Area Of Research Research Articles

Theoretical frameworks for ICT for development: Impact assessment of telecommunication infrastructure projects in Africa and the U.S

This review paper examines the theoretical frameworks for assessing the impact of Information and Communication Technology (ICT) on development, focusing on telecommunication infrastructure projects in Africa and the U.S. A comparative analysis highlights the socio-economic and technological disparities between these regions and their implications for telecommunication development. The paper discusses various theoretical frameworks, such as the Digital Divide Theory, Technology Acceptance Model, and Capability Approach, and their applicability to evaluating telecommunication projects. It emphasizes the importance of context-specific strategies, digital literacy, and cross-regional collaboration to enhance the developmental impact of telecommunication infrastructure. The paper concludes with recommendations for policymakers and suggests areas for further research, particularly in developing adaptable frameworks that address regional challenges and opportunities in telecommunication development.

The Role of Education in Fostering Political Empowerment among the Female School Teachers in Newly Merged Districts of Khyber Pakhtunkhwa

One of the fundamental institutions that exist in one form or another in every society on the planet is education. The present research focuses on how education supports women's political empowerment in Khyber Pakhtunkhwa's newly merged districts. The study's main goal was to determine how education contributes to the political empowerment of female teachers in the targeted community. In a similar vein, the study's goal was to ascertain how teachers in the research area support women's political empowerment. The research was quantitative in nature, and 225 sample size (female teachers) were chosen from the three districts of Bajawar, Kurram, and Mohmmand using the Sekran sample size determination table. The data were collected through structured questionnaires from (Primary, Middle, and High) school teachers, which were then analyzed using the Statistical Package for Social Sciences (SPSS) software. The analyzed data were then presented in the form of tables and frequencies. The researched data demonstrates that in Khyber Pakhtunkhwa's newly merged districts, education may promote women's political empowerment in the true sense. It is advised that training, seminars, and awareness-raising events on the value of education and how it gives women political power be required for both the general public and female teachers.

Raziskave konfucijanskega preporoda v Sloveniji

There has been a significant amount of research carried out in Slovenia on the revival of Confucianism, particularly since 2012 and 2014, when the first two research projects on this topic were approved. Members of the Department of Asian Studies at the Faculty of Arts, University of Ljubljana, have been consistently conducting research in Slovenia and various Chinese, Taiwanese, and East Asian archives and libraries. These efforts have led to several important publications in Slovene, English, and even Chinese, and represent an ongoing commitment that continues in the present day. Therefore, it is not surprising that the present special issue of the Slovenian academic journal Asian Studies, focusing on the Confucian revival, already marks the fourth special issue in this area of research. This review article’s objective is thus to summarize the key achievements of Slovenian research in this field and provide a comprehensive overview of the diverse Slovene research activities and publications related to Modern New Confucianism. It covers not only research on the revival of Confucianism in China and the broader Sinic region, but also includes Confucian studies in other East Asian regions and the wider Sinic area.

The role of traffic flow modeling in solving current problems of transport planning in Ukraine

The main current problems of the transport and planning infrastructure of Ukraine are identified. A brief description of traffic flows as an object of the street-road network is given including all off the factors that are affecting its functionality and the main reasons for the difficulty of studying them are listed and divided in. Modelling is proposed as one of the most effective tool in dealing with modern traffic problems with application of informational technologies. A brief history of the development of urban transport planning and modelling is given, highlighting its four most significant time periods with the description of notable changes during that time. Description of basic principles of transport modelling is structured and formalized. Four-stage computerized transport model is highlighted as the most common used and flexible, its basic assumptions and principles is mentioned. Various transport modeling techniques are proposed for consideration as one of the ways to aid in traffic flows managing. Definition of macromodelling, micromodelling, mesomodelling with its area of research, types of problems it is used to solve, basic advantages and disadvantages is given. The main software packages used today to solve various transport and planning tasks are listed, divided by the level of modelling it is capable to effectively perform. The main transport modelling software packages development companies is named with mentioning of its country-of-origin year of establishing and the most popular products. Along with that, a brief overview for the company's production is given taking into account their advantages and disadvantages and ability to be capable of working in different modelling levels at the same time. Conclusions are drawn summarizing the modern state of urban transport modelling in Ukraine with application of classical paradigm of traffic flow management and regarding further directions of research in this area for the most optimal use of modeling in issues of transport planning, such as creati ng of hybrid systems that make it possible to simultaneously study various characteristics of traffic flows at several levels of abstraction.

