Search Results (105)

Grid-forming (GFM) inverters are increasingly being deployed in distribution networks to improve system strength and provide synthetic inertia. However, their steady-state influence on hosting capacity (HC) is not yet fully understood, particularly across different distribution network topologies. This study shows that the relationship between GFM droop control and HC is strongly topology-dependent. In overvoltage-limited feeders, non-optimised GFM reactive power absorption reduces HC, whereas in undervoltage-limited feeders, the same reactive support mechanism significantly enhances HC. To analyse and optimise this behaviour, this study proposes the Coordinated Hosting-Capacity and Stability Algorithm (CHSA), which determines the optimal site-specific GFM droop parameters $(m_p,n_q,{\omega }_c)$ using a steady-state multi-constraint metaheuristic co-simulation framework. In this context, stability refers exclusively to steady-state voltage feasibility, that is, the maintenance of nodal voltages within statutory limits under varying DER injection levels. For the overvoltage-limited Australian-adapted IEEE 123-bus unbalanced multi-phase feeder, mid-range GFM settings introduce a GFM Hosting-Capacity Penalty ($C{P}_{\mathrm{GFM}}$) of 95.19 kW, reducing HC from the 950.00 kW grid-following (GFL) reference ceiling to 854.81 kW. By coordinating the reactive droop parameters, the proposed CHSA recovers 92.65 kW (97.3% of $C{P}_{\mathrm{GFM}}$), increasing HC to 947.46 kW while eliminating voltage violations, with all inverters operating at 33.3% of their rated apparent power capacity. For the undervoltage-limited IEEE 34-bus feeder, the same GFM reactive support mechanism increases HC from 19.90 kW under GFL operation to 237.41 kW with the optimised GFM configuration, representing a nearly twelve-fold improvement. Independent verification using Particle Swarm Optimisation confirms that both algorithms converge to the same operating point: on the representative run (seed 42), HC values differ by only 1.23 kW, while mean HC across 20 independent seeds differs by 0.99 kW (HBO: $947.70\pm 0.72$ kW; PSO: $948.69\pm 0.00$ kW), indicating that the results are governed by network characteristics rather than the choice of optimisation algorithm. These findings demonstrate that the HC limitations often associated with GFM technology arise mainly from suboptimal control coordination rather than inherent architectural constraints. Furthermore, the proposed CHSA provides a general framework for voltage-constraint-aware droop coordination across distribution networks with contrasting topological characteristics. Full article

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed CN approaches is the absence of independent hydrological validation; most machine learning models are trained and evaluated against the same SCS-USDA lookup values used to construct the training target, a circular scheme that measures statistical agreement rather than physical credibility. This study develops a reproducible geospatial workflow for distributed CN estimation across continental Ecuador, combining official MAG land use, soil surface texture natural drainage, and topographic slope layers at 1:25,000 scale with a Random Forest regression model at 10 m spatial resolution. The CN reference raster was derived from official geo-pedological layers and independently validated, not against tabulated assumptions, but against observed hydrological behaviour. Field hydraulic characterization across four dominant land cover classes in the Guamote microwatershed (Chimborazo Province), combined with HEC-HMS (US Army Corps of Engineers, Davis, CA, USA) rainfall-runoff modelling over 41 years (1981–2021), confirmed a mean annual discharge of 0.1568 m³ s⁻¹ consistent with the tabulated CN assignments. To our knowledge, this is the first nationally distributed CN map with field-anchored hydrological benchmarking for an Andean country. The Random Forest model achieved an RMSE = 10.4, an R² = 0.42, and an NSE = 0.41, a performance consistent with published field-based CN estimation studies and expected given the inherent scatter of the SCS-USDA method under real-world conditions. Zonal CN comparisons confirmed a mean absolute error below 5 CN units across the Andean highland and Amazon watersheds; the Guamote watershed showed a mean ∆CN below 4 units against the field-calibrated model. Land use and surface texture emerged as the dominant CN predictors, with natural drainage providing critical discrimination in volcanic and poorly drained soil environments. The resulting 10 m national CN map offers a physically grounded, spatially explicit parameterization layer for distributed hydrological modeling and water resources planning across data-scarce Andean and tropical territories, with direct relevance for flood risk screening, irrigation planning, watershed conservation, and climate adaptation under SDG 6, SDG 11, SDG 13 and SDG 15. Full article

