Adaptive Tool Research Articles

Toward Climate Resilient Asphalt Binder Selection in Canada

Climate change and extreme weather events present a fundamental challenge to pavement engineering and planning practice, given that transportation infrastructure has traditionally been planned and designed using historical climate data under the implicit assumption that climate is stationary and future conditions will resemble past ones. Transportation professionals should consider future risks scenarios in road design, material selection and system operations. This will be challenging given the inherent uncertainties in any climate projections. However, changes might be needed since transportation professionals are expected to deliver cost-effective and climate-resilient transportation infrastructure. The material selection plays an important role in the durability of flexible roads; therefore, it is important to consider the whole road design life cycle in terms of climate change and extreme weather events impact on asphalt selection as well as to consider different traffic speeds. This led to the development of the NRC Climate Adaptation and Asphalt Selection Tool (CAAST).

Monitoring of zero-inflated COM-Poisson processes

This paper investigates control charts for count data with a significant number of zeros. The proposed models are generalized to handle data exhibiting different types of dispersion, i.e. equidispersion, overdispersion, and underdispersion. To this effect, the zero-inflated Conway–Maxwell Poisson (ZICMP) distribution is employed. A Shewhart and an exponentially weighted moving average (EWMA) control chart are developed, referred to as ZICMP-Shewhart and ZICMP-EWMA charts, respectively. The ZICMP distribution incorporates various distributions, including the Conway–Maxwell–Poisson (COM-Poisson) and zero-inflated Poisson (ZIP), Geometric, and Bernoulli distributions, as special cases. Consequently, the flexibility and versatility of the ZICMP distribution enhance the applicability of the proposed control charts, thereby providing practitioners with an adaptable tool suitable for various scenarios.The control charts are examined for their ability to detect upward shifts in the process mean level, and their statistical performance is evaluated in terms of the average run-length (ARL). Through a simulation study, we demonstrate that the ZICMP-Shewhart chart is more effective for monitoring over-dispersed data, while the ZICMP-EWMA chart is more suitable for under-dispersed data. Finally, we provide two real-life examples to illustrate the applicability of the proposed charts.

Measuring Beyond the Standard: Informal Measurement Systems as Cognitive Technologies.

This paper explores the role of measurement as a cognitive technology across human history, emphasizing the coexistence of formal and informal measurement systems. While standardized systems dominate contemporary culture and are well documented across large-scale societies of the past, this manuscript highlights the less explored domain of informal measurement practices that have been integral to daily life from the past to the present. Through the examination of body-based measurement systems and proportional heuristics, we demonstrate how these informal strategies were not merely precursors to formal standards but essential adaptive tools for solving everyday problems. Often, these informal solutions come with practical advantages. This manuscript calls for a broader recognition of their significance in cultural and technological evolution.

Fostering language awareness through Citizen Science: results and implications of a project with Polish teenagers doing language-related research

This article reports on the rationale, implementation, results and evaluation of four rounds of co-creative Citizen Science (CS) projects (2021–2024) conducted with 10 public secondary schools in Opole, Poland. These educational interventions consisted in facilitating 18 team research projects designed and executed by 55 teenagers (15–17-year-olds) on topics related to communication, linguistic diversity, multiculturalism and language learning. The article shows how such interventions are conducive to pupils’ increased language awareness, their reflectivity about the scientific methods in linguistics, and an inclination towards future citizen engagement regarding literacy-­related issues. The article also focuses on the potential of CS for allowing pupils themselves to enhance their language awareness and socio-linguistic competences while producing experiential and situated knowledge that is relevant to their communities. The project evaluation that was undertaken subsequently traces which learning outcomes are likely to be achieved during co-creative CS and recommends some adaptable tools for monitoring the increase in language awareness in secondary language education, also beyond the Polish context.

