Search Results (3,510)

We present FilterForge, a chat-driven VS Code environment that pulls the synthesis, analysis, simulation, and optimization stages of microwave bandpass filter design, normally coordinated by hand across tools written in different languages, into one workflow. A deployed Model Context Protocol (MCP) server exposes deterministic Python implementations of coupling-matrix synthesis, uniform predistortion, topology reconfiguration, a genetic-algorithm transmission-zero selector, a mode-matching engine for H-plane iris-coupled rectangular waveguide geometries, and a skill that generates PyAEDT/HFSS notebooks for various dimensioning design-curves. A language-model orchestrator turns natural-language requests into typed tool calls, while every reported quantity stays inside the deterministic kernels, so the numerics remain reproducible and model-agnostic. We evaluate the call layer on a 45-task benchmark across the five tool categories: gemini-3-flash reaches $96.3\%$ tool-selection and $94.8\%$ full-call accuracy with an $88.9\%$ pass${}^3$ rate, which an ablation traces to the curated tool-selection prompt rather than to raw model capability. The mode-matching engine is validated against full-wave HFSS on a six-pole 4 GHz Chebyshev filter tuned from the chat panel, and on an 8 GHz WR-112 counterpart taken end-to-end with no engineer in the loop, where a deterministic critique gates each round until a manufacturable geometry is reached. We then exercise the full workflow on two folded six-pole WR-90 cross-coupled filters at $10\mathrm{GHz}$, a high-selectivity design synthesized against a stop-band mask and a group-delay-equalized variant whose positive cross-coupling uses a pair of side-wall irises, the latter settling to a peak-to-peak in-band group-delay ripple below $1.5\mathrm{ns}$ while recovering the synthesized return loss. Full article

Menopause marks a complex biopsychosocial transition defined by the permanent cessation of menstruation resulting from the loss of ovarian follicular activity. South Asian women tend to experience menopause earlier (45–47 years) than North American women, yet limited culturally appropriate interventions exist to address their symptoms. While hormone replacement therapy can reduce discomfort, its associated risks and limited cultural feasibility restrict its use in this population. There is a growing need to explore non-pharmacological and culturally relevant alternatives. Physical activity has been associated with potential well-being benefits during menopause. This study examined the prospective acceptability of a pedometer-based walking intervention, encouraging 10,000 steps daily, among South Asian immigrant women. The study was conducted in 2024 and completed within approximately seven months. A cross-sectional survey was conducted with 64 South Asian women aged 40–70+ years, who completed a questionnaire assessing the prospective acceptability and perceived barriers to participation. Overall, participants reported moderate to high levels of acceptability of the proposed walking intervention. Some participants perceived potential benefits for well-being; however, given the study design, effectiveness and symptom management outcomes were not assessed. Sociocultural factors—such as family responsibilities, modesty concerns, and limited access to supportive environments—were identified as potential barriers to participation. These findings suggest that a pedometer-based walking intervention may be acceptable to some South Asian immigrant women, though acceptability was not uniform and may be influenced by contextual factors, including opportunity costs. Further research using longitudinal or interventional designs is needed to evaluate feasibility, uptake, and effectiveness. Full article

“Power-to-ammonia” is widely regarded as a viable solution for large-scale consumption of wind and solar power, as well as for deep decarbonization in the energy and chemical sectors. However, the intermittent nature of renewable energy requires ammonia synthesis systems to operate across a wide and varying range of loads, posing challenges to their economic viability. To address this, we develop a simulation and optimization methodology for ammonia reactor operation under varying loads. Firstly, a high-fidelity reactor model is developed based on the reactor’s structural characteristics by incorporating reaction kinetics and thermodynamic mechanisms. This reactor model is then integrated with compression and separation units. To ensure computational efficiency, surrogate models are developed to approximate the ammonia synthesis and flash separation units. A case study of an ammonia plant with a nominal production rate of 100,000 tons/year is conducted to demonstrate the effectiveness of the proposed method. The results indicate that the feasible operation region of the reactor narrows significantly as the system production load decreases. System operation parameters, including reactor inlet temperature, reactor pressure, and ammonia separation temperature, are optimized for the ammonia synthesis loop over a wide operating window from 30% to 100% of nominal capacity. It is recommended to increase the system inlet temperature as the production load decreases, thereby compensating for the reduced heat release per unit product resulting from the decreased system pressure. Full article

