Search Results (208)

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

Digital trust in online interactions is commonly established through mechanisms such as decentralized identifiers (DIDs), verifiable credentials (VCs), and digital wallets. While these technologies support the correctness of individual components, they do not by themselves establish that an interaction as a whole is trustworthy. This limitation arises because real-world interactions consist of sequences of dependent steps, where inconsistencies may arise even when each step is locally valid. In this paper, we introduce the concept of executable trust, which models trust as a verifiable property of execution across complete interaction sequences. We formalize interactions as chains of TrustEvidence objects that capture step-level validity, constraint satisfaction, and cross-step dependencies. Based on this model, we show that step-level correctness alone is insufficient to characterize interaction-level trust under the stated execution assumptions. We further clarify the definition-induced modular structure of interaction-level trust and use a local failure-witness characterization to connect the formal model with scenario-based validation. We also present the Executable Trust Architecture (ETA), a five-layer architecture that operationalizes the proposed model through components for evidence generation, constraint enforcement, secure communication, and auditability. The feasibility of the approach is examined through scenario-based evaluation covering key trust properties—authenticity, integrity, privacy, and accountability—across nine scenarios comprising 68 test cases. The evaluation illustrates cases in which cross-step violations that pass conventional step-level verification are reflected as failures of ETA’s sequence-aware trust conditions under the evaluated assumptions. Full article

Y₂SiO₅ (YSO) and Lu₂SiO₅ (LSO) are orthosilicates used in photonic and scintillation applications. Isovalent substitution on the rare-earth sublattice in YSO–LSO solid solutions enables systematic tuning of lattice parameters and elastic properties without changing the underlying monoclinic structural framework. A systematic ab initio study of structural, elastic, and vibrational properties of Ce-free YSO–LSO solid solutions is performed within density functional theory using a localized Gaussian-type orbital basis. Nine compositions spanning the full range from YSO to LSO with a Lu content step of 12.5% are investigated. A total of 76 symmetry-independent Y/Lu substitution patterns are explicitly constructed. For each configuration, full geometry optimization and calculation of second-order elastic constants are carried out using the stress–strain approach. Bulk, shear, and Young’s moduli, as well as Poisson’s ratio, are obtained using the Voigt, Reuss, and Hill averaging schemes. Sound velocities and Debye temperatures are derived from the Hill-averaged elastic moduli and density. The unit-cell volumes decrease smoothly with increasing Lu content and follow Vegard’s law, indicating uniform lattice contraction. The Hill-averaged bulk modulus increases from 92 GPa (YSO) to 115 GPa (LSO), the Young’s modulus rises from 151 to 180 GPa, and a strong directional anisotropy (ratio ∼2) is preserved across the entire series. The Debye temperature decreases monotonically from 518 K to 439 K, indicating that the increase in mass density outweighs the stiffening-induced tendency toward higher sound velocities. These results provide quantitative guidance for composition selection and stress management in LYSO-based crystal detectors. Full article

Identification of data distribution and data normalization are fundamental steps in data pre-processing and statistical modeling. Probability distribution identification is essential for selecting an appropriate statistical model, whereas normalization transforms data into a comparable scale to improve the performance of machine learning algorithms. Although normalization techniques are extensively used in data pre-processing, they are generally applied without examining their impact on the underlying probability distribution of the data. This study systematically investigates the influence of normalization methods on probability distribution behavior and distributional transformation. In this work, datasets derived from four popular probability distributions—Gaussian, Exponential, Weibull, and Lognormal—are subjected to fourteen different normalizing approaches. Following normalization, statistical goodness-of-fit metrics and estimated distributional parameters are used to refit the converted observations into nine potential probability distributions. The suggested methodology offers a comparative examination of how different normalization techniques affect the data’s probabilistic properties, parameter estimation behavior, and distributional structure. The study further identifies normalization-specific distributional transition behavior and validates the results using independent univariate subsets of a publicly available real-world dataset. The experimental results show that normalization can significantly alter the original probability distribution: ED, WD, and LD datasets show significant distributional changes under several normalization methods, while GD-distributed data largely preserve their normality under TH, HT, LS, and PNN normalization techniques. While GD data mainly maintain their normality under TH, HT, LS, and PNN normalization approaches, ED, WD, and LD datasets show significant distributional shifts with various normalization procedures. The work uses separate univariate subsets of a publicly accessible real-world dataset to validate the results and further finds distributional transition behavior particular to normalization. This work’s primary contribution is the development of a methodical distribution-aware normalization analysis methodology that links probabilistic modeling behavior with data pre-processing. The results offer useful information for choosing appropriate normalizing methods in applications related to machine learning, statistical inference, risk analysis, Monte-Carlo simulation, and predictive modeling, which enhances the interpretability and dependability of the models. Full article