Survival and Life: A Study on Cultural Adaptation of Ethnic Minority Groups from the Perspective of Dietary Anthropology

Abstract: In the process of globalization and modernization, the diet culture of minority groups is undergoing profound changes and adjustments. Therefore, through the perspective of food culture, this paper studies the identity of ethnic minorities in cultural adaptation, cross-cultural integration and their resistance to foreign cultures. It is found that diet is not only an important carrier of cultural inheritance of ethnic minorities, but also a core way of cultural self-expression and social interaction. In the process of cultural adaptation, ethnic minorities not only maintain the uniqueness of traditional culture through the inheritance and change of eating habits, but also carry out innovation and integration under the influence of foreign cultures to ensure the continuation of culture and self-identity. At the same time, eating rituals in festivals and religious occasions have also become an important way for ethnic minorities to express their cultural identity. In addition, the commercialization of diet and the development of tourism have enabled the ethnic diet culture to spread widely in the context of globalization and brought economic benefits. Studies have shown that diet plays a key role in ethnic minorities' response to the impact of globalization, maintaining cultural identity and promoting cultural inheritance, and is an important tool and symbol in cultural adaptation.

A fingerprint of source-specific health risk of PM2.5-bound components over a coastal industrial city

The influence of specific local land-use activities (continuously redistributing elements across environments) and environmental conditions (altering the chemical composition of airborne particulate matter) on the intrinsic health risk of PM2.5 exposure is sparsely reported. To fill this gap, we employed a novel integrated approach to address the influence of short-term changes in source-specific PM2.5 composition on the exposure-response risk, while controlling for weather conditions. We combine receptor-based source apportionment with conditional logistic regression in a space-time-stratified case-crossover design. This approach is different from previous studies as it: i) controls the impact of spatiotemporal variations in air pollution and human mobility using multilocation-specific fixed and disjointed space-time strata ii) addresses the spatial heterogeneity of personal exposure separating its variable effect from other predictors by allowing different baseline hazards for each space-time stratum; iii) aligns case/control periods with strong/regular episodes of source-specific PM-multipollutant fingerprint contributions rather than health outcomes. This enabled comprehensive examination of the association between source-specific PM2.5-bound species and cardiorespiratory disease hospitalizations. The epidemiological findings were that primary anthropogenic emissions [industrial (ORs 2.5 – 4.8)] were associated with higher 1-day moving average PM-induced risks. Natural-related sources [fresh / aged sea salt aerosol, dust, soil resuspension] and secondary sulfate formation were consistently associated with higher health risks (ORs 1.0 – 1.54) after 1 to 5-days since exposure. The results emphasize the importance of source-specific air quality management in complex areas and our research provides an adaptable universal tool to support targeted place-based policy interventions to mitigate air pollution impacts on health.

Modular and extendable 1D-simulation for microfluidic devices

Microfluidic devices have been the subject of considerable attention in recent years. The development of novel microfluidic devices, their evaluation, and their validation requires simulations. While common methods based on Computational Fluid Dynamics (CFD) can be time-consuming, 1D simulation provides an appealing alternative that leads to efficient results with reasonable quality. Current 1D simulation tools cover specific microfluidic applications; however, these tools are still rare and not widely adopted. There is a need for a more versatile and adaptable tool that covers novel applications, like mixing and the addition of membranes, and allows easy extension, resulting in one comprehensive 1D simulation tool for microfluidic devices. In this work, we present an open-source, modular, and extendable 1D simulation approach for microfluidic devices, which is available as an open-source software package at https://github.com/cda-tum/mmft-modular-1D-simulator. To this end, we propose an implementation that consists of a base module (providing the core functionality) that can be extended with dedicated application-specific modules (providing dedicated support for common microfluidic applications such as mixing, droplets, membranes, etc.). Case studies show that this indeed allows to efficiently simulate a broad spectrum of microfluidic applications in a quality that matches previous results or even fabricated devices.

Improving dissemination products and practices for community-based organisations serving LGBTQ+ communities in the USA: a thematic analysis

BackgroundIn the USA, lesbian, gay, bisexual, transgender and queer (LGBTQ+) people report higher rates of tobacco use than non-LGBTQ+ people due to diverse factors, from anti-LGBTQ stigma to targeted marketing...

