Search Results (7,583)

The significance of access to energy sources for fostering innovation is increasing. Regions should, however, base their competitiveness not merely on innovation, but also on social cohesion and ecological ambitions. In this context, the objective of this article is to evaluate the sustainable competitiveness potential of Polish regions from the perspective of energy-related factors, as well as to identify the trends and the disparities observed over the past decade. The study employs a multidimensional comparative analysis (MCA), operationalized through the development of a Synthetic Measure of Potential (SMP) constructed from ten disaggregated indicators encompassing resource-related, economic, environmental, and social dimensions of energy. This approach is complemented by a cluster analysis using Ward’s method to identify patterns and groupings within the data. The empirical results demonstrate that sustainable competitiveness potential with regard to energy factors has generally increased, although it was not a linear process. The most favorable trend was observed for the generation of energy from renewable sources. An interesting side effect of transformation was observed in the energy balance. Further, despite the significant decrease in industrial electricity consumption per unit of gross value added, the energy poverty level increased. The study offers several practical implications for advancing the green transformation, emphasizing the uneven regional impacts of this process and underscoring the necessity of a coordinated policy framework to support the energy transition. Full article

(1) Background: The use of wearable technology for assessing running biomechanics in field-based sports has increased in recent years. Inertial measurement units (IMUs) are low-cost, non-invasive devices capable of estimating spatiotemporal gait-related metrics during overground locomotion. This study evaluated the accuracy and concurrent validity of a foot-mounted IMU system for estimating sprinting kinematics. (2) Method: Twenty-five elite and sub-elite athletes completed four maximal 10-metre fly efforts, with their kinematics measured concurrently using a three-dimensional motion analysis system and IMUs. (3) Result: The foot-mounted IMU system’s root mean square errors for stride length and duration were 0.22 m and 0.04 s, respectively. Mean biases (95% level of agreement) were −0.67 $\mathrm{m} · {\mathrm{s}}^{-1}$ (−1.19; −0.14) for peak velocity, −0.51 $\mathrm{m} · {\mathrm{s}}^{-1}$ (−1.10; 0.09) for instantaneous velocity, and 0.17 $\mathrm{m} · {\mathrm{s}}^{-2}$ (−1.04; 1.37) for instantaneous acceleration. Stride length, duration, and cadence were −0.07 m (−0.36; 0.23), 0.02 s (−0.02; 0.06), and −4.64 strides $· {\min }^{-1}$ (−15.82; 6.53), respectively. (4) Conclusions: End users implementing this technology in research and practice should interpret this study’s findings relative to their analytical objectives, logistical resources, and operational constraints. Therefore, its adoption should be guided by the specific performance metrics of interest and the extent to which the system’s capabilities align with the outcomes the end user aims to achieve. Full article

Background: Systemic inflammation is an essential factor in the formation of the tumor microenvironment and has an impact on patient response to immune checkpoint inhibitors. Although there is a growing interest in biomarkers of inflammation, there is a gap in understanding their predictive value for response to nivolumab in clinical practice. The objective of this research was to design and assess a multi-algorithmic machine learning (ML) model based on regular systemic inflammation measurements to forecast the response of treatment to nivolumab. Methods: An analysis of a retrospective real-world cohort of 177 nivolumab-treated patients was performed. Baseline inflammatory biomarkers, such as neutrophils, lymphocytes, platelets, CRP, LDH, albumin, and derived indices (NLR, PLR, SII), were derived. After preprocessing, 5 ML models (Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, and Neural Network) were trained and tested on a 70/30 stratified split. Accuracy, AUC, precision, recall, F1-score, and Brier score were used to evaluate predictive performance. The interpretability of the model was analyzed based on feature-importance ranking and SHAP. Results: Gradient Boosting performed best in terms of discriminative (AUC = 0.816), whereas Support Vector Machine performed best on overall predictive profile (accuracy = 0.833; F1 = 0.909; recall = 1.00; and Brier Score = 0.134) performance. CRP and LDH became the most common predictors of all models, and then neutrophils and platelets. SHAP analysis has verified that high CRP and LDH were strong predictors that forced the prediction to non-response, whereas higher lymphocyte levels were weak predictors that increased the response probability prediction. Conclusions: Machine learning models based on common inflammatory systemic markers give useful predictive information about nivolumab response. Their discriminative ability is moderate, but the high performance of SVM and Gradient Boosting pays attention to the opportunities of inflammation-based ML tools in making personalized decisions regarding immunotherapy. A combination of clinical, radiomic, and molecular biomarkers in the future can increase predictive capabilities and clinical use. Full article

