15288 research outputs found
Sort by
Towards an Integrative Model of Innovative Entrepreneurship Education for Institutional Sustainability
Get PDFThis paper advocates and posits the Integrative Model of Innovative
Entrepreneurship Education (IMIEE). This is achievable by exploring
entrepreneurship from the angle of educating students and aspiring
entrepreneurs to become innovative entrepreneurs rather than being imitative
and non-disruptive. However, given the current overly theoretical nature of
teaching schemes across higher institutions today, curriculum needs to be
completely overhauled to incorporate more innovative approach of practical
and hands-on experiences that fosters innovative entrepreneurial practice. An
integrative model for innovative entrepreneurship education becomes
imperative for insight and guidance for pedagogy and practice in a way that
drives institutional sustainability. Thus, this theoretical paper contributes to
existing literature by analysing various empirical works and previous models
such as the Design Thinking Approach, D.I.S.R.U.P.T, the Experiential model,
and National Innovation Systems (NIS). Some shortcomings in previous
models inform the need to posit an integrative model that synthesises vital
elements
SOCIO-POLITICAL CHALLENGES AND EDUCATION TOURISM IN FOREIGN INSTITUTIONS: A STUDY OF NIGERIAN TERTIARY LEVEL STUDENTS
Get PDFEducation tourism has been a sought-after option in recent years for tertiary level students in Nigeria who are in search of quality education in foreign institutions. Reports have revealed that Nigeria has the highest outbound student mobility in Africa. The decision for Nigerian tertiary level students to leave the country is motivated by the socio-political challenges that have plagued the public tertiary institutions in Nigeria. This study therefore examines how socio-political challenges have influenced education tourism in foreign institutions among Nigerian tertiary level students with a focus on three socio-political challenges - poor funding, incessant strikes, and insecurity. This study also analyses education tourism from 2019 to 2023. Hence, both primary data and secondary data were collected using literature search and interview. This study adopts the Human Capital Theory. According to this theory, lack of educational opportunities in the home country and its availability in the destination country are the primary motivation for moving. Data gathered were analysed using thematic method of analysis. This research reveals that the absence of sustainable funding of public tertiary institutions has hindered the quality of education in Nigeria, the constant disruption in academic calendars has resulted in students staying in school for more than the duration of their study, and the state of insecurity has led to the loss of lives, properties and disruption in schools’ administration. These have led to the prevalence of education tourism among Nigerian tertiary level students to foreign institutions, and more are willing to embark on education tourism if given the means and the opportunity to do so. The study also reveals that education tourism among Nigerian tertiary level students occurred most in 2022 and the United Kingdom, the United States, Canada, Germany, and Malaysia are the most popular destinations for Nigerian students to pursue foreign education. This study recommends that there should be the yearly allocation of 26% of the national budget to the education sector which will help close the gap that stands currently. Also, Nigerian tertiary institutions should ensure that facilities available in foreign institutions which are necessary for effective learning and teaching are made available. This should also include the creation of more scholarship opportunities which are likely to attract foreign students to study in Nigeria. With this, Nigerian tertiary schools will be able to compete globally with foreign institutions. Furthermore, there should be the creation of policies and programmes that create job opportunities for Nigerian graduates. This will help reduce unemployment and therefore reduce the rate of insecurity in the country. The study therefore concludes that there is a relationship between socio-political challenges and education tourism, and these socio-political challenges- poor funding of the education sector, incessant strikes, and insecurity- have motivated Nigerian tertiary level students to embark on education tourism in foreign institutions
A Review of Fabrication Techniques and Optimization Strategies for Microbial Biosensors
Get PDFChallenges of stability and specificity associated with early generation sensors
necessitate the fabrication and optimization of microbial biosensors. More so, the global
biosensors market size currently valued at USD25.5 billion in 2021 is expected to grow
at a compound annual growth rate (CAGR) of 7.5% to USD36.7 billion in 2026. Microbial
biosensors are bioanalytical systems that integrate microorganisms with a physical
transducer to generate signals, thus, aiding the identification of analytes. The biosensors
are fabricated through a series of steps comprising microbe selection, immobilization
onto a matrix, microfabrication, calibration, and validation. The transducers integrated
microorganisms generate quantifiable signals, enabling real-time monitoring of a
diversity of analytes within food samples. The optimization strategies are scrutinized,
with a particular focus on the integration of sundry nanoparticles, such as magnetic,
gold, and quantum-dot nanoparticles, which enhance sensor performance. Distinct
advantages offered by microbial biosensors promise to revolutionize food quality
assessment via cost-effectiveness, rapid sample testing, and the ability to provide access
to real-time data. Literature have highlighted certain limitations including interference
from complex matrices, instability of microorganisms, and microbial lifespan. In
assessing their economic importance, a comparative analysis is presented against
conventional food analytical methods like ELISA, PCR, and HPLC; thus, highlighting the
unique strengths of microbial biosensors. The future perspectives focus on the potential
of the technology in addressing the need for continuous monitoring challenges, and
research for further improvements in the biocompatibility of fabrication processes and
long-term reusability
Emerging Technology and Future Directions in Environmental Nanotoxicology
Get PDFEnvironmental nanotoxicology constitutes a specialized scientific discipline
that systematically investigates the multifaceted impact of nanomaterials on
ecosystems. The rapid advancements within this field have yielded pivotal
insights into the intricate behaviors exhibited by nanomaterials, elucidating
their toxicity profiles and unraveling the broader ecological consequences
ensuing from their introduction into various environmental compartments.
