Search Results (44)

The valuation of Goodwill (GW) has remained one of the several critical issues in financial analysis. This aspect is particularly important for mergers and acquisitions due to the significance of intangible assets. This study delves into the capitalization method of super profit (CMSP), a prominent technique for GW valuation, enhanced by the integration of statistical tools. Assessing a company’s excess profits over its average return on tangible assets is part of the CMSP. Finding the variables that have a significant impact on GW valuation, such as average profit, capital employed, and rate of return, is the main goal of this research. These issues are thoroughly investigated through statistical analysis to give stakeholders useful information for well-informed decision-making. Additionally, the study seeks to identify the external elements influencing this process as well as the internal aspects influencing GW valuation. Regression analysis, correlation matrices, response analysis and ANOVA are used to improve GW assessment and comprehension of the complex relationships between different factors. Full article

Herbal spices are an agricultural commodity, economically very important and beneficial in primary healthcare in the food and medicine sectors. Herbal spices are used as food flavoring agents as well as in phytotherapies throughout the world and have nutritive benefits. The food and medicine industries widely employ artificial or natural adulteration to retard the deterioration and utilization of these adulterants in food and medicine products has given rise to significant apprehension among consumers, primarily stemming from the potential health risks that they pose. Thus, their characterization for the purpose of identification, origin, and quality assurance is mandatory for safe human consumption. Here, we studied 22 samples of commonly traded herbal spices that belong to 20 different genera and 21 species comprising 14 families, investigated macroscopically or organoleptically as well as histologically under microscopic examination. In this study, we provide details on organoleptic features including appearance, taste, odor, color, shape, size, fractures, types of trichomes, and the presence of lenticels among the examined herbal spices and these features have great significance in the detection of both natural as well as artificial deterioration. In terms of microscopic characterization, each examined plant part comprising different anatomical characteristics has taxonomic importance and also provides useful information for authentication from natural adulterants. Furthermore, the studied taxa were also described with nutritive and therapeutic properties. For condiments, herbal beverages and medicinal purposes, different herbal parts such as leaves, floral buds, seeds, fruit, and accessory parts like mericarp, rhizome, bulbs, and bark were used and commercially traded. Similarly, in this study, the leaves of Cinnamomum tamala and Mentha spicata, the floral buds of Syzygium aromaticum, the seeds of Amomum subulatum, Brassica nigra, Punica granatum, Myristica fragrans, Phyllanthus emblica, and Elettaria cardamomum, the mericarp of Coriandrum sativum, and Cuminum cyminum were observed. As a result, we show the potential of herbal spices as a source of many valuable phytochemicals and essential nutrients for food, nutraceutical, and homoeopathic medicine. Full article

Artificial aggregate (AF), i.e., silico manganese (SiMn) slag aggregate, is a byproduct of ferromanganese and silico manganese alloy production. The utilization of industrial waste and industrial byproducts in construction has increased the aim of conserving natural resources to nurture a pollution-free environment. The current study examines the performance of the use of artificial aggregate (AF) and partial replacement of cement with fly ash (FA). The properties of fresh concrete, as well as the compressive and flexural strength and split tensile strength of concrete were evaluated. Seven mix proportions were prepared for M30-grade concrete. The first was a control mix (with 0% AF and FA), three other mixes contained varying amounts of AF (20%, 40%, and 60%) as a partial replacement of CA with AF. The average compressive strength of the control SCC was found to be 32.87 MPa (megapascals) at the age of 28 days, and after replacing 20% natural aggregate with artificial aggregate, the compressive strength increased by 8.27%, whereas for 40% and 60% replacement, it decreased by 4.46% and 12.55%, respectively. Further investigation was performed on the optimum value obtained by replacing 20% of CA with AF. At this percentage, cement was replaced by FA at (15%, 25%, and 35%) where at 15%, the average compressive strength increased by 7.41%, whereas for 25% and 35% replacement, it decreased by 7.47% and 17.19%, respectively. For SCAF20 and SCF15, all strengths were at maximum due to the increase in its density. The findings show that the development of advanced construction materials is environmentally sustainable. Full article

