Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification.

Cancer detection poses a significant challenge for researchers and clinical experts due to its status as the leading cause of global mortality. Early detection is crucial, but traditional cancer detection methods often rely on invasive procedures and time-consuming analyses, creating a demand for more efficient and accurate solutions. This paper addresses these challenges by utilizing automated cancer detection through AI-based techniques, specifically focusing on deep learning models. Convolutional Neural Networks (CNNs), including DenseNet121, DenseNet201, Xception, InceptionV3, MobileNetV2, NASNetLarge, NASNetMobile, InceptionResNetV2, VGG19, and ResNet152V2, are evaluated on image datasets for seven types of cancer: brain, oral, breast, kidney, Acute Lymphocytic Leukemia, lung and colon, and cervical cancer. Initially, images undergo segmentation techniques, proceeded by contour feature extraction where parameters such as perimeter, area, and epsilon are computed. The models are rigorously evaluated, with DenseNet121 achieving the highest validation accuracy as 99.94%, 0.0017 as loss, and the lowest Root Mean Square Error (RMSE) values as 0.036056 for training and 0.045826 for validation. These results revealed the capability of AI-based techniques in improving cancer detection accuracy, with DenseNet121 emerging as the most effective model in this study.

Advanced CNN models in gastric cancer diagnosis: enhancing endoscopic image analysis with deep transfer learning.

Introduction: The rapid advancement of science and technology has significantly expanded the capabilities of artificial intelligence, enhancing diagnostic accuracy for gastric cancer. Methods: This research aims to utilize endoscopic images to identify various gastric disorders using an advanced Convolutional Neural Network (CNN) model. The Kvasir dataset, comprising images of normal Z-line, normal pylorus, ulcerative colitis, stool, and polyps, was used. Images were pre-processed and graphically analyzed to understand pixel intensity patterns, followed by feature extraction using adaptive thresholding and contour analysis for morphological values. Five deep transfer learning models-NASNetMobile, EfficientNetB5, EfficientNetB6, InceptionV3, DenseNet169-and a hybrid model combining EfficientNetB6 and DenseNet169 were evaluated using various performance metrics. Results & discussion: For the complete images of gastric cancer, EfficientNetB6 computed the top performance with 99.88% accuracy on a loss of 0.049. Additionally, InceptionV3 achieved the highest testing accuracy of 97.94% for detecting normal pylorus, while EfficientNetB6 excelled in detecting ulcerative colitis and normal Z-line with accuracies of 98.8% and 97.85%, respectively. EfficientNetB5 performed best for polyps and stool with accuracies of 98.40% and 96.86%, respectively.The study demonstrates that deep transfer learning techniques can effectively predict and classify different types of gastric cancer at early stages, aiding experts in diagnosis and detection.

Strengthening facility-based integrated emergency care services for time sensitive emergencies at all levels of healthcare in India: An implementation research study protocol.

BACKGROUND: The healthcare system in India is tiered and has primary, secondary and tertiary levels of facilities depending on the complexity and severity of health challenges at these facilities. Evidence suggests that emergency services in the country is fragmented. This study aims to identify the barriers and facilitators of emergency care delivery for patients with time-sensitive conditions, and develop and implement a contextually relevant model, and measure its impact using implementation research outcomes. METHODS: We will study 85 healthcare facilities across five zones of the country and focus on emergency care delivery for 11 time-sensitive conditions. This implementation research will include seven phases: the preparatory phase, formative assessment, co-design of Model "Zero", co-implementation, model optimization, end-line evaluation and consolidation phase. The "preparatory phase" will involve stakeholder meetings, approval from health authorities and the establishment of a research ecosystem. The "formative assessment" will include quantitative and qualitative evaluations of the existing healthcare facilities and personnel to identify gaps, barriers and facilitators of emergency care services for time-sensitive conditions. On the basis of the results of the formative assessment, context-specific implementation strategies will be developed through meetings with stakeholders, providers and experts. The "co-design of Model 'Zero'" phase will help develop the initial Model "Zero", which will be pilot tested on a small scale (co-implementation). In the "model optimization" phase, iterative feedback loops of meetings and testing various strategies will help develop and implement the final context-specific model. End-line evaluation will assess implementation research outcomes such as acceptability, adoption, fidelity and penetration. The consolidation phase will include planning for the sustenance of the interventions. DISCUSSION: In a country such as India, where resources are scarce, this study will identify the barriers and facilitators to delivering emergency care services for time-sensitive conditions across five varied zones of the country. Stakeholder and provider participation in developing consensus-based implementation strategies, along with iterative cycles of meetings and testing, will help adapt these strategies to local needs. This approach will ensure that the developed models are practical, feasible and tailored to the specific challenges and requirements of each region.

