Abstract: The study of emotional body mapping has emerged as a critical tool for understanding the embodied mind, recently integrated into a tripartite framework comprising bottom-up physiological, top-down motor, and conceptual-metaphorical signals (Daikoku et al., 2025). However, current models remain largely descriptive, lacking a formalized account of functional lateralization as a predictive indicator of a subject’s cognitive stance. This paper proposes an integration of the Subjectica model (Shopin, 2025) into the body mapping paradigm to address this operational gap. By conceptualizing the body as a lateralized interface—distinguishing between the Personally-Oriented Left Side (PO-LS) and the Socially-Oriented Right Side (SO-RS) — we provide a methodology for interpreting Asymmetric Neurobehavioral Signals (ANS) through body segmental (BS). This paper introduces the concept of Sensory Circulation (SC) — a continuous flow of sensory signals that determines the level of somatic awareness and engagement through attentional mechanisms. Within the Subjectica framework, sensory circulation is analyzed through the lens of functional lateralization: the PO-LS and the SO-RS. This synthesis enables the interpretation of body maps not as passive affective reports, but as indicators of the subject's active cognitive stance. This approach shifts the analytical focus from the static localization of affect to the dynamic mapping of cognitive orientation. We posit that lateralized embodied patterns serve as a quantifiable link between hemispheric specialization and observable kinematics. This synthesis offers a rigorous neurophenomenological foundation for cognitive science, enabling the objective analysis of the "cognitive alphabet" expressed through the body.

Abstract:

The introduction of drones as part of a future logistics systems could enhance the efficiency of some goods movements but brings with it the prospect of a change to the environment and society. This paper reports on a study which seeks to identify how varied the concerns are amongst both practitioners and citizens and also how different the concerns of the public are from those of practice. The research uses Q-Sort methods to understand the critical variables and clusters of opinions which underlie policy controversies. A Q-Sort was first conducted with 53 professional stakeholders before a common, but reduced size Q-Sort was undertaken with a representative sample across three different local geographies (N = 610) in the UK. The findings suggest many common clusters of viewpoints across the expert and citizen samples, with the key interactions being between the degree of in principle support for drones for delivery and the degree of practical control over their introduction. However, the citizen group was dominated by drone sceptics worried about privacy, terrorism and environmental impacts in a way which was not manifested in the experts. Few differences occurred between places suggesting that simple urban-rural dichotomies do not define groups of opinions.

Abstract: This paper focuses on analyzing the dynamic process, strength and orientation of risk spillovers in the Chinese banking system under the exogenous shock scenario of the COVID-19 pandemic. Using the closing prices of 25 chosen banks on a daily basis, it stratifies the data into three periods: before, during, and after the pandemic. The HP-TVP-VAR-DY model is used to model risk heterogeneity and time-varying features in risk transmission processes. A dynamic topological directional graph is further used to track the core risk sources and paths in risk transmission processes. The key findings obtained from this paper are summarized below: (1) The Total Spillover Index for the banking system persisted at a high level following the outbreak of the COVID-19 pandemic, indicating its high sensitivity to abrupt and large-scale events. (2) Bank risk transmission paths are highly heterogeneous and time-varying in nature. Prior to the pandemic, CCBs were prominent in overall risk output; during the pandemic, JSCBs dominated; while in the post-pandemic period, again CCBs dominated overall risk output. In all periods, SOCBs and RCBs were identified as major risk receivables. (3) Concerning the structural change in interbank risk transmission paths, it exhibits phase-dependent features. In the pre-pandemic period, risk spillovers spread from CIB, CMB to ABC. However, in the pandemic period, interbank risk transmission paths became highly decentralized, indicating significant increases in risk outflows and inflows from RCBs and CCBs, respectively. Moreover, CMBC and SZRCB turned out to become key sources for risk radiation, while overall network mechanisms dominated risk absorption effects. However, in the post-pandemic period, interbank risk transmission paths tend to become re-centralized; BOC turned out to become a core source for risk transformation, indicating a revival in risk-output dominance in network topologies.

