Search Results (670)

The healthcare needs of refugees and people seeking asylum are often broad and complex, with a higher burden of communicable diseases. There are limited data describing migrants’ experiences of accessing healthcare in Ireland. This cross-sectional study describes the experiences of migrants accessing healthcare services through an Irish Infectious Diseases clinic. Individuals attending the infectious diseases services in our hospital who had migrated to Ireland were included. Data were collected via a questionnaire, focusing on factors that may limit access to care, including communication, accessibility, cost, and stigmatisation. Seventy-six patients participated in this study. N = 20 (26%) of patients reported a commuting time of more than two hours to attend our clinic. N = 11 (15%) had experienced being unable to access healthcare in Ireland due to cost. Trust in healthcare providers was high (88%), and patient-reported satisfaction with communication was high (>90%). Persons living in direct provision services were more likely to report issues around privacy and less likely to have registered with a general practitioner. Accessibility and privacy were among the biggest challenges faced by migrants attending infectious diseases services at our centre, while communication and trust in healthcare providers were identified as areas of strength. Considering the burden of infectious diseases in migrant populations, and the challenges that certain migrant populations face in accessing healthcare, it is important to identify potential barriers to accessing care in order to ensure equitable, effective care. This study seeks to identify and describe the challenges that migrants face when accessing care through an Irish infectious diseases clinic. The results can help inform service provision and allocation of resources at a local level, while also identifying an area for further research regarding the barriers to accessing care faced by migrant communities in Ireland. Full article

In special relativity, particle trajectories, whether mass-bearing or not, can be traced on the Minkowski spacetime manifold in (3+1)D. Meantime, in quantum mechanics, trajectories in the phase space are not strictly outlined because coordinate and linear momentum cannot be measured simultaneously with arbitrary precision since they do not commute within the Hilbert space formalism. However, from the density matrix representing a quantum system, the extracted information still produces an imperative description of its properties and, furthermore, by appropriately reordering the matrix entries, additional information can be obtained from the same content. Adhering to this line of work, the paper investigates the definition and the meaning of velocity and speed in a typical quantum phenomenon, the disentanglement for a bipartite system when dynamical evolution is displayed in a (3+1)D pseudo-spacetime whose coordinates are constructed from combinations of entries to the density matrix. The formalism is based on the definition of a Minkowski manifold with compact support, where trajectories are defined following the same reasoning and formalism present in the Minkowski manifold of special relativity. The space-like and time-like regions acquire different significations referred to entangled-like and separable-like, respectively. The definition and the sense of speed and velocities of disentanglement follow naturally from the formalism. Depending on the dynamics of the physical state of the system, trajectories may meander between regions of entanglement and separability in the space of new coordinates defined on the Minkowski manifold. Full article

Magnetically levitated artificial hearts impose stringent requirements on the blood-pump motor: zero friction, minimal heat generation and full biocompatibility. Traditional mechanical-bearing motors and permanent-magnet bearingless motors fail to satisfy all of these demands simultaneously. A bearingless switched reluctance motor (BSRM), whose rotor contains no permanent magnets, offers a simple structure, high thermal tolerance, and inherent fault-tolerance, making it an ideal drive for implantable circulatory support. This paper proposes an 18/15/6-pole dual-stator BSRM (DSBSRM) that spatially separates the torque and levitation flux paths, enabling independent, high-precision control of both functions. To suppress torque ripple induced by pulsatile blood flow, a variable-overlap TSF-PWM-DITC strategy is developed that optimizes commutation angles online. In addition, a grey-wolf-optimized fast non-singular terminal sliding-mode controller (NRLTSMC) is introduced to shorten rotor displacement–error convergence time and to enhance suspension robustness against hydraulic disturbances. Co-simulation results under typical artificial heart operating conditions show noticeable reductions in torque ripple and speed fluctuation, as well as smaller rotor radial positioning error, validating the proposed motor and control scheme as a high-performance, biocompatible, and reliable drive solution for next-generation magnetically levitated artificial hearts. Full article

