Search Results (25,907)

We introduce an “entangled clock” in which time is defined operationally by discrete measurement registrations on a singlet state. Locally, each party’s tick rate is fixed by the unbiased marginals. The nontrivial resource is the relational (coincidence-tick) stream: because the singlet’s information budget is entirely exhausted by joint properties, the only definite temporal structure resides in the correlations between the two parties. Operationally, after exchanging time tags and outcomes, Alice and Bob identify synchronized events (that is, the $++$ channel) and thereby obtain a joint tick record. Comparing the $++$ coincidence rate $R\left(\theta \right)=P_{++}(\overrightarrow{a},\overrightarrow{b})$ to Peres’ isotropic bomb-fragment local-hidden-variable model (yielding $R_{\mathrm{cl}}\left(\theta \right)=\theta /\left(2\pi \right)$), we find that for obtuse analyzer separations the quantum prediction exceeds this natural classical benchmark, with a maximal relative excess of about $13.6\%$ near $\theta \approx {140.5}^{\circ }$. We emphasize that this “faster ticking” refers to the rate of identified coincidence ticks under a specific operational convention, not to an improved local clock rate, precision, or stability. Finally, by using multiple settings and a Bell test, we outline “Certified Private Time”: a device-independent certification of unpredictability/privacy of the relational time-stamp record against adversaries lacking foreknowledge of the settings, analogous to certified randomness generation. Full article

Background: Malnutrition has bidirectional effects in childhood cancer, as nutrition affects treatment-related adverse effects and outcomes. In turn, the cancer diagnosis and treatment, along with related psychosocial factors, can affect nutritional status. Nutritional evaluation is challenging because of the heterogeneous nutritional risks associated with a patient’s cancer diagnosis and socioeconomic status, as well as because of the variation in available resources and capacity in different global settings. Methods: This review summarizes methods for evaluating nutritional status and proposes a structured approach for use across different cancer types and resource settings. Results: Conventional anthropometric measures, including weight, height, and body mass index, along with longitudinal growth curve plotting using World Health Organization or Centers for Disease Control and Prevention growth charts, are widely used but may not adequately detect changes in body composition. In resource-limited (limited-access) countries, where equipment and trained personnel are lacking, history taking, physical examination, and anthropometric measurements should be prioritized, along with basic body composition measures such as mid-upper arm circumference. In partial-access settings, biochemical assessments and bioelectrical impedance analysis may be added to identify micronutrient deficiencies and changes in lean and fat mass, respectively. In full-access settings, advanced body composition imaging techniques (e.g., dual-energy x-ray absorptiometry, computed tomography, and magnetic resonance imaging) can be incorporated. The approaches should also be adjusted based on the cancer diagnosis and treatment. Conclusions: Tailoring nutritional assessment strategies across diverse resource settings and diagnoses would be beneficial for targeted interventions that may improve clinical outcomes. Further research, quality improvement studies, and policy-level initiatives are necessary to develop effective assessments. Full article

In cold regions with prolonged subzero temperatures and abundant solar radiation, Trombe walls serve as high-efficiency passive solar building envelopes for improving indoor thermal comfort. This study aims to optimize the thermal performance of Trombe walls via a multimodal data analysis framework and a multiview measurement algorithm. Three distinct Trombe wall configurations were constructed and continuously monitored for 60 consecutive days under typical winter conditions (average temperature: −15 °C; solar radiation intensity: 800–1100 W/m²). Field-measured datasets, including solar radiation intensity, hourly air temperature distribution, and heat exchange efficiency, were systematically analyzed to quantify the impacts of ventilation mode, air gap width, and insulation thickness on thermal performance. The results demonstrate that the hourly peak surface temperature of the optimized Trombe wall reaches 25.7 °C at 13:00, which significantly improves indoor thermal comfort compared with conventional buildings. An air gap width of 6 cm minimizes indoor temperature fluctuations (fluctuation coefficient = 0.08), while a 20 mm insulation layer stabilizes heat loss reduction at 31.1% relative to non-insulated walls. The optimal operational parameter combination (6 cm air gap, 16 °C indoor set temperature) was determined based on the lowest temperature fluctuation and highest thermal efficiency, with experimental results deviating by less than 5% from established analytical models. This study verifies the reliability of the multimodal data analysis framework for Trombe wall performance evaluation, providing practical design guidelines for passive solar building envelopes in cold regions. Full article

