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Comparing the uplink performance of 3D and 2D antenna models in THz networks in the presence of joint human and wall blockages
Terahertz (THz) communication is considered as a key technology enabler for realizing Sixth Generation (6G) network. THz band communication offers several promising advantages, but numerous challenges are expected due to the inherent limitations of propagation at THz frequencies in the 6G network, such as path loss, interference, human and wall blockages, etc. In retrospect, THz band communication finds its use in indoor network deployments. In this paper, a framework is developed to analyze the impact of the uplink performance of a single-tier THz network, incorporating the impact of wall and human blockages in the indoor environment. To model a practical system, 3D antenna model have been employed, which accounts for both horizontal and vertical radiation patterns, whose performance have been benchmarked against 2D antenna model that accounts only for horizontal direction. This evaluation has enabled us to highlight the impact of practical antenna models on THz communication performance. Using the developed system model, generalized expressions for uplink mean interference, uplink coverage probability, and area spectral efficiency have been derived. The impact of THz uplink network performance has been analyzed using an antenna model with varying user equipment heights and different main lobe beam widths, as well as considering different path loss exponents for Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions. The analytical results obtained against different network conditions have been compared and validated against Monte Carlo simulations and both have been found in agreement.
Representing national images of self and others through China’s diplomatic discourse: A corpus-based study
Drawing on van Dijk’s Ideological Square framework, this paper adopts a corpus-based method to examine the discursive strategies in their responses by the spokespersons for China’s Ministry of Foreign Affairs during regular press conferences amid a public health crisis. The analysis focuses on how these discursive strategies shape the national images of China and the other four permanent members of the United Nations Security Council. The results show that (1) the spokespersons actively employed communicative discursive strategy to clarify China’s stance and international cooperation initiatives while also using offensive discourse strategy to counter criticisms from US-led Western nations and media regarding the virus and the pandemic; (2) although the spokespersons’ discourse generally aligns with van Dijk’s Ideological Square of positive self-presentation and negative other-presentation, this model is not fixed but subject to dynamic changes driven by the self-serving principle. It is argued that factors such as diplomatic ideology, geopolitical relations, and traditional Chinese culture underlie the spokespersons’ use of discursive strategies and national images representations. This study contributes to reconceptualizing an existing discourse model by offering data-driven insights into the operational mechanisms of ideological discourse in the contexts of global political communication and national image construction.
Joint optimization of task offloading and energy trading in edge-enabled smart grids using deep reinforcement learning
The proliferation of distributed energy resources (DERs) and the ubiquity of Internet of Things (IoT) devices are driving the integration of mobile edge computing (MEC) into smart grids. This convergence enables real-time data processing for prosumers but introduces a complex cyber-physical coupling: computational offloading decisions directly impact local energy consumption, thereby altering the prosumer’s status in the peer-to-peer (P2P) energy market. Conversely, dynamic market prices influence the economic viability of offloading. This paper addresses the joint optimization of computational task offloading and P2P energy trading in an edge-assisted smart grid ecosystem. We formulate the problem as a mixed-integer nonlinear programming (MINLP) model aimed at maximizing long-term system utility, balancing throughput, latency, and economic incentives under strict edge server capacity and community energy neutrality constraints. To tackle the curse of dimensionality and system stochasticity, we propose a hybrid framework combining Deep Q-Networks (DQN) with a constraint-aware heuristic mechanism. The DQN agent learns adaptive offloading policies from high-dimensional states, while a deterministic rule-based layer ensures strict adherence to community energy balance. Simulation results based on real-world solar generation and market data demonstrate that our proposed method outperforms baseline strategies—including local-only execution and greedy heuristics—improving average utility by 12.3% and reducing task delay by 16.5%, while maintaining robust operational feasibility.