Disposable Zirconium trisulfide-Reduced graphene oxide modified conducting thread based electrochemical biosensor for lung cancer diagnosis.

Flexible technology in sensors have received much attention in monitoring of human health through various physiological indicators. Thus, it drawn a lot of interest in the development of flexible substrate for the diagnosis of various diseases via analysis of analytes. Present work focusses on the development of ecofriendly, portable, flexible, conducting thread (Th) and used as smart substrate for fabrication of biosensor towards ultrasensitive detection of the lung cancer biomarker (cytoskeleton-associated protein 4; CKAP4). The zirconium trisulfide-reduced graphene oxide nanocomposite and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) modified cotton thread based biosensor was fabricated via dip coating method. Next, successive immobilization of monoclonal antibodies of CKAP4 (anti-CKAP4) and bovine serum albumin (BSA) was performed via drop cast approach using fabricated electrode [nZrS3@rGO/PEDOT:PSS/Th]. The response of fabricated electrode (BSA/anti-CKAP4/ZrS3@rGO/PEDOT:PSS/Th) was recorded electrochemically versus CKAP4 concentration via chronoamperometry (CA). The results showed wider linear detection range of 6.25-800 pg mL-1, excellent sensitivity of 85.2 µA[log(pg mL-1)]-1cm-2 with good stability up to 42 days. The response of fabricated biosensor was supported by investigating response of CKAP4 biomarker present in patients of lung cancer (concentration as determined through enzyme-linked immunosorbent assay) and obtained results exhibited excellent correlation with that of standard samples.

Educational intervention on perceived stress among adults with type 2 diabetes and metabolic syndrome: a non-randomized clinical trial.

Objective: To evaluate the effectiveness of Virtual Teaching (VT) Programme regarding palliative care on knowledge, self-efficacy and attitude among Nursing Personnel working in selected hospitals of North India. Methods: A quasi-experimental study with non-equivalent control group pre-test-post-test design was conducted on 121 Nursing Personnel, selected by convenient sampling technique. Knowledge, self-efficacy and attitude were assessed using structured knowledge questionnaire, Palliative Care Self-efficacy Scale, and Frommelt Attitudes toward care of dying scale respectively. Nursing personnel in experimental group received Virtual Teaching Programme regarding palliative care whereas those in comparison group received conventional teaching (CT). The study included a pre-test followed by the teaching (virtual/ conventional) on day one. The post-test was conducted on 15th day after the intervention. Results: The results showed that there was a significant difference in mean post-test knowledge (VT group: 17.11 to CT group: 25.05; t=9.25, p<0.001), self-efficacy (VT group: 39.27 to CT group: 43.38; t=6.39, p<0.001) and attitude (VT group: 108.86 to CT group: 133.23; t=9.27, p<0.001) scores between virtual teaching group and conventional teaching group. ANCOVA test revealed statistically significant differences in the mean scores of knowledge [F (1.11) = 86.61, p<0.001], self-efficacy [F (1.11) = 841.75, p<0.001] and attitude [F (1.11) = 82.92, p<0.001] between the groups, with higher means obtained in the CT group. Conclusion: Virtual Teaching programme and Conventional teaching both were effective in enhancing the knowledge, self-efficacy and attitude among Nursing Personnel regarding palliative care with conventional teaching being more effective.

Photophysical Investigation of One Pot Synthesized Novel Indenofluorene Derivative (BDP) as a Fluorescent Chemosensor for the Detection of Fe3+ Ion.

