Search Results (3,484)

We study equations with real polynomials of arbitrary degree, such that each coefficient has a small, individual error; this may originate, for example, from imperfect measuring. In particular, we study the influence of the errors on the roots of the polynomials. The errors are modeled by imprecisions of Sorites type: they are supposed to be stable to small shifts. We argue that such imprecisions are appropriately reflected by (scalar) neutrices, which are convex subgroups of the nonstandard real line; examples are the set of infinitesimals, or the set of numbers of order $\epsilon$, where $\epsilon$ is a fixed infinitesimal. The Main Theorem states that the imprecisions of the roots are neutrices, and determines their shape. Full article

Soft skills such as communication and collaboration are vital in both professional and educational settings, yet difficult to train and assess objectively. Traditional role-playing scenarios rely heavily on subjective trainer evaluations—either in real time, where subtle behaviors are missed, or through time-intensive post hoc analysis. Virtual reality (VR) offers a scalable alternative by immersing trainees in controlled, interactive scenarios while simultaneously capturing fine-grained behavioral signals. This study investigates how task design in VR shapes non-verbal and paraverbal behaviors during dyadic collaboration. We compared two puzzle tasks: Task 1, which provided shared visual access and dynamic gesturing, and Task 2, which required verbal coordination through separation and turn-taking. From multimodal tracking data, we extracted features including gaze behaviors (eye contact, joint attention), hand gestures, facial expressions, and speech activity, and compared them across tasks. A clustering analysis explored whether o not tasks could be differentiated by their behavioral profiles. Results showed that Task 2, the more constrained condition, led participants to focus more visually on their own workspaces, suggesting that interaction difficulty can reduce partner-directed attention. Gestures were more frequent in shared-visual tasks, while speech became longer and more structured when turn-taking was enforced. Joint attention increased when participants relied on verbal descriptions rather than on a visible shared reference. These findings highlight how VR can elicit distinct soft skill behaviors through scenario design, enabling data-driven analysis of collaboration. This work contributes to scalable assessment frameworks with applications in training, adaptive agents, and human-AI collaboration. Full article

Accurate and reliable characterization of current transformer (CT) performance is essential for maintaining grid stability and power quality in modern electrical networks. CT measurements are key to effective monitoring of harmonic distortions, supporting regulatory compliance and ensuring the safe operation of the grid. This paper addresses a method for the characterization of CTs across an extended frequency range from 50 Hz up to 150 kHz, driven by increasing power quality issues introduced by renewable energy installations and non-linear loads. Traditional CT calibration approaches involve measurement setups that offer ppm-level uncertainty but are complex to operate and limited in practical frequency range. To simplify and expand calibration capabilities, a calibration system employing a sampling ammeter (power analyzer) was developed, enabling the direct measurement of CT secondary currents of an unknown CT and a reference CT without any further auxiliary equipment. The resulting expanded magnitude ratio uncertainties for the wideband CT calibration system are 10 ppm ($k=2$) up to 10 kHz and less than 120 ppm from 10 kHz to 150 kHz; these uncertainties do not include the uncertainty of the reference CT. Additionally, the operational conditions and setup design choices, such as instrument warm-up duration, grounding methods, measurement shunt selection, and cable type, were evaluated for their impact on measurement uncertainty and repeatability. The results highlight the significance of minimizing parasitic impedances at higher frequencies and maintaining consistent testing conditions. The developed calibration setup provides a robust foundation for future standardization efforts and practical guidance to characterize CT performance in the increasingly important supraharmonic frequency range. Full article

Although various special materials have been studied for their potential for phosphorus removal in constructed wetlands, varying methodologies make direct comparisons of adsorption capacities observed in laboratory experiments difficult. This paper aims to establish a methodology for determining the optimal ratio of phosphate to material mass for different materials and for achieving the necessary contact time for adsorption isotherms. To minimise the number of experiments required, pretests over 24 h should be repeated to determine the phosphate-specific ratios until they show around 60% of the initial concentration. The tested materials included lava sand and expanded sand (ExS), which showed saturating kinetics curves after 24 to 48 h. However, aggregates containing calcium silicate hydrate (CSH) phases (autoclaved aerated concrete AAC, sand–lime brick SLB, and hydrothermal granules HTG) did not show saturating curves, complicating contact time determination. Consequently, adsorption velocity is proposed to identify the phase with the lowest adsorption rate, which is then used as the contact time in adsorption isotherm experiments. Using this method, adsorption times of 48 h were observed for HTG and SLB, while that for AAC was 24 h. This methodology is intended as an initial approach to establish a common basis for researchers investigating novel materials and make the results comparable. Full article

