A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study

Selecting the minimal best subset out of a huge number of factors for influencing the response is a fundamental and very challenging NP-hard problem because the presence of many redundant genes results in over-fitting easily while missing an important gene can more detrimental impact on predictions, and computation is prohibitive for exhaust search. We propose a modified memetic algorithm (MA) based on an improved splicing method to overcome the problems in the traditional genetic algorithm exploitation capability and dimension reduction in the predictor variables. The new algorithm accelerates the search in identifying the minimal best subset of genes by incorporating it into the new local search operator and hence improving the splicing method. The improvement is also due to another two novel aspects: (a) updating subsets of genes iteratively until the no more reduction in the loss function by splicing and increasing the probability of selecting the true subsets of genes; and (b) introducing add and del operators based on backward sacrifice into the splicing method to limit the size of gene subsets. Additionally, according to the experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms. Moreover, the mutation operator is replaced by it to enhance exploitation capability and initial individuals are improved by it to enhance efficiency of search. A dataset of the body weight of Hu sheep was used to evaluate the superiority of the modified MA against the genetic algorithm. According to our experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms including the most advanced adaptive best-subset selection algorithm.

A Modified XG Boost Classifier Model for Detection of Seizures and Non-Seizures

Diagnosis of Epilepsy is immensely important but challenging process, especially while using traditional manual seizure detection methods with the help of neurologists or brain experts’ guidance which are time consuming. Thus, an automated classification method is require to quickly detect seizures and non-seizures. Therefore, a machine learning algorithm based on a modified XGboost classifier model is employed to detect seizures quickly and improve classification accuracy. A focal loss function is employed with traditional XGboost classifier model to minimize mismatch of training and testing samples and enhance efficiency of the classification model. Here, CHB-MIT SCALP Electroencephalography (EEG) dataset is utilized to test the proposed classification model. Here, data gathered for all 24 patients from CHB-MIT Database is used to analyze the performance of proposed classification model. Here, 2-class-seizure experimental results of proposed classification model are compared against several state-of-art-seizure classification models. Here, cross validation experiments determine nature of 2-class-seizure as the prediction is seizure or non-seizure. The metrics results for average sensitivity and average specificity are nearly 100%. The proposed model achieves improvement in terms of average sensitivity against the best traditional method as 0.05% and for average specificity as 1%. The proposed modified XGBoost classifier model outperforms all the state-of-art-seizure detection techniques in terms of average sensitivity, average specificity.

Dual-responsive nanoplatform with feedback amplification improves antitumor efficacy of photodynamic therapy

A good photosensitizer (PS) delivery system could enhance efficiency and reduce side effects of anti-tumor photodynamic therapy (PDT) by improving accumulation in tumor, uptake by tumor cells, and intracellular release...

Blockchain Tokenisation in Grant Management

Grant management is a vital process that enables organisations to successfully deliver programmes that address social, economic, environmental, and other challenges. Globally, grant transfers represent significant sums, and their management is fraught with deficiencies that undermine the impact of the donated funds. Among the most urgent challenges in grant management are to improve transparency and reduce high administration costs. Digital transformation has done much to enhance efficiency, transparency, and accountability in grant administration; however, numerous shortcomings still exist. Blockchain, smart contracts, and tokenisation can be combined to achieve greater levels of efficiency and control in grant management than is currently possible. Through the study of fundamental blockchain concepts, the authors propose a grant-management solution that tokenises every expenditure item and uses smart contracts to automate payments, reporting, and budgetary compliance.

Identifying target sites for placental therapeutics through the comparison of normal term pregnancies and intrauterine growth restricted proteomes

A variety of pathologies, including intrauterine growth restriction (IUGR), have been linked to placental insufficiencies as important causal factors, however, little has been done to develop therapeutics that may improve placental development, structure and function. The placenta offers the opportunity to manipulate the in-utero environment without directly intervening with the fetus, accessible from the maternal circulation, a vital but temporary organ, the placenta is no longer required after birth. Developing therapeutics must involve multiple aspects of targeting and safety to ensure no off-target impact on the pregnant person or fetus as well as enhance efficiency of delivery. In addition to our studies on nanoparticle delivery to the placenta [1] we are developing targeting strategies to allow peripheral delivery via the maternal circulation. In this study we have performed the isolation of the microvillous membrane from human placental syncytiotrophoblast (the targeting cell) and via proteomic analysis identified potential targeting moieties, we have then compared this to publicly available data from pregnancies complicated by fetal growth restriction to ensure that we do not identify targets which would be reduced in FGR, resulting in less efficient targeting.