Large language models (LLMs) are increasingly being used in education, yet their correctness alone does not capture the quality, reliability, or pedagogical validity of their problem-solving behavior, especially in mathematics, where multi-step logic, symbolic reasoning, and conceptual clarity are critical. Conventional evaluation methods largely focus on final answer accuracy and overlook the reasoning process. To address this gap, we introduce a novel interpretability framework for analyzing LLM-generated solutions using undergraduate calculus problems as a representative domain. Our approach combines reasoning flow extraction and decomposing solutions into semantically labeled operations and concepts with prompt ablation analysis to assess input salience and output stability. Using structured metrics such as reasoning complexity, phrase sensitivity, and robustness, we evaluated the model behavior on real Calculus I–III university exams and compared it with the performances of students enrolled in the courses. Our findings revealed that LLMs often produce syntactically fluent yet conceptually flawed solutions with reasoning patterns sensitive to prompt phrasing and input variation. This framework enables a fine-grained diagnosis of reasoning failures, supports curriculum alignment, and informs the design of interpretable AI-assisted feedback tools. The framework was evaluated on Gemma 3, an open-access large language model, across zero-shot, retrieval-augmented generation, and contextual retrieval configurations, using nine real undergraduate calculus examinations from three course levels. To our knowledge, this is the first paper to apply a combined reasoning flow decomposition and prompt ablation framework to real undergraduate calculus examinations, benchmarked against actual student cohort performance, laying the foundation for the transparent and responsible deployment of AI in STEM learning environments. Full article

This study presents a discrete–continuous flux-guided shape-refinement framework for freeform shell geometries under self-weight. The method evaluates the directional relation between a prescribed support-directed transmission field and the shell surface normal, identifies locally underperforming regions, applies top-down geometric updates, and reconstructs a continuous surface at each step. It is intended as a transparent intermediate stage between intuitive freeform design and high-fidelity structural verification. The framework is demonstrated on nine shell cases with different geometries, support conditions, height ranges, and surface irregularities. Across all the cases, the results show reduced normal-component misalignment and increased tangential alignment relative to the prescribed transmission field. A representative finite-element comparison provides case-specific supporting evidence that under a linear-elastic gravity-load model the refined geometry can reduce deformation and stress levels over large surface regions; however, it does not prove general structural optimality or fully membrane-dominated behavior. Geometric roughness remains a key limitation requiring explicit regularization in future work. The approach is positioned as a lightweight geometric pre-optimization tool for conceptual shell design, rather than as a substitute for equilibrium-based form-finding or detailed structural optimization. Full article

Accurate short-term forecasting of Limnospira platensis biomass is essential for optimizing experimental scheduling and cultivation strategies, yet small datasets and strong temporal autocorrelation pose significant challenges for model reliability. In this study, we developed a leakage-safe, data-driven framework for direct multi-step forecasting of biomass concentration based on experimental time-series data from nine independent cultivation trials conducted under heterogeneous nutritional and environmental conditions. Gradient Boosting consistently outperformed a persistence baseline across all forecasting horizons (R² ≈ 0.915 at h = 1, 0.935 at h = 2, 0.814 at h = 3), demonstrating strong predictive capability under Leave-One-Experiment-Out cross-validation, which ensures generalization to unseen experiments. Residual analysis and prediction intervals confirmed robust uncertainty quantification and revealed condition-dependent variability in predictive performance. Overall, the results show that rigorously validated machine learning models can reliably forecast biomass trajectories beyond naïve baselines, even under limited and heterogeneous datasets. This approach provides a scalable and reproducible methodological framework for predictive modeling in algal biotechnology; however, because the training data were collected at flask scale, direct transfer to larger photobioreactor or outdoor systems should be considered a future validation step rather than an immediate deployment outcome. Full article

