Abstract: Precision nutrition is the personalization of dietary recommendations based on characteristics such as genetics, the microbiome, lifestyle, environment, and baseline metabolic state. One potential basis for the development of guidelines is the characterization of gene-diet interactions. In this narrative review, we evaluate the published literature reporting associations between salivary amylase gene copy number and metabolic health. The salivary amylase enzyme facilitates starch digestion and is encoded by AMY1, a gene copy number (CN) variant. Humans have 2–20 copies, and AMY1 CN has been associated with metabolic health conditions such as obesity and insulin resistance. Studies of these associations have conflicting findings. The objectives of this review are to assess the findings from studies testing associations between AMY1 CN and adiposity, glucose metabolism, and gut microbiome composition; to explore possible mechanisms underlying the effects on metabolic health; and to identify knowledge gaps requiring additional research. To identify relevant articles, we searched PubMed, Web of Science, Cumulative Index to Nursing and Allied Health Literature, and Centre for Agricultural and Biosciences International for articles focused on AMY1 CN and one or more of the following: body mass index, glucose metabolism, and the microbiome. Key findings are that AMY1 CN has a positive association with postprandial glucose response and that a high AMY1 CN is protective against insulin resistance. AMY1 CN alone does not appear to be an accurate predictor of adiposity, and the relationship is likely convoluted by habitual starch intake, genetic background, and lifestyle factors. Future studies are required to determine how AMY1 CN could be used as a biomarker or to inform precision nutrition protocols to achieve metabolic health outcomes.

Abstract: We propose a new clinical entity termed recovery-phase viral pancreatitis (RVP), characterized by acute pancreatitis arising during the recovery phase of viral infection. RVP frequently develops after apparent symptom improvement or hospital discharge. Although acute pancreatitis associated with viral infections, particularly coronavirus disease 2019 (COVID-19), has been increasingly recognized, most previous studies have focused on pancreatic injury occurring during the acute infectious phase. However, accumulating reports have described pancreatic inflammation and acute pancreatitis arising during apparent clinical recovery and even after hospital discharge. These manifestations are difficult to explain solely by direct viral cytopathic injury during acute infection and instead suggest the presence of a distinct recovery-associated inflammatory process. We performed a focused narrative literature review to identify cases of acute pancreatitis arising during the recovery phase of viral infections. Representative cases demonstrated a reproducible temporal pattern in which pancreatitis developed approximately 1–3 weeks after the onset of viral illness, frequently during convalescence or after hospital discharge. Similar acute pancreatitis during recovery-phase patterns were observed not only in SARS-CoV-2 infection but also in dengue virus, Epstein–Barr virus, and hepatitis A virus infections. Potential mechanisms may involve transient immune suppression during acute viral infection, permitting pancreatic viral infection and antigen persistence. Recovery-associated immune responses may subsequently trigger pancreatic inflammation and acute pancreatitis during the recovery phase or after hospital discharge. Although RVP shares certain conceptual similarities with immune reconstitution inflammatory syndrome (IRIS), it differs in that it appears to arise during the natural course of viral infection without external immune manipulation or profound baseline immunodeficiency. We therefore propose that RVP should be distinguished from classical IRIS.Because RVP may arise even after apparent recovery or hospital discharge, its association with preceding viral infection may be overlooked, leading to potential misclassification as conventional acute pancreatitis. Recognition of RVP may improve clinical awareness of post-viral pancreatic complications.