We present a fully discrete splitting-Galerkin scheme for second-order, non-autonomous abstract Cauchy problems with time-dependent perturbations. By reformulating the second-order equation as a first-order system in the product space, we apply a Galerkin semi-discretization in space of order $O\left(h^k\right)$ and a Strang splitting in time of order $O(\Delta t^2)$. An embedded Runge–Kutta controller provides adaptive time-stepping to handle rapid temporal variations in the perturbation operator $B\left(t\right)$. Under standard regularity and commutator assumptions on $A\left(t\right)$ and $B\left(t\right)$, we establish a priori error estimates $\underset{0\le t_n\le T}{max}{\parallel u\left(t_n\right)-u_n\parallel }_Z=O(h^k+\Delta t^2).$ Numerical experiments for a 1D perturbed wave equation confirm the theoretical convergence rates, illustrate stability thresholds in the unstable regime, and demonstrate up to 40% savings in computational cost via adaptivity. Full article

As smart cities evolve, integrating new technologies into school transportation is becoming increasingly important to ensure student comfort and safety. Monitoring and enhancing comfort during daily commutes can significantly influence well-being and learning readiness. However, most existing research addresses isolated factors, which limits the development of comprehensive and scalable solutions. This study presents the design and implementation of a low-cost, generalized IoT-based system for monitoring comfort in school transportation. The system processes multiple environmental and operational signals, and these data are transmitted to a cloud computing platform for real-time analysis. Signal processing incorporates standardized metrics, such as root mean square (RMS) values from ISO 2631-1 for vibration assessment. In addition, machine learning techniques, including a Random Forest classifier and ensemble-based models, are applied to classify ride comfort levels using both road roughness and environmental variables. The results show that stacked multisensor fusion achieved a significant improvement in classification performance compared with vibration-only models. The platform also integrates route visualization with commuting time per student, providing valuable information to assess the impact of travel duration on school mobility. Full article

To enhance the resistance to commutation failures of high-voltage direct current (HVDC) transmission systems under interphase short-circuit faults, an improved commutation failure prevention (CFPREV) control strategy is developed and validated. Initially, the adaptability of conventional CFPREV under interphase short-circuit faults is analyzed, and the time-varying mismatch between its three-phase criterion and the actual fault-phase voltage drop magnitude is quantified, thereby revealing the limitations of existing CFPREV control in handling interphase faults. Then, an innovative phase-by-phase real-time voltage drop calculation algorithm is proposed, which requires no integration and can be realized using only two adjacent sampling points. The calculated phase voltage indices remain stable during normal operation and respond rapidly after faults, ensuring the fast activation of advance firing control. On this basis, a real-time prediction algorithm for commutation voltage zero-crossing offset is further developed, and an improved CFPREV control strategy is designed. Subsequently, two real-time algorithms are proposed: a per-phase voltage drop magnitude calculation algorithm and a commuta-tion voltage zero-crossing shift prediction algorithm. Based on these, an improved control strategy is designed. Finally, simulation results using the PSCAD V46/EMTDC platform con-firm that the proposed strategy significantly improves commutation failure mitigation under interphase short-circuit faults compared to conventional CFPREV. Full article

We explore a timeless approach to quantum theory, in the form of the Page–Wootters mechanism, in which a gravitational interaction is introduced between the system and a finite-dimensional clock. The clock model used is the recently proposed quasi-ideal clock, a construction that can approximate the time–energy canonical commutation relation. We derive equations of motion for the case in which the system is in a pure and mixed state, obtaining a Schrödinger-like equation that leads to a non-linear equation exhibiting decoherence due to the non-ideal nature of the clock and gravitational coupling. A distinctive feature of this equation is that it exhibits dependence on the system’s initial conditions. Full article

This study first situates the discourse on Proudhon’s federalism and nationalism within the framework of his comprehensive economic, social, and philosophical system. Proudhon attempts to construct a federalism based on an associational and decentralized political structure that could accommodate plural groups and avoid the exclusive interpretation of sovereignty that prevailed in nationalism at the time. Such federalism is not only a design of political institutions but also a reflection of his economic mutualism and the idea of commutative justice. Then, this study proposes a relatively concise and intuitive dual critique framework to focus on how his federalism directly refutes nationalism. Proudhon’s federalism aims to protect the culture, language, and identity of minority groups from the oppression of the unitary nation-state internally, and advocates the establishment of an external confederation beyond national borders to eliminate national conflicts and achieve universal peace. Full article

