Search Results (1,441)

Methane (CH₄) emissions are a major contributor to greenhouse gases and pose significant challenges to global climate mitigation efforts. The accurate determination of CH₄ concentrations via remote sensing is crucial for emission monitoring but remains impeded by surface spectral heterogeneity—notably albedo variations and land cover diversity. This study systematically assessed the sensitivity of three mainstream algorithms, namely, matched filter (MF), albedo-corrected reweighted-L1-matched filter (ACRWL1MF), and differential optical absorption spectroscopy (DOAS), to surface type, albedo, and emission rate through high-fidelity simulation experiments, and proposed a dynamic regularized adaptive matched filter (DRAMF) algorithm. The experiments simulated airborne hyperspectral imagery from the Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) with known CH₄ concentrations over diverse surfaces (including vegetation, soil, and water) and controlled variations in albedo through the large-eddy simulation (LES) mode of the Weather Research and Forecasting (WRF) model and the MODTRAN radiative transfer model. The results show the following: (1) MF and DOAS have higher true positive rates (TP > 90%) in high-reflectivity scenarios, but the problem of false positives is prominent (TN < 52%); ACRWL1MF significantly improves the true negative rate (TN = 95.9%) through albedo correction but lacks the ability to detect low concentrations of CH₄ (TP = 63.8%). (2) All algorithms perform better at high emission rates (1000 kg/h) than at low emission rates (500 kg/h), but ACRWL1MF performs more robustly in low-albedo scenarios. (3) The proposed DRAMF algorithm improves the F1 score (0.129) by about 180% compared to the MF and DOAS algorithms and improves TP value (81.4%) by about 128% compared to the ACRWL1MF algorithm through dynamic background updates and an iterative reweighting mechanism. In practical applications, the DRAMF algorithm can also effectively monitor plumes. This research indicates that algorithms should be selected considering the specific application scenario and provides a direction for technical improvements (e.g., deep learning model) for monitoring gas emission. Full article

Organophosphate pesticides are among the most persistent and toxic contaminants in aquatic environments, requiring effective strategies for detection and remediation. In this work, density functional theory (DFT) calculations were employed to investigate the adsorption of nine representative organophosphates (glyphosate, malathion, diazinon, azinphos-methyl, fenitrothion, parathion-methyl, disulfoton, tokuthion, and ethoprophos) on mercaptobenzothiazole disulfide (MBTS) and MBTS-functionalized graphene (G–MBTS). All simulations were performed in aqueous solution using the SMD solvation model with dispersion corrections and counterpoise correction for basis set superposition error. MBTS alone displayed a range of affinities, suggesting potential selectivity across the organophosphates, with adsorption energies ranging from 0.27 to 1.05 eV, malathion being the strongest binder and glyphosate the weakest. Anchoring of MBTS to graphene was found to be highly favorable (1.26 eV), but the key advantage is producing stable adsorption platforms that promote planar orientations and $\pi$–$\pi$/dispersive interactions. But the key advantage is not stronger binding but the tuning of interfacial electronic properties: all G–MBTS–OP complexes show uniform, narrow HOMO-LUMO gaps (∼0.79 eV) and systematically larger charge redistribution. These features are expected to enhance electrochemical readout even when adsorption strength was comparable or slightly lower (0.47–0.88 eV) relative to MBTS alone. A Quantum Theory of Atoms in Molecules (QTAIM) analysis of the G–MBTS–malathion complex revealed a dual stabilization mechanism: multiple weak C–H$\dots \pi$ interactions with graphene combined with stronger S…O and hydrogen-bonding interactions with MBTS. These results advance the molecular-level understanding of pesticide–surface interactions and highlight MBTS-functionalized graphene as a promising platform for the selective detection of organophosphates in water. Full article

Background/Objectives: Older adults should be capable of reasoned judgments regarding their health, lifestyle, and disease management. Therefore, interventions to improve Health Literacy (HL) are essential for empowering older adults to make choices that improve their quality of life. Correct medication use is vital to maintaining and enhancing health outcomes in older adults. This study aimed to identify the most effective HL interventions with impact on medication use among older adults. Methods: A systematic review was conducted using MEDLINE (PubMed) and EMBASE to identify interventional studies evaluating HL interventions that have an impact on medication use in older adults. Results: Three studies satisfied the inclusion criteria. The evidence indicates that HL interventions have positive effects and can be effectively implemented by several healthcare professionals through tailored communication strategies. However, the review underscores a substantial lack of high-quality research on HL strategies aimed at improving medication use in older adults, particularly given the increasing prevalence of chronic diseases and polypharmacy in this population. Conclusions: This systematic review identifies substantial research gaps regarding HL interventions and their impact on medication use among older adults. While the included studies demonstrate encouraging outcomes, further rigorous research is necessary to develop specific HL interventions addressing medication-related challenges in older adults. Full article

