15288 research outputs found
Sort by
Greener Technology of Producing Polyhydroxyalkanoate using Anthracene as Carbon Source
Get PDFThe study investigated the potential of Bacillus cereus AAR-1 (OQ999178) to simultaneously
degrade anthracene, a toxic environmental pollutant, and produce polyhydroxyalkanoate (PHA), an ecofriendly
and sustainable biopolymer. Using a Taguchi L16 (4*3) array for optimization, it was found that
a 10% seed inoculum, grown for 8 days in a minimal salt medium containing 400 ppm of anthracene and
2 g/L of NH4Cl as carbon and nitrogen sources, respectively, maximized anthracene degradation and
PHA accumulation by B. cereus AAR-1. The bacterial biomass had a colony count of 1 x 106 cfu/ml and
produced 286 mg/L of biopolymer, as extracted using a hypochlorite-chloroform solvent method. Fouriertransform
infrared spectroscopy (FTIR) analysis confirmed the biopolymer as PHA. This study identifies
a key hydrocarbon-degrading bacterium at a municipal dumpsite, which plays a significant role in
environmental biotechnology by supporting cleaner and greener technologies. This contribution aligns
with the goals of SDG 12 and 1
Influence of synthetic carbon grade on the metabolic flux of polyhydroxyalkanoate monomeric constitution synthesized by Bacillus cereus AAR-1
Get PDFCarbon substrate is a pivotal factor influencing polyhydroxyalkanoate (PHA) properties
of varied industrial importance. Three synthetic sucrose samples with varying
manufacturing purity levels were selected as carbon substrates to synthesize diverse
PHAs using a wild-type Bacillus cereus AAR-1. Comparative monomeric analyses of the
extracted biopolymers revealed Poly (3-hydroxytetradecanoate) (P3HTD), Poly(3-
hydroxybutyrate-co-2-hydroxytetradecanoate) [P(3HB-co-2HTD)], and Poly(3-
hydroxybutyrate) (P3HB) with carbon elemental contents that ranged from 39 to 53 %
and no nitrogen detected. The decomposition temperature of [P(3HB-co-2HTD)] was
279 °C, indicating higher thermal stability than the individual monomeric units. Notably,
the homopolymer P3HTD exhibited an increased melting temperature of 172.4 °C and a
reduced crystallinity percentage (Xc % = 20.7 %), crucial properties for bioplastics and
medical sector applications. All the biopolymers displayed a low specific heat capacity
ranging between 0.03 and 0.05 J/g°C, suitable for applications such as thermal storage
materials and temperature-regulating textiles. The results suggest that different carbon
purity grades influenced homopolymer accumulated in Bacillus cereus AAR-1
Chemical deposition and corrosion perspectives on the development of pipe union steel in automobile industry
Get PDFnotably in terms of surface protection, durability enhancement, and corrosion resistance. This
comprehensive research dives into the domain of chemical deposition technologies, primarily
electroplating and its derivatives to strengthen steel surfaces against environmental, thermal, and
cyclic stressors inherent in car exhaust systems. Electroplating is a flexible technology with
numerous applications ranging from corrosion protection and microstructure formation to
alloying and the development of high-magnetic force actuators. The research examines
electroplating mechanisms, classifies chemical deposition methods, and defines the parameters
of an ideal electroplating process. It assesses thin film electrodeposition and autocatalytic or
electroless metal deposition, analyzing their benefits, disadvantages, and real-world applications
in the automotive industry. It also investigates current research on electroplating applications,
environmental problems, and pipe union steel challenges in engineering applications, such as
fracture inclinations and material fatigue. Furthermore, this study investigates studies that
address environmental contamination caused by electroplating applications, as well as novel
methods for reducing hazardous waste. Several experimental and computational researches on
steel compositions, alloys, and alternative coatings provide vital insights into enhancing the
durability and corrosion resistance of pipe union steel for automotive applications
The utilization of pulverized waste tire rubber in a soil–cement composite for sustainable compressed earth brick production
Get PDFThis study examined the suitability of blending waste tires with cement in indigenous soil for producing compressed
earth bricks (CEB) to achieve a sustainable environment. CEB was produced with clay soil and a combination of cement
at 5 and 10% levels with varying dosages of pulverized waste tire at 0, 2.5, 5, 7.5, and 10% mixture. Classification tests
were conducted to determine engineering properties such as the particle size distribution, Atterberg limit, specific
gravity, optimum moisture and unit weight of the soil. The physical properties of the pulverized tire were evaluated.
