Explore the vast range of titles available.

Dynamical downscaling provides physics-based high-resolution climate change projections across regional and local scales. This is particularly important for island nations characterized by complex terrain, where the coarse resolution of global climate model (GCM) output often prohibits direct use. One of the main motivations for dynamical downscaling is to reduce biases relative to the host GCM at the local scale, which can be quantified through assessing ‘added value’. However, added value from downscaling is not guaranteed; quantifying this can help users make informed decisions about how best to use available climate projection data. Here we describe the experiment design of the updated national climate projections for New Zealand based on dynamical downscaling. The global non-hydrostatic Conformal Cubic Atmospheric Model (CCAM) is primarily used for downscaling, with a global stretched grid targeting high resolution over New Zealand (12-km) and the wider South Pacific region (12–35-km). Focusing on the historical simulations, we assess added value for a range of metrics, climatological fields, extreme indices, and tropical cyclones. The main strengths of the downscaling include generally large improvements relative to the host GCM for temperature and orographic precipitation. Inter-annual variability in temperature is well captured across New Zealand, and several temperature and precipitation-based extreme indices show large improvements. The representation of tropical cyclones reaching at least category 2 intensity is generally improved relative to the large consistent under-representation in the host GCMs. The remaining biases are explored and discussed forming the basis for ongoing bias-correction work.

Healthy internet activity (eg, making use of eHealth and online therapy) is positively associated with well-being. However, unhealthy internet activity (too much online time, problematic internet use/PIU, internet dependency/ID, etc.) is associated with reduced well-being, loneliness, and other related negative aspects. While most of the evidence is correlational, some research also shows that internet activity can be predictive for well-being. The aim of this article is to elaborate on the question as to what extent internet activity is predictive of psychological well-being by means of (a) a scoping review and (b) theoretical understanding which model the interrelation of internet activity and psychological well-being. We searched different electronic databases such as Web of Science by using the search terms \"Internet\" OR \"App\" OR \"digital\" OR \"online\" OR \"mobile application\" AND \"Use\" OR \"Activity\" OR \"Behavior\" OR \"Engagement\" AND \"Well-being\" OR \"Loneliness\" for (a, the scoping review) or CCAM for (b, the theoretical understanding). The scoping review (a) summarizes recent findings: the extent to which internet activity is predictive for well-being depends on the internet activity itself: internet activity facilitating self-management is beneficial for well-being but too much internet activity, PIU and ID are detrimental to well-being. To understand (b) why, when and how internet activity is predictive for well-being, theoretical understanding and a model are required. While theories on either well-being or internet activity exist, not many theories take both aspects into account while also considering other behaviors. One such theory is the Compensatory Carry-Over Action Model (CCAM) which describes mechanisms on how internet use is related to other lifestyle behaviors and well-being, and that individuals are driven by the goal to adopt and maintain well-being - also called higher-level goals - in the CCAM. There are few studies testing the CCAM or selected aspects of it which include internet activity and well-being. Results demonstrate the potentials of such a multifactorial, sophisticated approach: it can help to improve health promotion in times of demographic change and in situations of lacking personnel resources in health care systems. Suggestions for future research are to employ theoretical approaches like the CCAM and testing intervention effects, as well as supporting individuals in different settings. The main aim should be to perform healthy internet activities to support well-being, and to prevent unhealthy internet activity. Behavior management and learning should accordingly aim at preventing problematic internet use and internet dependency.

ABSTRACT Background: Congenital Cystic Adenomatoid Malformations (CCAM) are rare congenital anomalies of the lungs characterised by bronchopulmonary foregut malformations due to a sudden arrest in the development of the bronchial tree in the first trimester of the gestational period. Aim: The present study was aimed to describe the clinical and histopathological profiles of the patients and study patient outcomes after 1 year of surgical resection. Methods: All patients diagnosed with CCAM by histological examination of tissue obtained on surgical resection during the study period were included in the study. Data, such as patient demographics and clinical, radiological and histopathological findings, were recorded, and follow-up information was taken on OPD follow-up till 1 year after surgery regarding respiratory infections, haemoptysis or mortality. Results: Out of 21 patients, 11 were female and included in the study between the ages of 1 month and 32 years, with >50% younger than 2 years. Most patients in the study had recurrent pneumonia, with difficulty in breathing being the second most common presenting complaint. All patients had undergone computed tomography (CT) of the lungs, which was able to diagnose cystic lesions accurately in >80% of cases. Histologically, all cases were classified based on recent Strocker's classification, and Type 1 was the most commonly observed with 13 cases, followed by Type 2 in five and Type 3 in three patients. There was no evidence of malignant transformation in any of the cases. There was 100% survival at the end of 1 year, with six patients having respiratory infections and none of the patients getting hospitalised over 1 year after surgery. Conclusion: CCAM is a rare congenital anomaly associated with significant morbidity and may present at any age. It can be histologically classified into three subtypes, with Type 1 being the most common. Early surgical management is mandatory to prevent complications such as recurrent infections, respiratory distress, pneumothorax, lung abscess and malignant transformation. All patients included in the study had undergone surgical resection, and there was 100% survival at 1-year follow-up.

