Variety Of Sources Research Articles

Landslides in a changing world

Abstract This article investigates the multifaceted dimensions to understand the interrelatedness among global change drivers and their implications for landslide hazards and disaster risk. Drawing on empirical research, it utilised a mixed-methods design; the research combined diverse data sources and experiential insights on the interdependencies bounded by local context and global scale. The findings underscore the urgent need for holistic approaches that consider the complex dimensions of landslides as socio-natural hazards and global change, emphasising the importance of collaboration, innovation, and international cooperation in building resilience and mitigating the adverse effects of landslide disaster risk on global systems and societies. Furthermore, the challenge of reducing landslide disaster risk lies in understanding and addressing the interplay between socio-environmental transformations and geodynamic processes. The escalating effects of climate change, urban expansion, and deforestation are anticipated to magnify the occurrence of landslides, thereby posing significant risks to human lives, infrastructure, ecosystems, and livelihoods. However, most importantly, these risks are further compounded by environmental, social, economic, political, cultural, and technological spheres associated with globalisation. The systemic nature of disaster risk, particularly landslides in a changing world, highlights the interconnectedness of different systems, resulting in complex causality and cascading impacts. These insights contribute to the broader discourse on sustainability by providing empirical evidence that supports integrated approaches to achieving long-term disaster risk reduction based upon the equitable and sustainable use of territories while integrating robust disaster risk management strategies to ensure resilient communities and ecosystems.

A Modified Bi-Directional Convolutional U-Net (BCDU-Net) Neural Network Approach for Lung CT Image Segmentation

In this paper, a modified Bi-directional Convolutional Long Short-Term Memory U-Net (BCDU-Net) neural network is presented, which aims at enhancing medical image segmentation for lung cancer diagnosis. By integrating BConvLSTM in the decoding path and densely connected convolutional layers in the encoding path, the proposed model achieves greater stability and precision in segmenting lung CT images. The addition of Batch Normalization (BN) after the up-convolutional layers accelerates convergence speed by six times. A notable feature of BCDU-Net is its adaptability to different imaging modalities, enabling it to generalize across diverse data sources by reducing overfitting, a limitation seen in many existing models. This adaptability allows the model’s ability to be integrated into various clinical environments, ensuring consistent and reliable results across different equipment. Another key contribution is the enhanced interpretability of the model, a critical improvement when compared with traditional U-Net models. BCDU-Net accurately segments complex anatomical structures, such as the left and right lungs, and precisely identifies tumors near central bronchi or trachea airways, which is crucial for lung cancer treatment planning. The proposed model was tested on the Cancer Imaging Archive (TCIA) dataset from the 2017 Lung CT Segmentation Challenge, achieving Dice Similarity Coefficients (DSC) of 97.97 for the right lung and 97.73 for the left lung. Overall, the BCDU-Net model demonstrates superior accuracy and interpretability in medical image segmentation and holds promise for broader applications in medical imaging beyond lung cancer diagnosis.

Unlocking Digital Start-up Funding: How Innovation Intermediaries make It Happen. An Empirical Evidence from Italy

Start-ups are pivotal to the contemporary economy, yet securing essential growth capital remains a considerable challenge due to financial constraints arising from asymmetric information, regulatory frameworks and market conditions. Extant research has rigorously examined both traditional and non-traditional funding sources, addressing the myriad obstacles that entrepreneurs encounter when seeking finance. Despite substantial scholarly attention to the role of innovation intermediaries in supporting start-ups, there remains a paucity of understanding regarding their facilitation of access to both conventional and alternative finance. This study, through a comprehensive analysis of 157 Italian digital start-ups, elucidates that innovation intermediaries significantly enhance funding opportunities across diverse sources, including equity crowdfunding, venture capital and traditional bank loans. The findings contribute to the academic literature highlighting the crucial role of innovation intermediaries in shaping the financial ecosystem for digital start-ups, providing nuanced insights crucial for fostering sustainable growth and innovation.

Bibliometric Analysis of Nontuberculous Mycobacteria Research in South Korea.

