The purpose of this study was to assess the performance of iterative reconstruction (IR) and deep-learning image reconstruction (DLR) algorithms developed by four CT vendors in terms of image quality. Acquisitions were performed on an image quality phantom at three dose levels (1.8, 6 and 11 mGy) using four CT systems (further referred to as G-CT, P-CT, U-CT, and C-CT). For each CT, raw data were reconstructed using the commonly used soft tissue kernel and level for IR and DLR algorithms. Noise power spectrum and task-based transfer function were computed to assess noise magnitude, noise texture (fav) and spatial resolution, respectively. Detectability indexes (d') were computed to model the detection of two abdominal lesions. Compared to IR, noise magnitude reduction with DLR was similar for all dose levels for G-CT (-21.1 ± 1.5 [standard deviation (SD)] %) and P-CT (-48.4 ± 0.1 [SD] %) but more pronounced at 1.8 mGy and decreased as the dose level increased for U-CT and C-CT. Noise texture was greater with DLR than IR at all dose levels for all CT systems, except for U-CT, which gave similar fav values. For both inserts, spatial resolution was better with DLR than with IR for all CT systems, except for the low-contrast insert with C-CT at 1.8 and 6 mGy and P-CT at 1.8 mGy. For both simulated lesions and all dose levels, d' values were greater with DLR than with IR by 77.5 ± 8.7 (SD) % for C-CT, 33.7 ± 5.6 (SD) % for G-CT, 112.7 ± 4.7 (SD) % for P-CT and from 158.3 % to 546.6 % on average for U-CT. Compared to IR, DLR algorithms reduce the image noise and improve detectability whilst providing similar or better noise texture and spatial resolution.

STAR Protocols question-and-answer series: RNA-seq.

This article addresses the global asymptotic stability problem of uncertain non-strict feedforward nonlinear systems. For multiple integrators with uncertain parameters and non-strict feedforward structure nonlinear perturbed terms, a novel nested saturated control design with state-dependent saturation levels is proposed, the complex perturbation terms of vector field homogeneity at least of first-order that destroy the strict feedforward structure and the uncertain parameters are dealt with successfully. By using direct calculation of inequalities, the reasonable control parameters are designed to ensure the non-integrability and slowly-varying property of the state-dependent saturation levels, then the convergence analysis can be carried out by calculating the time derivative of the boundary surface and simple Lyapunov function, besides, we can obtain explicit parameter conditions. A nonlinear liquid level control resonant circuit system is conducted to verify the validity of the saturated control scheme.

This study explores the feasibility of adopting a modern digital system for managing train tickets in Iraq, drawing on the experiences and best practices of leading countries in this field, such as the United States, Russia, and Turkey. The study employs a comparative analytical approach, supported by a conceptual system design. Through comparative analysis, it examines the gap between the current state of train management services in Iraq and more advanced international systems. The findings indicate several weaknesses, particularly in the digital ticketing infrastructure, data processing, and the system's level of digital integration. To address these issues, the study proposes a phased implementation plan, starting with simple digital services and evolving over time into a more integrated smart transportation system. Results also suggest that leveraging successful international practices, especially in areas such as e-ticketing, digital payment methods, and data utilization in operations, can improve efficiency and enhance passenger satisfaction. These improvements contribute to streamlining procedures, facilitating access to services, and supporting future integration with broader smart transportation solutions. Study concluded that Iraq has a real opportunity to develop a modern and efficient train ticketing system by leveraging international expertise that has achieved passenger satisfaction and significant economic gains, provided that this development is supported by sound infrastructure built according to international standards and principles, and by the gradual adoption of technology in line with international standards.

Continuous activity monitoring using motion sensors provides a non-invasive method to assess pig general behavioural activity and detect potential welfare issues on group level in commercial housing systems. In the current study, motion sensors were installed in rearing and fattening pens on four farms (18 pens in total, 18–40 pigs per pen) over multiple trials. Based on daily tail inspections 4977 observation days were classified into control (C, without tail lesion), pre-tail lesion (P, last seven days before tail lesion) and tail lesion (TL) days. Binary classification ablation studies were used to evaluate and understand model robustness, using pairs of classes and varying time windows for P days. Results indicated that a time window of 7 d before the occurrence of tail lesions showed improved classification performance compared to other time windows, allowing the binary distinction of C, P and TL days. Feature selection revealed that not only motion sensor data but also time of year and housing-related variables influenced model performance. The final multiclass model, based on a 7-day time window for P days, achieved a mean testing accuracy >0.7, ranging from 0.75 to 0.85 for the individual classes and lower accuracies for P days compared to TL and C days. However, leave-one-out cross-validation revealed limited generalisation to unseen farms, likely because the general behavioural activity features are not fully independent of farm. Overall, this study demonstrates the potential of motion sensor data for the early detection of tail lesion while indicating that further research is required to improve robustness and generalisability across farm environments.