Across the world’s electrical substations, water-treatment plants, and manufacturing lines, a single engineer with valid credentials and a laptop can today push new control logic to a programmable logic controller (PLC) and change the physical behaviors of safety-critical equipment within minutes. Firmware and ladder-logic updates on SCADA and industrial IoT systems are privileged operations: an attacker installing a malicious update controls the physical process. Existing protections concentrate install authority in a single party with no externally verifiable record; compromise of the vendor key, the engineering workstation, or any operator credential suffices to deliver an attacker-chosen payload to a PLC. CertiFlash binds every update to four independent approvals: a vendor signature, a FROST-Ed25519 threshold signature from an operator quorum, a per-session nonce from the PLC, and a monotonic counter. Every decision is recorded in an append-only Merkle transparency log. The PLC verifies the aggregate with a standard RFC 8032 Ed25519 verifier, requiring no FROST-specific device code. Four security properties (authenticity, authorization, rollback resistance, auditability) are machine-checked in Tamarin under a Dolev–Yao adversary with up to t − 1 compromised operators and corroborated through ten attack scenarios. The implementation runs with concurrent Modbus TCP and Siemens S7 traffic, blocks all attacks, signs in 27–192 ms (k = 3–10), keeps ML-DSA-65 within 6% of Ed25519 from 1 KiB to 10 MiB, and sustains 30.1 updates/s on 100 PLCs. The operator-quorum signature remains FROST-Ed25519: the design is partially post-quantum in the evaluated version. The framework maps to IEC 62443-3-3 SR 3.4 and NIS2 Article 21(2)(d–e). Full article

As extreme weather events increasingly threaten global food systems, accurately assessing climate risks and predicting regional crop yields remains a critical challenge. Conventional prediction models often rely on direct weather-to-yield relationships, bypassing continuous crop physiological responses and limiting their capacity to capture fine-grained temporal impacts of meteorological anomalies. To address this, we propose a novel two-stage spatiotemporal functional framework that integrates high-resolution daily weather trajectories with satellite-derived indicators, utilizing the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) to represent canopy structural vigor and hydraulic status, respectively. In the first stage, a Historical Functional Linear Model (HFLM) dynamically maps daily meteorological trajectories (temperature, precipitation, and solar radiation) onto continuous physiological curves under strict temporal causality constraints. This generates bivariate coefficient surfaces that reveal dynamic windows of vulnerability and capture divergent, lagged physiological responses to climate stress. In the second stage, a spatially heterogeneous functional additive model integrates these weather-shaped physiological trajectories alongside raw meteorological dynamics as joint predictors for county-level yields. By extracting functional principal components and modeling flexible non-linear biological responses while accounting for continuous spatial heterogeneity, this dual-channel frameworkcaptures key aspects of both chronic physiological stress and acute meteorological shocks. Validated across a 25-year (2000–2024) U.S. Corn Belt panel, the proposed DC-FAM achieves a mean weighted mean squared prediction error (WMSPE) of 242.33 (bu/acre)² and a median out-of-sample $R_{\mathrm{cv}}^2$ of 0.422, outperforming all benchmarks including a random forest. Attribution of the 2012 flash drought further demonstrates the framework’s capacity to mechanistically trace the complete disaster propagation chain from anomalous spring warming to mid-summer hydraulic failure. The proposed framework provides a transparent, biophysically grounded tool for decoding dynamic climate stress trajectories and disaster propagation chains, offering potential implications for adaptive farm management and precision agricultural insurance. Full article

