Abstract:Carbon emission is one of the main drivers of global warming, due to which environmental concerns are rapidly increasing. To reduce the issue, nations are moving towards green vehicles, which significantly minimize the impact of transportation on the environment. The study's objective is to investigate the role of green perceived quality and green product availability in the green purchase intention of green automobiles with the moderating role of environmental education. The study is based on quantitative methodology and Partial Least Squares Structural Equation Modeling was used for data analysis. The data was collected from 900 consumers of environmentally friendly vehicles in Pakistan, China and Saudi Arabia. The findings show that green perceived quality and product availability have positively and significantly relationships with green purchase intention. Environmental education also moderates the relationship between green perceived quality and green product availability towards green purchase intention. The findings suggest that consumers with environmental education are more likely to consider green attributes like green perceived quality and green product availability when making purchasing decisions. In addition, the importance of environmental education in shaping consumers' attitudes towards green products and their willingness to make sustainable purchasing decisions is also evident. The study recommends that manufacturers improve environmentally friendly automobiles' perceived quality and availability to promote green purchase intention. The study has practical and managerial contribution and implications for the automobile industry and policymakers and advances the attainment of sustainability development goals (SDGs).
Abstract:Achieving sustainability is one of the critical challenges of the 21st century. This study examines the relationships of green resources and green technology with green manufacturing towards sustainability. It also investigates how environmental regulations moderate these relationships. The theoretical framework is based on command-and-control theory, resource-based theory, sustainable manufacturing frameworks and 4R theory. This study employs quantitative research methodology and uses partial least square structural equation modelling was used for the data analysis. Data was collected through Likert scalebased questionnaire from 600 respondents including engineers and managers of the manufacturing units in China and Pakistan. Purposive sampling technique was used for the selection of respondents. The finding shows that green resources and green technology have positive relationships with green manufacturing with a beta 0.331, t-statistics 4.249, and pvalue 0.000; and a beta 0.468, t-statistics 6.335, and p-value 0.000 respectively. Similarly, green manufacturing also has a positive and significant relationship with sustainability with a beta 0.610, t-statistics 7.634, and p-value 0.000. The research findings also reveal that environmental regulations play a moderating role in the relationships between green resources and green technology with green manufacturing with a beta 0.323, t-statistics 8.623, and pvalue 0.000; and a beta 0.203, t-statistics 11.364, and p-value 0.000 respectively. The findings provide useful insights for policymakers, practitioners, and researchers to enhance the efficacy of sustainability initiatives in the manufacturing sector using green manufacturing as a tool. Furthermore, it also helps in the advancement of sustainable development goals (SDG-7, SDG9, SDG-12, SDG-13).
Abstract:A sensitive and rapid method was developed to evaluate the flavonoids, iridoid glycosides, and phenolic acids in Gardenia jasminoides J. Ellis (GJE) from various regions, utilizing multiple reaction monitoring (MRM) of UFLC/QTRAP-MS in conjunction with principal component analysis (PCA) for simultaneous quantification. The findings revealed that all target constituents were accurately identified in GJE samples, confirming the method's efficacy for the concurrent determination of 21 chemicals. Notable discrepancies in the content of GJE from various places were noted, indicating that geographical differences and similarities, together with processing methods and harvesting time, significantly impact GJE composition. The PCA findings corresponded with the quantitative analysis. GJE from Jurong City, Jiangsu Province, was chosen as the herbal material for further tests due to its active ingredient composition, with Jiangsu Province recognized as the ideal location for GJE cultivation. This study offers a methodological framework for the optimal selection and thorough assessment of GJE quality in various locales.
Abstract:The main features of the aerodynamics of small-sized unmanned aerial vehicles, appearing in flight modes at maximum range and duration and distinguishing this class of aircraft from large-sized vehicles, are currently attracting increased attention. This is due to the increasing use and intensity of application of this class of aircraft. Negative increment of the lift coefficient at low angles of attack, although not quite a common phenomenon, but this behavior of profile characteristics can lead to a significant change in the stability and controllability characteristics of aircraft using such profiles in the design of all-turn rudders, or other surfaces. This can significantly complicate control system algorithms and require the installation of additional sensors. In this paper, based on laboratory experiments, we analyze how the shape of the tail part of the airfoil affects the variation of the lift coefficient at low angles of attack.