An Indane-1-one derivative 11-(1-benzyl-1H-indol-3-yl)-10,12-dihydrodiindeno[1,2-b:2',1'-e]-pyridine (BDP) has been synthesized by the reaction of Indan-1-one with 1-benzyl-1H-indole-3-carbaldehyde. FT-IR, 1H-NMR, 13N-NMR and Mass spectroscopic techniques has been used to confirmed the structure of BDP. The observed photophysical changes in BDP across various solvents were associated. The impact of various interactions on photophysical parameters, including Stokes shift, dipole moment, oscillator strength, and fluorescence quantum yields, has been assessed in relation to solvent polarity. Moreover, BDP demonstrates potential as a selective fluorescent chemosensor for detecting Fe3+ ion within a range of cations in an aqueous DMSO environment. A thorough investigation into the recognition mechanism of BDP towards Fe3+ ion has been conducted using Benesi-Hildebrand and Stern-Volmer, measurements. BDP forms a 2:1 complex with the Fe3+ ion, exhibiting fluorescent quenching behaviour.

Optimizing agricultural sustainability: enriched organic formulations for growth, yield, and soil quality in a multi-crop system.

Utilizing agricultural and industrial wastes, potent reservoirs of nutrients, for nourishing the soil and crops through composting embodies a sustainable approach to waste management and organic agriculture. To investigate this, a 2-year field experiment was conducted at ICAR-IARI, New Delhi, focusing on a pigeon pea-vegetable mustard-okra cropping system. Seven nutrient sources were tested, including a control (T1), 100% recommended dose of nitrogen (RDN) through farmyard manure (T2), 100% RDN through improved rice residue compost (T3), 100% RDN through a paddy husk ash (PHA)-based formulation (T4), 75% RDN through PHA-based formulation (T5), 100% RDN through a potato peel compost (PPC)-based formulation (T6), and 75% RDN through PPC-based formulation (T7). Employing a randomized block design with three replications, the results revealed that treatment T4 exhibited the significantly highest seed (1.89 ± 0.09 and 1.97 ± 0.12 t ha-1) and stover (7.83 ± 0.41 and 8.03 ± 0.58 t ha-1) yield of pigeon pea, leaf yield (81.57 ± 4.69 and 82.97 ± 4.17 t ha-1) of vegetable mustard, and fruit (13.54 ± 0.82 and 13.78 ± 0.81 t ha-1) and stover (21.64 ± 1.31 and 22.03 ± 1.30 t ha-1) yield of okra during both study years compared to the control (T1). Treatment T4 was on par with T2 and T6 for seed and stover yield in pigeon pea, as well as okra, and leaf yield in vegetable mustard over both years. Moreover, T4 demonstrated notable increase of 124.1% and 158.2% in NH4-N and NO3-N levels in the soil, respectively, over the control. The enhanced status of available nitrogen (N) and phosphorus (P) in the soil, coupled with increased soil organic carbon (0.41%), total bacteria population (21.1%), fungi (37.2%), actinomycetes (44.6%), and microbial biomass carbon (28.5%), further emphasized the positive impact of T4 compared to the control. Treatments T2 and T6 exhibited comparable outcomes to T4 concerning changes in available N, P, soil organic carbon, total bacteria population, fungi, actinomycetes, and microbial biomass carbon. In conclusion, treatments T4 and T6 emerge as viable sources of organic fertilizer, particularly in regions confronting farmyard manure shortages. These formulations offer substantial advantages, including enhanced yield, soil quality improvement, and efficient fertilizer utilization, thus contributing significantly to sustainable agricultural practices.

A review on the role of pH-sensitive natural pigments in biopolymers based intelligent food packaging films.