This study investigates the wind field characteristics of long-span suspension bridges, with a particular focus on the disturbance effects introduced by the bridge deck on wind measurements. Field data are collected using anemometers installed on both the upstream and downstream sides at the midspan of the bridge girder. A comparative analysis of these measurements reveals notable discrepancies attributable to deck-induced flow disturbances. To systematically assess these effects, the disturbed wind parameters are identified, and their spatial distribution patterns are examined. A statistical model is then developed to quantify and correct the disturbance influence. This model isolates the disturbance component and establishes empirical correlations between the disturbed and actual wind parameters. The results confirm that the proposed correction approach effectively reduces measurement bias caused by deck interference, thereby enabling more accurate wind load evaluation for long-span suspension bridge structures. Full article

Ketosis is one of the most common metabolic disorders in high-yielding dairy cows in early lactation. It has a negative impact on milk yield, reproduction, and general health of the animals. The present review aims to systematize and critically analyze current scientific data on the monitoring, diagnosis, prevention, and treatment of subclinical and clinical ketosis, with the aim of developing an applicable protocol for good veterinary medical practices (GMP). Based on the comparative analysis method of data from the literature and clinical practice, β-hydroxybutyrate (BHBA) in blood is confirmed as the gold standard for diagnosis with specificity and sensitivity above 90%. Indicators such as fat/protein (F/P) > 1.4 and NEFA > 0.4 mmol/L, as well as reduced citrate content in milk, have been evaluated as useful screening tools, although with lower diagnostic value. Despite the advantages of some indirect methods (such as F/P), critical analysis shows that they are strongly influenced by physiological status, lactation stage, and diet and cannot replace direct blood tests. Preventive approaches emphasize the importance of stable nutrition in the pre- and post-calving period, restriction of ketogenic feeds, and use of oral glucose precursors. Literature analysis shows that propylene glycol is effective, but with prolonged use it can reduce appetite. Combined antiketotic products have also been introduced, providing not only energy support but also liver protection. Particular attention has been paid to monensin (applied in the commercial product “Kexxtone”)—a polyether antibiotic with sustained release, which reduces the incidence of ketosis by over 50% and increases milk yield in the first weeks after calving. However, its high cost, antibiotic nature, and need for veterinary supervision limit its universal use. The treatment protocol is differentiated according to the clinical type: glucose I. V. and propylene glycol in type I ketosis and avoidance of glucocorticoids in suspected type II (hepatic steatosis). In the critical analysis It is noted that improper use of glucocorticoids can lead to a worsening of the condition. A structured protocol for DVMP (Dairy Veterinary Medical Practice) is proposed, which includes targeted metabolic monitoring (NEFA, BHBA, F/P); proven preventive regimens (Kexxtone, propylene glycol, balanced rations), and staged prevention and treatment according to the form of ketosis. The adaptation of good practices to the scale of the farm and the level of knowledge and skills of the staff for their correct application remains a challenge. Providing training, a standardized control log, and access to field diagnostic tools is key to the success of any protocol. Full article