Correction: A new criterion for defining tunnel portal failure using the strength reduction method
Development and validation of search hedges for Transgender and Gender Diverse (TGD) populations in Ovid MEDLINE and Ovid APA PsycInfo
Introduction This paper describes the development and validation of highly sensitive search hedges for Ovid MEDLINE and Ovid APA PsycInfo that effectively identify literature on transgender and gender diverse (TGD) populations. Methods Two librarians developed the search hedges using relevant keywords and controlled vocabulary terms, building on previous work on identifying transgender populations in evidence synthesis. The hedges were tested and refined to capture diverse and expansive gender identities across cultures and disciplines. The hedges were validated for sensitivity using a gold standard set of 144 articles from the Knowsy portal of evidence syntheses tagged as Two-Spirit, transgender, or gender non-binary. To assess precision an international research team of subject experts independently screened a randomized sample of search results in a two-stage screening process with an additional screener resolving disputes. Results The final search hedges demonstrated 100% sensitivity in both MEDLINE and APA PsycInfo, identifying all 144 relevant articles from the Knowsy gold standard set. The MEDLINE search hedge achieved a 71% precision, and the APA PsycInfo hedge achieved a 67% precision. These results balance comprehensive retrieval while minimizing non-relevant articles for an efficient screening process. Conclusions These search hedges in MEDLINE and APA PsycInfo are valuable tools for researchers and librarians to more effectively identify literature on TGD populations. These tools will be crucial for ongoing work in addressing gaps in research and health disparities faced by TGD populations and will be particularly valuable for researchers conducting evidence synthesis projects related to this population.
Real-time detection of rare roadside obstacles using YOLOv8-n in autonomous vehicles
Rare road obstacles, including traffic cones, fallen trees, debris, barrels, and rocks, pose significant safety risks to autonomous vehicles. This paper presents a lightweight real-time detection framework using YOLOv8-n to accurately identify such obstacles on resource-constrained hardware. Multiple open source datasets containing annotated images of rare objects were combined and curated into a unified dataset. The model was refined using transfer learning, and its resilience to changing illumination and partial occlusion was enhanced by data augmentation techniques such brightness fluctuation, rotation, flipping, and geometric distortion. On a mid-range NVIDIA P100 GPU, the model maintained an inference speed of 68 frames per second while achieving a precision of 95.4%, recall of 93.9%, F1-score of 94.6%, and mean average precision (mAP@0.5) of 98.1%. These findings show that the framework is appropriate for edge-based autonomous driving systems where low latency and computational efficiency are crucial since it provides precise real-time detection without the need for expensive hardware.
Unimodal vs. multimodal deep learning for non-invasive MGMT promoter methylation prediction in glioblastoma: A systematic evaluation on the BraTS 2021 dataset
Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor in adults, with a median survival of 14.6 months under standard radiotherapy and temozolomide (TMZ) chemotherapy. The methylation status of the O⁶-methylguanine-DNA methyltransferase (MGMT) promoter is a critical biomarker predicting TMZ response; however, its determination currently requires invasive tissue sampling. Non-invasive prediction of MGMT promoter methylation from multiparametric MRI (mpMRI) through deep learning represents a compelling alternative, yet its clinical feasibility remains unresolved. Using the BraTS 2021 dataset (582 patients, four MRI sequences: FLAIR, T1w, T1wCE, T2w), we conducted a systematic comparative study of unimodal and multimodal deep learning approaches based on VGG-16, exploring 1,380 experimental configurations (unimodal: 192; multimodal: 1,188) across three imaging planes, eight slice counts, and three multimodal fusion strategies (early, intermediate, and late fusion). In the unimodal setting, the best model trained on T2w coronal images (32 slices, no transfer learning) achieved an accuracy of 0.6458 and an AUC of 0.6422 on the validation set, but dropped to 0.5586 and 0.5533 on the independent test set, revealing substantial overfitting attributable to limited dataset size. Strikingly, multimodal fusion consistently failed to outperform the best unimodal model, with all three fusion strategies plateauing at ~0.64 accuracy and ~0.64 AUC on validation data. Transfer learning improved generalization across train/test distributions at the cost of peak performance. These findings suggest, for the tested framework in this study, that MGMT methylation status prediction from mpMRI remains fundamentally constrained by dataset heterogeneity and size, irrespective of modality combination strategy, and that T2w coronal acquisitions could be more interesting in future data collection efforts.
