Limited Effort Research Articles

Trends in the Development of University Education in Nigeria: Colonial and Post-Colonial Perspectives

Trends in the development of university education in Nigeria revealed limited efforts by both the missionaries and the colonial authority that pioneered modern education in Nigeria. However, since independence; there has been a gradual increase in the number of universities, their programmes and spread, as a result of the establishment of state, federal and privately owned universities. The objective of this study is to chronicle how we have come to where we are in the development of University education in Nigeria, bothering on the issues of quantity and quality outcomes. Historical research design was adopted with both primary and secondary sources of data and largely depended on documentary analysis. The findings showed that quantitatively, universities are growing rapidly, even becoming a regular attribute under successive governments. While this expansion is commendable, the attainment of quality and production of adequate manpower in critical facets of the nation’s need is less desirable, in fact, has not been achieved. Findings further revealed that the astronomical increase of these institutions in the twenty-first century is not spared of inadequacies in staff needs, state of the art facilities and equipment, staff and students’ welfare and other challenges; including drop in quality of graduates churned out, unemployment after school, poor funding, lecturers’ agitations, indiscriminate proliferation of universities, brain drain, capital flight etc. It is therefore recommended that a positive political will is needed to change the inadequacies in the system for quality university education, in consonance with global best practices. Keywords: Trends, Development, University Education, Colonial and Post-Colonial Perspectives.

A case study of College English teachers' emotional experiences in the blended teaching context.

The current research on language teacher education has witnessed a surge in studies focusing on teacher emotions due to their recognized importance in teaching. However, limited efforts have been found to investigate the emotions of Chinese College English teachers in the blended teaching context. This qualitative case study aims to uncover the emotional experiences of four Chinese College English teachers and explore the causes of their emotions in the blended teaching context. Data for the study was primarily gathered through interviews and case documents. The findings indicate that teachers in Eastern China experienced more positive emotions than negative emotions in the blended teaching context, while teachers in Western China exhibited the reverse pattern. These emotions were caused by their continuous appraisal of the interaction between their personal goals and various ecological systems. The research findings underscore the crucial role of teacher emotion in blended teaching and provide implications for enhancing blended teaching practices.

Gender Stereotypes and Composition in the Chinese Film Industry: A Study of the Top 15 Grossing Films

ABSTRACT This study employed qualitative textual analysis to explore gender representation in the 15 highest-grossing films in China. Applying the doing gender framework, the study revealed that there are limited efforts toward gender equality, pervasive stereotypes, and patriarchal and heteronormative ideologies across different genres. Women are portrayed through family-oriented lenses, and an Iron Lady image has emerged, perpetuating shallow perceptions of femininity and maintaining male power. Furthermore, female actors composed only one-fifth of all actors, and there were no female cinematographers or editors. This study underscores the pressing need for the Chinese film industry to promote gender equality.

Boosting Learners' Confidence in Learning English: Can Self‐Efficacy‐Based Intervention Make a Difference?

AbstractSelf‐efficacy is theorized as a malleable construct that can be influenced by teachers' instructions; however, limited effort has been devoted to evaluating the effectiveness of self‐efficacy interventions. This study leverages a mixed methods design to evaluate the effectiveness of a self‐efficacy intervention over 16 weeks among Chinese English as a foreign language learners. Data were collected from 102 secondary school students (52 and 50 students in the intervention and comparison groups, respectively). We also interviewed nine students from the intervention class. Repeated measures mixed analyses of variance were employed to check the changes in self‐efficacy, intrinsic motivation, engagement, and academic achievement over time as well as to detect the differences in these aspects between the comparison and the intervention groups. Moreover, thematic analysis was adopted to handle the qualitative data. Results of the quantitative phase showed that the explicit self‐efficacy supports worked effectively in promoting self‐efficacy, intrinsic motivation, and language proficiency. Results of the qualitative phase triangulated the findings from the quantitative phase, illuminating the broad‐spectrum effect of self‐efficacy support. Implications and directions for future research are discussed.