Modern AI systems can strategically misreport information when incentives diverge from truthfulness, posing risks for oversight and deployment. Prior studies often examine this behavior within a single paradigm; systematic, cross-architecture evidence under a unified protocol has been limited. We introduce the Strategy Elicitation Battery (SEB), a standardized probe suite for measuring deceptive reporting across large language models (LLMs), reinforcement-learning agents, vision-only classifiers, multimodal encoders, state-space models, and diffusion models. SEB uses Bayesian inference tasks with persona-controlled instructions, schema-constrained outputs, deterministic decoding where supported, and a probe mix (near-threshold, repeats, neutralized, cross-checks). Estimates use clustered bootstrap intervals, and significance is assessed with a logistic regression by architecture; a mixed-effects analysis is planned once the per-round agent/episode traces are exported. On the latest pre-correction runs, SEB shows a consistent cross-architecture pattern in deception rates: ViT $80.0\%$, CLIP $15.0\%$, Mamba $10.0\%$, RL agents $10.0\%$, Stable Diffusion $10.0\%$, and LLMs $5.0\%$ (20 scenarios/architecture). A logistic regression on per-scenario flags finds a significant overall architecture effect (likelihood-ratio test vs. intercept-only: ${\chi }^2\left(5\right)=41.56$, $p=7.22\times {10}^{-8}$). Holm-adjusted contrasts indicate ViT is significantly higher than all other architectures in this snapshot; the remaining pairs are not significant. Post-correction acceptance decisions are evaluated separately using residual deception and override rates under SEB-Correct. Latency varies by architecture (sub-second to minutes), enabling pre-deployment screening broadly and real-time auditing for low-latency classes. Results indicate that SEB-Detect deception flags are not confined to any one paradigm, that distinct architectures can converge to similar levels under a common interface, and that reporting interfaces and incentive framing are central levers for mitigation. We operationalize “deception” as reward-sensitive misreport flags, and we separate detection from intervention via a correction wrapper (SEB-Correct), supporting principled acceptance decisions for deployment. Full article

Background/Objectives: Rectal cancer is a significant global health concern, requiring precise staging and response assessment to make treatment decisions. Magnetic resonance imaging (MRI) is the standard imaging modality for evaluating tumor stage and treatment response. Positron emission tomography/computed tomography (PET/CT) offers complementary insights into pelvic lymph node involvement, tumor response, and distant metastases. Integrating PET and MRI into a hybrid PET/MRI modality can provide superior assessment of tumor staging and response compared to conventional imaging techniques. This review shares an update on the role of PET/MRI in rectal cancer staging and treatment response assessment. Methods: A systematic review of the current literature was conducted by two independent reviewers. This study utilized databases including Embase, Biosis, PubMed, Scopus, and Web of Science, employing the following keywords as eligibility criteria: “PET/MRI” OR “PET/MR” AND “rectal cancer” OR “colorectal cancer” AND “staging” AND “treatment assessment” OR “planning”. The inclusion criteria were that studies must examine cancer staging and response assessment. The exclusion criteria for the search were letters to the editors, abstracts, and case reports; studies that included fewer than five patients; studies that included cancer other than rectal or colorectal cancer; studies that did not utilize PET/MRI for rectal cancer staging and assessment; and non-human studies. Results: PET/MRI demonstrates potential advantages over conventional imaging, providing superior soft tissue contrast, functional imaging capabilities, and improved lesion characterization. A total of ten studies suggest that PET/MRI may enhance tumor staging accuracy and better assess pelvic lymph node involvement than PET/CT and MRI alone; in four studies, PET/MRI also showed higher response accuracy. Challenges remain in standardizing imaging protocols, validating PET tracers, and encouraging widespread clinical adoption. Conclusions: PET/MRI has the potential to offer a superior imaging solution for rectal cancer staging and treatment response assessment. While preliminary studies highlight its advantages over PET/CT and MRI alone, further research is needed to establish standardized protocols, validate PET tracers for routine clinical use, and improve imaging quality through attenuation and motion correction. Full article

Background/Objectives: Adolescent idiopathic scoliosis (AIS) is a three-dimensional deformity of the spine that can negatively impact on quality of life, pulmonary function, and body image. Its conservative management includes various interventions, among which the Schroth method stands out. This approach is based on three-dimensional corrective exercises and rotational breathing. This review aimed to analyze the effectiveness of the Schroth method, applied either alone or in combination with other conservative therapies, on variables such as Cobb angle, angle of trunk rotation (ATR), pulmonary function, and health-related quality of life in patients with AIS. Methods: A scientific literature search was conducted using the PubMed database. We searched for randomized controlling trials (RCTs), systematic reviews, and meta-analyses reported in English from 2020 to 2025. Different combinations of the terms and MeSH terms “adolescent”, “idiopathic”, “scoliosis”, and “Schroth” connected with various Boolean operators. Results: Overall, 82 articles were reviewed from the selected database. After removing duplicated papers and title/abstract screening, 13 studies were included in our review. The results showed that the Schroth method proved effective in reducing the Cobb angle and ATR, particularly in patients with mild curves and in early stages of skeletal growth. Improvements were also observed in health-related quality of life and aesthetic perception, and to a lesser extent, in pulmonary function. Moreover, therapeutic adherence and treatment continuity were important to maintaining long-term benefits. Conclusions: The Schroth method could be an effective treatment associated with orthopedic treatment, yielding satisfactory results. Its implementation requires structured programs, professional supervision, and strategies to enhance therapeutic adherence. Nevertheless, to validate its long-term effectiveness, we need more homogeneous studies with longer follow-up durations. Full article