Background: Enhanced Recovery After Surgery (ERAS) protocols improve postoperative outcomes and promote multidisciplinary, evidence-based perioperative care. However, ERAS adoption in gynecological departments remains inconsistent, and the underlying implementation challenges are poorly understood. Objective: To identify key barriers, facilitators, and preferred implementation strategies influencing ERAS adoption in German gynecological departments, and to assess whether clinicians’ ERAS knowledge or institutional certification shapes these perceptions. Methods: We conducted a nationwide, web-based cross-sectional survey of gynecologic clinicians in Germany. The questionnaire assessed ERAS-related knowledge, current implementation status, and perceived barriers, facilitators, and strategies. Statistical analyses included equality of proportions tests, logistic regression, and internal consistency measurement. Results: A total of 116 clinicians participated; 66 provided data on barriers and 64 on facilitators and strategies. Only 37.9% reported routine ERAS use. The most frequently identified barriers were limited ERAS knowledge (40.9% “very important”) and insufficient personnel resources (40.9%). The strongest facilitators were improved patient well-being, reduced morbidity, and higher patient satisfaction (each >60% “very important”). High-impact implementation strategies included informational materials, workshops, and online training. Well-informed clinicians had significantly higher odds of reporting a positive professional impact of ERAS (OR = 9.0, p = 0.001). Conclusions: ERAS implementation in gynecological settings remains restricted by staff knowledge gaps and personnel limitations. Patient-centered benefits and interactive educational strategies serve as powerful facilitators. Enhanced staff education and multidisciplinary support structures may substantially improve ERAS uptake and contribute to greater professional satisfaction among clinicians. Full article

This article presents the findings of experimental research conducted to assess the stability of the force mode of the UR5e cobot from Universal Robots in the low-force range, from 1 N to 10 N. The set values of the robot’s forces and the physically measured values were verified by an OptoForce Hex six-axis Force/Torque sensor attached to the robot’s wrist, additionally coupled with an end-effector specially designed for research purposes. The results were recorded using proprietary software developed in the LabVIEW environment and a configured test lab station with a UR5e cobot. Three experimental tests were performed, in which the parameters of the effective force were measured while varying (1) the position of the task in the workspace of the robot, (2) the position and the level of force, and (3) the controller parameters of the force mode. The results of the experiments were compiled and presented in tables containing descriptions of, among other parameters, the following: the mean forces and their standard deviation; the mean maximum forces and its standard deviation; the mean root mean square error and its standard deviation; the mean absolute error and its standard deviation; the mean rate of force and its standard deviation; and the mean overshoot and its standard deviation. The findings of Experiment 1 demonstrated that when a setpoint of 10 N was employed, the UR5e cobot yielded an actual mean force ranging from 8.95 N to 13.26 N within the workspace plane. Experiment 2 showed that the average deviation from the set value within the 1–10 N range was approximately 0.38 N, with a maximum deviation of 0.61 N occurring at the limits of the working space. Experiment 3 showed that for the force range of 1–4 N, the best controller settings are Gain = 0.5 and Damping = 0.7; for the force range of 5–7 N: Gain = 1.0 and Damping = 0.6; and for the force range of 8–10 N: Gain = 2.0 and Damping = 0.8. Polynomial regression models were developed for each positioning scenario that can be used when making decisions regarding practical applications of the low-force mode. Full article

The FLT3-ITD mutation is a critical prognostic marker in acute myeloid leukemia (AML) and recent clinical trials demonstrate that FLT3-based measurable residual disease (MRD) is both prognostic and predictive, guiding therapeutic interventions in intensive and post-transplant settings. Conventional detection methods lack the sensitivity required for effective MRD monitoring. We developed a patient-specific droplet digital PCR (ddPCR) approach achieving analytical sensitivity of 10⁻⁵ (0.001%) for FLT3-ITD quantification. In our cohort, ddPCR enabled longitudinal monitoring of clonal dynamics, allowing the detection of re-emerging FLT3-ITD clones months before hematologic relapse and earlier than standard capillary electrophoresis. Notably, 25% of patients who relapsed as FLT3-ITD positive despite being classified as FLT3-negative at diagnosis harbored detectable microclones when retrospectively analyzed by ddPCR, suggesting that FLT3-ITD-positive relapse frequently originates from pre-existing subclones below conventional detection thresholds. These findings challenge current diagnostic classification and may influence risk stratification and treatment decisions, particularly regarding FLT3 inhibitor eligibility. While ddPCR is limited to tracking known dominant clones, it represents a practical, cost-effective solution for high-sensitivity MRD surveillance. In the era of targeted FLT3 therapies, integrating sensitive molecular monitoring into routine AML management may enable timely therapeutic adjustments and improve patient outcomes. Full article