Learning in the shadows: exploring primary school students and their parents’ perceptions of fee-charging private tutoring in Kazakhstan

ABSTRACT This mixed-methods study explored the experiences and perceptions of primary school students and their parents towards fee-charging private tutoring (PT) in Astana, Kazakhstan. The data were collected from 503 Grade 6 students (aged 11–12) using a close-ended questionnaire followed by semi-structured interviews with 21 parents. The questionnaire data from five mainstream schools found that 357 out of 503 (71%) students received PT, and highly educated parents invested in PT more than other parents. A total of 29% of students revealed that their parents spent 31,000–40,000 tenge (US$ 67–87) on PT per month, and 9 parents acknowledged that PT was financially burdensome for them. Both students and parents affirmed that preparing for the exams and securing a place in a highly selective school represented the main motives for having PT. Some parents mentioned the soft, intangible gains of PT such as responding appropriately to students’ social and emotional needs. Moreover, 48% of students were unaware of the identity of their tutors, and some parents criticised the spread of less qualified, costly tutors due to the unregulated PT market in Kazakhstan. However, none of the parents supported the idea of banning PT. This study suggests pedagogical implications and areas for ongoing research.

Mechanistic Understanding of Oxygen Activation on Bulk Au(111) Surface Using Tip-Enhanced Raman Spectroscopy.

Gaining mechanistic understanding of oxygen activation on metal surfaces is a topical area of research in surface science. However, direct investigation of on-surface oxidation processes at the nanoscale and the empirical validation of oxygen activation pathways remain challenging for the conventional analytical tools. In this study, we applied tip-enhanced Raman spectroscopy (TERS) to gain mechanistic insights into oxygen activation on bulk Au(111) surface. Specifically, oxidation of 4-aminothiophenol (4-ATP) to 4-nitrothiophenol (4-NTP) on Au(111) surface was investigated using hyperspectral TERS imaging. Nanoscale TERS images revealed a markedly higher oxidation efficiency in disordered 4-ATP adlayers compared to the ordered adlayers signifying that the oxidation of 4-ATP molecules proceeds via interaction with the on-surface oxidative species. These results were further validated via direct oxidation of the 4-ATP adlayers with H2O2 solution. Finally, TERS measurements of oxidized 4-ATP adlayers in the presence of H2O18 provided the first empirical evidence for the generation of oxidative species on bulk Au(111) surface via water-mediated activation of molecular oxygen. This study expands our mechanistic understanding of oxidation chemistry on bulk Au surface by elucidating the oxygen activation pathway.

Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing.

In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well-curated datasets, thereby unveiling latent knowledge crucial for informed decision-making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM-printed parts. This review delves into the challenges and opportunities emerging at the intersection of these two dynamic fields. It provides a comprehensive analysis of the publication landscape for ML-related research in the field of AM, explores common ML applications in AM research (such as quality control, process optimization, design optimization, microstructure analysis, and material formulation), and concludes by presenting an outlook that underscores the utilization of advanced ML models, the development of emerging sensors, and ML applications in emerging AM-related fields. Notably, ML has garnered increased attention in AM due to its superior performance across various AM-related applications. It is envisioned that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM-related research areas.

Enhanced Leaf Area Index Estimation in Rice by Integrating UAV-Based Multi-Source Data

The monitoring of crop growth, particularly the estimation of Leaf Area Index (LAI) using optical remote sensing techniques, has been a continuous area of research. However, it has become a challenge to accurately and rapidly interpret the spatial variation of LAI under nitrogen stress. To tackle these issues, this study aimed to explore the potential for precise LAI estimation by integrating multiple features, such as average spectral reflectance (ASR), vegetation index, and textures, obtained through an unmanned aerial vehicle (UAV). The study employed the partial least squares method (PLS), extreme learning machine (ELM), random forest (RF), and support vector machine (SVM) to build the LAI estimation model under nitrogen stress. The findings of this study revealed the following: (i) texture features generally exhibited greater sensitivity to LAI compared to ASR and VIs. (ii) Utilizing a multi-source feature fusion strategy enhanced the model’s accuracy in predicting LAI compared to using a single feature. The best RP2 and RMSEP of the estimated LAI were 0.78 and 0.49, respectively, achieved by RF through the combination of ASR, VIs, and textures. (iii) Among the four machine learning algorithms, RF and SVM displayed strong potential in estimating LAI of rice crops under nitrogen stress. The RP2 of the estimated LAI using ASR + VIs + texture, in descending order, were 0.78, 0.73, 0.67, and 0.62, attained by RF, SVM, PLS, and ELM, respectively. This study analyzed the spatial variation of LAI in rice using remote sensing techniques, providing a crucial theoretical foundation for crop management in the field.