Nitrogen fertilization and sowing date as wheat climate change adaptation tools under Mediterranean conditions

In the current situation, climate change has substantially disturbed precipitation occurrence in the Mediterranean region, by increasing its variability and decreasing the total annual amount, which both negatively affect rainfed crop productivity. We hypothesize that a simple cost-effective method for enhancing crop adaptation to new climate conditions would consist of modifying the crop sowing date. Traditional nitrogen (N) fertilization rates could also be adjusted to the current situation given the interdependent water/N relation in plant nutrition. Based on this hypothesis, during a 4-year field experiment with bread wheat (Triticum aestivum L., var. Pistolo), the effects of three sowing dates (October, November, February) and three N fertilization rates (54kgN ha-1, 27kgN ha-1, 0kgN ha-1) on crop development, yield, grain quality, soil N content and N use efficiency were analyzed. The results showed that water scarcity was the predominant limiting factor, because it outweighed N deficiency with half-fertilized crops being as productive as fully fertilized treatments. Nevertheless, sowing date was the most influential factor, with up to a 30% yield increase noted for the November-sown wheat compared to that sown in October, while delaying wheat sowing to February decreased crop yields. Grain protein content remained the same between the November- and October-sown crops, but increased in the February one crops. Optical sensor measurements showed that an optimal assessment of the current water/N nutritional status of crops can be achieved with these tools.

Neurocircle microsurgery model: Description of simulation-based training and exoscope

Background: Microsurgery is crucial in neurosurgery, requiring precise skills for interventions on delicate structures. Effective training is essential for developing these skills. In Peru and Latin America, however, there is a notable shortage of specialized training centers and high costs associated with foreign simulators, hindering the development of neurosurgical skills. To address this issue, the NeuroZone3D Research Center has initiated a project to create a national dataset on Peruvian craniometry and develop locally adapted training models. Methods: The “NeuroCircle Microsurgery Model” was created through a multi-phase process. Phase 1 involved designing a simulation platform using a 3D printer. Phase 2 focused on creating a realistic biomodel with polyurethane and advanced modeling techniques. In Phase 3, the biomodel was assembled and integrated into the platform. Phase 4 included using a wooden module, a cost-effective exoscope simulation, and surgical instruments to provide a realistic training environment. Results: The “NeuroCircle Microsurgery Model” provided a stable and effective training environment. Feedback from training sessions with neurosurgery residents and medical students indicated significant improvements in microsurgical skills. Participants valued the model’s realism and its role in enhancing their surgical proficiency. Conclusion: The “NeuroCircle Microsurgery Model” is a significant advancement in microsurgery training for Peru and Latin America. Its development provides a valuable, locally adaptable tool for improving surgical skills and addresses the critical training gaps in the region.

Summer School in Computational Physiology: A Collaborative Course in Modeling Excitable Tissues

ABSTRACT Computational modeling of physiology is a multiscale, multidisciplinary field requiring a diverse set of skills and backgrounds. The Simula Summer School in Computational Physiology aims to teach graduate students from around the world practical methods for multiscale modeling of excitable tissues (specifically, the heart and brain). This joint summer school is collaboratively based on the complementary expertise and shared educational goals of three institutions: Simula Research Laboratory, the University of Oslo, and the University of California San Diego. The summer school’s core goal is to promote successful research collaboration among the host institutions and to train excellent computational researchers. Keys to the success of this type of school include sustainable funding, close mentorship, and innovative, adaptive teaching tools. Using a combination of lectures, hands-on programming modules, and real-world projects in small groups with experienced scientific mentors, this unique program is an immersive way for young scientists to develop skills and network with future peers from around the world.

