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Background: Genetic causes of miscarriages have been extensively reported and studied in almost half of the first-trimester pregnancy losses, for that reason genetic investigations of the products of conceptions (POCs) are crucial to help identify possible genetic etiology. This information is helpful for the parents as it provides them with more acceptance of the loss and helps them recognize possible recurrence risks. Objective: This study aims to evaluate Quantitative Fluorescent-PCR (QF-PCR) for chromosomal aneuploidies detection in product of conception (POCs) as an alternate to conventional karyotyping and to explore potential parental chromosomal abnormalities causing recurrent spontaneous miscarriages. Methodology: A total of seventy-six products of conceptions (POCs) were obtained from the Gynecology department of Civil Hospital, Karachi. Further blood samples from forty-five couples were also collected in addition to ninety-three maternal blood samples. All of these samples were subjected to amplify short tandem repeat (STR) for common chromosomal aneuploidies (13, 18, and 21). Real-time QF-PCR was performed on 76 POCs on Sansure Biotech Inc, Changsha, China, using primers D13S631 and D13S634 for chromosome 13, D18S386 and D18S535 for chromosome 18, and D21S1411 and D21S1414 for chromosome 21. Parental blood samples were examined by both QF-PCR and karyotyping for cross-checking with their POC. Marker on Chromosome 16 was used as an internal control for the amplification of each reaction. Statistical analysis was done by using SPSS version 24. Results: QF-PCR analysis revealed chromosomal aneuploidies among twenty-nine POCs with the highest number of trisomy 13 followed by trisomy 21 and then trisomy 18 while all the parental samples were normal. Nine cases of chromosomal abnormalities were ruled out through karyotyping from parental blood samples further, a Robertsonian ttranslocation involving chromosome 21 was observed through conventional cytogenetics and interestingly the corresponding POC was found to have trisomy 21. Conclusion: This study’s findings endorsed QF-PCR as an efficient technique to detect chromosomal aneuploidies. Advantages of QF-PCR over conventional karyotyping is that it is less laborious, has lesser turnaround time, is more economical, and has a low failure rate.

The success of sustainability is mainly linked to the energy sector, which is governed by the mix of different fuel resources. This work aims to calculate the sustainability performance scores (SPSs) of the energy alternatives that constitute Pakistan’s current electric energy mix and future energy plans. For this purpose, a decision framework consisting of eighteen indicators is considered for evaluating the performance of energy alternatives. The SPSs of energy alternatives are calculated using the dimensional reduction feature of the principal component analysis technique. The resultant prioritization of energy alternatives favors solar resource the most for planning a roadmap for sustainable net-zero energy infrastructure development. Based on the outcomes, recommendations are put forward to improve the current energy situation so that the country could catch the pathway towards achieving energy sustainability.

Recommender systems are intelligent data mining applications that deal with the issue of information overload significantly. The available literature discusses several methodologies to generate recommendations and proposes different techniques in accordance with users' needs. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user's an item's context. The biggest challenge for a recommender system is to produce meaningful recommendations by using contextual user-item rating information. A context is a vast term that may consider various aspects; for example, a user's social circle, time, mood, location, weather, company, day type, an item's genre, location, and language. Typically, the rating behavior of users varies under different contexts. From this line of research, we have proposed a new algorithm, namely Kernel Context Recommender System, which is a flexible, fast, and accurate kernel mapping framework that recognizes the importance of context and incorporates the contextual information using kernel trick while making predictions. We have benchmarked our proposed algorithm with pre- and post-filtering approaches as they have been the favorite approaches in the literature to solve the context-aware recommendation problem. Our experiments reveal that considering the contextual information can increase the performance of a system and provide better, relevant, and meaningful results on various evaluation metrics.