The growing reliance on cyber deception as a defensive mechanism has revealed persistent limitations in existing deception infrastructures, particularly in their ability to scale, adapt, and provide continuous observability under realistic adversarial workloads. Conventional honeypot deployments are predominantly monolithic and statically configured, which constrains their responsiveness to dynamic attack conditions and limits their applicability in contemporary distributed environments. This work presents a microservice-oriented cyber deception platform that reconceptualizes deception infrastructure as a composition of loosely coupled, independently deployable services. The platform integrates containerized honeypots, a lightweight API-driven orchestration layer, and a centralized telemetry pipeline to enable rapid instantiation, dynamic reconfiguration, and high-resolution monitoring of attacker interactions. Unlike prior approaches that treat deployment, orchestration, and monitoring as separate concerns, the proposed design explicitly unifies these components within a single, measurable system architecture. To support principled reasoning about system behaviour, the paper introduces first-order analytical models that characterize deployment latency, resource utilisation, telemetry throughput, and operational cost as functions of attacker concurrency. These models are not intended as exact predictors, but as tractable abstractions that enable interpretation of system performance and guide capacity planning. Model parameters are empirically derived and validated through controlled experimentation. Evaluation is conducted within a reproducible cyber-range environment using scripted adversarial workloads that emulate reconnaissance, authentication attempts, and sustained interactive sessions. The results indicate that containerised deployment reduces instantiation latency to approximately 1.2 s under warm-start conditions, compared to tens of seconds for virtual machine-based baselines. Resource utilisation exhibits approximately linear scaling under moderate concurrency, while the telemetry pipeline sustains ingestion rates exceeding 18,000 events per minute without observable loss. Stress testing further reveals that telemetry processing, rather than orchestration, constitutes the primary scalability bottleneck. These findings suggest that microservice-based architectures can provide a viable and extensible infrastructure substrate for cyber deception, supporting both operational deployment and integration with higher-level adaptive and learning-based defence mechanisms. The contribution of this work lies not in introducing new deception strategies, but in enabling their practical realisation through a scalable and observable system design. Full article