Steering large language models (LLMs) toward desired behaviors while preserving privacy is a critical challenge in AI alignment. Existing differentially private (DP) steering methods, such as PSA, add high-dimensional noise that can severely degrade steering accuracy. We propose DP-JL, a novel approach that combines Johnson–Lindenstrauss (JL) random projection with differential privacy to reduce noise while maintaining formal privacy guarantees. DP-JL projects steering vectors into a lower-dimensional space (dimension k) before adding DP noise, reducing total noise magnitude from $O\left(\sqrt{d}\right)$ to $O\left(\sqrt{k}\right)$ where $k\ll d$, while the privacy budget $\epsilon$ remains unchanged. We evaluate DP-JL on seven behavioral datasets with LLaMA-2-7B, Mistral-7B, Qwen2.5-7B, and Gemma-2-9B, alongside general capability benchmarks (MMLU, TruthfulQA). All accuracy values are measured on held-out test sets. Results show that DP-JL achieves: (1) up to 22.76 percentage points higher steering accuracy than PSA on the myopic-reward dataset (at fixed privacy budget $\epsilon \approx 0.22$, $\delta ={10}^{-5}$); (2) 91.7% win rate on sycophancy with an average accuracy improvement of 3.01 percentage points; (3) systematic advantages in high-privacy regimes ($\epsilon <0.2$); and (4) superior capability preservation on related tasks (TruthfulQA), achieving 6.6 percentage points better accuracy than PSA. Furthermore, visualizations and layer-sensitivity analyses reveal that DP-JL faithfully preserves the geometric structure of activation spaces, explaining its robustness. Our findings demonstrate that DP-JL offers superior privacy–utility trade-offs while better preserving model capabilities. Full article

Hami melon is a widely consumed fruit worldwide, and its sweetness, characterized by soluble solids content (SSC), is a key indicator of fruit quality and commercial value. In this study, hyperspectral imaging combined with machine learning was systematically applied to develop non-destructive models for SSC prediction in multiple Hami melon varieties. Four varieties, namely ‘Xizhoumi’, ‘Jiashigua’, ‘Jinfenghuang’, and ‘Heimeimao’, with a total of 160 samples, were used as the test materials. Hyperspectral images were collected, and SSC was measured at two pulp positions for each sample (denoted as BRIX1 and BRIX2). After applying preprocessing methods including Standard Normal Variate (SNV) transformation and Savitzky–Golay smoothing, five machine learning models were compared: XGBoost, LightGBM, Random Forest (RF), Support Vector Regression (SVR), and Partial Least Squares Regression (PLSR). Furthermore, an ensemble modeling strategy based on residual predictive deviation (RPD) weighting from the validation set was proposed. The results show that all models could effectively predict SSC, with the ensemble model achieving the best performance: the coefficients of determination (R²) for BRIX1 and BRIX2 were 0.848 and 0.833, the root mean square errors (RMSEs) were 0.992 and 0.899, the Mean Absolute Percentage Errors (MAPEs) were 6.90% and 6.76%, and the RPD values were 2.57 and 2.45, respectively, demonstrating its strong quantitative analysis capability. This performance benefited from three core optimized designs adopted in this study: (1) a multi-cultivar experimental design that verified the stable correlation between sugar-related spectral features and internal SSC across different Hami melon varieties; (2) an RPD-weighted ensemble modeling strategy that balanced the fitting ability and generalization performance of linear and nonlinear models; and (3) a dual-position SSC measurement design that validated the robustness of the model for SSC prediction at different spatial positions in the pulp. This study confirms the feasibility of hyperspectral imaging technology for non-destructive SSC detection in the four tested Hami melon varieties under laboratory-controlled conditions. The proposed ensemble model achieved a marginal but stable improvement in overall prediction accuracy across the tested varieties compared with the optimal single model, providing a preliminary methodological reference and data support for the development of cross-cultivar non-destructive SSC detection models for Hami melon. Full article

Shannon’s differential entropy for continuous variables suffers from a fundamental limitation: it is not invariant under scale transformations. This makes entropy values dependent on the choice of measurement units rather than reflecting intrinsic properties of distributions. While Jaynes proposed the limiting density of discrete points (LDDP) as a theoretical solution, a concrete method for computing the required invariant measure has been lacking. This paper establishes a rigorous connection between Kullback–Leibler divergence and the invariant measure, providing theoretical proofs of invariance under affine transformations and a practical computational method. We prove that entropy normalized by the median of k-nearest neighbor distances is invariant under affine transformations (Theorems 1 and 2). The non-negativity of the resulting entropy has been validated empirically across all tested distribution families, though a complete theoretical proof remains an open question. This approach extends naturally to multivariate settings, enabling scale-invariant mutual information estimation. We provide open-source implementations in Julia (EntropyInvariant.jl) and Python (entropy_invariant) and demonstrate their advantages over traditional approaches, particularly for variables with disparate scales. Full article

