Search

In recent years, consumers have shown interest in non-destructive methods to assess the fruit's internal quality during ripening. The objective of this study is to construct an E-nose system using low-cost MQ sensors and evaluate fruit quality, specifically soluble sugar content (SSC) and hardness of mango during ripening. The correlation test was performed to compare sensor readings with SSC and hardness, and multiple linear regression (MLR) was used to establish linear equations for mango quality indices based on sensor variation. Over the storage period, the hardness of mango was decreased from the value of 15.4 kgf/cm² to 12.25 kgf/cm². Similarly, the SSC for mangoes increased from 19.7 %Brix to a final value of 24.66 %Brix. The sensor values also showed positive correlation with SSC and negative correlation with hardness of mango, respectively. Using the MLR analysis, the hardness and SSC of mango during the ripening stage, the correlation coefficient (r) of 0.847, standard error of 1.49 kgf/cm2 and 0.815, standard error of 1.696 %Brix for hardness and SSC prediction, respectively. These results indicate that MQ-based E-nose is the rapid and non-destructive method for predicting mango qualities during ripening stage.

Natural products are being developed as possible treatment options due to the rising prevalence of cancer and the harmful side effects of synthetic medications. Arctiin is a naturally occurring lignan found in numerous plants and exhibits different pharmacological activities, along with cancer. To elucidate the anticancer properties and underlying mechanisms of action, a comprehensive search of various electronic databases was conducted using appropriate keywords to identify relevant publications. The findings suggest that arctiin exhibits anticancer properties against tumor formation and various cancers such as cervical, myeloma, prostate, endothelial, gastric, and colon cancers in several preclinical pharmacological investigations. This naturally occurring compound exerts its anticancer effect through different cellular mechanisms, including mitochondrial dysfunction, cell cycle at different phases (G2/M), inhibition of cell proliferation, apoptotic cell death, and cytotoxic effects, as well as inhibition of migration and invasion of various malignant cells. Moreover, the study also revealed that, among the various cellular pathways, arctiin was shown to be more potent in terms of the PI3K/AKT and JAK/STAT signaling pathways. However, pharmacokinetic investigation indicated the compound's poor oral bioavailability. Because of these findings, arctiin might be considered a promising chemotherapeutic drug candidate.

This study aims to develop an automated image acquisition system for paddy leaves images and an image processing algorithm to estimate the severity level of the disease. The images were acquired using the developed system and processed using MATLAB software. By quantifying the total and infected leaf area based on pixel counting, the algorithm calculated the percentage of the infected leaf area using a ratio-based method. Through the image processing method, the study achieved a range of 15.53% to 41.23% of the total percentage of infected leaf area. In comparison, the leaf area meter method yielded a lower range of 23.49% to 28.98% of the total infected area. This indicates that the image-processing approach can capture a wider extent of infection. However, a deviation was observed between the two methods, ranging from -9.39% to 17.74% in the image processing results. Ensuring proper lighting conditions and installing an automatic camera movement system would significantly enhance the accuracy of disease severity measurements.

We investigate the hierarchical structure of Dhaka stocks' financial networks, known as an emerging market, from 2008 to 2020. To do so, we determine correlations from the returns of the firms over a one-year time window. Then, we construct a minimum spanning tree (MST) from correlations and calculate the hierarchy of the tree using the hierarchical path. We find that during the unprecedented crisis in 2010-11, the hierarchy of this emerging market did not sharply increase like in developed markets, implying the absence of a compact cluster in the center of the tree. Noticeably, the hierarchy fell before the big crashes in the Bangladeshi local market, and the lowest value was found in 2010, just before the 2011 Bangladesh market scam. We also observe a lower hierarchical MST during COVID-19, which implies that the network is fragile and vulnerable to financial crises not seen in developed markets. Moreover, the volatility in the topological indicators of the MST indicates that the network is adequately responding to crises and that the firms that play an important role in the market during our analysis periods are financial, particularly the insurance companies. We notice that the largest degrees are minimal compared to the total number of nodes in the tree, implying that the network nodes are somewhat locally compact rather than globally centrally coupled. For this random structure of the emerging market, the network properties do not properly reflect the hierarchy, especially during crises. Identifying hierarchies, topological indicators, and significant firms will be useful for understanding the movement of an emerging market like Dhaka Stock exchange (DSE), which will be useful for policymakers to develop the market.