Long-term trends in height, weight and body mass index of children and adolescents in Macao Special Administrative Region (China), 2005–2020
Objective To assess long-term trends in height, weight and body mass index (BMI) among children and adolescents from 2005 to 2020 in Macao Special Administrative Region (SAR), China. Methods Height, weight and BMI data for Macao children and adolescents aged 6–18 years were obtained from the Physical Fitness Reports of Macao SAR Residents in 2005, 2010, 2015, and 2020. Sex-specific two-way analysis of variance was used to estimate the differences in means. The Bonferroni post hoc test was used for multiple comparisons. Results During the entire period, the average height, weight and BMI increased by 2.1 cm (95% confidence interval (CI): 1.6 to 2.6 cm), 4.0 kg (95% CI: 3.2 to 4.8 kg), and 1.1 kg/m 2 (95% CI: 0.8 to 1.3 kg/m 2 ) for boys and 2.4 cm (95% CI: 1.9 to 2.9 cm), 2.6 kg (95% CI: 1.9 to 3.3 kg), and 0.5 kg/m 2 (95% CI: 0.3 to 0.8 kg/m 2 ) for girls, respectively ( p < 0.001). Boys and girls in most age groups experienced significant increases. The greatest increases in height occurred between 2005 and 2010 in both sexes. The weight and BMI of boys have continued to increase. The weight and BMI of girls continued to increase until 2015, and thereafter declined. Conclusion There were positive long-term trends in growth among Macao children and adolescents since 2005. Sex differences in changes of weight and BMI over the past five years may be related to the pandemic, and efforts are needed by governments and public health departments.
Time-to-event ensemble machine learning approach for predicting long-term survival of abdominal aortic aneurysm patients undergoing endovascular aneurysm repair
Background Endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) is associated with risks such as endoleaks and late aneurysm rupture, highlighting the importance of long-term survival prediction. Despite recent advancements in machine learning (ML), predictive models utilizing time-to-event analysis remain limited for AAA patients undergoing EVAR. We aimed to develop a stacking ensemble ML model to predict long-term outcomes in EVAR-treated AAA patients. Methods From 2002 to 2019, a total of 12,312 patients underwent EVAR. The primary outcome was AAA-related mortality, with follow-up until December 31, 2019. Using 5 ML algorithms, we developed a model comprising 34 variables. Model performance was assessed using the time-dependent C-index and Brier score. Variable importance was evaluated through permutation-based and partial dependent plots. Results The stacking ensemble model showed the best predictive performance among the tested models (time-dependent C-index: 0.759 at 30 days, 0.716 at 365 days). The time-dependent Brier scores generally increased slightly over time but remained stable across all ML algorithms. Important predictors included age, smoking status, duration between diagnosis and surgery, household income, renal function, and blood pressure. Variable importance differed over time, and each predictor presented a nonlinear relationship with AAA-related mortality risk. Conclusion The stacking ensemble ML model for time-to-event prediction identified dynamic, time-varying changes in predictor importance, providing improved risk stratification and phase-specific management after EVAR.
Who becomes a dermatologist? A repeated cross-sectional study on diversity in the Dutch dermatology workforce
Background Workforce diversity in dermatology is crucial for equitable, high-quality care, given the impact of skin tone, culture, and socio-economic status on skin conditions. Although this has been studied in other countries, data on the demographic makeup of Dutch dermatologists is lacking. This study aims to assess workforce diversity in Dutch dermatology over time. Methods We conducted a nationwide repeated cross-sectional study using pseudonymized microdata from Statistics Netherlands, including sex, migration background, and parental socio-economic indicators. Descriptive statistics were used to track demographic trends between 2005 and 2023, and multivariable logistic regression analyses were performed to evaluate which variables influenced the odds that registered physicians in the 2023 national healthcare professional register (BIG register) had to be a dermatologist. Results Female representation rose from 35.4% in 2005 to 61.7% in 2023. In 2023, 84.4% of dermatologists had no migration background or a European migration background. Dermatologists with Turkish, Moroccan, Surinamese, or Caribbean Dutch origins were underrepresented. Despite a net increase of 289 dermatologists, only 56 of this net increase consisted of dermatologists with a non-European background. Multivariable regression analysis showed that being female (OR 1.609 [1.143–2.266]), having parents in the top 20% assets bracket (OR 2.251 [1.272–3.984]), or having physician parents (OR 1.326 [1.011–1.740]) were associated with higher odds of being a registered dermatologist among the younger generation of physicians. Conclusions The findings highlight a persistent lack of ethnic and socio-economic diversity in the Dutch dermatology workforce, despite broader demographic shifts in the general population and medical student cohorts. The underrepresentation of dermatologists with a migration background may have implications for equitable patient care, particularly in the context of cultural and linguistic barriers, as well as differences in disease presentation across skin tones. Further research is warranted to explore the potential impact of workforce diversity on patient outcomes.