Introduction: Ropeginterferon alfa-2b is an emerging treatment for polycythemia vera, with growing interest in its application for essential thrombocythemia and early myelofibrosis due to its extended dosing intervals and favorable tolerability profile. However, real-world evidence regarding its dosing strategies and titration practices remains limited. Objective: This study examined seven younger patients, all under 60 years of age, who were treated with ropeginterferon alfa-2b. Materials and Methods: This study is a retrospective medical records review of consecutive patients from seven hospitals. Treatment was initiated at a dose of 250 micrograms, with a maintenance dose of 500 micrograms. Results: The regimen demonstrated good safety and tolerability in this real-world setting. Hematological responses were observed, along with a meaningful reduction in JAK2V617F variant allele frequency across the patient cohort. Conclusions: These findings show that the use of high-initial-dose accelerated titration (HIDAT) regimen of ropeginterferon alfa-2b is a safe and effective treatment option for younger patients with myeloproliferative neoplasms. Full article

Background: Our growing understanding of the brain basis of mind has seen an interest in evolutionarily ancient structures, most notably the brainstem. This paper offers an interesting example of this underexplored territory, by considering the mesencephalic component of the trigeminal nucleus. This largely uncelebrated brainstem structure is central to control of the jaw, and for the foundational acts of eating, oral exploration, and biting. Objectives: This paper explores the interesting anatomy of the mesencephalic trigeminal: unique in the nervous system as a centrally located sensory ganglion, which combines sensory and motor function for the jaw. An unexplored aspect of its anatomy is that the mesencephalic component of the nucleus lies directly adjacent to the brain’s core system for the experience of emotion, the peri-acqueductal gray (PAG). Results: The data suggest a role for the jaw, and more broadly the oral cavity, in relation to a range of feeling states, from pleasure to aggression. This is supported by behavioural and classic neuropsychological findings, such as the Klüver-Bucy syndrome. However, the proposal is not well-supported by findings of direct connections between the trigeminal nucleus and the PAG. Conclusions: While these contrasting findings present a conundrum, there may be a role for non-synaptic signalling, of the sort increasingly understood to be important for interoception and homeostasis. Full article

Most datasets encountered in computer vision and medical applications present symmetries that should be taken into account in classification tasks. A typical example is the symmetry by rotation and/or scaling in object detection. A common way to build neural networks that learn the symmetries is to use data augmentation. In order to avoid data augmentation and build more sustainable algorithms, we present an alternative method to mod out symmetries based on the notion of section of a principal fiber bundle. This framework allows to use simple metrics on the space of objects in order to measure dissimilarities between orbits of objects under the symmetry group. Moreover, the section used can be optimized to maximize separation of classes. We illustrate this methodology on a dataset of contours of objects for the groups of translations, rotations, scalings and reparameterizations. In particular, we present a 2-parameter family of canonical parameterizations of curves, containing the constant-speed parameterization as a special case, which we believe is interesting in its own right. We hope that this simple application will serve to convey the geometric concepts underlying this method, which have a wide range of possible applications. Full article

