Abstract: Accurate per-wagon occupancy accounting at freight stations — knowing which wagon entered or exited which track and when — is a prerequisite for automated shunting management, yet existing technologies — axle counters, RFID, computer vision, and LPWAN IoT — each provide only a subset of the required information and depend on dedicated infrastructure or favourable conditions. This paper investigates whether two fixed BLE gateways, combined with Eddystone-TLM beacon nodes proposed for mounting on freight wagon bodies, can classify passage direction from RSSI signals without training data, site-specific calibration, or track modification. The enabling mechanism is wagon-body attenuation: as a wagon passes between the receivers, its metallic body creates a temporal asymmetry in the RSSI envelopes that encodes travel direction. We present a five-stage online pipeline at O (1) memory per packet: a two-sided CUSUM detector with adaptive per-event baseline estimation segments the RSSI stream; a three-stage validation filter rejects partial passes, lateral paths, and near-gateway reversals; and direction is classified by the normalized Temporal Centroid shift — a speed-invariant feature requiring no training data — with a cascade fallback for ambiguous short windows. Combined with the beacon MAC address as a wagon identifier, the system generates structured occupancy events directly consumable by station management systems. Validated on 151 labelled events across eight scenario categories at Urtaul freight station and the TSTU test polygon, the pipeline achieves 96.7% accuracy (95% Wilson CI: [92.5%, 98.6%]) zero wrong-direction predictions across all 84 directional events (exact Clopper-Pearson 95% CI for the wrong-direction rate: [0%, 3.5%])", a Random Forest baseline on the same features confirms supervised learning adds no measurable benefit over the training-free approach within this feature space.

Abstract: Background/Objectives: The prognostic and predictive role of BMI in patients with HR+/HER2– MBC remains controversial, particularly in the era of CDK4/6 inhibitors. This study aimed to evaluate the association between baseline BMI and clinical outcomes in patients treated with palbociclib in a real-world setting. Methods: We conducted a multicenter retrospective observational cohort study including 326 patients with HR+/HER2− MBC treated with palbociclib in combination with endocrine therapy across six oncology centers in Romania. Only patients who received palbociclib for at least three months were included. Patients were stratified according to BMI into <25 kg/m² and ≥25 kg/m² groups. PFS and OS were the primary endpoints, while ORR and CBR were secondary endpoints. Results: Among the 326 patients, 66.6% were classified as overweight or obese (BMI ≥25 kg/m²). Median PFS was 23.66 months in the BMI <25 group and 26.78 months in the BMI ≥25 group, with no statistically significant difference (HR 0.86, 95% CI 0.62–1.20; p = 0.373). Median OS was not reached in the BMI <25 group and was 43.73 months in the BMI ≥25 group, also without significant difference (HR 0.82, 95% CI 0.52–1.30; p = 0.397). ORR (29.07% vs. 28.89%) and CBR (90.7% vs. 88.3%) were comparable between BMI groups. In multivariate analysis, liver metastases and brain metastases were independently associated with worse outcomes, whereas BMI was not an independent prognostic factor. Conclusions: In this selected real-world cohort of patients with HR+/HER2− MBC who tolerated at least three months of palbociclib, baseline BMI was not associated with treatment response, PFS, or OS. While clinically informative, these results should not be interpreted as definitive evidence that body weight has no influence on palbociclib efficacy, given the methodological constraints of the analysis. BMI alone may be insufficient to capture the complex interplay between body composition and treatment outcomes, highlighting the need for more refined biomarkers of body composition in this setting.

Abstract: As cities worldwide face challenges of rapid urbanization and declining public transit ridership, traditional fixed-route systems often fail to meet evolving mobility needs. Urban planning issues, such as suburban sprawl and fragmented land use, exacerbate these limitations, leading to underutilized services, higher operational costs, and accessibility gaps, particularly for underserved communities. Demand-Responsive Transit (DRT) systems have emerged as an effective solution, offering flexible, on-demand services that dynamically adjust routes based on user demand. This review synthesizes insights from 65 studies, including 20 real-world implementations, examining DRT's potential to enhance accessibility, cost efficiency, and environmental sustainability. Key findings demonstrate that DRT systems reduce operational costs by 25-35% while increasing ridership up to 300%. Integration of AI-driven routing algorithms improves service reliability by 90-98% and reduces travel times by 35-50%. Multiple booking interfaces increase adoption by 40-60%, while multimodal integration expands service coverage by 100-150%. However, significant barriers persist, with 58% of DRT system models requiring subsidies and 51% facing equity challenges. The study proposes hybrid funding models, integrated multimodal platforms, and inclusive design approaches to address these challenges. By aligning with urban design principles and leveraging advanced technologies, DRT systems can enhance urban resilience while promoting sustainable development.