Moreover, density, water absorption, and compressive strength were determined for hardened CEB samples produced
from mixtures of soil and various proportions of blended cement and pulverized waste tire materials. The classification
results showed that the soil was silty clay of low plasticity. The density of the CEB samples was observed to increase
slightly with the addition of blended cement-tire residue. Furthermore, a considerable improvement in the compressive
strength development of the CEB was observed; however, compared with those of the control, the peak compressive
strength of the CEB samples was greater when the soil was stabilized with 7.5% pulverized waste tire material and 10%
cement. A decrease in the water absorption capacity of CEB was observed with an increase in the amount of pulverized
waste tire and cement in the soil mixture. The response models corroborate the experimental findings indicating that
amount of waste tire rubber residue and cement had significant effects on the CEB performance. This study ensure the
beneficial recycling of waste tires blended with cement in soil for making medium-strength CEB to achieve sustainable,
resilient masonry construction applications
Bank Performance: A Measure of the Relationship of ERM Indicators with Net Financing Per Share of Deposit Money Banks in Nigeria
Get PDFThis research aims to quantify the correlation between ERM indicators and net
financing per share of Deposit Money Banks for a group of Nigerian banks through the
use of pooled OLS, WG-VC, and flexible GLS techniques. Banks must manage a
number of risks, including market, operational, interest rate, liquidity, and solvency (or
capital) risks. These risks have reduced the benefits of managing the banking sector.
One of the most noteworthy features of the Global Financial Crisis (GFC) was the
unique nature of the liquidity crisis it precipitated, which eventually materialised as a
variety of crises prior to reaching a catastrophic threshold. The outcomes demonstrated
a favourable and statistically significant relationship between the chief risk officer and
risk committee member parameters. Expanding these variables will result in an
increase in net financing per share because the two factors have a favourable impact on
net financing. However, there is no statistically significant difference between the
exchange rate, price, and credit risk hedging derivative instrument and the risk
mapping parameter. It demonstrates that these factors and net financing per share do
not positively correlate. Derivative tools for hedging foreign exchange rate risk, the
chief risk officer, and risk committee members all have positive coefficient values. This
evidence shows that these factors and net financing per share are positively correlated.
There is typically no positive correlation seen between risk mapping and derivative
instruments used to hedge credit risk and net financing per share. Our research leads
us to the conclusion that, since more than two ERM indicators have a positive impact on net financing per share, the expected or theoretical sign of the relationship between
enterprise risk management and net financing per share is maintained
Progressive energy management technique for smart load control
Get PDFElectricity demand is rapidly increasing in many countries especially because of the increase in smart appliances, smart cities, and communities. The use of electricity ranges from agricultural load, to commercial and industrial load, and also residential load which account for the largest contributor to the increase in peak demand. Thus, residential consumers play a massive role in the national demand for electrical energy and power. The increased demand puts increasing pressure on the energy suppliers who force consumers to cope with short peaks. The need arises for an energy management technique to reduce energy consumption by allowing for consumer load control. The method explores using a Raspberry Pi to collect and evaluate the energy consumed over time and allow consumers access to directly control their home appliances. A more accurate load curve is deduced using the new readings gotten from the end-users. The data obtained from the proposed system is sent to a progressive web application that allows users manage their energy consumption
NaijaCovidAPI: an application programming interface for retrieval of COVID19 data from the Nigerian Center for Disease Control web platform
Get PDFBackground: In this work, a COVID19 Application Programming
Interface (API) was built using the Representational State Transfer
(REST) API architecture and it is designed to fetch data daily from the
Nigerian Center for Disease Control (NCDC) website.
Methods: The API is developed using ASP.NET Core Web API
framework using C# programming language and Visual Studio 2019
as the Integrated Development Environment (IDE). The application
has been deployed to Microsoft Azure as the cloud hosting platform
and to successfully get new data from the NCDC website using
Hangfire where a job has been scheduled to run every 12:30 pm (GMT
+ 1) and load the fetched data into our database. Various API
Endpoints are defined to interact with the system and get data as
needed, data can be fetched from a single state by name, all states on
a particular day or over a range of days, etc.