Forestry, nursery, and planting tasks involve repetitive trunk flexion, squatting, and kneeling, as well as manual handling, increasing musculoskeletal load, and the need for mobility-related safety measures. Passive exoskeletons could mitigate postural exposure and reduce the overall body workload. We conducted a preliminary study (n = 14) to test the feasibility of a protocol and estimated model- and task-specific trends during standardized simulated nursery activities in a laboratory setting. Participants simulated planting and seeding tasks (loads of 0.5–2 kg) and material handling and preparation tasks (loads of 5–15 kg) without an exoskeleton (No-EXO) and with three passive models (EXO 1–EXO 3). EXO 3 was excluded from the planting tasks for feasibility reasons. Whole-body kinematics were recorded using an IMU-based motion capture system and converted into time-based ergonomic exposure outcomes (OWAS and RULA). Physiological load was monitored via heart-rate (HR) measurements. Compared to the No-EXO condition, exoskeleton use shifted posture exposure towards lower-risk categories. The largest improvements were observed with EXO 2 and EXO 3 during material handling (OWAS: −18%/−20%; RULA action-level reduction: −25%/−39%) and with EXO 2 during planting/seeding (OWAS: −15%; RULA: −26%). HR_max did not increase across tasks or conditions and HR tended not to rise with higher workload when exoskeletons were used. Overall, the results suggest positive ergonomic and workload trends related to the model and tasks. Field validation on uneven terrain with full personal protective equipment and harness integration is needed to confirm usability and support and to define implementation requirements (fit, compatibility with PPE, and safe-use conditions). Full article

Salinity stress severely limits barley production by disrupting physiological and biochemical processes critical for growth and yield. Although numerous studies have examined individual physiological or antioxidant responses to salinity, an integrated multivariate understanding of how these mechanisms collectively contribute to yield stability at the flowering stage remains limited. This study aimed to elucidate the integrated antioxidant and physiological mechanisms underlying salinity tolerance in barley genotypes during flowering. Barley plants were subjected to controlled salinity treatments, and a comprehensive set of phenolic compounds, antioxidant capacity indices, physiological traits, and yield components were measured. Multivariate analyses, including redundancy analysis (RDA) and partial least squares regression (PLSR), identified key traits contributing to yield stability under salinity. Multivariate analyses revealed also genotype-specific physiological strategies underlying contrasting salinity tolerance levels. Antioxidant defenses, such as total phenolics, DPPH and ABTS radical scavenging activities, and α-tocopherol, along with osmotic regulators like proline and soluble sugars, were closely associated with improved water status and reduced oxidative damage. These coordinated responses correlated strongly with yield components, including thousand-grain weight and main spike seed number. Notably, this study provides new insights into the predictive relevance of selected biochemical and physiological markers for yield performance under salt stress using PLSR at the flowering stage. PLSR further demonstrated the high predictive power of a limited subset of biochemical and physiological markers for yield traits under salt stress. Collectively, these findings reveal that the interplay between antioxidant machinery and osmotic adjustment at flowering is critical for barley resilience to salinity, providing valuable physiological markers to inform breeding strategies aimed at improving salt tolerance. Full article

The topic of sustainability reporting by SMEs is gaining significant importance in European contexts such as Italy. However, recent regulations, constantly evolving in terms of legal requirements and practical standards, do not yet provide solid foundations to guide small and medium-sized enterprises. This study aims to examine how Italian listed SMEs address sustainability issues in terms of Sustainable Development Goals (SDGs) in their sustainability reports, in light of the recent requirements set out in European directives (i.e., Directive 2022/2464/EU—Corporate Sustainability Reporting Directive (CSRD) and Directive 2025/794/EU—Stop the Clock). The analysis is based on a content review of 17 sustainability reports published in 2023 by Italian SMEs listed on Euronext Growth Milan of Borsa Italiana. The research protocol was structured around the key SDG themes found in the reports, using Python 3.14.2 libraries including Pandas, NumPy, NLTK, and Matplotlib. The findings highlight heterogeneous approaches to sustainability. Most firms adopt symbolic approaches based on formal narrative disclosures without addressing sustainability reporting’s substantive dimensions. They overlook both the principle of double materiality, actually recommended by the CSR Directive, and the provision of assurance statements on reports. Although mandatory sustainability reporting is not imminent, particularly in light of the “Stop the Clock” measure, this research offers significant insights into both theoretical and practical implications. From a theoretical standpoint, it contributes to the growing body of literature on sustainability practices among SMEs. From a managerial standpoint, it underscores the importance of designing tailored reporting practices for SMEs that avoid administrative costs and overload issues, at the same time fostering a substantive approach to disclosure able to convey meaningful information to stakeholders. Full article