As food packaging evolves, consumer interests are shifting from traditional to intelligent food packaging systems. Intelligent packaging includes active components that display changes in a visual or interactive form perceivable by consumers. This offers real-time monitoring of the quality and shelf life of the packaged food and enhances transparency. For example, pH-sensitive natural pigment-based films change color in response to variations in pH levels, enabling the film/labels to reflect alterations in the acidity or basicity of the food inside the package. Natural pigments like anthocyanins, curcumin, betalains, chlorophyll, and carotenoids have been comprehensively reported for developing biodegradable pH-sensitive films of starch, protein, chitosan, and cellulose. Natural pigments offer great compatibility with these biopolymers and improve the other performance parameters of the films. However, these films still lack the strength and versatility of petroleum-based synthetic plastic films. But these films can be used as an indicator and combined with primary packaging to monitor freshness, time-temperature, and leak for muscle foods, dairy products, fruits and vegetables, and bakery products. Therefore, this review provides a detailed overview of pH-sensitive pigments, their compatibility with natural polymers, their role in film performance in monitoring, and their food packaging applications.

Recent Biochemical Advances in Antitubercular Drugs: Challenges and Future.

Tuberculosis (TB) is one of the leading causes of death world-wide after AIDS. It infects around one-third of global population and approximately two million people die annually from this disease because it is a very contagious disease spread by Mycobacterium tuberculosis. The increasing number of drug-resistant strains and the failure of conventional treatments against this strain are the challenges of the coming decades. New therapeutic techniques aim to confirm cure without deterioration, to reduce deaths, contagions and the formation of drug-resistant strains. A plethora of new diagnostic tests are available to diagnose the active tuberculosis, screen latent M. tuberculosis infection, and to identify drug-resistant strains of M. tuberculosis. When effective prevention strategies do not prevail, high rates of early case detection and successive cures to control TB emergence would not be possible. In this review, we discussed the structural features of M. tuberculosis, Multi drug resistance tuberculosis (MDR-TB), extremely drug-resistant tuberculosis (XDR-TB), the mechanism of M. tuberculosis infection, the mode of action of first and second-line antitubercular drugs, the mechanism of resistance to the existing drugs, compounds in preclinical and clinical trial and drugs presently available for the treatment of tuberculosis. Moreover, the new diagnostic techniques to detect M. tuberculosis are also discussed in this review.

Insights from multi-omic modeling of neurodegeneration in xeroderma pigmentosum using an induced pluripotent stem cell system.

Xeroderma pigmentosum (XP) is caused by defective nucleotide excision repair of DNA damage. This results in hypersensitivity to ultraviolet light and increased skin cancer risk, as sunlight-induced photoproducts remain unrepaired. However, many XP patients also display early-onset neurodegeneration, which leads to premature death. The mechanism of neurodegeneration is unknown. Here, we investigate XP neurodegeneration using pluripotent stem cells derived from XP patients and healthy relatives, performing functional multi-omics on samples during neuronal differentiation. We show substantially increased levels of 5',8-cyclopurine and 8-oxopurine in XP neuronal DNA secondary to marked oxidative stress. Furthermore, we find that the endoplasmic reticulum stress response is upregulated and reversal of the mutant genotype is associated with phenotypic rescue. Critically, XP neurons exhibit inappropriate downregulation of the protein clearance ubiquitin-proteasome system (UPS). Chemical enhancement of UPS activity in XP neuronal models improves phenotypes, albeit inadequately. Although more work is required, this study presents insights with intervention potential.

Mechanisms of the initial stage of non-enzymatic oxidation of wine: A mini review.

Non-enzymatic oxidation is a primary factor affecting wine quality during bottling or aging. Although red and white wines exhibit distinct responses to oxidation over time, the fundamental mechanisms driving this transformation remain remarkably uniform. Non-enzymatic oxidation of wine commences with the intricate interplay between polyphenols and oxygen, orchestrating a delicate redox dance with iron and copper. Notably, copper emerges as an accelerant in this process. To safeguard wine integrity, sulfur dioxide (SO2) is routinely introduced to counteract the pernicious effects of oxidation by neutralizing hydrogen peroxide and quinone. In this comprehensive review, the initial stages of non-enzymatic wine oxidation are examined. The pivotal roles played by polyphenols, oxygen, iron, copper, and SO2 in this complex oxidative process are systematically explored. Additionally, the effect of quinone formation on wine characteristics and the intricate dynamics governing oxygen availability are elucidated. The potential synergistic or additive effects of iron and copper are probed, and the precise balance between SO2 and oxygen is scrutinized. This review summarizes the mechanisms involved in the initial stages of non-enzymatic oxidation of wine and anticipates the potential for further research.

