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An additive based on iron oxides was applied to reduce the amount of produced sludge and to increase the production and quality of biogas. The C/N ratio was 11.0–11.3 and the pH of the sludge mixture was 7.3 before the anaerobic digestion. The determined optimal dose of the additive was 0.35 g/g of sludge dry matter over 20 days. This allowed a reduction in the sludge retention time up to 6–11 days. Consequently, maximum biogas production was reached on average 1.6 times faster, volatile solids degradation increased by 56.7%, biogas production increased by 75%, specific biogas production increased by 11.5%, and methane concentration in the biogas increased by 8.4%–18.2%. When the additive was applied, the quantity of phosphate phosphorus in the supernatant was reduced by up to 19%, and hydrogen sulfide reduction efficiency in the biogas ranged between 55% and 62%. In sludge treatment facilities, using an iron oxide-based additive could reduce the dewatering and drying costs for digested sludge by up to 35% .

Many businesses utilize cost-efficient cloud services to execute their industrial and scientific workflow applications. Business continuity is a very important issue for both cloud users and providers. To have reliable workflow execution, the engagement of reliable resources is a challenging job that can supply business continuity. In addition, the lowest execution time and monetary cost are two cost features that adhere users to providers. In this regard, the task scheduling algorithm is very prominent in reducing costs in favor of users and providers. To address the issue, a system framework and different cost-type models are suggested. Then, the task scheduling issue is formulated into an optimization problem with an overall cost reduction viewpoint. To solve this NP-Hard problem, a hybrid genetic algorithm (HGA) is presented for reliable and cost-efficient task scheduling of workflow execution in a heterogeneous cloud computing environment. The proposed HGA has different phases chief amongst them is to apply new crossover and mutation operators for global search, and a Walking around procedure to enhance the quality of local search solutions. It makes a good balance between local and global searches in a huge search space that leads to efficient results. To verify the proposed hybrid algorithm, it has been tested in different twelve scenarios with variable communication to computation ratios datasets. The results of extensive simulations in twelve datasets scenarios prove that HGA significantly dominates other state-of-the-art in terms of three prominent cost metrics, namely, makespan, monetary cost, and failure cost in the amount of 14.10%, 18.70%, and 42.30% cost reduction respectively. [ABSTRACT FROM AUTHOR]

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The removal of crystal violet (CV) and methylene blue (MB) from an aqueous solution was successfully accomplished using biochar derived from Moroccan sheep manure (SM). Process optimization revealed adsorption efficiencies of 96.77 % and 98.95 %, with maximum adsorption capacities of 244.08 mg/g and 295.14 mg.g−1 for MB and CV dyes, respectively. This was achieved at pH 11, with an adsorbent dose of 0.5 g.L−1 at 40 μm, over a 120 minutes at 25 °C. The results indicate that the Freundlich isotherm model is the best fit for both dyes. The pseudo-second-order model accurately describes the kinetics of the processes studied for both dyes. The thermodynamic characteristics clearly demonstrated the endothermic and spontaneous nature of the dye elimination. The ΔH° values of 34.3293 and 31.3338 KJ.mol−1for the MB and CV dyes respectively, suggest that physisorption and chemisorption may be involved in the adsorption process. The re-adsorption study revealed that biochar can be reused for up to 5 cycles with both dyes. Adsorption of dyes onto biomass is facilitated by electrostatic, π-π, n-π and hydrogen bonding interactions. Additionally, SMB300 has been identified as an effective, cost-efficient, and environmentally friendly material for removing cationic dyes.

Energy management is important for both consumers and utility providers. Utility providers are concerned with identifying and reducing energy wastage and thefts. Consumers are interested in reducing their energy consumption and bills. In Pakistan, residential and industrial estates account for nearly 31,000 MW of the maximum total demand, while the transmission and distribution capacity has stalled at about 22,000 MW. This 9000 MW gap in demand and supply, as reported in 2022, has led to frequent load shedding. Although the country now has an excess generation capacity of about 45,000 MW, the aging transmission and distribution network cannot deliver the requisite power at all times. Hence, electricity-related problems are likely to continue for the next few years in the country and the same is true for other low- and middle-income countries (LMICs). Several energy monitoring systems (EnMS) have been proposed, but they face limitations in terms of cost, ease of application, lack of universal installation capability, customization, and data security. The research below focused on the development of an economical, secure, and customizable real-time EnMS. The proposed EnMS comprises low-cost hardware for gathering energy data with universal compatibility, a secured communication module for real-time data transmission, and a dashboard application for visualization of real-time energy consumption in a user-preferred manner, making the information easily accessible and actionable. The experimental results and analysis revealed that approximately 40% cost savings in EnMS development could be achieved compared to other commercially available EnMSs. The performance of the EnMS hardware was evaluated and validated through rigorous on-site experiments. The front-end of the EnMS was assessed through surveys and was found to be interactive and user-friendly for the target clients. The developed EnMS architecture was found to be an economical end-product and an appropriate approach for small and medium clients such as residential, institutional, commercial, and industrial consumers, all on one platform. [ABSTRACT FROM AUTHOR]

