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The current study explored the experiences of Asian International Students (AISs) in terms of mental health, disclosure and help-seeking within Higher Education in Scotland, UK. A qualitative study using individual semi-structured interviews with AISs (n = 20) was used and an inductive thematic approach to analysis was conducted. Three major themes were developed: (1) Negative beliefs, stigma and fear of judgment, (2) Adaptation and acculturation difficulties and (3) Barriers in communication, social disconnection and loneliness. Supporting AISs involves challenging negative judgements surrounding mental health, increasing mental health literacy and addressing barriers that may inhibit disclosure and help-seeking behaviour. The need for culturally sensitive mental health practitioners and awareness of diverse understandings of mental health issues is essential to improving support for AISs. [ABSTRACT FROM AUTHOR]

Abstract Visual assessment, while the primary method for pigmentation and erythema evaluation in clinical practice, is subjective, time-consuming, and may lead to variability in observations among clinicians. Objective and quantitative techniques are required for a precise evaluation of the disease's severity and the treatment's efficacy. This research examines the precision and utility of a newly developed skin imaging system in assessing pigmentation and erythema. Sixty participants were recruited, and their facial images were analyzed with the new OBSERV 520 x skin imaging system, compared to DERMACATCH for regional analysis and VISIA for full-face examination. The degree of skin pigmentation was clinically graded using the MASI scores evaluated by dermatologists. The data revealed positive correlations between the novel skin imaging system and the two conventional instruments in quantifying pigmentation and erythema, whether in regional or full-face analysis. Furthermore, the new skin imaging system positively correlated with the clinical MASI scores (r = 0.4314, P

The fragmented design of intelligent transportation systems creates isolated intelligent systems. Resource competition and information gaps are fierce and widespread, worsening traffic issues and degrading overall service levels. Therefore, empowered by advanced technologies, an evolution toward an autonomous transportation system (ATS) is observed. This evolution aims to develop a collaborative and sustainable ecosystem, prompting interoperability within the cloud-edge-device continuum. It can, accordingly, dismantle internal resource barriers and achieve a systematic balance between demand and supply with less human intervention. Despite the promising vision of an ATS, it encounters three key challenges: disparate data, deficient models, and conflicting interests in supporting autonomous and personalized mobility. Hence, as an innovative solution, a trustworthy, private, and equal-serving framework called TPE is designed. It seamlessly integrates blockchain, federated learning, and large-scale models to deploy a trustworthy operating environment, process private data for globally shareable knowledge, and develop a foundation model for personalized adaptation, respectively. Consequently, ATSs empowered by TPE can serve diverse user groups both privately and equally.

Reconfigurable metasurface, also known as Reconfigurable Intelligent Surfaces (RIS), with its flexible beamforming, low-cost, and easy industrial deployment characteristics, presents many interesting solutions in wireless application scenarios. This paper presents a sophisticated reconfigurable metasurface architecture that introduces an advanced concept of flexible full-array space-time wavefront manipulation with enhanced dynamic capabilities. The practical 2-bit phase-shifting unit cell on the RIS is distinguished by its ability to maintain four stable phase states, each with 90° differences, and features an insertion loss of less than 0.6 dB across a bandwidth of 200 MHz. All reconfigurable unit cells are equipped with meticulously designed control circuits, governed by an intelligent core composed of multiple Micro-Controller Units (MCUs), enabling rapid control response across the entire RIS array. Owing to the capability of each unit cell on the metasurface to independently switch states, the entire RIS is not limited to controlling general beams with specific directional patterns but also generates beams with more complex structures, including multi-focus 3D spot beams and vortex beams. This development substantially broadens its applicability across various industrial wireless transmission scenarios. Moreover, by leveraging the rapid-respond space-time coding and the full-array independent programmability of the RIS prototyping operating at 10.7 GHz, we have demonstrated that: 1) The implementation of 3D spot beams scanning facilitates dynamic beam tracking and real-time communication under the indoor near-field scenario; 2) The rapid wavefront rotation of vortex beams enables precise modulation of signals within the Doppler domain, showcasing an innovative approach to wireless signal manipulation; 3) The beam steering experiments for blocking users under outdoor far-field scenarios, verifying the beamforming capability of the RIS board.