Abstract: We find a mapping between the attractive Fermi-Hubbard model and the repulsive Bose-Hubbard model at finite temperature and at imaginary chemical potential μ=iθ. We show, by using a large N-expansion, that the partition functions of the two models are related by a simple shift θ→θ+π. This condition maps the BCS–BEC crossover of attractive fermions to a Bose–Fermi crossover (fermion-like occupation) of repulsive bosons. Central feature of this correspondence plays the thermal kernel g(βE,ϕ), whose analytic continuation gB(βE,ϕ)=gF(βE,ϕ+π) governs the bosonic and fermionic sectors. Interestingly, we are able to find that the special angles ϕ=2π/3,4π/3 for fermions correspond to ϕ=π/3,5π/3 for bosons, marking the boundaries of a universal thermal window. We further argue that the present mechanism shows that fermionization can occur at finite interaction strength through a thermodynamic effect induced by the imaginary chemical potential. This suggests that it is a new way of fermionization (not a change in statistics but a fermion-like behaviour) unlike the Tonks–Girardeau limit, where fermionization arises from an infinite repulsive interaction and anyonic or Floquet-engineered systems where transmutation emerges from modified statistics or dynamics. Essentially, the phase ϕ is a statistical parameter; by twisting the thermal phase, it generates fermion-like behaviour without hard-core constraints or infinite repulsion but only by using thermodynamics. We derive the gap equation and number equation for the bosonic model, highlighting the role of the imaginary chemical potential as a statistical regulator. Our results provide a unified framework for understanding crossovers in interacting lattice systems.

Abstract: Waste lubricating oils (WLOs) represent a major stream of hazardous petroleum-based residues, with global generation exceeding 24 million tons annually. Improper disposal of WLOs poses risks to soil, water, and air quality, while their chemical composition makes them a potential secondary resource within circular economy frameworks. This review summarizes conventional, advanced, and emerging technologies reported for the recycling and valorization of WLOs into high-value petrochemicals and carbon-based materials. Established processes such as acid–clay treatment, solvent extraction, and vacuum distillation are discussed together with more recent approaches, including catalytic upgrading, hydrotreatment, membrane separation, and thermochemical conversion methods such as pyrolysis and catalytic cracking. Reported data on process performance, environmental considerations, and economic aspects are comparatively analyzed to outline current trends and technical challenges in WLO recycling. Particular attention is given to thermochemical pathways capable of generating carbonaceous materials, including carbon black, porous carbons, and functional carbon nanostructures with potential applications in adsorption, catalysis, electrochemical systems, and tribological formulations. Hybrid and integrated process configurations described in the literature are highlighted for their potential to improve recovery efficiency, enhance product quality, and reduce environmental burdens. In addition, recent life cycle assessment (LCA) and techno-economic analysis (TEA) studies are reviewed to provide insight into the environmental and economic implications of advanced re-refining systems. Overall, the reviewed literature indicates that WLO recycling represents not only an important element of sustainable lubricant management, but also a promising waste-to-carbon strategy for the production of value-added carbon-based materials and petrochemical products.

Abstract: This study investigates the impact of age, sex, and coat color on hair cortisol and dehydroepiandrosterone (DHEA) levels as chronic stress indicators in sheltered and adopted domestic cats (Felis catus). A total of 21 cats, comprising both males and females, were enrolled and categorized into six groups based on age (2 vs. 3 years; n = 12), sex (female vs. male; n = 15), and coat color (light vs. dark; n = 9). Hair samples were collected from the shoulder region at shelter entry (Initial Hair Sample; IHS), 8 weeks later while in the shelter (Post-Sheltered Hair Sample; PHS), and 8 weeks after adoption (Post-Adopted Hair Sample; PAHS). Statistical analysis was performed using the GLM procedure of SAS software. Results revealed no significant differences in hair cortisol and DHEA levels or their ratio based on age or sex. However, cats with dark coat colors exhibited significantly higher cortisol and DHEA levels compared to light-coated cats (p < 0.05). Sheltered cats demonstrated elevated hair cortisol and DHEA concentrations over the two-month shelter period, while adopted cats showed significantly reduced levels by the end of the study period. These findings confirm that coat color and living environment (sheltered vs. adopted) are principal determinants of hair cortisol and DHEA levels in cats, whereas age and sex do not appear to play significant roles. Adoption is associated with reduced long-term stress, highlighting its pivotal role in improving feline welfare.

