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Corrosion Inhibition Behaviour of Calf Thymus Gland DNA on Mild Steel in 10% Sulphamic Acid
Get PDFThe use of corrosion inhibitors is a major practical method for reducing the corrosion of mild
steel in corrosive environments. Weight loss, potentiodynamic polarization (electrochemical)
measurements and SEM analyses were used to examine the corrosion inhibition behaviour of
calf thymus gland DNA (CTGDNA) in 10% sulphamic acid. Weight loss data demonstrated that
the highest inhibition efficiency of 82.71% was reached at 303 K and 6 h of immersion with calf
thymus DNA at a concentration of 2.5 mg/L. The electrochemical test, with a change in
Ecorr < 85 mV seen in potentiodynamic polarisation curves, verified that CTGDNA functions as a
mixed inhibitor, by creating a barrier on the mild steel's surface, it inhibited both the anodic
dissolution of the metal and the cathodic oxygen reduction. CTGDNA adsorption on mild steel
modelled the Langmuir isotherm with a linear regression coefficient of 0.99. The increase in the
activation energy from − 37.54 to 52.5 kJ/mol after 2 h immersion; with a similar trend for 4 and
6 h demonstrated that addition of CTGDNA favoured chemisorption. The small and negative value
of entropy was an indication that the adsorption of CTGDNA was spontaneous. SEM images
demonstrated that the addition of CTGDNA significantly decreased the mild steel surface
deterioration in the uninhibited solution. It is the conclusion of this study that CTGDNA is an
effective inhibitor of mild steel corrosion in 10% sulphamic acid
Surface Modification and Integration of Organic/Inorganic Additives Into The Matrix of The Membrane: The Governing Interaction Mechanisms of Dye Adsorption on Adsorptive Membranes
No full textThe contamination of water from dye industries is considered one of the most global urgent concerns as it compromises the esthetic feature of water bodies, inhibits plant growth,
and might stimulate toxicity and carcinogenicity. Adsorptive membranes are highly viewed as one of the prospective technologies that have demonstrated competency in wastewater
treatment due to their capacity to make wastewater clean enough for reuse. The adsorption mechanism that lies between adsorptive membranes and dye molecules depends on the
individual properties and characteristics. Novel hybrid composite membranes with organic/inorganic additives have been considered for adsorptive membranes as they are expected
to advance the effectual removal of dyes from wastewater. The impact of organic/inorganic additives on hybrid adsorptive membranes is highlighted based on the bulk polymer
properties like mechanical and chemical resistance together with the structural configurations of the membrane. As such, it is important to understand the interaction mechanisms
between adsorbents and dyes for effectual removal of dyes from wastewater. Here, we review the governing interaction mechanisms between dye and adsorptive membrane in the
membrane separation process together with the modified adsorptive membranes. Despite the fact that adsorptive membranes possess outstanding effectiveness and capability in
wastewater treatment for reuse which provides them a great chance to be employed as prospective technologies for dye adsorption; adsorptive membranes are still racked with some
drawbacks. Hence, we present different modification methods used in combating these drawbacks which will subsequently improve the performance of adsorptive membranes
New droop-based control of parallel voltage source inverters in isolated microgrid
Get PDFMicrogrids, featuring distributed generators like solar energy and hybrid
energy storage systems, represent a significant step in addressing challenges
related to the greenhouse effect and outdated transmission infrastructures.
The operation and control of islanded microgrids, particularly in terms of
grid voltage and frequency, rely on the synchronization of multiple parallel
inverters connected to the distributed generators. However, to determine the
necessary grid parameters for effective control, the presence of circulating
currents from unbalanced grid voltages arises as a challenge. This situation
necessitates the development of a new approach to achieve phase angle
locking for grid synchronization, with the aim of maintaining the voltage
within acceptable limits in islanded microgrids. This objective is realized
through the creation of a microgrid network model, design of an adaptive
filter, utilizing the double second-order generalized integrator–phase-locked
loop (DSOGI-PLL), for dynamic voltage transformation. The design is
evaluated by simulation using MATLAB/Simulink. The primary goal is to
investigate the DSOGI-PLL-based droop control and compare its
performance with the conventional synchronous reference frame–phaselocked
loop (SRF-PLL) control approach. Notably, the DSOGI-PLL
successfully eliminates the ripples in phase angle estim
Automated gas-controlled cooker system design and implementation
Get PDFCooking from ancient times has evolved from using open fires to wood, gas
cookers, using liquefied petroleum gas (LPG). This has also come with
various adverse effects ranging from gas leakages to burnt food due to
absent-mindedness, thereby creating a significant disaster that could lead to
loss of life and property damage. The study aimed to reduce the rate of
liquefied petroleum gas related accidents in domestic usage and improve the
safety of domestic gas users. An automated method to enforce safety was
proposed to avoid unwanted cooking gas flow consequences, especially in
homes. The paper presents a control system using an Arduino Uno with a
control design interfaced with a utensil sensor, solenoid valve, and a timer
circuit to allow gas flow to commence and ignite a flame automatically. The
automatic ignition apparatus, which has a high-voltage electric circuit,
begins to function once the utensil detector comes in contact with silverware.