The flap strut fairing system is made of movable construction inside, but the outer skin is made of composite material. Each day, we can face an expensive repair for movable construction and outer skin as most of the aviation companies are dealing with mechanical issues that are not solved during a long period of time or somebody misses it due to human errors. In this abstract, we will look for options for easier and faster solutions to avoid the expensive operation process. [ABSTRACT FROM AUTHOR]

Air pollution is inarguably a common tragedy in the today’s world: a resultant effect of industrialization and civilization. This work considered two of the most common domestic air pollutants – particulate matter (PM) and carbon monoxide (CO). This developed device is able to detect the presence of these two pollutants in the atmosphere, and trigger an alarm when the levels of these pollutants is above the safe level with respect to the World Health Organization (WHO) standards. NOVA SDS011 and MQ135 were used as the particulate matter and carbon monoxide sensors respectively, 20 x 4 Liquid Crystal Display (LCD) was used as the display unit, and a buzzer as the alarm device which is triggered when the pollutant level is high. The device utilizes Arduino Uno R3 as its microcontroller for controlling the operation of the device. The key contribution to knowledge of this work is the design of a low-cost, portable and modern pollutant detector that can be traditionally deployed in either closed or open environments. On testing the device under different conditions for 500 seconds per condition, the indoor PM2.5, P.M10 and CO levels ranged between 16-19 µg/m3, 43-80 µg/m3 and 0.6-1.3 parts per million (PPM) respectively. The outdoor PM2.5, PM10 and CO levels were between 17-23 µg/m3, 19-62 µg/m3 and 0.3-0.6 PPM respectively. These levels are considered reasonable enough compared to World Health Organization safe limits of below 25 µg/m3, below 54 µg/m3 and 9 PPM for the PM2.5, PM10 and CO respectively. The device was further exposed to the combustion of fuels and to a dusty environment to read very unsafe limits. This work helped to develop a cost-efficient pollution detector; even as optimal operating efficiency was retained.

Due to the high computational demands inherent in Deep Neural Network (DNN) executions, multi-tier environments have emerged as preferred platforms for DNN inference tasks. Previous research on partitioning strategies for DNN models typically involved leveraging all layers of the DNN to identify optimal splits aimed at reducing latency or cost. However, due to their computational complexity, these approaches face scalability issues, particularly with models containing hundreds of layers. The novelty of our work lies in uniquely identifying specific split points within various DNN models that consistently lead to efficient latency or cost partitioning. Under the assumption that per unit computing cost decreases in higher tiers and that bandwidth is not free, we show that only these specific split points need to be considered to optimize latency or cost. Importantly, these split points are independent of different infrastructure configurations and bandwidth variations. The key contribution of our work is the significant reduction in the computational complexity of DNN partitioning, making our strategy applicable to models with a large number of layers. Introducing DNNSplit, an adaptive strategy, enables dynamic split decisions in varying conditions with the least complexity. Evaluated across nine DNN models varying in size and architecture, DNNSplit exhibits exceptional effectiveness in optimizing latency and cost. Even for a more substantial model containing 517 layers, it identifies only 5 points as potential split points, thereby reducing the partitioning complexity by more than 100x. This makes DNNSplit especially advantageous for managing larger models. DNNSplit also demonstrates significant improvements for multi-tier deployments compared to single-tier execution, including up to 15x latency speedup, 20x cost reduction, and 5x throughput enhancement.

Edge-cloud collaboration fully utilizes the advantages of sufficient computing resources in cloud computing and the low latency of edge computing and better meets the needs of various Internet of Things (IoT) application scenarios. An important research challenge for edge-cloud collaboration is how to offload tasks to edge and cloud quickly and efficiently, taking into account different task characteristics, resource capabilities, and optimization objectives. To address the above challenge, we propose a fast and efficient task offloading approach in edge-cloud collaboration systems that can achieve a near-optimal solution with a low time overhead. First, it proposes an edge-cloud collaborative task offloading model that aims to minimize time delay and resource cost while ensuring the reliability requirements of the tasks. Then, it designs a novel Preprocessing-Based Task Offloading (PBTO) algorithm to quickly obtain a near-optimal solution to the Task Offloading problem in Edge-cloud Collaboration (TOEC) systems. Finally, we conducted extended simulation experiments to compare the proposed PBTO algorithm with the optimal method and two heuristic methods. The experimental results show that the total execution time of the proposed PBTO algorithm is reduced by 87.23%, while the total cost is increased by only 0.0004% compared to the optimal method. The two heuristics, although better than PBTO in terms of execution time, have much lower solution quality, e.g., their total costs are increased by 69.27% and 85.54%, respectively, compared to the optimal method. [ABSTRACT FROM AUTHOR]