Over the last decades, cement has been observed to be the most adaptive material for global development in the construction industry. The use of ordinary concrete primarily requires the addition of cement. According to the record, there has been an increase in the direct carbon footprint during cement production. The International Energy Agency, IEA, is working toward net zero emissions by 2050. To achieve this target, there should be a decline in the clinker-to-cement ratio. Also, the deployment of innovative technologies is required in the production of cement. The use of alternative binding materials can be an easy solution. There are several options for a substitute to cement as a binding agent, which are available commercially. Non-crystalline alkali-aluminosilicate geopolymers have gained the attention of researchers over time. Geopolymer concrete uses byproduct waste to reduce direct carbon dioxide emissions during production. Despite being this advantageous, its utilization is still limited as it shows the quasi-brittle behavior. Using different fibers has been started to overcome this weakness. This article emphasizes and reviews various mechanical properties of fiber-reinforced geopolymer concrete, focusing on its development and implementation in a wide range of applications. This study concludes that the use of fiber-reinforced geopolymer concrete should be commercialized after the establishment of proper standards for manufacturing. Full article

The study utilizes a dataset with seven critical constraints and creates models that are estimated based on various metrics. The goal is to categorize and properly predict the water quality index (WQI) using the suggested models. The outcomes show that the implied models can accurately assess water quality and forecast WQI with high rates of success. Temperature, pH, dissolved oxygen (DO), conductivity, total dissolved solids (TDS), turbidity, and chlorides (Cl-) are some of the six crucial factors used in the study’s dataset. The mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R²) are some of the metrics used to develop and assess the Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models. The study also makes use of heat maps and correlation graphs to shed further light on the connections between various water quality measures. The color-coded values of the seven parameters, which represent the water quality level of the sample, are displayed on the heat map. The link between the two parameters is shown by the correlation graph between TDS and turbidity, which depicts their correlation coefficient. The study’s results show how effective machine learning algorithms may be as a tool for observing surface water quality. Himachal Pradesh is the tourist hub, so with the rapid increase in the volume of surface water contamination, the application of artificial intelligence will give a better view of data analytics and help with prediction and modeling. It was obtained from the study that the mean square error and root mean square error of ANN and LSTM lie between 0.52–6.0 and 0.04–0.21, respectively. However, the LSTM model’s accuracy is 95%, which is higher than the ANN model. The study highlights the importance of leveraging machine learning techniques in water quality monitoring to ensure the protection and management of water resources. With advancements in machine learning, artificial intelligence (AI) techniques have emerged as a promising tool for surface water quality monitoring. The major goal of the study is to explore the potential of two types of machine learning algorithms, namely artificial neural networks (ANNs) and long short-term memory (LSTM) models, for surface water quality monitoring. Full article

In the present study, the quality of the surface water of the Gomti river (Lucknow, India) was investigated. Lucknow is situated in the centre of Uttar Pradesh, which is most the populated state in India. The locality has experienced rapid, unregulated development activities and population growth in recent decades, both of which have had a negative impact on its ecosystem and environment. Continuous monitoring is required to maintain the ecosystem at the desired level. Nine samples of river water were collected from the Gomti River in Lucknow, and they were analysed for a total of nine different characteristics, including pH, turbidity (Tur), dissolved oxygen (DO), total dissolved solids (TDSs), chemical oxygen demand (COD), chloride ion (Cl-) concentration, temperature (T), biochemical oxygen demand (BOD₅) and total hardness (TH). The observed data were analysed using multivariate statistical methods. A cluster analysis (CA) was used to sort the sampling locations into different groups, and a principal component analysis (PCA) was used to find the different sources of pollution. Using a cluster analysis, all the water quality parameters were divided into three groups. Cluster 1 represented the less polluted sites, cluster 2 represented the moderately polluted sites and cluster 3 represented the highly polluted sites. Sampling sites SS8, SS4, S99 and SS7 were highly polluted because of nearby pollution sources such as domestic wastewater and runoff storm water. The principal component analysis yielded two meaningful components that explained 82.4% of the total variation in the data. The first factor and second factor explained 59.022 and 23.363 percentages of the total variance, respectively. It was noticed that major sources of pollution for the Gomti river are storm water runoff and the release of domestic and industrial wastewater from residents and industries, respectively. This study will help policy makers to ensure sustainable practices and reduce negative impacts on the availability and quality of water, allowing for the most efficient use of the Gomti River. Full article