Metal oxoanions present significant challenges because of their high solubility in water and their charge characteristics. Among these, carcinogenic chromate and radioactive pertechnetate are particularly concerning. Conventional adsorbents often suffer from low recyclability and reduced selectivity to remove oxoanionic pollutants. Herein, we report a guanidinium-based porous organic polymer (POF-3) that selectively adsorbs CrO4 2-and TcO4 - from real-life water samples. POF-3 displays maximum adsorption capacities of 123mg g-1 for chromate and 177mg g-1 for perrhenate at pH=6. The removal efficiency of POF-3 at low concentrations of chromate and pertechnetate is very far below the corresponding EPA limit. The adsorption mechanism mainly follows electrostatic interaction and the ion exchange mechanism. Reducing carcinogenic Cr(VI) to Cr(III) is a crucial step; introducing thiophene in the structural backbone of POF-3 facilitates the redox properties. Morphological transformation into a sheet-like structure confirms the effective sorption of chromate and perrhenate. In this context, NMR titration of monomeric compound also suggests interaction of POF-3(M) with guanidium sites of material towards chromate and perrhenate effectively, as proven by solid phase study. These effective adsorption capacities, faster kinetics, and ion exchange ability make it a promising solution for environmental remediation.

Abstract The blackbody radiation (BBR) shift is a significant systematic shift for atomic clocks on the 10 -18 scale. We investigate the BBR shift of the hydrogen and hydrogen-like systems and present a method to reduce the uncertainty due to the BBR shift of the 1S--2S clock transition to the 10 -21 range. This feature is due to the single-electron system's energy level structure and a close dipole-allowed resonance to the excited 2S state. We present detailed calculations and analyses of the magic temperature where the BBR shift mathematically cancels. We also present the operational magic temperature, a stationary point where the shift becomes insensitive to the temperature. This shift is precisely calculable, in contrast to all other optical clocks.

Frequency response analysis plays an important role in many applications, allowing for the assessment of a system's vibration response level when subjected to operational loading. This assessment enables the approval or proposal of improvements in the design project, or corrections of systematic failures related to structural performance or damping treatment. In large-scale industrial manufacturing processes, the integration of numerical simulations with experimental validations is widely used, demonstrating efficiency in cost reduction, and minimizing development and reprocessing time. However, oversimplification in constitutive models or uncertainties regarding material properties, constraints, or loading types can lead to divergences between simulated predictions and measured responses, representing a bottleneck in the new product development process. Therefore, the appropriate characterization of each material and component of the system is essential for correctly calibrating the mathematical model used in assessing the frequency response, to ensure accurate predictions of the system's behavior. In the present work, an actual case of a pipeline system composed of a vulcanized rubber hose is studied to enhance the prediction accuracy of the system's response. For modeling the coupled mechanical system, the finite element method is used, utilizing the commercial simulation tool ABAQUS to determine the frequency response function of the transmissibility. This involves applying an excitation force at one end of the pipeline system and measuring the response at the other end. The vulcanized rubber hose is modeled to exhibit viscoelastic behavior. For this purpose, a dynamic mechanical analysis is carried out to obtain the storage modulus and loss factor, both as functions of frequency. Subsequently, a fractional derivative constitutive model for the rubber hose is identified. This numerical model is subjected to validation and calibration through several experimental tests. It is expected that the results will not only ensure accurate predictions for the response of this specific pipeline system but also enable the development of an efficient methodology, which can be systematically applied in processes that involve developing new components and systems of higher complexity.

We compared fish assemblage structure and total mercury (THg) bioaccumulation between a natural floodplain lake and a constructed irrigation canal in central Brazil. A total of 473 individuals representing 34 species were recorded, and dorsal muscle samples from 62 specimens representing shared species or species occupying comparable trophic positions were analyzed for THg (Curimatella immaculata, Hemiodus microlepis, Astyanax aff. bimaculatus, Triportheus albus, Geophagus sveni, Pimelodus blochii, Pygocentrus nattereri, Lycengraulis batesii, and Cichla kelberi). The floodplain lake exhibited higher species richness, diversity, and evenness, whereas the irrigation canal supported a simplified assemblage dominated by fewer species. Total Hg concentrations were significantly higher in the lake than in the irrigation canal; however, this pattern was observed only for the carnivorous guild (t = 5.384, p < 0.0001) and the detritivorous guild (t = 4.183, p = 0.0001). THg increased significantly with trophic level in both systems, from detritivores to carnivores (F2,4 = 15.127, p = 0.009), yielding comparable trophic magnification slopes (lake: 1.46, 95% CI: 1.11–1.81; canal: 1.36, 95% CI: 0.94–1.77). Despite lower diversity and THg concentrations in the irrigation canal, Hg transfer efficiency across trophic levels was conserved between systems.

