Tunable luminescence and electrical properties of cerium doped strontium aluminate (SrAl2O4:Ce3+) phosphors for white LED applications

In new research growth long after glow material is a potential candidate due to its physical properties, chemical stability and wide application in modern solid-state lightning (LED), display devices, dosimetry and sensors. A cerium doped strontium aluminate phosphor (SrAl₂O₄:Ce³⁺) was synthesized by conventional solid-state reaction method. The crystal structure and morphology of phosphors, while doping rare earth metal and lithium metal ion was investigated by using X-ray diffraction, Raman spectroscopy and field emission scanning electron microscopy. Fourier transformed infrared spectrum results of the synthesized phosphor composition conforms the characteristic vibration bands of synthesized phosphor. Surface composition analysis of the prepared samples was examined using X-ray photoelectron spectroscopy. Photoluminescence emission band observed at ∼420 nm, ∼490 nm and ∼610 nm region under the excitation wavelength of 256 nm. Wight light emission was confirmed using the Commission Internationale de L'Eclairage (CIE) chromatic coordinate graph. The correlated colour temperature (CCT) value of 0.5% Ce³⁺ doped SAO of phosphors was calculated is in the range of 1543 K, which is indicated the synthesized phosphors performance as warm white light source. The obtained phosphor has a high dielectric constant and low loss tangent which is useful to optoelectronic devices.

Unsteady ternary hybrid-nanofluid flow over an expanding/shrinking cylinder with multiple slips: A Yamada-Ota model implementation

The primary objective of this investigation is to examine the thermal state of an unsteady ternary hybrid-nanofluid flow over an expanding/shrinking cylinder. The influence of radiation along with a non-uniform thermal source/sink is taken into account to expedite heat distribution. Multiple slips are considered at the cylinder interface. The mathematical model is simplified by incorporating appropriate transformations. A numerical solution is obtained using the bvp4c algorithm. The flow characteristics and behavior of the trihybrid nanoliquid exhibit significant changes when the cylinder expands or contracts. The effects of various emerging parameters are analyzed using graphical representations. The velocity field shows an opposite trend when the unsteadiness and mass transfer parameters are increased. The thermal field improves with higher values of the non-uniform source/sink parameter but deteriorates with an increase in the thermal slip parameter. The drag force increases with higher values of the unsteadiness parameter, while it decreases with amplified values of the mass suction and velocity slip parameters. A strong correlation is observed with previous studies which validates and strengthens the credibility of the present analysis.

Acoustic Wave Reflection in Water Affects Underwater Wireless Sensor Networks

The phenomenon of acoustic wave reflection off fluid-solid surfaces is the focus of this research. This research aims to measure the effect of material physical qualities on oblique incidence acoustic attenuation across a large frequency range. To construct the extensive comparison shown in the supporting documentation, reflection coefficient curves were generated by carefully adjusting the porousness and permeability of the poroelastic solid. The next stage in determining its acoustic response is to determine the pseudo-Brewster angle shift and the reflection coefficient minimum dip for the previously indicated attenuation permutations. This circumstance is made possible by modeling and studying the reflection and absorption of acoustic plane waves encountering half-space and two-layer surfaces. For this purpose, both viscous and thermal losses are taken into account. According to the research findings, the propagation medium has a significant impact on the form of the curve that represents the reflection coefficient, whereas the effects of permeability, porosity, and driving frequency are relatively less significant to the pseudo-Brewster angle and curve minima, respectively. This research additionally found that as permeability and porosity increase, the pseudo-Brewster angle shifts to the left (proportionally to porosity increase) until it reaches a limiting value of 73.4 degrees, and that the reflection coefficient curves for each level of porosity exhibit a greater angular dependence, with an overall decrease in magnitude at all incident angles. These findings are given within the framework of the investigation (in proportion to the increase in porosity). The study concluded that when permeability declined, the angular dependence of frequency-dependent attenuation reduced, resulting in iso-porous curves. The study also discovered that the matrix porosity largely affected the angular dependency of the viscous losses in the range of 1.4 × 10-14 m2 permeability.