Central to the research in environmental nanotoxicology is the comprehensive
comprehension of nanoparticle interactions with both organisms and their
surrounding environments. This encompasses an in-depth analysis of the
physicochemical properties of nanomaterials, their fate and transport within
ecosystems, as well as their potential uptake and bioaccumulation by living
organisms at different trophic levels. In the quest for a more thorough
understanding of nanoparticle impacts, cutting-edge technologies have
become instrumental in pushing the boundaries of research. High-throughput
screening methodologies enable the rapid assessment of a multitude of
nanomaterials, expediting the identification of potential hazards. Omics
techniques, encompassing genomics, transcriptomics, proteomics, and
metabolomics, offer a comprehensive profiling of molecular responses to
nanoparticle exposure, unraveling intricate cellular and organismal dynamics.
Furthermore, computational modeling plays a pivotal role in simulating and
predicting the behavior of nanomaterials in complex environmental matrices,
providing valuable insights into their transport, transformation, and potential
ecological risks. The trajectory of environmental nanotoxicology is now
propelled toward the integration of multi-omics data, aiming for a holistic
understanding of the underlying mechanisms governing nanoparticle-induced
toxicity. This integrated approach holds the promise of unraveling complex
biological pathways, enabling the identification of key molecular signatures
associated with nanomaterial exposure. Moreover, it facilitates the
development of predictive toxicology models, enhancing our capability to
forecast the potential environmental impacts of various nanomaterials.
Anticipated future directions in this field involve leveraging these innovations
to refine risk assessment methodologies, thus contributing to the
establishment of robust regulatory frameworks. The ongoing quest is not only
to deepen our insights into nanoparticle behavior at the molecular and
ecological levels but also to channel this knowledge towards the development
of sustainable nanotechnology applications. By aligning research endeavors
with the principles of sustainability, environmental nanotoxicology strives to
ensure that the benefits of nanotechnology can be harnessed responsibly,
mitigating potential adverse effects on ecosystems and human health
Nanochitosan-Based Enhancement of Fish Breeding Programs
Get PDFThe exploration of nanochitosan’s potential in stimulating fish spawning
represents a promising frontier in aquaculture. Its unique properties, including
antimicrobial action and compatibility with aquatic environments, offer
possibilities for enhancing reproductive outcomes. However, this innovation
requires careful consideration of various factors. Environmental impact
assessment, regulatory compliance, efficacy optimization, and risk mitigation
are crucial aspects to ensure the responsible use of nanochitosan in fish
breeding. Rigorous research involving species-specific studies, mechanistic
understandings, and ecotoxicological assessments is pivotal for informed
decision making and regulatory adherence. Collaboration, knowledge
dissemination, and ongoing innovation are essential in advancing this
technology sustainably. By leveraging nanochitosan’s benefits while
addressing limitations and risks, the aim is to develop a balanced approach
that contributes to sustainable aquaculture practices. With continual
advancements and a commitment to responsible implementation,
nanochitosan-based spawning stimulation holds the potential to revolutionize
fish breeding, promoting sustainable practices and the health of aquatic
ecosystems
Fecal Carriage of Colibactin-Encoding Escherichia coli Associated With Colorectal Cancer Among a Student Populace
Get PDFFecal carriage of the colibactin (clb) gene in Escherichia coli is described as a source that could promote carcinogenesis, progressing to
colorectal cancer. The present study investigated the demographic, dietary, and antibiotic consumption variables as correlates for
fecal carriage of clb+/E coli among the student populace. In a randomized cross-sectional survey, E coli (N = 136) from the fecal
samples of eligible students were characterized and evaluated for antibiotic resistance, β-lactamase (blm), biofilm, virulence factor
production, and strain tryptophan reverse mutagenic activity. The encoded clb+/E coli were analyzed for correlates with principal
component analysis. Of all the E coli strains, a low rate of 2 clb+/E coli (1.5%) and higher rates of biofilm (13.2%) and blm producers
(11.8%) were recorded among the mutant strains as compared with the nonmutant types. All the clb+/E coli showed complete
resistance to amoxicillin, Augmentin (amoxicillin and clavulanate), gentamicin, and trimethoprim/sulfamethoxazole. The fecal