We introduce the Hamiltonian pseudorandom function (HPRF), a new symmetric cryptographic primitive in which the function family $\left\{F_k\right\}$ is defined by $F_k\left(q\right)=\nabla S_k\left(q\right)$, the gradient of the generating function of a secret Lagrangian submanifold $L_k$ on the symplectic torus ${\mathbb{T}}^{2n}$. The key k specifies a composition of kicked-rotor maps in the strongly chaotic regime, whose classical Lyapunov exponents grow as $log(K/2)$ per kick. The HPRF is best understood as a seeded one-way function with high min-entropy output: $F_k$ is smooth ($C^{\infty }$), so its raw output is not directly usable as a uniform keystream, but it is computationally hard to invert. We construct three symmetric encryption modes—Mode A (key-dependent coordinate frame), Mode C (Lagrangian keystream), and Mode AC (hybrid)—in which the HPRF supplies the hardness and a key derivation function (HKDF) supplies bit-level uniformity. Standard symmetric composition then yields IND-CPA and IND-CCA2 security. Classical security reduces to the Lagrangian identification problem (LIP), shown as equivalent to the Hamiltonian inversion problem of recovering the kick parameters, which we state as an explicit hardness assumption supported by a precision/sample-complexity obstruction from the positive Lyapunov exponents, by the empirical failure of concrete attacks, and (more heuristically) by topological suggestiveness from the Arnold conjecture and Floer theory. We validate a gradient-fitting attack and an algebraic-structure attack and show that both fail. For quantum security, we propose what we believe is the right framing: that the composed Floquet operator ${\widehat{U}}_K^r$ is a candidate pseudorandom unitary (PRU) in the sense of Ji–Liu–Song. We provide three independent pillars of evidence—Wigner–Dyson spectral statistics, Lyapunov-rate scrambling, and conjectural approximate-design behaviour—and reduce the HPRF quantum security to the PRU conjecture for ${\widehat{U}}_K^r$. We then retire the dynamical-localisation argument of previous drafts as inapplicable at cryptographic parameters; the chaotic-pseudorandomness regime that the operator actually inhabits is, we argue, a stronger foundation than the one that localisation would have provided. A deterministic fixed-point arithmetic core ensures cross-platform bit-exact consistency. A reference implementation validates correctness across all modes, and an NIST SP 800-90B analysis of the output min-entropy fixes the parameter sets. As a foundational proposal, the HPRF is intended for settings that seek a symmetric hardness assumption structurally independent of the algebraic problems underlying current cryptography, for example, as a hedge primitive in defence-in-depth designs, or as a basis for further study of geometry- and chaos-based cryptography, rather than as a drop-in replacement for AES or lattice-based schemes at this stage. Full article

To accurately assess foundation behaviour under urban conditions, it is essential to integrate geotechnical analysis with continuously evolving hydrogeological parameters. In rapidly developing cities such as Astana, long-term fluctuations in seasonal groundwater levels, salinity dynamics, and changes in soil permeability significantly influence stress–strain behaviour and structural settlement. This study employs multiple software tools, supported by detailed hydrogeological monitoring, laboratory testing, and integrated numerical simulations, to analyse the development of settlement and stress–strain characteristics for both the synagogue and the Independence Palace. The results show that between 2002 and 2020, groundwater salinity at the synagogue site increased from 1.10 g/L to 3.39 g/L, accompanied by a 23% rise in soil permeability. At the Independence Palace site, salinity reached 2.01 g/L, with an 18% increase in permeability. Numerical simulations conducted using GEO5, PLAXIS 2D, and LIRA SAPR revealed consistent trends but varying magnitudes of subsidence. PLAXIS 2D predicted settlement values approximately 15–25% higher than GEO5, while LIRA SAPR produced estimates 10–20% lower. Among the models, GEO5 demonstrated the closest agreement with field observations. The Independence Palace underwent relatively rapid stabilisation due to an effective drainage system, with consolidation occurring over approximately 100–150 days. In contrast, the synagogue experienced prolonged settlement over a period of 10–15 years, driven by high groundwater saturation and river recharge. These findings confirm that hydrochemical evolution plays a critical role in governing soil permeability. Consequently, cross-validation using multiple modelling platforms is essential, and long-term settlement assessments in complex hydrogeological environments must account for time-dependent changes in permeability. Full article