Aquatic food production system raises aquatic organisms including fish, shellfish and seaweeds for human consumption and associated value chains. Moreover, as the global human population continues to expand at a high rate and is expected to reach over 9 billion by 2030, developing a cost-efficient production method is a significant challenge in the future development of the aquatic food production industry to provide food and nutritional security with high-quality animal protein. Recent advancements in biotechnological tools and approaches provided a new toolset that can be used to design and optimize the existing processes such as food preservation, fermentation, packaging, quality control and setting proper guidelines to manufacture and process genetically modified fish. At the same time, with the refinement of technology, these are becoming easier applicable and transferable to several other aquatic species production systems. These trends have resulted in exploiting new and unconventional microbial systems with sophisticated properties, which render promising results in the production industry. Here, we highlight the recent advances in the newly emerging biotechnological technology in the production of fish and fish products and discuss the potential of these tools as a sustainable platform for centuries to come with a significant impact on the aquatic food production industry. [ABSTRACT FROM AUTHOR]

As drivers of change and learning in future generations, teachers are required to have the grit to learn and adapt to different practices and what is the trend. The School Learning Action Cell is a tool for filling in instructional gaps and for exchanging practices to deepen and broaden the teaching and learning process for the consistency of attaining the curriculum goal. This study used a qualitative research design utilizing a phenomenological approach. Purposive sampling was used in the study to choose the participants and it was participated by 8 Master Teachers in one of the secondary public schools in the Philippines. Based on the results of the research, conceptual framework was emerged in the study: cost-efficient learning, teaching effectiveness, and lack of needs assessment. With these, the School Learning Action Cell is a cost-efficient platform that develops and manages teaching effectiveness and addressing to fill the gaps in the delivery of instruction through needs assessment and it is a key for professional development among teachers. More than the change to the new educational norm, active engagement in the School Learning Action Cell helped to improve, reskill, and retool the teachers' capacities for addressing the instructional gaps. This finding suggests its usefulness in providing in-depth knowledge for implementing School Learning Action Cell (SLAC) in the school for future directives.

Summary: With the rapid development of the Internet of Things (IoT), fog computing has emerged as a complementary solution to address the issues faced in cloud computing. However, it is a challenging issue to ensure both of the high Quality of Service (QoS) and low cost for different requests when dealing with computing resources. In this article, we propose a new approach on adaptive cost‐efficient and QoS‐aware application placement for fog computing called DATSS. Specifically, this approach consists of a QoS state driven strategy and a credibility rating mechanism. The algorithm divides the QoS into three different states by calculating the QoS violation rate and adaptively selects the best scheduling strategy. Then evaluates the stability of the node based on the task history, and prioritize the task on the node with a higher rating. Performance results show that the cost, delay and energy consumption of the proposed algorithm can be reduced by 20.9%, 15.7%, and 12.6% respectively compared with other algorithms at most. The proposed algorithm can effectively improve cost efficiency and stability under QoS constraints in fog computing environments. [ABSTRACT FROM AUTHOR]

With the development of cloud-native technologies, Kubernetes becomes the standard of fact for container scheduling. Kubernetes provides service discovery and scheduling of containers, load balancing, service self-healing, elastic scaling, storage volumes, etc. Although Kubernetes is mature with advanced features, it does not consider reducing the cost in Kubernetes clouds using the factor of communication frequent between pods while scheduling pods, nor does it have a rescheduling mechanism to save cost. Hence, we propose a cost-efficient scheduling algorithm and a rescheduling algorithm to reduce the cost of communication-intensive and periodically changing web applications deployed in Kubernetes, respectively. Network communication-intensive pods are scheduled to the same node by the scheduling algorithm based on Improved Beetle Antennae Search. According to the changing pod intimacy relationship, the rescheduling algorithm is completed through the replacement of new and old pods to reduce the cost. In addition, this paper evaluates the proposed algorithms in terms of cost and performance on a Kubernetes cloud. The result shows that the cost consumption of Kubernetes clusters in cloud environment is reduced by 20.97% on average compared with the default Kubernetes framework. [ABSTRACT FROM AUTHOR]

As drivers of change and learning in future generations, teachers are required to have the grit to learn and adapt to different practices and what is the trend. The School Learning Action Cell is a tool for filling in instructional gaps and for exchanging practices to deepen and broaden the teaching and learning process for the consistency of attaining the curriculum goal. This study used a qualitative research design utilizing a phenomenological approach. Purposive sampling was used in the study to choose the participants and it was participated by 8 Master Teachers in one of the secondary public schools in the Philippines. Based on the results of the research, conceptual framework was emerged in the study: cost-efficient learning, teaching effectiveness, and lack of needs assessment. With these, the School Learning Action Cell is a costefficient platform that develops and manages teaching effectiveness and addressing to fill the gaps in the delivery of instruction through needs assessment and it is a key for professional development among teachers. More than the change to the new educational norm, active engagement in the School Learning Action Cell helped to improve, reskill, and retool the teachers' capacities for addressing the instructional gaps. This finding suggests its usefulness in providing in-depth knowledge for implementing School Learning Action Cell (SLAC) in the school for future directives. [ABSTRACT FROM AUTHOR]