clb-encoded E coli (1.5%) were not associated with demographic status, fiber-based food (odds ratio [OR], 1.03; 95% CI, 56.74–138.7;
P = .213), alcohol (OR, 1.27; 95% CI, 61.74–147.1; P = .221), antibiotic consumptions (OR, 1.11; 95% CI, 61.29–145.3; P = .222), and
handwashing (OR, 1.17; 95% CI, 60.19–145.5; P = .216). The hierarchical cluster of blm+/E coli revealed high-level resistance with a
multiantibiotic resistance index ≥0.2 (P < .05). Only 12% of all strains were tryptophan mutant/blm+, and 1.5% of clb+/ECblm+ were
observed in fecal samples with a 452–base pair size. Trimethoprim/sulfamethoxazole and biofilm production positively regressed with
clb expression (P > .05). Principal component analysis score plot indicated an association of clb+/ECblm+ with dietary pattern, alcohol,
blm, and hemolysin production. The combined activity of blm and biofilm production in the gut microbiota could promote clb+/E
coli colonization, facilitating genotoxin production and possible colorectal cancer induction
An in-silico analysis of OGT gene association with diabetes mellitus
Get PDFO-GlcNAcylation is a nutrient-sensing post-translational modification process. This cycling process involves two
primary proteins: the O-linked N-acetylglucosamine transferase (OGT) catalysing the addition, and the glycoside
hydrolase OGA (O-GlcNAcase) catalysing the removal of the O-GlCNAc moiety on nucleocytoplasmic proteins.
This process is necessary for various critical cellular functions. The O-linked N-acetylglucosamine transferase (OGT)
gene produces the OGT protein. Several studies have shown the overexpression of this protein to have biological
implications in metabolic diseases like cancer and diabetes mellitus (DM). This study retrieved 159 SNPs with
clinical significance from the SNPs database. We probed the functional effects, stability profile, and evolutionary
conservation of these to determine their fit for this research. We then identified 7 SNPs (G103R, N196K, Y228H,
R250C, G341V, L367F, and C845S) with predicted deleterious effects across the four tools used (PhD-SNPs, SNPs&Go,
PROVEAN, and PolyPhen2). Proceeding with this, we used ROBETTA, a homology modelling tool, to model the
proteins with these point mutations and carried out a structural bioinformatics method– molecular docking– using
the Glide model of the Schrodinger Maestro suite. We used a previously reported inhibitor of OGT, OSMI-1, as the
ligand for these mutated protein models. As a result, very good binding affinities and interactions were observed
between this ligand and the active site residues within 4Å of OGT. We conclude that these mutation points may be
used for further downstream analysis as drug targets for treating diabetes mellitus
Geophysical Investigation of the Subsurface Structural Competency Around College of Computing and Communication Studies, Bowen University, Iwo, Osun State, South West Nigeria
Get PDFMapping of the subsurface structures within the environment is highly essential in engineering site
characterization. The subsurface structure around the college of computing and communication studies,
Bowen University Nigeria was studied using the very low frequency electromagnetic (VLF-EM) and the
geo-electrical resistivity method. This was aimed with a view to investigate the weak and competent
geological zones. In this study, five traverses were generated for both the VLF-EM and geoelectrical
resistivity method, using the VLF-EM 16 equipment and OHMMEGA-Terrameter respectively. The results
of the VLF-EM revealed the presence of positive and negative anomalies responses. The positive anomalies
indicate the weak zones, which may be caused by geological structures such as faults/fractures, and contacts
between rocks and the negative anomalies indicate the competent zones, which may be due to hard
rock/laterite. The geo-electrical resistivity results revealed the vertical and lateral inverted resistivity values
of the subsurface structure. The result revealed four layers such as top soil, weathered layer, fractured and
fresh basement. From both the interpreted results, the weak/fractured zones mapped in the basement are
points of interest in this research. The results of the investigation revealed that the probable cause(s) of the
structural failures within the study area are evidence of geological features mapped as fracture and clayey
formation that is present in the study area. Therefore, in order to evade future structural problems and
minimize capitals used in restoring distressed structures in the University, a geophysicist services should be
engaged for pre-foundation studies, which will act as a guide before and during construction
DEMAND FORECASTING AND PRODUCTION PLANNING IN THE FASHION INDUSTRY IN LAGOS STATE, NIGERIA.