Abstract: The educational pathway for expertise in Intraoperative Neurophysiological Monitoring (IONM) is complex and lengthy, requiring a solid foundation in neuroscience, neurophysiology, and neuroanatomy. It also demands direct familiarity with a broad range of neurosurgical scenarios, including supratentorial, infratentorial, and spinal procedures, gained through exposure to at least ten distinct surgical approaches. Intraoperative neurophysiology must be tailored to each patient's preoperative assessments. It relies on a variety of methods to collect, analyse, and report neurophysiological signals that are relevant to the surgical procedure. Despite its importance, there remains a substantial shortage of training tools designed to support realistic practice and skill development. To address this gap, we developed a comprehensive framework (ION-Sim) that integrates all laboratory testing modalities and adapts them to the operating room environment. ION_sim supports the simulation and analysis of spontaneous EEG and EMG activity, a wide range of evoked potentials, and intraoperative stimulus–response testing protocols. The framework provides a unified environment for practising, testing, and validating the core neurophysiological procedures employed during neurosurgical interventions. In addition, it incorporates a robust data-management architecture, maintaining a database with system setups, user profiles, educational performance metrics, and automatically generating reports. This structure enables the longitudinal tracking of objective skill acquisition and facilitates standardised assessments of trainee progress. ION_Sim is distributed both as a ready-to-use application, suitable for direct integration into teaching and training programmes, and as a modular scientific library. Through its dedicated APIs, users can design customised configurations, create novel simulation scenarios, and extend the platform to support additional research or educational objectives. It is available upon request for educational purposes, open-source and released under the GNU General Public License, ensuring transparency, reproducibility, and long-term accessibility for the scientific and clinical communities.

Abstract: One of the most frequent reasons why pipeline structures fail is corrosion. Corrosion may occur on the inside, exterior, or even both surfaces of the pipeline, and it is particularly challenging for insulated pipelines. While insulation helps prevent corrosion damage, there is still a potential for corrosion under insulation (CUI). The current inspection methods require removing the insulation layer, which is time-consuming and expensive. In this research, the Pulsed Eddy Current (PEC) method was applied to detect CUI. Several factors affect PEC signals, including sample material thickness, insulation material, insulation material thickness, and coil parameters. Understanding these characteristics is crucial for designing a suitable PEC system. Comparative analysis using eddy current reveals that thicker insulation materials generally result in higher initial signal strength, with the highest values observed for 5mm insulation across all materials. However, as the carbon steel thickness increases, the signal strength consistently decreases for all insulation types. Wood insulation maintains the highest signal strength across all thicknesses, followed by acrylic, which shows higher signal strength than rubber at comparable thicknesses. Overall, increasing the thickness of the carbon steel substrate consistently reduces the signal strength. Based on error analyses, the thickness of samples and insulation should be considered carefully as they impact accuracy.