Background/Objectives: General movement assessment (GMA) is a clinical assessment tool used to predict risk for cerebral palsy (CP) in young infants. Equal access is challenging since GMA-trained personnel is a limited resource. An implementation study aimed to offer all high-risk infants born in the Central Norway Regional Health Authority equal access to GMA as part of the standard follow-up. This study explored the local health care personnel (HCP) experiences with early risk assessment for CP in young infants using remote GMA. Methods: This was a qualitative study with one focus group and four individual interviews. Participants were HCP from the local follow-up clinics who had experience with GMA. Analyses were inspired by Malterud’s systematic text condensation. Results: Attitudes towards GMA were, in general, positive, and GMA was considered an important and gentle examination contributing to earlier initiation of correct follow-up actions and appropriate treatment. The GMA results could improve communication between HCP and parents, and lead to a closer local municipality follow-up if GMA result was abnormal. Parents were given an active role with home video recording, which was considered family empowering. Especially pediatricians wanted more detailed information about the qualities of spontaneous movements to support clinical decision-making. Conclusions: This study indicated that further implementation of the GMA method to assess the risk of CP in high-risk infants could be recommended, and that GMA was a gentle method for the purpose. As suggested by pediatricians in this study, more detailed assessments using the GMA beyond FMs could be further explored as support to clinical decisions. The insight from this study may inform implementation in similar contexts. Full article

Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such as arc voltage, current, wire feed rate, and torch travel speed, requiring advanced monitoring and adaptive control strategies. In this study, a vision-based monitoring system integrated with a reinforcement learning framework was developed to enable intelligent in situ control of WAAM. A custom optical assembly employing mirrors and a bandpass filter allowed simultaneous top and side views of the melt pool, enabling real-time measurement of layer height and width. These geometric features provide feedback to a tabular Q-learning algorithm, which adaptively adjusts voltage and wire feed rate through direct hardware-level control of stepper motors. Experimental validation across multiple builds with varying initial conditions demonstrated that the RL controller stabilized layer geometry, autonomously recovered from process disturbances, and maintained bounded oscillations around target values. While systematic offsets between digital measurements and physical dimensions highlight calibration challenges inherent to vision-based systems, the controller consistently prevented uncontrolled drift and corrected large deviations in deposition quality. The computational efficiency of tabular Q-learning enabled real-time operation on standard hardware without specialized equipment, demonstrating an accessible approach to intelligent process control. These results establish the feasibility of reinforcement learning as a robust, data-efficient control technique for WAAM, capable of real-time adaptation with minimal prior process knowledge. With improved calibration methods and expanded multi-physics sensing, this framework can advance toward precise geometric accuracy and support broader adoption of machine learning-based process monitoring and control in metal additive manufacturing. Full article

Determining a high-precision national geoid is a fundamental step in modernizing Kazakhstan’s vertical reference system. However, the country’s vast territory, complex topography, and limited coverage of modern terrestrial and airborne gravimetric surveys present significant challenges. In this context, Soviet-era gravimetric maps at a 1:200,000 scale remain the only consistent nationwide data source, yet their reliability has not previously been rigorously assessed within modern gravity standards. This study presents the first comprehensive validation of Soviet-era gravimetric surveys using two independent approaches. The first approach is about the comparison of gravity anomalies with the global geopotential models EGM2008, EIGEN-6C4 and XGM2019e_2159. The second approach is about the direct evaluation against absolute gravity measurements from the newly established Qazaqstan Gravity Reference Frame (QazGRF). The analysis demonstrates that, after applying systematic corrections, the Soviet-era gravimetric survey retains high information content. The mean discrepancy with QazGRF measurements is 0.7 mGal with a standard deviation of 2.5 mGal, and more than 90% of the evaluated points deviate by less than ±5 mGal. Larger inconsistencies, up to 20 mGal, are confined to mountainous and geophysically complex regions. In addition, several artifacts inherent to the global models were identified, suggesting that the integration of validated regional gravimetric data can also support future improvements of global gravity models. A key finding was the detection of an artifact in the global models on sheet M43. Its presence was confirmed by comparison with terrestrial gravimetric data and inter-model differences. It was established that the anomaly is caused by inaccuracies in the terrestrial “fill-in” component of the EGM2008 model, which subsequently inherited by later global solutions. The results confirm that Soviet gravimetric maps, once critically re-evaluated and tied to absolute observations, can be effectively integrated with global models. This integration delivers reliable, high-resolution inputs for regional gravity-field modeling. It establishes a robust scientific and practical foundation for constructing the first national geoid of Kazakhstan and for implementing a unified state coordinate and height system. It also helps enhance the accuracy of global geopotential models. Full article

