Abstract: Robo-advisors have expanded access to automated investment services, but many platforms continue to rely on relatively static onboarding procedures and limited forms of user interaction. This study examines how participants with investment experience respond to two next-generation robo-advisory design features: financial digital twins, understood as dynamic investor profiles that integrate goals, risk tolerance, cash-flow patterns, and anticipated life events, and conversational artificial intelligence (AI), understood as an interactive interface for explaining recommendations. Using a scenario-based randomized 2 × 2 online experiment, 336 adult respondents with self-reported investment experience, recruited through professional and academic networks, were assigned to one of four robo-advisor scenarios that varied the personalization architecture, standard versus digital twin, and the interface style, plain dashboard versus conversational AI, while holding the portfolio recommendation constant. The results show that digital-twin personalization increases perceived personalization and privacy concern, indicating that more adaptive advisory architectures may be viewed as both more relevant and more data-intensive. Conversational AI increases the perceived interactive quality of the advisory experience, while the clearest adoption-related patterns emerge when it is combined with digital-twin personalization, particularly for selected indicators of stated behavioral willingness. Given the limited internal consistency of several secondary composite measures, the findings are best interpreted as evidence of scenario-based investor responses rather than as validated evidence of actual adoption behavior or confirmed psychological mechanisms. The study contributes to behavioral FinTech research by clarifying the personalization-privacy tension in AI-enabled robo-advisory services and by offering design implications for more transparent, interactive, and responsibly personalized digital wealth management systems.

Abstract: Background/Objective: Vitamin K (VK) comprises a family of quinone compounds with potential involvement in cell death-related pathways through their redox properties. However, consistent findings have not been obtained regarding the clinical significance of VK in breast cancer (BC). Thus, we used the FDA Adverse Event Reporting System (FAERS) to examine the co-reporting patterns of BC-related adverse-event terms among VK-related reports. Methods: Reporting disproportionality analysis was conducted using FAERS data spanning the first quarter of 2004 to the third quarter of 2024. BC-related reports were defined using all valid Preferred Terms included in the relevant narrow-scope Standardized MedDRA Query (SMQ). Reporting odds ratios (RORs) and proportional reporting ratios were calculated for all VK types and each homolog, followed by exploratory comparisons with other compounds containing quinone structures. Results: In total, 32,156 VK-related reports were identified, including 136 BC-related reports. VK-related reports showed significantly lower reporting disproportionality for breast cancer-related reports (ROR = 0.486, 95% confidence interval = 0.411–0.575). In homolog-specific analyses, similar trends were observed for the quinone-type homologs phytomenadione, menatetrenone, and menadione, whereas no significant reporting disproportionality was detected for the hydroquinone-type homolog menadiol. Conclusions: The differences in reporting patterns among quinone-type VK homologs, hydroquinone-type VK, and other quinone-containing compounds suggest that differences in redox properties may be partially related to the structure of reporting disproportionality. Although this study did not demonstrate causality or clinical efficacy, it provides a hypothesis-generating basis for linking basic, epidemiological, and clinical research using FAERS data. Future validation through mechanistic research and analytical epidemiological studies with stricter control of confounding is warranted.

Abstract: Organizational resilience has become a strategic priority for firms facing environmental, economic, and institutional disruptions, particularly in emerging economies where access to financial and technological resources remains limited. This study examined the influence of organizational psychological resources on sustainable business resilience through the mediating role of green innovation in formalized firms in northern Peru. A quantitative, cross-sectional, and explanatory design was applied to data from 130 firms, each represented by a manager or coordinator. The model included three latent constructs: organizational psychological resources, green innovation, and sustainable business resilience, measured through 27 Likert-scale indicators. Data were analyzed using covariance-based structural equation modeling with the WLSMV estimator in lavaan. The results showed strong measurement quality, with standardized loadings between 0.898 and 0.988, Cronbach’s alpha values from 0.985 to 0.990, composite reliability above 0.992, and AVE above 0.929. The structural model showed satisfactory fit and confirmed that organizational psychological resources positively influenced green innovation, while green innovation positively influenced sustainable business resilience. The indirect effect was significant, indicating partial mediation. The findings suggest that psychological well-being, work engagement, and empowerment constitute key internal resources for transforming green innovation into sustainable resilience.