Get PDFThe purpose of this study is to evaluate the influence of demand forecasting on production
planning in the fashion industry in Lagos State, Nigeria. The study aims to address the
challenges of inventory control, resource allocation, and pricing strategies by analyzing the
effectiveness of simple moving average (SMA), weighted moving average (WMA), and
exponential smoothing in predicting demand and optimizing production processes. Utilizing
a mixed-methods approach, the study combines primary data collected through 105 filled
questionnaires using random and purposive sampling techniques, analyzed using SEM-PLS,
alongside secondary data from a clothing store, organized and analyzed with Microsoft Excel
for historical sales and inventory data. The findings reveal that demand forecasting
significantly impacts production planning (β=0.565, t=9.132, p<0.05). Exponential
smoothing is found to be particularly effective in forecasting prices, while SMA proves
beneficial for inventory management despite its limitations. The study highlights that while
SMA provides basic forecast accuracy, WMA and exponential smoothing offer superior
precision and adaptability in resource allocation and pricing strategies. These insights
underscore the critical importance of selecting appropriate forecasting methods to optimize
various aspects of production planning in the fashion industry, ultimately enhancing
operational efficiency and strategic decision-making
ENSEMBLE MACHINE LEARNING APPROACH FOR IDENTIFYING THREATS IN SECURITY OPERATIONS CENTER
Get PDFCyberattacks can be prevented by identifying threats before they cause damage, requiring
robust cybersecurity measures. However, recent years have seen an increase in cyber threats
and data breaches, often exploiting infrastructure weaknesses. These attacks lead to significant
financial losses and compromised personal information, necessitating proactive defence
strategies. Traditionally, detecting threats involves laborious log analysis, but machine learning
can automate this process in intrusion detection systems (IDS). This study aims to implement
a blended ensemble approach for cyberattack detection in security operation centers,
combining predictions from base classifiers like Random Forest, XGBoost, HMM, and LSTM,
Feature selection was performed by aggregating importance scores from these classifiers, with
selected features used to improve the model's performance. A web application interface was
developed using the Python Flask framework. The integration of trained models into the
application programming interface (API) facilitated model training and dependency
management. The testing and evaluation were performed on both real production network
traffic flows and the testing set of the CICIDS2017 Thursday-WorkingHours-
Morning.pcap_ISCX.csv dataset, as well as the generated real-time network traffic dataset.
Real web attacks were intentionally executed on the server where the API/Intrusion Detection
System was implemented, and these unlabelled attack network flows were accurately labelled
by the IDS. To implement the ensemble model, the "Thursday-WorkingHours-Morning-
WebAttacks.pcap_ISCX.csv" was extracted from the renowned CICIDS2017 Thursday
Morning Hours Dataset was utilized to train the model. To enhance the diversity of network
traffic patterns and potential security incidents, real-time network traffic was generated using
Sqlite, Zenmap Nmap, ID2T, and Python. The generated real-time network traffic was also
used to train the model to detect unseen attacks. The proposed model performed well on the
balanced Thursday Morning Dataset. With precision, recall, and F1-score all at 0.99, the model
achieved an overall accuracy of 99% across the binary classification task, highlighting its
robustness and effectiveness in handling real-time malicious traffic. These findings validate
the model's ability to detect real-time network traffic patterns, particularly in the context of
potential security incidents. The proposed model demonstrated high performance on the
generated dataset, achieving a precision of 1.00 for detecting malicious threats, thereby
correctly identifying all instances without false positives. The recall of 1.00 further underscored
its capability to detect all actual instances of malicious activity. An F1-score of 1.00 for
legitimate traffic reflected the model's balanced precision and recall, ensuring reliable
classification across categories. Additionally, the cross-validation results exhibited consistently
high accuracy, with an average accuracy of approximately 0.999 across five folds. This
outcome confirms the model's robustness and generalizability across various data subsets,
highlighting its potential for reliable real-time threat detection and enhanced cybersecurity in
practical applications