Shared immersive environment sports venues, virtual classrooms, and collaborative workspaces require multiple users to stream 360° videos simultaneously over the same edge network, yet every existing adaptive bitrate system optimises each viewer in isolation. This self-interested behaviour triggers a bandwidth auction that chronically starves the most uncertain viewers: Jain’s Fairness Index for ten independently optimised agents routinely falls below 0.85. We present FairEdge360, a hierarchical multi-agent reinforcement learning framework that reformulates multi-user 360° streaming as a Decentralised Partially Observable Markov Decision Process (Dec-POMDP) and proves, formally, that fairness and quality are complementary rather than competing objectives. Three tightly coupled innovations make this possible. First, a Lightweight Uncertainty Estimator (LUE) a compact 8385-parameter four-layer MLP evaluates per-device viewport prediction confidence $c_t^i=\sigma \left(w_4^{\top }{h}_3\right)$ in under approximately 2.1 ms on commodity smartphones (95th percentile, iPhone 12 A14 Bionic), enabling selective edge offloading that reduces device energy consumption by 38.9%. Second, a variational Graph Neural Network compresses each agent’s 256-dimensional GRU state into a 32-byte INT8 latent, transmitted over a dynamic RTT-gated neighbourhood graph at 96 bytes per agent per 500 ms 75% less overhead than competing approaches. Third, the edge coordinator maximises the Nash social welfare objective $\mathrm{NSW}={\left({\prod }_{i=1}^N{Q}_i\right)}^{1/N}$, whose gradient $\partial \mathrm{NSW}/\partial Q_i\propto 1/Q_i$ automatically prioritises the most disadvantaged viewer; a formal proof guarantees that every Pareto-optimal policy satisfies $Q_i/{\sum }_j{Q}_j\ge 1/N$. Counterfactual advantage estimation correctly attributes each agent’s marginal contribution to the global reward, eliminating the credit-assignment ambiguity inherent in standard multi-agent baselines. Evaluated on 284 users, 52 omnidirectional videos, and 10,000 real network traces spanning 4G LTE, 5G mmWave, HSDPA, and campus WiFi, FairEdge360 raises Jain’s Fairness Index from 0.934 to 0.976 (+4.5%), improves worst-case user quality-of-experience from MOS 2.54 to MOS 3.21 (+26.4%), and halves rebuffering rate from 2.1% to 1.1%, all within a 20 ms motion-to-photon budget on a commodity smartphone. Full article

This study investigated context-associated variation in vocalizations in Malinois dogs through acoustic parameter analysis. Vocalizations from thirty adult Malinois dogs (15 males, 15 females) aged 2 to 3 years were recorded across 11 behaviourally defined contexts. Using Praat software, key acoustic parameters—fundamental frequency (F0), harmonic-to-noise ratio (HNR), and formant frequencies—were extracted and analyzed. Results indicated that different vocalization types (barking, whimpering, growling, snarling, howling) exhibited distinct acoustic profiles. Whimpering and howling showed significantly higher F0 values than barking (p < 0.05), with whimpering uniquely displaying both low and high F0 components. Dogs in contexts expected to be positively valenced (e.g., food anticipation) showed lower HNR than those in contexts expected to be negatively valenced (e.g., separation) (p < 0.05). However, the actual internal states were not independently verified. Formant analysis revealed that snarling and howling had lower Formant 1 (F1) values (p < 0.05), while formant dispersion varied with emotional state. These findings suggest that acoustic analysis of dog vocalizations can provide objective insights into dogs’ motivational and arousal changes, thereby improving our understanding of canine vocal communication, social behavior, and the human–dog bond. This approach has potential applications for working-line Malinois breeding programs and for enhancing human–working dog interactions. Full article

Expansive soils pose significant challenges in geotechnical engineering due to their volume changes with moisture variations. A critical distinction exists between a soil’s inherent potential to swell or shrink (governed by intrinsic parameters such as clay content, plasticity index, and activity index) and its actual behaviour under specific site conditions (governed by state parameters like porosity and water content). This paper critically evaluates the reliability of widely used single-index and multi-index classification methods against direct oedometer measurements of swelling pressure. Analysis of nearly 600 tests on natural active clays from four different sites in Romania reveals that, for these soils and site conditions, no single intrinsic parameter—nor any simple pair of parameters—correlates reliably with swelling pressure, demonstrating that these indices merely indicate potential, not actual, behaviour. In contrast, state parameters provide more meaningful insights. Drawing on parallels with collapsible soil mechanics, the study introduces the concept of “saturation-independent pressure” (si_p), the stress level beyond which saturated and natural-moisture soil behaviours converge. Furthermore, a practical calculation method is proposed for estimating both foundation heave (upon saturation) and shrinkage (upon drying), based on double oedometer compressibility curves. Notably, a strong correlation (R² = 0.79–0.86) is demonstrated between swelling pressure and the specific swelling strain measured under an initial load of 12.5 kPa, offering a rapid and inexpensive screening tool for identifying potentially problematic active clays. Full article