Abstract: Background: The expanding interest of chatbots within the medical domain underscores the imperative for a comprehensive understanding of their capabilities and limitations, particularly in the context of anatomical education. Chatbots possess the potential to comprehend intricate anatomical concepts, deliver both advanced and contextually relevant information, and could serve as a valuable resource for medical students and educators. This study aimed to evaluate the proficiency and constraints of chatbots in the domain of neuroanatomy. Methods: We developed 30 questions and administered them to ChatGPT-4, Google Gemini, Microsoft Copilot, and Perplexity.ai, in their open versions. Questions were collaboratively constructed by the research team, selected through a semi-randomized process within the domain of neuroanatomy. Chatbots' responses were evaluated in a blinded manner for validity and appropriateness, utilizing a 5-point Likert scale. Results: The optimal performance was exhibited by ChatGPT-4 and Perplexity.ai, which achieved scores of 4.6 ± 0.5 and 4.5 ± 0.5, respectively. Microsoft Copilot (4.4 ± 0.5) and Google Gemini (4.1 ± 1.0) followed. The least successful performance was observed in the task of generating a neuroanatomical structure: only Microsoft Copilot attempted to fulfill the request, albeit with a dramatically flawed outcome. Conversely, Google Gemini and Perplexity.ai provided web links to anatomical illustrations. Conclusions: Despite technological advancements, AI models have not yet reached a level of sophistication sufficient to entirely supplant the role of educators or facilitators in a neuroanatomy course; however, they can serve as valuable adjunct tools for medical educators and students when utilized with careful consideration.

Abstract: Building energy conservation and emission reduction have become global priorities. Conventional sports facilities, owing to their substantial spatial dimensions, predominantly depend on mechanical HVAC systems, leading to elevated energy consumption and operational expenses. Consequently, the judicious application of natural ventilation is crucial for attaining a sustainable transformation of these structures. This study focuses on the National Fitness Center in Shenyang, a representative city located in a chilly climate. Utilizing the Ladybug Tools platform alongside computational fluid dynamics (CFD) numerical simulation techniques, multi-scenario simulations are performed for omnidirectional wind conditions and two varieties of window openings. An analysis is conducted on the indoor airflow distribution and wind speed characteristics across several functional regions of the large-space gymnasium under different wind directions. The study developed methodologies for identifying optimal ventilation durations and target wind velocities annually, quantified the influence of incident wind angles on ventilation efficacy, confirmed that appropriate building orientation can enhance ventilation efficiency by roughly 45%, and clarified the mechanisms and selection criteria for window types affecting indoor airflow patterns. The research findings offer a solid theoretical foundation and practical technical assistance for the ventilation design of national fitness centers to accommodate climatic conditions.

Abstract: This study reports on the design and characterisation of thermo-reversible gelatine hydrogels incorporating beeswax-structured oil-in-water emulsions as novel 3D-printable food inks. Beeswax oil-in-gel emulsions (BOGEs) were prepared by varying the sunflower oil to beeswax (SFO:BW) mass fraction (1:0, 3:1, 1:1, 1:3 and 0:1) at a fixed lipid loading (10% wt.) within a 4% wt. gelatine matrix. The BOGEs were evaluated in terms of microstructure, thermophysical properties, small and large amplitude oscillatory shear rheological behaviour, instrumental hardness, and 3D printability. CLSM images revealed a progressive transition from an emulsion-filled to a bigel-like microstructure with increasing beeswax content, driven by partial crystallisation and percolation of lipid droplets. Differential scanning calorimetry confirmed that beeswax incorporation progressively suppressed the gelatine hydrogel fusion enthalpy, indicating that wax crystal lattices govern the supramolecular organisation of the gelatine network. SAOS tests showed that BW enhanced the elastic modulus, with a critical solid fat content threshold (Φc = 0.294) above which lipid droplet percolation provided an additional structural reinforcement. LAOS characterisation revealed a type III nonlinear viscoelastic response, with delayed yielding and enhanced structural integrity at higher BW fractions. Instrumental hardness measurements confirmed the active filler role of BW at mass fractions ≥0.5. 3D printing assessment demonstrated that intermediate SFO:BW ratios (3:1 and 1:1) afforded the highest printing fidelity, combining favourable extrusion flow with adequate post-deposition shape retention. Overall, the results demonstrate that beeswax-structured emulsions can effectively tailor the structure–function properties of gelatine hydrogels, enabling the development of clean-label, multiphase food inks suitable for extrusion-based 3D printing applications.