In autonomous driving, motion sickness (MS) arises from physical or visual stimuli, or a combination of both. However, objective quantification of MS level (MSL) remains limited beyond questionnaire-based assessments. Using multimodal human signals (physiological and behavioral) collected in an autonomous driving simulator, this study addresses the association between these signals and MSL, across these MS types, by (i) screening and curating a decade of human-signal MS studies (HS-Set) to establish a data-driven foundation for selecting target sensor domains and features, (ii) constructing a dataset with subjective measures of MSL (fast motion sickness scale and simulator sickness questionnaire (SSQ)), alongside human signals (electroencephalogram (EEG), photoplethysmogram (PPG), electrodermal activity (EDA), skin temperature, and head/eye movement), (iii) conducting a correlation analysis between MSL and the identified features from HS-Set, and (iv) quantifying multivariable contributions at the feature and sensor domains through an explainable boosting machine (EBM). Key correlations include head amplitude/energy (pitch/surge) with SSQ total/oculomotor, eye entropy with nausea/oculomotor (positive), and EDA with nausea (negative). The EBM-based contribution analysis highlights EEG connectivity and head kinematics as dominant contributors; excluding EEG, the interpretability of single-domain models remains limited. Additionally, a combination of Head, PPG, and EDA domains retains over 80% of the full model’s interpretability. Full article

The paper presents a comprehensive methodological system for assessing the level of economic security of Russian regions, based on the synthesis of several complementary approaches and accounting for regional specifics. The central idea is a shift from static monitoring to dynamic analysis, which allows not only for capturing the current state but also for identifying the direction and stability of trends over time. The proposed methodology based on four stages: forming a set of indicators, normalizing their values, aggregating them into integral indices, and then visualizing them for operational decision-making. An important feature of sustainable development is the introduction of mechanisms to account for regional specifics through the clustering of regions and adjustment coefficients, which helps to mitigate the influence of geographical and structural differences on the results comparability. Together, they form an integrated system for diagnosing, planning, and monitoring the economic security of regions. The paper provides examples of threshold values for indicators such as the share of households with internet access, the length of the road network, birth rate, the volume of building commissioning, and innovation expenditures. A classification of regions into stability zones and recommendations for policy measures within each zone accompany the threshold analysis. In particular, for digitalization and transport infrastructure, measures are proposed to enhance monitoring, improve service accessibility, and invest in infrastructure; for the demographic component, measures are proposed to support families and improve quality of life. The practical significance of the research lies in creating a universal, yet flexible, toolkit for monitoring, ranking, and planning regional policy in the field of economic security. The proposed system was designed for application both at the federal level and for interregional analysis, including scenario planning and modeling the impact of management decisions. Thus, this study contributes to the literature by bridging the theory of economic security, the imperatives of sustainable regional development, and the practical potential of information technologies. It offers a concrete, scalable methodology for transforming regional economic security management into a data-driven, forward-looking, and context-sensitive process. In the future, the authors intend to further develop the methodology by considering the sectoral specialization of regions, integrating with medium- and long-term forecasting systems, and creating an automated monitoring platform. Full article

Machine learning (ML) models can complement traditional measurement-based approaches by supporting large-scale screening, spatial analysis, and prioritization of buildings for testing of indoor radon, a leading cause of lung cancer among non-smokers. Originating from uranium decay in soil and rock, radon enters homes via foundation cracks and accumulates indoors, influenced by building characteristics, ventilation, urbanization, and geogenic factors. As part of the Zagreb pilot within the “Evidence Driven Indoor Air Quality Improvement” (EDIAQI) project, this is the first ML application for indoor radon analysis in Croatia. This research evaluates residential indoor radon concentrations in Zagreb using ML applied to a dataset of 80 households. Several linear regression and tree-based ensemble methods were tested. The best-performing model (GBR) achieved an R² of 0.99 on the training set and 0.57 on the test set, with an RMSE of 33 Bq/m³ and MAE of 26 Bq/m³. Although predictive performance was moderate and generalization limited, key building characteristics such as construction year, dwelling type, occupancy details, and floor level were identified as relevant variables. The results suggest that machine learning may support radon risk prioritization in urban environments, but cannot replace direct measurements for regulatory purposes. Full article