Abstract: Traffic sign detection in autonomous driving faces challenges including multi-scale objects, complex backgrounds, and limited edge-computing power. To address insufficient multi-scale feature representation and high false negatives for small traffic signs in YOLOv8n, this study proposes an improved algorithm integrating the VoVGSCSP module with a Multi-scale Contextual Attention (MCA) mechanism. The original C2f module is replaced with VoVGSCSP, enhancing feature representation through parallel residual branches and cross-stage connections. A lightweight neck, SlimNeck, is designed and combined with MCA, employing multi-branch pooling and dynamic weight fusion to capture geometric features and color semantics. The PAN-FPN path is optimized with cross-level connections and learnable weights for adaptive multi-scale fusion. Experiments on the GTSRB dataset show that the improved model reduces parameters to 2.66 M (an 11.6% decrease) and computational complexity to 7.49 GFLOPs, while mAP@0.5 increases from 94.7% to 96.3% and FPS improves from 82.3 to 90.6. The proposed algorithm achieves comprehensive gains in lightweighting, accuracy, and speed, demonstrating its effectiveness and practical applicability.

Abstract: Prostate-specific membrane antigen (PSMA) targeting radiopharmaceuticals have been successfully used for the diagnosis and therapy of prostate cancer. Most of the PSMA molecules for the diagnosis and treatment are based on the peptidomimetic glutamate-urea-lysine (Glu-CO-Lys) pharmacophore connected to various linker groups. Optimization of the available agents is desirable to improve tumor uptake and reduce uptake in non-target organs. This can be achieved, for instance, via linker modifications and/or multivalent approaches. In this study, we synthesized several new Glu-CO-Lys-based PSMA ligands, each connected to different linkers to explore the role of these linkers on cell binding and tumor targeting potential. Additionally, a bivalent (bis) PSMA ligand, containing two PSMA targeting motifs (Glu-CO-Lys) in the same structure, was synthesized by conventional Fmoc-based solid-phase synthe-sis. DOTA- or Aoa-coupled PSMA conjugates showed high radiolabeling efficiency (≥ 90%) with [68Ga] and [18F] and resulted in the formation of one major radiolabeled product. Also, a high stability of the PSMA conjugates was found in human plasma. The [68Ga/18F]-labeled PSMA ligands exhibited the nanomolar affinity (<95 nM) specific to the PSMA-positive LNCaP tumor cell line. In the PSMA-positive tumor xenograft model, the radiolabeled PSMA ligands exhibited rapid clearance from the blood and excretion primarily via the renal system. Biodistribution and imaging studies revealed high accumulation of bis-PSMA ligand in LNCaP tumor xenografts. These render that bis-PSMA may be a promising ligand for diagnostic imaging of PSMA-positive pros-tate cancer.

Abstract: Urban models circulate toward African cities with claims to universal applicability, yet they consistently produce outcomes that diverge from their initial promises. This article argues that the explanation lies not in local implementation failures but in the very mechanics of circulation. Building on the critical trajectory from policy transfer theory through policy mobilities to assemblage thinking, the article constructs an original analytical framework organised around the fragmentation matrix. This matrix identifies five families of fragments composing any urban model in circulation: conceptual, metric, iconographic, institutional, and narrative. Each family exhibits differential mobility, travelling through distinct channels and producing different consequences for the receiving context. Three types of legitimation arenas selectively structure this diffusion, generating what the article theorises as asymmetric fragmentation: the selective, hierarchised, and unequally operational circulation of heterogeneous components. Applied to the African continent, the framework reveals that model dysfunction is not a contingent failure but a structural feature of the circulation mechanism itself. The article concludes that overcoming this structural inadequacy requires reconfiguring the epistemic conditions under which urban knowledge is produced and legitimised.

