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A sensorless efficiency-optimizing vector control scheme for an induction motor drive
Get PDFIn this paper, an energy-saving scheme in rotor field-orientated vector control is
developed for induction motor drives. The energy-saving scheme minimizes
copper and core losses in induction motors, which are equally applicable to
induction generators. In efficiency optimization, an optimal stator angular
velocity is uniquely obtained, and consequently, a corresponding optimal slip
at any given rotor speed is determined. The challenge of determining a reference
for rotor flux linkage that guarantees the minimal copper and core loss regime is
overcome by developing a load torque observer loop. The torque observer is
developed alongside a rotor speed observer for a sensorless speed operation. The
observed mechanical torque is further used to enhance the outer-loop rotor
speed control that generates an electromagnetic torque command used in
building the reference for the inner-loop stator current control. The results
obtained justified the effective operation of the torque and rotor speed
observer, which consequently verified the effective minimal electrical loss
regime at the optimal stator angular velocity and optimal rotor flux linkage.
The results are further compared to results obtained from the equivalent
induction machine drive on a finite-set model predictive control (FS-MPC)
scheme with the same values of the optimal stator angular velocity and
optimal rotor flux linkage. The developed efficiency-optimized vector control
scheme gave lower ripples in developed electromagnetic torque and dampened
overshoot better during step change in load torque
Development of a Machine Learning Based Fault Detection Model for Received Signal Level in Telecommunication Enterprise Infrastructure
Get PDFThis research develops a machine-learning fault detection model for received signal levels
in telecommunication infrastructure. The methodology involves modeling an enterprise
point-to-multipoint wireless network using pathloss 5.0 software. Data from the simulated
network, including free space pathloss, transmit power output, transmit antenna gain,
transmitter loss, miscellaneous loss, and receiver loss, is used to train three regression
models: gradient boosting regression (GBR), random forest regression (RFR), and KNearest
Neighbor (KNN). The algorithm compares the received signal levels (RSL) of
new data with a threshold value, triggering a "Fault" or "No-fault" condition. A "Fault"
indicates a deviation in the RSL, prompting maintenance by the field support team. A
"No-fault" means the RSL is within the accepted range, requiring no maintenance.
Performance evaluation metrics such as mean absolute error (MAE), mean square error
(MSE), R-squared, and root mean square error (RMSE) were compared to select the
optimal model. Experimental results show that the RFR model outperforms GBR and
KNN with MAE: 0.007101, MSE: 0.000610, R-squared: 0.999992, and RMSE: 0.024697.
Leveraging these machine learning-based fault detection models enables telecom service
providers to optimize network performance, reduce downtime, and increase customer
satisfaction
Navigating academic excellence: Understanding how university vision impacts staff effectiveness
Get PDFVision statements are seen as important factors that come before the development of strategies. They excel in encapsulating the strategic direction of a company, clearly defining its scope, boundaries, and the process of creating value. Vision statements function as comprehensive frameworks that guide the development of several strategic elements, including mission, strategic capabilities, strategic intent, objectives, goals, core values, standards of behavior, and business models. However, research on this subject remains scanty, particularly within academia. Therefore, this study examined the impact of university vision on staff effectiveness building on the transformational leadership theory and the strategic leadership theory. The study adopted positivist research philosophy. The research employed a cross-sectional study design. This study employed a descriptive research design. Questionnaires were devised by the researcher to collect data from a randomly selected group of 186 academic personnel from the four colleges in a private university using stratified sampling. Data obtained was subject to validity checks using composite reliability, average variance extracted (AVE) estimate, and Cronbach Alpha coefficient. The findings reveal that the vision of the university had a significant but weak impact on the effectiveness of staff. Based on the findings and conclusions, the study suggests that the University’s organizational vision should be consistently improved and effectively conveyed to staff members to direct their efforts toward achieving the university’s vision
Typology of State Actors' Behavior in Cyber Space
Get PDFCyberwar is no longer subject to "if" but "when." Despite growing interest in cyberspace and cyber war readiness and resilience among academics, researchers, policymakers, and the media, the area needs to be more robust with different terminology that strategically captures the activities of state actors in cyberspace. The paper aims to provide a strategic classification of the activities of state actors in cyberspace. A typology is developed to encapsulate the strategic complexity of the activities of state actors in this terrain. The typology can illuminate the current global proportion and payoff of each type
Retrograde Assemblages in the Muscovite-Biotite Gneiss of Oluyole Southwestern Nigeria, an Indication of Shear-Zone Environment
Get PDFPetrographic and whole-rock geochemical study of biotite-muscovite gneiss was determined in order to interpret
the metamorphic evolution of the Basement Complex of Southwestern, Nigeria. The gneiss shows a millimetric banding, and
in some cases the quartzo-feldspathic bands running up to 10 cm. The gneiss has mineral assemblage biotite + plagioclase +
quartz + garnet + K-feldspar + muscovite + chlorite + ilmenite ±titanite. Chlorite occurs along cleavage planes of biotite, and
in some cases forms reaction rims around porphyroblasts of garnet. K-feldspar crystals are surrounded by muscovite. Titanite
crystals are sub-idioblastic to xenoblastic in form, and have inclusions of ilmenite. Titanite, where present, occurs in close