The system is designed to function in different modes to ensure safety and
prevent gas flow. The prototype serves as a means of curbing gas wastage
and increasing the safety of people who use LPG as a source of fuel for
cooking
ENHANCED IN-CONTEXT LEARNING FOR CODE ANALYSIS WITH COMPACT LARGE LANGUAGE MODELS AND MONTE CARLO TREE SEARCH
Get PDFLarge Language Models (LLMs) demonstrate impressive reasoning abilities, yet their performance can falter when dealing with extensive context lengths. Techniques like Retrieval Augmented Generation (RAG) and Chain-of-Thought prompting seek to bridge this gap, but they face limitations when applied to large code-based contexts due to the complexity of representing inter-object relationships. Monte Carlo Tree Search (MCTS), a heuristic search algorithm, offers a potential solution by aiding LLMs in identifying crucial code repository aspects, thus facilitating downstream tasks. This research focuses on applying MCTS to enhance the performance of "Compact LLMs" - models small enough to run inference on consumer-grade GPUs. Our findings confirm that MCTS indeed boosts performance compared to the baseline Compact LLM. However, these compact models, even with MCTS, still lag behind larger models in performance
Deep Reinforcement Learning Applications For Coexistence in Television Whitespace: A Mini-Review
Get PDFThe ever-increasing demand for wireless communication services, coupled with the scarcity
of available radio frequency spectrum, necessitates innovative approaches to spectrum
management. Television White Space (TVWS) and Cognitive radio (CR) technology have
emerged as a pivotal solution, enabling intelligent and dynamic spectrum sharing among
secondary users while respecting the rights of primary, licensed users. However, a notable
challenge to its effective utilization lies in the interference between primary and secondary
users, as well as interference among secondary users themselves. In such networks,
network entities must make local decisions to optimize network performance in the face of
unknown network conditions. Reinforcement learning has effectively been utilised to help
network entities choose the best policies, such as decisions or actions, based on their
states when the state and action spaces are limited. However, in complex and large-scale
networks, the state and action spaces are typically vast. Deep reinforcement learning, a
fusion of reinforcement learning and deep learning, has been created to address these
limitations. This paper explores the coexistence issue and evaluates the use of deep
reinforcement learning (DRL) methods to enhance spectrum sharing in cognitive radio
networks
Development of an Automated Service Level Agreement Negotiation Framework for SaaS Cloud E-Marketplace
Get PDFWith the increasing number of Software-as-a-Service (SaaS) users, catering to their diverse
needs while ensuring Quality of Service (QoS) has become imperative. Although SaaS
providers regularly introduce new applications with varying offerings and QoS parameters,
the current Cloud e-marketplace may overlook the individual QoS requirements of each
user. To tackle this challenge, a Service Level Agreement (SLA) negotiation framework is
employed for cloud service selection, ensuring SaaS user satisfaction, often facilitated by
brokers. These brokers secure optimal offers from providers, taking into account the user's
preferences. To initiate an agreement, brokers rank cloud providers, proceeding to
negotiations only if the user rejects the top-rated provider's offer. Given the multitude of
users and providers in today's cloud e-marketplace, selecting the most suitable provider
and negotiating QoS parameters can be a complex task for brokers. This paper introduces
a negotiation framework designed to enhance the customer satisfaction value for SaaS
users through a service broker. Our proposed framework employs multi-agent systems
(MAS) as the methodology, incorporating the level of competition, current negotiation time,
and opportunities to formulate an improved negotiation model for SaaS users in the cloud
marketplace. By adopting this proposed negotiation framework, SaaS users can acquire
services from providers that precisely meet their requirements. Experimental results from
simulations demonstrate that the proposed framework outperforms previous studies in
terms of satisfaction level, response time, and negotiation success rate
Design and Fabrication of a Bone Crushing Machine/Hammer Mill for Sustainable Livestock Feed Production
Get PDFThis study focuses on the design and construction of an improved crushing machine with a capacity