Low-cost sensors have demonstrated their advances in acquiring hyper-localized data compared to traditional, high-maintenance air quality monitoring stations. The study aims to leverage the mobility of participants equipped with low-cost wearable monitors (LWMs) by comparing their exposure to particulate matter (PM) across indoor-home, outdoor-walking, and hybrid-commuting micro-environments. The LWMs would be calibrated first through field co-location and the multiple linear regression models. The coefficient of determination (R²) of PM_1.0 and PM_2.5 increased to over 0.85 after calibration, along with the reduced root mean square error of 2.25 and 3.46 μg/m³, respectively. The 26-day PM data collection with geographic locations could identify individual exposure patterns, local source contributions, and hotspot maps. Commuting constituted a small fraction of daily time (4–8%) but contributed a disproportionate impact, accounting for 11% of individual PM exposure. Indoor-home PM_2.5 exposure varied significantly among the urban districts. Based on the PM_2.5 hotspot map, the elevated concentration was mainly concentrated in dense residential areas and historical industrial areas, as well as interchanges of major roads and the highway system. LWMs acting as non-regulatory instruments can complement monitoring stations to provide missing short-term and hyper-localized air pollution data. Future studies should integrate long-term monitoring and citizen science across seasons and geographical regions to address pollutant spatiotemporal variability for building and city sustainability. Full article

Against the backdrop of rapid new urbanisation and the ongoing integration of urban and rural areas, the evolving spatial dynamics between public service facilities and population distribution have increasingly garnered scholarly interest. The present study employs a grid-based spatial unit and a coupling coordination model as a foundation. This model integrates POI data, Baidu heat maps, and other sources of spatial and temporal information. The objective is to explore the dynamic matching pattern of public service facilities and population distribution. The study’s findings are as follows: The population within the core urban area displays a strong propensity for agglomeration during the morning and evening peak hours, thereby forming a highly coordinated public service network characterised by high-density and piecemeal distribution of public service facilities. The population residing within the transition zone between urban and rural areas is commuting in a substantial number, and the relationship between the supply of and demand for facilities demonstrates cyclical fluctuations. Local areas are subject to time-periodic pressure on the supply of and demand for facilities. In rural areas, due to the continuous population outflow and dispersed residence, the layout of service facilities is fragmented, exhibiting the island effect. The study reveals a structural contradiction between traditional homogeneous planning and the gradient difference between urban and rural areas, providing a scientific basis for Shandong Province to promote new urbanisation and rural revitalisation strategies in an integrated manner. Full article

Traffic congestion remains a major urban challenge, impacting economic productivity, environmental sustainability, and commuter well-being. This systematic review investigates how artificial intelligence (AI) techniques contribute to detecting traffic congestion. Following the SPAR-4-SLR protocol, we analyzed 44 peer-reviewed studies covering three data categories—spatiotemporal, probe, and hybrid/multimodal—and four AI model types—shallow machine learning (SML), deep learning (DL), probabilistic reasoning (PR), and hybrid approaches. Each model category was evaluated against metrics such as accuracy, the F1-score, computational efficiency, and deployment feasibility. Our findings reveal that SML techniques, particularly decision trees combined with optical flow, are optimal for real-time, low-resource applications. CNN-based DL models excel in handling unstructured and variable environments, while hybrid models offer improved robustness through multimodal data fusion. Although PR methods are less common, they add value when integrated with other paradigms to address uncertainty. This review concludes that no single AI approach is universally the best; rather, model selection should be aligned with the data type, application context, and operational constraints. This study offers actionable guidance for researchers and practitioners aiming to build scalable, context-aware AI systems for intelligent traffic management. Full article

Historically, traditional transportation planning has promoted public policies focused on building and maintaining infrastructure for private cars to improve travel efficiency. This approach presents a significant challenge for cities in the Global South due to their unique socioeconomic conditions and urban development patterns. Dedicated public transport infrastructure can make better use of the road network by moving more people and reducing congestion. Beyond its environmental benefits, it also provides the population with greater accessibility, creating new development opportunities. This study uses Mexicali, Mexico, a medium-sized city with dispersed urban growth and a high dependence on cars, as a case study. The goal is to identify the relationship between the supply of public bus routes and actual work-related commuting patterns. The methodology considers that, given the scarcity of economic resources and prior studies in the Global South, using Geographic Information Systems (GIS) for the spatial analysis of travel is a key tool for redesigning more inclusive and sustainable public transport systems. Specifically, this study utilized origin–destination survey data from 14 urban areas to assess modal coverage, work-related commuting patterns, and the spatial distribution of employment centres. The findings reveal a marked misalignment between the existing public transport network and the population’s travel needs, particularly in marginalized areas. Users face long travel times, multiple transfers, low service frequency, and limited connectivity to key employment areas. This configuration reinforces an exclusionary urban structure, with negative impacts on equity, modal efficiency, and sustainability. The study concludes that GIS-based spatial analysis generates sufficient evidence to redesign the public transport system and reorient urban mobility policy toward sustainability and social inclusion. Full article