The efficacy of polymer-based nanopesticides (NPs) is strongly governed by carrier concentration and surface charge, which affect shell thickness, drug release kinetics, and photostability. However, the influence of these two factors in pesticide release and delivery performance remains unclear. This study introduces a NIR-II fluorescence dye-tracing strategy to enable high-resolution monitoring of NP behavior in model plants. By systematically varying polymer concentration and copolymer blocks, we investigate their impact on release behavior, photostability, and stem uptake. As the polymer concentration increased, NPs demonstrated a controlled slow release and better photostability, yet a lower pesticide loading capability. In model plants, PISNPs transport quickly and can accumulate at wound sites, effectively offering antifungal properties. This work provides experimental evidence for optimizing polymer carrier design to achieve efficient, controlled release while minimizing photodegradation risks, offering practical guidelines for developing high-performance, low-risk nanopesticide formulations. Full article

The aim of this systematic literature review was to analyse how hydrological–hydraulic modelling, through the assessment of surface stormwater runoff behaviour, can support the participatory management of socio-environmental risks such as flooding, flash floods, and landslides. For this, 31 publications dating from 2015 to 2025 were selected from Scopus, ScienceDirect and Web of Science databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, to examine the importance of integration between modelling and community participation for risk management. The results indicate that, despite recent advances, most studies still prioritise either the technical application of modelling or community participation, without articulating the two approaches in risk analysis and management processes. There is a scarcity of methods that effectively combine local knowledge into the collaborative construction of scenarios and in the continued use of modelling as a tool for monitoring flood risks to disseminate community information. It was observed that studies carried out in developing countries use simpler methods, using community participation as an alternative to the absence of data. In developed countries, however, studies use more advanced methodologies through institutionalised processes. In contexts marked by high vulnerability, the integration of community participation and technical tools, such as hydrological–hydraulic modelling, represents a promising pathway toward more equitable and efficient risk management practices, aligning with sustainability agendas such as the Sustainable Development Goals (SDGs). Full article

Objectives: Multimodal large language models (MLLMs) have shown potential for medical image classification. We evaluated four optimization strategies in two MLLMs—GPT-4o (gpt-4o-2024-08-06) and Gemini 2.5 Flash-Lite—for ultrasound-based thyroid nodule malignancy classification using two public datasets and a clinical cohort of nodules with atypia of undetermined significance (AUS) cytology. Methods: Text prompting, few-shot learning, fine-tuning, and a hybrid strategy combining fine-tuning with few-shot learning were evaluated for each model. Performance was assessed using the Digital Database of Thyroid Images (DDTI; n = 80), a 1000-image test subset of TN5000, and an institutional AUS cohort with surgical pathology (n = 84). In the AUS cohort, the best-performing strategy was compared with the consensus classification of three endocrinologists and the American Thyroid Association (ATA) ultrasound risk stratification. Results: For GPT-4o, the hybrid strategy achieved the highest area under the receiver operating characteristic curve (AUC) in DDTI (0.866), TN5000 (0.689), and the AUS cohort (0.836). In the AUS cohort, its specificity was higher than that of endocrinologist consensus and ATA risk stratification when only high-suspicion nodules were classified as malignant (95.1% vs. 70.7% and 70.7%; p = 0.002 and p = 0.001, respectively), while sensitivity did not differ significantly (72.1% vs. 74.4% and 79.1%, respectively; both p > 0.05). However, the hybrid model misclassified 12 of 43 malignant nodules, corresponding to a false-negative rate of 27.9%. When high- and intermediate-suspicion ATA categories were classified as malignant, ATA sensitivity increased to 83.7% and specificity decreased to 56.1%; the hybrid model had a higher AUC than ATA risk stratification (0.836 vs. 0.749; p = 0.017). For Gemini 2.5 Flash-Lite, few-shot learning, fine-tuning, and the hybrid strategy did not improve AUC relative to text prompting in any dataset. Conclusions: The hybrid strategy produced the most consistent performance gains for GPT-4o across the three datasets but did not improve Gemini 2.5 Flash-Lite. The optimized GPT-4o model achieved high specificity in the diagnostically challenging AUS cohort, although its false-negative rate limits its use as a stand-alone diagnostic tool. Further validation in larger, prospective multicenter cohorts is required before clinical use. Full article