Abstract: The crop system of soybean (summer)—maize or other cereals (fall/winter) succession has been adopted widely in the Neotropics. It inadvertently provides food in sequence to stink bugs (Hemiptera: Heteroptera: Pentatomidae), forming green bridges, which favor their outbreaks. Attempts to control these outbreaks, usually consists of chemical control on isolated crop scenarios. Analyzing the literature available, it is possible to conclude that stink bugs must be managed having a broader and more holistic perspective, taking the whole landscape into consideration, rather than the usual individualized perspective. Multidisciplinary recommendations should include insect pests plus weed and disease controls, crop harvest, sowed cultivars or varieties, and neighboring vegetation (cultivated or native) for effective stink bug management. In conclusion, during the first crop season, stink bugs should be controlled only in the reproductive stage of soybean (from R3 to R6 plant development stage), when population is equal or higher than ET (2 stink bugs.m⁻¹). Biologicals should be used instead of chemicals whenever possible. When ET is surpassed at R7 or R8, more tolerant maize varieties (fast growing) should be sowed in the second crop season with the adoption of seed treatment. Always, grain losses during harvest and the presence of weeds must be avoided at the end of soybean season. Additionally, chemical insecticides sprayings on maize might still be necessary if Diceraeuss spp. outbreaks equal or surpass three insects.m⁻¹ during maize early stages. This more precise and less impactful management of the agroecosystem will promote more sustainable and resilient management of these polyphagous pests.

Abstract: Let $\mathbb{K}$ be a non-Archimedean valued field. Let \begin{align*} p(z)=a_0+a_1z+\cdots+a_{n-1}z^{n-1}+a_nz^n\in \mathbb{K}[z], \quad a_n \neq 0. \end{align*} If $\lambda \in \mathbb{K}$ satisfies $p(\lambda)=0$, then we show that \begin{align*} |\lambda|\leq \min \left\{1, \frac{1}{|a_n|^\frac{1}{n}}\left(\max_{0\leq j \leq n-1}|a_j|\right)^\frac{1}{n}\right \} \end{align*} or \begin{align*} 1\leq |\lambda|\leq \frac{1}{|a_n|}\max_{0\leq j \leq n-1}|a_j|. \end{align*} This is the non-Archimedean version of the Cauchy upper bound for every root of a complex polynomial derived by Cauchy in 1829. Our bound is different from the non-Archimedean bound obtained by Nica and Sprague [Am. Math. Mon., 2023].

Abstract: Capsizing, sinking, and flooding accidents occurring in the coastal waters of the Republic of Korea constitute a persistent marine safety concern, accounting for approximately 17% of total fatalities associated with marine accidents. Previous statistical analyses of accident causation have identified key contributing factors such as adverse weather conditions, improper cargo loading, and deficiencies in vessel maintenance; however, the complex interdependencies among these factors have not been sufficiently quantified. To address this limitation, this study proposes a Fuzzy Bayesian Network (FBN) model to systematically evaluate and quantify the risk factors associated with capsizing, sinking, and flooding accidents. A total of 164 adjudicated marine accident cases that occurred in Korean coastal waters over a 10-year period (2015–2024) were analyzed to estimate prior probabilities for six major causal categories. Conditional Probability Tables (CPTs) were derived through a structured Delphi survey conducted with marine safety experts possessing more than 10 years of professional experience. To mitigate the subjectivity inherent in expert judgment, Triangular Fuzzy Numbers (TFNs) and centroid-based defuzzification were applied. Sensitivity analysis identified sea state (SI = 0.0155) and cargo loading condition (SI = 0.0125) as the two most influential factors affecting the probability of capsizing. Scenario analysis further revealed that when adverse weather conditions and improper cargo loading occur simultaneously, the probability of capsizing increases to 39.3%, representing a 5.3 percentage point increase compared to the baseline. In addition, the model demonstrated a close agreement with observed accident outcome distributions, with a Kullback–Leibler (KL) divergence of 0.038, indicating differences within 1.3 percentage points across all outcome categories. The findings of this study provide practical implications for targeted marine safety interventions and the prioritization of regulatory measures in the coastal waters of the Republic of Korea.