A European research project named FIBRA (2017-2021) has been conducted under the Conference of European Directors of Roads (CEDR) on fostering the implementation of fibre-reinforced asphalt mixtures by ensuring its safe, optimised and cost-efficient use. This paper presents a Dutch case study on demonstration of a fibre-reinforced PA 8, top layers of two layer porous asphalt, on Dutch motorway A73. The results indicate that PA 8 can be reinforced using both polyacrylonitrile and aramid fibres instead of polymer modification through evaluation of lab performance, production process and initial pavement performance. The addition of fibres may contribute positively to the strength of the mixture, the stiffness and possibly aging resistance of the mortar. The workability of fibre-reinforced PA 8 is better than that with polymer modification. The accelerated load testing results indicate that the PA 8 with fibre reinforcement has an expected service life similar to that with polymer modification. [ABSTRACT FROM AUTHOR]

One possible step for reducing humans' use of fossil fuel due to transport tasks is to replace diesel trucks with battery electric ones. This paper introduces the energy distribution diagram, which makes it easy to visualise the trucks' daily energy consumption over their full service life. The energy distribution is used to investigate which driving patterns are suitable for cost-effective battery electric trucks when compared to commercial diesel trucks. It is shown that the battery capacity that results in the lowest cost per kilowatt-hour propulsion energy depends on the driving pattern, and an algorithm for selecting the most cost-effective capacity is presented. In many instances, it was found that battery electric trucks competed favourably with diesel trucks, especially when the trucks had low variations in daily energy consumption. It is beneficial to determine the circumstances under which they may be cheaper, as this will facilitate the transition to battery electric trucks in segments with a reduced overall cost of ownership. [ABSTRACT FROM AUTHOR]

SRAM or Static Random-Access Memory is the most vital memory technology. SRAM is fast and robust but faces design challenges in nanoscale CMOS such as high leakage, power consumption, and reliability. Quantum-dot Cellular Automata (QCA) is the alternative technology that can be used to address the challenges of conventional SRAM. In this paper, a cost-efficient single layer SRAM cell has been proposed in QCA. The design has 39 cells with a latency of 1.5 clock cycles and achieves an overall improvement in cell count, area, latency, and QCA cost compared to the reported designs. It can therefore be used to design nanoscale memory structures of higher order. [ABSTRACT FROM AUTHOR]

The increased use of DC renewable energy resources and DC storage systems, combined with the necessary reduction of energy waste, is boosting the development of DC smart grids. In this scenario, DC load emulation is of great importance. From the hardware point of view, DC buses stability of smart grids and the different DC/DC converter topologies must be tested. From the software point of view, smart grid strategies and job schedulers must be tested with different power absorption profiles. Moreover, DC load emulation can be useful for many other purposes, such as battery characterization, power supply testing, photovoltaic I-V curve measurements, etc. In this work, a cost-efficient DC Active Load (AL) solution is proposed. The principle of the circuit topology is a buck-boost-derived converter. This solution can be designed and tested considering the required voltage, current, and maximum input power. Both simulation and experimental results are shown on a 400 W size prototype. Thermal and electrical results validate the simulation model and the AL feasibility.

To reach EU's carbon neutrality target by 2050, emission reductions in the land-use sector are needed. Agricultural peatlands attribute for half of the greenhouse gas emissions of cropland in both in EU and Finland. High greenhouse gas emissions from agricultural peatlands are primarily caused by CO 2 emissions following aerobic peat decomposition due to deep drainage, and studies have shown that raising water-table has potential to slow down the decomposition process. Here we studied the emission reduction potential and cost of implementing controlled drainage, paludiculture, peatland restoration and afforestation to current land use on agricultural peatlands in Finland. We created three scenarios with increasing amount of wet field use on cultivated organic soils and estimated their effects on national greenhouse gas emissions and farmers' income. The yearly emission reduction ranged from 0.3 to 5.0 Mt CO 2 equivalents in the different scenarios compared to the current state in Finland. Emission reductive land use had a negative impact on the farmers income, which should be compensated. Assuming the government compensates the lost income for the farmers, the cost of emission reduction ranged from −4 to 45 € per ton of CO 2 equivalents. Rewetting provided the most emission reduction per area and was the most cost effective. We conclude that substantial emission reduction is attainable by rewetting agricultural peatlands. The cost of emission reduction is inexpensive compared to average carbon price in European Union emission trading system, or to the costs of technical carbon capture and storage in Finland. • Three scenarios with increasing wet field use on cultivated peatlands in Finland. • The yearly emission reduction ranged from 0.3 to 5.0 Mt CO 2 equivalents. • Scenarios had a negative impact on the farmers income, which should be compensated. • The median cost of emission reduction ranged from −4 to 45 € per ton of CO 2 equivalents. • Rewetting and paludiculture were the most cost-effective to reduce GHG emissions. [ABSTRACT FROM AUTHOR]