The positioning accuracy of a reconfigurable intelligent surface (RIS) assisted indoor positioning system (IPS) can be improved by developing a fingerprinting database for different RIS configurations. Every RIS configuration generates a different radio map of the indoor environment such that the variations in the received power are used to localize unknown receivers. However, creating a diverse fingerprinting database is challenging and time consuming. To this end, we compare three end-to-end (E2E) propagation modeling techniques that include a full-wave electromagnetic (EM) simulator-based model (FWS-E2E), and two hybrid models called HYB1-E2E and HYB2-E2E. The FWS-E2E technique models the RIS and the entire indoor environment in a full-wave EM simulator. On the other hand, the hybrid techniques mostly rely on analytical equations while using some important data from the EM simulator. In this paper, we also discuss methods and algorithms to identify useful RIS configurations having the potential to generate diverse radio maps for increasing the positioning accuracy of the IPS. Both hybrid methods significantly reduce the complexity of generating the radio map. Experimental results are also provided to compare the performance of the E2E models.

The concept of the peer-to-peer local energy market (P2P LEM) is no longer novel to the energy community. Yet, its large-scale implementation within the current electricity network remains a complex challenge. One key reason is the lack of understanding of the supplier licensing models in different countries. For instance, in the UK, up to year 2023, a consumer is only allowed to have a single supplier at a time under its single licence supplier model. This directly contradicts the existing P2P trading models that allow a consumer to purchase electricity from multiple sellers within the local market. Given this context, this article conducts a review of recent literature and government policies in different countries on the P2P LEM and identifies the barriers behind the lack of large-scale P2P trading implementation in today’s electricity markets. We explain how these barriers can be overcome by engaging prosumers in traditional and private distribution networks through either licensed or license-exempt suppliers. Particularly, we discuss six P2P LEM frameworks that can be utilised to address the supplier licensing issue. Finally, this review presents a summary of risks, and recommendations to aid the regulatory framework to implement P2P LEM.

The Intelligent Transportation System (ITS) has the potential to significantly improve communication reliability, efficiency, and manageability in transportation systems. Vehicular ad hoc networks (VANETs) are particularly valuable for sustaining ITS features in dynamic, high-density environments. Integrating the Internet of Things (IoT) into modern transportation systems can further enhance ITS capabilities by providing real-time data and connectivity. Challenges like insufficient spectrum utilization and increased end-to-end latency in single-radio data transmission systems have led to the development of advanced techniques such as dual-radio multi-channel systems. However, these approaches often compromise network performance due to interference. To address these limitations, this paper proposes drone-assisted cooperative routing (DACR), incorporating IoT elements for improved connectivity and data exchange. Performance evaluation, based on comparative analysis with existing state-of-the-art schemes, indicates that the proposed DACR outperforms in packet delivery ratio, end-to-end delay, and control message overhead. DACR demonstrates a 22% and 52% reduction in control message overhead compared to U2RV and CRRA, a 23% improvement in ETE delay over U2RV, a 33% improvement over CRRA, and a 14.5% and 44% increase in PDR, respectively.

Unprecedented growth in wireless data traffic, and ever-increasing demand for highly secured, and low-latency wireless communication has motivated the research community to move towards sixth-generation (6G) technology, where networks can cater to the rising need for ubiquitous secure wireless connectivity. One of the promising technologies for 6G wireless communication is the reconfigurable intelligent surface (RIS) concept that is proposed to successfully deal with increasing security threats by smartly controlling the wireless channel conditions. This survey paper presents a detailed literature review on RIS-assisted physical layer security (PLS) for next-generation wireless communications. Firstly, we briefly discuss the PLS concept, its importance, the PLS performance metrics, and its applicability in different wireless networks. Next, we discuss the applications of RIS in the 6G scenario. Then, a detailed and systematic classification of the various RIS-assisted wireless system topologies exhibiting multiple scenarios, system models, channel fading models, performance metrics and objectives is done. The existing state-of-art approaches for PLS such as secret key generation (SKG), optimization algorithms, namely semidefinite relaxation-successive convex approximation (SDR-SCA) to optimize RIS coefficients, and optimal placement of RIS units are discussed for single-input single-output (SISO) case. For multiple-input single-output (MISO) case, PLS strategies such as inducing artificial noise (AN), optimization algorithms, alternating optimization (AO), machine learning (ML) and deep learning (DL), and reflect matrices are discussed. Similarly, for multiple-input multiple-output (MIMO) setup, block coordinate descent (BCD) and AN induction are some of the PLS methods used to analyze the secrecy. Lastly, we present some of the technical challenges and future directions based on the survey.