Abstract: Lipid nanoparticles (LNPs) are central to modern mRNA therapeutics, including COVID‑19 vaccines. Far from passive carriers, their ionizable lipids actively interact with cellular membranes. Evidence from cellular, transcriptomic, and proteomic studies indicates that LNPs, with or without nucleic acid, alter gene and protein expression, thereby initiating inflammatory, detoxification, and stress responses at the membrane. Key pathways affected include lipid metabolism and detoxification, with roles for Peroxisome Proliferator-Activated Receptor Gamma (PPARγ) and cytochrome P450 enzymes. We hypothesize that the phosphatidylinositol (PI) cycle is the primary site of LNP-induced perturbations, regulating membrane restructuring and organelle trafficking during endocytosis. Disruption of this cycle triggers downstream signaling cascades, including Nuclear Factor kappa B (NF-κB), Mitogen-Activated Protein Kinases (MAPKs), Janus kinase/signal transducers and activators of transcription (JAK/STAT), and Mechanistic Target of Rapamycin (mTOR). We term this systemic effect lipid-nanoparticle-driven membrane dysfunction (L‑DMD), characterized by dysregulated cellular communication, stress responses, and energy balance. This review provides a mechanistic framework for understanding the persistent biological effects of modified modRNA-LNP exposure and emphasizes a systems-level intracellular perspective.

Abstract: Acute and chronic diseases such as sepsis, trauma, cancer cachexia, heart failure, COPD, and organ failure share a common metabolic feature: the hypercatabolic state (HCS). HCS is driven by systemic inflammation and neuroendocrine activation, leading to a marked increase in basal metabolic rate, a profound energy deficit, and accelerated skeletal muscle proteolysis with concomitant anabolic resistance. In this context, skeletal muscle functions as a reservoir of amino acids (AAs), which are mobilized to sustain energy production, gluconeogenesis, and biosynthetic processes essential for immune and organ function. If inadequately addressed, this metabolic adaptation results in loss of lean body mass, sarcopenia, and cachexia, conditions that independently worsen clinical outcomes. Standardized protein recommendations are often insufficient due to the high interindividual variability of metabolic responses in HCS. Moreover, AAs are not metabolically equivalent: beyond serving as substrates, they act as signaling molecules (metabokines) that regulate key metabolic pathways. This underscores the limitation of calorie-centered nutritional strategies, which fail to capture the functional and regulatory roles of proteins and AAs. This narrative review highlights the need for an integrated nutritional paradigm that jointly considers energy intake, protein quality, AAs composition, and individual physiology to optimize metabolic management in hypercatabolic conditions.

Abstract: Controllable symbolic music generation must preserve a reference melody while remaining responsive to style prompts. Existing hierarchical diffusion systems typically reuse a shared condition vector across harmony, rhythm, and timbre stages, which can entangle stylistic factors and weaken melody preservation. We present HCDMG++, a hierarchical diffusion framework that addresses these two limitations through Stage-Aware Style Routing and Differentiable Melody Regularization. The routing module uses a residual Multi-Layer Perceptron (MLP) to project text-derived style embeddings into stage-specific subspaces, whereas the regularization branch aligns soft pitch histograms and contour trajectories with the conditioning melody during training. We evaluate the integrated system on a 384-sample benchmark covering four melodies, eight styles, four random seeds, and three denoising budgets. HCDMG++ produces valid four-track outputs in all runs and reaches a peak pitch-histogram similarity of 0.508 under a 64-step budget. A matched legacy-compatible reference further shows substantially stronger pitch-histogram alignment than Legacy-HCDMG. These results indicate that stage-specific conditioning and differentiable structural guidance improve controllability in symbolic music diffusion.

Abstract: In faecal sludges (FS) from non-sewered sanitation systems, bound moisture consti-tuted 46-67% of total moisture across all sanitation types investigated, yet the energet-ic basis for its resistance to removal has not previously been characterized. Existing classifications of moisture fractions lack quantitative binding energy data, leaving the thermodynamic limits of solid–liquid separation undefined for FS. This study investi-gates the distribution and binding energies of bound moisture fractions in FS obtained from ventilated pit latrines, urine-diverting dehydrating toilets, and septic tank sys-tems. Bound moisture fractions were determined using moisture sorption isotherms, low-temperature convective drying, nuclear magnetic resonance, and thermogravi-metric–differential scanning calorimetry analyses. Results show that interstitial mois-ture constituted 37–50% of total moisture, followed by vicinal (6–14%) and intracellu-lar (3–9%) fractions, with net isosteric heat rising sharply below 20–30% moisture content (w.b.). Evaporation enthalpy exceeded that of bulk water at moisture contents below ~30% (w.b.), consistent with EPS-mediated adsorption and capillary confine-ment contributing to increased energy requirements for moisture removal and indi-cating a transition from capillary-controlled to structure-influenced retention. These findings provide a thermodynamic basis for interpreting why conventional mechani-cal dewatering stalls at a residual moisture content that differs systematically between VIP, UDDT, and septic tank sludges. These insights are relevant for improving FS treatment strategies, particularly in selecting appropriate combinations of dewatering, drying, and pre-treatment processes.