The Good Life with Dementia approach: A realist-informed qualitative study of a peer-tutored course, co-produced with and for people living with dementia
People with dementia often report a lack of post-diagnostic support, and much of the current dementia training available is for staff or carers, not for the person diagnosed. The Good Life with Dementia course was designed with and for people with a diagnosis of dementia and is co-delivered by peer-tutors living with dementia, supported by a trained facilitator. This study used realist-informed methods, underpinned by a co-productive ethos which values all sources of expertise equally, to better understand the core constructs underpinning the Good Life approach and how these operate to produce outcomes. The resultant, evidence-based programme theory suggests that – in a context characterised by shared experience, equality and positive expectations – three key mechanisms can trigger: sharing of experiences and resources; peer-led learning and responding; and the taking on of meaningful roles. Qualitative evidence indicates that these mechanisms are likely to lead to four interconnected outcomes: enjoyment; feeling valued (personhood); (re)building social confidence and connections; and positive reframing of life with dementia, meaning participants felt more prepared to face the challenges ahead. Not everyone diagnosed with dementia will want to take part in a peer-led course, but interventions like a Good Life with Dementia could be part of a suite of post-diagnostic options available to help people with dementia to live as well as possible. The next step will be to establish whether the approach can be manualised, delivered with different communities and evaluated in trial conditions. This will be assessed via an inclusive feasibility study already underway and due to conclude in August 2027.
Role of Najran University Scholarship Students in the United Kingdom in cultural bridging and civilizational dialog
This study explores the role of Najran University scholarship students in the United Kingdom in promoting cultural bridging and civilizational dialog. To achieve this objective, a structured questionnaire was designed and administered. The instrument demonstrated high validity and reliability, with a Cronbach’s alpha coefficient of 0.879. The study sample consisted of 59 Saudi scholarship students from Najran University currently studying in the United Kingdom. The questionnaire was distributed during the second semester of the 2024–2025 academic year. Taking into consideration the reported limitations of the study, the analysis revealed that Najran University scholarship students in the United Kingdom actively contribute to cultural bridging and civilizational dialog within academic institutions in their host country. Their engagement extends beyond the university setting, encompassing residential life and community interactions, where they help promote Saudi cultural and civilizational values. Furthermore, they play a vital role in fostering cross-cultural relationships with international peers. Statistical analysis revealed no significant differences and very weak effect sizes in terms of gender, age, marital status, academic level, or field of study. Additionally, the study identified key mechanisms and strategies utilized by Najran University scholarship students to introduce and represent Saudi culture and to foster constructive civilizational engagement within the multicultural context of the United Kingdom.
Effects of tibialis anterior contraction on the medial longitudinal arch and hallux valgus angle: A functional anatomical perspective
This study aimed to elucidate the effects of the tibialis anterior (TA) muscle contraction on the medial longitudinal arch (MLA) and hallux valgus (HV) angle, primarily focusing on the potential functional role of the TA in foot alignment. Twenty-five healthy adults (mean age: 20.7 ± 1.0 years) participated. Electrical stimulation was applied to the TA without ankle dorsiflexion. A triaxial accelerometer was attached to the skin over the navicular bone, and electrogoniometers were placed over the interphalangeal joints of the hallux and ankle. The navicular displacement was calculated through double integration of the acceleration data. Contraction of the TA caused navicular displacement in the supination direction (upward, posterior, and medial), suggesting a temporary elevation of the MLA. The hallux showed an average varus shift of 1.36°; the shift was observed in 24 of 25 participants (p < 0.001). A significant positive correlation was found between upward displacement of the navicular bone and the extent of varus change in the HV angle (r = 0.463, p = 0.020). Additionally, a greater initial HV angle was associated with a larger varus shift in the hallux after TA contraction (r = 0.433, p = 0.030). No significant correlations were observed between the HV angle change and ankle dorsiflexion, suggesting that the medial hallux shift may result from structural changes in the MLA rather than isolated joint motion. These findings provide novel evidence that TA contractions can elevate the MLA and induce varus displacement of the hallux. These findings further suggest that the TA contributes to foot biomechanics not only as an isolated muscle but also as part of a coordinated muscle system involved in arch dynamics and toe alignment. This suggests the potential clinical applications of TA activation in noninvasive interventions for mild-to-moderate HV, such as exercise therapy or neuromuscular electrical stimulation.