The evolution of Connected, Cooperative, and Automated Mobility (CCAM) systems represents a shift in transportation, potentially achieving benefits in efficiency, sustainability, and safety. Large-scale deployments of CCAM systems are, however, still constrained by fragmented Operational Design Domain (ODD) and limited infrastructure readiness. This paper reviews the state of the art regarding operational, infrastructural, and technological enablers for predictive and extendable ODDs. First, a literature review of existing definitions and ongoing standardization work is presented, focusing on gaps in the formalization and validation of ODD boundaries. Second, the influence of physical infrastructure elements on vehicle performance and safety is analyzed. Third, technological and organizational enablers, which include digital twins, data-driven simulation models, and governance frameworks, are discussed in depth as essential in adaptive and resilient CCAM operations. The review concludes that predictive and extendable ODDs require a data-driven and interoperable mobility ecosystem linking vehicles, infrastructure, and governance. Future research should focus on developing measurable indicators for infrastructure readiness, advancing simulation tools for dynamic ODD monitoring, and integrating human-in-the-loop systems for safe mixed traffic. Aligning these advances with Safe System Design and AI governance frameworks will enable scalable and trustworthy automated mobility.

The planetary boundary layer height (PBLH) is one of the key factors in influencing the dispersion of the air pollutants in the troposphere and, hence, the air pollutant concentration on ground level. For this reason, accurate air pollutant concentration depends on the performance of PBLH prediction. Recently, ceilometers, a lidar instrument to measure cloud base height, have been used by atmospheric scientists and air pollution control authorities to determine the mixing level height (MLH) in improving forecasting and understanding the evolution of aerosol layers above ground at a site. In this study, ceilometer data at an urban (Lidcombe) and a rural (Merriwa) location in New South Wales, Australia, were used to investigate the relationship of air pollutant surface concentrations and surface meteorological variables with MLH, to validate the PBLH prediction from two air quality models (CCAM-CTM and WRF-CMAQ), as well as to understand the aerosol transport from sources to the receptor point at Merriwa for the three case studies where high PM10 concentration was detected in each of the three days. The results showed that surface ozone and temperature had a positive correlation with MLH, while relative humidity had negative correlation. For other pollutants (PM10, PM2.5, NO2), no clear results were obtained, and the correlation depended on the site and regional emission characteristics. The results also showed that the PBLH prediction by the two air quality models corresponded reasonably well with the observed ceilometer data and the cause and source of high PM10 concentration at Merriwa can be found by using ceilometer MLH data to corroborate back trajectory analysis of the transport of aerosols to the receptor point at Merriwa. Of the three case studies, one had aerosol sources from the north and north west of Merriwa in remote NSW, where windblown dust is the main source, and the other two had sources from the south and south east of Merriwa, where anthropogenic sources dominate.

Weather and climate extremes, such as heat waves (HWs), have become more frequent due to climate change, resulting in negative environmental and socioeconomic impacts in many regions of the world. The high vulnerability of South African society to the impacts of warm extreme temperatures makes the study of the effect of climate change on future HWs necessary across the country. We investigated the projected effect of climate change on future of South Africa with a focus on HWs using an ensemble of regional climate model downscalings obtained from the Conformal Cubic Atmospheric Model (CCAM) for the periods 2010–2039, 2040–2069, and 2070–2099, with 1983–2012 as the historical baseline. Simulations were performed under the Representative Concentration Pathway (RCP) 4.5 (moderate greenhouse gas (GHG) concentration) and 8.5 (high GHG concentration) greenhouse gas emission scenarios. We found that the 30-year period average maximum temperatures may rise by up to 6 °C across much of the interior of South Africa by 2070–2099 with respect to 1983–2012, under a high GHG concentration. Simulated HW thresholds for all ensemble members were similar and spatially consistent with observed HW thresholds. Under a high GHG concentration, short lasting HWs (average of 3–4 days) along the coastal areas are expected to increase in frequency in the future climate, however the coasts will continue to experience HWs of relatively shorter duration compared to the interior regions. HWs lasting for shorter duration are expected to be more frequent when compared to HWs of longer durations (over two weeks). The north-western part of South Africa is expected to have the most drastic increase in HWs occurrences across the country. Whilst the central interior is not projected to experience pronounced increases in HW frequency, HWs across this region are expected to last longer under future climate change. Consistent patterns of change are projected for HWs under moderate GHG concentrations, but the changes are smaller in amplitude. Increases in HW frequency and duration across South Africa may have significant impacts on human health, economic activities, and livelihoods in vulnerable communities.