Objective: This study aimed to evaluate patients who developed hypersensitivity reactions (HSRs) during rituximab treatment and report the outcomes of desensitization protocols implemented to allow treatment continuation. Methods: We retrospectively reviewed the institutional data of 76 patients who received rituximab therapy at the Adult Hematology Department between January 2022 and September 2023. Among these, 11 patients who experienced immediate hypersensitivity reactions during infusion were analyzed. The overall frequency of rituximab-associated HSRs was 14.47% (11 out of 76 patients). Demographic data, underlying diseases, timing and type of HSRs, and details of the desensitization protocols were recorded. Results: The overall frequency of rituximab-associated HSRs was 14.47% (11 out of 76 patients). Among the 11 patients, eight were male and three were female, with a median age of 56 years (range: 19–72). Eight patients had CD20-positive non-Hodgkin lymphoma (NHL) and three had acute B-lymphoblastic leukemia (B-ALL). HSRs occurred during the first rituximab exposure in nine patients, at the fourth dose in one patient, and at the eighth dose in another. Symptoms included widespread rash, pruritus, flushing, chills, shivering, dyspnea, dysphagia, back pain, dizziness, syncope, and throat discomfort. All the patients were consulted by the Allergy and Immunology Clinic. Based on prick and intradermal test (IDT) results and the planned rituximab dose, desensitization protocols consisting of a three-dilution/12-step and a four-dilution/16-step regimen were prepared. Overall, 46 desensitization procedures were successfully completed in 11 patients. Notably, no severe anaphylactic events or treatment discontinuations due to drug toxicity occurred during the implementation of the protocols. Conclusions: Although the number of patients was limited, our findings indicate that in patients with hematologic malignancies receiving rituximab who develop early HSRs, desensitization represents a safe and effective strategy before considering treatment modification. These results support that, in appropriately selected patients, desensitization protocols are an important approach to continue therapy without interruption while minimizing adverse reactions. Full article

Background: Mass spectrometry imaging (MSI) enables in situ mapping of metabolite distributions within tissues, and spatial clustering is a key step for delineating metabolically distinct regions. Nevertheless, spatial clustering methods have not been systematically benchmarked for spatial metabolomics data. Methods: Here, we evaluated the effects of ion filtering and clustering method selection on clustering performance and established a dual-metric framework that jointly assesses the spatial continuity of cluster labels and inter-cluster metabolic heterogeneity. We benchmarked 30 clustering algorithms across 12 heterogeneous MSI datasets spanning three major ion sources, four mass analyzers, and multiple spatial resolutions, covering approaches from non-spatial methods to advanced spatially aware models. Results: Noise filtering markedly improved the spatial continuity of results generated by non-spatial methods (mean improvement, approximately 28%) but provided limited benefit for spatially aware methods. Across the 12 datasets, a median of only 11 methods satisfied both evaluation criteria simultaneously, whereas SSC and DRSC met the dual-metric thresholds in at least nine datasets. In the mbrain2_pos50 dataset, the top-ranked method based on the composite dual-metric score achieved 22% higher concordance between cluster assignments and cell-type annotations than the lowest-ranked method. Conclusions: Together, the proposed evaluation framework and the online platform SMcluster provide a standardized resource for benchmarking and selecting MSI clustering methods. Our results highlight the critical roles of preprocessing and method selection in determining spatial clustering performance and offer practical guidance for spatial metabolomics studies. Full article

Background: Southeast Asian Americans (SEAAs) experience a disproportionately high burden of hepatocellular carcinoma (HCC), with incidence in several subgroups (i.e., Cambodian, Laotian, and Vietnamese individuals) reaching up to nine times that of non-Hispanic Whites. HCC in SEAAs is largely driven by chronic hepatitis B (HBV), hepatitis C (HCV), metabolic dysfunction–associated steatotic liver disease (MASLD), and alcohol-associated liver disease (ALD). Despite established screening guidelines, under-detection and delayed diagnosis remain common. Objective: To summarize epidemiologic patterns, risk factors, screening challenges, and potential interventions aimed at reducing HCC disparities among SEAAs. Design and Methods: This narrative review synthesized evidence from population based epidemiologic studies, community-based interventions, health services research, and policy analyses. Attention was given to studies reporting disaggregated SEAA subgroup data. Findings derived from SEAA specific studies were distinguished from evidence drawn from broader Asian American or general cirrhosis populations, with inferential steps explicitly noted where subgroup specific data were limited. Key Findings: HCC incidence varies widely across SEAA subgroups, with elevated HBV- and HCV-related HCC in Vietnamese, Cambodian, and Laotian communities, and increasing MASLD-related HCC including among lean individuals who fall outside many surveillance frameworks. Screening and surveillance remain suboptimal, with fewer than 30% of patients with cirrhosis receiving recommended semiannual HCC surveillance and even lower uptake among SEAAs. Barriers include low HBV/HCV screening rates, limited disease awareness, language barriers, underinsurance, provider knowledge gaps, and lack of automated EHR-based reminders. Structural challenges such as poverty, transportation barriers, and limited access to specialty care further delay diagnosis. Proposed Interventions: Culturally tailored outreach programs, bilingual navigators, and community-based screening initiatives have demonstrated improved HBV/HCV testing and linkage to care. AI-enabled EHR tools may enhance identification of high-risk patients, streamline follow-up, and increase surveillance adherence. Expanded use of non-invasive fibrosis assessment and recognition of MASLD-related risk in non-obese individuals may support earlier detection. Policy priorities include mandatory Asian subgroup data disaggregation, expanded insurance coverage, and strengthened community-level healthcare infrastructure. Conclusions: SEAAs face a substantial and preventable HCC burden. A coordinated approach combining culturally tailored community engagement, improved provider support systems, and policy reforms is essential to improving early detection and reducing HCC disparities in this diverse population. Full article