The Construction of Helical Carbon‐Based Skeletons for Enhanced Electrocatalytic Performance

AbstractAs an excellent class of electrode materials, carbon‐based materials have garnered sustained attention. Constructing carbon‐based skeleton with unique structure has become a vital research area in the fields of electrocatalysis. Recently, surface curvature has been extensive discussed as a compelling factor in electronic modulation for electrocatalyst design. Benefitting from its distinctive regulation, the intrinsic activity and service stability of catalysts during electrocatalytic process can be significantly improved. Therefore, a helical structure that adeptly integrates well‐regulated, highly ordered surface curvatures is considered to be of great research value for the synthesis of carbon‐based skeleton. In this Concept article, we systematically summarize the up‐to‐date reports on the synthetic methods of carbon‐based skeletons with helical structures and present major challenges in this field. We also emphasize that the helical carbon‐based skeleton could be combined with transition metals for cooperative coupling, potentially leading to the further development of high‐efficiency electrocatalysts.

Trust, risk perception, and intention to use autonomous vehicles: an interdisciplinary bibliometric review

AbstractAutonomous vehicles (AV) offer promising benefits to society in terms of safety, environmental impact and increased mobility. However, acute challenges persist with any novel technology, inlcuding the perceived risks and trust underlying public acceptance. While research examining the current state of AV public perceptions and future challenges related to both societal and individual barriers to trust and risk perceptions is emerging, it is highly fragmented across disciplines. To address this research gap, by using the Web of Science database, our study undertakes a bibliometric and performance analysis to identify the conceptual and intellectual structures of trust and risk narratives within the AV research field by investigating engineering, social sciences, marketing, and business and infrastructure domains to offer an interdisciplinary approach. Our analysis provides an overview of the key research area across the search categories of ‘trust’ and ‘risk’. Our results show three main clusters with regard to trust and risk, namely, behavioural aspects of AV interaction; uptake and acceptance; and modelling human–automation interaction. The synthesis of the literature allows a better understanding of the public perception of AV and its historical conception and development. It further offers a robust model of public perception in AV, outlining the key themes found in the literature and, in turn, offers critical directions for future research.

Mutual-Modality Adversarial Attack with Semantic Perturbation

Adversarial attacks constitute a notable threat to machine learning systems, given their potential to induce erroneous predictions and classifications. However, within real-world contexts, the essential specifics of the deployed model are frequently treated as a black box, consequently mitigating the vulnerability to such attacks. Thus, enhancing the transferability of the adversarial samples has become a crucial area of research, which heavily relies on selecting appropriate surrogate models. To address this challenge, we propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme. Our approach is accomplished by leveraging the pre-trained CLIP model. Firstly, we conduct a visual attack on the clean image that causes semantic perturbations on the aligned embedding space with the other textual modality. Then, we apply the corresponding defense on the textual modality by updating the prompts, which forces the re-matching on the perturbed embedding space. Finally, to enhance the attack transferability, we utilize the iterative training strategy on the visual attack and the textual defense, where the two processes optimize from each other. We evaluate our approach on several benchmark datasets and demonstrate that our mutual-modal attack strategy can effectively produce high-transferable attacks, which are stable regardless of the target networks. Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.

Controllable Mind Visual Diffusion Model

Brain signal visualization has emerged as an active research area, serving as a critical interface between the human visual system and computer vision models. Diffusion-based methods have recently shown promise in analyzing functional magnetic resonance imaging (fMRI) data, including the reconstruction of high-quality images consistent with original visual stimuli. Nonetheless, it remains a critical challenge to effectively harness the semantic and silhouette information extracted from brain signals. In this paper, we propose a novel approach, termed as Controllable Mind Visual Diffusion Model (CMVDM). Specifically, CMVDM first extracts semantic and silhouette information from fMRI data using attribute alignment and assistant networks. Then, a control model is introduced in conjunction with a residual block to fully exploit the extracted information for image synthesis, generating high-quality images that closely resemble the original visual stimuli in both semantic content and silhouette characteristics. Through extensive experimentation, we demonstrate that CMVDM outperforms existing state-of-the-art methods both qualitatively and quantitatively. Our code is available at https://github.com/zengbohan0217/CMVDM.