Abstract: The unprecedented rate of technological advances, accelerated industry disruptions and social and environmental sustainability crises are requiring very different business organizations from the traditional paradigm. The main research question for this paper is: What change (paradigm shift) is needed for organizations to be future-fit? The aim is to contribute an integrated, transdisciplinary paradigmatic model of an emerging, progressive future business organization, and an understanding of the paradigm shift required in our socially constructed reality for organizations to be future-fit. A methodology based on complexity theory and a transdisciplinary approach was developed and applied. The researcher’s transdisciplinary conceptualization of a ‘paradigm’, focusing on language-based representations, serves as the foundation. Textual analyses, including corpus linguistics, of practitioner-focused literature were used to elicit concept maps (or domain models) of the shared, societal mental models of a business organization for two periods: (1) the Traditional Business Organization, and (2) a Progressive Future Business Organization. The outcomes were compared using a novel qualitative method, resulting in a set of societal level ontological shifts required for progressive future business organizations. The study shows a paradigm shift to complexity and social responsibility, and the need for transdisciplinarity to reflect complex, integrated organizational realities.

Abstract: Background/Objectives: Following the market authorization of innovative medicinal products, continuous monitoring of safety signals is essential to ensure a favorable benefit–risk balance. This is particularly relevant for biological therapies used in oncology, where complex mechanisms of action and patient-related risk factors may contribute to rare but serious adverse events. The objective of this paper is to describe and critically evaluate the internal pharmacovigilance processes applied by a pharmaceutical company in response to a potential post-marketing safety signal of stroke associated with a newly authorized biological medicinal product for colorectal cancer. Methods: A structured signal management approach was applied in line with international regulatory guidance. The evaluation included detailed clinical assessment of reported post-marketing cases, review of safety data from completed and ongoing clinical trials, epidemiological comparison with background stroke incidence, assessment of potential risk factors, and review of the pharmacological and biological plausibility. Additional data sources, including scientific literature, safety databases, and periodic safety reports, were systematically reviewed. Multidisciplinary collaboration among pharmacovigilance, medical, and regulatory teams supported the assessment and regulatory interactions. Results: The cumulative evidence did not allow confirmation of a definitive causal relationship between the medicinal product and stroke but identified the event as a potential safety signal requiring close monitoring. The assessment supported the preparation of an evidence-based company position and informed discussions with regulatory authorities, including the European Medicines Agency. Conclusions: This case illustrates the practical implementation of structured signal management in the post-marketing setting. Timely evaluation, interdisciplinary coordination, and regulatory engagement are essential to ensure patient safety, maintain regulatory compliance, and support informed benefit–risk decision-making for innovative biological medicinal products.

Abstract: The intensification of the ecosocial crisis has revealed the structural limitations of economic paradigms based on growth. In this context, degrowth emerges as a transformative framework that proposes the deliberate reduction of production and consumption, prioritizing well-being, equity, and ecological sustainability. However, the role of education in the transition toward post-growth societies remains insufficiently developed. This article analyzes how formal educational systems reproduce growth-oriented subjectivities through human capital frameworks and neoliberal governance. Based on a critical review of the literature and a conceptual analysis, both the structural limitations of the dominant educational model and the emergence of alternative pedagogies grounded in sufficiency, care, and the commons are identified. This article proposes a reorientation of educational aims, contents and practices favouring ecosocial literacy and collective agency, with implications for educational policy and systemic transformation.

