The Activation of Residual Dormant Follicles by Human Chorionic Gonadotropin (HCG) in Vivo: a Novel Treatment for Premature Ovarian Insufficiency

BackgroundWith the influence of factors such as ovarian surgery, high-dose radiotherapy and chemotherapy, environmental degradation, and bad living habits, the occurrence of premature ovarian insufficiency(POI) is getting younger and younger, and many young women's ovaries have entered the aging stage earlier. While many studies have investigated the patients with POI, which is still a challenge in reproductive medicine as the treatments available now are not ideal. POI patients have varying amounts of residual dormant follicles in the ovaries. Therefore, it is critical to further our understanding of primordial follicle activation in order to treat.This study aimed to investigate the activation of residual follicles in POI patients with injection of HCG, whether they could obtain embryos and become pregnant.Methods Four patients with POI were pretreated with dehydroepiandrosterone, Coenzyme Q10, estrogen and medroxyprogesterone. The prescribed amounts of estrogen and medroxyprogesterone were adjusted to maintain the level of FSH at ˂15 mIU/ml and the level of LH˂10 mIU/ml. When the treatments failed to induce the appearance of follicles after 3 months, the patients received treatment with 10000 IU of HCG. Results The residual dormant follicles in POI patients can be activated using our approach to obtain embryos and conceive by injection of HCG. ConclusionsPOI patients may conceive their own genetic children by activating dormant follicles in vivo. These findings may represent a new simple and feasible solution for the treatment of patients with POI to conceive their own genetic children.

Solving interval-valued transportation problem using a new ranking function for octagonal fuzzy numbers

In this paper, we introduce a new method to solve Interval-Valued Transportation Problem (IVTP) to deal with those problems of transportation wherein the information available is imprecise. First, a newly proposed fuzzification method is used to convert the IVTP to octagonal fuzzy transportation problem and then with the help of ranking function proposed in this paper, the fuzzy transportation problem is converted into crisp transportation problem. Lastly, Initial Basic Feasible Solution (IBFS) of this problem is obtained using Vogel’s Approximation Method and the solution is improved using Modified Distribution (MODI) method. A numerical example with interval data is solved using the proposed algorithm to make comparison of the solution with some other methods. Also, a numerical example with parameters in the form of octagonal fuzzy numbers is illustrated to compare the effectiveness of the proposed ranking technique. The proposed fuzzification and ranking technique can be used in the other fields of decision making dealing with the data in the same form as considered in this paper.

Raciocínio Baseado em Casos para a Configuração de Modelos de Alocação de Banda

The extensive adoption of computer networks, especially the Internet, using services that require extensive data flows, has generated a growing demand for computational resources, mainly bandwidth. Bandwidth Allocation Models (BAM) have proven to be a viable alternative to network management where the bandwidth resource is shared to meet the high demand for the network. However, managing these networks has become an increasingly complex task, and solutions that allow for nearly autonomous configuration with less intervention of the network manager are highly demanded. The use of Case-Based Reasoning (CBR) techniques for network management has proven satisfactory for decision making and network management. This work presents a proposal for network reconfiguration based on the CBR cycle, intelligence, and cognitive module for MPLS (Multi-Protocol Label Switching) networks. The results show that CBR is a feasible solution for auto-configuration with autonomic characteristics in the MPLS using bandwidth allocation models (BAMs). The proposal improved the general network performance.

Investigations on Off-Grid Hybrid Renewable Energy Microgrid for Sustainable Development Growth

The principal aim of Sustainable Development Goals (SDGs) or Global Goals is to attain a much better and more sustainable future for all. In recent times, microgrids have attracted much attention, given the transformation of software systems and raising the bar for customers in terms of sustainability, reliability and cost predictability. Consequently, there is a need for microgrid development in remote areas. Our paper proposes a hybrid renewable energy microgrid (HREM) that focuses on the affordable, efficient, reliable and sustainable growth of energy systems. This paper presents an optimized off-grid HREM for a remote locality in the south Indian state of Tamil Nadu. The proposed approach employs a configuration of photovoltaic (PV) arrays, AC loads, a diesel generator set, a wind turbine and a battery energy storage system (BESS) connected to AC/DC buses and designed to satisfy the power requirements of a remote, rural community residential area. The primary objective was to carry out a cost optimization of the designed system. A sensitivity analysis was also conducted to obtain a feasible solution from the optimized results and evaluate the robustness of the design. The primary objective was to carry out a cost optimization of the designed system. A sensitivity analysis was also conducted to obtain a feasible solution from the optimized results and evaluated the robustness of the design. A detailed analysis was made by comparing the [Formula: see text] [Formula: see text] and [Formula: see text] [Formula: see text] and it was found that the former offered better results for the particular location. Overall, it was observed that our proposed system offers renewable energy for residential loads that is relatively inexpensive, reliable and sustainable.