Abstract: The Gielis superformula is a powerful parametric tool that generates an infinite variety of natural and organic curves and surfaces through a compact set of parameters. However, classical differential geometry has lacked a unified framework for analyzing their curvature, torsion, and intrinsic geometric properties. This study addresses this gap by developing a novel superelliptic geometric framework that integrates the superformula 6with the differential geometry of curves and surfaces. We define the superelliptic inner and cross products, the star derivative, and the superelliptic Frenet frame to extend Euclidean and Riemannian interpretations of curvature and torsion to a more flexible parametric structure. The framework provides a uniform geometric characterization of all Gielis curves and surfaces, independent of their classical parametric expressions; even singular cases are regularized so that their curvature and torsion reduce exactly to those of a circle. This unifies the entire family under a common, robust foundation while preserving orthonormality and differentiability. This superelliptic approach offers a consistent and computationally 14tractable model that bridges mathematical abstraction with real-world morphology, with 15the superformula serving as a representative example of the framework’s broad generality for diverse geometric structures.

Abstract: The design and reliability assurance of embedded systems is a complex issue, since they need to handle not only digital behavior but also physical quantities such as time, cost, and sometimes randomness. In addition, since many embedded systems, such as networks and automobiles, have systems in which errors can be fatal, design verification for reliability assurance is an important research topic. With the above background, we adopt the approach of specifying and verifying embedded systems by formal models. Specifically, we focus on a priced probabilistic timed automaton as a specification description language, and propose a reachability analysis method based on Counterexample-guided abstraction refinement (CEGAR) to reduce the state explosion. To demonstrate the effectiveness of the proposed method, we attempt to verify the design of important wireless sensor networks (WSNs). In this paper, we model WSNs by a priced probabilistic timed automaton that can express their power characteristics in terms of cost, uncertainty in terms of probability, real-time in terms of time, and attribute WSNs’ characteristics to the cost bound probabilistic reachability problem. To the best of our knowledge, this paper is the first CEGAR development and implementation of a priced probabilistic timed automaton. We have developed a prototype of the verifier and confirmed that it is verifiable.

Abstract: In Europe, bitumen classification has traditionally relied on empirical tests, namely penetration and the Ring‐and‐Ball softening point, originally developed for unmodified binders and insufficient for modern modified materials. As an alternative, a rheology‐based method, the Bitumen Typisierungs Schnell Verfahren (BTSV), has been developed in Germany to characterize high service temperature performance, with performance requirements introduced in 2025. In this study, the performance of five bitumen types commonly used in Hungarian road construction was investigated using the BTSV method. During testing, the softening temperature corresponding to the rheological threshold value of G* = 15.0 kPa (TBTSV) and the phase angle (δBTSV) were determined. The results are compared with each other, with softening point values determined by the standardized Ring‐and‐Ball method, and with German bitumen classification systems. A total of 137 samples from production control were analyzed, including paving grade, SBS‐modified, and chemically stabilized rubber‐modified binders. Statistical evaluation included mean values and 95% confidence intervals. For rubber‐modified bitumens, the recoverable, insoluble rubber content was deter‐mined using Soxhlet extraction. Based on the results, it can be concluded that with increasing rubber content, the TBTSV value shows an increasing trend, while the δBTSV value decreases. A strong linear relationship was observed between the investigated parameters in the TBTSV–δBTSV diagram, with a coefficient of determination of R² = 0.99.

Abstract: A numerical bifurcation analysis is presented for inductively coupled plasmas and wall stabilized arcs for argon and hydrogen. Because of the non–linear transport and radiative properties both problems admit multiple solutions, up to three for argon and up to four for hydrogen. The multiplicity structure primarily dependents on the non–linear and especially the non–monotonic relationship between thermal conductivity and temperature. As a result of the non-monotonicity a multipoint energy equilibrium between Joule heating (heat generation) and heat dissipation by conduction and radiation exists, giving rise to the multiplicity which is a characteristic feature of both radiating and non–radiating arcs. Despite the relatively simple one–dimensional model employed the agreement with the experimental data is good.