During the last decade, artificial intelligence (AI) has enabled key technological innovations within the modern dementia and frailty healthcare and prevention landscape. This has boosted the impact of technology in the clinical setting, enabling earlier diagnosis with improved specificity and sensitivity, leading to accurate and time-efficient support that has driven the development of preventative interventions minimizing the risk and rate of progression. Background/Objectives: The rapid ageing of the European population places a substantial strain on the current healthcare system and imposes several challenges. COMFORTage is the joint effort of medical experts (i.e., neurologists, psychiatrists, neuropsychologists, nurses, and memory clinics), social scientists and humanists, technical experts (i.e., data scientists, AI experts, and robotic experts), digital innovation hubs (DIHs), and living labs (LLs) to establish a pan-European framework for community-based, integrated, and people-centric prevention, monitoring, and progression-managing solutions for dementia and frailty. Its main goal is to introduce an integrated and digitally enabled framework that will facilitate the provision of personalized and integrated care prevention and intervention strategies on dementia and frailty, by piloting novel technologies and producing quantified evidence on the impact to individuals’ wellbeing and quality of life. Methods: A robust and comprehensive design approach adopted through this framework provides the guidelines, tools, and methodologies necessary to empower stakeholders by enhancing their health and digital literacy. The integration of the initial information from 13 pilots across 8 European countries demonstrates the scalability and adaptability of this approach across diverse healthcare systems. Through a systematic analysis, it aims to streamline healthcare processes, reduce health inequalities in modern communities, and foster healthy and active ageing by leveraging evidence-based insights and real-world implementations across multiple regions. Results: Emerging technologies are integrated with societal and clinical innovations, as well as with advanced and evidence-based care models, toward the introduction of a comprehensive global coordination framework that: (a) improves individuals’ adherence to risk mitigation and prevention strategies; (b) delivers targeted and personalized recommendations; (c) supports societal, lifestyle, and behavioral changes; (d) empowers individuals toward their health and digital literacy; and (e) fosters inclusiveness and promotes equality of access to health and care services. Conclusions: The proposed framework is designed to enable earlier diagnosis and improved prognosis coupled with personalized prevention interventions. It capitalizes on the integration of technical, clinical, and social innovations and is deployed in 13 real-world pilots to empirically assess its potential impact, ensuring robust validation across diverse healthcare settings. Full article

Background/Objectives: Parkinson’s disease (PD) and multiple sclerosis (MS) are neurological conditions that result in debilitating non-motor symptoms, such as anxiety and depression, which significantly reduce quality of life and often persist despite pharmacological treatment. As a result, effective alternative treatment strategies are needed. Exercise therapy—particularly aerobic training—has shown promise in alleviating non-motor symptoms, potentially through neuroplastic adaptations. However, traditional aerobic exercise is often time-consuming and monotonous, limiting long-term adherence. High-intensity interval training (HIIT) offers a time-efficient and potentially more engaging alternative, though its effects on non-motor symptoms in PD and MS remain understudied. Methods: This transdiagnostic randomized controlled trial will enroll 48 participants (24 PD, 24 MS) with clinically significant affective symptoms (hospital anxiety and depression scale [HADS] ≥ 8). The participants will be randomly assigned to one of three 8-week interventions: (1) HIIT, 5–6 intervals of 45 s of high-intensity cycling; (2) continuous aerobic training (CAT), 50 min of low-intensity cycling; (3) movement advice (MA), step goals, and physical education. The primary (affective symptoms) and secondary outcomes (cognition, fatigue, sleep, motor function) will be assessed at four time points: 4 and 1 weeks pre intervention, and 1 and 4 weeks post intervention. Weekly blood samples and pre/post brain imaging will be collected to study biofluid and MRI measures for potential neuroplasticity. Linear mixed models will analyze the time and group effects. Discussion: This trial will assess whether HIIT can more effectively improve non-motor and motor symptoms in PD and MS than CAT or MA. A multimodal approach will explore both the clinical outcomes and underlying mechanisms, informing scalable and engaging rehabilitation strategies. Full article