The farmers' welfare and its interlinkages to energy efficiency and farm sustainability has attracted global scientific interest within the last few decades. This study examines the contribution of Agriculture 5.0 to the prosperity of the farmers in the post-pandemic era and the gradual transition to an energy-smart farm. To obtain an insight into the attributes of Agriculture 5.0 and the emerging technologies in the field, Bibliometrix analysis with the use of an R package was conducted based on 2000 data consisting of peer-reviewed articles. The data were retrieved from the Scopus database. A bibliometric approach was employed to analyze the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of Agriculture 5.0 and energy-smart farming. Emerging technologies that are part of Agriculture 5.0 in combination with alternative energy sources can provide cost-effective access to finance, weather updates, remotely monitoring, and future energy solutions for the establishment of smart farms. Keywords such as "renewable energy," "Internet of Things," and "emission control" remain the trending keywords. Moreover, thematic analysis shows that "economic and social effects", "energy efficiency", "remote sensing", and "Artificial Intelligence" with their associated components such as "anaerobic digestion", "wireless sensor network," "agricultural robots", and "smart agriculture" are the niche themes of Agriculture 5.0 in combination with green energy sources, which can lead to the cut cost, energy-efficient, and sustainable energy-smart farms. [ABSTRACT FROM AUTHOR]

Stream processing is a new memory computing paradigm that deals with dynamic data streams efficiently. Storm is one of the stream processing frameworks, but the default stream processing scheduler of storm also has some problems. For example, it does not consider reducing the cost in the cloud environment while ensuring the performance requirements. In this paper, a cost-efficient scheduling algorithm for Storm framework (CE-Storm) is proposed to reduce cost while satisfying deadline constrain. First, a new cost-efficient model (including resources usage cost, energy cost and communication cost) based on Storm framework is built. Then, based on the cost model, a cost-efficient scheduling algorithm which integrated resource monitoring module and communication detection module is designed. The nodes in the cluster are prioritized according to the cost-efficient information, and the nodes with higher priority are assigned tasks first to minimize the total cost of the cluster. Furthermore, this algorithm also reduces the communication cost between nodes and improves the cost effectiveness of the Storm cluster. We have performed extensive experiments on Storm clusters using Hibench's workloads in cloud environment. The result shows that the cost consumption of Storm clusters in cloud environment is reduced by 19.25% on average compared with the traditional scheduling algorithm. In others words, the proposed algorithms effectively improve the cost efficiency of Storm cluster in the cloud environment while satisfying the performance constrains. [ABSTRACT FROM AUTHOR]

Task offloading in edge computing has become an effective way to expand the computing power of user equipment, since it migrates computing-intensive applications from user equipment to edge servers. The execution of a task may require multiple services. Today, many works study the edge computing about service placement or migration with single service tasks. However, it may not meet the need of applications on large scale. In this paper, we study a computational offloading method for multi-service tasks. Here, the execution of each task requires the collaboration of multiple services, and each service is indispensable. Specifically, we design an evaluation metric about system cost, and aim to find the decision to minimize this metric to solve the mobile edge computing (MEC) problem with multi-services tasks. Since this problem is NP-hard, we design the multi-service task computing offload algorithm (MTCOA) to realize the optimal solution. The simulation results show that the algorithm can effectively reduce the cost of computing offloading, and it has higher resource utilization than the existing algorithms. [ABSTRACT FROM AUTHOR]

Background: Handwritten documentation process has drawbacks. Therefore, anaesthesia electronic record (AER) is becoming commonplace in operating theatres to help clinicians improve perioperative quality. This study aims to evaluate the efficiency of AER are compared to handwritten records in terms of completeness of information, time and cost, and assessing user satisfaction. Methods: A hospital-based cohort study was conducted, which was an Ambi-directional study that included a phase 1 part of the study that was retrospective (using a checklist) and phase 2 that was prospective (using a questionnaire at King Faisal Specialist Hospital and Research Center, Riyadh (KFSHRC), Saudi Arabia. For phase 1, data were collected for 165 handwritten records during 2016 - 2017 and 77 AERs during 2018 - 2019, both from the operating room at KFSHRC. For phase 2 survey was conducted on 47 anaesthetists. Results: The AER shows significantly higher completion of information than the handwritten records (AER: 62.3% versus handwritten: 48%; P = 0.02). In terms of time, the meantime for the handwritten report was 45.9 min compared to 53.9 min for AER, therefore, showing the handwritten method's efficiency compared to AER. The satisfaction survey found that 53% of the respondents recognise the growing role of AER in streamlining workflow and improving the quality of services. Conclusion: The study demonstrates that AER has better completion of information, which supports the enhancement of documentation quality. On the contrary, lesser time is taken for filling handwritten records than AER, therefore, handwritten records are cost-efficient. This also shows how the time spent impacts the cost in operating theatre. We recommend a user-friendly environment for AER with adequate training for its users. [ABSTRACT FROM AUTHOR]