Useful quantum neural networks should not merely explore large Hilbert spaces but should organise their expressive capacity according to the symmetries of the learning problem. We introduce symmetry-organised complexity as an ansatz-level, representation-theoretic trajectory diagnostic for quantum neural networks. The diagnostic combines symmetry-sector organisation, cross-irreducible representation organised complexity, and symmetry metastability into a composite index, which is then multiplied by a compliance factor that penalises apparent complexity arising from symmetry violation. This compliance factor is defined at the level of the implemented trainable generators rather than as a representation-independent channel metric. The representation-theoretic basis of the construction is that, for an exactly equivariant network, the effective trainable operators lie in the commutant of the group action and are controlled by multiplicity dimensions rather than by the full Hilbert-space dimension. We show that joint sector collapse and state freezing force the index to vanish under an explicit multiplicity–purity condition and that networks with identical qubit and parameter counts can have different values of the index. Two analytically tractable four-qubit examples with excitation number and total spin symmetry illustrate how the diagnostic separates sector-collapsed, symmetry-organised, and symmetry-breaking behaviour. A controlled $U\left(1\right)$-compatible teacher–student classification task further shows that, in this validation setting, the ordering of the composite index across equivariant, hybrid, and non-equivariant ansatze agrees with the ordering of generalisation accuracy. The framework is most informative when the relevant symmetry of the learning problem is known. Full article

Background: Lipid peroxidation is a primary driver of biological membrane damage and mediates the relationship between environmental exposure and adverse health outcomes. Malondialdehyde (MDA) is a widely recognized biomarker for quantifying oxidative stress intensity. Despite numerous studies on oxidative stress and metal exposure, nonlinear relationships between physiological characteristics, serum metal profiles and MDA levels in pubertal children remain insufficiently studied. Methods: The study included 105 conditionally healthy children aged 12–14 years from urban and rural regions of Tatarstan, Russia. Serum MDA concentrations were determined spectrophotometrically using the thiobarbituric acid assay, while Zn, Cu, Fe, Sr and Pb concentrations were measured by atomic absorption spectrometry. A multilayer perceptron neural network was applied to model nonlinear relationships between MDA levels, environmental exposure indicators and morphophysiological characteristics. Because the original relational dataset contained partially replicated participant-derived relational structures, primary validation was performed using independently reconstructed datasets without repeated observations. Additional repeated cross-validation and SHAP-based feature importance analysis were performed. Results: Urban-residing children demonstrated significantly higher serum MDA levels than rural counterparts, independent of sex, with girls consistently showing higher values. Reduction of predictor dimensionality improved model generalization behaviour. Validation using independently reconstructed datasets without repeated observations demonstrated reproducible exploratory predictive behaviour of the reduced neural network model, with independently reconstructed validation datasets yielding mean R² values of 0.901 ± 0.052 and 0.914 ± 0.046, respectively. SHAP analysis demonstrated that zinc, copper and iron consistently represented the dominant contributors to the nonlinear model, although substantial variability in the relative ranking of zinc and copper was observed between validation datasets. Conclusions: The proposed neural network model demonstrated the ability to capture reproducible nonlinear relationships between oxidative stress markers and environmental exposure parameters in a limited biomedical dataset. The model should primarily be interpreted as an exploratory explanatory tool rather than an individual clinical prediction instrument. Because of the limited dataset size, partially reconstructed relational structure and exploratory study design, the findings require cautious interpretation and further external validation. Full article

