Genotypic selection and trait variation in sweet orange (Citrus sinensis L. Osbeck) dataset of Bangladesh.

The dataset primarily focused on selecting genotypes of sweet oranges based on their phenotypic performances. The dataset resulted significant variations in the best linear unbiased predictions (BLUPs) of 20 out of 21 traits, including leaves, flowers, fruits, and seeds. A strong positive correlation (r= 0.73 to 0.95) was observed among the majority of morphological traits. The sweet orange genotypes demonstrated considerable genetic variance, surpassing 65% for almost all traits, with a selection accuracy exceeding 92%. Using the multi-trait genotype-ideotype distance index (MGIDI), CS Jain-001 emerged as the top-ranked genotype, followed by BAU Malta-3 and CS Jain-002 in order of desirability. The broad sense heritability of selected traits was above 75.60%, and the selection gain reached a maximum of 12.60. These identified genotypes show promise as potential parent donors in breeding programs, leveraging their strengths and weaknesses to develop promising varieties in Bangladesh.

Demand and Supply Disruptions During the Covid-19 Crisis on Firm Productivity.

This paper explores the supply chain (SC) disruption impacts to the performance outcomes of a semiconductor company during the Covid-19 pandemic and proposes appropriate risk mitigation strategies to overcome the crisis. The research uses a single case study methodology and 24 SC employees from Belgium and Germany who take part in the survey. To measure the effect of SC disruptions to the firm's financial performance, some quarterly financial statement data are used from 2018 to 2021. The regression analysis results show that there is no significant impact of SC disruptions to the firm's productivity and non-financial performance. The paired samples t-test suggests that there is no significant change in the firm's financial performance before and during Covid-19 either due to the market's political and economic stability or the semiconductor company develops effective SC risk management strategies.

Genome-wide investigation of SnRK2 gene family in two jute species: Corchorus olitorius and Corchorus capsularis.

BACKGROUND: Sucrose non-fermenting-1 (SNF1)-related protein kinase 2 (SnRK2), a plant-specific serine/threonine kinase family, is associated with metabolic responses, including abscisic acid signaling under biotic and abiotic stresses. So far, no information on a genome-wide investigation and stress-mediated expression profiling of jute SnRK2 is available. Recent whole-genome sequencing of two Corchorus species prompted to identify and characterize this SnRK2 gene family. RESULT: We identified seven SnRK2 genes of each of Corchorus olitorius (Co) and C. capsularis (Cc) genomes, with similar physico-molecular properties and sub-group patterns of other models and related crops. In both species, the SnRK2 gene family showed an evolutionarily distinct trend. Highly variable C-terminal and conserved N-terminal regions were observed. Co- and CcSnRK2.3, Co- and CcSnRk2.5, Co- and CcSnRk2.7, and Co- and CcSnRK2.8 were upregulated in response to drought and salinity stresses. In waterlogging conditions, Co- and CcSnRk2.6 and Co- and CcSnRK2.8 showed higher activity when exposed to hypoxic conditions. Expression analysis in different plant parts showed that SnRK2.5 in both Corchorus species is highly expressed in fiber cells providing evidence of the role of fiber formation. CONCLUSION: This is the first comprehensive study of SnRK2 genes in both Corchorus species. All seven genes identified in this study showed an almost similar pattern of gene structures and molecular properties. Gene expression patterns of these genes varied depending on the plant parts and in response to abiotic stresses.

The effect of global price movements on the energy sector commodity on bitcoin price movement during the COVID-19 pandemic.

This study aims to determine the effect of global price movements for energy sector commodities, especially Crude Oil and Natural Gas Prices, on cryptocurrency price movements. This study focuses more on the Bitcoin cryptocurrency. This study uses quantitative methods, and the data collection used is secondary data with weekly data and the period from January 1, 2020-July 31, 2021. The number of observations used in this study amounted to 79 observations. Secondary data sources are obtained through the website finance.yahoo.com. The data processing technique will be carried out using Stata and SPSS software, the Multiple Linear Regression method, and the Classical Assumption Test. The results of this study show that global prices for energy sector commodities, especially Crude Oil, Natural Gas, have a positive effect on Bitcoin price movements. These results indicate a link between energy and Bitcoin caused by Bitcoin miners who are mining Bitcoin using energy so that when the price of Bitcoin rises, the price of energy will also increase.

The Effect of Transformational Leadership and Remote Working on Employee Performance During COVID-19 Pandemic.

The COVID-19 outbreak has emphasized the importance of leadership style in achieving organizational performance. It also implies changes in administrative processes to remote working, impacting employee activities, and performance. Employee performance is one of the aspects that might influence a company's success. If employees are productive and provide high-quality work, the company's performance will increase. This study aims to analyze the effect of Transformational Leadership and Remote Working on Employee Performance during the COVID-19 pandemic. This study was conducted by performing a questionnaire distribution survey and acquiring 136 respondents. The research was conducted on a Jakarta area company that had implemented Remote Working. The number of samples in this study was determined using a simple random sampling procedure, in which the sampling was done at random without consideration for the population's existing strata. This study uses a quantitative approach method based on factual data and research data in statistical figures related to concluding research problems. The source of data in this study is primary data that was directly obtained from sources without going through intermediaries. This study shows that Transformational Leadership has no significant impact on Employee Performance during the COVID-19 pandemic, and Remote Working has a significant impact on Employee Performance during the COVID-19 pandemic.