The impact of ear growth on identification rates using an ear biometric system in young infants

Background Accurate patient identification is essential for delivering longitudinal care. Our team developed an ear biometric system (SEARCH) to improve patient identification. To address how ear growth affects matching rates longitudinally, we constructed an infant cohort, obtaining ear image sets monthly to map a 9-month span of observations. This analysis had three main objectives: 1) map trajectory of ear growth during the first 9 months of life; 2) determine the impact of ear growth on matching accuracy; and 3) explore computer vision techniques to counter a loss of accuracy. Methodology Infants were enrolled from an urban clinic in Lusaka, Zambia. Roughly half were enrolled at their first vaccination visit and ~half at their last vaccination. Follow-up visits for each patient occurred monthly for 6 months. At each visit, we collected four images of the infant’s ears, and the child’s weight. We analyze ear area versus age and change in ear area versus age. We conduct pair-wise comparisons for all age intervals. Results From 227 enrolled infants we acquired age-specific datasets for 6 days through 9 months. Maximal ear growth occurred between 6 days and 14 weeks. Growth was significant until 6 months of age, after which further growth appeared minimal. Examining look-back performance to the 6-month visit, baseline pair-wise comparisons yielded identification rates that ranged 46.9–75%. Concatenating left and right ears per participant improved identification rates to 61.5–100%. Concatenating images captured on adjacent visits further improved identification rates to 90.3–100%. Lastly, combining these two approaches improved identification to 100%. All matching strategies showed the weakest matching rates during periods of maximal growth (i.e., <6 months). Conclusion By quantifying the effect that ear growth has on performance of the SEARCH platform, we show that ear identification is a feasible solution for patient identification in an infant population 6 months and above.

Linear interpolated multi-objective economic emission scheduling using grey wolf optimizer: a strategic balance and solution with diverse load pattern

Purpose The purpose of this paper is to focus on the cost-effective and environmentally sustainable operation of thermal power systems to allocate optimum active power generation resultant for a feasible solution in diverse load patterns using the grey wolf optimization (GWO) algorithm. Design/methodology/approach The economic dispatch problem is formulated as a bi-objective optimization subjected to several operational and practical constraints. A normalized price penalty factor approach is used to convert these objectives into a single one. The GWO algorithm is adopted as an optimization tool in which the exploration and exploitation process in search space is carried through encircling, hunting and attacking. Findings A linear interpolated price penalty model is developed based on simple analytical geometry equations that perfectly blend two non-commensurable objectives. The desired GWO algorithm reports a new optimum thermal generation schedule for a feasible solution for different operational strategies. These are better than the earlier reports regarding solution quality. Practical implications The proposed method seems to be a promising optimization tool for the utilities, thereby modifying their operating strategies to generate electricity at minimum energy cost and pollution levels. Thus, a strategic balance is derived among economic development, energy cost and environmental sustainability. Originality/value A single optimization tool is used in both quadratic and non-convex cost characteristics thermal modal. The GWO algorithm has discovered the best, cost-effective and environmentally sustainable generation dispatch.