Aiming at multi-agent interest demands and environmental benefits, a distributionally robust game-theoretic optimization algorithm based on a green certificate–carbon trading mechanism is proposed for uncertain microgrids. At first, correlated wind–solar scenarios are generated using Kernel Density Estimation and copula theory and the probability distribution ambiguity set is constructed combining 1-norm and $\infty$-norm metrics. Subsequently, with gas turbines, renewable energy power producers, and an energy storage unit as game participants, a two-stage distributionally robust game-theoretic optimization scheduling model is established for microgrids considering wind and solar correlation. The algorithm is constructed by integrating a non-cooperative dynamic game with complete information and distributionally robust optimization. It minimizes a linear objective subject to linear matrix inequality (LMI) constraints and adopts the column and constraint generation (C&CG) algorithm to determine the optimal output for each device within the microgrid to enhance its overall system performance. This method ultimately yields a scheduling solution that achieves both equilibrium among multiple stakeholders’ interests and robustness. The simulation result verifies the effectiveness of the proposed method. Full article

Aimed at the problems of traditional wood species identification relying on manual experience, slow identification speed, and insufficient robustness, this study takes hyperspectral images of cross-sections of 10 typical wood species commonly found in Puer, Yunnan, China, as the research object. It comprehensively applies various spectral and texture feature extraction technologies and proposes an intelligent wood species identification method based on the fusion of multimodal texture-dominated features and deep learning. Firstly, an SOC710-VP hyperspectral imager is used to collect hyperspectral data under standard laboratory lighting conditions, and a hyperspectral database of wood cross-sections is constructed through reflectance calibration. Secondly, in the spectral space construction stage, a comprehensive similarity matrix is built based on four types of spectral similarity indicators. Representative bands are selected using two Max–Min strategies: partitioned quota and coverage awareness. Multi-scale wavelet fusion is performed to generate high-resolution fused images and extract interest point features. Thirdly, in the texture space construction stage, three types of texture feature matrices are generated based on the PCA first principal component map, and interest point features are extracted. Fourthly, in the complementary collaborative learning stage, the ST-former model is constructed. The weights of the trained SpectralFormer++ and TextureFormer are imported, and only the fusion weights are optimized and learned to realize category-adaptive spectral–texture feature fusion. Experimental results show that the overall classification accuracy of the proposed joint model reaches 90.27%, which is about 8% higher than that of single-modal models on average. Full article

Over the years, neurons undergo functional changes initially linked to the maturation of the brain and then are progressively linked to normal aging. The curious relationship between brain decay, aging, and neuronal diseases has aroused the interest of numerous studies to better understand and contrast the evolution of these pathologies. The objective of this research is to apply the non-equilibrium thermodynamic theory with the internal variables of the study of the rheological properties of the brain, focusing on the study of viscoelastic properties. After a thermodynamic introduction of the principal rheological phenomena, this paper discusses the results by the application of our mathematical technique, which revealed a prevalence of anelastic properties in the old central nervous system compared to the young one. Furthermore, the entropy production trend tested identifies a greater disorder in the young brain in respect to the old one. The results obtained highlight that a lower stiffness in the old central nervous system may be interpreted with dendritic regression associated with neuronal death, both being potential consequences of an increased production of free radicals due to reduced antioxidant defenses and/or an altered mitochondrial dysfunction in aging. Full article

Ultra-Wideband (UWB) technology enables centimeter-level indoor positioning, but it remains highly sensitive to channel dynamics, multipath and Non-Line-of-Sight (NLoS) propagation. Recent studies increasingly apply Machine Learning (ML) methods to address these issues by modeling nonlinear channel behavior and mitigating ranging bias. This paper presents a comprehensive review and provides a critical synthesis of 169 research works published between 2020 and 2024, offering an integrated overview of how ML techniques are incorporated into UWB-based Indoor Positioning Systems (IPSs). The studies are grouped according to their functional objective, learning algorithm, network architecture, evaluation metrics, dataset, and experimental setting. The results indicate that most approaches apply ML to channel classification and ranging error mitigation, with Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid CNN–Long Short-Term Memory (LSTM) architectures being among the most common choices due to their ability to capture spatial and temporal patterns in the Channel Impulse Response (CIR). Despite the reported accuracy improvements, scalability and cross-environment generalization remain open challenges, largely due to the scarcity of public datasets and the lack of standardized evaluation protocols. Emerging research trends highlight growing interest in transfer learning, domain adaptation, and federated learning, along with lightweight and explainable models suitable for embedded and multi-sensor systems. Overall, this review summarizes the progress made in ML-driven UWB localization, identifies current gaps, and outlines promising directions toward more robust and generalizable indoor positioning frameworks. Full article