Abstract: Prior authorization in the United States relies on payer coverage policies expressed as unstructured narrative text, creating fundamental barriers to automation, consistency, and auditability. Large language model (LLM) approaches to policy interpretation suffer from hallucination, nondeterminism, and clinically unsafe outputs—we argue these failures stem not from model capability but from a representation problem: policies written for human interpretation are not inherently computable. We introduce policy computability: the representation of coverage policies in machine-interpretable forms that support deterministic execution, formal verification, provenance tracking, and reproducible reasoning. To operationalize this concept, we present a six-layer neuro-symbolic framework that transforms payer policy documents into executable policy artifacts. Neural components are constrained to language-processing tasks—document ingestion, ontology normalization, and structured rule extraction under symbolic guardrails—while all coverage determinations are executed by a symbolic verification engine using deterministic logical evaluation. The framework incorporates ontology mapping, rule-graph construction, a Python-embedded domain-specific language (DSL), logical conflict resolution, and provenance-aware reasoning traces. We validate the symbolic pipeline using a lumbar fusion prior authorization policy across six synthetic clinical scenarios, demonstrating reproducible coverage determinations with complete reasoning traces. In a preliminary evaluation of the neural extraction layer, Llama 3.2 3B achieved 100% recall on inclusion and exclusion criteria from a narrative policy document across three trials, though extraction quality depended on prompt formulation. Comparative analysis of two representative payer policies reveals clinically meaningful variation—including greater than twofold differences in required conservative therapy duration—highlighting the need for structured policy representations. This work establishes a pathway from narrative payer policies toward deterministic, transparent, and machine-executable coverage systems, providing a foundation for trustworthy automation in prior authorization.

Abstract: Background: This study evaluated the use of circulation time (Tcirc) calculated from polysomnogram (PSG) with pulse oximetry to identify poor cardiac function with low left ventricular ejection fraction (EF). Methods: Subjects over 18 years with sleep apnea (apnea-hypopnea index (AHI) >5/hr) diagnosed by PSG who had transthoracic echocardiography (TTE) within 1 year of PSG were included in this retrospective study. Tcirc of each sleep stage (N2, N3, and REM) were measured and averaged and EF was recorded. Statistical analysis was done using Wilcoxon rank sum test, logistic regression and Youden index. Results: There were 89 sub-jects who met inclusion criteria, 14 with EF ≤45% (Group A) and 75 with EF ≥ 50% (Group B). All 14 Group A subjects had prolonged overall Tcirc with a median time of 27.8 seconds (range 14.1 - 39.6 sec), compared to Group B subjects with median Tcirc of 23.5 seconds (range 14.3 – 37.6 sec), p = 0.311. The op-timal cut-point for overall sleep Tcirc with moderate discrimination (AUC = 0.6) was 28.6 sec. Those with to-tal sleep Tcirc > 28.6 sec were 2.5 x more likely to have low EF with OR =2.56 (95% CI, 0.55-11.16). Con-clusions: In sleep apnea patients, total sleep Tcirc > 28.6 seconds is associated with low ejection fraction with specificity = 0.78.

Abstract: Background/Objectives: The Geri-Fit® program, recognized by the National Council on Aging, is known to improve strength in older adults, yet it lacks robust evidence on clinical outcomes. The current study was performed to assess the change in clinical outcomes in addition to patient-reported change in mobility and general well-being Methods: A total of 227 adults aged 60 and older were recruited from clinics and community sites across Galveston and Harris counties and participated in 45-minute classes twice weekly for 12 weeks, led by trained Geri-Fit® instructors. A mixed-methods approach includes pre- and post-collection of biometric measures of Hemoglobin A1c, total cholesterol, weight, and waist circumference. Participants also completed mid- and post-program surveys reporting changes in health behaviors, psychosocial outcomes, and physical changes, and provided qualitative feedback. Results: showed that 44% of participants lost weight, nearly half reduced their waist circumference, 43.5% improved their Hemoglobin A1c, and total cholesterol decreased significantly (from 167.77 to 155.04 mg/dL; p=0.02). Self-reported outcomes indicated that almost 100% of participants showed improvement or maintenance in mobility, strength, physical activity, and well-being. Conclusions: These findings suggest that Geri-Fit® is associated with favorable clinical outcomes and improved functional health, supporting its potential as a community-based intervention to enhance physical activity, improve self-management, or reduce the risk of chronic disease among older adults.