Background/Objectives: This study investigates the hypothesis that transient oxygen depletion is the mechanism of the skin sparing effect of ultra-high dose-rate irradiation, commonly referred to as FLASH irradiation. Methods: Two skin tattoo dots were placed approximately 1.0 cm apart on the thigh of FVB/N mice. The area overlapping the dots was irradiated with a single dose of 27 Gy protons delivered with either FLASH (~120 Gy/s) or 0.5 Gy/s conventional dose-rate (CDR) irradiation. Skin contraction was assessed by measuring the distance between the tattoo dots and complemented by histopathological skin analyses. Mice were placed in a 1.4 L chamber flushed with 5%, 7%, 20.9% or 100% oxygen (balance nitrogen, where applicable) prior to and during irradiation. Skin oxygenation was measured non-invasively using the phosphorescence quenching method. Results: Compared to air-breathing mice, skin contraction increases in mice breathing 100% oxygen and decreases when breathing 7% and 5% oxygen following CDR irradiation, showing that skin is neither fully oxygenated nor hypoxic. FLASH irradiation reduced skin contraction, epidermal thickening, and fibrosis in air-breathing mice compared to CDR irradiation. The difference between FLASH and CDR skin contraction decreases as the inspired gas oxygen content is reduced from 20.9% to 7%. Under 5% oxygen breathing conditions, the FLASH sparing effect is eliminated. Conclusions: Mean normal tissue pO₂ does not reveal the presence of cells at low pO₂ that could become susceptible to FLASH-induced radiobiological hypoxia at doses lower than would be predicted from the mean tissue pO₂ value. In the absence of oxygen, FLASH skin sparing for the late normal tissue effect, skin contraction, is eliminated. Full article

Genetic programming (GP) has been widely used in quantitative finance for discovering formulaic alpha factors that can predict asset returns. However, GP often produces overgrown expressions that are difficult to interpret and expensive to evaluate. This paper proposes a large language model (LLM)-assisted pruning framework that reviews expression trees generated by GP, with the LLM acting as a semantic reviewer that flags redundant or financially implausible branches based on structural complexity and contextual reasoning. The proposed method is formalized as a closed-loop Trigger–Evaluate–Decide–Execute (TEDE) process. We present mathematical formulations, algorithmic design, and examples showing how redundant nested functions can be simplified while monitoring predictive performance. Experiments with high-frequency cryptocurrency market data, using DeepSeek-V4-Flash as the semantic engine, show lower expression complexity and higher rubric-based interpretability scores for the pruned symbolic factors. Under the reported test setup, the LLM-pruned configuration has higher Information Ratio (IR) values than the listed baselines and more compact expression trees than the GP baselines. Full article