Un dialogo possibile? Teoria delle organizzazioni e studio dei partiti

Literature devoted to party organizations rarely relies on Organization Theory. With the exception of the fundamental contribution by Panebianco, which dates back to 1982, limited efforts have been made towards a mutual contamination between the study of party organizational development and the vast body of theories, approaches and analytical frameworks that crowd Organization Theory. Moreover, while Organization Theory has recently paid attention to the study of political parties, party scholars are still committed to accumulating empirical knowledge or, at best, elaborating ideal-typical models. The aim of this contribution is to favour a cross-fertilization between these two literatures, by building on the premises of organizational institutionalism and on the organizations as multi-dimensional rational open systems perspective. Despite our effort is primarily conceptual, our arguments are conceived to be properly translated into empirical observations.

(Invited) Atomic Layer Deposition on 1D Nanomaterials for Various Applications

One-dimensional nanomaterials – materials with one dimension outside the nanoscale, further noted as 1D NMs – represent a class of very important nanomaterials with continuously increasing importance. Due to their intrinsic features, unique properties and diversity of functionalities, they count among the most widely studied materials nowadays. While considerable research efforts have been spent to synthesize various 1D NMs (e.g. nanopores, nanotubes or nanofibers), limited efforts have been devoted to surface modification and property tailoring of these materials.However, it is their surface that comes into direct contact with various media (air, gases, liquids, solids) and influences the reactivity, stability and biocompatibility of these materials. The surface and aspect ratio (defined as their diameter to length ratio) influence the performance of these materials in various applications. Considering these facts, it is more relevant to tailor the surface of these materials and to be able to influence their properties and reactivity at the nanoscale, rather than to deal with tailoring their own bulk material composition.The focus of this presentation is on the modification of two types of 1D nanomaterials – nanotubes and nanofibers. Numerous techniques can be utilized for this purpose, such as for example wet chemical or physical deposition techniques. However, it is only the Atomic Layer Deposition (ALD) that is capable of really uniform and homogenous coating of these 1D nanomaterials, in particular those of very high-aspect ratio.The presentation will be mainly focused on modification of TiO2 nanotube layers and various nanofibers of different aspect ratios via ALD.Experimental details and some very recent application examples in photocatalysis, catalysis, sensors, batteries, nanorobots, etc. [1-11] and structural characterizations of these modified materials will be discussed. Sopha et al (2017), Appl. Mater. Today, 9, 104.Sopha et al. (2018), Electrochem. Commun.,97, 91.Ng et al. (2017), Adv. Mater. Interfaces, 1701146.Dvorak et al. (2019), Appl. Mater. Today, 14, 1.Sopha et al. (2019), FlatChem 17, 100130.Motola et al. (2019), Nanoscale 11, 23126.Ng et al. (2020), ACS Appl. Mater. Interfaces, 12, 33386.Motola et al. (2020) ACS Appl. Bio Mater. 3, 6447.Rihova & M. Knez & J. M. Macak et al. (2021), Nanoscale Adv., 3, 4589Galstyan, T. Djenizian, J. M. Macak (2022), Appl. Mater. Today, 29, 101613Villa & M. Pumera & J. M. Macak et al. (2022), Small, 2106612

COMPUTATIONAL WEARABLES DESIGN: SHOE SOLE MODELING AND PROTOTYPING

Computational wearables design is based on the integration of algorithms into the design process. The use of computational design tools offers a series of advantages in the wearable product design modelling because a complete family of products can be produced with a limited effort from the designer, while at the same time geometries with high complexity can be dealt with a manageable way. The present paper aims to create the appropriate code for modelling shoe soles. The use of a visual programming language leads to a better understanding of the produced code and aims in controlling and optimising the output models. Following the proposed methodology, 3D scanning technologies and plantar pressure diagrams are used for introducing the sole customised geometry, and then the proposed algorithm creates a variety of alternatives of the final shoe sole geometry. As a result, both the customisation and the high number of alternative designs lead to increased creativity and customer satisfaction. Due to the complexity of the geometry produced, 3D printing technologies are incorporated for the prototype implementation.