Abstract: Glioblastoma (GBM) remains among the most treatment-refractory human malignancies, shaped by profound molecular heterogeneity, extensive genomic instability and an immunosuppressive tumor microenvironment. Radiotherapy represents a cornerstone of current management; however, its therapeutic benefit is frequently limited by adaptive resistance and ineffective antitumor immunity. Emerging evidence indicates that ionizing radiation acts not only as a cytotoxic modality, but also as a potent immunological trigger through the release of damage-associated molecular patterns (DAMPs) and the induction of immunogenic cell death (ICD). In this review, we synthesize recent advances describing how canonical DAMPs—including HMGB1, ATP, calreticulin exposure, mitochondrial and nuclear DNA fragments—coordinate innate and adaptive immune activation via TLR-, RAGE- and cGAS–STING-dependent pathways. We further discuss the dual nature of DAMP signaling, which can either promote durable antitumor immunity or foster chronic inflammation, myeloid reprogramming and tumor tolerance, depending on radiation dose, fractionation, tumor context and concomitant therapies. Special emphasis is placed on how different radiation qualities, particularly proton versus photon irradiation, differentially modulate DAMP release, ICD dynamics and microenvironmental remodeling. Finally, we highlight translational opportunities to exploit DAMP-related signatures as liquid-biopsy biomarkers of response, as rational selectors for combination strategies (including immunotherapy and radiosensitizers), and as biological guides for personalized and adaptive radiotherapy in GBM. Collectively, DAMP-centered radiobiology provides a conceptual framework to integrate immunity into radiation planning and may enable a new generation of biologically informed treatment strategies for glioblastoma.

Abstract: The digital economy, while a pivotal engine for growth, presents a dual challenge in the context of climate goals. Utilizing panel data from 267 Chinese cities covering the period from 2011 to 2022, this study investigates the nonlinear relationship between the digital economy and carbon dioxide emissions, testing its conformity with the Environmental Kuznets Curve (EKC) hypothesis. Moving beyond a static verification, this research introduces a dynamic framework by examining how three exogenous shock variables—green finance, local government fiscal pressure, and climate policy uncertainty—reshape the EKC curve. Specifically, construct an extended EKC model with interaction terms to empirically assess how these exogenous shock variables shift the inflection point horizontally and vertically and alter the curve's slope. Our findings reveal that these factors significantly influence both the timing and peak level of emissions, as well as the efficiency of decarbonization before and after the turning point. This study provides a nuanced understanding of the digital economy's environmental impact, offering policymakers critical insights to navigate the green transition in the digital era.

Abstract:

Maintenance organizations face growing volumes of spare parts, requiring robust classification methodologies to support decision-making. Practitioners continue reliance on simple and single-criterion-specialized methodologies, while research advances toward criteria and threshold specialized classification optimization for operationally visible spare parts or predefined classes revealing criteria dependencies and data completeness requirements. The literature review identifies a gap showing that existing classification methodologies lack inclusion of all spare parts with maintainable asset relevance, consequently excluding, under-prioritizing, or misclassifying essential spare parts leading to the wrong forecasts and inventory policies. Applying design science research, this study develops a holistic spare parts portfolio classification methodology that increases spare parts inclusion and enables class-based decision-making strategy development to address the gap. The methodology classifies spare parts based on their absence and presence across equipment bill of materials, maintenance history, inventory, and inventory policies, enabling identification and inclusion of operationally invisible spare parts. A case study of 32,521 spare parts demonstrates the interventional effects of the methodology. The intervention improved decision-making efficiency by 91%, increased decision throughput ninefold, and transformed a non-transparent decision-making approach with 9% scope completion and 1.7% stock value increase into a transparent strategy-based approach yielding full scope completion and 33.6% scope stock value reduction.

Abstract: Background. Conservative management of port-related bacteremia often includes locally administered antimicrobials, known as antimicrobial lock therapy (ALT). Current guidelines recommend daily replacement of antimicrobial lock solutions (ALS). We aimed to evaluate whether ALS could remain effective with extended dwell times of up to 10 days. Methods. In this randomized clinical trial, patients with noninfected, recently implanted ports were assigned to one of five ALS dwell-time groups, ranging from 1 to 10 days. Each ALS contained heparin plus an antimicrobial at standard intraluminal concentrations: vancomycin 2 mg/mL, teicoplanin 10 mg/mL, linezolid 1.8 mg/mL, daptomycin 5 mg/mL, or tigecycline 4.5 mg/mL. The primary endpoint was the time at which intraluminal drug concentrations decreased below 1 mg/mL (ClinicalTrials.gov NCT01592032). Results. Vancomycin and linezolid concentrations fell significantly below 1 mg/mL after 3 days of dwell time. Daptomycin and tigecycline concentrations decreased significantly after 7 days but remained above 1 mg/mL. Teicoplanin concentrations did not decline significantly after 7 days. Conclusions. Optimal ALS dwell time depends on the antimicrobial agent. Vancomycin and linezolid locks require daily replacement, whereas daptomycin, tigecycline, and teicoplanin locks maintain therapeutic concentrations for up to 7 days. These findings support individualized ALS replacement strategies, potentially reducing the need for daily interventions.