Solar Salt with Carbon Nanotubes as a Potential Phase Change Material for High-Temperature Applications: Investigations on Thermal Properties and Chemical Stability

Nano-enhanced phase change materials are highly employed for an enhanced heat-transfer process. The current work reports that the thermal properties of solar salt-based phase change materials were enhanced with carbon nanotubes (CNTs). Solar salt (60:40 of NaNO₃/KNO₃) with a phase change temperature and enthalpy of 225.13 °C and 244.76 kJ/kg, respectively, is proposed as a high-temperature PCM, and CNT is added to improve its thermal conductivity. The ball-milling method was employed to mix CNTs with solar salt at various concentrations of 0.1, 0.3, and 0.5% by weight. SEM images display the even distribution of CNTs with solar salt, with the absence of cluster formations. The thermal conductivity, phase change properties, and thermal and chemical stabilities of the composites were studied before and after 300 thermal cycles. FTIR studies indicated only physical interaction between PCM and CNTs. The thermal conductivity was enhanced with an increase in CNT concentration. The thermal conductivity was enhanced by 127.19 and 125.09% before and after cycling, respectively, in the presence of 0.5% CNT. The phase change temperature decreased by around 1.64% after adding 0.5% CNT, with a decrease of 14.67% in the latent heat during melting. TGA thermograms indicated the weight loss was initiated at about 590 and 575 °C before and after thermal cycling, after which it was rapid with an increase in temperature. Thermal characterization of CNT-enhanced solar salt indicated that the composites could be used as phase change materials for enhanced heat-transfer applications.

NAMSTCD: A Novel Augmented Model for Spinal Cord Segmentation and Tumor Classification Using Deep Nets

Spinal cord segmentation is the process of identifying and delineating the boundaries of the spinal cord in medical images such as magnetic resonance imaging (MRI) or computed tomography (CT) scans. This process is important for many medical applications, including the diagnosis, treatment planning, and monitoring of spinal cord injuries and diseases. The segmentation process involves using image processing techniques to identify the spinal cord in the medical image and differentiate it from other structures, such as the vertebrae, cerebrospinal fluid, and tumors. There are several approaches to spinal cord segmentation, including manual segmentation by a trained expert, semi-automated segmentation using software tools that require some user input, and fully automated segmentation using deep learning algorithms. Researchers have proposed a wide range of system models for segmentation and tumor classification in spinal cord scans, but the majority of these models are designed for a specific segment of the spine. As a result, their performance is limited when applied to the entire lead, limiting their deployment scalability. This paper proposes a novel augmented model for spinal cord segmentation and tumor classification using deep nets to overcome this limitation. The model initially segments all five spinal cord regions and stores them as separate datasets. These datasets are manually tagged with cancer status and stage based on observations from multiple radiologist experts. Multiple Mask Regional Convolutional Neural Networks (MRCNNs) were trained on various datasets for region segmentation. The results of these segmentations were combined using a combination of VGGNet 19, YoLo V2, ResNet 101, and GoogLeNet models. These models were selected via performance validation on each segment. It was observed that VGGNet-19 was capable of classifying the thoracic and cervical regions, while YoLo V2 was able to efficiently classify the lumbar region, ResNet 101 exhibited better accuracy for sacral-region classification, and GoogLeNet was able to classify the coccygeal region with high performance accuracy. Due to use of specialized CNN models for different spinal cord segments, the proposed model was able to achieve a 14.5% better segmentation efficiency, 98.9% tumor classification accuracy, and a 15.6% higher speed performance when averaged over the entire dataset and compared with various state-of-the art models. This performance was observed to be better, due to which it can be used for various clinical deployments. Moreover, this performance was observed to be consistent across multiple tumor types and spinal cord regions, which makes the model highly scalable for a wide variety of spinal cord tumor classification scenarios.