Fused Filament Fabrication (FFF) is a widely used additive manufacturing technology due to its versatility, low cost, and broad material compatibility. However, achieving high dimensional accuracy in FFF parts remains challenging because dimensional deviations are affected by material shrinkage, process parameters, and part geometry. This study analyses the dimensional deviations of PLA hollow cylindrical specimens manufactured by FFF, with particular attention to the different behaviour of outer and inner diameters. The methodology combines an iterative design-adjustment procedure with a neural-network-based compensation approach. First, specimens with different geometries were printed and measured to evaluate the evolution of dimensional error after successive design corrections. Then, the influence of print speed and layer thickness was analysed through the volumetric material flow rate, and the resulting data were used to train separate feedforward neural networks for the outer and inner diameters. The results showed that outer and inner diameters followed different deviation trends, confirming that they should be analysed independently. Print speed, layer thickness, and material flow affected dimensional accuracy in different ways depending on the measured diameter. The proposed neural network approach provided a practical means of estimating compensated design diameters within the experimental domain analysed, reducing the need for repeated trial and error adjustments. However, the results should be interpreted within the experimental limits of the study, particularly regarding the use of a single material, a single printer, and a limited validation dataset. Overall, the study provides a practical workflow for improving dimensional accuracy in FFF parts and highlights the importance of diameter-specific compensation strategies. Full article

Background/Objectives: Artificial intelligence (AI) tools are increasingly integrated into acute stroke imaging workflows, but real-world performance for ischemia detection on non-contrast CT (NCCT) remains incompletely validated by investigators independent of the developer. This study externally validated the BrainScan AI system in an unselected, consecutively enrolled emergency cohort. Methods: Consecutive adult patients undergoing NCCT under the routine acute stroke protocol at a single tertiary centre between January and December 2025 were prospectively enrolled. The reference standard was the post-consensus radiological diagnosis, supplemented where available by follow-up imaging and clinical course. Primary outcomes were diagnostic accuracy for ischemia and intracranial haemorrhage detection, assessed by sensitivity, specificity, predictive values, likelihood ratios, and area under the ROC curve (AUC; DeLong). Pre-specified secondary analyses included regional sensitivity, confidence-score behaviour, artefact robustness, threshold sensitivity, a cluster-robust bootstrap for within-patient correlation, and a quantitative bias analysis under non-differential reference-standard misclassification. Sample size adequacy was assessed using a precision-based framework. Results: A total of 1419 NCCT examinations from 1260 patients were analysed. Ischemia sensitivity was 59.2% (95% CI 52.1–66.1) and specificity was 99.8% (99.4–100), with an AUC of 0.930 (0.906–0.954). The Youden-optimal threshold (0.055) recovered sensitivity to 86.1% with negligible specificity loss, reflecting a markedly bimodal score distribution. Regional sensitivity was lower in infratentorial structures. Bias-corrected estimates were stable across all reference-standard parameters consistent with the data. Haemorrhage detection performed substantially better (sensitivity 96.7%; AUC 0.983). Conclusions: The system shows excellent specificity and strong discrimination but moderate sensitivity for ischemia, supporting its role as a rule-in adjunct rather than a stand-alone tool, pending multicentre validation and site-specific threshold recalibration. Full article