With the expansion of ammonia energy applications, long-distance liquid ammonia pipelines are expected to support large-scale cross-regional ammonia transport. In the liquid ammonia pipeline commissioning process, gaseous ammonia purging involves ammonia–nitrogen mixing and possible liquefaction, while liquid ammonia injection may induce flashing and severe local cooling, all of which can affect commissioning safety. To characterize these thermodynamic phenomena, a transient gas–liquid two-phase flow model was established and validated using OLGA 2022.1.0 software for simulating the long-distance liquid ammonia pipeline commissioning. The model adopts the cross-sectionally averaged one-dimensional approach. A volume-corrected Soave–Redlich–Kwong (SRK) equation of state for ammonia was adapted, validated, and used to generate OLGA-compatible thermodynamic property tables. The results show that, during gaseous ammonia purging, a higher flowrate shortens the displacement time by accelerating nitrogen removal, and this effect is more pronounced at higher ambient temperatures due to enhanced molecular diffusion. Along the pipeline, pressure gradually decreases from frictional resistance, with a steeper drop near the outlet caused by gas acceleration, and temperature gradually approaches ambient through heat exchange with the pipe wall and surrounding soil. A high gaseous ammonia flowrate can cause partial liquefaction, regasification, and temperature fluctuations. During liquid ammonia injection, local condensation and slight liquid accumulation occur before the liquid front arrives, and the low-temperature region moves with the liquid front. The liquid ammonia mass flowrate has the strongest influence on the injection process, as it reduces the completion time but increases the outlet temperature, outlet pressure, and the low-temperature risk downstream of the valve. Therefore, it should be controlled within an appropriate range to balance efficiency and low-temperature safety risks. This work provides a rapid and efficient prediction model for key thermo-hydraulic parameters during liquid ammonia pipeline commissioning, and the overall analyses offer insights for on-site process design and safety control. Full article

Peach (Prunus persica) fruit pigmentation is largely associated with anthocyanin accumulation, whereas colorless flavonols such as kaempferol glycosides may reflect alternative use of shared flavonoid precursors. To examine the relationship between anthocyanin and selected kaempferol glycoside accumulation, we analyzed 15 peach accessions classified by red, white, or yellow flesh pigmentation. Skin color was quantified using the a*/b* ratio, where a* represents redness/greenness and b* represents yellowness/blueness. Red-fleshed accessions showed higher skin a*/b* values and accumulated higher levels of total anthocyanins, particularly cyanidin-3-glucoside, than white and yellow accessions. In contrast, kaempferol-3-rhamnoside preferentially accumulated in white-fleshed accessions. Expression analysis of flavonoid pathway genes showed that dihydroflavonol 4-reductase (PpDFR) was more highly expressed in red accessions, whereas flavonol synthase (PpFLS) was more highly expressed in white accessions; chalcone synthase (PpCHS), flavanone 3-hydroxylase (PpF3H), flavonoid 3′-hydroxylase (PpF3′H), and anthocyanidin synthase (PpANS) showed no significant differences among color groups. Heterologous overexpression of PpF3′H in Arabidopsis thaliana, a well-characterized model plant for flavonoid biosynthesis, was associated with increased seed anthocyanin accumulation and a lower kaempferol-to-quercetin ratio, supporting its catalytic capacity to influence flavonoid composition in an exogenous system. Overall, these results indicate that differential anthocyanin and selected kaempferol glycoside accumulation in peach is associated with the relative expression patterns of branch-related flavonoid genes, particularly PpDFR and PpFLS. This study provides targeted metabolic and transcriptional evidence for understanding peach flesh and skin pigmentation and provides mechanistic insight into flavonoid branch competition linking gene expression patterns with metabolite allocation, and identifies candidate genes for improving fruit color and flavonoid-related nutritional quality. Full article