Quercetin (QUA), a flavonoid compound, is ubiquitously found in plants and has demonstrated a diverse range of biological activities. The primary objective of the current study is to assess the potential antiemetic properties of QUA using an in vivo and in silico approach. In this experiment, 4-day-old chicks were purchased to induce emesis by orally administering copper sulfate pentahydrate (CuSO4·5H2O) at a dose of 50 mg/kg (orally). Domperidone (DOM) (6 mg/kg), Hyoscine (HYS) (21 mg/kg), and Ondansetron (OND) (5 mg/kg) were treated as positive controls (PCs), and distilled water and a trace amount of Tween 80 mixture was employed as a negative control (NC). QUA was given orally at two distinct doses (25 and 50 mg/kg). Additionally, QUA (50 mg/kg) and PCs were administered separately or in combination to assess their antagonistic or synergistic effects on the chicks. The binding affinity of QUA and referral ligands towards the serotonin receptor (5HT3), dopamine receptors (D2 and D3), and muscarinic acetylcholine receptors (M1–M5) were estimated, and ligand–receptor interactions were visualized through various computational tools. In vivo findings indicate that QUA (25 and 50 mg/kg) has a significant effect on reducing the number of retches (16.50 ± 4.65 and 10.00 ± 4.19 times) and increasing the chick latency period (59.25 ± 4.75 and 94.25 ± 4.01 s), respectively. Additionally, QUA (50 mg/kg) in combination with Domperidone and Ondansetron exhibited superior antiemetic effects, reducing the number of retches and increasing the onset of emesis-inducing time. Furthermore, it is worth noting that QUA exhibited the strongest binding affinity against the D2 receptor with a value of −9.7 kcal/mol through the formation of hydrogen and hydrophobic bonds. In summary, the study found that QUA exhibited antiemetic activity in chicks, potentially by interacting with the D2 receptor pathway.

Abstract Much research has been done on time series of financial market in last two decades using linear and non-linear correlation of the returns of stocks. In this paper, we design a method of network reconstruction for the financial market by using the insights from machine learning tool. To do so, we analyze the time series of financial indices of S&P 500 around some financial crises from 1998 to 2012 by using feature ranking approach where we use the returns of stocks in a certain day to predict the feature ranks of the next day. We use two different feature ranking approaches—Random Forest and Gradient Boosting—to rank the importance of each node for predicting the returns of each other node, which produces the feature ranking matrix. To construct threshold network, we assign a threshold which is equal to mean of the feature ranking matrix. The dynamics of network topology in threshold networks constructed by new approach can identify the financial crises covered by the monitored time series. We observe that the most influential companies during global financial crisis were in the sector of energy and financial services while during European debt crisis, the companies are in the communication services. The Shannon entropy is calculated from the feature ranking which is seen to increase over time before market crash. The rise of entropy implies the influences of stocks to each other are becoming equal, can be used as a precursor of market crash. The technique of feature ranking can be an alternative way to infer more accurate network structure for financial market than existing methods, can be used for the development of the market.

Magnolin is a naturally occurring, multi-bioactive lignan molecule with inherent anticancer effects. This study aims to summarize the botanical origins and anticancer properties of magnolin. For this, a recent (as of March 2023) literature review was conducted using various academic search engines, including PubMed, Springer Link, Wiley Online, Web of Science, Science Direct, and Google Scholar. All the currently available information about this phytochemical and its role in various cancer types has been gathered and investigated. Magnolin is a compound found in many different plants. It has been demonstrated to have anticancer activity in numerous experimental models by inhibiting the cell cycle (G1 and G2/M phase); inducing apoptosis; and causing antiinvasion, antimetastasis, and antiproliferative effects via the modulation of several pathways. In conclusion, magnolin showed robust anticancer activity against many cancer cell lines by altering several cancer signaling pathways in various non- and pre-clinical experimental models, making it a promising plant-derived chemotherapeutic option for further clinical research.

The feature ranking method of machine learning is applied to investigate the feature ranking and network properties of 21 world stock indices. The feature ranking is the probability of influence of each index on the target. The feature ranking matrix is determined by using the returns of indices on a certain day to predict the price returns of the next day using Random Forest and Gradient Boosting. We find that the North American indices influence others significantly during the global financial crisis, while during the European sovereign debt crisis, the significant indices are American and European. The US stock indices dominate the world stock market in most periods. The indices of two Asian countries (India and China) influence remarkably in some periods, which occurred due to the unrest state of these markets. The networks based on feature ranking are constructed by assigning a threshold at the mean of the feature ranking matrix. The global reaching centrality of the threshold network is found to increase significantly during the global financial crisis. Finally, we determine Shannon entropy from the probabilities of influence of indices on the target. The sharp drops of entropy are observed during big crises, which are due to the dominance of a few indices in these periods that can be used as a measure of the overall distribution of influences. Through this technique, we identify the indices that are influential in comparison to others, especially during crises, which can be useful to study the contagions of the global stock market.

Genomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identified 3334545 mutations with an average of 14.01 mutations per isolate. Globally, single nucleotide polymorphism (SNP) is the most prevalent mutational event. The prevalence of C > T (52.67%) was noticed as a major alteration across the world followed by the G > T (14.59%) and A > G (11.13%). Strains from India showed the highest number of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. D416G, F106F, P314L, UTR:C241T, L93L, A222V, A199A, V30L, and A220V mutations were found as the most frequent mutations. D1118H, S194L, R262H, M809L, P314L, A8D, S220G, A890D, G1433C, T1456I, R233C, F263S, L111K, A54T, A74V, L183A, A316T, V212F, L46C, V48G, Q57H, W131R, G172V, Q185H, and Y206S missense mutations were found to largely decrease the structural stability of the corresponding proteins. Conversely, D3L, L5F, and S97I were found to largely increase the structural stability of the corresponding proteins. Multi-nucleotide mutations GGG > AAC, CC > TT, TG > CA, and AT > TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C > T, A > G, and A > T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T > C, G > A, and G > T mutations, respectively. T > G/A, C > G/A, and A > T/C are not anticipated in the future. Since SARS-CoV-2 is mutating continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide.

It has, nowadays, become indispensable to get multi-functional properties from a single piece of fabric or garment in the sector of textile and fashion. Cotton fabric is now extremely being utilized in garments all over the world. At the raw stage, cotton fabric has less reflectance, high ignition tendency, moreover the proclivity to be attracted by microorganisms. The strength of the fabric might be deteriorated because of frequent processing. In this study, a single jersey raw (grey) cotton fabric of 150 GSM was processed with a pertinent chemical treatment to convert the reflectance to about 100 % and impart flame resistance property together with the other functional properties. Due to the attainment of high reflectance and flame resistance traits, the heat-absorbing tendency of the fabric became low which improved the comfortability of the fabric. The strength of the processed fabric was increased by 4 %. Crease recovery angle was increased by 57 % in the course way and by 82 % in wales way direction that attributed crease-resistance and more stability to the modified fabric. Moreover, the processed fabric was also able to exhibit antimicrobial properties. Strength improvement, attainment of crease-resistance, and antimicrobial property could enhance the longevity of the fabric. The attainment of multifunctional properties together with the strength improvement simultaneously provided a unique feature to the modified cotton fabric. It was a permanent chemical treatment, and it modified the surface of the fabric, which ultimately enhanced the proclivity of cotton fabric to be more compact or crystalline and more stable. The addition of the new quality of the cotton fabric could endure even after washing. The modified cotton fabric with newly added characteristics could be a good choice to prepare garments with felicitous longevity to protect from heat, fire flame sensitivity. It really could create an opportunity in multi-functional fabric processing as well as modern textiles.

The consumption of various forms of plastic has been increased in recent days due to the boost in industrialization and other human activities. Most of the plastic wastes are abandoned and require large landfill area for storage. More importantly, the low biodegradability of plastic poses a serious threat to environment protection issue. Various methods have been followed for the disposal of plastic in an attempt to reduce the negative impact of the plastic on the environment. Recently, various types of plastic have been incorporated in concrete to minimize the exposure of plastic to the environment. The aim of this study is to investigate the properties of concrete containing polyethylene terephthalate (PET), and high density polyethylene (HDPE) plastic that were used as partial replacement of coarse aggregate (CA). In this study, four compositions of stone aggregate(S): plastic waste ratios have been used by volume basis: 100% S: 0% Plastic (control concrete), 90% S: 10% PET, 90% S: 10% HDPE, and 90% S: 5% PET+5% HDPE. The effects of waste plastic addition on the mechanical properties of concrete are presented in this paper. Test results reveal that minimum reduction in compressive strength has been found 35% in case of 10% PET plastic replaced concrete whereas splitting tensile strength for 10% PET replaced concrete has been increased by 21% while compared to control concrete. In addition, fresh unit weight of concrete containing plastic waste has been decreased by 4% in comparison to control concrete.

This paper examines the torque ripple and cogging torque variation in surface-mounted permanent-magnet synchronous motors (PMSMs) with skewed rotor. The effect of slot/pole combinations and magnet shapes on the magnitude and harmonic content of torque waveforms in a PMSM drive has been studied. Finite element analysis and experimental results show that the skewing with steps does not necessarily reduce the torque ripple but may cause it to increase for certain magnet designs and configurations. The electromagnetic torque waveforms, including cogging torque, have been analyzed for four different PMSM configurations having the same envelop dimensions and output requirements. Index Terms--Back-EMF (BEMF) harmonics, phase advancing, phase lagging, step skew, torque ripple.