Abstract: Synthetic aperture radar (SAR) target images are highly sensitive to aspect angle, while practical data acquisition usually provides only sparse observations over limited viewpoints. This leads to severe data scarcity at unseen aspect angles and makes cross-angle generation prone to scattering-structure distortion and background-statistics mismatch. Existing SAR image generation methods either focus on distribution matching without explicitly modeling target scattering mechanisms, or emphasize angle conditioning while failing to jointly preserve physically plausible scattering-structure evolution and realistic background speckle statistics at unseen views. To address this issue, we propose a physically consistent framework for SAR image generation at unseen aspect angles. The proposed method employs attributed scattering centers as an intermediate physical representation to explicitly model scattering-structure evolution across aspect angles, and uses this evolving representation to guide the generator toward synthesizing SAR images with structurally plausible scattering layouts. In addition, a dual-consistency scheme is introduced to jointly enforce target-region scattering consistency and background-region statistical consistency, thereby improving the physical realism of the generated results from both target and background perspectives. Extensive experiments under strict unseen-angle interpolation and hold-out protocols demonstrate that the proposed method consistently outperforms representative baselines in image fidelity, target-region scattering consistency, background statistical consistency, and angle-condition consistency. Further visualization and ablation studies verify the critical role of attributed scattering-center evolution modeling in physically consistent SAR view completion.

Abstract: Lexical inferencing is a key contributor to reading development in sighted children, yet its role in Braille reading remains underexplored. This study investigated the developmental trajectory of lexical inferencing among Chinese primary school students with blindness and examined the relationships among compounding awareness, lexical inferencing, vocabulary knowledge, and Braille text reading comprehension. Results showed that (1) students with blindness showed lower lexical inferencing performance than sighted students at both middle and upper grade levels, although lexical inferencing improved with grade level; (2) lexical inferencing significantly predicted both vocabulary knowledge and Braille reading comprehension among students with blindness; (3) compounding awareness significantly predicted lexical inferencing in both middle-grade students and upper-grade students; (4) the relative role of compounding awareness and lexical inferencing differed by grade group. In middle-grade students, both compounding awareness and lexical inferencing contributed to vocabulary knowledge and Braille reading comprehension, with vocabulary knowledge also predicting reading comprehension. In upper-grade students, lexical inferencing remained a significant predictor of both vocabulary knowledge and Braille reading comprehension, whereas compounding awareness no longer directly predicted either outcome. These findings indicate a developmental shift in which compounding awareness is more influential in earlier stages, whereas lexical inferencing becomes the central mechanism supporting vocabulary growth and text-level comprehension in later stages.

Abstract: Marine diesel engines generate high concentrations of sub-micron particulate matter (PM) that requires effective exhaust aftertreatment. While conventional wire-in-tube electrostatic precipitators (ESP) offer a low-drag solution, their practical efficiency is limited by particle re-entrainment at elevated flow velocities. This study investigates a novel application of corrugated cylindrical ducts—standard vibration-compensating couplings—as electrostatic collectors. A fully coupled two-dimensional axisymmetric COMSOL Multiphysics model was developed, integrating turbulent flow (k–ε), electrostatics, ion charge transport, and particle tracing. Numerical results demonstrate that while smooth and corrugated geometries yield identical theoretical Deutsch–Anderson efficiency (61.1% at U_in = 0.5 m/s, the corrugated profile significantly suppresses re-entrainment. The corrugations reduce wall shear stress by a factor of 7.7 to 13.5 at flow velocities of 0.3–0.8 m/s, maintaining aerodynamic conditions below critical particle detachment thresholds. With a pressure drop penalty representing less than 6% of the localized corona power, these findings show that existing marine exhaust infrastructure can be repurposed as high-efficiency, zero-re-entrainment particle collectors through the integration of cold plasma electrodes.