A novel Confidential Consortium Blockchain framework for peer to peer energy trading

The vital dependence of peer to peer (P2P) energy trading frameworks on creative Internet of Things (IoT) has been making it more vulnerable against a wide scope of attacks and performance bottlenecks like low throughput, high latency, high CPU, memory use, etc. This hence compromises the energy exchanging information to store, share, oversee, and access. Blockchain innovation as a feasible solution, works with the rule of untrusted members. To alleviate this threat and performance issues, this paper presents a Blockchain based Confidential Consortium (CoCo) P2P energy trading system that works on the trust issues among the energy exchanging networks and limits performance parameters. It reduces the duplicate validation by creating a trusted network on nodes, where participants identities are known and controlled. A Java-script-based smart contract is sent over the Microsoft CoCo system with Proof of Elapsed Time (PoET) consensus protocol. Also, a functional model is designed for the proposed framework and the performance bench-marking has been done considering about latency, throughput, transaction rate control, success and fail transaction, CPU and memory usage, network traffic. Additionally, it is shown that PoET’s performance is superior to proof of work (PoW) for multi-hosting conditions. The measured throughput and latency moving toward database speeds with more flexible, business-specific confidentiality models, network policy management through distributed governance, support for non-deterministic transactions, and reduced energy consumption.

OECD/G20 PILLAR TWO: IS IT A COMPATIBLE AND FEASIBLE SOLUTION?

This study aims to contribute to the continuing discussion about the compatibility and feasibility of the OECD/G20 Pillar Two measures as a solution to address the remaining base erosion and profit-shifting (BEPS) issues. One triggering such a discussion is the significance of Pillar Two for developing countries. In so doing, a literature review is conducted to gain relevant considerations to the Pillar Two implementation. The analysis lead to the comprehension of the issues surrounding Pillar Two, i.e. justification, complicated design, fairness issues, and effectiveness.

Optimization of Fuzzy Species Pythagorean Transportation Problem under Preserved Uncertainties

The transportation of big species is essential to rescue or relocate them and it requires the optimized cost of transportation. The present study brings out an optimized way to handle a special class of transportation problem called the Pythagorean fuzzy species transportation problem. To deal effectively with uncertain parameters, a new method for finding the initial fuzzy basic feasible solution (IFBFS) has been developed and applied. To test the optimality of the solutions obtained, a new approach named the Pythagorean fuzzy modified distribution method is developed. After reviewing the literature, it has been observed that till now the work done on Pythagorean fuzzy transportation problems is solely based on defuzzification techniques and so the optimal solutions obtained are in crisp form only. However, the proposed study is focused to get the optimal solution in its fuzzy form only. Getting results in the fuzzy form will lead to avoid any kind of loss of information during the defuzzification process. A comparative study with other defuzzification-based methods has been done to validate the proposed approach and it confirms the utility of the proposed methodology.

Hybridization of Green Energy Sources for the Electrification of Remote Villages: A Case Study at Adem Tuleman of Ethiopia

Background: Energy deeply influences the life of rural communities. The industrialized countries depend primarily on modern energy while the developing countries like Ethiopia heavily rely on traditional biomass. Thus, in Ethiopia, the energy sector faces dual challenges: one limited access to modern energy and the second is heavy reliance on traditional biomass energy sources to meet growing energy demand. The modern energy of the country is predominantly from hydropower which accounts for 90% and fuelwood accounts for more than 80% of households' energy supply today, this leads to deforestation and severe land degradation in the country.Objective: This study aim at providing the way to diversify energy sources through integrated hybrid energy sources (wind, solar and diesel generator) to obtain a sustainable autonomous power supply system for remote site. Method: Standalone hybrid system configuration was design by using HOMER software and finds an optimal combination of clean energy as well as comparing it with other energy sources for Adem Tuleman one of the remote sites in Ethiopia. HOMER is optimization tool to determine the possible optimal architecture and control strategy of the system. Results: The study found that the village had a 204.04 kWh/day average energy demand with a 31 kW/day peak load, a 4.5 kWh/day deferrable load, and 0.9kWh/day peak deferrable load. Simulation results demonstrated that the proposed system was a feasible solution to electrify Adem Tuleman. A financial analysis indicated that the project would have an initial capital cost of $24,817.00, an operating and maintenance cost of $12,862.00, and a total net present value of $189,233.00.The minimum cost of energy obtained was $0.195/kWh.Conclusion: The simulation result indicates that the proposed standalone hybrid system would be a climate smart and feasible solution for electrify remote village. Moreover, hybrid energy systems allow the effective way of utilizing available renewable energy in the village and providing clean energy which can alleviate energy poverty in many remote sites of Ethiopia.