association with biotite and opaque minerals (ilmenite). Also, titanite forms a reaction rim around apatite. Mylonitic texture,
fine-grained matrix of mica and quartz ribbons were observed. In addition, there is stretching of the quartz crystals. The SiO2
content is greater than 60 wt %, while CaO ranges from 3.05-6.91 wt %. The M1 foliation comprise of mineral biotite some of
which are included in the opaque mineral, M2 represents the metamorphism which gave rise to porphyroblasts of ilmenite,
while the M3 gave rise to foliations that forms a wraparound structure on the porphyroblasts of ilmenite. The last metamorphism
gave rise to retrograde minerals; chlorite, titanite, and muscovite. The study suggests that this area of the Basement Complex
has been subjected to multiple deformations, as well as multiple episodes of metamorphism. The structures observed are similar
to those associated with shear zone environment
Phase Transition in Silicon from Machine Learning Informed Metadynamics
Get PDFInvestigating reconstructive phase transitions in large-sized systems requires a highly efficient
computational framework with computational cost proportional to the system size. Traditionally,
widely used frameworks such as density functional theory (DFT) have been prohibitively expensive
for extensive simulations on large systems that require long-time scales. To address this challenge,
this study employed well-trained machine learning potential to simulate phase transitions in a largesize
system. This work integrates the metadynamics simulation approach with machine learning
potential, specifically deep potential, to enhance computational efficiency and accelerate the study of
phase transition and consequent development of grains and dislocation defects in a system. The
new method is demonstrated using the phase transitions of bulk silicon under high pressure. This
approach has revealed the transition path and formation of polycrystalline silicon systems under
specific stress conditions, demonstrating the effectiveness of deep potential-driven metadynamics
simulations in gaining insights into complex material behaviors in large-sized systems
MAP REDUCE SECURITY MODEL FOR ASTHMA PREDICTION IN CHILDREN USING FEDERATED XGBOOST
Get PDFA substantial number of asthma development prediction models in children, such as conventional methods involving risk factors, logistic regression, the hybrid of statistical methods, and machine learning based approaches, exist. The problem associated with conventional methods of asthma prediction in children is low predictive accuracy of the model. However, using centralised machine learning approaches in healthcare requires training the learning models on large datasets. Besides cost, data privacy and security represent the main problems with centralised machine learning. The objective of this study is to develop a Map Reduce Security for asthma prediction in children using a federated XGBoost as a response to the aforementioned limitations associated with the existing asthma prediction model for children. This study leveraged two diverse datasets: the Nigerian Hospital Asthma dataset and the National Survey of Children's Health dataset for benchmarking purposes. After preprocessing the dataset, the symmetrical uncertainty and normalization interaction gain, as well as the undersampling approach, were employed for feature selection and dataset balancing. The system was trained, and tested using Federated Artificial Intelligence (AI) Technology Enabler (FATE) and extended to XGBoost model with one central server for federated algorithm averaging. The map reduce security measure was employed for the input data during training to avoid data leakage. The study was implemented with the Python programming language on Google Collaboratory (Collab) environment. The results of the analysis showed considerable high accuracy of 0.98, precision (0.98%), recall (0.98%), and F1-score (0.99%) for the asthmatic class and precision of 0.98%, as well as F1-score of 0.99% for the non-asthmatic class. The implemented model was benchmarked with the existing study on asthma prediction model in children using the same NSCH dataset of 23 features and 50212 samples. This study achieved a prediction accuracy of 93.8%, outperforming the existing models. The simulated attack on the implemented model results show that the model correctly identified whether a data point was used in the training process with predictive accuracy of 97.94%, membership inference attack, and data loss of 0.014%, respectively. In conclusion, the study finds that the model developed by the researchers performed better in predicting childhood asthma than some of the state-of-the-art machine learning algorithms
Risk Assessment and Management in Nanotoxicology
Get PDFRisk assessment and management in the realm of nanotoxicology represents
an indispensable and multifaceted discipline that is profoundly committed to
comprehending and mitigating the potential perils inherently associated with
nanoparticles. Nanotoxicology, as a central component of this field, delves
into the systematic exploration of the detrimental effects that nanoparticles
can impose upon both living organisms and the delicate environment. It is
thus imperative to meticulously scrutinize and evaluate the multifarious risks
posed by nanoparticles. It is a nonnegotiable imperative that these risks are
subjected to thorough analysis and subsequently managed with a suite of
highly effective strategies, all oriented toward preserving human health and
the ecological equilibrium. The armamentarium of these strategic approaches
encompasses a diverse array of tools, including the formidable instrument of
regulatory oversight. This not only serves as a sentinel guarding against
potential hazards but also lays down the law when it comes to the utilization of
nanoparticles, making sure that it is consistent with safety and environmental
preservation. Research and development emerge as another cornerstone in
this protective agenda. This involves a rigorous and relentless pursuit of
knowledge, where the toxicological aspects of nanoparticles are painstakingly
scrutinized and safer alternatives are earnestly sought. Furthermore,
workplace safety protocols stand as a bulwark against potential perils. These
protocols codify the correct methods for handling, storing, and disposing of
nanomaterials, taking into account critical elements such as engineering
controls, personal protective equipment, and comprehensive worker training.