Purpose: The synthesis of nanoscale particles with antibacterial properties has garnered significant attention in pharmaceutical research, driven by the escalating threat of antibiotic-resistant bacteria. This study investigates the antibacterial efficacy of Zn–Co ferrite nanoparticles against virulent, antibiotic-resistant, and biofilm-forming strains of Escherichia coli. Methods: Three nanoparticle variants—S1 (Zn_0.7Co_0.3Fe₂O₄), S2 (Zn_0.5Co_0.5Fe₂O₄), and S3 (Zn_0.3Co_0.7Fe₂O₄)—were synthesized using the solution combustion method by systematically varying the Zn:Co molar ratio. The Scanning Electron Micrograph, X-ray diffraction analysis, Complementary Fourier-transform infrared, Minimum Inhibitory Concentration, and Minimum Bactericidal Concentration were performed. Results: The SEM spectroscopy study revealed distinct morphological differences as a function of the cobalt substitution level within the spinel ferrite matrix. At the highest level of cobalt substitution (Zn_0.3Co_0.7Fe₂O₄), the microstructure displayed significant irregularities, with enhanced agglomeration and a notably broader particle size distribution. X-ray diffraction analysis confirmed the formation of crystalline structures, with an average crystallite size of 12.65 nm. Complementary Fourier-transform infrared spectroscopy revealed characteristic absorption bands in the 400–600 cm⁻¹ range, indicative of the cubic spinel structure of the ferrite nanoparticles. The higher-frequency band was associated with metal–oxide stretching in the tetrahedral sites, while the lower-frequency band corresponded to stretching in the octahedral sites. The Minimum Inhibitory Concentration and Minimum Bactericidal Concentration assays revealed that Zn–Co ferrite nanoparticles possess potent antibacterial activity against virulent, antibiotic-resistant, and biofilm-forming strains of E. coli. Conclusion: Increasing the molar ratio of Zn to Co enhances the antibacterial activity of the nanoparticles. These findings suggest that Zn–Co ferrite nanoparticles could serve as a promising alternative to conventional antibacterial agents for combating multidrug-resistant pathogenic bacteria in the future. Full article

Patient-tailored, 3D-printed surgical guides offer significant potential to improve precision and therapeutic efficacy in cardiac ablation surgery. However, reliable post-sterilization material performance presents a critical yet underexplored barrier to clinical adoption. This study investigates how disinfection and sterilization impact the mechanical and thermal properties of photopolymer resins. Specimens from two 3D-printing resins (Bioflex A80 MB™, 3Dresyns; MED625FLX™, Stratasys) were treated with four combinations of disinfection techniques (low-temperature manual cleaning; high-temperature machine washing) and sterilization techniques (H₂O₂ vs. autoclaving). We assessed post-sterilization properties by mechanical (material integrity, bending tests), thermal (differential scanning calorimetry, thermogravimetric analysis), and viscoelastic (dynamic mechanical analysis) studies. Statistical analysis was performed using one-way ANOVA with Bonferroni post hoc tests (α = 0.05). From this preliminary study, we conclude that MED625FLX maintains integrity and flexibility across all tested disinfection and sterilization methods. Bioflex A80 MB is only suitable for low-temperature disinfection–sterilization, as high-temperature treatments cause surface cracking. Neither resin is appropriate for cryogenic conditions due to the risk of brittleness. Further research into post-sterilization properties is essential to ensure the safety and clinical reliability of these materials in cardiac procedures. Full article