Incomplete absorption correction procedures in single-crystal diffraction experiments leave a characteristic trace—a “fingerprint”—in the residuals. Specifically, weak intensities are systematically overestimated, contributing disproportionately and sometimes even dominantly to the chi-square sum in least squares refinements. An analysis of six published crystal structures spanning a wide range of absorption coefficients reveals a consistent positive shift of the weighted residuals, which were significant for crystals with $>5.02$ mm⁻¹. This shift is all the stronger the greater the absorption coefficient and is accompanied by a proportionally increasing fraction of positive excess residuals. The simultaneous increase in the mean value of the residuals and the fraction of positive excess residuals proves that the shift is not caused by strong reflections or isolated outliers, but rather by the systematic overestimation of many weak intensities. Diagnostic plots and statistical metrics are presented for additional published data sets, supporting the generality of the findings. These findings can support the development of improved methods for absorption correction, which lead to physically meaningful thermal motion parameters even with strong absorption. Full article

This paper investigates whether ChatGPT, a large language model, can assist in the implementation of lattice-based cryptography and fully homomorphic encryption algorithms, specifically the Ring Learning-with-Errors encryption scheme and the Brakerski–Fan–Vercauteren FHE scheme. To the best of our knowledge, this study represents the first systematic exploration of ChatGPT’s ability to implement these cryptographic algorithms. Fully homomorphic encryption, despite its theoretical and practical significance, poses significant challenges due to its computational complexity and efficiency requirements. This study evaluates ChatGPT’s capability as a development tool from both algorithmic and implementation perspectives. At the algorithmic level, ChatGPT demonstrates a solid understanding of the Rring Learning-with-Errors lattice encryption scheme but faces limitations in comprehending the intricate structure of the Brakerski–Fan–Vercauteren FHE scheme. At the code level, ChatGPT can generate functional C++ implementations of both encryption schemes, significantly reducing manual coding effort. However, debugging and corrections remain necessary, particularly for the more complex Brakerski–Fan–Vercauteren scheme, where additional effort is required to ensure correctness. The findings highlight ChatGPT’s potential and limitations in supporting cryptographic algorithm development, offering insights into its application for advancing implementations of complex cryptographic systems. Full article

Modular process plants represent a promising strategy to address the increasing need for flexibility and accelerated market deployment in the production of fine and specialty chemicals. However, these modular systems are inherently susceptible to wear and fault development, while condition monitoring methods tailored to such systems remain scarce. This study presents a proof of concept for a targeted fault diagnosis approach of the modular hydraulic systems of such modular process plants and reports on its experimental validation. The methodology comprises two stages: First, model-based symptoms are calculated independently for each module and subsequently utilized within a centralized diagnostic system. This rule-based diagnosis incorporates generalized module interactions, quantified fault degrees, and the plant topology. Importantly, uncertainties arising from measurement equipment, model fidelity, and parameter variability are incorporated and systematically propagated throughout the diagnosis. The validation was conducted on a modular test rig specifically designed to simulate a range of single-fault scenarios across more than 1200 stationary operating points. The results underscore the robustness of the proposed approach: the correct fault was consistently identified, with the estimated fault magnitudes closely aligning with the actual values, exhibiting an average discrepancy of 0.029 for internal leakage of a positive displacement pump. The overall discrepancy for the experimental validation of all fault types was 0.12. Notably, no false alarms were observed, and the displayed uncertainty was considered plausible, though there remains potential for refinement. In summary, this study demonstrates the successful application of model-based symptoms for a rule-based diagnosis, representing a significant advancement toward reliable fault detection in modular hydraulic systems. Full article