Abstract: This study looks into the predictive performance of linear econometric and deep learning methodologies for the South African unemployment rate quarterly data. In this paper, the Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) model was compared to the Long Short-Term Memory (LSTM) network using unemployment rate quarterly data. Exploratory Data Analysis (EDA) suggested that the unemployment rate series is non-stationary, with structural breaks around 2020 and time-varying volatility. Stationarity tests established the need for differencing, whereas diagnostic tests revealed the presence of autocorrelation and ARCH effects in the raw data. The ARIMAX model added labour market covariates, and the differenced Not Economically Active (NEA) variable was statistically significant, whereas Discouraged workers were not. Although the ARIMAX model provided a good in-sample fit, residual diagnostics showed deviations from normality. Out-of-sample forecast study revealed moderate predictive accuracy, with relatively substantial forecast errors and increasing prediction intervals over time. In contrast, the LSTM model showed significant learning capacity, with early convergence and well-behaved residuals that meet both independence and homoskedasticity criteria. The model achieved significantly lower forecast errors, with RMSE, MAE, and MAPE values much lower than those of the ARIMAX model. Comparative forecast analysis using Diebold-Mariano (DM) test and model confidence Set (MCS) method and bootstrap confidence intervals consistently demonstrated the statistical superiority of the LSTM model. The findings give strong evidence that the LSTM model outperformed the ARIMAX model for projecting South African unemployment rate. The findings emphasise the importance of nonlinear modelling approaches in capturing complex labour market dynamics while also demonstrating the limitations of classic linear models. These findings also emphasise the importance of using nonlinear machine learning algorithms in macroeconomic forecasting.

Abstract: Introduction: In hemodialysis patients, Body Mass Index is insufficient in assessing their nutritional status due to the ‘obesity paradox’ and the impact of body composition on inflammation. The aim of the study was to assess the relationship between body composition, traditional inflammatory markers, and the new NETosis indicators (neutrophil extracellular traps), as well as to determine their impact on 12-month mortality. Methods: The study included 99 patients with end-stage renal disease. Their body composition was assessed using bioelectrical impedance analysis (Seca mBCA 525). Blood serum was tested for inflammatory markers (hs-CRP, IL-6, TNF-α, IL-1B), NETosis markers (citrullinated histone H3, MPO, elastase), and nutritional status parameters (albumin, transferrin). Results: No correlation between BMI and inflammation was demonstrated. Higher contents of the adipose tissue, particularly visceral, were significantly associated with increased levels of IL-6 and hs-CRP, while muscle mass was negatively correlated with inflammation. The use of dialysis catheters stimulated NETosis (higher CH3 levels), which had a negative effect on albumin concentrations. Low albumin levels and high TNF-α levels were independent predictors of death. Conclusions: It is body composition, and not BMI, that determines the severity of inflammation. Visceral obesity promotes inflammation, while muscle mass has a protective effect. Dialysis catheters, by stimulating NETosis, contribute to a decrease in albumin levels and a poorer prognosis.

Abstract: Genomic prediction is now routine in crop improvement, but its main bottleneck has shifted from marker density to environmental complexity. Breeders rarely need predictions for one fixed environment; they need to rank genotypes across target populations of environments that differ in weather, soils, management, and stress timing. This makes genotype-by-environment interaction a primary breeding problem rather than a secondary statistical nuisance. This review examines how genomic, environmental, and phenomic information can be integrated to improve multi-environment prediction in crop breeding pipelines. The review is narrative rather than PRISMA-style, but the literature search and selection logic were structured and explicitly defined. Peer-reviewed English-language studies were identified through structured searches of Web of Science Core Collection and Scopus, supplemented by backward citation screening, with emphasis on literature published from January 2023 to March 2026. Four conclusions emerge. First, environmental information is most useful when it is developmentally aligned, biologically interpretable, and matched to the target population of environments. Second, strong structured statistical baselines remain highly competitive, especially in moderate-sized or highly unbalanced datasets, whereas gains from more flexible machine-learning and deep-learning approaches are most evident in large, sparse, heterogeneous, and multimodal settings. Third, phenomic markers often improve prediction for complex traits, especially yield, because they capture realized crop responses not fully represented by markers alone. Fourth, practical value depends less on isolated gains in predictive accuracy than on evaluation under realistic deployment scenarios, including untested genotype and untested environment settings. Progress therefore requires transparent reporting, benchmark design, stage-aware envirotyping, multimodal integration, uncertainty reporting, and cost-aware deployment.