Background: Rhythmic sensory stimulation, including structured musical interventions, has gained renewed interest as a non-pharmacological strategy that may modulate cortical excitability and network stability in focal epilepsy. Although several small studies have reported changes in seizure frequency or epileptiform activity during rhythmic or music exposure, the underlying mechanisms and translational relevance remain insufficiently synthesized. Objective: This narrative review summarizes clinical evidence on music-based and rhythmic sensory interventions in focal epilepsy, outlines plausible neurophysiological mechanisms related to neural entrainment and large-scale network regulation, and discusses emerging opportunities for digital delivery of rhythmic protocols in everyday self-management. Methods: A structured search of recent clinical, neurophysiological, and rehabilitation literature was performed with emphasis on rhythmic auditory, tactile, and multimodal stimulation in epilepsy or related conditions. Additional theoretical and translational sources addressing oscillatory dynamics, entrainment, timing networks, and patient-centered digital tools were reviewed to establish a mechanistic framework. Results: Existing studies—although limited by small cohorts and heterogeneous methodology—suggest that certain rhythmic structures, including specific musical compositions, may transiently modulate cortical synchronization, reduce epileptiform discharges, or alleviate seizure-related symptoms in selected patients. Evidence from neurologic music therapy and rhythmic stimulation in other neurological disorders further supports the concept that externally delivered rhythms can influence timing networks, attentional control, and interhemispheric coordination. Advances in mobile health platforms enable structured rhythmic exercises to be delivered and monitored in real-world settings. Conclusions: Music-based and rhythmic sensory interventions represent a promising but underexplored adjunctive approach for focal epilepsy. Their effectiveness likely depends on individual network characteristics and on the structure of the applied rhythm. Digital integration may enhance personalization and adherence. Rigorous clinical trials and mechanistic studies are required to define optimal parameters, identify responders, and clarify the role of rhythmic stimulation within modern epilepsy care. Full article

Background/Objectives: Extracellular vesicles (EVs) are nanosized carriers with high biocompatibility, low immunogenicity, and the ability to cross biological barriers, making them attractive for drug delivery. Despite growing interest, the clinical translation of drug-loaded EVs remains limited. This systematic review aimed to summarize current evidence on EV sources, loading strategies, therapeutic applications, and translational challenges. Methods: Following PRISMA 2020 guidelines, a systematic search was conducted in Embase, PubMed, Reaxys, and Scopus for the period 2020–2025. Eligible studies included original articles on drug-loaded EVs from human, animal, plant, or other sources. Data on EV source, drug type, particle size, loading method, administration route, and therapeutic application were extracted. Clinical trials were identified through ClinicalTrials.gov. Results: A total of 65 studies were included after screening 5316 records, along with two clinical trials. Human mesenchymal stem cell (MSC)-derived EVs were the most frequent source in oncology, while plant-derived EVs predominated in non-oncology applications. Anti-cancer drugs such as doxorubicin, gemcitabine, and docetaxel were most frequently loaded, alongside curcumin, berberine, and atorvastatin. EV sizes generally ranged from 50 to 200 nm, with larger vesicles reported for plant-derived EVs. Intravenous administration predominated, with most studies demonstrating sustained release and enhanced therapeutic efficacy. Passive loading was most common, especially for hydrophobic drugs, whereas active methods such as electroporation and sonication were preferred for hydrophilic cargo. Two clinical trials showed preliminary therapeutic benefits with favorable safety. Conclusions: Drug-loaded EVs represent a promising and versatile drug delivery platform, yet their clinical translation is hindered by variability in isolation and loading methods, production scalability, and safety evaluation. Further standardization and large-scale studies are needed to advance EV-based therapeutics toward clinical use. Full article