Bayesian active learning isolation forest (B-ALIF): A weakly supervised strategy for anomaly detection

Due to the high cost of labelling large datasets, unsupervised Anomaly Detection models have been widely applied in many applications. In many scenarios, Anomaly Detection is typically embedded in Decision Support Systems, where users can monitor complex systems with increased efficiency. However, due to the subjective or domain-specific definition of anomalies, unsupervised models tend to perform poorly as they are based on general definitions of anomaly that might not coincide with the end-user expectations. In many cases human tagging of small amount of data can be feasible: For such scenarios, strategies to properly embed such information in the model and to guide the tagging may be extremely useful. Indeed to solve this problem, scientists are proposing models able to interactively query the end-user, training the detector towards the expected anomaly definition while reducing the human labelling effort; such scenario is especially feasible when Decision Support Systems are in place, since users have simple ways to interact with the Anomaly Detection modules. In this context, a novel Active Learning algorithm is here proposed, called Bayesian Active Learning Isolation Forest (B-ALIF), that, starting from the solution provided by a popular detection model named Isolation Forest, it is able to query the user and update by means of a Bayesian strategy. B-ALIF employs the unsupervised solution provided by the Isolation Forest as its prior, and then improves its detection capabilities as the user interacts with it. Experimental results on real datasets show that, with a limited human effort, the proposed approach is able to learn faster than other state-of-art weakly supervised and supervised approaches.

Exploring the impacts of public-private partnership investments and collaborative solutions for tackling inadequate funding in French language education

Educational institutions are shifting from traditional government bonds to the dynamic landscape of Public-Private Partnerships and collaborative funding models. However, limited research efforts have been directed toward studying the effects of Public-Private Partnerships and collaborative funding solutions in education. The methodology of the paper involves a comprehensive literature analysis to examine the existing body of knowledge related to the implementation of Public-Private Partnerships (PPPs) in French language education. This study delineated the critical financial constraints faced by educational institutions, emphasizing the need for innovative approaches. Within the public-private partnership sphere, the study scrutinizes the effectiveness and viability of these collaborative arrangements. In tandem, the study covered perspectives on the multifaceted impacts of collaborative investments, recognizing the potential of these collaborations to ameliorate the financial gaps in French language education. The study proposed strategies for fostering effective Public-Private Partnerships, offered guidance for educational leaders and policymakers, and illuminated future considerations and emerging trends that may transform the funding landscape in French language education. The culmination of this study is a synthesis of key opinions and findings, shedding light on the implications for the future of French language education and the prospects of addressing inadequate funding. The study provided valuable perspectives and recommendations for stakeholders, policymakers, and educators grappling with the persistent challenge of funding within the sphere of French language education.

A mathematical model for rapid estimation of solar radiation in urban canyons with trees and its applications

Street-level solar radiation estimation can help to better understand the influence of solar radiation on urban microclimate, outdoor thermal comfort and health, solar energy utilization, and building energy savings. However, existing urban canyon radiation models based on Monte Carlo ray tracing are challenging to apply in real practice due to limited applicability and high computational efforts. A flexible and robust mathematical model for solar radiation in urban canyons is proposed in this study to estimate solar radiation potential at the street level quickly and can be applied to the urban scale. The newly developed model considers the effect of trees on street canyon radiative transfer based on some assumptions and simplifications. Using the matrix method to resolve the infinite number of multi-reflections, the model accurately predicts solar radiation inside the street canyon compared to experimental data. The maximum root mean square error (RMSE) in the model prediction is 52.7 W/m2 and 14.5 W/m2, respectively, for urban canyons without and with trees in the studied case. And the Pearson correlation coefficient (r) is above 0.987 for all scenarios. An open-source Python script module for the model application is provided and can be integrated with other models or software platforms. The model has been used to estimate solar potential on the street and urban scales taking Chongqing city, China as an example. It is proven that the model is helpful for rapid assessment of solar radiation at the street and urban scales, serving urban design and planning.