Abstract: The 2026 multinational outbreak of Andes virus (ANDV) linked to the cruise ship MV Hondius represents an unprecedented epidemiological event with established person-to-person transmission. As of mid-May 2026, 11 confirmed cases (with 2 probable) have been identified across multiple countries, with 3 documented fatalities (case fatality ratio 27%). This outbreak underscores urgent clinical need for novel therapeutics against ANDV-induced hantavirus cardiopulmonary syndrome (HCPS), for which no FDA-approved antivirals exist.This perspective synthesizes structural, genomic, and clinical evidence to support urgent investigation of remdesivir as a therapeutic candidate for ANDV infection.Key findings: (1) ANDV L protein (RdRp) shows 72% amino acid identity with HTNV, with 70–75% conservation of catalytic residues; (2) Remdesivir mechanism depends on nucleotide-binding pocket geometry, substantially conserved across hantavirus species; (3) New ANDV strain shows high genomic stability with selective constraints on RdRp evolution; (4) Meta-analysis of New World hantavirus mortality identifies urgent clinical prognostic markers; (5) Cross-species nucleoside analogues demonstrate efficacy against both Old-World and New-World hantaviruses.Conclusion: Combined structural, genomic, and clinical evidence provides compelling rationale for immediate experimental evaluation of remdesivir against ANDV.

Abstract: Melasma is a chronic hyperpigmentation disorder that significantly impacts quality of life. Given the persistent challenges in melasma management, there is a need to evaluate therapies that may offer long-term treatment. This review analyzes placebo- and hydroquinone (HQ)-controlled interventional studies of melasma published between January 1, 2014, and December 31, 2024. Screening, data extraction, and discussion synthesis were performed with artificial intelligence assistance under human oversight. Treatments were grouped into five categories: HQ-based Standard Treatments, Isolated Molecules as Depigmenting Therapies, Botanical and Antioxidant-Based Therapies, Regenerative and Microenvironment-Modulating Therapies, and Procedure-Assisted and Combination Treatments. HQ remained a key benchmark, although recurrence and tolerability limitations were frequently observed. Several non-HQ or adjunctive approaches demonstrated benefit when administered orally, topically, intradermally, or via iontophoresis. Botanical antioxidants, synbiotics, epidermal growth factor, and platelet-rich plasma also showed promising efficacy. Nevertheless, the evidence base was constrained by small sample sizes, heterogeneous comparators, inconsistent endpoints, mixed objective and subjective assessments, and variable follow-up durations, which prevented meta-analysis. Research on melasma treatment is growing worldwide, with several promising non-HQ and adjunctive strategies emerging. However, standardization of outcomes, comparator selection, and longer follow-up periods is needed to clarify efficacy, tolerability, and relapse prevention throughout diverse skin tones.

Abstract: World models have emerged as a pivotal research direction, with recent breakthroughs in generative AI underscoring their potential for advancing artificial general intelligence. For embodied AI, world models are critical for enabling robots to effectively understand, interact with, and make informed decisions in real-world physical environments. This survey systematically reviews recent progress in embodied world models, under a novel technical taxonomy. We hierarchically organize the field by model architectures, training methodologies, application scenarios, and evaluation approaches, thus offering researchers a clear technical roadmap. We first thoroughly discuss vision-based generative world models and latent space world models, along with their corresponding training paradigms. We then explore the multifaceted roles of embodied world models in robotic applications, from functioning as cloud-based simulation environments to on-device agent brains. Additionally, we summarize important evaluation dimensions for benchmarking embodied world models. Finally, we outline key challenges and provide insights into promising future research directions within this crucial domain. We summarize the representative works discussed in this survey at https://github. com/tsinghua-fib-lab/Awesome-Embodied-World-Model.