Abstract: Precise step length estimation (SLE) is a key necessity for not only navigation systems design but also gait health monitoring in neurological conditions. Among existing solutions, non-invasive inertial sensor-based approaches operating without dedicated infrastructure are more cost-effective. Many such methods, however, rely on bodily-affixed inertial sensors rather than freely held smartphone sensors. Traditional signal processing approaches, on the other hand, offer varying accuracy across diverse gait patterns due to user parameter calibration. This study, thus, proposes a regression-based SLE framework employing eight regression algorithms: linear regression (LR), k-nearest neighbours, support vector machine, decision tree, elastic network, random forest, histogram-based gradient boosting (HGB) regressor, and artificial neural network (ANN). Their extensive and rigorous evaluations across varied window sizes, using a dataset collected in normal and fast walking modes with two device positions (hand-held and trouser-pocket) during three evaluation scenarios, demonstrate the ANN’s exceptional generalization capacity over the other models and the previous method IRT-SD-SLE in unseen test evaluations with an achieved mean absolute error (MAE) not exceeding 6.3 cm. In seen test evaluations and leave-one-out cross-validation, the HGB regressor’s outstanding performance, achieving the lowest MAE below 1 cm across most contexts, is also reported. The extensive evaluations of training and testing times reveal the highest computational efficiency for LR, moderate efficiency for the HGB regressor, and the highest training cost for the ANN, indicating a clear trade-off between MAE and computational expense. Additionally, this study includes an insightful discussion on the performance results, including the trade-offs between accuracy and efficiency.

Abstract: This study examines customer retention intention in neobanking environments using a theory-informed explainable machine learning framework. Existing digital banking research typically relies on linear modelling approaches to explain retention behaviour, potentially overlooking nonlinear, value-range-dependent, and interaction-based predictive patterns. Using a publicly available survey of 305 neobank users, this study compares regularized linear models, a partial least squares structural equation modelling (PLS-SEM)-inspired benchmark, and XGBoost under repeated nested cross-validation. SHapley Additive exPlanations (SHAP)-based explainability, SHAP interaction analysis, generalized additive model (GAM) diagnostics, construct-level aggregation, and construct-sensitivity checks are used to interpret model behaviour and assess robustness. The results show that XGBoost substantially outperforms the linear benchmarks, achieving the lowest average RMSE and highest average R² across 100 out-of-sample test-fold estimates. Trust-related indicators provide the largest share of model-based predictive importance, followed by perceived security and switching costs. SHAP and GAM diagnostics suggest that trust and switching costs may contribute to retention intention in heterogeneous and nonlinear ways, while perceived security displays a more stable positive predictive pattern. Age-related nonlinearities appear weak and should be interpreted cautiously given the young sample profile. The analysis also suggests possible non-additive relationships between trust and perceived security. The study contributes to digital banking and FinTech research by showing how explainable machine learning can complement theory-driven retention models, identify potentially nonlinear predictive patterns, and preserve interpretability. The findings offer practical insight for trust-building, visible security assurance, and retention diagnostics in neobanking contexts.