Volunteer computing is an Internet-based distributed computing in which volunteers share their extra available resources to manage large-scale tasks. However, computing devices in a Volunteer Computing System (VCS) are highly dynamic and heterogeneous in terms of their processing power, monetary cost, and data transferring latency. To ensure both of the high Quality of Service (QoS) and low cost for different requests, all of the available computing resources must be used efficiently. Task scheduling is an NP-hard problem that is considered as one of the main critical challenges in a heterogeneous VCS. Due to this, in this article, we design two task scheduling algorithms for VCSs, named Min-CCV and Min-V. The main goal of the proposed algorithms is jointly minimizing the computation, communication, and delay violation cost for the Internet of Things (IoT) requests. Our extensive simulation results showthat proposed algorithms are able to allocate tasks to volunteer fog/cloud resources more efficiently than the state-of-the-art. Specifically, our algorithms improve the deadline satisfaction task rates around 99.5% and decrease the total cost between 15 to 53% in comparison with the genetic-based algorithm. [ABSTRACT FROM AUTHOR]

Reliability and cost are two important targets for distributed storage systems. For many years, numerous schemes have been proposed to improve the reliability or cost of distributed storage systems, and they can be divided into three categories: (1) data redundancy schemes; (2) data placement schemes; and (3) data repair schemes. However, it is still unclear regarding how to build a reliable and cost-efficient distributed storage system, because (i) insufficient considerations on the combinations of different schemes; and (ii) insufficient considerations on failures and recoveries of different subsystems (racks, nodes, disks, and sectors). To measure the reliability and cost caused by different schemes, we design and implement CR-SIM, a Comprehensive Reliability SIMulator for distributed storage systems. It considers various affecting factors, such as the system topology, the data redundancy scheme, the data placement scheme, the data repair scheme, and the failure/recovery models of different subsystems. By using CR-SIM, we conduct various simulation-based experiments, and the experimental results reveal several important findings, which are helpful to build reliable and cost-efficient distributed storage systems. For public use, we have open-sourced our source code at https://github.com/yichuan0707/CR-SIM.

Fillet yield (FY) and harvest weight (HW) are economically important traits in Nile tilapia production. Genetic improvement of these traits, especially for FY, are lacking, due to the absence of efficient methods to measure the traits without sacrificing fish and the use of information from relatives to selection. However, genomic information could be used by genomic selection to improve traits that are difficult to measure directly in selection candidates, as in the case of FY. The objectives of this study were: (i) to perform genome-wide association studies (GWAS) to dissect the genetic architecture of FY and HW, (ii) to evaluate the accuracy of genotype imputation and (iii) to assess the accuracy of genomic selection using true and imputed low-density (LD) single nucleotide polymorphism (SNP) panels to determine a cost-effective strategy for practical implementation of genomic information in tilapia breeding programs. The data set consisted of 5,866 phenotyped animals and 1,238 genotyped animals (108 parents and 1,130 offspring) using a 50K SNP panel. The GWAS were performed using all genotyped and phenotyped animals. The genotyped imputation was performed from LD panels (LD0.5K, LD1K and LD3K) to high-density panel (HD), using information from parents and 20% of offspring in the reference set and the remaining 80% in the validation set. In addition, we tested the accuracy of genomic selection using true and imputed genotypes comparing the accuracy obtained from pedigree-based best linear unbiased prediction (PBLUP) and genomic predictions. The results from GWAS supports evidence of the polygenic nature of FY and HW. The accuracy of imputation ranged from 0.90 to 0.98 for LD0.5K and LD3K, respectively. The accuracy of genomic prediction outperformed the estimated breeding value from PBLUP. The use of imputation for genomic selection resulted in an increased relative accuracy independent of the trait and LD panel analyzed. The present results suggest that genotype imputation could be a cost-effective strategy for genomic selection in Nile tilapia breeding programs.

Development of environment-friendly natural adsorbents to treat heavy metal, contaminated wastewater stands as a foremost area of investigation, due to its less price and environment-friendly nature. In the present experimental investigations' adsorption of iron against aqueous mixture by means of chemically activated coconut shell is studied in batchwise. Adsorption capability in elimination of iron metal ion has been examined by various influencing criteria such as pH, time of contact, agitation speed, initial ion concentration and adsorbent dosages. This study is done to find a cost-efficient adsorbent as well as understand the process aimed at removal of heavy metal in wastewater with the aid of the natural adsorbent. Activated carbon prepared from coconut shell is made using chemical activation technique. Experimental results showed that the adsorption rate relies on size of pores, surface area, amount of the chemically activated carbon. Therefore, chemically activated carbon will turn out to be a decent adsorbent for adsorption of CO2 against flue gas and wastewater treatment. Kinetic study showed that the elimination efficiency and adsorption rate remain greatly reliant on concentration of adsorbent, time of agitation and initial ion concentration. Iron adsorption is figured to be reliant on pH, and maximum elimination is observed at pH 7–8. The maximum elimination of metal ion is figured with adsorbent dosage of 0.5 g, contact time of 90 min and speed of agitation of 300 rpm. Experimental information closely fitted with Freundlich isotherm and adsorption exhibited a pseudo-second-order reaction mechanism. [ABSTRACT FROM AUTHOR]