Current research on nontuberculous mycobacteria (NTM) is multidisciplinary; thus, proper organization is necessary to obtain comprehensive insights. Therefore, a bibliometric analysis was performed to identify NTM research characteristics in South Korea. The Web of Science was searched for NTM articles authored by Koreans at Korean institutions until March 2023. We collected data on authors, publication year, article type, study design, research area, citations, research institutes, and funding sources for analysis. Of the 28,092 articles on NTM, Koreans authored 868. After excluding 167 unrelated studies, 701 relevant articles were analyzed. The first study was from 1992, with publication rates markedly increasing from 2004 onwards. Basic research constituted 41.3% (n=290) of papers, whereas clinical research represented 44.7% (n=313). Basic research consisted mostly of biochemistry studies (n=74, 10.6%), while clinical research primarily involved retrospective studies (n=113, 16.1%). Median citations were highest for systematic reviews and meta-analyses (median 23, interquartile range 15-93). Fifty-four institutions participated in NTM research, with the top five contributing to 71% (n=498) of the publications. The National Research Foundation of Korea was the most significant funding source, supporting 181 studies (32.5% of funded articles). The quality and quantity of NTM research in South Korea has improved. However, it was concentrated within a few institutions and largely funded by few sources. Future research should use more diverse funding sources, be conducted in more institutions, and prioritize prospective study designs to enhance the understanding and treatment of NTM.

Envisioning the 'Jefore' Landscape and Its Attributes: Evidence from Gurage Countryside

Contemporary understandings of landscapes are focusing on their attributes for sustainable human-environment interactions. This article explores the socio-cultural, aesthetic, and therapeutic attributes of Jefore as a peculiar socio-ecological landscape of Gurage countryside. As research design and tools of data collection, ethnographic research design and ethnographic data collection tools such as key informant interviews, focus group discussions, observations, and a transect walk were used to generate data from three districts of West Gurageland. Scoring from diverse data sources, Jefore is the long-existing local landscape tradition. It has diverse attributes that embeds aspects of Gurage's life such as identity, social interactions, socio-cultural and economic productions, livelihoods, and well-being. For socio-cultural attributes, it remains the core of cultural production and reproduction, social interactions, social alliances, identity as well as heritages for the group. Aesthetic and therapeutic attributes magnify the wider well-being associations of the landscapes for its embodiment effects. Largely, maintaining Jefore landscape tradition ensuring sustainable socio-ecological landscape values and attributes that serve long generations. Hence, diverse stakeholders need to consider indigeneity of Jefore landscape and its diverse attributes in the midst of their decisions and interventions.

Are glucagon-like peptide-1 receptor agonists anti-consummatory drugs?

Incretin-based treatments, such as glucagon-like peptide-1 receptor (GLP-1R) agonists (eg liraglutide and semaglutide), have rapidly transformed obesity treatment. The well-documented weight loss effect from these agents is considered to be primarily a result of their actions on food intake, but frequent anecdotal reports from varied sources have suggested that they might also broadly affect consummatory behavior, including alcohol and drugs of abuse, suggesting a potential modulatory effect on reward behavior. Herein, we critically review the extant literature on the behavioral effects of GLP-1R agonists in humans, including their impact on feeding behavior, alcohol/drug intake, and overall reward response. We also consider the physiological and neurobiological underpinnings of GLP-1 actions, with a focus on its distinct central and peripheral roles, as well as its relationships with the broader energy homeostasis network. We conclude with a discussion on the implications of this line of research on how behavior is conceptualized, and the potential future directions for research.