Antioxidant, antibacterial, in vitro, and in silico α-glucosidase inhibition activities and chemical profiling of Usnea cornuta Korb
The total phenolic content and flavonoid content of the Usnea cornuta extract were evaluated as 210.31 ± 2.87 mg GAE/g and 22.42 ± 0.78 mg QE/g, respectively. The crude extract exhibited strong antioxidant activity (IC 50 : 32.91 ± 1.27 µg/mL) and notable anti-diabetic effects via α-glucosidase inhibition, with IC 50 values of 2.59 ± 2.23 µg/mL for the dichloromethane extract. LC-MS analysis identified eleven metabolites like D-mannitol (1), galbinic acid (2), conhypoprotocetraric acid (3), roccellaric acid (4), diffractatic acid (5), haemathamnolic acid isomer (6), conprotocetraric acid (7), constictic acid I (8), salazinic acid II (9), menegazziaic acid (10), and one unknown compound (11). Among these, menegazziaic acid exhibited the strongest binding affinity of −9.7 kcal/mol with the target (PDB ID 3A4A), favorable molecular dynamics, binding free energy (MM/GBSA, and pharmacokinetic profiles. Furthermore, the extract showed strong antimicrobial activity, with inhibition zones of 23 mm and 26 mm at 10 mg/mL against Staphylococcus aureus ATCC 29213 and ATCC 245, respectively. These findings highlight the therapeutic potential of Usnea cornuta , specifically for managing oxidative stress, microbial infections, and type 2 diabetes.
Conceptual thermal constraints on the growth of the first tree on a terraformed Mars
The environmental conditions on present‑day Mars are far outside the range tolerated by known complex terrestrial life. Conceptual climate studies have suggested that, in hypothetical terraforming scenarios, artificially enhancing the greenhouse effect could restore Mars to more habitable surface conditions. Early colonizing terrestrial life on a warming Mars would plausibly consist of lichens and high‑alpine or high‑arctic plants. Here, we consider a later, more demanding step and investigate the thermal conditions under which the first tree could, in principle, grow on the Martian surface. Based on empirical treeline studies, we adopt thermal thresholds for a representative high‑elevation conifer: a growing season of at least 110 sols during which daily minimum temperatures exceed −6 °C, daily means exceed 6 °C, and daily maxima remain below 40 °C. In addition to liquid water and suitable substrates, O₂ at ~1 hPa and non‑toxic CO₂ levels are likely required; however, these non‑thermal constraints are not explicitly modelled and make the temperature thresholds necessary but not sufficient for tree viability. We use a high‑resolution surface energy balance model of Mars, assuming a pure CO₂ atmosphere with prescribed grey infrared opacity and neglecting the coupled water cycle, full atmospheric dynamics, photochemistry, and surface radiation, to estimate spatio‑temporal thermal windows for potential tree growth as a function of CO₂ surface pressure and additional greenhouse forcing. For a 100 hPa CO₂ atmosphere, near‑surface temperatures satisfying the treeline thresholds first appear when the added grey infrared opacity is ~ 0.39 optical depths. In our simulations, these thermal criteria are initially met not in the tropics (±25°), but in the low‑lying Hellas Basin. As either the CO₂ surface pressure or the imposed grey opacity is increased beyond the values required to open the thermal window, large regions of the southern hemisphere subsequently become thermally overheated and thus unsuitable for tree growth. In this sense, the thermal windows identified in our simulations mark conditions under which temperature would no longer be the primary limiting factor for tree growth, assuming that other essential environmental constraints (such as water availability, radiation environment, substrate properties, and atmospheric composition) are satisfied. We emphasize that this study deals with temperature only, which is an important factor in tree growth on Mars. Other factors that affect tree growth, including water, CO₂ limits, O 2 limit, UV and ionizing radiation, and soil nutrients and microbial population, are not considered explicitly here.