Improving road safety at horizontal curves using V2I speed warning messages

Introduction: According to the Federal Highway Administration, a quarter of fatal collisions has occurred at horizontal curves. The average collision rate at horizontal curves was found to be three times higher than other types of highway segments. The lack of compliance with the speed limit and driver-related factors are among the main contributing factors to those collisions. Vehicle to Infrastructure (V2I) communications can address these limitations by providing drivers with valuable in-vehicle warning messages based on operational and safety data. There is limited effort investigating the impact of different types of V2I warning messages at horizontal curves and among different profiles of drivers. This study aims to thoroughly examine drivers’ behavior and compliance with different V2I warning messages when approaching horizontal curves. Methods: A driving simulator experiment and self-reported survey were conducted. Three main hypotheses were analyzed in this study. First, whether supplying drivers with in-vehicle V2I warning messages will positively affect drivers’ behavior at horizontal curves compared to the standard road signs. Second, whether there will be a significant difference in drivers’ behavior when receiving text and audio V2I warning messages. Third, whether seniors and female drivers will comply more with speed limit advisory provided through V2I message than younger and male drivers. Results: The Analysis of Covariance confirmed the first two hypotheses. Two main measures of drivers’ behavior found to be lower in the V2I communication scenarios compared to the base one. The audio warning message was found to be more promising in increasing drivers’ compliance with speed limit advisory when approaching the curves. Analyzing the third hypothesis revealed that younger and male drivers had higher curve initiation speed compared to females and seniors. Practical Applications: The findings of this study can be used by transportation researchers, authorities, and car manufacturers to improve the effectiveness of in-vehicle V2I warning messages among different profiles of drivers.

Patterns and predictors of racial/ethnic disparities in HIV care continuum in the Southern USA: protocol for a population-based cohort study

IntroductionHealth disparities exist at every step of the HIV care continuum (HCC) among racial/ethnic minority population. Such racial/ethnic disparities may have significantly delayed the progress in HCC in the Southern...

Feature detection of mineral zoning in spiral slope flow under complex conditions based on improved YOLOv5 algorithm

In actual processing plants, the quality and efficiency of the traditional spiral slope flow concentrator still rely on workers to observe the changes in the mineral belt. However, in realistic complex working conditions, the formation of mineral separation zones is subject to large uncertainties, and coupled with the limited efforts, experience, and responsibility of workers, it becomes important to free up labour and improve the efficiency and profitability of the beneficiation plant. Therefore, to solve the problem of difficult detection of fuzzy small target mineral separation point features in real scenes, an improved YOLOv5-based algorithm is proposed. Firstly, the dataset quality is well improved by image enhancement and pre-processing techniques, after that an innovative CASM attention mechanism is added to the backbone of the YOLOv5 model, followed by a multi-scale feature output and prediction enhancement in the neck part of the model, and an optimized loss function is designed to optimize the whole feature learning process. The improved effect of the model and the specific detection performance were tested using real mine belt image datasets, the ablation experiment verified the comprehensive effectiveness of the proposed improved method and finally compared it with the existing high-level attention mechanism and target detection algorithms. The experimental results show that the improved YOLOv5 algorithm proposed in this study has the best overall detection performance carrying a MAP of 0.954, which is over 20% better than YOLOv5. It is worth mentioning that the improvement to achieve this performance only increases the parameter values by 0.8M and GFLOPs by 1.8, moreover, in terms of the inference speed, it also achieves a respectable 63 FPS, implying that the proposed improved method achieves a better balance between the performance enhancement and the computational complexity of the model, the overall detection results fully satisfy the industrial requirements.