The G Block of Daqing Oilfield is a crucial area for sustainable development and stable production. In addressing the technical bottlenecks of high-resolution logging data interpretation for reservoir evaluation in the Block, this study proposes a resistivity curve prediction method based on machine learning algorithms. Traditional interpretation models relying on DLS logging data face two major challenges when applied to 0.2 m high-resolution logging: first, the interpreted effective thickness of the reservoir tends to be overestimated, and second, the accuracy of fluid property identification declines. Additionally, the lack of corresponding well-test data for new logging datasets further constrains the development of interpretation models. To tackle these challenges, this study employs the XGBoost algorithm to construct a high-precision resistivity prediction model. Through systematic analysis of various logging parameter combinations, the optimal feature set comprising HAC, MSFL, and GR curves was identified. Training and testing results demonstrate that the model achieves a mean absolute error (MAE) of 0.94 Ω·m and a root mean square error (RMSE) of 1.79 Ω·m in predicting resistivity. After optimization, the model’s performance improved significantly, with MAE and RMSE reduced to 0.75 Ω·m and 1.31 Ω·m, respectively. To evaluate the model’s reliability, an external validation test was conducted on Well GFX2, yielding MAE and RMSE values of 0.91 Ω·m and 1.43 Ω·m, confirming the model’s strong generalization capability. Furthermore, the RLLD-AC and RLLD-DEN crossplots constructed from the predicted results exhibit excellent fluid identification performance in practical applications, achieving an accuracy rate exceeding 89%, which aligns well with production test data. The findings of this study provide new technical support for fine reservoir characterization in the study area and offer significant practical guidance for development plan adjustments. Full article

Community Land Trusts (CLTs) have emerged as alternative housing models mainly taken up by civil society organizations aiming to de-commodify land and ensure long-term affordable housing, while fostering participatory democratic governance and (re)claiming the right to homeownership. Drawing on empirical evidence from the CLT in Leuven (Belgium) and research conducted between November 2022 and February 2025, this study examines state-led CLTs and their potential in providing affordable housing and democratizing housing systems. The leading role of local authorities serves as a catalyst facilitating access to land and resources while setting up democratic and collaborative governance processes towards the creation of housing commons. However, their involvement introduces market mechanisms that undermine long-term affordability. This research mobilizes the literature on commons and commoning, housing affordability debates and governance theories to explore the paradox of state-led CLTs: Can they democratize housing governance, or does state involvement inevitably reinforce the market mechanisms they seek to counteract? The paper argues that states can initiate commons without fully co-opting them, provided governance is polycentric and reflexive. The contribution of state-led housing commons lies not in radical rupture but in incremental decommodification and emergent commoning, showing how commons can evolve within capitalist states. Full article

Objectives: This study investigated the acute effects of pre-exercise cold-water immersion (CWI) on performance, muscle oxygen saturation, and mechanical muscle tension during calf raise training. Method: Twenty-four trained individuals (5 females, 19 males) were randomly assigned to either a CWI group (5 min at 10 ± 1 °C) or a non-CWI group (no intervention). Both groups performed three sets of standing calf raises to failure using a standardized protocol. Load lifted, repetitions, and rate of perceived exertion (RPE) were recorded. Muscle oxygenation (SmO₂, total hemoglobin) and mechanical muscle properties (frequency and stiffness) were measured before and after each set. Results: The CWI group showed a significantly greater increase in barbell load from set 1 to set 2 compared to the non-CWI group (from 94.5 ± 18.1 kg to 98.0 ± 18.7 kg, p < 0.01). Repetitions decreased and RPE increased across sets in both groups. The non-CWI group exhibited earlier increases in muscle stiffness and frequency, whereas these responses were delayed in the CWI group. Gastrocnemius SmO₂ increased during the protocol in the non-CWI group only. Total hemoglobin change was greater in the CWI group in set 1. Conclusions: These findings suggest that pre-exercise CWI may acutely enhance performance and delay neuromuscular fatigue without negatively affecting perceived effort. Full article

Background: Some dogs with severe mitral valve regurgitation can have chronic cough, but its cause is unclear. Many of these dogs exhibit left principal (synonym mainstem) bronchial collapse or compression. Some clinicians believe that an enlarged heart, particularly the dilated left atrium, compresses the left principal bronchus, while others argue that a healthy bronchus cannot be compressed and that the cough is due to concurrent airway disease, such as bronchomalacia. In this study, we aimed to investigate the correlation between chronic cough and radiographic signs of bronchial narrowing in dogs with cardiomegaly secondary to primary mitral valve regurgitation. Methods: Four radiologists independently evaluated 51 sets of thoracic radiographs from client-owned dogs with cardiomegaly due to severe mitral valve regurgitation, including 26 sets of radiographs belonging to dogs with chronic (>8 weeks) cough and 25 without cough. The presence or absence of bronchial narrowing was assessed while the radiologists were blinded to patient characteristics and each other’s findings. Results: Interobserver variability was high, and the radiologists could not reliably predict which dogs had a chronic cough. Dogs with left atrial enlargement and airway narrowing sometimes coughed and sometimes did not. Likewise, some dogs with left atrial enlargement but no signs of airway compression or collapse coughed, and some did not. It was impossible to distinguish the groups based on these features. Conclusions: Bronchial compression by an enlarged heart and left atrium is an unlikely cause of chronic cough in dogs with severe primary mitral valve regurgitation. Full article

