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آرتھر ہیورڈ

مسٹر آرتہر ہیورڈ

لندن کے امپرئیل کالج آف سائنس میں مسٹر آرتہر ہیورڈ سائنس سے متعلق ایک تجربہ کرتے کرتے وفات پاگئے، وہ کچھ عرصہ سے فن تصویر کشی (فوٹوگرافی) سے متعلق تجربات میں مشغول تھے، اور آخری تجربہ ایک تاریک کمرہ کے اندر کررہے تھے، کمرہ میں روشنی کا کسی۔۔۔۔ ان سے گذر نہ تھا، اور اس کی چھت اور دیواریں سیاہ رنگ سے رنگی ہوئی تھیں، کمرہ اندر سے بند تھا، کہ وفعتہ زور سے ایک تڑاقا ہوا، مسٹر موصوف کے استاد پروفیسر بون باہر تھے، وہ یہ آواز سن کر دوڑے کمرہ کے اندر انھوں نے جھانکا تو معلوم ہوا بجلی کی روشنی ہورہی ہے، ایک ہتھوڑا لے کر انہوں نے دروازہ توڑا اور اندر گئے تو دیکھا کہ مسٹر ہیورڈ خون میں شرابور پڑے ہوئے ہیں اور دیکھتے ہی دیکھتے ختم ہوگئے، پروفیسر بون کی رائے میں جس آلہ سے وہ تجربہ کررہے تھے، اتفاقاً پھٹ گیا اور اس کے اندر جو زہریلی گیس تھی، اس کے صدمہ سے ان کی گردن سخت مجروح ہوئی اور یہی باعث ہلاکت ہوا۔ (فروری ۱۹۲۱ء)

PASTEL Analysis of Micro, Small and Medium Enterprises After Covid-19 in India

Micro, Small and Medium Enterprises are major sector in Indian economy in relation with GDP (Gross Domestic Product), Export and Employment generation for the country. According to Ministry of Statistic and Programme Implementation (MOSPI), the share of MSME for Gross Value Added (GVA) in total GVA during the year 2016-17 was 31.8% which is considered as significant contribution to economy. As per Directorate General of Commercial Intelligence and Statistics (DGCIS) the portion of MSME related products in total export from India during 2018-19 was 48.10% with this it is indicated most important sector for economy but after declaration of lockdown due to Covid-19 that lead to major impact on MSME sector. In this study researcher try to identify the PESTEL Environment after Covid-19 and ATMA-NIRBHAR BHARAT Abhiyan initiated by Indian Government on 12th May 2020. The major finding of the study indicated major decision are taken by government of India and Atma-Nirbhar Bharta Abhiyan give boost to MSMEs in future and widely increases number of MSMEs. In India movement also started Vocal for Local that lead to strengthen MSMEs in future.

Additive Main Effect and Multiplicative Interaction Analysis in Bread Wheat-Derived Lines Across Environment

Identification of high yielding stable genotypes is an integral objective of plant breeding programs. Testing of genotypes across environments is required to determine yield stability of genotypes. The specific objective of the current study was to analyze genotype by environment interaction (GEI) of grain yield for 50 genotypes using the additive main effects and multiplicative interaction (AMMI) model. Experiments were planted in an alpha lattice design with two replicates in Peshawar (E-1 and E-3), Hangu (E-2 and E-4) and Kohat (E-5) Khyber Pakhtunkhwa province, Pakistan during 2011/12 and 2012/13. Analysis of variance revealed significant differences among genotypes for all traits, while interactions due to genotype by environment were significant for all traits except days to emergence and 1000-grain weight. Significant GEI justified environment-specific as well as AMMI analysis to identify genotypes with specific and wider adaptation. The AMMI analysis revealed that the first interaction principal component analysis (IPCA 1) captured 64.0% of GEI sum of squares while the second interaction principal component analysis (IPCA 2) explained 25.8% of the interaction sum of square. The AMMI biplot identified G30 as a high yielding genotype followed by G19 and G49, whereas low yielding genotypes were G13, G8 and G7. Being close to IPCA1 axis, the most stable genotype with wider adaptability was G30 followed by G31 and G25. Based on AMMI stability value (ASV), genotypes G18 (2.15), G5 (2.78), G27 (3.72), G44 (4.31), G25 (4.43), G42 (4.57), G43 (5.78), G11 (5.82), G1 (7.66) and G29 (7.81) were found in the given order of relative stability. GGE biplot analysis explained 79.9% (PCA1=56.6 and PCA2= 23.3%) of the total variation. Genotype G19 positioning on vertex in sector E-3, E-4 and E-5, while G30 in sector E-1 and E-2 revealed their specific suitability to respective environments. GGE biplot identified environment E-4 as the most representative environment, whereas G49, G30, G22 and G45 as the high yielding genotypes. Shifted multiplicative model (SHMM) grouped genotypes into four clusters based on similarity/dissimilarity index for grain yield. High yielding and stable genotypesG19, G49 and G30 were placed in group B. Grain yield had positive association with tillers m-2 (r =0.73**), grain weight spike-1 (r =0.57**), biological growth rate (r =0.44**), grain growth rate (r = 0.80**), biological yield (r = 0.41**) and harvest index (r = 0.55*). The SHMM clustering and correlations of yield with other traits inferred that tillers m-2, grain weight spike-1, biological growth rate, grain growth rate, biological yield and harvest index contributed towards higher grain yield. Therefore, these traits could be used as selection criteria for the improvement of grain yield in bread wheat. Stability analysis identified G49 (Wafaq × Ghaznavi-98-3) as a high yielding stable genotype among breeding lines which can be commercialized after fulfilling procedural requirements
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