Abstract: Aerobic composting is an important pathway for the resource utilization of agricultural waste. However, nitrogen loss during composting not only reduces the nutrient value of the final product but also causes environmental burdens, particularly through ammonia (NH₃) volatilization and nitrous oxide (N₂O) emissions. This review critically examines the sources, pathways, and mechanisms of nitrogen loss during aerobic composting of agricultural waste, with emphasis on nitrogen transformation and the major loss routes, including NH₃ volatilization, N₂O emissions, and nitrate leaching. From a multiscale perspective, the review synthesizes control strategies spanning feedstock pretreatment (e.g., C/N ratio optimization, adsorbent amendment, and microbial inoculation), in-process regulation (e.g., aeration, moisture, temperature, pH), and post-treatment approaches for nitrogen stabilization and resource recovery. The supporting roles of reactor innovation, intelligent process control, and policy and regulatory measures are also discussed. Finally, current bottlenecks and future research directions are summarized from environmental and economic perspectives, with particular emphasis on interdisciplinary integration and technological innovation to enhance nitrogen retention during composting.

Abstract: Refugees are exposed to cumulative pre-migration, migration, and post-migration stressors that increase vulnerability to depressive disorders and impaired quality of life. Aim of this study was to assess the prevalence and severity of depressive symptoms among adult refugees in Serbia and associations with sociodemographic characteristics, traumatic experiences, social support, and Health Related Quality of Life (HQoL). This study included 324 refugees in four reception centers in Serbia. Data were collected between November 2022 and April 2023 using self-report questionnaires. Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), while HQoL was evaluated using the SF-36 Health Survey. Sociodemographic, migration-related, and psychosocial variables were collected through a structured questionnaire. The mean PHQ-9 score indicated mild to moderate depressive symptomatology. Significant depressive symptoms were present in 41.4% of participants, while more than 70% reported mild symptoms. Depressive symptom severity was negatively correlated with energy/fatigue, emotional well-being, social functioning, general health, and pain. Energy/Fatigue emerged as the strongest independent predictor of depressive symptom severity. Depressive symptoms are highly prevalent among refugees and are closely associated with impaired quality of life and psychosocial stressors. These findings highlight the need for systematic screening and psychosocial interventions targeting mental health issues in refugees.

Abstract: Drinking water quality is essential for public health and requires monitoring approaches able to capture both regulatory compliance and short-term variability. This study presents a high-frequency IoT-based comparative physicochemical assessment of two drinking-water sources in Bragança, NE Portugal: treated municipal water derived from surface water and groundwater abstracted from a decentralized supply system. A low-cost IoT monitoring system was used to measure pH, electrical conductivity, temperature, oxidation-reduction potential, and total dissolved solids. Monitoring campaigns were conducted between January and March 2026 at two treated-water points within the public supply system and three groundwater points, complemented by municipal records from 2023 to 2025. The treated municipal supply showed a more stable physicochemical profile and lower variability, whereas groundwater was associated with higher mineralization and stronger temporal fluctuations. Significant differences were found for electrical conductivity, total dissolved solids, oxidation-reduction potential, temperature, and pH. High-frequency monitoring enabled the identification of dynamic patterns and transient fluctuations that would be difficult to detect through discrete sampling alone.

Abstract: Let Γ be a commutative with identity multiplicative hyperring and S ⊆ Γ be a multiplicatively closed subset of Γ. In this study, we will discuss the definition and general properties of weakly Γ-prime hyperideals. Let Ω be a hyperideal of Γ disjoint from S. We say that Ω is a weakly S− prime hyperideal of Γ if there exists an s ∈ S such that for all ϱ,σ ∈ Γ , if {0} ̸ = ϱ⋄σ ⊆ Ω then s⋄ϱ ⊆ Ω or s◦y ⊆ Ω . We will also examine the relationship between weakly−S prime hyperideals, weakly prime, and S−prime hyperideals.