Fortifying cloud environments against data breaches: A novel AI-driven security framework

As cloud computing continues to dominate the modern technological landscape, organizations face growing challenges in preventing data breaches and sophisticated cyber threats. The increasing complexity and scale of cloud environments require advanced security mechanisms to address evolving threats. This paper introduces "SecureCloudAI," a cutting-edge AI-driven security framework designed to fortify sensitive data within cloud infrastructures. SecureCloudAI leverages a hybrid approach that combines machine learning models like Random Forest and deep learning techniques, including Long Short-Term Memory (LSTM) networks, to detect, classify, and respond to potential security breaches in real-time. The system offers robust malware detection, network traffic analysis, and web intrusion detection while maintaining scalability and efficiency across large cloud environments. Experimental results demonstrate SecureCloudAI's high accuracy, with malware detection at 94.78% and network traffic classification at 90.92%, ensuring the system can handle both complex and emerging threats. This AI-driven solution marks a significant advancement in cloud security, providing organizations with an adaptive and scalable tool to safeguard their data against the ever-growing cyber threat landscape.

Adaptable Plasmonic Membrane Sensors for Fast and Reliable Detection of Trace Low-Micrometer Microplastics in Lake Water.

In freshwater environments, low-micrometer microplastics (LMMPs) have captured significant attention due to their prevalence and toxicity. Yet, rapid detection of LMMPs (1-10 μm) at the single-particle level within complex freshwater matrices remains a hurdle. We developed an adaptable plasmonic membrane sensor for fast detection of individual LMMPs in eutrophic lake waters. The plasmonic membrane sensor functions both as a membrane filter and as a sensor for LMMP collection and analysis. Among the four types of membrane sensors, polycarbonate track-etch (PCTE) membrane sensors exhibit superior imaging quality for LMMPs due to their flat and homogeneous surfaces. Besides the significantly improved imaging contrast and reduced background interferences, the Raman intensity of LMMPs is enhanced by 48% ± 25% on PCTE membrane sensors compared to unmodified membranes. The increased Raman intensities of a chemical probe with an increasing gold layer thickness and a decreasing membrane pore size suggest a surface-enhanced Raman scattering effect from the membrane sensors. The membrane sensors achieve a detection limit of 1 μg/L and an ultrafast scanning time of 0.01 s for individual LMMPs across natural eutrophic lake water. The developed membrane sensors offer an adaptable tool for the swift and reliable detection of individual LMMPs in complex environmental matrices.

Informing infodemic management capacity building by piloting an adaptable capability tool

Abstract Background Infodemic management competencies and indicators have been included in key WHO pandemic preparedness documents and guidance such as the Preparedness and Resilience for Emerging Threats (PRET) initiative, the Health Emergency preparedness and response (HEPR) framework and in the WHO benchmarks for strengthening health emergency capacities. Training and capacity building is needed to strengthen infodemic management skills and capacity, and a tool to define capabilities and inform training needs was needed. Objectives An adaptable tool was developed and piloted to measure infodemic management capacity to inform training and capacity building needs. The tool was informed by previous work and adapted to cover a wide set of capabilities. It was piloted in March - May 2024. Results The tool was developed to cover the breadth of infodemic management work practice and structural support and included: who is conducting infodemic management work; structures in place to support; main activities; health topics focus areas; partnerships with other organisations; social listening and infodemic insights generation; as well as challenges, opportunities and training needs. The tool can be adapted to the specific situation, with a full survey able to be disseminated via an online survey platform as well as a modified interview guide for key informants or settings where that is more appropriate. Conclusions Preliminary results show that this tool can be a useful way to measure infodemic management competencies and to inform training and capacity building needs. The ability to adapt to specific settings is important to ensure the results are as useful and tailored as possible. As further testing in different setting continues the tool will continue to be iterated upon. Key messages • Infodemic management is an important component of pandemic preparedness and resilience and across the whole emergency response cycle. • A new adaptable tool to measure capabilities can help inform infodemic management trainings and capacity building.