Targeting Autophagy Using Long Non-Coding RNAs (LncRNAs): New Landscapes in the Arena of Cancer Therapeutics

Cancer has become a global health hazard accounting for 10 million deaths in the year 2020. Although different treatment approaches have increased patient overall survival, treatment for advanced stages still suffers from poor clinical outcomes. The ever-increasing prevalence of cancer has led to a reanalysis of cellular and molecular events in the hope to identify and develop a cure for this multigenic disease. Autophagy, an evolutionary conserved catabolic process, eliminates protein aggregates and damaged organelles to maintain cellular homeostasis. Accumulating evidence has implicated the deregulation of autophagic pathways to be associated with various hallmarks of cancer. Autophagy exhibits both tumor-promoting and suppressive effects based on the tumor stage and grades. Majorly, it maintains the cancer microenvironment homeostasis by promoting viability and nutrient recycling under hypoxic and nutrient-deprived conditions. Recent investigations have discovered long non-coding RNAs (lncRNAs) as master regulators of autophagic gene expression. lncRNAs, by sequestering autophagy-related microRNAs, have been known to modulate various hallmarks of cancer, such as survival, proliferation, EMT, migration, invasion, angiogenesis, and metastasis. This review delineates the mechanistic role of various lncRNAs involved in modulating autophagy and their related proteins in different cancers.

Monosodium Glutamate Perturbs Human Trophoblast Invasion and Differentiation through a Reactive Oxygen Species-Mediated Pathway: An In-Vitro Assessment

The overproduction of reactive oxygen species (ROS) has been associated with various human diseases. ROS exert a multitude of biological effects with both physiological and pathological consequences. Monosodium glutamate (MSG), a sodium salt of the natural amino acid glutamate, is a flavor-enhancing food additive, which is widely used in Asian cuisine and is an ingredient that brings out the “umami” meat flavor. MSG consumption in rats is associated with ROS generation. Owing to its consumption as part of the fast-food culture and concerns about its possible effects on pregnancy, we aimed to study the impact of MSG on placental trophoblast cells. MSG exposure influenced trophoblast invasion and differentiation, two of the most critical functions during placentation through enhanced production of ROS. Similar findings were also observed on MSG-treated placental explants, as confirmed by elevated Nrf2 levels. Ultrastructural studies revealed signs of subcellular injury by MSG exposure. Mechanistically, MSG-induced oxidative stress with endoplasmic reticulum stress pathways involving Xbp1s and IRE1α was observed. The effect of MSG through an increased ROS production indicates that its long-term exposure might have adverse health effect by compromising key trophoblast functions.

Nanoparticles in the diagnosis and treatment of cancer metastases: Current and future perspectives

Metastasis accounts for greater than 90% of cancer-related deaths. Despite recent advancements in conventional chemotherapy, immunotherapy, targeted therapy, and their rational combinations, metastatic cancers remain essentially untreatable. The distinct obstacles to treat metastases include their small size, high multiplicity, redundancy, therapeutic resistance, and dissemination to multiple organs. Recent advancements in nanotechnology provide the numerous applications in the diagnosis and prophylaxis of metastatic diseases, including the small particle size to penetrate cell membrane and blood vessels and their capacity to transport complex molecular 'cargo' particles to various metastatic regions such as bones, brain, liver, lungs, and lymph nodes. Indeed, nanoparticles (NPs) have demonstrated a significant ability to target specific cells within these organs. In this regard, the purpose of this review is to summarize the present state of nanotechnology in terms of its application in the diagnosis and treatment of metastatic cancer. We intensively reviewed applications of NPs in fluorescent imaging, PET scanning, MRI, and photoacoustic imaging to detect metastasis in various cancer models. The use of targeted NPs for cancer ablation in conjunction with chemotherapy, photothermal treatment, immuno therapy, and combination therapy is thoroughly discussed. The current review also highlights the research opportunities and challenges of leveraging engineering technologies with cancer cell biology and pharmacology to fabricate nanoscience-based tools for treating metastases.

Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS-GA-HSA Approach

Since the discovery of petrol-based products, a surge in energy-requiring equipment has been established across the world. Recent depletion of the existing crude oil resources has motivated researchers to opt for and analyze potential fuels that could potentially provide a cost-effective and sustainable solution. The current study selects a waste plant known as Eichhornia crassipes through which biodiesel is generated, and its blends are tested in diesel engines for feasibility. Different models using soft computing and metaheuristic techniques are employed for the accurate prediction of performance and exhaust characteristics. The blends are further mixed with nanoadditives, thereby exploring and comparing the changes in performance characteristics. The input attributes considered in the study comprise engine load, blend percentage, nanoparticle concentration, and injection pressure, while the outcomes are brake thermal efficiency, brake specific energy consumption, carbon monoxide, unburnt hydrocarbon, and oxides of nitrogen. Models were further ranked and chosen based on their set of attributes using the ranking technique. The ranking criteria for models were based on cost, accuracy, and skill requirement. The ANFIS harmony search algorithm (HSA) reported a lower error rate, while the ANFIS model reported the lowest cost. The optimal combination achieved was 20.80 kW, 2.48047, 150.501 ppm, 4.05025 ppm, and 0.018326% for brake thermal efficiency (BTE), brake specific energy consumption (BSEC), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC), and carbon monoxide (CO), respectively, thereby furnishing better results than the adaptive neuro-fuzzy interface system (ANFIS) and the ANFIS-genetic algorithm model. Henceforth, integrating the results of ANFIS with an optimization technique with the harmony search algorithm (HSA) yields accurate results but at a comparatively higher cost.

Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data

The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has greatly improved the capability for Human Activity Recognition (HAR). By utilizing Machine Learning (ML) techniques and data from these sensors, various human motion activities can be classified. This study performed experiments and compiled a large dataset of nine daily activities, including Laying Down, Stationary, Walking, Brisk Walking, Running, Stairs-Up, Stairs-Down, Squatting, and Cycling. Several ML models, such as Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Multinomial Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine, were trained on sensor data collected from accelerometer, gyroscope, and magnetometer embedded in smartphones and wearable devices. The highest test accuracy of 95% was achieved using the random forest algorithm. Additionally, a custom-built Bidirectional Long-Short-Term Memory (Bi-LSTM) model, a type of Recurrent Neural Network (RNN), was proposed and yielded an improved test accuracy of 98.1%. This approach differs from traditional algorithmic-based human activity detection used in current wearable technologies, resulting in improved accuracy.

Effects of COVID-19 on Synaptic and Neuronal Degeneration

Neurons are the basic building blocks of the human body's neurological system. Atrophy is defined by the disintegration of the connections between cells that enable them to communicate. Peripheral neuropathy and demyelinating disorders, as well as cerebrovascular illnesses and central nervous system (CNS) inflammatory diseases, have all been linked to brain damage, including Parkinson's disease (PD). It turns out that these diseases have a direct impact on brain atrophy. However, it may take some time after the onset of one of these diseases for this atrophy to be clearly diagnosed. With the emergence of the Coronavirus disease 2019 (COVID-19) pandemic, there were several clinical observations of COVID-19 patients. Among those observations is that the virus can cause any of the diseases that can lead to brain atrophy. Here we shed light on the research that tracked the relationship of these diseases to the COVID-19 virus. The importance of this review is that it is the first to link the relationship between the Coronavirus and diseases that cause brain atrophy. It also indicates the indirect role of the virus in dystrophy.