Ammonia is a promising zero-carbon energy carrier for maritime decarbonisation, but its deployment is limited by safety risks that are not adequately addressed by conventional marine fuel safety frameworks. This study critically reviews safety assessment, risk management and control strategies for ammonia storage and handling in maritime applications using a PRISMA-informed literature synthesis. Evidence is analysed across hazard characterisation, storage configurations, transfer operations, risk assessment methods, mitigation barriers and regulatory frameworks. The review shows that ammonia safety is governed by coupled release–exposure–barrier interactions shaped by storage condition, tank configuration, pressure–temperature behaviour, material compatibility, transfer mode, ventilation, ship geometry and human intervention. Existing methods, including HAZID, HAZOP, risk matrices and QRA, support hazard screening and prioritisation, but remain limited in representing flashing two-phase releases, dense gas dispersion, confined-space accumulation, exposure duration, ventilation effectiveness and safeguard timing under maritime conditions. CFD, FTA, Bayesian approaches and Monte Carlo analysis offer higher analytical resolution, but their reliability is constrained by limited validation data, uncertain leak-frequency inputs and simplified assumptions for human exposure and emergency response. Effective risk control therefore requires a toxicity-centred barrier strategy linking containment integrity, ammonia-compatible materials, gas and process monitoring, emergency shutdown, ventilation, water-based mitigation, PPE, competency-based training and emergency planning. Current regulatory and classification guidance provides an essential foundation but remains fragmented and insufficiently aligned with ammonia-specific requirements for exposure modelling, safety-zone definition and approval pathways. This review contributes a maritime-specific synthesis of ammonia storage and handling safety by connecting hazard behaviour, storage design, transfer operations, risk assessment limitations, control-barrier logic and regulatory approval needs. The findings highlight the need for validated source-term models, full-scale release and dispersion data, exposure-based safety criteria and harmonised regulatory pathways to support the safe and scalable use of ammonia in maritime decarbonisation. Full article

Purpose: Vasomotor, sexual, and musculoskeletal symptoms are common adverse effects of adjuvant endocrine therapy in breast cancer survivors. Social media use has not been investigated with altered symptom perception in patients receiving adjuvant endocrine therapy. This study aimed to investigate whether social media use or addiction independently predicts endocrine therapy-related symptom burden in breast cancer survivors. Methods: A cross-sectional survey study was conducted among 153 breast cancer survivors receiving adjuvant endocrine therapy. The Social Media Use Scale (SMUS) and Bergen Social Media Addiction Scale (BSMAS) were assessed using validated Turkish versions of each scale. Endocrine therapy-related toxicities (specifically hot flashes, vaginal dryness, loss of libido, and musculoskeletal pain severity) were evaluated using specific self-reported 5-point Likert scale items. Results: All of the patients were female and menopausal, either neutral or induced with ovarian function suppression. In the univariate analysis, the BSMAS score showed a weak positive correlation with vasomotor/sexual symptoms (r = 0.194; p = 0.017), but this association disappeared after adjustment for clinical variables. Younger age was associated with greater vasomotor/sexual symptoms in univariate testing. Neither the SMUS nor BSMAS independently predicted musculoskeletal symptom severity in univariate and multivariate models, while higher educational attainment remained the only independent predictor of musculoskeletal pain severity (OR = 1.96; 95% CI: 1.06–3.57; p = 0.031). Conclusions: This study is unique in investigating unstructured social media use and endocrine therapy-related physical symptoms. In this cohort, unstructured social media use was not associated with the endocrine therapy-related physical symptom burden. While these cross-sectional findings do not support social media behavior as a significant predictor, clinical assessments should continue to prioritize established determinants such as age and educational background. Full article

Session dialogue assessment based on machine learning is gradually becoming an effective solution for therapeutic alliance measurement which is an important factor for successful psychotherapy. However, most existing models assume clean and pre-structured dialogue transcripts, whereas real-world counseling documentation often contains heterogeneous case reports. This gap limits the applicability of current automated assessment models in realistic documentation scenarios. In this work, we propose a framework for automated working alliance assessment from complex, multilingual reports. First, language-specific BERT models are fine-tuned to process case reports across different languages, enabling accurate speaker role delineation and dialogue structuring. Second, Gemini-2.5-Flash is leveraged to annotate the dialogues with working alliance ratings. Third, a hybrid feature representation strategy is then developed to jointly capture linguistic style and semantic content from the counseling dialogues. Furthermore, an entropy-based mutual information analysis is conducted to identify the most informative linguistic features. Finally, the extracted hybrid features serve as inputs to XGBoost for alliance assessment. In experiments, the proposed framework shows better performance in the comparison with SOTA methods and generalization ability. Full article