Behaviourism, Innatism, Cognitivism: Considering the Dominance to Provide Theoretical Underpinning of Language Acquisition Conjecture

The language specialists have discerned that language is a species-specific and a biologically determined scheme for the human beings After a child is born it goes under pre-linguistic and linguistic stages of language acquisition Although there are many different approaches to learning three basic kinds of learning theory are prominent like Behaviourism Innatism and Cognitivism All these theories centered around nature and nurture theories or on empiricism and nativism concepts According to empirical research usually knowledge comes through experience from the environment Nativism holds that at least some knowledge is not acquired from the environment but is genetically transmitted and innate The theoreticians never agree or disagree with any of these theories whether environmentalist or nativist The principle focus of this study is to investigate the dominance among three main doctrines by delving into the fundamental differences among them The specification of these theories is also given prominence in this article Finally in the findings session it has been tried to trace the dominance of one particular theory among other

Effect of Explainable Artificial Intelligence and Decision Task Complexity on Human-Machine Symbiosis

Artificial Intelligence (AI) is a tool that augments various facets of decision making. This disruptive technology is helping humans perform better and faster with accuracy (Grigsby 2018). There are tasks where AI decides in real-time without human intervention. For example, AI can approve or decline a credit card application without any human intervention. On the other hand, there are tasks where both AI and human reasoning is required to make the decision. For instance, automated employee selection decision requires a higher level of human involvement. Interaction between humans and machines is required in such decisions. Grigsby (2018) posits that the interaction becomes effective when the machine understands human and human understands machine. This interplay is called human-machine symbiosis that merges the best of the human with the best of the machine. The human decision-makers need to understand how the machine is reaching to a specific prediction. One tool that facilitates this understanding by increasing the interpretability of the algorithm is Explainable AI (XAI). XAI is a tool that explains the results to the decision-maker in a human-understandable manner (Rai 2020). As a result, the decision is more transparent and fairer. Other than the benefits of transparency and fairness, there is an emerging regulatory requirement for explaining machine-driven decisions. The General Data Protection Regulation addresses the right to explanation by enabling the individuals to ask for an explanation for algorithm’s output (Selbst and Powles 2017). That is why the decision-makers need to convert their decision-making tool from a black box to a glass box. To enhance the explainability and interpretability, two broad categories of XAI techniques are model-specific XAI and model-agnostic XAI (Rai 2020). The model-specific techniques incorporate interpretability in the inherent structure of the learning model whereas the model-agnostic techniques use the learning model as an input to generate explanation. These models ensure transparency and fairness in human-machine decision making. Another important factor for effective human-machine symbiosis is decision task complexity (Grigsby 2018). Task complexity in decision making can be characterized by the number of desired outcomes, conflicting interdependencies among outcomes, path multiplicity, and uncertainty (Campbell 1988). When the decision-making task is unstructured and complicated, then the decision-maker’s need for understanding the algorithmic process increases. Moreover, decision task complexity is a factor of trust in the autonomous system, and trust is a factor of human-machine symbiosis (Grigsby 2018). Furthermore, decision task complexity is related to the mental workload and cognitive ability of the decision-makers (Grigsby 2018; Speier and Morris 2003). In the extant literature, there is a gap in explaining how the interplay between XAI techniques and decision task complexity impacts the decision makers perception about the human-machine symbiosis. Therefore, the objective of this research is to investigate the effect of XAI and decision task complexity on perceived human-machine symbiosis. Using the theories of information overload and algorithmic transparency, we develop a causal model to explain the relationship. We will run a randomized 2×2 factorial experiment to test the model. The paper will have theoretical and practical implications

An Exertion to Alleviate Stitch Defects During Garments Production

In garments industry sewing is the most critical phase during an apparel production. Different types of sewing and stitching defects are occured in this phase due to various problems. As today’s world each customer is expecting a very high quality garments with product variety, it has become a very challenging task for garments quality management. Here all the data were collected from Gardenia Wears Limited situated at Barmi- Sreepur, Gazipur, Dhaka and data were analyzed for reducing Defects per Hundred Unit (DHU%) and also top 10 stitch defects were identified and analyzed later. Finally the work shows reduction of DHU% from 5.23% to 3.48% and also reduce the top ten stitch defects with comparison to before trial and after trial data of ten days and it is proved that an industry can achieve higher production capability & profitability with improved quality product and also saving cost due to reducing DHU%

Potentially mineralizable nitrogen in ten important sub - tropical soil series of Bangladesh