Abstract: Accurate simulation of hyperspectral cloud radiance remains challenging under optically thick cloud conditions, where conventional layered radiative transfer (RT) models tend to underestimate cloud-induced backscattering and return radiance in the visible to shortwave infrared (VIS–SWIR) range. In this study, we propose an extinction-dependent interlayer reflective augmentation within a Curtis–Godson (CG)–based layered RT framework. Instead of introducing explicit cloud-top or cloud-bottom boundaries, the method adds a reflective coupling term at all discretized sublayer interfaces, scaled by local extinction properties, to compensate for the underrepresented backward radiative contribution in standard solvers. The proposed approach is designed for optically thick, plane-parallel cloud conditions and aims at improving forward radiance simulation rather than detailed microphysical retrieval. The formulation is constructed so that the reflective augmentation vanishes as the local extinction decreases, although the present experiments focus on optically thick cloud cases. Validation using Gaofen-5A (GF-5A) hyperspectral observations further confirms improved spectral fidelity of simulated cloud radiance in real scenes. Compared with conventional layered RT, the proposed method provides a favorable balance between computational efficiency and accuracy, making it suitable as a fast forward module for hyperspectral cloud radiance simulation of optically thick cloud scenes.

Abstract:

Background: Overlapping endemic infections often present with non-specific systemic features, which could initially lead to delayed recognition and inappropriate treatment. Strongyloides stercoralis and Coccidioides spp. are rarely encountered together, yet both may cause pulmonary disease, constitutional symptoms, and eosinophilia, complicating diagnosis. Corticosteroid exposure in particular can unmask severe strongyloidiasis, highlighting the importance of early detection. Case Presentation: We present the case of a 30-year-old man from the Dominican Republic with recent travel to Brazil and Mexico, who presented with a 3-week history of fever, cough, myalgias, rash, and 13-pound weight loss. Initial treatment for presumed asthma exacerbation and bacterial pneumonia with corticosteroids and multiple antibiotics failed to relieve symptoms. Laboratory evaluation revealed marked eosinophilia (absolute eosinophil count 3,400/µL) and elevated inflammatory markers. Chest CT demonstrated diffuse bilateral tree-in-bud and micronodular opacities. Bronchoalveolar lavage contained 44% eosinophils. Serologic testing was positive for Strongyloides IgG, Coccidioides IgM/IgG, and β-D-glucan. The patient improved with ivermectin and fluconazole but experienced a relapse of coccidioidomycosis after antifungal discontinuation, requiring reinitiation of long-term azole therapy. Discussion: Coinfection with Strongyloides stercoralis and Coccidioides spp. poses a difficult diagnosis due to overlapping respiratory and systemic manifestations that could mimic common bacterial, fungal or allergic processes. Corticosteroid exposure can precipitate Strongyloides hyperinfection while promoting fungal proliferation, worsening disease severity. Recognition of eosinophilia in patients with a compatible travel history should prompt evaluation for parasitic and fungal etiologies. This case emphasizes the need for early serologic testing and targeted therapy while providing close follow-up to prevent relapses and complications in overlapping endemic infections. Conclusion: This case shows the difficulty of diagnosing overlapping infections like Strongyloides stercoralis and Coccidioides, which can easily be mistaken for bacterial pneumonia. It highlights the risk of giving corticosteroids before ruling out parasitic diseases and stresses the value of screening those at risk. The patient’s relapse after stopping treatment reflects the chronic nature of coccidioidomycosis and the need for close follow-up. Clinicians should keep an open, exposure-based approach when evaluating unexplained pulmonary symptoms, especially in people from endemic areas. This case underscores the importance of broad differentials, timely diagnosis, and long-term monitoring in patients with complex overlapping infections.