Strengthening Automotive Cybersecurity: A Comparative Analysis of ISO/SAE 21434-Compliant Automatic Collision Notification (ACN) Systems

The increasing usage of autonomous and automatic systems within the automotive industry is steering us towards a more interconnected world. This enhanced interconnectivity fosters a more streamlined driving experience, reduces costs, and provides timely driver assistance. The electric/electronic (EE) architectures of modern vehicles are inherently complex due to the multitude of components they encompass. Contemporary architectures reveal that these components converge at an electronic control unit (ECU) called the central gateway, which could potentially represent a single point of failure. While this central unit is typically adequately safeguarded, the same cannot be said for the connected components, which often remain vulnerable to cyber threats. The ISO/SAE 21434 standard paved the way for automotive cybersecurity and could be used in parallel with other standards such as ISO 26262 and ISO PAS 21488. Automatic collision notification (ACN) is one of the most typical systems in a vehicle, and limited effort has been dedicated to identifying the most suitable architecture for this feature. This paper addresses the existing security and privacy gap of this feature by conducting a comparative analysis of security threats in two distinct ACN architectures. Notably, despite ACN architectures exhibiting inherent similarities, the primary distinction between the two architectures lies in their strategies for crash estimation and detection, followed by subsequent communication with emergency response teams. A rigorous security assessment was conducted using the ISO/SAE 21434 standard, employing the TARA and STRIDE methodologies through the Ansys medini analyze software. This analysis identified an average of 310 threats per architecture, including a significant number of high-level threats (11.8% and 15%, respectively), highlighting the importance of a comprehensive evaluation.

Multi-site, Multi-domain Airway Tree Modeling.

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).

Active learning for medical image segmentation with stochastic batches.

The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image segmentation tasks because of the limited expert availability and intensive manual effort required. To reduce manual labelling, active learning (AL) targets the most informative samples from the unlabelled set to annotate and add to the labelled training set. On the one hand, most active learning works have focused on the classification or limited segmentation of natural images, despite active learning being highly desirable in the difficult task of medical image segmentation. On the other hand, uncertainty-based AL approaches notoriously offer sub-optimal batch-query strategies, while diversity-based methods tend to be computationally expensive. Over and above methodological hurdles, random sampling has proven an extremely difficult baseline to outperform when varying learning and sampling conditions. This work aims to take advantage of the diversity and speed offered by random sampling to improve the selection of uncertainty-based AL methods for segmenting medical images. More specifically, we propose to compute uncertainty at the level of batches instead of samples through an original use of stochastic batches (SB) during sampling in AL. Stochastic batch querying is a simple and effective add-on that can be used on top of any uncertainty-based metric. Extensive experiments on two medical image segmentation datasets show that our strategy consistently improves conventional uncertainty-based sampling methods. Our method can hence act as a strong baseline for medical image segmentation. The code is available on: https://github.com/Minimel/StochasticBatchAL.git.

Framework for development of digital records preservation in the cloud in Botswana

The deployment of digital systems in the delivery of public services by the Government of Botswana has seen an increase in the generation of digital records. Without preservation efforts over time, the continued availability of digital records is not guaranteed. The reverse side of this is that a focused records preservation regime ensures access to and accurate digital records in the long term, regardless of the challenges of media failure and technological change. Lack of an adequate legal and policy framework on digital material preservation; gaps in human resources in terms of knowledge, skills, and competencies to drive digital material preservation; lack of appropriate standards for digital records preservation; and limited collaborative efforts towards digital records preservation are some of the challenges in digital records preservation in Botswana. Despite these challenges related to digital records preservation, developing countries in Africa are increasingly embracing the use of cloud computing technology for the management and preservation of digital records due to its benefits. In the developed world, cloud computing has matured into a viable option for the management and preservation of digital records. This study utilised a review of literature, supplemented by document analysis, to propose a framework for digital preservation of records in the cloud in the context of e-government in the Botswana public sector. It is guided by concepts from the Policy, Strategy and Resources troika model as a lens to frame the study. The model provides the key elements of policy, strategy, and resources as necessary for the preservation of records in Botswana’s public sector, using cloud computing as a platform. The proposed framework for the preservation of digital records may benefit public sector organisations that endeavour to adopt cloud computing for records preservation.