A new procedure for volcanic glass hydrogen isotope studies of tuffaceous sediments and its application in the North American Cordillera

Hydrated volcanic glass δD (δDg) values of pure tuffs have been used to reconstruct paleoprecipitation δD in paleoelevation and paleoclimate studies. These hydrated glasses in tuffaceous sediments hold the potential for providing extended and continuous paleoprecipitation δDw records due to their abundance in the geologic record. However, glasses in such materials may be recycled and subjected to surface alteration, and the δDw values of the water that hydrated the glasses can vary across different depositional environments. Here we first develop a method to reduce the influence of recycled glass and surface alteration, then apply the method to the Eocene−Oligocene tuffaceous sediments in the Sage Creek basin, southwestern Montana, USA, to examine the variation of δDg in different depositional environments and test a previous interpretation of early Oligocene surface uplift in southwestern Montana. Our integration of microscopic observations of glass morphology and lab experiments of hydrofluoric acid (HF) abrasion of glasses for various durations (30 s to 30 min) shows that 10% HF abrasion for at least 5 min removes most of the recycled glass in tuffaceous sediments and does not change the δDg values and hydration water contents. Our data also show that pumice and cuspate glasses have higher δDg and water content than blocky glasses, which explains some of the δDg variations. The extended HF abrasion method was applied to 91 tuffaceous sandstone samples from floodplain and alluvial/fluvial channel facies in the Renova Formation in the Sage Creek basin. The results show that the δDg values exhibit greater variations in trunk and avulsion channels and floodplain facies than in paleosols. These variations are likely due to the mixing of waters derived from diverse elevations and sources, with differing evaporation degrees and glass types. This study underscores the significant potential of utilizing hydrated volcanic glass δDg from tuffaceous sediments for paleoclimate and paleoelevation studies. Nonetheless, it is important to assess the influence of depositional environment when interpreting such data. Paleosols are more suitable than other facies for reconstructing basinal precipitation δDw, but it is crucial to evaluate the effect of evaporation on paleosol δDg before interpretation. Additionally, the study reveals an absence of the anticipated early Oligocene negative δDg shift, calling for further investigation of this hypothesis.

To stay or to go: resource diversity alters the dispersal behavior of sympatric cryptic marine nematodes

Animals can use specific environmental cues to make informed decisions about whether and where to disperse. Patch conditions are known to affect the dispersal behavior of animals, but empirical studies investigating the impact of resource diversity on the dispersal of closely related species are largely lacking. In this study, we investigated how food diversity affects the dispersal behavior of three co-occurring cryptic species of the marine bacterivorous nematode complex Litoditis marina (Pm I, Pm III and Pm IV). Using microcosms composed of a local patch (inoculation plate), a connection tube, and a distant patch (dispersal plate), we examined nematode dispersal patterns with bacteria serving as the food source. Food treatments included low-, medium-, and high-diversity bacterial mixtures of 5, 10, and 15 bacterial strains, respectively. Additionally, a single-strain food resource Escherichia coli was used as a control treatment. Both local and distant patches had either identical food treatments (‘homogeneous patches’) or E. coli in the local patches and more diverse food (low-, medium-, or high-diversity food) in distant patches (‘heterogeneous patches’). Our results show that the dispersal behavior of the cryptic species varies depending on food diversity, indicating that L. marina acquire information about their environment when making dispersal decisions. All three cryptic species tend to disperse faster toward food patches that increase fitness. Pm I and Pm IV exhibited faster dispersal toward patches with a more diverse food source, while Pm III showed similar dispersal rates toward E. coli, medium-diversity, and high-diversity food. This indicates that resource diversity can alter the dispersal behavior of cryptic species and may be an important mechanism to achieve species coexistence in the field.

Islamic Education Management in Promoting Multiculturalism, Democracy and Harmony

Islamic education plays a vital role in promoting multiculturalism and fostering democratic harmony by instilling values of tolerance, respect, and coexistence. This study aims to explore how the principles of Islamic education can contribute to building a society that values diversity and upholds democratic ideals. To achieve this, a qualitative approach is employed, utilizing both primary and secondary sources such as religious texts, educational curricula, and scholarly articles. The study examines the teachings of Islam on social justice, equality, and respect for different cultures and religions, and how these values can shape educational practices and support societal integration. The findings indicate that when Islamic education is taught inclusively, it has the potential to bridge cultural divides and foster cooperation among diverse communities. In conclusion, the study emphasizes the significance of incorporating Islamic educational values into the development of a more harmonious and democratic society. The Prisma framework was used to ensure a systematic review of relevant literature and the integration of diverse sources. Key research questions explored in this study include: How can Islamic education promote multicultural understanding? What principles within Islamic teachings support democratic values and diversity? How can Islamic education be implemented to enhance social cohesion in multicultural societies?