In recent years, the number of connected vehicles has been steadily increasing. Vehicles that can exchange information with each other or with infrastructure elements enable services for cooperative manoeuvring and address central problems related to road traffic (e.g., growing traffic volume, sustainability, traffic safety, etc.). The development, standardisation, and implementation activities of Cooperative Intelligent Transport System (C-ITS) services have been advancing in recent years in numerous projects and research efforts. Impact assessment plays a central role in this process, whereby the approaches and methods used can differ significantly. This paper aims to review existing related work and future directions related to C-ITS impact assessment in the form of a structured literature review. Within this scope, it is elaborated which C-ITS services have already been the subject of past studies and under which conditions the respective impact assessment studies have taken place. Furthermore, the methods used in the individual studies as well as the impact categories and KPIs used to quantify the impacts are explained and summarised. Finally, potentials for future research are derived based on the literature analysed. This paper is the first review of its kind to focus specifically on the impact assessment of C-ITS. The results of this paper provide a comprehensive overview of current research efforts in the field of C-ITS impact assessment, provide input for future research directions, and thus contribute to further development in the direction of cooperative connected and automated mobility.

Model simulations for Borneo suggest potential difficulties in capturing the variability of rainfall on regional scales due to complex topography and climate interactions. This study utilizes a high-resolution Conformal Cubic Atmospheric Model (CCAM) to address the impacts of climate change on a regional scale over Borneo by analyzing key parameters such as precipitation, surface temperature, mean sea level pressure, and relative humidity over Borneo for the near future (2023-2033). We also found that the MAM (March-April-May) and SON (September-October-November) seasons show higher rainfall than the DJF (December-January-February) season. Meanwhile, relative humidity tends to decrease in the future compared to previous years. Mean sea level pressure (MSLP) tends to increase over the border of Indonesia and Malaysia in the JJA (June-July-August) season but decreases over southern Borneo in the MAM (March-April-May) and SON (September-October-November) seasons. There is a noticeable increase in temperature from the MAM (March-April-May) to SON (September-October-November) season in the east-south region. However, further research is required to validate these projections.

In the upcoming decade and beyond, the Cooperative, Connected and Automated Mobility (CCAM) initiative will play a huge role in increasing road safety, traffic efficiency and comfort of driving in Europe. While several individual vehicular wireless communication technologies exist, there is still a lack of real flexible and modular platforms that can support the need for hybrid communication. In this paper, we propose a novel vehicular communication management framework (CAMINO), which incorporates flexible support for both short-range direct and long-range cellular technologies and offers built-in Cooperative Intelligent Transport Systems’ (C-ITS) services for experimental validation in real-life settings. Moreover, integration with vehicle and infrastructure sensors/actuators and external services is enabled using a Distributed Uniform Streaming (DUST) framework. The framework is implemented and evaluated in the Smart Highway test site for two targeted use cases, proofing the functional operation in realistic environments. The flexibility and the modular architecture of the hybrid CAMINO framework offers valuable research potential in the field of vehicular communications and CCAM services and can enable cross-technology vehicular connectivity.

Autonomous vehicles and smart roads are not new concepts and the undergoing development to empower the vehicles for higher levels of automation has achieved initial milestones. However, the transportation industry and relevant research communities still require making considerable efforts to create smart and intelligent roads for autonomous driving. To achieve the results of such efforts, the CCAM infrastructure is a game changer and plays a key role in achieving higher levels of autonomous driving. In this paper, we present a smart infrastructure and autonomous driving capabilities enhanced by CCAM infrastructure. Meaning thereby, we lay down the technical requirements of the CCAM infrastructure: identify the right set of the sensory infrastructure, their interfacing, integration platform, and necessary communication interfaces to be interconnected with upstream and downstream solution components. Then, we parameterize the road and network infrastructures (and automated vehicles) to be advanced and evaluated during the research work, under the very distinct scenarios and conditions. For validation, we demonstrate the machine learning algorithms in mobility applications such as traffic flow and mobile communication demands. Consequently, we train multiple linear regression models and achieve accuracy of over 94% for predicting aforementioned demands on a daily basis. This research therefore equips the readers with relevant technical information required for enhancing CCAM infrastructure. It also encourages and guides the relevant research communities to implement the CCAM infrastructure towards creating smart and intelligent roads for autonomous driving.