Abstract:The development of the circular economy model in Vietnam is still facing several shortcomings. One of the main issues is the lack of participation from farmers, which varies significantly among different localities and regions. This poses a significant challenge for researchers and policymakers who must collaborate to find effective solutions. To address this issue, a study was conducted using survey data from 600 farmers in the Mekong Delta region of Vietnam. By applying a Binary Logistic Regression model, the study identified nine key factors that affect farmers` participation in the circular economy. These factors include their attitude towards the circular economy, access to credit, level of interaction with agricultural extension staff, subjective norms, agricultural land area, non-agricultural income, education level, age of the household head, and distance from home to the nearest market.
Abstract:Gastric cancer, another name for stomach cancer, is a kind of cancer that begins as a cell growth in the stomach and has a poor diagnosis. The world`s pathologist shortage offers a unique chance to implement AI support systems to cut down on labor and boost diagnostic accuracy. It is believed that genetic instability, manifesting as either chromosomal instability or microsatellite instability, is a precursor to stomach carcinogenesis in the majority of cases of stomach cancer. The new categorization of stomach cancers based on histologic features, genetics and molecular phenotypes has improved early identification, prevention and therapy by illuminating the features of each subtype. Located directly below the ribs in the upper central region of the abdomen is the stomach. This research develops a solution using deep learning algorithms to aid in the pathological diagnosis of gastric cancer over Gastric Histopathology Sub-size Image Database, a publicly available database for medical image analysis. An advanced algorithm is created by combining three different algorithms, and it is then used to diagnose cancer more accurately. The combination of these three algorithms-Multitask Net, Fusion Net, and Global Net-creates a powerful ensemble model that leverages the strengths of each approach, leading to improved gastric cancer classification performance. This hybrid approach can aid in early diagnosis and treatment planning, ultimately improving patient outcomes.
Abstract:Assyrian plum or Lasura (Cordia myxa L.) is a widely recognized as diverse traditional medicinal plant. Different parts of this species have been utilized for the treatment of various diseases. However, information about the bioactive compounds and antimicrobial properties of Assyrian plum is scarce. This study was designed to analyze bioactive compounds, such as total antioxidants, phenolics, flavonoids, alkaloids, tannins, terpenoids, and saponins in 20% ethanol, methanol, acetone and aqueous extracts from different segments of Assyrian plum. The investigation revealed substantial amounts of these bioactive compounds in the studied segments, indicating the plant's potential therapeutic value. In addition, the highest amount of total protein content was found in ethanol extracts of root, fruit, leaves and stem (34.7 ± 1.08 mg/ml, 34.2 ± 1.16 mg/ml, 34.1 ± 1.12 mg/ml, 30.0 ± 0.02 mg/ml) respectively. Methanol extract of fruit exhibited the maximum quantity of total sugar (54.0 ± 0.32 mg/mL), while methanol extract of fruit displayed significantly higher levels of reducing sugar (0.159 ± 0.0004 mg/ml) compared to other parts of Assyrian plum extracts. The extracts of different parts of Assyrian plum strongly inhibited the growth of human pathogenic bacteria Escherichia coli and Staphylococcus aureus. Furthermore, 20% extracts of different segments of Assyrian plum also successfully ceased the growth of Aspergillus fumigatus and Aspergillus niger. This study provides valuable information about the bioactive composition of Assyrian plum and highlights its potential as a source of natural antimicrobial agents, supporting its traditional medicinal applications.