Abstract: A finite receiver–spectral closure is constructed for copy-time quantum information. Thereceiver side defines an operational copy-time interface, validator-centred shell geometry, a six-cell certified contour, exact two-channel propagation, receiver-visible and receiver-null leakagelaws, hydrodynamic reduction, gap-length reconstruction, and platform-independent tests. Thespectral side gives the finite internal algebra, real structure, chiral module, unimodular gaugequotient, finite action terms, endpoint holonomies, flavour matrices, neutral Majorana data, anda reproducibility layer. The construction is organized by a primitive physical closure principle:retained finite degrees of freedom must remain distinguishable after receiver quotient, fixed-point-free copy opposition, oriented endpoint transport, and first-order opposite locality, withquotient-null spectators removed. This principle yields the six-cell contour and derives a singleoriented complex phase carrier, a pseudoreal opposition carrier, an irreducible three-endpointshield, primitive direct-sum composition, and observer-null spectator removal. The resultingfinite Wedderburn classification is exhaustive and selectsAF ≃C ⊕H ⊕M3(C).The endpoint denominator follows from the explicit action of half-turn transport on C6/I ≃Z3.For q = 2r, the transport is Qr, the forward orientation condition is r ≡1 (mod 3), oppositionseparation requires r odd, receiver faithfulness requires gcd(r,6) = 1, and proper flavour liftingrequires q >6. A primitive holographic ground principle then selects the unique nondegenerateentropy-saturating branch, r= 7 and q= 14; the arithmetically admissible values q= 26,38,...are higher-winding excitations and not primitive ground sectors. The same finite construc-tion gives a neutral Dirac generator DνF, an entry-by-entry Majorana matrix Mν = U∗qΛνU†q,θPMNS13 = π/21, a negative leptonic Jarlskog invariant, and a normal-ordered neutrino masssum imi = 0.072810131 eV. The receiver gauge-exchange sector, chiral spoke sector, anomaly-free matter completion, rest-gap reconstruction protocol, hydrodynamic likelihood, CKM/PMNSholonomy exposures, electroweak prediction covariance, explicit falsification stack, and global-spectral uniqueness audits are given in the main text and supplementary information. Numericaltables, operator-trace ledgers, holographic ground-denominator audit, Morita-rigidity audit, pre-diction ledgers, flavour prediction ledgers, hydrodynamic reconstruction files, and supplementarydata are provided.

Abstract: Residential buildings must now be designed and retrofitted as adaptive climate-health-work systems rather than as static housing units. This structured literature review synthesises peer-reviewed journal and conference evidence on residential taxonomy, ventilation, indoor environmental quality, overheating, airborne infection resilience, post-pandemic occupancy changes and future performance benchmarks. The review shows that single-family and multifamily buildings remain the most practical first-order categories because they differ in envelope exposure, ventilation pathways, system ownership, governance, retrofit feasibility and occupant control. Single-family dwellings generally provide greater household autonomy, roof-based renewable potential and room-level intervention flexibility, but can also carry higher envelope losses, lower density and stronger dependence on occupant operation. Multifamily buildings benefit from compactness and shared infrastructure, yet face additional risks from common services, vertical shafts, stack effects, corridor pressurisation, inter-zonal airflow and collective maintenance. Ventilation evidence indicates that natural, exhaust-only, supply, balanced heat-recovery, hybrid, demand-controlled and filtration-based strategies cannot be ranked universally; their effectiveness depends on climate, airtightness, pollutant source, occupancy, maintenance and governance. The review further shows that overheating, cooling-demand growth, airborne infection preparedness and remote work are shifting residential performance from winter-centric energy efficiency toward year-round thermal resilience, clean-air delivery and prolonged-occupancy functionality. A future taxonomy is therefore proposed around adaptive performance attributes, including thermal resilience, clean-air capacity, ventilation controllability, energy flexibility, remote-work readiness, vulnerability and retrofit potential. The core contribution is an implementation-oriented framework for aligning residential design, retrofit and policy with health, indoor environmental quality, energy efficiency and carbon performance.

Abstract: Crack opening and reinforcement stress are two complementary indicators of the service state of reinforced concrete hydraulic structures, yet they are often predicted separately.This study develops a data-driven multi-task temporal fusion framework for joint 48 hahead prediction of dam crack responses and rebar stress using multi-source monitoring data. The measured data comprise five crack-monitoring series, five rebar-stress series,local temperature channels, reservoir water level, antecedent rainfall, and an auxiliary environmental signal from 2021-03-11 to 2025-03-06. Target responses are aligned only at commonmeasuredtimestamps; no synthetic target observations are introduced. A residual multi-task temporal fusion network (MTTF-Net) is proposed with a shared Transformer 10 encoder, attention pooling, task-specific decoders, and a response-continuity regularization term. The model is compared with persistence, Ridge regression, random forest, Extra Trees, XGBoost, and GRU baselines under a chronological train/validation/test split. On the independent test period, Ridge regression obtains the lowest overall RMSE (2.2968), whereas MTTF-Net provides the lowest crack RMSE (0.0141), the lowest overall MAE (1.0035), and the second-best overall RMSE (2.3813). These results indicate that the monitoring data contain a strong linear autoregressive component, while multi-task temporal fusion improves nonlinear crack-response prediction and remains competitive for stress forecasting. The source code is prepared as a public implementation package, whereas the measured monitoring dataset is subject to data-owner restrictions.