Abstract: Global Positioning System (GPS) spoofing poses severe threats to navigation safety, necessitating robust detection mechanisms with enhanced interpretability. This study proposes Stack-TabNet, a novel stacked ensemble learning framework integrating XGBoost, Random Forest, and the attentive transformer-based TabNet network. To address model opacity, an interpretable feature attribution mechanism is employed to quantify feature contributions and guide optimization. Experiments are conducted on a complex dataset comprising authentic and spoofed GPS signals across four classes, characterized by high-dimensional signal metrics and severe class imbalance. The initial model utilizing all available features demonstrates robust detection capability. Subsequently, an optimized variant utilizes a subset of top-ranked features identified by the interpretation mechanism, yielding further improved accuracy. Comparative analysis confirms that the proposed framework surpasses all traditional machine learning and deep learning baselines. The analysis identifies Pseudorange and Time of Code Delay as the most discriminative features. These results indicate that combining ensemble learning with interpretable feature selection significantly enhances detection accuracy and training efficiency for GPS anti-spoofing applications.

Abstract: Background Tuberculosis-related stigma remains a substantial psychosocial burden among patients with multidrug-resistant tuberculosis, particularly in resource-constrained settings where prolonged treatment, social vulnerability, and barriers to care may further compromise well-being and engagement with health services. Stigma may adversely affect patients’ treatment experience, healthcare-seeking behavior, and continuity of care. This study aimed to assess perceived stigma and examine its association with tuberculosis-related knowledge, attitudes, and practices among patients with multidrug-resistant tuberculosis in Vietnam. Methods We conducted a cross-sectional study among 528 patients with multidrug-resistant tuberculosis in Vietnam. Perceived stigma was assessed using the Van Rie tuberculosis stigma scale. Knowledge, attitude, and practice scores were derived from structured questionnaire items. Spearman correlation analysis was used to assess bivariate associations between stigma and the knowledge, attitude, and practice domains. Multivariable linear regression was performed to identify factors independently associated with stigma. Results The mean age of participants was 42.61 years (standard deviation, 13.62), and 68.8% were male. The mean stigma score was 23.68 (standard deviation, 4.30), with a median of 24.0 and an interquartile range of 21.0-27.0, indicating a considerable burden of perceived stigma. In bivariate analysis, stigma was inversely correlated with knowledge score (rho = -0.095, p = 0.030), attitude score (rho = -0.270, p < 0.001), and total knowledge-attitude-practice score (rho = -0.192, p < 0.001), while the correlation with practice score was not statistically significant (rho = 0.081, p = 0.064). In multivariable analysis, a higher attitude score remained independently associated with lower stigma (beta = -0.229, 95% confidence interval: -0.306 to -0.153, p < 0.001), whereas knowledge and practice scores were not independently associated with stigma. Being on treatment was also associated with lower stigma (beta = -1.966, 95% confidence interval: -2.716 to -1.216, p < 0.001). Conclusions Patients with multidrug-resistant tuberculosis in Vietnam experienced a considerable burden of perceived stigma. More favorable tuberculosis-related attitudes were independently associated with lower stigma, underscoring the importance of integrating stigma reduction, psychosocial support, and patient-centered educational interventions into multidrug-resistant tuberculosis care. Such approaches may help improve treatment experience and strengthen sustained engagement in care, particularly in settings facing persistent social and health-system challenges.

Abstract: The Nicolas criterion gives an equivalent formulation of the Riemann Hypothesis as an inequality involving the Euler totient function evaluated at primorial numbers. A natural strategy for establishing this inequality is to prove that a suitable subsequence of the associated ratio sequence is eventually strictly decreasing under the assumption that the Riemann Hypothesis is false. The present work shows that such a subsequence exists. When this monotonicity property is combined with the known limiting behavior of the ratio sequence and the Nicolas equivalence, a contradiction emerges: assuming the Riemann Hypothesis is false forces the subsequence to converge to a limit that is simultaneously equal to $e^{\gamma}$ (by a subsequence argument) and strictly less than $e^{\gamma}$ (by strict monotonicity). The Riemann Hypothesis therefore follows as a direct consequence.