Abstract: Neurolymphomatosis (NL), a rare manifestation of non-Hodgkin’s lymphoma affecting the peripheral nervous system, remains a diagnostic challenge. This study aimed to define an optimal diagnostic approach for timely and effective identification of NL. We analyzed 559 NL cases from 231 articles published from 1951 to 2022, examining how patient outcomes correlated with various diagnostic modalities, including magnetic resonance imaging (MRI), computed tomography (CT), [18F]fluorodeoxyglucose positron emission tomography (FDG-PET), electromyography-nerve conduction studies (EMG-NCS), ultrasound, and tissue biopsy when used individually or in combination. Separate analyses were performed in a mutually exclusive fashion to minimize confounding effects from multiple modalities. The results of this investigation revealed that patients with biopsies had a longer time interval from treatment 1 to progression (Kruskal-Wallis p< 0.0001), survival from diagnosis (overall survival) (p< 0.0001), and survival from symptom onset (p< 0.0001), but not symptom onset to diagnosis (p=0.2134). Pairwise comparisons of biopsy plus 2, 3, or 4 diagnostic modalities revealed a positive trend for the combination of biopsy + PET + MRI + EMG-NCS. A majority of patients without biopsy had secondary NL. In this non-biopsied population, no diagnostic modality had a significant correlation with outcome. The collective data indicate that histological confirmation of NL from biopsy was associated with a positive patient outcome. Management of NL patients requires timely testing using PET, MRI, and EMG-NCS to quickly identify a site for image-guided nerve biopsy.

Abstract: This study examines the implications of El Niño on the Indian industrial economy in the context of climate change, with a focus on sectoral risks, economic disruptions, and emerging growth opportunities. The study adopts a qualitative and analytical approach using historical El Niño trends, secondary economic data, sectoral performance analysis, and climate-related industrial indicators to evaluate the impact on major industries in India. The findings indicate that El Niño negatively affects agriculture, commodity supply chains, and food inflation due to weak monsoon conditions and rising temperatures. However, industries related to cooling appliances, irrigation and water technologies, renewable energy backup systems, healthcare, and consumer durables show strong growth potential during El Niño years. Climate change is further accelerating the demand for climate-resilient infrastructure and adaptive industrial strategies. This study provides an integrated perspective linking climate phenomena with industrial economics in India. It highlights how El Niño acts not only as an environmental risk but also as a catalyst for industrial transformation, investment opportunities, and climate-resilient economic development.

Abstract: In this article we study semi-Markov decision processes (SMDPs) where the pay-off criterion is limiting ratio average, generally known as undiscounted pay-off. Here we consider the action space of the decision maker to be possibly countably infinite. However, we do not put any restriction on the reward function. We prove the existence of a near-optimal or ϵ-optimal strategy of the decision maker which turns out to be a deterministic semi-stationary. An efficient algorithm is discussed to compute a near-optimal pure semi-stationary strategy for such SMDP model. Also under some standard ergodicity conditions, we propose an optimality equation of these SMDP models.

Abstract: Lithium-ion batteries (LIBs) are ubiquitous in modern technology, powering consumer electronics, electric vehicles, and energy-storage systems. As these systems age, internal structural degradation can lead to reduced performance, diminished lifetime, and increased safety risks, including thermal instability. Because many forms of degradation occur internally and are not detectable through external measurements, accurate assessment of structural health can be observed by non-destructive imaging and robust analysis techniques. In this study, a transfer learning-based deep learning framework for classifying the structural health conditions of 18650-format LIB cells using X-ray micro-computed tomography (µCT) imaging is proposed. This approach includes preprocessing that extracts radial CT slices and core-region cropping to capture localized 3D structure. The dataset is balanced and augmented with transformations and rotations, and a pretrained InceptionResNet-V2 model is fine-tuned to distinguish between various cell conditions. Modified classification layers with dropout and class weighting improve robustness. Initial results demonstrate that the model can identify internal structural differences with promising accuracy, supporting the development of automated µCT-based battery health assessment and safety diagnostics.

Abstract: This study investigated the relationships between ambivalent sexism, social roles, and body compassion in Albanian and Italian women. The participants were 251 Albanian and 280 Italian women who completed validated measures assessing hostile and benevolent sexism, social roles transcendence and link to social roles, and three subdimensions of body compassion (defusion, common humanity, and acceptance). Path analyses indicated excellent model fit across samples. In Albanian women, hostile sexism negatively predicted social roles transcendence and positively predicted a link to social roles, both of which were associated with lower body compassion. Benevolent sexism was positively associated with social roles transcendence, which in turn was related to higher body compassion. In contrast, Italian women showed a different pattern: benevolent sexism positively predicted a link to social roles, while social roles transcendence and link to social roles were both negatively related to defusion. Age positively predicted defusion and acceptance, highlighting a possible protective effect. Explained variance was higher in the Italian sample, particularly for the link to social roles. Overall, findings suggest that sexist attitudes and adherence to stereotyped social roles influence women’s body compassion differently across cultural contexts, revealing ambivalent and sometimes contradictory associations. The study highlights the need for culturally sensitive approaches in promoting positive body image.