Background/Objectives: Acute infected necrotizing pancreatitis remains associated with substantial morbidity and mortality. The step-up approach combines minimal-invasive drainage with endoscopic transgastric or percutaneous necrosectomy and has been shown to improve outcomes compared with open surgery. Laparoscopic-assisted necrosectomy (LAPN) may be performed in cases of infected walled-off necrosis (WON) following percutaneous drainage and is typically carried out using laparoscopic instrumentation. A newly implemented interdisciplinary approach includes sinus tract endoscopy, guided necrosectomy (STEN), which employs flexible endoscopy through a surgically created sinus tract and offers a less invasive and more targeted alternative to LAPN, providing improved visualization of complex necrotic cavities and facilitating repeatable step-up debridement. This study aimed to assess the introduction of STEN compared with LAPN in the management of infected WON within a step-up approach. Methods: A retrospective analysis of patients with infected walled-off necrosis (WON) treated using a step-up approach between 2019 and 2025 was conducted. Patients who underwent CT-guided percutaneous drainage followed by either STEN or LAPN were included. Demographic characteristics and clinical outcomes were collected. The primary endpoint was a composite outcome comprising major complications and 6-month mortality. Secondary outcomes included overall complication rates, need for reinterventions, and length of hospital stay. Results: During the study period, 17 patients were included. All patients were managed using a step-up approach: nine underwent STEN and eight underwent LAPN. In the STEN group, six patients (66.7%) met the primary endpoint, all due to major complications, with no mortality observed. In the LAPN group, the primary endpoint occurred in four patients (50.0%), including one death and three major complications. Conclusions: Our study showed that both STEN and LAPN were effective in treating infected WON within a step-up approach. STEN and LAPN showed comparable outcomes. However, these findings should be interpreted as exploratory and with caution given the retrospective design and the small sample size of this study. Further studies with larger patient cohorts are warranted to confirm these findings and to better define the role of this technique in the management of infected necrotizing pancreatitis. Full article

The present research article deals with the thermal degradation study of epoxy resins filled with hybrid nanostructured forms of carbon under oxidative conditions. In particular, the formulated polymer composites (denoted as HYB_0.1%_CNTs:GNs and HYB_0.5%_CNTs:GNs, respectively) consist of two kinds of fillers, namely multi-walled carbon nanotubes (CNTs) and graphene nanosheets (GNs), mixed together with two different total mass amounts: 0.1 and 0.5%. In both kinds of nanocomposites, three different CNT:GN mixing ratios were considered (5:1, 1:1, and 1:5, respectively), thus providing a total of six hybrid samples. The thermal behavior of these samples was studied by simultaneous thermogravimetry and differential thermal analysis (TG/DTA) under flowing air, and two processes took place in distinct temperature ranges. In each step, about 50% of mass loss is detected with an exothermic effect in the corresponding DTA curve, with the second one accompanied by an intense heat release. The kinetic analysis of the two-stage oxidative thermal degradation was investigated using a model-free isoconversional approach. A non-Arrhenian behavior of the temperature function k(T) was assumed, and lifetime prediction was estimated at temperatures close to those of the possible applications. Isoconversional analysis shows nearly constant activation energies for all composites except HYB_0.1%_5:1 (from 142 to 96 kJ·mol⁻¹), while lifetime predictions indicate that thermal stability increases with graphene content at 0.1% loading (HYB_0.1%_1:5) and with CNT content at 0.5% loading (HYB_0.5%_5:1), with uncertainties below 7%. Finally, because of the π–π bond interactions between the CNTs and the GNs dispersed in the epoxy resin matrix, an effective and remarkable electrical performance was found and a correlation with both electrical and morphological properties was established. In this regard, Tunneling Atomic Force Microscopy (TUNA) proved to be particularly powerful in allowing the simultaneous mapping of topography and localized conductive networks with exceptional sensitivity to nanofiller dispersion, such as CNTs and GNs. DC conductivity increased by up to nine orders of magnitude at 0.1 wt% hybrid loading (up to 3.73 × 10⁻⁴ S/m vs. 1.06 × 10⁻¹³ S/m for CNT-only), with nanoscale TUNA currents (−1.9 to 4.5 pA) mirroring macroscopic trends, while at 0.5 wt% all hybrids reached 10⁻² S/m, indicating reduced synergy once a fully developed conductive network is established. Full article