of 0.15 (150 kg/hr) tons per hour, 15 Hp, and 2910 rpm speed. The design follows criterion design
guidelines to ensure the improved service life of the component. When the values produced from the
current design approach were contrasted with the values and outcomes received from the analysis
using the Ansys package, the design should be reliable. The hammers produced are subjected to
carburisation process using bio-wastes such as coconut shells, saw dust, and palm kernel shells to
enhance the reliability of the machine. The bending of the shaft is controlled during the rotation at
rated speed rpm when a load is applied to the shaft. The critical speed of the shaft is experienced
with deflection when the shaft rotates freely. The natural frequency and speed were put under check
in order to avoid failure. The von Mises stress was employed as a yielding criterion for the shaft. It
states that if the components of stress operating on a body are more than the criterion, the body will
yield
Willingness to Pay for an Electricity Connection: A Choice Experiment Among Rural Households and Enterprises in Nigeria
Get PDFRural electrification initiatives worldwide frequently encounter financial planning challenges
due to a lack of reliable market insights. This research delves into the preferences
and marginal willingness to pay (mWTP) for upfront electricity connections in
rural and peri-urban areas of Nigeria. We investigate discrete choice experiment data
gathered from 3,599 households and 1,122 Small to Medium-sized Enterprises (SMEs)
across three geopolitical zones of Nigeria, collected during the 2021 PeopleSuN project1
survey phase. Employing conditional logit modeling, we analyze this data to explore
preferences and marginal willingness to pay for electricity connection. Our findings
show that households prioritize nighttime electricity access, while SMEs place a higher
value on daytime electricity. When comparing improvements in electricity capacity
to medium or high-capacity, SMEs exhibit a sharp increase in willingness to pay for
high-capacity, while households value the two options more evenly. Preferences for the
electricity source vary among SMEs, but households display a reluctance towards diesel
generators and a preference for the grid or solar solutions. Moreover, households with
older heads express greater aversion to connection fees, and male-headed households
show a stronger preference for nighttime electricity compared to their female-headed
counterparts. The outcomes of this study yield pivotal insights to tailor electrification
strategies for rural Nigeria, emphasizing the importance of considering the diverse
preferences of households and SMEs
In-Situ Based Observation and Reanalysis-Derived Wind Data for Offshore Wind Energy Potential Assessment in the Gulf of Guinea
Get PDFThis study investigates offshore wind energy resources over five synoptic stations offshore in Nigeria,
located within Guinea Gulf of Guinea (GoG). It utilizes buoy-station observations of wind from 1979 to
2015 to validate the use of high-resolution RegCM4 Regional Climate Model CORDEX-CORE
simulations driven by ERA-Interim (ERA). It employs the Weibull distribution function to estimate
parameters for the evaluation of offshore wind energy potential based on characteristics intrinsic to
energy conversion in the study area. Mann–Kendal test was carried out to evaluate the statistical
significance of the observed trends and inter-annual variability. A series of standardized criteria such as
wind power density (WPD), coefficient of variation (CV), monthly variability index (MVI),
accessibility, extreme wind speed, and distance to the coast were adopted to find the most appropriate
sites for offshore wind energy exploitation over the study area. The results revealed that the ERA
simulations have fairly good agreements and fit with the field observations of sea surface wind speed.
Low and insignificant (at p = 0.05) negative model bias, MB (−0.07 ≤ MB ≤ −0.28 ms−1), and percentage
mean bias, PMB (−2.12 ≤ PMB ≤ −7.06%) were obtained in 80% of the stations. The wind power
potential in the GoG varied with the distance of the site from the coast. Agbami station showed the best
potential for wind power resources with daily mean and annual total WPD of 2.50 ± 0.50 kW m−2 and
1.02 ± 0.17 MW m−2, respectively. There were indications of abundant wind power availability and
generation at the selected sites, going by the low variability and intermittency obtained in terms of
trends, range, CV, MVI, and extreme wind speed episodes