Analysis of Enterprise Internet of Things Maturity Models: A Review

Maturity models are valuable tools when assessing the readiness and progress of technology incorporation in organizations, providing information for decision-making, resource allocation, and competitive advantage. The Internet of Things is a technology paradigm of global importance, especially for organizations, as it supports productivity improvements, real-time analysis, and customer satisfaction. Therefore, adopting and implementing this technology in enterprises brings several challenges, such as technological, organizational, security, and maturity issues. However, secondary studies that systematically compile the existing literature on these specific mechanisms for the enterprise domain are still being determined. This article aims to address this knowledge gap by conducting a review to deepen and synthesize the existing knowledge. This research followed established methodologies and protocols to synthesize and analyze the state of the art in the area; 489 documents were retrieved from seven bibliographic databases, and, applying inclusion and exclusion criteria, 36 primary studies were selected. The results indicate that the typical structures of maturity models incorporate technological, organizational, human, performance, and security dimensions through graded levels that denote the sophistication of the Internet of Things. Measurement techniques and metrics vary from model to model. There are few empirical validations or standardized improvement frameworks. The main conclusion is that there is a diversity of models, dimensions, indicators, and methods and a need for more comprehensive, adaptable, and user-friendly tools to help companies assess their Internet of Things maturity and inform future development strategies.

Adopting Digital Tools in Higher Education: Opportunities, Challenges and Theoretical Insights

ABSTRACTThe digital transformation of higher education, driven by technological advancements and evolving societal needs, presents both significant opportunities and critical challenges. This study integrates the technology acceptance model (TAM), diffusion of innovations (DOI), comprehensive literature review, in‐depth case studies and empirical survey data to explore these dynamics. The findings reveal the potential of digital technologies—such as online platforms, adaptive learning tools and immersive technologies—to expand access, personalise learning and foster collaboration. However, the research also identifies major barriers, including the digital divide, faculty resistance and data privacy concerns. By combining qualitative thematic analysis and quantitative statistical analysis, this study underscores the importance of strategic planning, inclusive policies and ongoing professional development in maximising the benefits of digital education. The paper contributes to the field by offering actionable insights, theoretical implications and practical recommendations for institutions to implement adaptive pedagogical strategies, ensure equitable access to technology and build robust, secure digital learning environments.

BLINKRESNET: COST-EFFECTIVE EYELID LOCALIZATION FOR USE IN A BLINK REFLEXOMETER

Four keypoints corresponding to upper and lower eyelid positions may be localized in the scan videos of a blink reflexometer, an edge device. Images from prior reflexometer scans are tagged in order to train a residual neural network, dubbed BlinkResNet. In order to provide adequate demographic coverage, comprehensive data collection procedures are implemented. The architecture is thoroughly parameterized, allowing for a multidimensional space of compatible architectures to be swept through using a random search. Experimental results on a dataset of annotated reflexometer frames demonstrate significant improvement over a previous algorithm for eyelid localization. A random search identified variants of BlinkResNet best-suited for reflexometer frame annotation, with BlinkResNet-Default achieving a [Formula: see text] score of 0.966 on the validation dataset. Requirements for deployment on an edge device are met: training ease, generalizable inference performance, and low computational cost. A sweep may be conducted on any combination of hyperparameters and datasets, making it an adaptable tool for the continual development of customized machine learning architectures. The analysis of sweep results provides valuable insights into optimal model organization for specific tasks. Thus, methodical data annotation and sweeps over a parameterized machine learning routine are investigated as measures to achieve rapid, accurate inference results.