In this paper, we consider a stacked intelligent metasurface (SIM) with the ability to perform beamforming in the electromagnetic (EM) wave domain. We develop a path-loss model that allows us to compute the received power of the signal after passing through the SIM. Based on the proposed path-loss model, we formulate an optimization problem to maximize the power at a desired target location in space. We develop a gradient ascent algorithm that can be applied when the phases of the meta-atoms of the SIM can be continuously varied. Also, we develop an alternating optimization (AO) algorithm for the same problem when the meta-atoms can only apply discrete phase shifts. In addition, we formulate an optimization problem whose objective is to produce a given target radiation pattern on a 2D plane located at a certain distance from the center of the SIM. The corresponding algorithms for the continuous and discrete values for the transmission coefficients applied by the SIM are provided. We show that, thanks to the use of multiple layers, complex target radiation patterns in a 2D plane are easily generated. For continuous-valued transmission coefficients, more than 90% of the radiated power is concentrated at the desired points with only three layers. For discrete-valued transmission coefficients with two phase shifts, we show, on the other hand, that nine layers are required to concentrate 90% of the power towards the desired locations. Compared to a single-layer SIM, notably, the power ratio increases by almost 50% by using only three layers.

Vitiligo, a psychologically distressing pigmentary disorder characterized by white depigmented patches due to melanocyte loss, necessitates non-invasive tools for early detection and treatment response monitoring. High-cellular-resolution full-field optical coherence tomography (CRFF-OCT) is emerging in pigmentary disorder assessment, but its applicability in vitiligo repigmentation after tissue grafting remains unexplored. To investigate the feasibility of CRFF-OCT for evaluating vitiligo lesions following tissue grafting, our investigation involved ten vitiligo patients who underwent suction blister epidermal grafting and laser ablation at a tertiary center between 2021 and 2022. Over a six-month period, clinical features, dermoscopy, and photography data were recorded. Utilizing CRFF-OCT along with artificial intelligence (AI) applications, repigmentation features were captured and analyzed. The CRFF-OCT analysis revealed a distinct dark band in vitiligo lesion skin, indicating melanin loss. Grafted areas exhibited melanocytes with dendrites around the epidermal-dermal junction and hair follicles. CRFF-OCT demonstrated its efficacy in the early detection of melanocyte recovery and accurate melanin quantification. This study introduces CRFF-OCT as a real-time, non-invasive, and in vivo evaluation tool for assessing vitiligo repigmentation, offering valuable insights into pigmentary disorders and treatment responses.

Fused in sarcoma (FUS) is an intrinsically disordered RNA-binding protein that helps to regulate transcription and RNA transport while reversibly assembling into membraneless organelles (MLOs). Some mutations of FUS can promote irreversible aggregation, contributing to neurodegenerative diseases. We previously reported a multi-scale computational framework combining a series of molecular dynamics simulations (MD) followed by lattice Monte Carlo (MC) simulations to describe the tendency and dynamics of the assembly and disassembly of intrinsically disordered proteins (IDPs) using wild-type (WT)-FUS as an illustrative example. In this study, we utilized our computational model to simulate three FUS mutants widely experimented with glycine point mutation G156E, arginine point mutation R244C, and deletion of the C-terminal nuclear localization signal (ΔNLS). MD simulation results conveyed that G156E has improved sticker contact probability compared to WT-FUS, while R244C has slightly lower contact probability, which is also complemented by change of net interactions according to the molecular mechanics Poisson Boltzmann surface area (MMPBSA) method. The MC simulation results revealed that G156E has a higher aggregation propensity than the WT-FUS, while ΔNLS has more liquid-like assemblies. R244C demonstrated higher dynamics at the beginning, while over the evolution of MC simulations, it tends to aggregate compared to WT-FUS. In addition, the G156E mutant has more stable protein aggregates, lacking the rapid dynamics shown in all other scenarios. From the peak height of radial distribution functions (RDFs) of the assemblies, the phase separation propensity in ascending order is ΔNLS < FUS-WT < R244C < G156E. Moreover, interpreting the dynamic assembly propensity (DAP) parameter over time, the fluidity of the assemblies in ascending order is G156E < FUS-WT < R244C < ΔNLS. The results obtained from this study support that the computational model is able to predict the effect of mutation down to single amino acid substitution on the phase separation behavior of FUS. This efficient in silico method can be generalized to investigate the phase separation propensity of other IDPs and their mutants.