A Comparative Analysis of European Media Coverage of the Israel–Gaza War Using Hesitant Fuzzy Linguistic Term Sets

Representing and interpreting human opinions within an unstructured framework is inherently complex. Hesitant fuzzy linguistic term sets offer a comprehensive context that facilitates a nuanced understanding of diverse perspectives. This study introduces a methodology that integrates sentiment analysis with hesitant fuzzy linguistic term sets to effectively aggregate and compare news from diverse sources. By employing linguistic scales, our approach enhances the interpretation of various perceptions and attitudes, facilitating comprehensive knowledge extraction and representation. The main objective of this research is to conduct a comparative analysis of news coverage across European countries in relation to the Israel–Gaza war. This analysis aims to capture the multifaceted sensitivities surrounding the ongoing situation, highlighting how different nations perceive the conflict.

Automated Academic Supervisor Allocation Using the C4.5 Decision Tree Algorithm: A Scalable Web-Based Solution

Efficient allocation of academic supervisors is a critical yet challenging process in higher education, often hindered by mismatches in expertise and uneven workload distribution. This study introduces a web-based recommendation system leveraging the C4.5 decision tree algorithm to address these issues. By assessing supervisor expertise, workload, and alignment with student research topics, the system generates data-driven, accurate recommendations. Developed using the Laravel framework and the waterfall development model, the system emphasizes scalability and modularity. Functional testing demonstrated a 96.7% accuracy rate for recommendations, while usability testing reflected high user satisfaction, with an average score of 92% for ease of use and relevance. These results underscore the system’s effectiveness in optimizing supervisor assignments, enhancing administrative efficiency, and providing a scalable solution for educational institutions. Future work will focus on integrating diverse data sources and AI-driven features to further improve adaptability and responsiveness to evolving academic demands.

A conceptual model for multivariate data integration in reservoir modeling: exploring the role of neural networks in petrophysical analysis

Reservoir modeling is crucial for optimizing oil and gas production, as it helps characterize subsurface properties like porosity, permeability, and fluid saturation. Integrating diverse data sources such as well logs, seismic data, and core analyses presents a significant challenge due to their differing scales, resolutions, and data types. Traditional methods often struggle to accurately integrate these data sources, resulting in suboptimal predictive accuracy. This review proposes a conceptual model that leverages neural networks to integrate multivariate data for enhanced reservoir characterization. Neural networks, with their ability to handle nonlinear relationships and large, complex datasets, offer a transformative approach to unify diverse data inputs, resulting in more accurate predictions of reservoir properties. The model focuses on using feedforward neural networks (FNNs), convolutional neural networks (CNNs), and autoencoders to merge well logs, seismic interpretations, and core sample data. This integration aims to improve the identification of productive zones, reservoir boundaries, and fluid distributions, thereby enhancing reservoir models. Additionally, the review explores how neural networks can manage data gaps, predict missing values, and quantify uncertainty, which are common challenges in reservoir studies. By automating data processing and facilitating real-time analysis, neural networks can accelerate decision-making in reservoir management and field development. This framework highlights the potential of AI-driven techniques to revolutionize reservoir modeling, offering a pathway to more efficient, accurate, and adaptive resource management. The conceptual model aims to advance predictive capabilities in reservoir analysis, addressing the complexities of multivariate data integration and opening doors for future innovations in the energy industry. Keywords: Conceptual Model, Multivariate Data, Reservoir Modeling, Petrophysical Analysis.