Self-powered photodetectors: a device engineering perspective.

Nanoscale self-powered photodetectors that can work without any external source of energy are required for future applications. There is potential demand for these devices in areas like wireless surveillance, weather forecasting, remote monitoring, and places where the availability of power is scarce. This study provides an overview of state of the art research trends and improvements in self-powered photodetectors. A device engineering perspective for improvement in the figures of merit has been presented along with a description of additional effects like pyro-phototronic, piezo-phototronic, and surface plasmonics.

Effectiveness of virtual teaching programme regarding palliative care on knowledge, self-efficacy and attitude of Nursing Personnel in North India / Efectividad de un programa de enseñanza virtual sobre cuidados paliativos en los conocimientos, la autoeficacia y la actitud del personal de enfermería en el norte de la India / Eficácia de um programa virtual de ensino de cuidados paliativos sobre conhecimento, autoeficácia e atitude da equipe de enfermagem no norte da Índia

Objective.To evaluate the effectiveness of Virtual Teaching (VT) Programme regarding palliative care on knowledge, self-efficacy and attitude among Nursing Personnel working in selected hospitals of North India. Methods. A quasi-experimental study with non-equivalent control group pre-test-post-test design was conducted on 121 Nursing Personnel, selected by convenient sampling technique. Knowledge, self-efficacy and attitude were assessed using structured knowledge questionnaire, Palliative Care Self-efficacy Scale, and Frommelt Attitudes toward care of dying scale respectively. Nursing personnel in experimental group received Virtual Teaching Programme regarding palliative care whereas those in comparison group received conventional teaching (CT). The study included a pre-test followed by the teaching (virtual/ conventional) on day one. The post-test was conducted on 15th day after the intervention. Results. The results showed that there was a significant difference in mean post-test knowledge (VT group: 17.11 to CT group: 25.05; t=9.25, p<0.001), self-efficacy (VT group: 39.27 to CT group: 43.38; t=6.39, p<0.001) and attitude (VT group: 108.86 to CT group: 133.23; t=9.27, p<0.001) scores between virtual teaching group and conventional teaching group. ANCOVA test revealed statistically significant differences in the mean scores of knowledge [F (1.11) = 86.61, p<0.001], self-efficacy [F (1.11) = 841.75, p<0.001] and attitude [F (1.11) = 82.92, p<0.001] between the groups, with higher means obtained in the CT group. Conclusion. Virtual Teaching programme and Conventional teaching both were effective in enhancing the knowledge, self-efficacy and attitude among Nursing Personnel regarding palliative care with conventional teaching being more effective.

Objetivo. Evaluar la efectividad de un programa de Enseñanza Virtual (EV) sobre cuidados paliativos en cuanto a conocimientos, autoeficacia y actitud entre el personal de enfermería que trabaja en hospitales seleccionados del norte de la India. Métodos. Se llevó a cabo un estudio cuasiexperimental con un diseño de grupo de control no equivalente. Se realizaron pre y post-prueba a 121 miembros del personal de enfermería, seleccionados mediante una técnica de muestreo por conveniencia. Se evaluaron los conocimientos, la autoeficacia y las actitudes mediante un cuestionario estructurado de conocimientos, una escala de autoeficacia en cuidados paliativos y una escala de actitudes de Frommelt hacia el cuidado de personas al final de su vida. El personal de enfermería del grupo experimental recibió un programa de EV sobre cuidados paliativos, mientras que el del grupo de comparación recibió enseñanza convencional (EC). El estudio incluyó una preprueba seguida de la enseñanza virtual o convencional el primer día y una prueba posterior al decimoquinto día después de la intervención. Resultados. Los resultados mostraron que había una diferencia significativa en las puntuaciones medias post-test entre los grupos en: conocimientos (EV: 17.11 y EC: 25.05; t=9.25, p<0.001), autoeficacia (grupo VT: 39.27 y grupo CT: 43.38; t=6.39, p<0.001) y actitud (grupo EV: 108.86 y grupo EC: 133.23; t=9.27, p=<0.001) La prueba ANCOVA también reveló diferencias estadísticamente significativas en las puntuaciones medias de conocimientos [F (1.11) = 86.61, p=<0.001], autoeficacia [F (1.12) =841.75, p=<0.001] y actitud [F (1.11) = 82.91, p<0.001] entre los grupos, obteniéndose medias más altas en el grupo CT. Conclusión. Tanto el programa de enseñanza virtual como la enseñanza convencional fueron efectivos para mejorar los conocimientos, la autoeficacia y la actitud del personal de enfermería en relación con los cuidados paliativos, siendo el beneficio mucho mayor la enseñanza convencional.