Knowledge on nitrogen (N) supplying capacity of inherent soil organic matter helps farmers and extension workers to determine the N fertilization rate. However, this information is very scanty particularly in developing countries like Bangladesh. Thus, this research work was conducted to estimate potentially mineralizable nitrogen (N) in ten important soil series of Bangladesh namely Jamalpur, Silmondi, Sonatala, Ghatail, Tejgaon, Chandra, Khilgao, Kalma, Brahmaputra and Sherpur. Net N mineralization in these soil series was measured during 100 days laboratory incubation at 200C at field capacity. Findings revealed that eight out of ten soil series had silt loam texture and the rest two were loams. All soils were slightly acidic in reaction. The organic carbon and total N contents of the soils varied from 5.24 to 17.4 and 0.53 to1.48 g kg soil-1, respectively with the C:N ratios of 9.89 to 11.76. The amount of net N mineralization and nitrification (NO3-1-N) increased linearly with time; however, ammonification (NH4+-N) did not follow any definite trend. Majority of mineralized N undergone nitrification falling within the ranged between 30 and 128 mg/kg soil/98 days. The net N mineralization (mg NH4+-N+NO3-1-N kg-1 soil) varied from 51 mg N kg-1 soil in Tejgaon soil series to 154 mg N kg-1 soil in Khilgaon soil series accounting for 4.3 to 10.8% of total N mineralized 120 days-1. The rate of N mineralization also varied widely from 1.19 to 0.28 (mg N kg-1 soil day-1) like the net N mineralization. The highest rate of N mineralization was observed in Khilgaon soil series followed by Chandra, Ghatail, Silmondi, Sherpur, Jamalpur, Sonatola, Kalma, Bramaputra silt and Tejgaon soil series. N mineralization was significant and positively correlated with organic carbon and total N and NH4+ -N contents of soils. Khilgaon and Chandra soil series have shown greater potentiality in supplying nitrogen under aerobic condition

Vulnerability to HIV and AIDS: a social research on cross border mobile population from Bangladesh to India

"Baseline Research on cross border migration was initiated to understand the drivers of mobility, access to services for migrants at source and destination, and to understand the risk and vulnerabilities associated with migration and HIV and AIDS. The study was conducted using quantitative methods and a separate qualitative study was conducted to enhance and complement the quantitative data.

Understanding online information avoidance behavior during a crisis

Online information avoidance is a behavior of delaying or rejecting information consumption from online sources. It is an understudied construct in information systems research; however, information avoidance is studied extensively in economics, psychology, health, and media disciplines. Economists argue that rational agents avoid information when they feel it is detrimental to their economic outcome (Golman et al., 2017; Gul, 1991). Psychologists identify different predictors of information avoidance behavior, such as individual differences, motivations, and situation factors (Sweeny et al., 2010). Health information researchers also identify different psychological factors as predictors of information avoidance behavior, particularly in terminal diseases such as cancer (Miles et al., 2008). Crisis literature suggests that people receive information from different sources in such unprecedented times, and online platforms have become one of the dominant sources. Crisis information from different online sources provides different psychological stimuli, shaping people's perceptions and behaviors in a crisis (Savage, 2020). While prior studies provide explanations of individual information avoidance behavior, there is not much attempt to identify how these findings relate to online information avoidance in a crisis. To understand the online information avoidance behavior in a crisis, we investigate online information avoidance in two different crises: a health crisis and a humanitarian crisis. Using the COVID-19 pandemic as the health crisis, essay one investigates how individuals' fear and situational motivation impact online information avoidance. Using the self determination and information avoidance theories, we argue that fear and situational motivation constructs impact online information avoidance through response efficacy, optimism, and coping self-efficacy. From a pooled cross-sectional survey study, we find that fear and external regulation increase online information avoidance, whereas identified regulation is a significant inhibitor of online information avoidance. We also find that response efficacy, optimism, and coping self-efficacy mediate the relationship. Our robustness analysis using Important Performance Map Analysis (IPMA) and Artificial Neural Network (ANN) robustness checks support these results. Information sources often take a partisan position during a humanitarian crisis such as the Russia-Ukraine war. In that scenario, individuals with a need to consume information framed in a neutral way or individuals with a partisan view may not find information that matches their worldview. This deviation is referred to as expectation violation in communication and media research. Extant literature explains how information consumer's expectation violation can impact objectivity and trust; however, how these relationships will hold in a humanitarian crisis and how these mechanisms lead to online information avoidance are major research questions. Using expectation violation, objectivity, and trust theories, essay two argues that violation expectedness, source importance, and valence will impact online information avoidance through the mediation of perceived objectivity and source trust. We have generated interesting insights from a multi-country survey study based in Poland and the United States. In Poland, violation expectedness increases online information avoidance significantly, and the importance of the relationship with the information source is a significant inhibitor of online information avoidance. Moreover, both trust and perceived objectivity mediate the relationship. In the USA, source importance and valence are important inhibitors of online information avoidance. However, only trust mediates the relationships. Our IPMA and ANN robustness analyses support these results. While focusing on two different contexts, our studies contribute to the broader information systems research literature and specifically to the information avoidance literature during a crisis. Our study contributes to the literature by introducing online information avoidance as a vital outcome behavior after people are exposed to a myriad of information during a crisis. At a practical level, our studies’ findings will be helpful for online information providers, governments, response organizations, and communities who utilize online platforms, forums, and related outlets to reach larger audiences for disseminating pertinent information and recommendations during a crisis