Digital photoelasticity enables non-contact, full-field stress analysis through optical fringe patterns, yet its practical deployment is often constrained by experimental complexity and the limited availability of open, standardized datasets. The emergence of multi-polarizer array cameras provides polarization-resolved measurements with high information content, enabling advanced analysis strategies beyond conventional single-image approaches. This work presents a public experimental dataset composed of synchronized image sequences acquired using a polarizer array camera and a conventional RGB camera under incremental mechanical loading. The dataset comprises nine experiments, including four benchmark specimens and five bio-inspired geometries, each recorded over 720 load steps. In total, the dataset releases 25,920 polarization-resolved images and 6480 RGB images, all provided in lossless format and accompanied by experiment-specific segmentation templates. Although classical and hybrid load-stepping methods are used to demonstrate the utility of the dataset, its scope is not limited to this application. The dataset is intended as a flexible platform for exploring a wide range of photoelastic analysis techniques that leverage polarization information, while enabling direct comparison with conventional color demodulation techniques. Full article

Wastewater-based epidemiology (WBE) allows for early surveillance of viral pathogens, including SARS-CoV-2. Simplified low-cost approaches are needed to deploy WBE surveillance in resource-limited small-island settings, where high sensitivity must be maintained. In this study, we optimized key upstream steps in an electronegative membrane virus adsorption–elution (VIRADEL) workflow, including sample acidification, composite sampling duration, and RT-qPCR inhibition mitigation. Wastewater influent was sampled at a pump station in Grenada using 12 h and 24 h time-weighted composite samples, concentrated using electronegative membrane VIRADEL with and without sample acidification (pH 3.5), and used Phi 6 (enveloped virus) and MS2 (non-enveloped virus) bacteriophages as process controls and PMMoV as a fecal-derived normalization target. Targets for SARS-CoV-2 N1 and a non-enveloped virus surrogate were measured by RT-qPCR. Quantitative wastewater data were compared to reported clinical cases in the community. Sample acidification significantly increased recovery of the enveloped process control, Phi 6 (p < 0.01) indicating improved efficiency in capturing enveloped viral targets during filtration. Twelve-hour composite samples had a false-negative percentage of 88%, while 24 h samples had only 6% false negatives and were able to mirror clinical case trends. Wastewater viral signals were detected 3–5 days prior to an increase in clinical cases. Hydraulic travel time within the contributing sewer network was not directly measured; therefore, the reported 3–5 day lead time reflects the combined effect of shedding dynamics, sampling integration, and sewer transport. This optimized workflow was deployed for nine months showing sustained analytical performance and operational feasibility. Full article

This study presents an analysis of selected approaches to transforming the Polish power system towards a net-zero greenhouse gas (GHG) emission economy by 2060. The generation-side system models primarily comprise renewable energy sources (RES), supported by nuclear power plants. Two system balancing scenarios were examined: Model G, based on biomethane-fired gas turbines and electrolysers utilising surplus energy; and Model H, which relies primarily on reversible fuel cells (RFCs) operating in a Power-to-Power configuration. Both models were considered under two demographic projections for Poland in 2060: maintaining the current population level (100%) and a decline to 71%. Simulations were performed with an hourly time step over a nine-year period, starting from 2060, using weather data from 2015 to 2023. The total electricity demand in the analysed scenarios ranges from 352 to 542 TWh/year, representing 2.1–3.2 times the current level. The proposed systems include 64 GW of onshore wind capacity, 33 GW of offshore wind, 136 GW of PV, 10 GW of nuclear generation, and extensive storage systems for electricity, heat, and gases (biomethane and hydrogen). In Model G, biomethane and hydrogen storage play a crucial role, requiring storage capacities of 5.8–7.5 billion Nm³ for biomethane and 6.2–7.0 billion Nm³ for hydrogen. In Model H, long-term storage relies on hydrogen reservoirs (approximately 12.5 billion Nm³) integrated with RFC units. The results demonstrate that the choice of architecture dictates the scale and technical requirements of the storage infrastructure. Notably, hydrogen serves as an effective energy storage medium, enabling the elimination of peak gas turbines from the system. Consequently, biomethane resources can be redirected to support the decarbonisation of other sectors of the economy. Full article