Abstract: Against the backdrop of rural revitalization, traditional villages in Guizhou's ethnic minority regions face the dual challenges of preservation and development. Existing research predominantly focuses on macro-scale morphological descriptions, lacking an operable spatial classification method that can directly guide planning and management. To address this gap, this paper takes Fengxi Village in Dejiang County as a case study, integrates Conzenian urban morphology theory with the concept of "management units", and proposes a spatial unit classification method for traditional villages based on the overlay analysis of "morphological regions + property parcels". First, the Conzenian plan analysis method is employed to systematically deconstruct Fengxi Village's land use, road system, plot combinations, and building types, thereby delineating its morphological regions. Subsequently, three evaluation factors—building value, quality, and appearance—are innovatively introduced. Through quantitative evaluation, all 702 buildings in the village are classified into five categories: preservation units, restoration and improvement units, comprehensive renovation units, demolition and renewal units, and new development units, with the quantities and proportions of each unit type statistically analyzed. Building on this foundation, differentiated control guidelines and development strategies are proposed for each unit category. The research indicates that this method achieves a transformation from "morphological description" to "implementable control", breaking down the vague goal of "holistic preservation" into concrete "unit-based guidance" actions, and provides a replicable technical pathway for the refined planning and management of traditional villages. The innovation of this paper lies in constructing a complete technical framework of "morphological analysis - factor evaluation - unit control", addressing the deficiency of existing research at the micro-operational level.

Abstract: Upper respiratory tract infections such as the common cold are highly prevalent and impose a substantial health and economic burden, with many individuals relying on over-the-counter (OTC) medications for symptomatic relief despite limited real‑world evidence on perceived effectiveness. This non‑interventional, retrospective, cross‑sectional survey evaluated the product attributes most valued when selecting cold and flu medications and assessed perceptions of the effectiveness, quality‑of‑life impact, and overall attitudes toward MED‑LEMON. A total of 249 adults completed a structured questionnaire covering symptom relief priorities, medication attributes, perceived effectiveness, quality of life outcomes, and post-intake attitudes. Relief of fever, sore throat, headache, and sinus congestion, along with fast action, long‑lasting relief, and ease of use, were identified as key drivers of OTC cold and flu medication choice. MED‑LEMON was widely perceived as effective, with over 90% of participants reporting overall symptom improvement and strong relief of pain, headache, and fever. Adherence to the recommended dosing regimen was associated with better symptom control and improved quality of life, including sleep, emotional well‑being, and daily functioning. Overall attitudes toward MED‑LEMON were highly positive. From a public health perspective, these findings highlight the role of effective OTC treatments in supporting health promotion and responsible self-care during cold/flu period.

Abstract: Dry reforming of methane (DRM) is an attractive route for H2 production and simultaneous CO₂ utilization, but its practical implementation is limited by catalyst deactivation. This study experimentally investigates the catalytic performance of Ni/Al₂O₃ and Gd-doped ceria–promoted Ni/GDC–Al₂O₃ catalysts for DRM in a fixed-bed quartz reactor over 400–800 °C at gas residence times of 0.1 s and 0.4 s. Increasing temperature and residence time enhanced CH₄ and CO₂ conversion as well as H₂ and CO yields for both catalysts. The GDC-promoted catalyst exhibited markedly improved activity, achieving conversions and product yields at 0.1 s comparable to those of Ni/Al₂O₃ at 0.4 s and reaching complete CH₄ conversion at about 650 °C, approximately 100 °C lower than the Ni/Al₂O₃. Long-term testing demonstrated high durability of Ni/GDC–Al₂O₃ at 650 °C with no detectable carbon deposition, consistent with thermodynamic equilibrium analysis.