Heat Transfer Characteristics of Fullerene and Titania Nanotube Nanofluids under Agitated Quench Conditions

Distilled water and aqueous fullerene nanofluids having concentrations of 0.02, 0.2, and 0.4 vol % and titania (titanium dioxide, TiO₂) nanofluids of 0.0002, 0.002, and 0.02 vol % were analyzed for heat transfer characteristics. Quenching mediums were stirred at impeller speeds of 0, 500, 1,000, and 1,500 RPMs in a typical Tensi agitation system. During the quenching process, a metal probe made of ISO 9950 Inconel was used to record the temperature history. The inverse heat conduction method was used to calculate the spatial and temporal heat flux. The nanofluid rewetting properties were measured and matched to those of distilled water. The maximum mean heat flux was 3.26 MW/m², and the quickest heat extraction was 0.2 vol % fullerene nanofluid, according to the results of the heat transfer investigation.

Effect of Kariba Weed Biodiesel Blended with n-Pentane on the Chosen Parameters of a Ceramic-Coated Thermal Barrier Direct Injection Diesel Engine

In this experimental investigation, Kariba weed biodiesel (KSB) blended with n-pentane has been tested in conventional and ceramic-coated thermal barrier engines, and the results have been compiled and presented. A single-cylinder, four-stroke, direct injection diesel engine has been used as the test engine with eddy current dynamometer loading as used in the experimental setup. The tests were repeated in various ambient conditions to get an optimal value. Ceramic coating has been done with partially stabilized zirconia by the plasma arc spraying process. Among the quantum of tests conducted, 90% KSB blended with 10% n-pentane showed appreciable results when it was compared with the test fuel (neat diesel). The brake thermal efficiency and brake-specific fuel consumption were found to be better when compared with neat diesel. At increasing load, unburnt hydrocarbon, carbon monoxide, and smoke opacity emissions were appreciably reduced.

Structural Morphology and Optical Properties of Strontium-Doped Cobalt Aluminate Nanoparticles Synthesized by the Combustion Method

The study of structural morphology and the optical properties of nanoparticles produced by combustion methods are gaining significance due to their multifold applications. In this regard, in the present work, the strontium-doped cobalt aluminate nanoparticles were synthesized by utilizing Co_1-xSr_xAl₂O₄ (0 ≤ x ≤ 0.5) L-Alanine as a fuel in an ignition cycle. Subsequently, several characterization studies viz., X-ray diffraction (XRD), energy-dispersive X-ray (EDX) analysis, high-resolution scanning electron microscopy (HRSEM), Fourier transform infrared spectroscopy (FTIR), ultraviolet (UV) spectroscopy and vibrating sample magnetometry (VSM) were accomplished to study the properties of the materials. The XRD analysis confirmed the cubic spinel structure, and the average crystallite size was found to be in the range of 14 to 20 nm using the Debye-Scherrer equation. High-resolution scanning electron microscopy was utilized to inspect the morphology of the Co_1-xSr_xAl₂O₄ (0 ≤ x ≤ 0.5) nanoparticles. Further, EDS studies were accomplished to determine the chemical composition. Kubelka-Munk's approach was used to determine the band gap, and the values were found to be in the range of 3.18-3.32 eV. The energy spectra for the nanoparticles were in the range of 560-1100 cm^-1, which is due to the spinel structure of Sr-doped CoAl₂O₄ nanoparticles. The behavior plots of magnetic induction (M) against the magnetic (H) loops depict the ferromagnetic behavior of the nanomaterials synthesized.

Aggressive and Drug-resistant Pancreatic Cancer: Challenges and Novel Treatment Approaches

Pancreatic cancer, especially pancreatic ductal adenocarcinoma (PDAC), has a mortality rate that is among the highest. The single therapy option that has the potential to be curative for the illness is surgery, which is generally accepted as being the only effective treatment available. This is because most pancreatic tumors are intractable to chemotherapy. Because of the severe nature of the illness that these tumors cause, only around twenty percent of these tumors can be surgically removed when the first symptoms appear. This is due to the harm that these tumors cause. Since surgery in late-stage cases does not usually offer benefits, here we shed light on the molecular mechanisms of the most aggressive pancreatic tumors that are highly resistant to drugs. We also describe two latest novel treatment approaches that are used to combat this fierce tumor: targeting exosome-mediated tumor-enhancement mechanisms and radiation therapy in combination with adverse effect-mitigating agents. Effective treatments for pancreatic cancer are needed to meet this urgent medical need.