Background/Objectives: Rhabdomyolysis is a potentially life-threatening condition characterized by acute skeletal muscle breakdown. In addition to well-described external triggers, a genetic contribution is increasingly recognized. We aimed to (I) review the genetic diagnostic approach of rhabdomyolysis, (II) evaluate the clinical characteristics indicative of a genetic susceptibility with the ‘RHABDO’ acronym, and (III) assess the predictive value of the presence RHABDO features for identifying genetic variants. Methods: In this retrospective two-center study, 122 patients underwent genetic testing for rhabdomyolysis since the introduction of whole exome sequencing (WES) in 2013. The presence of RHABDO features was compared between those with (likely) pathogenic variants and those with benign or no identified variants or variants of uncertain significance. Results: The testing methods included panel-based WES (82%), Sanger sequencing (49%), and full WES (24%), of which 52 patients (43%) underwent multiple methods. A (likely) pathogenic variant was identified in 13 patients (11%), in all of whom ≥2 RHABDO features were present. The positive predictive value for ≥2 features was 14%, while the negative predictive value was 100%. Conclusions: These results highlight the relevance of WES in further elucidating the genetic basis of rhabdomyolysis and demonstrated that RHABDO is a valuable tool for selecting patients who should undergo genetic testing. Full article

The food additive sodium benzoate (SB) has been used for decades as an antimicrobial to prevent food spoilage. SB has been deemed to pose no risk to human health when consumed at levels under 5 mg/kg body weight per day; however, when many of the supporting studies were conducted, the importance of the gut microbiome to human health was not yet appreciated. Given SB’s known antimicrobial qualities, it is important to assess the effect of this food additive on the human gut microbiome. The ex vivo SIFR^® (Systemic Intestinal Fermentation Research) technology was used to test the effect of SB on microbial communities from 24 donors, aged infants to older adults. A dose of 3.5 g/L SB elicited a drop in the Pseudomonadota phylum for multiple age groups but did not alter the alpha or beta diversity within any of these groups. This was accompanied by changes in the functional outputs that included an overall rise in butyrate and a drop in propionate production. This higher butyrate correlates with an increase in the abundance of several known butyrate producers in the presence of SB, although the genetic potential for its production in the community did not change. Overall, despite using a dose ten times higher than the accepted daily intake limit, the effect on the gut microbiome was minimal. Full article

Symmetry and asymmetry between past and future knowledge are at the heart of continual learning. Deep neural networks typically lose the temporal symmetry that would preserve earlier knowledge when the network is trained sequentially, a phenomenon known as catastrophic forgetting. Dynamically expandable networks (DENs) attempt to restore symmetry by allocating a dedicated module—such as a feature extractor or a task token—for every new task while freezing all previously learned modules. Although this strategy yields high average accuracy, we observe a pronounced asymmetry: earlier tasks still degrade over time, indicating that frozen modules alone do not guarantee knowledge conservation. Moreover, feature bias, arising from the imbalance between old and new samples, further exacerbates the forgetting issue. This raises a fundamental challenge: how can multiple feature extractors be coordinated more effectively to mitigate catastrophic forgetting while enabling the robust acquisition of new tasks? To address this challenge, we propose two asymmetric, contrastive auxiliary losses that exploit rich information from previous tasks to guide new task learning. Specifically, our approach integrates features extracted by both frozen and current modules to reinforce task boundaries while facilitating the learning process. In addition, we introduce a feature adjustment mechanism to alleviate the bias caused by class imbalance. Extensive experiments on benchmarks, including DyTox and MCG, demonstrate that our approach reduces catastrophic forgetting and achieves state-of-the-art performance on ImageNet-100. Full article