Determinants of Stock Market Investors’ Behavior in COVID-19: A Study on the Pakistan Stock Exchange

As one of the most contagious diseases in history, Corona Virus (COVID-19) spread rapidly around the world infecting millions of people in the year 2020. Besides killing huge number of persons, the calamity not only ignited severe panic and chaos among them, it even affected vast businesses and stock markets around the globe. This study was undertaken to investigate those determinants, which affected the extent of stock market investors’ behavior in Pakistan during spread of COVID-19. Data was collected from various individual investors, representing a high response rate of about 86.5% from 167 respondents. From the analysis it was indicated that most important and significantly influencing determinants on investment decisions in Pakistan Stock Markets are pertinent to: ‘getting rich quickly, loss aversion, fear of losses, expected corporate earnings and dividends, gut feelings on the economy, previous performances of firm’s stock and opinions of its majority shareholders, and eventually, the recommendations of brokers and family/friends. Our findings would first, assist in understanding the most common behavioral patterns of investors and secondly, determine to show the adequate paths, which lead towards the growth of Pakistan Stock Market

Combining sociocognitive-transformative approach and form-focused instruction: effects on L2 learners’ complexity, accuracy and fluency in writing

Many pedagogical approaches have attempted to systematically integrate form-focused instruction (FFI) into L2 writing, namely the process approach, the product approach, the post-process approach, and the process-genre approach. However, these approaches continue to provide conflicting findings on how they can improve students’ overall writing skills and grammatical accuracy and fail to consider the sociocognitive aspect of L2 writing. Thus, the current study examined the effects of a combined sociocognitive-transformative (ST) approach and FFI on L2 writers’ complexity, accuracy, and fluency (CAF) in writing. This quasi-experimental study involved 72 students from a private university in Pakistan. The findings revealed that L2 writers significantly improved in all fluency measures and in certain accuracy and complexity measures after being exposed to the treatment. The improvement in writing fluency was attributed to their increased rhetorical awareness and focus on content during writing. Meanwhile, the improvement in accuracy was linked to the contextualised teaching of linguistic items and learners’ psycholinguistic readiness in learning these items. Finally, the mixed results in fine-grained measures of accuracy and complexity were linked to the possible interaction between these measures. Implications for L2 writing pedagogy and future studies are discussed

Clay mineralogy of the Gandak megafan and adjoining areas, Middle Gangetic Plains, India / Minéralogie des argiles du " mégafan " de Gandak et des régions avoisinantes, Moyennes Plaines du Gange, Inde

Soils of the Gandak megafan and adjoining areas in the Middle Gangetic Plains, India, are of ages less than 11 ka and contain mainly illite, kaolinite, interstratified kaolinite-smectite, chlorite, vermiculite and small amounts of mixed layer illite-vermiculite-chlorite. The amount and variability of clay minerals are significantly related to duration of pedogenesis in different parts of the area. On the Gandak megafan, chlorite is present in very young soils (Active Floodplain and Young Plain of the Gandak) and is replaced by vermiculite in older soils (Older Gandak Plain) and vermiculite, too, decreases drastically in still older soils (Oldest Gandak Plain). Kaolinite and interstratified kaolinite-smectite increase with increase in development of soils from the Active Floodplain and Young Plain of the Gandak to the Oldest Gandak Plain.Minéralogie des argiles du "mégafan" de Gandak et des régions avoisinantes, Moyennes Plaines du Gange, Inde — Les sols du "mégafan" de Gandak dans les Moyennes Plaines du Gange (Inde) ont un âge inférieur à 11 ka et contiennent principalement de l'illite, de la kaolinite, des interstratifiés kaolinite-smectite, de la chlorite, de la vermiculite et de faibles quantités d'interstratifiés illite-vermiculite-chlorite. Les teneurs et variations des minéraux argileux sont fonction de la durée de la pédogenèse dans les différentes parties du secteur étudié. Sur le "mégafan" de Gandak, la chlorite est présente dans les sols très jeunes (plaine alluviale active et plaine jeune du Gandak) et est remplacée par la vermiculite dans les sols plus anciens (plaine plus ancienne du Gandak) ; et la vermiculite à son tour voit sa teneur diminuer beaucoup dans les sols encore plus anciens (plaine la plus ancienne du Gandak). La kaolinite et les interstratifiés kaolinite-smectite augmentent en fonction du degré de la pédogenèse, depuis la plaine alluviale active et jeune, jusqu'à la plaine la plus ancienne du Gandak.Mohindra Rakesh, Parkash B. Clay mineralogy of the Gandak megafan and adjoining areas, Middle Gangetic Plains, India / Minéralogie des argiles du " mégafan " de Gandak et des régions avoisinantes, Moyennes Plaines du Gange, Inde. In: Sciences Géologiques. Bulletin, tome 43, n°2-4, 1990. Minéraux argileux dans les sols et les sédiments. pp. 203-212

Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively

An analytical and experimental study of performance on jatropha biodiesel engine

Biodiesel plays a major role as one of the alternative fuel options in direct injection diesel engines for more than a decade. Though many feed stocks are employed for making biodiesel worldwide, biodiesel derived from domestically available non-edible feed stocks such as Jatropha curcas L. is the most promising alternative engine fuel option especially in developing countries. Since experimental analysis of the engine is pricey as well as more time consuming and laborious, a theoretical thermodynamic model is necessary to analyze the performance characteristics of jatropha biodiesel fueled diesel engine. There were many experimental studies of jatropha biodiesel fueled diesel engine reported in the literature, yet theoretical study of this biodiesel run diesel engine is scarce. This work presents a theoretical thermodynamic study of single cylinder four stroke direct injection diesel engine fueled with biodiesel derived from jatropha oil. The two zone thermodynamic model developed in the present study computes the in-cylinder pressure and temperature histories in addition to various performance parameters. The results of the model are validated with experimental values for a reasonable agreement. The variation of cylinder pressure with crank angle for various models are also compared and presented. The effects of injection timing, relative air fuel ratio and compression ratio on the engine performance characteristics for diesel and jatropha biodiesel fuels are then investigated and presented in the paper

Do Socio-Economic factors really Influence risk taking Behavior of individual Investors?

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Enoki Mushroom (Flammulina velutipes (Curtis) Singer) Breeding

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Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed

The effects of recalling and imagining prompts on writing engagement, syntactic and lexical complexity, accuracy, and fluency: A partial replication of Cho (2019)

This replication study examined the effects of writing prompt type on second language (L2) learners’ writing performance. Fifty undergraduate academic and professional writing course pupils wrote narrative essays about a past event (recalling group/high formulation demand condition) or a future event (imagining group/high conceptualization demand condition). Writers completed a freewriting draft and were then given unlimited opportunities to revise. The writing was subjected to syntactic complexity, fluency, accuracy, and lexical complexity analyses. Writer engagement was computed as the time spent revising drafts. The previous study’s results were confirmed in that the recalling group exhibited more complexity and less accuracy in their writing than the imagining group. The recalling group also exhibited a higher level of writing fluency and possessed a higher level of engagement. Furthermore, the results of our study showed that the imagining group produced writing that was slightly more lexically complex than the recalling group. The pedagogical importance of writing prompts and their potential for affecting writing performance and writing engagement was discussed

Water and Nitrogen Dynamics in Drip Fertigated Tomato for Water of Different Qualities under Polyhouse Conditions

Water and NO3-N dynamics in the soil during the growing season is an important tool in improving the nitrogen management and environmental protection. HYDRUS-2D has been widely used to predict the water and NO3-N distribution in the soil. The objective of this study was to simulate the water and NO3-N distribution in the soil under drip fertigated tomato irrigated with different water qualities under polyhouse conditions. Field data were collected on spatial and temporal distribution of water and available NO3-N during growing season. The model was calibrated for the hydraulic conductivity and parameters were used for the validation of the model. The model performance in simulating the water and NO3-N was evaluated by using coefficient of determination (R2), root mean square error (RMSE), index of agreement and Nash–Sutcliffe model efficiency (NSE). For both calibration and validation, the higher values of R2 from 0.70 to 0.99 for water distribution and 0.70 to 0.96 for NO3-N distribution showed that observed and predicted values are highly correlated. The value of RMSE ranges from 0.004 to 0.0016 for water and 0.002-0.006 for NO3-N distribution. The index of agreement value varied from 0.86-0.98 for water distribution and 0.89-0.99 for NO3-N distribution. The values of NSE (nearer to 1) i.e. 0.17 to 0.98 for water distribution and -0.09 to 0.94 for NO3-N distribution show that HYDRUS-2D was predicting with good accuracy. From these results, it can be concluded that the model performs well for predicting the water and NO3-N distribution in the tomato crop irrigated with different water qualities under polyhouse conditions