Unraveling the Potential Role of NEDD4-like E3 Ligases in Cancer

Cancer is a deadly disease worldwide, with an anticipated 19.3 million new cases and 10.0 million deaths occurring in 2020 according to GLOBOCAN 2020. It is well established that carcinogenesis and cancer development are strongly linked to genetic changes and post-translational modifications (PTMs). An important PTM process, ubiquitination, regulates every aspect of cellular activity, and the crucial enzymes in the ubiquitination process are E3 ubiquitin ligases (E3s) that affect substrate specificity and must therefore be carefully regulated. A surfeit of studies suggests that, among the E3 ubiquitin ligases, neuronal precursor cell-expressed developmentally downregulated 4 (NEDD4)/NEDD4-like E3 ligases show key functions in cellular processes by controlling subsequent protein degradation and substrate ubiquitination. In addition, it was demonstrated that NEDD4 mainly acts as an oncogene in various cancers, but also plays a tumor-suppressive role in some cancers. In this review, to comprehend the proper function of NEDD4 in cancer development, we summarize its function, both its tumor-suppressive and oncogenic role, in multiple types of malignancies. Moreover, we briefly explain the role of NEDD4 in carcinogenesis and progression, including cell survival, cell proliferation, autophagy, cell migration, invasion, metastasis, epithelial-mesenchymal transition (EMT), chemoresistance, and multiple signaling pathways. In addition, we briefly explain the significance of NEDD4 as a possible target for cancer treatment. Therefore, we conclude that targeting NEDD4 as a therapeutic method for treating human tumors could be a practical possibility.

Ocular myasthenia gravis saccades as a measure of extraocular muscle function

Background

It is important to understand the pathophysiology of ocular myasthenia gravis (OMG) to improve treatment.

Aim

To use modern video-oculography to characterise saccadic eye movements in patients with OMG, including anti-AChR, anti-MuSK, anti-LRP4, and seronegative OMG.

Methods

In total, 21 patients with OMG and five age-matched healthy control subjects underwent video-oculography. Participants performed a sequence of horizontal saccades (3 minutes each) at ±5°, ± 10°, and ±20°, followed by 3 minutes of saccades directed at randomly presented targets at ±5°, ± 10°, and ±15°. We recorded the direction, amplitude, duration, peak, and average velocity of each saccade for each task for each participant.

Results

Saccadic amplitude, duration, and average velocity were all lower in OMG patients than in control subjects (p < 0.021). Saccadic amplitude and velocity decreased over time, but this decrease was similar in OMG patients and control subjects. Fixation drift and ocular disparity tended to be greater in OMG patients than in control subjects. Saccadic intrusions occurred more frequently in OMG patients than in control subjects (p < 0.001). No significant effects of time or group by time on fixation drift or ocular disparity were found.

Discussion

Saccadic velocities in OMG patients differed from those in normal control subjects, which suggests that OMG affects fast-twitch fibres, although fast-twitch fibres were still able to generate "twitch" or "quiver" movements in the presence of even severe ophthalmoplegia. Slow-twitch muscle fibres involved in gaze holding were also affected, accounting for increased fixation drift following saccades. Our objective finding of increased fixation drift and a larger number of saccadic intrusions mirror our anecdotal experience of patients with OMG who report significant diplopia despite minimal ophthalmoplegia on examination. Such microsaccades may be a surrogate for compensation of a gaze-holding deficit in MG.