Barite-Free Non-Aqueous Drill-In Fluid System Maximizes Productivity in High Temperature Wells

Minimizing formation damage is vital for maximizing productivity when an openhole (slotted liner) completion strategy is used, and it is particularly challenging in high temperature wells with bottomhole static temperature approaching 190°C (374°F). A barite-weighted fluid system for such high temperature wells was identified as unsuitable due to lack of ability to remediate via acid treatment. This paper discusses how a customized barite-free non-aqueous drill-in fluid system was used to successfully achieve productivity objectives for three such wells. Based on reservoir and well data provided, a 1.15 to 1.20 sg (9.60 to 10.0 lbm/gal) barite-free, non-aqueous drill-in fluid system was designed using a high density calcium chloride/calcium bromide brine as the internal phase to compensate for the absence of barite as a weighting agent. An engineered acid-soluble bridging package was included to protect the reservoir from excess filtrate invasion and allow for potential remediation by acid treatment at a later stage. The fluid system was subjected to formation response testing, and the results obtained proved satisfactory, confirming the fluid system was suited for drilling the reservoir. A similar solids-free system using higher density brine as the internal phase, was also formulated. This was spotted in the open hole once drilling was completed to help eliminate any potential for solids settling before running the slotted liner. Three wells were successfully drilled and completed. The barite-free system remained stable, allowed for trouble-free fluids-handling and drilling operations, and performed as expected. To aid in minimizing fluid invasion into the reservoir, onsite particle size distribution (PSD) measurements were performed in order to optimize bridging material additions while drilling and enhance efficiency in managing the solids control system. Because of the extremely high bottomhole temperature, a mud cooler was installed to help control the flowline temperature below 60°C (140°F); this helped maintain fluid stability and preserve functionality of downhole tools in this hostile environment. The solids-free system was successfully spotted in the open hole after drilling the section before well completion. This eliminated any settling potential and reduced flowback of solids during production. The recorded productivity of these wells met expectations.

The Efficiency of a Learning Software Compared to e-Books in Dental Education

Objectives Due to time-consuming curricular and extracurricular activities, students in dentistry and medicine can profit from efficient learning strategies. One strategy could be the preparation with individually designed educational software that embed different multimedia sources. The aim of this study was to determine the efficiency of such a program compared with an e-book similar to a traditional textbook. Materials and Methods Dentistry students of the Johannes Gutenberg-University of Mainz passed an entrance multiple-choice test on the topic of odontogenic tumors and were then randomized into two groups. Afterward, both groups had 14 days to study on the topic of odontogenic tumors either with a learning software or an e-book. A final exam was then taken and the two groups were compared. Statistical Analysis A least significant difference post hoc analysis comparing the group average values was performed. The level of significance was p <0.05. Results Seventy-one students took part in the study. While students from the first and second clinical semester showed significantly better results and improvements with the e-book, an opposite effect was observed in students from the third and fifth clinical semester with significantly better results and improvements with the software. Conclusion Depending on the clinical experience and knowledge, a multimedia educational software can help students in dentistry to enhance efficiency in the preparation for exams.

A decision model for efficient service design in the sharing economy: a service triad perspective

PurposeSharing economy-based service platforms are a relatively new way of delivering services that have received increasing attention from both practitioners and researchers. However, current research in the area is still developing in terms of offering practical insight in conjunction with a decision model that may help to determine optimal strategies for efficient service design in the sharing economy from a service triad perspective. Exploring this gap in the literature, this paper aims to develop and apply a decision model that enables managers to identify and prioritise the efficiency attributes of sharing economy-based services. It also aids in designing optimal strategies to enhance efficiency over time based on the insights obtained from users (buyers and sellers) and platform providers.Design/methodology/approachThis study adopts a mixed-methods approach. The qualitative approach comprised an extensive literature review followed by in-depth interviews, and the quantitative approach adopted the quality function deployment (QFD) integrated optimisation technique to design and prioritise the most optimal strategy emanating from the application of a decision model.FindingsThe findings revealed that establishing global distribution, continued technological research and development (R&D) and enhancing the transactional platform are the most important strategies in the context of sharing economy platform providers (e.g. accommodation-based-platform service providers). This study also revealed that as the importance weights of the efficiency attributes changed over time, so too did the portfolio of strategies used to attain an optimal efficiency level.Originality/valueThe decision model brings a richer conceptual understanding of the dynamic changes over time that occur in the business ecosystem. It also allows managers of sharing economy-based platforms to select optimal strategies and make astute decisions towards achieving efficient service design.