Modeling Groundwater Resources in Data-Scarce Regions for Sustainable Management: Methodologies and Limits

Groundwater modeling in data-scarce regions faces significant challenges due to the lack of comprehensive, high-quality data, impacting model accuracy. This systematic review of Scopus-indexed papers identifies various approaches to address these challenges, including coupled hydrological-groundwater models, machine learning techniques, distributed hydrological models, water balance models, 3D groundwater flow modeling, geostatistical techniques, remote sensing-based approaches, isotope-based methods, global model downscaling, and integrated modeling approaches. Each methodology offers unique advantages for groundwater assessment and management in data-poor environments, often combining multiple data sources and modeling techniques to overcome limitations. However, these approaches face common challenges related to data quality, scale transferability, and the representation of complex hydrogeological processes. This review emphasizes the importance of adapting methodologies to specific regional contexts and data availability. It underscores the value of combining multiple data sources and modeling techniques to provide robust estimates for sustainable groundwater management. The choice of method ultimately depends on the specific objectives, scale of the study, and available data in the region of interest. Future research should focus on improving the integration of diverse data sources, enhancing the representation of complex hydrogeological processes in simplified models, and developing robust uncertainty quantification methods tailored for data-scarce conditions.

Spatiotemporal Patterns and Driving Forces Analysis of Ecological Carbon Sink from 2001 to 2022 in Qinling-Daba Mountains,China

The Qinling-Daba Mountain area, an essential ecological conservation zone in China, occupies a pivotal position in the pursuit of carbon neutrality. Using diverse data sources, including temperature, precipitation, solar radiation, and the Normalized Difference Vegetation Index, we refined the CASA model by replacing model indicators. This enhanced model simulated the net primary productivity of vegetation in the Qinling-Daba Mountain area from 2001 to 2022. Additionally, we employed a method that subtracts soil respiration （Rh） from the simulated vegetation net primary productivity to calculate the regional net ecosystem productivity （NEP）, thereby characterizing the ecological carbon sink. Employing techniques such as linear regression analysis, MK trend testing, partial correlation analysis, and composite correlation analysis, we examined the spatiotemporal evolution characteristics of NEP over the past 22 years in the Qinling-Daba Mountain area and assessed the degree of influence of various factors. Our findings revealed that： ① Over the past 22 years, the vegetation NEP in the Qinling-Daba Mountain area displayed a fluctuating upward trend, with an average annual increase of 168 g·m-2·a-1, resulting in a total increase of 52.2 Tg in regional vegetation NEP （in terms of C） over the 22-year period. ② Spatially, the Qinling-Daba Mountain area predominantly functioned as a carbon sink, with only 0.3% of the area serving as a carbon source, primarily concentrated in a punctate pattern on the eastern side of the Qinling-Daba Mountain area. ③ Changes in the carbon sink in the Qinling-Daba Mountain area were the consequence of the interaction of multiple factors, with 60% of the contribution stemming from climatic factors. The combined weak driving force of temperature, precipitation, and solar radiation accounted for as much as 29%, distributed in a scattered manner across the central and eastern regions of the Qinling-Daba Mountain area.

The Molecular Biology of Placental Transport of Calcium to the Human Foetus.

From fertilisation to delivery, calcium must be transported into and within the foetoplacental unit for intracellular signalling. This requires very rapid, precisely located Ca2+ transfers. In addition, from around the eighth week of gestation, increasing amounts of calcium must be routed directly from maternal blood to the foetus for bone mineralisation through a flow-through system, which does not impact the intracellular Ca2+ concentration. These different processes are mediated by numerous membrane-sited Ca2+ channels, transporters, and exchangers. Understanding the mechanisms is essential to direct interventions to optimise foetal development and postnatal bone health and to protect the mother and foetus from pre-eclampsia. Ethical issues limit the availability of human foetal tissue for study. Our insight into the processes of placental Ca2+ handling is advancing rapidly, enabled by developing genetic, analytical, and computer technology. Because of their diverse sources, the reports of new findings are scattered. This review aims to pull the data together and to highlight areas of uncertainty. Areas needing clarification include trafficking, membrane expression, and recycling of channels and transporters in the placental microvilli; placental metabolism of vitamin D in gestational diabetes and pre-eclampsia; and the vascular effects of increased endothelial Orai expression by pregnancy-specific beta-1-glycoproteins PSG1 and PSG9.

The gut-brain axis: Unveiling the impact of xenobiotics on neurological health and disorders.