A Review and Analysis on Fake News Detection Based on Artificial Intelligence and Data Science

Along with the development of the Internet, the emergence and widespread adoption of the social media concept have changed the way news is formed and published. News has become faster, less costly and easily accessible with social media. This change has come along with some disadvantages as well. In particular, beguiling content, such as fake news made by social media users, is becoming increasingly dangerous. The fake news problem, despite being introduced for the first time very recently, has become an important research topic due to the high content of social media. Writing fake comments and news on social media is easy for users. This study attempts to investigate advanced and state-ofthe-art fake news detection mechanisms pensively. We begin with highlighting the fake news consequences. The objective of this study was to synthesize the current possibilities of using AI to combat disinformation. Disinformation is currently a growing problem, and it is increasingly important to understand how we can utilize AI to help address this issue. Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) and Natural Language processing (NLP) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies this study made a review analysis by considering the authors their own perceptions in the previous years was presented in this paper.

Liturgy’s outlook on the oikumene exposes distorted attitudes and memories of ecumenism

This article explored the interplay between ecumenism and people’s distorted attitudes. Viewed from a South African historical perspective, faith communities have endured a robust experience of denominationalism. The authors were thus interested in the influential impact of attitudes and people’s memories in a praxis that recognises the importance of ecumenism but demonstrates limited efforts to promote it. This aspect is evident in the descriptive-empirical section of this research. Ironically, people are firm in their confession, which explicitly embraces ecumenicity. However, distorted attitudes present challenges in the praxis thereof. The authors applied visualisation as part of the research methodology, and executed the research as developing from the description to systemising (exploring practical wisdom and understanding) to strategising (practising strategic, practical theology). The notion of the oikumene pinpoints the kingdom’s interest, but people are focused on liturgical and other differences, which challenges ecumenical relationships. The authors presented the following research questions: How can an elucidation of ecumenical liturgy serve as a means to uncover negative attitudes and bad memories while simultaneously providing building blocks for fostering the realisation of the oikumene? We offer systemising perspectives to denote the importance of crossing borders to promote ecumenical relations and debate the power of liturgy to enhance awareness.Intradisciplinary and/or interdisciplinary implications: The authors concluded the article by emphasising that liturgical elements should incorporate an outlook on the oikumene. The attitudes of people should be altered, and a liturgical praxeology can play a pivotal role in facilitating this process.

Range area and the fast–slow continuum of life history traits predict pathogen richness in wild mammals

Surveillance of pathogen richness in wildlife is needed to identify host species with a high risk of zoonotic disease spillover. While several predictors of pathogen richness in wildlife hosts have been proposed, their relative importance has not been formally examined. This hampers our ability to identify potential disease reservoirs, particularly in remote areas with limited surveillance efforts. Here we analyzed 14 proposed predictors of pathogen richness using ensemble modeling and a dataset of 1040 host species to identify the most important predictors of pathogen richness in wild mammal species. After controlling for research effort, larger species geographic range area was identified to be associated with higher pathogen richness. We found evidence of duality in the relationship between the fast–slow continuum of life-history traits and pathogen richness, where pathogen richness increases near the extremities. Taxonomic orders Carnivora, Proboscidea, Artiodactyla, and Perissodactyla were predicted to host high pathogen richness. The top three species with the highest pathogen richness predicted by our ensemble model were Canis lupus, Sus scrofa, and Alces alces. Our results can help support evidence-informed pathogen surveillance and disease reservoir management to prevent the emergence of future zoonotic diseases.