Globally, lung cancer remains one of the leading causes of cancer-related mortality, warranting the exploration of novel and effective therapeutic approaches. Soy-fermented food products have long been associated with potential health benefits, including anticancer properties. There is still a lack of understanding of the active components of these drugs as well as their underlying mechanistic pathways responsible for their anti-lung cancer effects. In this study, we have undertaken an integrated approach combining network pharmacology and molecular docking to elucidate the mechanism of action of soy-fermented food products against lung cancer through simulation and in vitro validation. Using network pharmacology, we constructed a comprehensive network of interactions between the identified isoflavones in soy-fermented food products and lung cancer-associated targets. Molecular docking was performed to predict the binding affinities of these compounds with key lung cancer-related proteins. Additionally, molecular simulation was utilized to investigate the stability of the compound–target complexes over time, providing insights into their dynamic interactions. Our results identified daidzein as a potential active component in soy-fermented food products with high binding affinities towards critical lung cancer targets. Molecular dynamic simulations confirmed the stability of the daidzein–MMP9 and daidzein–HSP90AA1 complexes, suggesting their potential as effective inhibitors. Additionally, in vitro validation experiments demonstrated that treatment with daidzein significantly inhibited cancer cell proliferation and suppressed cancer cell migration and the invasion of A549 lung cancer cells. Consequently, the estrogen signaling pathway was recognized as the pathway modulated by daidzein against lung cancer. Overall, the findings of the present study highlight the therapeutic potential of soy-fermented food products in lung cancer treatment and provide valuable insights for the development of targeted therapies using the identified bioactive compounds. Further investigation and clinical studies are warranted to validate these findings and translate them into clinical applications for improved lung cancer management. Full article

With increasing population and industrialization, the water quality of freshwater sources like rivers, lakes, and ponds is becoming increasingly degraded. Most of the rivers in India are becoming polluted, including the Godavari. With the construction of dams, new industries and unsustainable agricultural practices in the Godavari basin, the water characteristics are degrading spatially and temporally. The present study emphasizes the analysis of water quality parameters like temperature, pH, Dissolved Oxygen (DO), conductivity, Biological Oxygen Demand (BOD), nitrate, and faecal coliform concentration in the Godavari basin. This was achieved by analysis of data taken from the Central Pollution Control Board, India (CPCB) for 21 stations around the Godavari basin over a span of five years from 2015 to 2019. The Pearson Correlation coefficient for the water quality parameters was assessed to study the relationship among the parameters. Variation in the water quality parameter is observed from the graphs for each station for respective years. It was found that conductivity and DO, temperature and pH and DO and faecal coliform are negatively correlated. It was also observed that DO has a negative correlation with pH, BOD and faecal coliform, indicating the utilization of dissolved oxygen at higher rates due to increasing degradation of organic matter by aerobic microorganisms in the river. One-way ANOVA was applied to find out significant temporal variations and it was observed that temperature, pH, and faecal coliform level had significantly changed the overdue course of time (F(4, 115) = 2.451, p < 0.05). The obtained results from the analysis indicate that the selected water quality parameters have varied significantly spatially, whereas temporally, according to the ANOVA coefficient, only temperature, pH and faecal coliform had shown significant differences during the selected timeframe. Hence, the present study highlighted the deteriorating water quality of the Godavari River over time. Full article

The failure of an embankment causes loss of lives, massive damage to infrastructure and the interruption of basic facilities; it has thus drawn increasing attention from researchers. When compared to other types of embankment disasters, overtopping-related embankment breaches are much more frequent. The study of the breach mechanism of embankments due to overtopping is becoming more and more essential for developing evacuation plans, early warning systems and damage assessment. To recognize the breach activities of embankments, it is necessary to find out discrete breach considerations like breach depth, breach initiation, breach width, etc. In the present study, a total of six tests were performed in a narrow flume using an embankment model. By conducting different experiments, it was observed that embankment breaching may be described in three stages, i.e., initial erosion, headcut erosion and lateral erosion. Furthermore, erosion is a three-dimensional process that occurs during embankment breaching, with the majority of erosion movement being associated with lateral broadening. The rate of headcut migration also has an impact on the widening rate. Furthermore, it depends upon the type of fill material and dam geometry. Also, the observed effect of moisture content on breach widening proved that the rate of widening was strongly influenced by water content. A drop of about 50% in moisture content causes approximately a 20% decrease in time to failure. In the present study, it is observed that breach shape could not be assumed to be regular shape like rectangle or trapezoid, as described in the literature. The trials were carried out in a narrow flume under constant hydraulic conditions, which are two of the study’s limitations. Full article