Vanadium borate (V₃BO₆) has recently been synthesized and identified as a promising material for use in energy storage applications, particularly as a potential anode for lithium-ion batteries. However, despite previous studies highlighting its electrochemical performance, a comprehensive understanding of its intrinsic mechanical, thermal, and vibrational properties remains limited. The compound crystallizes in an orthorhombic phase with the Pnma (No. 62) space group. To explore its intrinsic physical characteristics, full geometry optimization of the unit cell and atomic positions was performed using density functional theory (DFT) within the CASTEP framework. The Perdew–Burke–Ernzerhof (PBE) functional under the generalized gradient approximation (GGA) was used to model exchange–correlation effects. A plane-wave cut-off of 408 eV and a 6 × 6 × 13 Monkhorst–Pack grid were employed to ensure numerical convergence. The optimized lattice constants (a = 9.9025 Å, b = 8.4751 Å and c = 4.5354 Å) are highly consistent with experimental data, which confirms the reliability of the computational approach adopted. The elastic behaviour was further investigated using the first-principles strain-energy method, yielding nine independent elastic constants consistent with orthorhombic symmetry. The calculated bulk and shear moduli, along with the anisotropy parameters, suggest that V₃BO₆ has a favourable balance of mechanical robustness and moderate ductility. A Vickers hardness of 10.95 GPa and a B/G ratio of approximately 1.93 corroborate these findings. Additional parameters, such as Poisson’s ratio, Debye temperature and average sound velocities, were derived to gain deeper insight into the material’s thermomechanical performance. These results provide a solid theoretical foundation for understanding the mechanical stability and potential anode suitability of V₃BO₆ in lithium-ion battery systems. Full article

Background: To evaluate the association between glaucomatous visual field loss and self-reported driving limitation, and to explore potential threshold ranges of visual field loss associated with an increased likelihood of driving restriction. Methods: In this cross-sectional study, 100 patients with primary open-angle glaucoma underwent standard automated perimetry. Visual function was assessed using Mean Deviation (MD) and Visual Field Index (VFI) from the better eye. Driving status, driving limitation, and self-reported driving difficulties were assessed using a structured questionnaire. Multivariable logistic regression was performed to determine independent associations between visual field parameters and driving limitation, adjusting for age, sex, cataract status, and systemic comorbidities. Because MD and VFI are closely related indices of visual field loss, separate multivariable models were constructed for each parameter. Receiver operating characteristic (ROC) analysis was used to explore threshold values associated with driving limitation. Results: Driving limitation increased progressively with worsening functional severity, affecting 17% of participants with preserved function, 48% of those with borderline impairment, and 72% of those with definite impairment (p < 0.001). Reduced VFI was independently associated with driving limitation (OR = 0.972, 95% CI: 0.948–0.996; p = 0.021). In a separate model, more negative MD was also independently associated with driving limitation (OR = 0.924, 95% CI: 0.875–0.976; p = 0.004). Male sex was associated with a lower likelihood of driving limitation. ROC analysis identified threshold values of VFI ≤ 71% (AUC = 0.663) and MD ≤ −13.36 dB (AUC = 0.650), both characterized by high specificity but limited sensitivity. Participants who had ceased driving demonstrated worse visual field indices than active drivers, whereas never-drivers showed no consistent association with visual field loss. Conclusions: Glaucomatous visual field loss was significantly associated with self-reported driving limitation and behavioural self-regulation. Objective perimetric parameters, particularly VFI and MD in the better eye, may help identify patients more likely to report driving difficulties. The reported threshold values should be interpreted as exploratory reference points rather than clinically actionable criteria and require further validation before clinical application. Full article

Silicon photomultipliers (SiPMs) are vital for calorimetric applications in high-energy physics and medical imaging due to their high gain, compactness, and insensitivity to magnetic fields. However, their finite pixel count induces non-linear response behaviour at high photon fluxes, affecting energy resolution and systematic accuracy. This work presents a comprehensive methodology to characterise SiPM response functions and derive correction curves using a single-step laser-based measurement approach. Three SiPMs with varying pixel sizes (15, 25 and 50 µm) are studied under controlled temperature conditions, with response functions extracted across different overvoltages and integration windows. The correction method, independent of precise light source calibration, effectively linearises the response up to saturation levels exceeding 100% of the pixel count, achieving deviations of the order of 3% across a broad operational parameter space, and outperforming the traditional calibration model. The analysis demonstrates minimal dependence of the correction on temperature, overvoltage, and pixel size, indicating universal applicability. These findings enhance SiPM performance in high-energy calorimetry and offer a practical framework for improving detector linearity and dynamic range extensions in large-scale applications. Full article