کائنات کے نظام کا مطالعہ کرنے سے پتہ چلتا ہے کہ یہاں کسی چیز کے لئے کبھی ہمیشگی نہیں ہے، سب کی ایک عمر مقرر ہے وہ اپنی عمر مکمل کرنے کے بعد ختم ہوجاتی ہے، اس کائنات کو کمال درجہ حکمت سے بنایا گیا ہے جس کی ہر شئے میں ایک قانون کار فرما ہے۔ دنیا میں نیکی و بدی، ظلم و عدل اور تعمیر و تحریک کرنے کے بعد مٹی میں مل جانا یہ حقیت نہیں ہے بلکہ ہر اچھائی اور برائی کا بدلہ ضرور ملے گا۔
حافظ عمران ایوب لاہوری اپنی کتاب ’’آخرت کی کتاب‘‘ میں لکھتے ہیں کہ:
"آخرت ان تمام امور کا نام ہے جو مرنے کے بعد انسان کو پیش آئیں گے آخرت کے لیے کہا گیا ہے کہ یہ دنیا کے بعد آنے والی ہے آخرت پر ایمان پر قائم ان میں سے ایک اہم رکن ہے کہ جب تک آخرت کے متعلق اللہ تعالی کی بتلائی ہوئی ہر خبر پر دل ایسا مطمئن نہ ہو جائے کہ کسی قسم کا شک و شبہ باقی نہ رہے تب تک ایمان بالآخرت کا حصول ممکن نہیں اور ایسا اعتقاد رکھنے والا ہی حقیقی مومن ہے اس کے علاوہ آخرت پر ایمان کا یہ فائدہ ہے کہ اس سے رغبت، خوف اور اعمال صالحہ کی بجاآوری کے ذریعے رب العالمین کی خوشنودی حاصل کرنے کا جذبہ پیدا ہوتا ہے ہر جاندار نے موت کا ذائقہ چکھنا ہے اور جو بھی ذی روح اس دنیا میں آیا ہے اس نے بالآخر یہاں سے رخصت ہونا ہے کوئی بھی موت سے نہیں بچ سکتا چاہے وہ خود کو مضبوط قلعوں میں بند ہی کیوں نہ کرلے اس لئے موت کو ہمیشہ یاد رکھنا چاہیے نبی صلی اللہ علیہ وسلم نے نصیحت فرمائی ہے کہ موت کو کثرت سے...
This study aims to analyze the relationship between customer relationship learning and service quality at Bank Pembangunan Daerah Jawa Timur Plc, Surabaya Region. This research uses quantitative methods with a descriptive research approach. The population was 1,329 savings customers in 10 locations of the Bank Pembangunan Daerah Jawa Timur Plc network in the Surabaya region, representing the eastern, western, northern and southern regions of Surabaya with 307 respondents. The results showed that customer relationship learning had an effect on the quality of PT Bank Pembangunan's service. The area of East Java Plc in the Surabaya area, this shows that with a customer relationship learning strategy, it will be able to increase service quality. Service quality is the totality of the characteristics of goods and services that demonstrate its ability to satisfy customer needs.
Environments for algorithms can be categorized as static or dynamic. A static environment remains stationary throughout the execution of the algorithm, while in a dynamic environment the environment changes during the execution of the algorithm. The algorithms for planning in static and dynamic environments can be divided into offline and online algorithms. This research implements an online algorithm for an unknown environment and combined exploration and planning in a hybrid architecture. A simulated system of agents based on swarm intelligence is presented for route optimization and exploration. Two versions of the system are implemented and compared for performance- i.e., a simulated ant agent system and a simulated niche based particle swarm optimization. A simulated ant agent system is presented to address the issues involved during route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated ant agent system (SAAS) is proposed using a modified ant colony optimization algorithm for dealing with online route planning. The SAAS generates and optimizes routes in complex and large environments with constraints. The traditional route optimization techniques focus on good solutions only and do not exploit the solution space completely. The SAAS is shown to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints, and its efficiency has been tested in a mine field simulation with different environment configurations. It is capable of tracking a stationary as well as a non-stationary goal and performs equally well as compared to moving target search algorithm. Route planning for dynamic environment is further extended by using another optimization technique for generation of multiple routes. Simulated niche based particle swarm has been used for dynamic online route planning, optimization of the routes, and it has proved to be an effective technique. It efficiently deals with route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated niche based particle swarm optimization (SN-PSO) is proposed using a modified particle swarm optimization algorithm for dealing with online route planning. The SN-PSO generates and optimizes multiple routes in complex and large environments with constraints. The SN-PSO is shown to be an efficient technique for providing safe, short,and feasible routes under dynamic constraints. The efficiency of the SN-PSO is tested in a mine field simulation with different environment configuration, and it successfully generates multiple feasible routes. Finally, the swarm based techniques are further compared with an evolutionary algorithm (genetic algorithm) for performance and scalability. Statistical results showed that evolutionary techniques perform well in less cluttered environments and their performance degrades with the increase in environment complexity. For small size maps, the evolutionary technique performs well but its efficiency decreases with an increase in map size.