Abstract:Some sources of nitrogen fertilizer can be easily lost in the soil and result with yield reduction in grain sorghum. The objective of this study was to determine the effects of nitrogen fertilizer sources and field conditions on yield of grain sorghum. A 2 x 5 x 2 factorial field experiment arranged in a split-split plot design was executed in North-West University experimental farm during 2018/19 and 2019/20 planting seasons. The main plots were two field conditions (irrigated and rainfed), the subplots were two sorghum cultivars (Titan and Avenger) and the sub-subplots were nitrogen sources (LAN, Urea, ammonium sulphate, ammonium sulphate nitrate and control). The measured yield parameters were plant population, field biomass yield, panicle mass, grain yield and 1000 grain mass. Though no significant differences were observed amongst the nitrogen sources, sorghum treated with limestone ammonium nitrate showed a higher panicle mass of 6173.14 and 5979.08 kg/ha during the 2018/19 and 2019/20 planting seasons respectively. During the 2018/19 planting season, sorghum treated with limestone ammonium nitrate and ammonium sulphate nitrate had a higher grain yield of 4132.43 and 4088.33 kg/ha respectively. During the 2019/20 planting season, sorghum treated with ammonium sulphate nitrate, limestone ammonium nitrate and urea had a higher grain yield of 4656 kg/ha each. Field condition had a significant effect on sorghum panicle mass and grain yield during the 2018/19 and 2019/20 planting seasons. Sorghum planted under irrigation had a significantly higher panicle mass of 6316.53 and 6781.75 kg/ha during both planting seasons respectively. Sorghum planted under irrigation had a significantly higher grain yield of 4198.02 and 4823.59 kg/ha during both planting seasons respectively. In this study, limestone ammonium nitrate and urea are recommended to farmers since both fertilizers were found to improve sorghum biomass, panicle mass and grain yields. Irrigation field condition was found to enhance panicle mass and grain yield of sorghum.
Abstract:One of today's most quickly developing technologies is the Internet of Things (IoT). There are now more threats and risks to its security than ever before. In order to tackle present and future IoT issues, machine learning is an effective technology that can be used to identify risks and threats in intelligent systems. In today’s world, credit card is the most popular payment mode for both online and offline. Consumers rely on online shopping and online bill payment, which cases of fraud associated with it are also increasing. With the developments in the communication channels, fraud is spreading all over the world resulting in huge financial losses. Fraud detection is the essential tool and probably the best way to stop fraud types. There is a technique of finding an optimal solution for a problem and implicitly generate the results using machine learning and genetic algorithm. The aim is to develop a model to detect fraudulent transactions and improves a credit card fraud detection solution with some machine learning algorithms such as GA, DT, LR, KNN, SVC, and ANN based on the RUST and SMOTE techniques. The experiments are conducted on the BCCFDD and DCCCD datasets to analyze the model using the dimension reduction transformers (T-SNE, PCA, and Truncated SVD). The performance of the classification model analyzed in terms of confusion matrix, the model ROC curve analysis, and accuracy. The evaluation finding is analyzed and compared. As proof of concept, a Credit Card Fraud Detection System (CCFDS) is developed to detect the credit card fraud based on the principles of the GA and showed the effectiveness of proposed approach. This algorithm is an optimization technique and evolutionary search based on the principles of genetic and natural selection, heuristic used to solve high complexity computational problems.
Abstract:Salinity is one of the major constraints prevailing in the environment, affecting plant growth, agricultural productivity, and soil fertility. The application of beneficial silicon-rich bio-stimulants presents an alternative strategy to ensure agricultural sustainability, as their use and expansion can help mitigate the adverse effects of salinity and reduce the excessive use of synthetic chemicals. The objective of this study was to evaluate the interaction between salinity levels and the use of Farina di Basalto Type XF (FdBXF) on the germination and early seedling growth of two alfalfa (Medicago sativa L.) varieties (Gabes and Azzura). Five levels of sodium chloride (0, 50, 100, 150, and 200 mM) and five levels of Farina di Basalto Type XF (0, 1, 3, 5, and 10%) were tested in 30 treatments under a completely random design. Germination percentage, mean germination time, radicle length, hypocotyl and cotyledon length, and biomass were measured at the end of the experiments. Sodium chloride significantly reduced all germination and seedling growth parameters. The reduction in the measured parameters was inversely proportional to increasing concentrations of NaCl. The application of Farina di Basalto Type XF mitigated the negative effects of NaCl (p<0.01). The most significant attenuation was recorded for a 3% concentration of Farina di Basalto Type XF. This study provides insights into the role of Farina di Basalto Type XF in salt stress tolerance in M. sativa.