Globally, breast cancer (BC), the second-biggest cause of cancer death, occurs due to unregulated cell proliferation leading to metastasis to other parts of the human organ. Recently, the exploration of naturally derived anticancer agents has become popular due to their fewer adverse effects. Among the natural products, soybean is a very well-known legume that contains important bioactive compounds such as diadazine, glycetin, genistein, and genistin. Therefore, keeping its therapeutic potential in mind, multi-targeted molecular docking and simulation studies were conducted to explore the potential role of soybean-derived isoflavone genistin against several breast cancer-signaling proteins (ER-alpha, ER-Beta, collapsin response mediator protein 2, CA 15-3, human epidermal growth factor receptor 2). A comparative study of the genistin-protein docked complex was explored to investigate its potential role in BC. The molecular binding energy (∆G) of the docked complex was calculated along with ADMET properties. The molecular docking score of genistin with ubiquitin-like protein activation complex-a type of Cancer Antigen (CA) 15.3 (PDB ID-2NVU, 5T6P, and 1YX8) showed the highest binding energy, ranging from −9.5 to −7.0 Kcal/mol, respectively. Furthermore, the highest docking scores of the complex were additionally put through molecular dynamics (MD) simulation analysis. MD simulations of the selected complex were performed at 100 ns to study the stability of the genistin-ubiquitin-like protein CA 15.3 complex, which appeared to be quite stable. Additionally, the ADMET study demonstrated that genistin complies with all drug-likeness standards, including Lipinski, Egan, Veber, Ghose, and Muegge. Therefore, based on the results, genistin can be considered as one of the potential drugs for the management and treatment of BC. In addition, the obtained results suggest that genistin could pave the way for new drug discovery to manage breast cancer and has potential in the development of nutraceuticals. Full article

The study of embankment breaching is not an easy practice, as it includes various parameters to meet the suitability of the design approach, especially when we consider it for the long term. Embankment breach studies generally include the prediction of different breach parameters. The important physical and hydrodynamic parameters of the flood wave generated from the embankment failure are breach width, breach slope, formation time, peak outflow, and time to failure. Out of these parameters, peak outflow is a very important breach parameter, as it deflects the magnitude of destruction on the downstream side of the embankment and affects the evacuation plans for the downstream population. The prediction of breach peak outflow due to overtopping of the embankment is very essential for dam failure prevention and mitigation, as well as for the design of an early warning system. Many researchers have used dam failure data, comparative studies, experimental studies, or regression techniques to develop various models for predicting peak outflow. The present paper analyzes the results of the design for forty experiments carried out in two different laboratory water channels (flumes). Different embankment models are overtopped with the objective of studying the breach behavior during overtopping. The study was inspired by reports in the open literature of embankment failures that resulted in catastrophic conditions. With experimental data, an efficient model is developed for predicting breach peak outflow (Q_p) by correlating with other independent embankment breach parameters for cohesive as well as non-cohesive embankments. The model is validated with historical and laboratory data compiled in the past. For the validation of current investigational work, the experimental data of the present study are compared with the model already developed by other researchers. From the study and analysis, it is observed that breach peak outflow depends upon hydraulic, geometric, and geotechnical parameters of embankments. Being a phenomenon that is active for a short duration only, the manual measurement of various parameters of the modeling process poses some limitations under laboratory conditions. Full article

One of the most valuable approaches in spatial analysis for a better understanding of the hydrological response of a region or a watershed is certainly the analysis of the well-known land use land cover (LULC) dynamicity. The present case study delves deeper into the analysis of LULC dynamicity by using digital Landsat TM and Landsat OLI data to classify the Kolkata Metropolitan Development Authority (KMDA) into seven classes with over 90% classification accuracy for decadal level assessments of 30 years (for the years 1989, 1999, 2009, and 2019). The change index, the Dematel method for analyzing the cause-effect relationship among the LULC classes, the Jaccard Similarity Index for measuring the nature of similarity among the LULC classes, and the Adherence Index for measuring the consistency of the LULC classes after the transition was used in this study to analyze the LULC transformation. In more detail, the present study considers how urban land use is altering at the expense of other land uses. Besides the shifting pattern of mean centers of the LULC classes through time, also gives a very significant insight into the LULC dynamics over 30 years of span. The current study of LULC dynamicity and transformation patterns over the 30 years of the KMDA area is expected to assist land and urban planners, engineers, and administrators in sustainable decisions and policies to ensure inclusive urbanization that accommodates population growth while minimizing the impact on potential natural resources within the whole study area. Full article