Background: Mild cognitive impairment (MCI) is the transition stage between the cognitive decline expected in normal aging and more severe cognitive decline such as dementia. The early diagnosis of MCI plays an important role in human healthcare. Current methods of MCI detection include cognitive tests to screen for executive function impairments, possibly followed by neuroimaging tests. However, these methods are expensive and time-consuming. Several studies have demonstrated that MCI and dementia can be detected by machine learning technologies from different modality data. This study proposes a multi-stream convolutional neural network (MCNN) model to predict MCI from face videos., Results: The total effective data are 48 facial videos from 45 participants, including 35 videos from normal cognitive participants and 13 videos from MCI participants. The videos are divided into several segments. Then, the MCNN captures the latent facial spatial features and facial dynamic features of each segment and classifies the segment as MCI or normal. Finally, the aggregation stage produces the final detection results of the input video. We evaluate 27 MCNN model combinations including three ResNet architectures, three optimizers, and three activation functions. The experimental results showed that the ResNet-50 backbone with Swish activation function and Ranger optimizer produces the best results with an F1-score of 89% at the segment level. However, the ResNet-18 backbone with Swish and Ranger achieves the F1-score of 100% at the participant level., Conclusions: This study presents an efficient new method for predicting MCI from facial videos. Studies have shown that MCI can be detected from facial videos, and facial data can be used as a biomarker for MCI. This approach is very promising for developing accurate models for screening MCI through facial data. It demonstrates that automated, non-invasive, and inexpensive MCI screening methods are feasible and do not require highly subjective paper-and-pencil questionnaires. Evaluation of 27 model combinations also found that ResNet-50 with Swish is more stable for different optimizers. Such results provide directions for hyperparameter tuning to further improve MCI predictions., (© 2024. The Author(s).)

Flow-diverter stents offer clinicians an effective solution for treating intracranial aneurysms, especially in cases where other devices may be unsuitable. However, strongly deviating success rates among different centres, manufacturers, and aneurysm phenotypes highlight the need for better in-situ studies of these devices. To support research in this area, virtual stenting algorithms have been proposed that, combined with computational fluid dynamics, provide insights into the hemodynamic alterations induced by the device. Yet, many existing algorithms rely on uncertain parameters, such as the forces applied during operation, fail to predict the length of the device after deployment, or lack robust validation steps, raising concerns about their reliability. Therefore, we developed a robust deployment technique that builds upon the geometrical features of the vessel and includes advancements from previous works. The algorithm is detailed and validated against literature examples, in-vitro experiments, and patient data, achieving a mean angular error below 5° in the latter. Furthermore, we describe and demonstrate how the deployed device can be embedded in a computational mesh using open-source tools and anisotropic meshing routines., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Advances in computational fluid dynamics continuously extend the comprehension of aneurysm growth and rupture, intending to assist physicians in devising effective treatment strategies. While most studies have first modelled intracranial aneurysm walls as fully rigid with a focus on understanding blood flow characteristics, some researchers further introduced Fluid-Structure Interaction (FSI) and reported notable haemodynamic alterations for a few aneurysm cases when considering wall compliance. In this work, we explore further this research direction by studying 101 intracranial sidewall aneurysms, emphasizing the differences between rigid and deformable-wall simulations. The proposed dataset along with simulation parameters are shared for the sake of reproducibility. A wide range of haemodynamic patterns has been statistically analyzed with a particular focus on the impact of the wall modelling choice. Notable deviations in flow characteristics and commonly employed risk indicators are reported, particularly with near-dome blood recirculations being significantly impacted by the pulsating dynamics of the walls. This leads to substantial fluctuations in the sac-averaged oscillatory shear index, ranging from -36% to +674% of the standard rigid-wall value. Going a step further, haemodynamics obtained when simulating a flow-diverter stent modelled in conjunction with FSI are showcased for the first time, revealing a 73% increase in systolic sac-average velocity for the compliant-wall setting compared to its rigid counterpart. This last finding demonstrates the decisive impact that FSI modelling can have in predicting treatment outcomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Goetz, Jeken-Rico, Pelissier, Chau, Sédat and Hachem.)