An example of a field service plan for oceanographic submerged moorings

Oceanographic mooring missions are critical for advancing our understanding of the world’s oceans and their role in the broader Earth system. These missions rely on the deployment and maintenance of complex observing systems, comprising subsurface, profiling, and surface moorings, which are designed to collect valuable data from the ocean interior. The success of these missions depends on meticulous planning and strategic execution, which are facilitated by the use of Field Service Plans (FSPs). FSPs are comprehensive and adaptable planning tools that integrate safety measures, contingency plans, and timelines to ensure the successful and safe recovery, maintenance, and deployment of mooring systems. In this paper, we provide a comprehensive guide to drafting, implementing, and adapting FSPs tailored to the unique challenges posed by oceanographic mooring missions. We highlight the critical role of FSPs in optimizing safety, data quality, and mission success, while also underscoring the need for flexibility and adaptation. Our aim is to provide practical guidance and a template for researchers, expedition leaders, and institutions engaged in oceanographic research.

Novel Multi-Criteria Decision Analysis Based on Performance Indicators for Urban Energy System Planning

Urban energy systems planning presents significant challenges, requiring the integration of multiple objectives such as economic feasibility, technical reliability, and environmental sustainability. Although previous studies have focused on optimizing renewable energy systems, many lack comprehensive decision frameworks that address the complex trade-offs between these objectives in urban settings. Addressing these challenges, this study introduces a novel Multi-Criteria Decision Analysis (MCDA) framework tailored for the evaluation and prioritization of energy scenarios in urban contexts, with a specific application to the city of Bozen-Bolzano. The proposed framework integrates various performance indicators to provide a comprehensive assessment tool, enabling urban planners to make informed decisions that balance different strategic priorities. At the core of this framework is the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is employed to systematically rank energy scenarios based on their proximity to an ideal solution. This method allows for a clear, quantifiable comparison of diverse energy strategies, facilitating the identification of scenarios that best align with the city’s overall objectives. The flexibility of the MCDA framework, particularly through the adjustable criteria weights in TOPSIS, allows it to accommodate the shifting priorities of urban planners, whether they emphasize economic, environmental, or technical outcomes. The study’s findings underscore the importance of a holistic approach to energy planning, where trade-offs are inevitable but can be managed effectively through a structured decision-making process. Finally, the study addresses key gaps in the literature by providing a flexible and adaptable tool that can be replicated in different urban contexts to support the transition toward 100% renewable energy systems.

A GIS-MCDA approach to map environmental suitability of Posidonia oceanica meadows as blue nature-based solutions in the Mediterranean eco-region

The growing environmental risks induced by interacting climate and human-induced pressures threaten the survival and growth of marine coastal ecosystems (MCEs) and the ecosystem services they provide. Nature-based solutions (NBS), consisting of ecosystem-based approaches, have emerged as vital tools for climate adaptation and mitigation facing biodiversity loss and societal challenges. Identifying suitable environmental conditions for implementing Blue-NBS in marine coastal areas is a key priority to drive robust and cost-effective nature-based adaptation pathways. This study developed a suitability model for Blue-NBS, with a specific focus on Posidonia oceanica meadows in the Mediterranean Sea under a baseline scenario. GIS-based Multiple Criteria Decision Analysis (MCDA) was applied for data integration and prioritization of different environmental variables in geomorphological (e.g., depth), water quality (WQ) (e.g., salinity), and climatic (e.g., thermal stress) sub-groups. Suitability classes and scores for each variable were determined using statistical distributions, ensuring a data-driven approach to defining environmental suitability. Variables' weights were derived from the Analytic Hierarchy Process (AHP) based on expert judgment and then combined with scores to generate suitability maps for managing Blue-NBS on seagrasses. Depth was found to be the most dominant environmental variable, with shallow areas (e.g., Northern Adriatic, Gulf of Gabés) showing higher suitability. The southern part of the Mediterranean (e.g., Egypt) reported relatively low scores for both climate and WQ, while the Northern Adriatic had the lowest WQ scores. This study represents the first attempt to evaluate Blue-NBS suitability for seagrass meadows at the eco-regional scale with geomorphologic, WQ, and climatic variables, providing decision support for the selection and allocation of Blue-NBS in different environmental settings. The resulting environmental suitability maps represent a basis for the integration of socio-economic and governance-related indicators into a more complex, multi-tier approach to support NBS mainstreaming.