Abstract: Atmospheric rivers (ARs) play a critical role in producing high-impact weather events including extreme precipitation, flooding, gusty winds, and rapid temperature changes. Building upon the recently published EDARA (ERA5-based Dataset for Atmospheric River Analysis), we present S-EDARA, a supplementary dataset that enhances AR impact assessment capabilities through a newer AR detection algorithm and additional impact-related metrics. S-EDARA includes AR shapes identified by the tARget version 4 (ARS4) algorithm, strong integrated vapour transport (SIVT) indicators, and pseudo total precipitation rate (PTPR) fields. The dataset features both numerical data and interactive graphical catalogues displaying ARS4, SIVT, PTPR, gusty winds, and 24-hour temperature changes at 6-hourly intervals. These enhancements enable more comprehensive analysis of AR impacts and characteristics, particularly for regions experiencing rapidly changing meteorological conditions during AR events. The dataset covers the period from 1940 to present and is publicly available through the Federated Research Data Repository.

Abstract: The principles of regeneration are found in different types of cultures, from long time ago. As an interdisciplinary field of research, regenerative medicine, play a great role in tissues and organs damaged reparation with a higher potential in transplantation if necessary. The definition of stem cells can be extended. From this point of view, we can mention taking in consideration the idea in which it is known that these cells form the base of the building body.

Abstract: We establish the estimation of solutions of two classes of weakly singular Wendroff-type integral inequalities of multiple variables with multiple nonlinear terms, apply them to fractional partial differential equations, and give the proof procedures for uniqueness, boundedness and continuous dependence.

Abstract: Forensic ethanol gas standards are used for, among other, the calibration and metrological verification of evidential breath analysers as described in OIML-R126. A correction for the amount fraction ethanol in forensic gas standards due to cylinder wall adsorption is described. The correction was developed for both the national primary measurement standards as well as for derived primary reference materials. A novel method based on the well-known decanting principle was developed and assessed using two suites of gas mixtures with ethanol amount fractions between 50 μmol mol⁻¹ to 1000 μmol mol⁻¹ in nitrogen. From the results, it is inferred that the initial adsorption loss is a function of the amount fraction and an interpolation formula was developed accordingly. To account for differences in adsorption between cylinders, a mixed effects model was used to describe the adsorption loss data with an excess standard deviation to account for between-cylinder effects.

Abstract: Recent conflicts indicate a structural inversion in the economics of warfare. The exposed human warfighter, requiring prolonged training, continuous sustainment, and high replacement cost, now operates at a growing disadvantage relative to low cost, rapidly replaceable machine systems. This paper argues that modern warfare is increasingly governed not by individual skill or platform sophistication, but by logistics, replacement speed, and cost asymmetry under sustained attrition. Using attrition economics and battlefield evidence from the ongoing war in Ukraine, the analysis demonstrates that humans are being displaced from exposed combat roles not primarily by ethical preference, policy choice, or doctrinal failure, but by binding logistical and regeneration constraints. As low cost systems absorb risk at scale, the human role shifts away from the highest-attrition layer toward remote command, supervision, and coordination, while machines assume primacy at the point of contact. This transition is observable in current conflicts and reflects a reversal in the cost structure that has historically defined military effectiveness, rendering the exposed human warfighter economically non-viable under sustained attrition.

Abstract: Federated Learning (FL) has emerged as a transformative distributed learning paradigm that enables collaborative model training without sharing raw data, thereby preserving privacy across large, diverse, and geographically dispersed clients. Despite its rapid adoption in mobile networks, IoT systems, healthcare, finance, and edge intelligence, FL continues to face several persistent and interdependent challenges that hinder its scalability, efficiency, and real-world deployment. In this survey, we present a systematic examination of six core challenges in federated learning: heterogeneity, computation overhead, communication bottlenecks, client selection, aggregation and optimization, and privacy preservation. We analyze how these challenges manifest across the full FL pipeline, from local training and client participation to global model aggregation and distribution, and examine their impact on model performance, convergence behavior, fairness, and system reliability. Furthermore, we synthesize representative state-of-the-art approaches proposed to address each challenge and discuss their underlying assumptions, trade-offs, and limitations in practical deployments. Finally, we identify open research problems and outline promising directions for developing more robust, scalable, and efficient federated learning systems. This survey aims to serve as a comprehensive reference for researchers and practitioners seeking a unified understanding of the fundamental challenges shaping modern federated learning.