Abstract: In contemporary research on natural bioactive compounds, increasing emphasis is placed on the development of efficient and sustainable extraction technologies. This study aimed to develop and optimize an innovative extraction process for wild cyclamen (Cyclamen purpurascens Mill.) tubers to maximize the yield of total extractives using a Box-Behnken design. The effects of four extraction parameters were evaluated on the system response. A second-order polynomial model accurately described the extraction process, yielding a coefficient of determination of 0.919. The liquid-to-solid ratio was identified as the dominant factor affecting the extraction efficiency compared to the other factors investigated. The optimal extraction conditions were as follows: extraction time of 15.5 min, 13% (v/v) ethanol, liquid-to-solid ratio of 13.5 mL/g, and extraction temperature of 34 °C, resulting in a yield of 53.44%. The optimized process yielded a significant saponin content of 16.19 g/100 g, while the levels of phenolic compounds (132.52 mg GAE/100 g) and flavonoids (12.04 mg QE/100 g) were also quantified. UHPLC–ESI–MS/MS analysis confirmed the presence of triterpene saponins, flavonoids, and terpenoids. DPPH, ABTS⁺, and CUPRAC assays indicated the antioxidant potential of the extract, while the minimum inhibitory concentration assay showed antibacterial activity against Staphylococcus aureus and Escherichia coli. The established chemical profile and observed biological activities provide the basis for further evaluation of wild cyclamen tubers as a source of bioactive secondary metabolites.

Abstract: This paper presents the development and implementation of a psychological model aimed at predicting the mental states of Air Traffic Controllers (ATCOs) within an Exploratory research project, entitled CODA (The Controller Adaptative Digital Systems Assistant), within the SESAR 3 Joint Undertaking and European Union’s Horizon Europe research and innovation programme. The proposed model aims to advance human–machine collaboration in air traffic management by enabling the precise prediction of critical operator cognitive and affective states, including mental workload, fatigue, stress, and attentional engagement. By formally integrating core cognitive processes—namely perception, comprehension, and decision-making—within its architecture, the model provides a principled framework for the continuous monitoring and real-time adaptation of support systems. Such adaptive capabilities are intended to optimize the allocation of assistance provided by artificial agents, thereby strengthening human–system coordination and contributing to enhanced operational safety and efficiency within the complex and highly dynamic environment of air traffic control. To estimate the parameters of the model, several air traffic simulations were conducted with expert controllers. In these simulations changes to traffic situations were introduced. Those changes could affect the controllers' mental states. The results of these changes were observed in the measured verbal and psychophysiological dependent variables. This paper presents results that partially validate the initial parameters of the models. These results will contribute to a future improvement of the model by refining the parameters of the proposed formulas for calculating mental workload, fatigue, stress, and vigilance in the air traffic control task.

Abstract: Artificial intelligence has achieved enormous visibility in recent years, mostly thanks to the success of deep learning and its generative AI applications. Still, current state-of-the-art models remain brittle and struggle to provide reliable predictions under settings that differ, often even marginally, from those that generate their training data. The issue is only compounded when one attempts to enable machines to continually learn from data, in an imitation of humans’ lifelong learning experience. While recognizing this issue under various names (e.g., ‘overfitting’ or ‘model adaptation’), traditional machine learning seems unable to address it in radical ways. We argue that a real breakthrough requires a proper mathematical treatment of the ‘epistemic’ uncertainty stemming from a forcibly partial knowledge of the world, which in turn links to both the continual learning from new data and the injection of knowledge in a neurosymbolic sense. Our position supports the creation of a new continual learning paradigm designed to provide worst-case guarantees on model predictions throughout the learning process, coupled with the extension of neurosymbolic AI under epistemic uncertainty, as the two main channels to reduce the latter via additional knowledge.