Background: The Low profile visualized intraluminal support (LVIS)/LVIS Jr is a self-expanding braiding stent for the treatment of intracranial aneurysm. This study is to determine the safety and effectiveness of the LVIS/LVIS Jr for the treatment of intracranial aneurysms in a real-world setting., Methods: This prospective, observational, multicenter study enrolled patients with unruptured, ruptured and recanalized intracranial aneurysms treated with the LVIS stents, between February 2018 to December 2019. Primary endpoint was the cumulative morbidity and mortality rate (CMMR) assessed at 12 months follow-up (FU)., Results: A total of 130 patients were included (62.3 % women, mean age 55.9 ± 11.4) on an intention-to-treat basis. Four patients (3.1 %) had 2 target aneurysms; 134 total aneurysms were treated. The aneurysms were mainly located on the middle cerebral artery (41/134; 30.6 %) and the anterior communicating artery (31/134; 23.1 %). The CMMR at 1 year linked to the procedure and/or device was 4.6 % (6/130). The overall mortality was 1.5 % (2/130), none of these deaths adjudged as being linked to the procedure and/or device. All aneurysms (134/134, 100 %) were successfully treated with LVIS stent and/or other devices. At a mean FU of 16.8 months post-procedure, complete/nearly complete occlusion was achieved in 112 aneurysms (92.6 %), and only 3 patients (2.5 %) required aneurysm retreatment., Conclusion: This study provides evidence that the LVIS/LVIS Jr devices are safe and effective in the treatment of complex intracranial aneurysms, with very high rates of adequate occlusion at FU. These angiographic results are stable over time with an acceptable complication rate., Trial Registration: ClinicalTrial.gov under NCT03553771., Competing Interests: Declaration of Competing Interest MP reports support for attending meetings and/or travel from Balt, and stock or stock options (Basecamp Vascular, Synchron, Radical Catheter, Vastrax, Intradys). LP reports consulting fees from Balt, Microvention, Phenox. FC reports consulting fees from Balt, Medtronic, Microvention, Stryker, stock or stock options (Collavidance, Intradys), and leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid (Artedrone). PM reports consulting fees from Medtronic, Stryker, Artiria, payment to his institution (Codman), and served as chairman of the adverse events monitoring committee for this study, sponsored by Microvention. GM reports consulting fees from Microvention, Balt, Stryker, honoraria for lectures (Medtronic, Johnson & Jonhson). AR reports consulting fees from Balt. The other authors report no conflicts., (Copyright © 2023 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