Objetivo. Avaliar a eficácia de um programa de Aprendizagem Virtual (VE) sobre cuidados paliativos em termos de conhecimento, autoeficácia e atitude entre a equipe de enfermagem que trabalha em hospitais selecionados no norte da Índia. Métodos. Um estudo quase experimental foi conduzido com um desenho de grupo controle não equivalente. Foram realizados pré e pós-testes em 121 membros da equipe de enfermagem, selecionados por meio de técnica de amostragem por conveniência. Foram utilizados os instrumentos: questionário de conhecimento estruturado, escala de autoeficácia em cuidados paliativos e escala de atitudes de Frommelt em relação ao cuidado de pessoas em fim de vida. A equipe de enfermagem do grupo experimental recebeu um programa de VE sobre cuidados paliativos, enquanto o grupo controle recebeu ensino convencional (CE). O estudo incluiu um pré-teste seguido de ensino virtual ou convencional no primeiro dia e um pós-teste no 15º dia após a intervenção. Resultados. Os resultados mostraram que houve diferença significativa nas médias dos escores pós-teste entre os grupos em: conhecimento (EV: 17.11 e EC: 25.05; t=9.25, p<0.001), autoeficácia (grupo VT: 39.27 e grupo CT: 43.38; t=6.39, p<0.001) e atitude (grupo EV: 108.86 e grupo EC: 133.23; t=9.27, p=<0.001). O teste ANCOVA também revelou diferenças estatisticamente significativas nas pontuações médias de conhecimento [F (1.11) = 86.61, p=<0.001], autoeficácia [F (1.12) =841.75, p=<0.001] e atitude [F (1.11) = 82.91, p<0.001] entre os grupos, obtendo maiores médias em o grupo CT. Conclusão. Tanto o programa de ensino virtual quanto o ensino convencional foram eficazes na melhoria do conhecimento, da autoeficácia e da atitude da equipe de enfermagem em relação aos cuidados paliativos, sendo o benefício muito maior com o ensino convencional.

Sinomenine protects against atherosclerosis in apolipoprotein E-knockout mice by inhibiting of inflammatory pathway.

Atherosclerosis, a multifaceted and persistent inflammatory condition, significantly contributes to the progression of cardiocerebrovascular disorders, such as myocardial infarctions and cerebrovascular accidents. It involves the accumulation of cholesterol, fatty deposits, calcium and cellular debris in the walls of arteries, leading to the formation of plaques. Our aim is to investigate the potential of sinomenine to counteract atherosclerosis in mice lacking Apolipoprotein E (ApoE-/-) Mice. We employed the high-fat diet-induced method to induce atherosclerosis in ApoE-/- mice, and the mice were treated with sinomenine (5, 10, and 15 mg/kg) and simvastatin (0.5 mg/kg) for 12 weeks. Body weight, water intake, and food intake were assessed. Lipid parameters, oxidative stress, inflammatory cytokines, and mRNA levels were estimated. Sinomenine treatment remarkably (P < 0.001) suppressed body weight, along with food and water intake. Sinomenine altered the levels of total cholesterol (TC), high-density lipoprotein (HDL), triglyceride (TG), low-density lipoprotein (LDL), and very low-density lipoprotein (VLDL), which were modulated in the atherosclerosis group. Sinomenine treatment also altered the levels of oxidative stress parameters such as glutathione peroxidase (GPx), catalase (CAT), malonaldehyde (MDA), superoxide dismutase (SOD) and glutathione (GSH). In addition, it modulated cardiac parameters like C-reactive protein (CRP), endothelin-1 (ET-1), thromboxane B2 (TXB2), nitric oxide (NO), cardiac troponin I (cTnI), lactate dehydrogenase (LDH), and creatinine kinase isoenzymes (CK-MB). Inflammatory cytokines interleukin (IL)-1α, IL-1ß, TNF-α, IL-6, and IL-10 were also affected. Sinomenine further suppressed the mRNA expression of IL-6, IL-17, IL-10, tumor necrosis factor-α (TNF-α), Il-1ß, monocyte chemoattractant protein-1 (MCP-1), MCP-2, MCP-3, transforming Growth Factor-1ß (TGF-1ß), vascular cell adhesion molecule 1 (VCAM-1), and intercellular adhesion molecule-1 (ICAM-1). The results suggest that sinomenine remarkably suppressed the development of atherosclerosis in the early stage.