Abstract: Background: China and India represent a large proportion of the Asian birth cohort and have produced extensive but heterogeneous evidence on neonatal hearing screening. This scoping review summarizes studies published between 2005 and 2025 on otoacoustic-emission-based neonatal hearing screening programs in these countries, with emphasis on program implementation, screening coverage, prevalence of congenital and bilateral hearing loss, follow-up, and intervention pathways. Methods: Searches were conducted in PubMed, Scopus, and Google Scholar using predefined keywords. Studies reporting screening protocols, coverage, prevalence, or follow-up outcomes were included. The standard English language filter was used. A total of 19 papers were considered for this review. Results: The data from the two assessed Asian states show two clearly different screening implementation profiles. In China, Universal hearing screening has evolved into a large-scale and increasingly standardized system, supported by technical specifications and regional or municipal databases; The reported screening coverage was 85.8% in early rural programs, 93.6% in Shanghai, and 97.9% in Liuzhou, while national institutional surveys indicate that UNHS has been substantially implemented in many regions. Reported Hearing Loss prevalence estimates generally ranged from 1.66 to 3.43 per 1,000 newborns, although follow-up and regional equity remain problematic, especially in rural settings. In India, the evidence is dominated by tertiary-hospital feasibility studies rather than a uniformly implemented national program. Reported Hearing loss prevalence estimates varied more widely, from 0.29 to 5.60 per 1,000 screened newborns, largely reflecting differences in study design, screening timing, referral completion, and population risk profile. Across both countries, OAE-based two-stage or sequential OAE+AABR protocols reduced referral rates and improved case identification, but loss to follow-up remained a recurrent limitation. Conclusions: China and India provide complementary models of neonatal hearing screening expansion: China demonstrates the effects of system-level scale-up, whereas India highlights the feasibility and constraints of hospital-based implementation in a highly diverse healthcare environment. Future priorities include stronger follow-up systems, harmonized reporting standards, and broader dissemination of outcome data through peer-reviewed publications.

Abstract: Background/Objectives: Children growing up in families with alcohol-related problems are considered a high-risk group for developmental, emotional, and cognitive difficulties, although this condition is not classified as a clinical diagnosis in DSM-5 or ICD-11. The aim of this study was to develop a neurofunctional profile of such children based on electroencephalographic (EEG) markers, in order to identify indicators of neurodevelopmental risk and explore their potential relevance for pedagogical and social interventions. Methods: The study employed resting-state EEG recordings in children aged 6–10 years from alcohol-affected families and a control group. Quantitative EEG (qEEG) indices were analyzed, including theta–beta ratio (TBR), frontal alpha asymmetry (FAA), temporal beta activity, and beta2 power in parietal regions. Standard preprocessing procedures were applied, and between-group comparisons were conducted using Welch’s t-tests with correction for multiple comparisons. Results: Children from alcohol-affected families exhibited significantly elevated TBR indices (global, frontal, prefrontal, and midline), increased temporal beta activity and SMR composite values, and higher beta2 power in parietal regions. Additionally, reduced alpha power in the prefrontal region (Fp1) was observed. These patterns are consistent with differences in attention, executive functioning, emotional regulation, and stress reactivity. No significant differences were found for frontal alpha asymmetry after correction. Conclusions: The findings indicate the presence of distinct group-level EEG patterns associated with children from alcohol-affected environments. These results may contribute to understanding developmental variability in high-risk populations; however, they should not be interpreted as indicators of individual impairment or causal mechanisms. The study highlights the potential, but still limited, applicability of EEG-based measures in informing educational and social support strategies and underscores the need for further research integrating neurophysiological and environmental perspectives.

Abstract: Soil liquefaction is a significant geotechnical hazard that can lead to severe structural damage during seismic events. Traditional liquefaction assessment methods, such as those based on the Standard Penetration Test (SPT) and Cone Penetration Test (CPT), rely on empirical correlations but often struggle to capture the complex, nonlinear interactions between soil properties and seismic parameters. Recent advancements in machine learning (ML) offer data-driven approaches that can improve liquefaction prediction accuracy. This study evaluates and compares the performance of Random Forest (RF) and Artificial Neural Networks (ANNs) for liquefaction potential prediction using a dataset containing 480 field observations derived from CPT-based studies. The dataset was preprocessed using min-max normalization, and models were trained and optimized through hyperparameter tuning. Model performance was assessed using accuracy, precision, recall, F-measure, Cohen’s kappa, and AUC-ROC analysis. The results show that RF achieved the highest accuracy (89%), outperforming both ANN (86%) and the traditional CPT-based liquefaction assessment method (87%). Additionally, ROC-AUC values of 0.932 for RF and 0.872 for ANN indicate the superior classification capability of machine learning models. Feature importance analysis in RF revealed that cone tip resistance (qc), cyclic stress ratio (CSR), and peak ground acceleration (amax) are the most influential factors in liquefaction prediction. These findings demonstrate that machine learning techniques, particularly RF, provide more reliable liquefaction predictions compared to conventional empirical methods. The study highlights the potential of ML models in improving seismic risk assessments and guiding engineering decision-making processes.