The reversible assembly of intrinsically disordered proteins (IDPs) to form membraneless organelles (MLOs) is a fundamental process involved in the spatiotemporal regulation in living cells. MLOs formed via liquid-liquid phase separation (LLPS) serve as molecule-enhancing hubs to regulate cell functions. Owing to the complexity and dynamic nature of the protein assembly via a network of weak inter- and intra-molecular interactions, it is challenging to describe and predict the LLPS behavior. We have developed a multiscale computational model for IDPs, using the fused in sarcoma (FUS) protein and its variants as illustrative examples. To simplify the description of protein, FUS is represented as a linear chain of stickers interspaced by spacers, as inspired by the associative polymer theory. Low-complexity aromatic-rich kinked segments (LARKS) available in FUS were identified using LARKSdb and represented as "stickers". The pairwise potential energies of each pair of stickers and their β-sheet-forming propensity were estimated via molecular docking and all atomistic molecular dynamics (AA-MD) simulations. Subsequently, FUS chains were randomly positioned in a cubic lattice as coarse-grained (CG) beads, with the bead assignment based on the Kuhn length estimation of stickers and spacers. Stochastic FUS movements were modeled by Monte Carlo (MC) simulations. In addition to the Metropolis algorithm, discretized pair potential distributions between stickers were considered in the move acceptance criteria. The chosen pair potential represents one of the possible binding energy states, with its probability determined by the frequency of the binding energy distribution histogram. The fluctuations of averaged radial distribution functions (RDFs) in successive MC trial move intervals of equilibrated lattice MC simulations were used to indicate the dynamic nature of assembly/disassembly of the protein chains. This multiscale computational framework provides an economical and efficient way of predicting and describing the LLPS behavior of IDPs.

In the development of cancer vaccines, antigens are delivered to elicit potent and specific T-cell responses to eradicate tumour cells. Nonetheless, successful vaccines are often hampered by the poor immunogenicity of tumour antigens, rapid clearance by the innate immunity, and limited cross-presentation on MHC-I to activate CD8 + T-cells arm. To address these issues, we developed dextran-based nanogels to promote antigen uptake, storage, and cross-presentation on MHC-I, while directing immunogenic maturation of the antigen-presenting cells (APCs). To promote the nanocarriers interaction with cells, we modified DX with L-arginine (Arg), whose immunomodulatory activities have been well documented. The ArgDX nanogel performance was compared with the nanogel modified with L-histidine (His) and L-glutamate (Glut). Moreover, we introduced pH-sensitive hydrazone crosslinking during the nanogel formation for the conjugation and controlled release of antigen ovalbumin (OVA). The OVA-laden nanogels have an average size of 325 nm. We demonstrated that the nanogels could rapidly release cargoes upon a pH change from 7 to 5 within 8 days, indicating the controlled release of antigens in the acidic cellular compartments upon internalization. Our results revealed that the ArgDX nanogel could promote greater antigen uptake and storage in DCs in vitro and promoted a stronger immunogenic maturation of DCs and M1 polarization of the macrophages. The OVA signals were co-localized with lysosomal compartments up till 96 hours post-treatment and washing, suggesting the nanogels could facilitate prolonged antigen storage and supply from endo-lysosomal compartments. Furthermore, all the tested nanogel formulations retained antigens at the skin injection sites until day 21. Such delayed clearance could be due to the formation of micron-sized aggregates of OVA-laden nanogels, extending the interactions with the resident DCs. Amongst the amino acid modifications, ArgDX nanogels promoted the highest level of lymph node homing signal CCR7 on DCs. The nanogels also showed higher antigen presentation on both MHC-I and II than DX in vitro . In the in vivo immune studies, ArgDX nanogels were more superior in inducing cellular and humoral immunity than the other treatment groups on day 21 post-treatment. These results suggested that ArgDX nanogel is a promising self-adjuvanted nanocarrier for vaccine delivery.

Computational fluid dynamics is intensively used to deepen our understanding of aneurysm growth and rupture in an attempt to support physicians during therapy planning. Numerous studies assumed fully rigid vessel walls in their simulations, whose sole haemodynamics may fail to provide a satisfactory criterion for rupture risk assessment. Moreover, direct in vivo observations of intracranial aneurysm pulsation were recently reported, encouraging the development of fluid-structure interaction for their modelling and for new assessments. In this work, we describe a new fluid-structure interaction functional setting for the careful evaluation of different aneurysm shapes. The configurations consist of three real aneurysm domes positioned on a toroidal channel. All geometric features, employed meshes, flow quantities, comparisons with the rigid wall model and corresponding plots are provided for the sake of reproducibility. The results emphasise the alteration of flow patterns and haemodynamic descriptors when wall deformations were taken into account compared with a standard rigid wall approach, thereby underlining the impact of fluid-structure interaction modelling.