Results: The results from the data showed that Lagos and Abuja FCT
in Nigeria were the hardest-hit states in terms of Total Confirmed
cases while Lagos and Edo states had the highest death causalities
with 465 and 186 as of August 2020. This analysis and many more can
be easily made as a result of this API we have created that
warehouses all COVID19 Data as presented by the NCDC since the first
contracted case on February 29, 2020. This system was tested on the
BlazeMeter platform, and it had an average of 11Hits/s with a
response time of 2905milliseconds.
Conclusions: The extension of NaijaCovidAPI over existing COVID19
APIs for Nigeria is the access and retrieval of previous data. Our
contribution to the body of knowledge is the creation of a data hub for
Nigeria's COVID-19 incidence from February 29, 2020, to dat
Hybridization of the Q-learning and honey bee foraging algorithms for load balancing in cloud environments
Get PDFLoad balancing (LB) is very critical in cloud computing because it keeps nodes from being overloading while others are idle or underutilized. Maintaining the quality of service (QoS) characteristics like response time, throughput, cost, makespan, resource utilization, and runtime is difficult in cloud computing due to load balancing. A robust resource allocation strategy contributes to the end user receiving high-quality cloud computing services. An effective LB strategy should improve and deliver required user satisfaction by efficiently using the resources of virtual machines (VM). The Q-learning method and the honey bee foraging load balancing algorithm were combined in this study. This hybrid combination of a load balancing algorithm and a machine learning method has reduced the runtime of load balancing activities and makespan, and increased task throughput in a cloud computing environment thereby enhancing routing activities. It achieved this by continuously tracking the usage histories of the VMs and altering the usage matrix to send jobs to the VMs with the best usage histories
Hybridization of the Q-learning and honey bee foraging algorithms for load balancing in cloud environments
Get PDFLoad balancing (LB) is very critical in cloud computing because it keeps nodes from being overloading while others are idle or underutilized. Maintaining the quality of service (QoS) characteristics like response time, throughput, cost, makespan, resource utilization, and runtime is difficult in cloud computing due to load balancing. A robust resource allocation strategy contributes to the end user receiving high-quality cloud computing services. An effective LB strategy should improve and deliver required user satisfaction by efficiently using the resources of virtual machines (VM). The Q-learning method and the honey bee foraging load balancing algorithm were combined in this study. This hybrid combination of a load balancing algorithm and a machine learning method has reduced the runtime of load balancing activities and makespan, and increased task throughput in a cloud computing environment thereby enhancing routing activities. It achieved this by continuously tracking the usage histories of the VMs and altering the usage matrix to send jobs to the VMs with the best usage histories
A MULTI-CRITERIA RANKING SYSTEM FOR EVALUATING GENE REGULATORY NETWORK INFERENCE METHODS
Get PDFIn this study, a multi-criteria ranking algorithm was introduced for evaluating gene regulatory network inference (GRNi) methods called RankGRN. This algorithm integrates a technique for determining the weights of evaluation criteria based on priorities defined by the user for ranking GRNi methods. Built upon the Weighted Sum Model, RankGRN, first generates the weight of each evaluation criteria considering the user-specified priority, after which it calculates the score of each GRNi method from all the selected weighted evaluation criteria, then it ranks the GRNi methods and identifies the best method according to the priority of each evaluation criteria and the overall best method. Subsequently, a web-based framework was developed for benchmarking and ranking GRNi methods called GRN Evaluator. The newly developed multi-criteria ranking algorithm was integrated in GRN Evaluator for ranking the GRNi methods after benchmarking. GRN Evaluator is a novel framework designed to address limitations in existing GRN benchmarking frameworks such as BEELINE, GReNaDIne, NetBenchmark, etc. Key advancements in GRN Evaluator include the incorporation of machine learning methods for GRN inference, addition of more datasets (both single-cell and bulk RNA-Seq data), inclusion of additional evaluation metrics, and integrating visualization tools for better interpretation of gene networks. Additionally, GRN Evaluator offers a user-friendly web interface, enhancing accessibility and usability. The systematic approach to evaluating GRNi methods across multiple datasets used in this study demonstrates their performance in various contexts. The framework effectively ranks these methods, providing valuable insights for researchers. The findings from this study serve as a guideline for selecting appropriate GRNi methods based on users’ specific needs