Abstract: The rapid development of Artificial Intelligence (AI) and Machine Learning (ML) poses new challenges for high-performance system developers. The performance of such systems is often limited not by computational power, but by the efficiency of memory subsystem interaction. Cache behavior optimization becomes critically important, yet existing analysis tools fail to meet the demands of modern AI applications. They either provide only aggregated statistics or are characterized by a "semantic gap", presenting data in machine addresses rather than source code, which makes them ill-suited for analyzing the complex software systems typical of AI. This paper introduces CATS (C Annotated Trace-based Cache Simulator), a novel hybrid method and toolset for detailed cache efficiency analysis, designed to overcome these limitations. CATS combines dynamic tracing with static source code analysis to generate semantically annotated memory traces. This approach is particularly relevant for optimizing AI applications, as it allows precise identification of which data structures (e.g., weight matrices, tensors, or input vectors) are causing cache misses. For analyzing long-running tasks, such as training AI models, our method leverages AI techniques, specifically ML, for intelligent trace sampling, significantly reducing analysis time without sacrificing representativeness. The paper describes the methodology and architecture of CATS and presents experimental evaluation results. In the long term, the data collected by CATS can be used to train AI models capable of automatically providing developers with code refactoring recommendations to improve performance. Early CATS application identifies and resolves cache issues before final implementation, cutting performance optimization costs

Abstract: Preventing large-scale illegal migration is one of the EU's highest priorities. In this study, we analyse the potential for integrating and fusing remote sensor data with a wider range of data streams to enhance border security situational awareness, specifical-ly targeting illegal migration. The aim was to develop a dynamic predictive risk analysis model to identify high-risk zones for illegal border crossings at Croatia's external EU borders. The model’s methodological framework is based on the integration of Geo-graphic Information Systems (GIS), Multi-Criteria Analysis (MCA), and the Analytic Hi-erarchy Process (AHP). The model utilizes various environmental and infrastructure var-iables derived from the open-source databases ESA WorldCover and OpenStreetMap to generate a categorised risk map showing areas of lowest, moderate, and highest risk for illegal border crossing. High-resolution historical satellite imagery showing activities re-lated to illegal migration is used for model verification and generation of labelled da-tasets for AI training. Features such as suspicious vans, river boats, tyre tracks, tents, il-legal campsites, and clusters of individuals were observed in high-resolution Airbus and Maxar historical satellite images. The model can be used for various practical applica-tions, including the strategic allocation of surveillance resources and the enhancement of frontier and pre-frontier intelligence, enabling more informed actions and optimised op-erations.

Abstract: The non-coding regions (NCRs) of the genome, once called “dark matter” and considered useless, are now understood to be key to cancer immunity. While only 2% of the genome is protein-coding, some NCRs can produce small non-canonical peptides that originate from long non-coding RNAs (lncRNAs), untranslated regions, pseudogenes, transposable elements, or aberrant RNA transcripts. Recent advances in proteogenomics and immunopeptidomics have shown that these peptides can bind to major histocompatibility complex (MHC) molecules and be presented to the immune system, thereby constituting non-coding neoantigens (NCNAgs). Because they’re not typically found in healthy cells, the immune system can identify them as foreign. This helps CD8⁺ and CD4⁺ T cells recognize and destroy cancer cells. A similar mimicry at the viral level can activate the innate immune system via some of these signals. Compared with central immune tolerance observed for T antigens, NCNAgs show low expression in normal tissues and high antigenicity in tumors. Therefore, they are ideal candidates for cancer therapy. They could be used in personalized cancer vaccines, adoptive T-cell therapies, and as biomarkers that predict how well patients respond to immunotherapy. However, detecting these antigens is difficult, and limitations in current computational methods may lead to errors. Additionally, tumor heterogeneity, divergence in Human Leukocyte Antigen (HLA) types, and the risk of off-target effects complicate their use. Several emerging technologies, including AI-based prediction, ribosome profiling, and spatial multi-omics, are being applied to improve the accuracy of cancer inter-architecture mapping. In conclusion, targeting the non-coding genome may be a new approach in future cancer therapy.

