وصیتِ علم و عمل
وجود ِ انسانی کے ارتقا کی تاریخ کو نظر ِ غائر سے دیکھا جائے تو اس کی تمام تر ترقی ’’ علم ــ‘‘ کی مرہون منت ہے۔علم ہی وہ اکائی ہے جس میں تہذیب و تمدن اور تربیت کے سوتے پھوٹتے دکھائی دیتے ہیں۔علم کی خصوصیت کی وجہ سے انسان اشرف المخلوقات ہے اس کے سبب سے اسے فرشتوں پر فضیلت ملی اور اسی کی بدولت خلافت کا تاج سر پرسجا۔حد تو یہ ہے کہ پہلی وحی کا آغاز ہوا۔ارشاد ربانی ہے ترجمہ:۔ ’’اپنے پروردگار کے نام سے پڑھ جس نے انسان کو جمے ہوئے خون سے پیدا کیا‘‘۔یہ بھی ارشاد ر بانی سنتے چلیے ۔ ترجمہ:۔’’ اللہ تم میں سے ایمان والوں اور علم والوں کے درجات بلند فرماتا ہے‘‘۔قرآن کریم میں ہی اللہ پاک نے اپنے نبی مکرم ﷺ کو یہ دعا عطا فرمائی ۔ترجمہ:۔ ’’کہو ،اے میرے رب میرے علم میں اضافہ فرما‘‘۔ حدیث شریف میں آتا ہے کہ ’’ علم حاصل کرناہر مسلمان (مرد اور عورت)پر فرض ہے‘‘ یہی وہ علم ہے جس کی افضلیت کے پیش نظر حضرت علی کرم اللہ وجہ فرماتے ہیں’’ ہم اللہ تعالیٰ کی اس تقسیم پر راضی ہیں کہ اس نے ہمیں علم عطا کیا اور جاہلوں کو دولت دی کیوں کہ دولت تو عنقریب فنا ہوجائے گی اور علم کو زوال نہیں‘‘۔
تاریخ انسانی میں ایک خواہش جو اپنے تمام تر مدارج سمیت جھلک رہی ہے وہ یہ ہے کہ ہر شخص اپنی جدا گانہ شناخت اور منفرد پہچان کا متمنی ہے اور اس خواہش کی تکمیل کے لیے مثبت اعمال و افعال بروئے کار لا کر ہی ازلی و ابدی پہچان تک رسائی حاصل کر لینا اصل شناخت اور پہچان ہے ۔اہل علم جانتے ہیں کہ یہ اسی وقت ممکن ہے جب علم کواوڑھنا بچھونابنا لیا جائے اور فضل باری تعالیٰ...
Strengthening rehabilitation in health systems and integrating rehabilitation across all levels of care depends on a mix of strategies, however all depend on an appropriately trained, resourced and organized workforce. Indeed, among the ten areas for action described in the World Health Organization 2030 initiative is developing a strong multidisciplinary rehabilitation workforce that is suitable for country context, and promoting rehabilitation concepts across all health workforce education.
The rehabilitation workforce is constantly evolving as it strives to provide safe practices and treatment choices based on the best available evidence to improve function, promote independence and help people reach their maximum potential. However, barriers to this evolution include a lack of well-resourced training programs, variations in the competencies expected within the standard entry-level curriculum, limited opportunities for continuing professional development, geopolitical instability, competing demands for limited health budgets and persistent de-prioritization of rehabilitation.
Yield forecasting is becoming increasingly important in the context of climate variability and change using approaches like remote sensing and crop modeling. Climate variability and change are affecting crops and efficiency of input resources. The situation is demanding efficient management of input resources. Changing climate is also affecting current production technology which needs modification using modern tools. Two field experiments were conducted at Water Management Research Center, University of Agriculture Faisalabad, Pakistan. Field experiments addressed above mentioned issues. The first experiment included full (100%) and three reduced levels (80%, 60% and 40%) of irrigation with four levels of nitrogen (160, 200, 240 and 280 kg ha-1) at different critical growth stages of maize. Second experiment involved four sowing dates (i.e.27 January, 16 February 8 March and 28 March) and three maize hybrids (i.e. P-1543, DK6103 and NK8711) during the years 2015 and 2016. Different system approaches were used to optimize the volume of irrigation, amount of nitrogen (N) and sowing date for maize hybrids. CERES-Maize model was calibrated and evaluated with second experiment. Calibrated model was used to explore the effects of climate variability and climate change (CC) at regional scale. Different adaption strategies were developed to mitigate the negative effects of climate change. Remote sensing framework was used for regional yield forecasting that can assess seasonal and interannual variability. In first experiment results from model and economic analysis showed that the N rates of 235, 229, 233, and 210 kg ha-1 were the most economical optimum N rates to achieve the economic yield of 9321, 8937, 5748 and 3493 kg ha-1 at 100%, 80%, 60% and 40% irrigation levels, respectively. The optimum level of irrigation was 250 mm. The results of second experiment revealed that grain yield was continuously decrease with delay in Planting date, among maize hybrids Poineer-1543 performed best in spring season. Model parametrization results showed a reasonably good result in prediction of biological and grain yield with RMSE values of 963 kg ha-1 and 451 kg ha-1. Different GCMs were used for understanding the CC impacts, which indicated that there would be increase of 3.4°C in maximum and 3.8°C in minimum temperature in hotdry GCM. The reduction in maize yield due to rise in temperature will be 27% under mid-century (2040-2069). Different adaptations options could be used with RAPs then maize yield would be increased by 15%.Landcover classification of maize were done by Machine Learning algorithms which estimate 14% less area reported by Reporting Service (CRS) of Punjab Pakistan for 2015 and 2016. For yield forecasting, seasonal multitemporal, a total of 8 LST and NDVI values for 64 farms were taken to develop model. Model was used to predict the yield of previous 10 year (2007-2016) which showed a high accuracy with mean % error of 1.25. Seasonal mean Tmax and Tmin of 10 years with predicted yield showed a negative relationship with Tmax. (R2= 0.76) and Tmin. (R2= 0.69). It can be concluded from the study that modern tools are very helpful to optimize input resources to ensure food security.