Consumer safety requires proper labeling and transparent disclosure of
nanoparticle usage that are critical components of this approach, for they
enable consumers to make informed choices and therefore reduce potential
health risks. The mitigation of long-term hazards associated with nanoparticle
waste is coupled with measures to prevent unintended releases into the
environment. An equally potent strategy involves collaborative research and
information sharing, where the combined efforts of scientists, regulatory
authorities, and industries are harnessed to collectively assess risks, identify
best practices, and forge comprehensive safety guidelines. This harmonious
collaboration fosters transparency, shaping the responsible nanoparticle use
while facilitating early hazard identification, risk mitigation, and informed
decision-making, all of which are instrumental in shielding public health and
the environment from potential harm. The aim of the chapter is to present the
secure and responsible deployment strategies of nanoparticles while diligently
minimizing their potential adverse impacts on society and the environment.
The report represents a vanguard of vigilance, ensuring that the vast potential
of nanotechnology is harnessed without jeopardizing the well-being of
humanity or the ecological balance of ecosystems
Performance evaluation of SARS-CoV-2 rapid diagnostic tests in Nigeria: A cross-sectional study
Get PDFThe COVID-19 pandemic challenged health systems globally. Reverse transcription polymerase
chain reaction (RT-PCR) is the gold standard for detecting the presence of SARSCoV-
2 in clinical samples. Rapid diagnostic test (RDT) kits for COVID-19 have been widely
used in Nigeria. This has greatly improved test turnover rates and significantly decreased
the high technical demands of RT-PCR. However, there is currently no nationally representative
evaluation of the performance characteristics and reliability of these kits. This study
assessed the sensitivity, specificity, and predictive values of ten RDT kits used for COVID-
19 testing in Nigeria. This large multi-centred cross-sectional study was conducted across
the 6 geo-political zones of Nigeria over four months. Ten antigen (Ag) and antibody (Ab)
RDT kits were evaluated, and the results were compared with RT-PCR. One thousand,
three hundred and ten (1,310) consenting adults comprising 767 (58.5%) males and 543
(41.5%) females participated in the study. The highest proportion, 757 (57.7%), were in the
20–39 years’ age group. In terms of diagnostic performance, Lumira Dx (61.4, 95% CI:
52.4–69.9) had the highest sensitivity while MP SARS and Panbio (98.5, 95% CI: 96.6–
99.5) had the highest specificity. For predictive values, Panbio (90.7, 95% CI: 79.7–96.9)
and Lumira Dx (81.2, 95% CI: 75.9–85.7) recorded the highest PPV and NPV respectively.
Ag-RDTs had better performance characteristics compared with Ab-RDTs; however, the
sensitivities of all RDTs in this study were generally low. The relatively high specificity of Ag-
RDTs makes them useful for the diagnosis of infection in COVID-19 suspected cases where
positive RDT may not require confirmation by molecular testing. There is therefore the need
to develop RDTs in-country that will take into consideration the unique environmental factors,
interactions with other infectious agents, and strains of the virus circulating locally. This
may enhance the precision of rapid and accurate diagnosis of COVID-19 in Nigeria
MULTILATERAL DIPLOMACY AND POVERTY ALLEVIATION IN NIGERIA
Get PDFThis research aims to examine the relationship of multilateral diplomacy on poverty alleviation in Nigeria by analysing three key strategies: attracting Foreign Direct Investment (FDI), acquiring Official Development Assistance (ODA), and campaigning for debt relief. The study seeks to determine if these strategies, facilitated through Nigeria's multilateral diplomatic relations, have effectively reduced poverty. Using the World System Theory, the study explores how global capitalism and the core-peripheral structure influence Nigeria's international economic engagements. It employs an exploratory research design; data were gathered with in-depth interviews and secondary sources. Data was then analysed using thematic analysis. The findings reveal that these diplomatic strategies have not significantly alleviated poverty in Nigeria, largely due to domestic challenges that impede their effectiveness. Addressing these internal issues is essential for multilateral diplomacy to have a meaningful effect on poverty reduction in the country. This study further recommends that Nigerian policymakers should use multilateral diplomacy to target human development variables such as education, healthcare, and social welfare to effectively alleviate poverty. This study therefore concludes that there is an insignificant relationship between multilateral diplomacy and poverty alleviation in Nigeria