Abstract: Background: The SPARK (Symptoms of PCOS ameliorated by responses to Ke-to-adaptation) pilot study investigates whether nutritional ketosis can improve repro-ductive and metabolic outcomes in women with Polyendocrine Metabolic Ovarian Syndrome (PMOS). PMOS is a common endocrinopathy characterized by anovulation, hyperandrogenism, and metabolic dysfunction. Objective: To evaluate if a ketogenic diet (KD) or exogenous ketone supplementation (KS) with Qitone™ (bis-octanoyl-(R)-1,3-butanediol) can restore ovulation and menstrual activity. Methods: This ongoing 12-week randomized trial assigns women with PMOS to either a KD or KS intervention. Assessments include body composition by DXA, metabolic and reproduc-tive biomarkers (e.g., insulin, testosterone, SHBG), and cycle tracking via basal body temperature. Results: Preliminary data from seven participants (KD: n=3; KS: n=4) show that 100% demonstrated spontaneous menstrual activity within 2–7 weeks. Full menstrual reinstatement occurred in 86% of participants (KD=3, KS=3), including those with long-standing amenorrhea. Ovulation was predicted in 43% of the cohort. Key metabolic improvements included significant insulin reductions, favorable body composition changes (fat mass loss with lean mass preservation), and enhanced neurobehavioral well-being, including reduced anxiety and improved cognitive clarity. Conclusion: Preliminary findings suggest that both sustained and intermittent ketogenic interven-tions can restore menstrual and ovulatory function through multi-node endo-crine-metabolic recovery, independent of significant weight loss. These results highlight the potential of ketogenic therapies for PMOS but require confirmation in larger trials.

Abstract: The increasing adoption of generative artificial intelligence (AI) in higher education has created new opportunities to enhance Learning Management Systems (LMS) with personalized feedback, adaptive assessment, and learning analytics. Despite these advances, many LMS platforms remain primarily focused on content delivery and grade management, with limited support for metacognitive assessment and intelligent feedback. This study presents CONF.i, a confidence-informed assessment and AI feedback framework integrated with Canvas LMS using Google Apps Script and Google Gemini AI. Developed through a design-based research approach, the framework combines traditional assessment scores with student self-reported confidence levels to support personalized formative feedback and diagnostic learning insights. The proposed system integrates Canvas LTI standards, a Google Apps Script backend, and Gemini AI services to automate scoring, confidence tracking, and AI-generated educational feedback within existing institutional infrastructure. A prototype implementation was evaluated using simulated learner profiles representing different combinations of performance and confidence patterns. The framework identified four illustrative assessment profiles: aligned mastery, underconfident competence, overconfident struggle, and aligned struggle. These patterns demonstrate how confidence-informed assessment can reveal metacognitive dimensions of learning that are not visible through conventional grading alone. Preliminary usability observations indicated positive perceptions regarding the integration within the familiar Canvas environment and the relevance of AI-generated feedback, while also identifying limitations related to response latency and feedback specificity. The findings suggest that integrating confidence-informed assessment with generative AI may support more personalized and reflective learning experiences without requiring major institutional infrastructure changes or commercial licensing costs. This study contributes an exploratory prototype framework for AI-enhanced formative assessment in higher education and provides a practical model for institutions seeking to extend existing LMS platforms with confidence-aware analytics and personalized feedback capabilities.