In body-to-body networks (BBNs), a group of closely located wireless body area network (WBAN) users aggregates their network resources to improve the overall physiological data uploading performances. This enables users with the poor Internet connection, i.e., requesting users, to upload their data through their nearby WBAN users with a good Internet connection, i.e., gateway users. In this letter, we investigate the data uploading problem for BBN, where the requesting users incentivize the gateway users for data uploading. However, since the gateway users incur an additional cost (energy and Internet access cost) for uploading data, and they are heterogeneous in terms of their cost, it is more challenging to design the incentive scheme for them. To address this, we formulate a Stackelberg game, where the requesting users as leaders propose the prices, and the gateway users as followers decide the amount of requesting users’ data they would upload. We prove the existence of Stackelberg equilibrium (SE) of the game using backward induction. Finally, the numerical results validate the effectiveness of the proposed incentive mechanism. [ABSTRACT FROM AUTHOR]

• In coastal tropical climate, an increase of 34% of comfort concentration corresponds to the optimal Pareto front. • Comfort potential was the highest in altitude tropical region and the lowest in hot tropical region. • The best solution allowing to reach Nearly Zero Carbon emission leads to 99.4% of the CO 2 reduction. The main objective of this research is to evaluate, analyse and compare the thermal comfort, the energy consumption, and the carbon emission rate in two types of neighbourhoods (existing and suggested neighbourhoods), located in Madagascar island. The same neighbourhood was designed in 14 cities selected in six climate zones which 13 of these cities, were located in the coastal zone. The simulation was carried out under the basic of Design Builder software which is one of the more recognized tools in energy and carbon optimization. The results showed that the comfort rate was estimated at 38% higher in suggested than existing neighbourhoods. The best solution allowing to reach simultaneously the objectives of "Net Zero Energy residential building", and, "Nearly Zero Carbon emission", required an annual expenditure of 1046$, with an emission concentration of 4783.2 kgCO 2 (or 2.6kgCO 2 /m2), during the operational phase of neighbourhoods. This solution leads to 7.9% savings in the total costs together with a 99.4% reduction in the CO 2 release. The best solution only allowing to reach "Net Zero Energy Costs "required an expenditure of 979$ per year, and accompanied by an emission of 51200 kg of CO 2. This solution leads to 13.6% savings in the costs; together with a 5.1% reduction in the embodied carbon release. This point of Pareto Front showed an improvement of comfort hours of 13.1% in the case of suggested neighbourhoods; of 33.8% in the case of existing neighbourhoods. [ABSTRACT FROM AUTHOR]

Liquid desiccant dehumidifiers (LDDs) can be improved by adding internal cooling. However, the addition of excessive cooling power may deteriorate the system's cost-efficiency, whereas the addition of insufficient cooling power leads to negligible performance improvements. The objective of this study is to determine the suitable cost-efficient cooling power range for improving the performance of internally cooled LDDs (IC-LDDs). A novel method and a set of criteria related to the moisture removal rate, cooling-power efficiency (η c) and coefficient of dehumidification performance from cooling power (DCOP cooling) were proposed to determine cost-efficient cooling power. The internally cooled ultrasonic atomization liquid desiccant system (IC-UADS), together with a well-validated model based on the conservation laws of mass and energy and the sensible heat balance, was adopted to demonstrate the analysis. The results showed that, although the dehumidification performance improves with increasing cooling power, the improvement rate decreases, while η cand DCOP coolingdecline quickly (by 87.9%). For cost-efficient improvement, the necessary power proportion of internal cooling to the system's target dehumidification capacity tends to be stable, which was about 29% for the IC-UADS, and independent of the operating conditions. The results may help to determine the reasonable cooling power range for cost-efficient improvement of IC-LDDs. [ABSTRACT FROM AUTHOR]

Cloud gaming (CG) system could pursue both high-quality gaming experience via intensive computing, and ultimate convenience anywhere at anytime through any energy-constrained mobile devices. Despite the abundance of efforts devoted, state-of-the-art CG systems still suffer from multiple key limitations: expensive deployment cost, high bandwidth consumption and unsatisfied quality of experience (QoE). As a result, existing works are not widely adopted in reality. This paper proposes a Transparent Gaming framework called T-Gaming that allows users to play any popular high-end desktop/console games on-the-fly over the Internet. T-Gaming utilizes the off-the-shelf consumer GPUs without resorting to the expensive proprietary GPU virtualization (vGPU) technology to reduce the deployment cost. Moreover, it enables prioritized video encoding based on the human visual feature to reduce the bandwidth consumption without noticeable visual quality degradation. Last but not least, T-Gaming adopts adaptive real-time streaming based on deep reinforcement learning (RL) to improve user's QoE. To evaluate the performance of T-Gaming, we implement and test a prototype system in the real world. Compared with the existing cloud gaming systems, T-Gaming not only reduces the expense per user by 75 percent hardware cost reduction and 14.3 percent network cost reduction, but also improves the normalized average QoE by 3.6-27.9 percent. [ABSTRACT FROM AUTHOR]