Background/Objectives: To compare changes in the thickness of retinal layers between patients with amyotrophic lateral sclerosis (ALS) and healthy controls using optical coherence tomography. Amyotrophic lateral sclerosis is a degenerative disease of the upper and lower motoneurons with a rapidly progressive course, but non-motor symptoms such as decreased ocular motility and reduced visual acuity have also been reported. Specific biomarkers or surrogate parameters assessing neurodegeneration in ALS are of interest. Methods: In a retrospective, longitudinal study using optic coherence tomography of the retinal layers, we compared changes in the thickness of the layers between patients with ALS and healthy controls. Correlations to clinical scores, such as the modified ranking scale, were analyzed. Results: In our cohort of patients with early ALS (disease duration 5.15 ± 21.4 months at baseline), we neither observed differences in retinal layer thickness at baseline nor did the thickness changes in any retinal layer differ in comparison to healthy controls at baseline. Moreover, we observed no significant thickness changes over the course of the observational period in our patients with ALS. However, a correlation analysis revealed a negative association of the thickness change rates in the complex of ganglion cell and inner plexiform layer and the inner nuclear layer with a higher modified Rankin scale at follow-up. Conclusions: This study adds to the notion that OCT may not be a suitable tool to monitor atrophy and disease progression in ALS. However, further longitudinal studies with longer follow-up times and larger cohorts are warranted. Full article

Pulpitis is the inflammatory response of the dental pulp to microbial challenge and can range from mild to severe in nature, with severe pulpitis traditionally resulting in pulp removal and root canal treatment (RCT). In the pursuit of more conservative treatments, recent clinical practice guidelines have recommended strategies that preserve the vitality of the dental pulp, rather than RCT, when possible. This has increased the focus on improving the accuracy of pulp diagnosis, which will direct treatment and improve management outcomes. Unfortunately, current point-of-care (PoC) tools are subjective, lack discrimination and rely on the stimulation of pulpal neurons, limiting dentists’ ability to objectively identify the level of inflammation. Molecular biomarker assessment has the potential to dynamically analyse pulpitis and correlate this with inflammatory thresholds and treatment outcomes. Numerous chemokines, cytokines, proteases and growth factors exhibit altered expression during pulpitis and can be collected intraoperatively as part of routine dental treatment. Although current data indicate several markers that could be used as next-generation diagnostic chairside tools for pulpitis, there are currently no commercial kits. Considering the interest in vital pulp treatment, there is an urgent need to engage researchers, industry, dentists and other stakeholders in the development of PoC diagnostic assays for pulpitis. Full article

Background/Objectives: Biosimilars are central to the modernization of veterinary pharmacology, improving access to complex biological therapies while maintaining quality, safety, and efficacy. Hemoproteins such as cytochrome c, used to support liver function and manage metabolic disorders in animals, are of particular interest. However, their structural complexity and species-specific pharmacology create significant analytical and regulatory challenges for biosimilar development and life-cycle management. Addressing these issues is critical for improving therapeutic outcomes and enabling the broader adoption of biosimilars in veterinary practice. Methods: This narrative review examines the scientific and regulatory principles underlying the development of veterinary biosimilars of hemoproteins, with cytochrome c as a representative model. Regulatory guidelines and relevant scientific literature were analyzed to identify key challenges, knowledge gaps, and required adaptations from human to veterinary medicine, with a focus on biosimilar assessment and life-cycle management. Results: Veterinary biosimilar frameworks are largely informed by EU and US regulatory pathways, emphasizing the stepwise demonstration of biosimilarity through extensive analytical and functional characterization. Long-term safety and efficacy depend on robust Pharmaceutical Quality Systems and effective life-cycle management to ensure manufacturing consistency. For cytochrome c, interchangeability may be acceptable when analytical similarity is exceptionally high. Critical Quality Attributes include polypeptide integrity, heme–protein interaction, iron redox state, and correct three-dimensional conformation. Quality by Design approaches are essential to control manufacturing variability. Despite regional regulatory differences, core scientific principles remain consistent. Conclusions: Hemoprotein biosimilars hold significant promise in veterinary medicine, provided their development is supported by rigorous analytical characterization, strong life-cycle management, and science-based regulatory approaches. Full article