Deep Phenotyping of Non-Alcoholic Fatty Liver Disease Patients with Genetic Factors for Insights into the Complex Disease

Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver disorder characterized by the excessive accumulation of fat in the liver in individuals who do not consume significant amounts of alcohol, including risk factors like obesity, insulin resistance, type 2 diabetes, etc. We aim to identify subgroups of NAFLD patients based on demographic, clinical, and genetic characteristics for precision medicine. The genomic and phenotypic data (3,408 cases and 4,739 controls) for this study were gathered from participants in Mayo Clinic Tapestry Study (IRB#19-000001) and their electric health records, including their demographic, clinical, and comorbidity data, and the genotype information through whole exome sequencing performed at Helix using the Exome+$^\circledR$ Assay according to standard procedure (www$.$helix$.$com). Factors highly relevant to NAFLD were determined by the chi-square test and stepwise backward-forward regression model. Latent class analysis (LCA) was performed on NAFLD cases using significant indicator variables to identify subgroups. The optimal clustering revealed 5 latent subgroups from 2,013 NAFLD patients (mean age 60.6 years and 62.1% women), while a polygenic risk score based on 6 single-nucleotide polymorphism (SNP) variants and disease outcomes were used to analyze the subgroups. The groups are characterized by metabolic syndrome, obesity, different comorbidities, psychoneurological factors, and genetic factors. Odds ratios were utilized to compare the risk of complex diseases, such as fibrosis, cirrhosis, and hepatocellular carcinoma (HCC), as well as liver failure between the clusters. Cluster 2 has a significantly higher complex disease outcome compared to other clusters. Keywords: Fatty liver disease; Polygenic risk score; Precision medicine; Deep phenotyping; NAFLD comorbidities; Latent class analysis.Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 11 page

Cynomolgus macaque TRIMCyp-resistant HIV-1

Old World monkey TRIM5α strongly suppresses human immunodeficiency virus type 1 (HIV-1) replication. A fusion protein comprising cynomolgus macaque (CM) TRIM5 and cyclophilin A (CM TRIMCyp) also potently suppresses HIV-1 replication. However, CM TRIMCyp fails to suppress a mutant HIV-1 that encodes a mutant capsid protein containing a SIVmac239-derived loop between α-helices 4 and 5 (L4/5). There are seven amino acid differences between L4/5 of HIV-1 and SIVmac239. Here, we investigated the minimum numbers of amino acid substitutions that would allow HIV-1 to evade CM TRIMCyp-mediated suppression. We performed random PCR mutagenesis to construct a library of HIV-1 variants containing mutations in L4/5, and then we recovered replication-competent viruses from CD4+ MT4 cells that expressed high levels of CM TRIMCyp. CM TRIMCyp-resistant viruses were obtained after three rounds of selection in MT4 cells expressing CM TRIMCyp and these were found to contain four amino acid substitutions (H87R, A88G, P90D and P93A) in L4/5. We then confirmed that these substitutions were sufficient to confer CM TRIMCyp resistance to HIV-1. In a separate experiment using a similar method, we obtained novel CM TRIM5α-resistant HIV-1 strains after six rounds of selection and rescue. Analysis of these mutants revealed that V86A and G116E mutations in the capsid region conferred partial resistance to CM TRIM5α without substantial fitness cost when propagated in MT4 cells expressing CM TRIM5α. These results confirmed and further extended the previous notion that CM TRIMCyp and CM TRIM5α recognize the HIV-1 capsid in different manners

Implementation of the World's largest measles-rubella mass vaccination campaign in Bangladesh: a process evaluation

Background: Gavi, the Vaccine Alliance, supported a mass vaccination Measles-Rubella Campaign (MRC) in Bangladesh during January–February 2014