Abstract:

The recovery of water and other resources from urban water system (UWS) has long been practiced in many Mediterranean countries, but very little in Croatia, although EU policy is encouraging. The threats posed by climate change, the growing problem of water and food supply, the energy crisis, and environmental pollution encourage resources recovery by applying the circular economy principles within integrated resource management (IRM) framework. The paper analyzes UWS sustainable circulation processes of water, nutrients and energy and their components in coastal tourist areas that strengthening urban system (US) and environment sustainability. The concept that is explored in this paper use dissipative structures theory to analyze the complexity and sustainability of UWS, urban systems (US) and circular economy processes. The paper discusses the potential of UWS as a local resource of nutrients, water and energy, and considers a possible integrated approach to selecting a locally sustainable recovery concepts. It was established that at the heart of effective water, energy and nutrient management in urban areas lays the principle of IRM, which treats entire urban life support systems as an interconnected system. Fitting circular economy strategy within IRM framework increases efficiency of resource recovery, and overall sustainability of tourist environment, economy and ensure sustainable well-being.

Abstract: Prior research has consistently shown that students’ SAT scores are influenced by factors beyond academic ability, including socioeconomic background and ethnicity. This study employed aggregated school-level data from Massachusetts and New York City (NYC) to assess the quantitative relationships between average SAT scores and school-level demographics and interventions. The assessment aims to help regional and national education policymakers identify factors related to school academic merits and devise inclusive and effective ways to promote educational equality. Three analytical methods, multiple linear regression, relaxed Least Absolute Shrinkage and Selection Operator (LASSO), and decision trees, were conducted sequentially to decipher the complex relationships among variables. The analysis showed that schools with high percentages of Black, Hispanic, and low-income students tend to have lower average scores than schools with high percentages of White, Asian, and well-off students. Moreover, socioeconomic disadvantage is the most powerful and consistent predictor of lower SAT scores, with race and good academic preparation (i.e., percent attending college) functioning as secondary influences. The results indicate that SAT score disparities reflect structural inequities, and more SAT preparation resources are needed at schools with higher percentages of Black, Hispanic, and low-income students to level the playing field in SAT testing.

Abstract:

Almost 10% of the global population suffers from chronic kidney disease (CKD), a severe, progressive and irreversible condition that usually leads to the necessity of life-sustaining renal replacement therapy. The inexistence of a therapeutic intervention able to restore renal function loss motivates the scientific community to develop experimental and preclinical studies in search for new drugs and treatments. Most of these studies require animal models of CKD in order to resemble human nephropathy and human pathophysiological responses, and one of the main employed animals for this purpose is the rat (Rattus norvegicus). Among the variety of available rat CKD models described in the literature, the sub-total nephrectomy model, achieved through the 5/6 renal ablation, stands out, since it better mimics human CKD development and progression. However, there are still no consensus on the most appropriate rat strain for this purpose. The aim of this study was to compare the development and severity of the nephropathy associated to the 5/6 renal ablation model in Wistar, Lewis and Fischer rats. In summary, we observed that, even submitted to the very same surgical procedure of renal mass reduction, the 3 studies rat strains presented completely distinct patterns of CKD progression: Wistar rats exhibited faster, rapidly-progressive and sustained renal function loss, with exuberant hypertension, proteinuria and renal inflammation, and can be considered as excellent animal models to study rapidly progressive, severe human nephropathy and to develop quick tests on new therapies and drugs. Lewis animals, in turn, presented mild and low-progressive CKD, which make this rat strain especially useful to simulate intermediate degrees of human CKD and to develop long-term drug tests. Finally, Fischer rats submitted to the same 5/6 renal ablation model, not even developed hypertension nor proteinuria or structural glomerular damage. We also demonstrated that, compared to Wistar rats, both Lewis and, especially Fischer control rats have a relative higher basal number of nephrons, which may have consistently contributed to the observer renoprotection exhibited by this last rat strain.