Navigating Real-World Partial Label Learning: Unveiling Fine-Grained Images with Attributes

Partial label learning (PLL), a significant research area, addresses the challenge of annotating each sample with a candidate label set containing the true label when obtaining accurate labels is infeasible. However, existing PLL methods often rely on generic datasets like CIFAR, where annotators can readily differentiate candidate labels and are unlikely to confuse, making it less realistic for real-world partial label applications. In response, our research focuses on a rarely studied problem, PLL on fine-grained images with attributes. And we propose a novel framework called Shared to Learn, Distinct to Disambiguate (SoDisam). Within the candidate label set, the categories may exhibit numerous shared attribute features, posing a challenge in accurately distinguishing them. Rather than perceiving it as an impediment, we capitalize on these shared attributes as definitive sources of supervision. This insight guides us to learn attribute space visual representation to focus on the information from these shared attributes. Moreover, we introduce an attribute attention mechanism tailored to harness the remaining distinct attributes. This mechanism directs the originally holistic feature towards specific regions, capturing corresponding discriminative features. In addition, a dynamic disambiguation module is introduced, continuously adjusting the two aforementioned mechanisms and achieve the final disambiguation process. Extensive experiments demonstrate the effectiveness of our approach on fine-grained partial label datasets. The proposed SoDisam framework not only addresses the challenges associated with fine-grained partial label learning but also provides a more realistic representation of real-world partial label scenarios.

MagiCapture: High-Resolution Multi-Concept Portrait Customization

Large-scale text-to-image models including Stable Diffusion are capable of generating high-fidelity photorealistic portrait images. There is an active research area dedicated to personalizing these models, aiming to synthesize specific subjects or styles using provided sets of reference images. However, despite the plausible results from these personalization methods, they tend to produce images that often fall short of realism and are not yet on a commercially viable level. This is particularly noticeable in portrait image generation, where any unnatural artifact in human faces is easily discernible due to our inherent human bias. To address this, we introduce MagiCapture, a personalization method for integrating subject and style concepts to generate high-resolution portrait images using just a few subject and style references. For instance, given a handful of random selfies, our fine-tuned model can generate high-quality portrait images in specific styles, such as passport or profile photos. The main challenge with this task is the absence of ground truth for the composed concepts, leading to a reduction in the quality of the final output and an identity shift of the source subject. To address these issues, we present a novel Attention Refocusing loss coupled with auxiliary priors, both of which facilitate robust learning within this weakly supervised learning setting. Our pipeline also includes additional post-processing steps to ensure the creation of highly realistic outputs. MagiCapture outperforms other baselines in both quantitative and qualitative evaluations and can also be generalized to other non-human objects.

Insect immunity - adaptive mechanisms and survival strategies

Host-pathogen interactions play an important role in understanding the dynamics of the insect immune system. The analysis of environmental modulators, both biotic and abiotic, directs our attention to the impact of the surroundings on the effectiveness of immunological responses. This knowledge is essential for the comprehensive understanding of insect’s immune reaction after infection with a pathogen. This article discusses the role of the immune system in insect, with a special emphasis on greater wax moth Galleria mellonella and highlights its adaptive capabilities. The processes are not only extremely interesting area of scientific research but also indicate potential practical applications in the context of plan protection, pest population control and medicine.

Does activation of higher-order thinking skills lead to students (dis)satisfaction with their academic experience?

ABSTRACT Although satisfaction with academic experience in the context of higher education has been an area of research for nearly 40 years, it is still unclear how pedagogical practices in the development of thinking skills are related to students’ satisfaction. This study aims to investigate the relationships between students’ satisfaction and the levels of thinking skills aligned with Bloom’s revised taxonomy (BRT) taking into account students’ characteristics. Relying on survey data (14 341 undergraduate students from five US universities), this study shows that when students are engaged in thinking skills higher on cognitive hierarchy of BRT, they are more likely to feel satisfied. Whereas engaging students in low thinking skills activities does not increase satisfaction with their academic experience. This study contributes to better understanding of how course design can be associated with student satisfaction and how instructors can implement the principles of BRT in their courses.