The mTOR/Akt pathway is involved in regulating astrocyte growth and GLT-1 expression during cerebral ischemia-reperfusion
Stroke is one of the most prevalent causes of death and disability worldwide and places a heavy economic burden on families and society. High glutamate accumulation and subsequent excitotoxicity after ischemia and hypoxia are important pathogenetic mechanisms of brain injury after ischemic stroke. Glutamate transporter 1 (GLT-1) on astrocyte membranes is responsible for 90% of glutamate clearance. The involvement of the mTOR/Akt pathway in the upregulation of GLT-1 expression in astrocytes under oxygen-glucose deprivation and reoxygenation conditions has been demonstrated. Nevertheless, it is still unclear whether there is a negative feedback pathway from mTOR to Akt during cerebral ischemia/reperfusion (I/R). It has also not been elucidated whether the mTOR/Akt pathway is involved in the expression of astrocyte GLT-1 in cerebral I/R injury. In this study, we established a middle cerebral artery occlusion-reperfusion rat model to investigate the interactions and mechanisms of the mTOR/Akt cascade with GLT-1 under cerebral I/R conditions. These results provide evidence that brain I/R injury activates the mTOR/Akt pathway in the ischemic penumbra, increases astrocyte activation, and downregulates the expression of GLT-1. Inhibition of the mTOR pathway reversed GLT-1 downregulation and inhibited astrocyte activation by blocking the mTOR pathway, thereby attenuating neurological dysfunction, inflammatory response, and apoptosis caused by brain I/R injury. In contrast, inhibiting the Akt pathway did not provide neuroprotection, with no significant decrease in the number of astrocytes, inflammatory response, or apoptosis in the model group. Additionally, the inhibition downregulated GLT-1 expression and promoted the lengthening and thickening of astrocyte processes in cerebral ischemic rats. Thus, the mTOR/Akt cascade may be involved in regulating astrocyte growth and GLT-1 expression during brain I/R injury. Furthermore, inhibiting the mTOR pathway may mitigate apoptosis and the release of inflammatory factors, thereby fostering neuronal survival and safeguarding the central nervous system.
Multicomponent Stapling of Glucagon‐Like Peptide‐1 Enables Receptor‐Guided PROTAC Delivery
ABSTRACT Achieving cell‐selective targeted protein degradation remains a major challenge for translating proteolysis‐targeting chimeras (PROTACs) into therapeutics. Although pancreatic β‐cells are well vascularised and readily accessible to circulating peptides, selective receptor‐mediated drug delivery remains challenging. Here, we exploit the glucagon‐like peptide‐1 receptor (GLP‐1R) as a β‐cell‐specific entry route and report, for the first time, a multicomponent stapled glucagon‐like peptide‐1 (GLP‐1) analogue constructed by tryptophan‐mediated multicomponent Petasis reaction (TMPR). This modular stapling strategy affords a conformationally stabilised GLP‐1 peptide bearing a chemically orthogonal handle for late‐stage conjugation, displaying markedly enhanced α‐helicity and improved receptor potency, compared with the wild‐type peptide. Linking this improved analogue to a bromodomain‐containing protein 4 (BRD4)‐directed degrader furnishes the first GLP‐1‐guided PROTAC, which retains GLP‐1R agonism and induces selective BRD4 degradation in GLP‐1R‐positive cells, consistent with receptor‐guided uptake and intracellular activation of the degrader payload. Together, these results provide strong proof‐of‐concept evidence that a TMPR‐stapled GLP‐1 peptide can serve as a β‐cell‐directed delivery platform for receptor‐defined protein degradation.