Abstract: Classical tool life models, including Taylor’s law, have been extensively used in machining practice due to their simplicity and empirical robustness. However, these formulations neglect the explicit influence of machining vibrations, which become critical in turning operations involving slender workpieces and dynamic cutting conditions. This limitation often leads to significant discrepancies between predicted and experimentally observed tool life. In this study, a vibration-informed extension of Taylor’s tool life law is proposed by explicitly incorporating the maximum transverse displacement of the workpiece as a governing dynamic parameter. The vibration amplitude is obtained from a nonlinear beam model accounting for large deflections and realistic boundary conditions, representative of turning configurations. The dynamic response is evaluated through a semi-analytical formulation and validated experimentally using measured displacement signals during cutting operations. Tool life experiments conducted under varying cutting speeds and vibration levels demonstrate that the proposed model significantly improves prediction accuracy compared to the classical Taylor formulation. The results reveal a strong correla-tion between increased transverse displacement amplitudes and accelerated tool wear, highlighting the critical role of vibration-induced dynamic effects on wear mechanisms. The proposed approach provides a physically grounded framework for coupling machining dynamics and tool wear, offering enhanced predictive capability for tool life estimation in vibration-sensitive turning processes. The findings of this work contribute to a deeper understanding of the interaction between structural dynamics and wear evolution in machining and offer practical insights for process optimization and chatter-aware tool life management.

Abstract: This case report describes longitudinal gait mechanics in a 62-year-old man who underwent left total knee arthroplasty (TKA) and subsequently laparoscopic sleeve gastrectomy with massive weight loss. Gait was evaluated using a wearable inertial sensor system at 6 months (T1) and 12 months (T2) after arthroplasty, alongside functional mobility and endurance tests (Timed Up and Go and 2-minute walk). Body mass decreased from 120 kg (BMI approximately 43 kg/m²) to 69 kg (BMI approximately 25 kg/m²) between T1 and T2. Despite the substantial reduction in mechanical loading, spatiotemporal gait parameters and pelvic kinematics remained largely stable, with persistent asymmetry and a compensatory gait strategy. Acceleration-derived estimates suggested approximately 42.5% mechanical offloading after weight loss. These findings indicate that, in this patient, massive postoperative weight loss did not translate into marked improvements in gait mechanics within the first year after TKA, underscoring the need for targeted rehabilitation even after successful weight reduction.

Abstract:

The escalating prevalence of occupational burnout constitutes a global public health crisis, exacerbating the existing supply-demand disparity in mental healthcare provision. This paper investigates the transformative potential of Artificial Intelligence (AI) as an adjunctive and autonomous modality in the treatment of burnout, employing a dialectical framework to assess the tension between algorithmic scalability and clinical nuance. We analyze the utility of Natural Language Processing (NLP) for sentiment analysis and the emergence of Digital Phenotyping as a mechanism for objective behavioral quantification. Furthermore, we critically evaluate the efficacy of CBT-based conversational agents versus the indispensable nature of the human therapeutic alliance. The analysis reveals that while AI significantly lowers barriers to entry and reduces stigma, it introduces profound ethical paradoxes regarding surveillance, algorithmic bias, and the ”Black Box” of machine cognition. We conclude that the future of psychiatric care lies not in replacement but in Augmented Intelligence—a ”Human-in-the-Loop” (HITL) hybrid model that synthesizes computational precision with intersubjective empathy.

Abstract: Video Question Answering (VideoQA) presents significant challenges, demanding comprehensive understanding of dynamic visual content, object interactions, and complex temporal-causal logic. While Multimodal Large Language Models (MLLMs) offer powerful reasoning capabilities, existing approaches often provide singular, potentially flawed reasoning paths, limiting the robustness and depth of VideoQA models. To address these limitations, we propose Contextualized Diverse Reasoning (CDR), a novel framework designed to furnish VideoQA models with richer, multi-perspective auxiliary supervision. CDR comprises three key innovations: a Diverse Reasoning Generator that leverages MLLMs with distinct viewpoint prompts to generate multiple, complementary reasoning pathways; a Reasoning Pathway Refiner and Annotator that purifies these paths by removing explicit answers and enriching them with semantic type annotations; and a Context-Aware Reasoning Fusion module that dynamically integrates these refined, multi-dimensional reasoning cues with video and question features using an attention-based mechanism. Extensive experiments on several benchmark datasets demonstrate that CDR consistently achieves state-of-the-art performance, outperforming leading VideoQA models and MLLM-based methods. Our ablation studies confirm the crucial role of each CDR component, while qualitative analysis and human evaluations further validate the superior correctness of answers and the coherence, completeness, and helpfulness of the generated reasoning pathways.