The cyclical nature of dualistic trends in urban design. Historical analysis of the creative attitudes of selected masters over the centuries

In this article, the authors raise issues of dualistic solutions in urban design and city development. It should be noted at the outset that the very concept of duality is not obvious and is inherently ambiguous. The numerous definitions that exist are very general. We can distinguish several disciplines and scientific areas where the concept of dualism occurs or can appear. At the same time, the definition of each of these phenomena will be different, depending on the area in which it occurs. Hence, the authors have made a far-reaching reduction in the types of dualisms, narrowing the area of research to the creative attitudes of urban planners, philosophers of space, artists, and architects in historical terms. The aim of the search for dual features in spatial forms is to find the key to a better understanding of the laws governing urban planning and urbanisation processes. Verification of opposites and dual characteristics takes place on three levels: community, form, and function. The urban conditions that occur contribute to the creation of new qualities and new spatial forms, which help redefine the city and its development. The paper is based on the thesis that dualism is a permanent feature of spatial solutions (a recurring feature) across the spectrum of history.

Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland

National soil organic carbon (SOC) maps are essential to improve greenhouse gas accounting and support climate-smart agriculture. Large-scale SOC models based on wall-to-wall soil information from remote sensing remain a challenge due to the high diversity of natural soil conditions and the difficulty of accounting for the spatial location of the soil samples. In this study, we tested if the implementation of local ensemble models (LEM) can be used to improve the SOC predictions from Landsat-based soil reflectance composites (SRC) for Germany. For this, we divided the research area into 30 times 30 km tiles and calculated local generalized linear models (GLM) based on random, nearby observations. Based on the GLMs, local SOC maps were predicted and aggregated using a moving window approach. The local variable importance was analyzed to identify spatial dependencies in the correlation between the SRC and SOC. For the final SOC map, a Random Forest (RF) model was trained using the aggregated local SOC predictions, the SRC, and a full set of training samples from the agricultural soil inventory. The results show that the LEM was able to improve the accuracy (R2 = 0.68; RMSE = 5.6 g kg−1), compared to the maps based on a single, global model (R2 = 0.52; RMSE = 6.8 g kg−1). The local variable importance of the spectral bands showed clear spatial patterns throughout the research area. Differences can be explained by the local soil conditions, influencing the correlation between SOC and the spectral properties. Compared to the widely adopted integration of distance covariates such as geographical coordinates, the LEM was able the reduce the spatial autocorrelation to a greater extent and to improve the prediction accuracy, especially for underrepresented SOC values. The LEM presents a new method to integrate spatial information and increase the interpretability of DSM models.

The Relationship between Physical Activity, Physical Fitness, Cognition, and Academic Outcomes in School-Aged Latino Children: A Scoping Review.

This scoping review provides an overview of the relationship between physical activity, physical fitness, cognition, and academic outcomes in Latino school-aged children and identifies areas for future research. A primary search was conducted in PubMed, PsycINFO, Web of Science, and ERIC for original-research articles meeting the inclusion criteria; the search results were uploaded into PICO Portal and assessed by two independent reviewers. Of the 488 initial search results, 50 articles were eligible for full-text review, and 38 were included in this review. Most studies were cross-sectional, conducted in the United States or Chile, and included children 5-18 years old. Overall, the majority of articles reported positive associations between physical activity or physical fitness and cognitive outcomes (n = 11/12; 91.7%), and physical activity or physical fitness and academic outcomes (n = 22/28; 78.6%). In sum, this review provided consistent evidence for higher amounts of physical activity and greater physical fitness to be associated with various positive cognitive and academic outcomes in a school-aged Latino population. This scoping review also elucidated a substantial gap in the research regarding study design, with a discernible lack of interventional efforts. Future studies should test physical activity interventional strategies to optimize cognitive and academic outcomes in school-aged Latino populations.

A3C: An Image-Association-Based Computing Device Authentication Framework for People with Upper Extremity Impairments

Current computing device authentication often presents accessibility barriers for people with upper extremity impairments (UEI). In this paper, we present a framework called Accessible image-Association-based Authentication for Computing-devices (A3C) , a novel recognition-based graphical authentication framework specifically designed for people with UEI to authenticate to their computing devices. A3C requires users to provide a set of primary images that the user knows that are recognizable to them and subsequently associate each primary image with a secondary image. To evaluate the efficacy of the A3C framework, we instantiated the framework by implementing a version of A3C called A3C-FA , which uses images of faces of people the user knows as the primary image and animal images as the secondary image. We then performed three studies to evaluate A3C-FA: a shoulder-surfing attack study ( N=319 ), a close-adversary attack study ( N=268 ), and a usability study with people with UEI ( N=14 ). We found that A3C was robust against both shoulder-surfing and close-adversary attacks. We also performed a detailed study to evaluate the accessibility of A3C-FA. Our participants reported that A3C-FA was more usable and more secure than the authentication approaches with which they were familiar. Based on these findings, we suggest four areas of future research to further improve the design of A3C framework.