Abstract: Critical binding of quantum states in Screened Coulomb Potentials such as Yukawa/Debye, Hulthén, and ECSC (Exponential Cosine Screened Coulomb) potentials is of perennial interest and relevance in many fields of science, ranging from nuclear and particle physics; plasma physics, astrophysics, cosmology, and nuclear fusion; physical chemistry, condensed matter, and materials physics; to synthetic nanostructures and nanophotonics. The purpose of this paper is to heuristically explore two related mysteries, one new, the other more than 50 years old. The solutions to these mysteries have implications for a much broader class of potentials, those addressed by Klaus and Simon. In our recent paper [1], we presented numerical calculations using the Phase Method (PM), accurate to 60 digits and to screening lengths D ≤ 10³ au, l = 0–20, of the critical binding parameters for these potentials; an for Yukawa and ECSC, l = 0–12, to D ≤ 10⁵ au, at 30 digits. In doing so, we discovered anomalous period-40 sawtooth structure in the critical parameters of the ECSC potential that is not observed for the Yukawa potential. In this second paper, we quantitatively explain the origin and periodicity of this newly discovered structure. To do so, we use two complementary approaches: a “neoclassical” (NC) variant of conventional semiclassical phase space quantization; and the PM for very precise fullyquantum calculations. The observed period-40 sawtooth structure is quantitatively explained in terms of a novel “tick-tock” mechanism. The periodicity is calculated in terms of the ratio of phase-space integrals for the primary and secondary potential wells. A quartic double-well potential is used as a simple model to further illustrate the tick-tock mechanism. Using NC, an approximate expression is derived to predict the locations of tick-tock glitches from higher order wells; it is confirmed by a PM calculation up to D ≤ 10⁶ au. The second mystery is a strangely linear dependence of the total number of bound states vs screening length for both the Yukawa and ECSC potentials. Using the PM we confirm and extend these empirical relations. We show using the PM that an approximate trivariate linear relation between the square root of the critical screening length √D_c, state number n, and angular momentum l applies to these potentials. This, plus a geometrical state accumulation argument solve the second mystery. We show these properties derive from the scaling relation between screening length and coupling constant, and as such are predicted to be applicable to the whole class of potentials. These results are expected to be of both theoretical interest and experimental relevance when interpreting spectra or calculating thermal properties. The significance of these results, and the applicability of these methods and conclusions to a vast array of related potentials is briefly discussed. Tables of critical screening parameters for Yukawa, Hulthén and ECSC D ≤ 10⁵ and l = 0 − 12 are posted as supplementary data.

Abstract: Cesarean section (CS) rates have been steadily increasing worldwide beyond medical needs. Globally, CS rates have surpassed the World Health Organization's recommended 10–15% range, with potential implications for maternal and neonatal health. The first mode of delivery influences future pregnancies. This study aimed to determine the prevalence, indications, and immediate fetal and maternal outcomes of cesarean section among primigravida women delivered at Bugando Medical Centre (BMC), Mwanza, Tanzania, from January 2022 to 2025. A retrospective cross-sectional study reviewed the medical records of 868 primigravida women who underwent CS during the study period. Data was extracted from the Electronic Health Management System and analyzed using descriptive statistics and inferential tests in SPSS version 26. The prevalence of CS among primigravida deliveries was 25% (868/3,515). Most women were aged 20–34 years (87.3%), delivered at term (83.9%), and underwent emergency CS (94.6%). The leading maternal indication was prolonged/obstructed/poor progress of labor (63.4%), while fetal indications included fetal distress/non-reassuring fetal status (14.2%). Maternal outcomes showed no complications in 75.7% of cases, with PPH (12.7%) as the most common issue. Neonatal outcomes included normal birth weight in 86.0%, NICU admission in 15.8% (primarily due to respiratory distress syndrome [37%]). Prevalence of CS in primigravida at BMC is higher than WHO range, mainly driven by labor-related maternal indications. No immediate complications to mother and child highlight the safe nature of CS.