Pakistan has an annual deforestation rate of 4.6% which is the second highest in Asia. It has been described by the Food and Agriculture Organization (FAO) that the deforestation rate increased from 1.8–2.2% within two decades (1980–2000 and 2000–2010). KPK (Khyber Pakhtunkhwa), Pakistan’s northwestern province, holds 31% of the country’s total forest resources, the majority of which are natural forests. The Malam Jabba region, known for its agro-forestry practices, has undergone significant changes in its agricultural, forestry, and urban development. Agricultural and built-up land increased by 77.6% in the last four decades, and significant changes in land cover especially loss in forest, woodland, and agricultural land were observed due to flood disasters since 1980. For assessing and interpreting land-cover dynamics, particularly for changes in natural resources such as evergreen forest cover, remote sensing images are valuable assets. This study proposes a framework to assess the changes in vegetation cover in the Malam Jabba region during the past four decades with Landsat time series data. The random forest classifier (RF) was used to analyze the forest, woodland, and other land cover changes over the past four decades. Landsat MMS, TM, ETM+, and OLI satellite images were used as inputs for the random forest (RF) classifier. The vegetation cover change for each period was calculated from the pixels using vegetation indices such as NDVI, SAVI, and VCI. The results show that Malam Jabba’s total forest land area in 1980 was about 236 km² and shrank to 152 km² by 2020. The overall loss rate of evergreen forests was 35.3 percent. The mean forest cover loss rate occurred at 2.1 km²/year from 1980 to 2020. The area of woodland forest decreased by 87 km² (25.43 percent) between 1980 and 2020. Other landcover increased by 121% and covered a total area of 178 km². The overall accuracy was about 94% and the value of the kappa coefficient was 0.92 for the change in forest and woodland cover. In conclusion, this study can be beneficial to researchers and decision makers who are enthusiastic about using remote sensing for monitoring and planning the development of LULC at the regional and global scales. Full article

On a local and regional level, climate change has had a significant impact on precipitation in the global climatic state. The purpose of this research is to examine the trend and character of urban precipitation in the world’s most densely inhabited metropolis. From 1981 to 2020, 40 years of monthly and annual precipitation data from 50 major metropolitan cities throughout the world, based on population statistics, were analysed. The monthly and annual precipitation analysis was done using a homogeneity test, shifting point test, non-parametric Modified Mann Kendall test, and also through computing the magnitude of the trend using Sen’s slope estimate. According to the findings of the study, the most homogeneous data was obtained in May (90 %) and the least in September (74%). In 2002, the highest number of breakpoints were found in July (9 cities) and August (8 cities). The month of January has the largest significant positive trend (10 cities) whereas annually it has 20 cities. The monthly maximum of the significant negative trend was discovered in February (4 cities) and annually in 2 main cities. In November, the maximum positive and minimum positive Sen’s slope values were found to be 82% and 56%, respectively. The findings of this study are important for future water resource projections, flood or drought predictions, and engineering, scientific, industrial, agricultural, and social studies. The goal of this research is to come up with a good plan for dealing with urban flash floods and droughts as precipitation acts as the key parameter of the hydrological cycle. Full article

This work is a climatological evaluation of terrestrial water storage anomalies (TWSAs), which act as driving forces for sustainable development, in one of the most populous countries of the world. The objective of this work is to evaluate RL06 mascon data from the GRACE and GRACE-FO satellite missions over India to explore seasonal and interannual changes in terrestrial water storage, encompassing an area of ~3.29 million km² with 285 grid points, from 2002 through to 2020. Several statistical tests are performed to check the homogeneity (i.e., Pettitt’s test, the BRT, the SNHT, and the VNRT). Most of the homogeneous data are found in winter, pre-monsoon, and post-monsoon, approximately above 42% to 47%, and the least are found in monsoons and annual with only 33%, at a 95% significance level. According to Pettitt’s test, the majority of the breakpoints are present in 2014 for winter, 2012 for pre-monsoon, 2011 for monsoons and post-monsoon, and 2008 as well as 2011 for annual. Furthermore, to detect trends and magnitudes we employed the nonparametric MK test, the MMK test, Sen’s slope estimator, and the parametric SLR test. According to the MK and MMK tests, the most significant negative and positive trends indicate the chances of droughts and floods, respectively. The Indo–Gangetic region shows the highest declination. According to Sen’s slope and the SLR test, the most declining magnitude is found in Delhi, Panjab, Uttrakhand, the northern part of Rajasthan, and Uttar Pradesh. Based on our findings, the average declining rate of yearly terrestrial water storage data from the MK, MMK, and SLR tests is −0.0075 m (−0.75 cm/year) from 2002 to 2020. Koppen–Geiger climate zones are also used to depict the seasonal and interannual descriptive statistics of TWSA trends. Interestingly, the annual means of arid desert cold (−0.1788 cm/year) and tropical savanna (−0.1936 cm/year) have the smallest declining trends when compared to other climatic zones. Northern Indian regions’ temperate dry winter, hot/warm summer, and dry arid steppe hot regions show the maximum declining future trend. This study could be useful in planning and managing water resources, agriculture, and the long-term growth of the country by using an intelligent water delivery system. Full article