Modulation of Physical and Thermal Properties in Wild Banana (Musa balbisiana Colla) Seed Powder by Moisture Variations.

Engineering and flow properties of banana seed powder as a function of moisture content are important for processing, handling, packaging, and transport processes. The bulk density, tapped density, and porosity increased from 377.37 to 427.36 kg m-3, 622.08 to 746.33 kg m-3, and 38.99-43.74%, respectively, within the increasing moisture content range. The Hausner ratio (Hr) and Carr's compressibility index (CI) significantly (p < 0.05) increased with an increase in moisture content (6.16-19.56% db) of banana seed powder, whereas HR fell in the range of 1.4-2.0, indicating cohesive characteristics of banana seed powder. The angle of repose, angle of spatula, and angle of fall exhibited a linear increase, ranging from 40.6° to 49°, 33.4° to 39.4°, and 35.6° to 42.6°, respectively, with increasing moisture content. The static coefficient of friction was found to be highest for aluminium and glass surfaces and least for stainless steel. The water activity and swelling power of banana seed powder showed a significant increase, while the solubility and oil absorption capacity exhibited a significant decrease within the range of increasing moisture content. The thermal characteristics of wild banana seed powder, such as thermal conductivity (0.16 to 0.20 Wm-1 K-1) and volumetric specific heat (0.58 to 0.99 MJm-3 K-1), demonstrated an increasing trend as the moisture content increased. However, the thermal diffusivity showed a decrease from 0.31 to 0.19 (×10-6 m2s-1) with the increase in moisture content.

Satellite or ground-based measurements for air pollutants (PM2.5, PM10, SO2, NO2, O3) data and their health hazards: which is most accurate and why?

Air pollution is growing at alarming rates on regional and global levels, with significant consequences for human health, ecosystems, and change in climatic conditions. The present 12 weeks (4 October 2021, to 26 December 2021) study revealed the different ambient air quality parameters, i.e., PM2.5, PM10, SO2, NO2, and O3 over four different sampling stations of Delhi-NCR region (Dwarka, Knowledge park III, Sector 125, and Vivek Vihar), India, by using satellite remote sensing data (MERRA-2, OMI, and Aura Satellite) and different ground-based instruments. The ground-based observation revealed the mean concentration of PM2.5 in Dwarka, Knowledge park III, Sector 125, and Vivek Vihar as 279 µg m-3, 274 µg m-3, 294 µg m-3, and 365 µg m-3, respectively. The ground-based instrumental concentration of PM2.5 was greater than that of satellite observations, while as for SO2 and NO2, the mean concentration of satellite-based monitoring was higher as compared to other contaminants. Negative and positive correlations were observed among particulate matter, trace gases, and various meteorological parameters. The wind direction proved to be one of the prominent parameter to alter the variation of these pollutants. The current study provides a perception into an observable behavior of particulate matter, trace gases, their variation with meteorological parameters, their health hazards, and the gap between the measurements of satellite remote sensing and ground-based measurements.

Automated detection and recognition system for chewable food items using advanced deep learning models.

Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.