The Gut-Brain Axis (GBA) is a crucial link between the gut microbiota and the central nervous system. Xenobiotics, originating from diverse sources, play a significant role in shaping this interaction. This review examines how these compounds influence neurotransmitter dynamics within the GBA. Environmental pollutants can disrupt microbial populations, impacting neurotransmitter synthesis-especially serotonin, gamma-aminobutyric acid (GABA), and dopamine pathways. Such disruptions affect mood regulation, cognition, and overall neurological function. Xenobiotics also contribute to the pathophysiology of neurological disorders, with changes in serotonin levels linked to mood disorders and imbalances in GABA and dopamine associated with anxiety, stress, and reward pathway disorders. These alterations extend beyond the GBA, leading to complications in neurological health, including increased risk of neurodegenerative diseases due to neuroinflammation triggered by neurotransmitter imbalances. This review provides a comprehensive overview of how xenobiotics influence the GBA and their implications for neurological well-being.

Effects of prebiotics from diverse sources on dysbiotic gut microbiota associated to western diet: Insights from the human Mucosal ARtificial COLon (M-ARCOL)

Effects of prebiotics from diverse sources on dysbiotic gut microbiota associated to western diet: Insights from the human Mucosal ARtificial COLon (M-ARCOL)

"I Came Across a Junk": Understanding Design Flaws of Data Visualization from the Public's Perspective.

The visualization community has a rich history of reflecting upon visualization design flaws. Although research in this area has remained lively, we believe it is essential to continuously revisit this classic and critical topic in visualization research by incorporating more empirical evidence from diverse sources, characterizing new design flaws, building more systematic theoretical frameworks, and understanding the underlying reasons for these flaws. To address the above gaps, this work investigated visualization design flaws through the lens of the public, constructed a framework to summarize and categorize the identified flaws, and explored why these flaws occur. Specifically, we analyzed 2227 flawed data visualizations collected from an online gallery and derived a design task-associated taxonomy containing 76 specific design flaws. These flaws were further classified into three high-level categories (i.e., misinformation, uninformativeness, unsociability) and ten subcategories (e.g., inaccuracy, unfairness, ambiguity). Next, we organized five focus groups to explore why these design flaws occur and identified seven causes of the flaws. Finally, we proposed a research agenda for combating visualization design flaws and summarize nine research opportunities.

A xylenol orange-based pH-sensitive sensor array for identification of bacteria and differentiation of probiotic drinks.

The development of straightforward and cost-efficient methods for bacterial identification is very important. In this study, we utilized xylenol orange, metal ions, and diverse bacterial carbon sources to construct a sensor array, achieving precise bacterial identification. Initially, we examined the absorbance variations of xylenol orange with five metal ions (Mn2+, Co2+, Ni2+, Cu2+, and Zn2+) at pH levels ranging from 4 to 7, observing significant changes at 570 nm or 580 nm. Given that bacteria can generate varying amounts of acid in different carbon sources, we employed a blend of xylenol orange and the five metal ions as pH-sensitive probes to characterize bacterial metabolism in three carbon sources, resulting in the development of a five-sensor array that effectively differentiated seven bacteria. Additionally, by utilizing xylenol orange with Co2+, we successfully identified six different bacterial mixtures and five types of probiotic drink products. These findings highlight the potential of our methods for broader practical application in bacterial identification in practical use.

Sirombu in West Nias during the 2004 and 2005 earthquakes and tsunamis: A preliminary study

Sirombu, a coastal settlement and trading hub on the western coast of Nias Island in Indonesia, was severely impacted by the devastating earthquakes and tsunamis that struck Aceh and the western offshore islands of Sumatra on December 26, 2004, and again on March 27, 2005. This paper examines the immediate aftermath of these catastrophic events in Sirombu. Employing a historical methodology and drawing upon diverse data sources, the study reveals the rapid transformation of life in Sirombu from normalcy to chaos and fear as the disasters unfolded. The earthquakes and tsunamis resulted in significant loss of life and injuries, widespread destruction of homes, markets, the seaport, and other infrastructure, and the displacement of the local population.