This paper investigates the associations between urban form, neighbourhood characteristics, socioeconomic factors and commuting mode choice and neighbourhood-level active travel (walking and cycling) in Windhoek, Namibia. Despite growing interest in sustainable mobility, limited research has examined these relationships in medium-sized African cities, particularly in distinguishing between commuting and neighbourhood travel behaviour. To address this gap, the study explores three interrelated research questions: (1) In what ways are urban form, accessibility, and socioeconomic factors associated with residents’ choices between motorised and non-motorised commuting modes? (2) What factors determine the propensity of cycling within neighbourhoods? (3) How are similar factors associated with walking propensity at the neighbourhood level? Using survey data from 1000 residents across nine constituencies and spatial analysis through GIS, the study applies binary logistic and multiple linear regression models to analyse commuting and local travel patterns. The findings show that commuting mode choice is significantly associated with socioeconomic status, car ownership, commuting time, and urban sprawl around homes, all of which reduce the likelihood of walking or cycling. Neighbourhood walking, in contrast, is largely driven by necessity in underserved, high-density areas and is positively associated with population density, perceived safety, and community belonging but constrained by inadequate infrastructure and car access. Cycling, though less frequent, is associated with perceived security, access to local amenities, and cycling competence, while negatively constrained by inexperience and cultural norms. The study concludes that fragmented urban form and socioeconomic disparities reinforce mobility exclusion and calls for equity-oriented transport planning that integrates infrastructure and behavioural change. Full article

Individuals are routinely exposed to traffic-related air pollution on their commutes, which has significant health impacts. Mitigating exposure to traffic-related pollution is a key urban sustainability concern. In Denver, Colorado, low-income Americans are more likely to rely on buses and spend time waiting at bus stops. Evaluating the contribution of traffic emissions at bus stops can provide important information on risks experienced by these populations. We measured PM_2.5 constituents at eight bus stops and one background reference site in Denver, in the summer of 2023. Source profiles, including gasoline emissions from traffic, were estimated using Positive Matrix Factorization (PMF) analysis of PM_2.5 constituents collected at a Chemical Speciation Network site in our study region. The contributions of the different sources at each bus stop were estimated by regressing the vector of species concentrations at each site (dependent variable) on the source-profile matrix from the PMF analysis (independent variables). Traffic-related emissions (~2.5–6.6 μg/m³) and secondary organics (~3–5 μg/m³) contributed to PM_2.5 at the bus stops in our dataset. The highest traffic-related emissions-derived PM_2.5 concentrations were observed at bus stops near local sources: a gas station and a car wash. The contribution of traffic-related emissions was lower at the background site (~1 μg/m³). Full article

In order to analyze and solve the problem of excessive commutation spark of DC motor in ship electric propulsion system, which leads to a decrease in output power and low torque, this paper first establishes a mathematical model of the ship DC motor, builds its simulation model based on the mathematical model, and conducts simulation verification. Secondly, the Cassie arc model is introduced to model the commutation spark, and the Cassie arc model is connected in series in the armature winding of the DC motor to achieve virtual injection of excessive spark fault of the DC motor. Finally, the Fourier transform and wavelet analysis are used to process the data of the armature winding current and excitation current of the DC motor. The simulation results show that when an arc fault occurs in the DC motor, the ripple coefficient of the armature current and excitation current will increase, and the high-frequency component will increase. DB8 is an adopted wavelet function that decomposes the armature current and excitation current six times, and calculates the energy changes before and after the fault of each decomposed signal layer. It is found that without considering the approximate components, the D4 layer wavelet energy of the armature current and excitation current has the largest proportion in the detail components. The D1, D2, and D3 layers’ wavelet decomposition signals of the armature current and excitation current have significant energy changes; that is, the energy increase in the middle and high frequency parts exceeds 20%, and the D3 layer wavelet decomposition signal has the largest energy change, exceeding 40%. This can be used as a fault characteristic quantity to determine whether the DC motor has a large spark fault. This study can provide reference and guidance for online detection technology of excessive sparks in ship DC motors. Full article