In Optical WDM networks, a single fiber carries enormous amount of data and consequently designing schemes to prevent the user traffic from disruption and techniques to recover from failures still remains an important area of research. In this paper we address the problem of protecting all-optical WDM mesh networks from single link failure. We propose an offline protection scheme that is not only cost-efficient in terms of network resource consumption but can also provide quick recovery from a link failure. In our scheme, backup paths are combined to reduce cost of protection and to facilitate fast recovery from a failure. We address a critical issue related to selection of backup paths to be combined, which was not addressed earlier. Instead of choosing backup paths for connection requests arbitrary as done in an existing scheme, our scheme is designed to select backup paths in a way that they are suitable for combination and can maximize resource saving as well. We first provide an Integer Linear Program for the problem, and then propose a heuristic eStreams that can provide near optimal solution in polynomial time. Performance comparisons with some well-known schemes show that our protection scheme is definitely an improvement in terms resource conservation while ensuring fast recovery from a link failure. The figure shows how a primary (working) path is protected with the help of a link-disjoint backup path in WDM optical networks. A primary path is used to carry traffic under normal fault free condition. In the event of a link fault (fiber-cut) the traffic is switched over to the backup path. [ABSTRACT FROM AUTHOR]

Solid-state drive (SSD) has become one of the most dominant storage devices and is rapidly replacing conventional storage devices. The core component of SSD is NAND flash memory (NFM), where the actual data are stored. Cost pressure is the most critical factor limiting the further deployment of SSDs and past researches have focused on developing cost-effective high-density NFM. Although the cost-driven technology development increases per-chip capacity, it reduces channel-/way-level parallelisms for the given device capacity, resulting in the performance degradation. Such observation directs us to focus on a novel NFM architecture exploiting plane-level parallelism. The distinct features of this architecture are: 1) enabling a decoupled word-line (WL) selection for the mated planes and 2) segmenting each plane into subplanes for further maximizing the plane-level parallelism. The experimental results show that decoupled WL selection improves the throughput by up to 21.3% with a marginal overhead of less than 1%, compared to the conventional NFM architecture. In addition, adopting the plane segmentation improves the throughput by up to 43.9% with an additional overhead of 14%. Considering the tradeoff between performance and overhead, the proposed NFM architecture is a cost-efficient method to secure high performance under decreasing channel-/way-level parallelisms in high-density NFM. [ABSTRACT FROM AUTHOR]

Pre-exposure prophylaxis (PrEP) is considered one of the five "pillars" by UNAIDS to reduce HIV transmission. Moreover, it is a tool for female self-protection against HIV, making it highly relevant to sub-Saharan regions, where women have the highest infection burden. To date, Truvada is the only medication for PrEP. However, the cost of Truvada limits its uptake in resource-constrained countries. Similarly, several currently investigated, patent-protected compounds may be unaffordable in these regions. We set out to explore the potential of the patent-expired antiviral efavirenz (EFV) as a cost-efficient PrEP alternative. A population pharmacokinetic model utilizing data from the ENCORE1 study was developed. The model was refined for metabolic autoinduction. We then explored EFV cellular uptake mechanisms, finding that it is largely determined by plasma protein binding. Next, we predicted the prophylactic efficacy of various EFV dosing schemes after exposure to HIV using a stochastic simulation framework. We predicted that plasma concentrations of 11, 36, 1287 and 1486ng/mL prevent 90% sexual transmissions with wild type and Y181C, K103N and G190S mutants, respectively. Trough concentrations achieved after 600 mg once daily dosing (median: 2017 ng/mL, 95% CI:445–9830) and after reduced dose (400 mg) efavirenz (median: 1349ng/mL, 95% CI: 297–6553) provided complete protection against wild-type virus and the Y181C mutant, and median trough concentrations provided about 90% protection against the K103N and G190S mutants. As reduced dose EFV has a lower toxicity profile, we predicted the reduction in HIV infection when 400 mg EFV-PrEP was poorly adhered to, when it was taken "on demand" and as post-exposure prophylaxis (PEP). Once daily EFV-PrEP provided 99% protection against wild-type virus, if ≥50% of doses were taken. PrEP "on demand" provided complete protection against wild-type virus and prevented ≥81% infections in the mutants. PEP could prevent >98% infection with susceptible virus when initiated within 24 h after virus exposure and continued for at least 9 days. We predict that 400 mg oral EFV may provide superior protection against wild-type HIV. However, further studies are warranted to evaluate EFV as a cost-efficient alternative to Truvada. Predicted prophylactic concentrations may guide release kinetics of EFV long-acting formulations for clinical trial design. [ABSTRACT FROM AUTHOR]