Describing the electronic and excitonic properties of two-dimensional metallic materials is challenging due to the reduced dielectric screening, which enhances many-body interactions and influences the optical response. In this work, we present a comprehensive study of many-body effects on the optical properties of two-dimensional (2D) metallic MXenes—a large family of emerging layered materials with significant potential for optoelectronic, sensing, and energy-harvesting applications. Using state-of-the-art methods, we explicitly treat intraband transitions and make use of a full frequency description of the screened Coulomb interaction, two aspects that are particularly important when treating many-body effects in metals. Our results reveal that many-body effects substantially modify the band structures of these metallic monolayers, reflecting the limited screening characteristic of atomically thin systems. The GW corrections lead to pronounced changes in the absorption spectra already at the independent-particle level. In contrast, the inclusion of electron-hole interactions through the Bethe-Salpeter equation (BSE) produces comparatively smaller modifications, which we attribute to the finite density of states at the Fermi level in these metallic systems. Overall, our findings highlight the necessity of explicitly accounting for many-body interactions to achieve reliable predictions of the optical properties of 2D metallic materials, and they establish key design principles for MXene-based optoelectronic applications.

This work presents a space-time variable-order (V-O) fractional framework for the analytical investigation of nonlinear longitudinal wave propagation in magneto-electro-elastic (MEE) materials. The model contains Caputo-type V-O derivatives, which account for spatial and temporal memory effects and nonlocal interactions inherent to coupled mechanical, electrical, and magnetic fields. On this basis, exact traveling-wave solutions of the resulting nonlinear fractional wave equation were derived using an exponential expansion methodology, in the form of periodic, kink-type, and solitary waves. The effect of the V-O parameters on the wave amplitude, dispersion characteristics, and stability behavior is systematically analyzed. Stability and bifurcation analyses show parameter-dependent transition of both stable and unstable regimes, whereas insertion of random perturbations illustrates the development of chaotic dynamics. The proposed V-O model offers increased modeling flexibility and parameter-dependent stability regimes in the reduced system compared to constant-order (C-O) fractional formulations. This enhancement provides increased analytical flexibility at the theoretical level for coupled field interactions in MEE media. These results suggest that the V-O fractional modeling represents a valid tool for the analysis of complex nonlinear electromechanical phenomena and could lead to a better understanding of the wave dynamics at a theoretical level in multifunctional material systems.

Abstract This article presents a comprehensive overview of the legislative and policy frameworks adopted at the EU Member State level in healthcare systems and analyses the development of their implementation regarding the 2030 digital targets. The comparative analysis of national strategies for the digitalisation of health services aims to provide information on the existence of the framework for digitalisation of the sector and on the implementation of the e-health record system, and to assess the feasibility of achieving the goal in each Member State and in a cumulative way for the European Union and its Digital Decade Policy Programme. The output of the analysis is an essential contribution to the current process of creating a common European health data space, for which the creation of an e-health information system that is digital and shareable is a prerequisite for interoperability, while ensuring digital inclusion for everyone.

Pathways toward a nature-positive financial system

Most superconducting materials exhibit a vanishing density of states at the Fermi level and Anderson's theorem posits that the superconducting gap is robust against nonmagnetic disorder. Although dilute magnetic impurities lead to localized in-gap states, these states typically have no bearing on the material's bulk superconducting properties. However, numerous experiments reveal a finite density of states at the Fermi level in systems with an apparently negligible number of magnetic impurities. Here, using scanning tunneling microscopy and self-consistent Bogoliubov-de Gennes calculations, we find that gapless superconductivity emerges in 2H- at remarkably low magnetic impurity concentrations. Furthermore, our density functional theory calculations and in-gap quasiparticle interference measurements demonstrate that the Se-S substitution significantly modifies the band structure. This modification favours nesting and dictates the in-gap scattering for , in stark contrast to the dominant charge density wave interactions in pure 2H- . Our findings reveal an unusual superconducting response to disorder and highlight the importance of incorporating material-specific band structures in the understanding of a superconductor's response to even very low concentrations of magneticimpurities.

Traditional milkfish pond farming represents and importan livelihood source for coastal communities, however, empirical information regarding income level and business efficiency in low input production systems remains limited. This study aimed to analyze productions cost s, revenue, income and economic efficiency of traditional milkfish farming in Sungai Undang village, Seruyan regency. A quantitatif survey approach was applied to 100 farmers selected using snowball sampling during one production cycle in 2025. Date were analyzed descriptively through the calculation of total cost, total revenue, income and the revenue cost ratio (R/C). The results showed an average production 0f 625 kg per cycle, generating an income of IDR 2.950.000 per cycle and an R/C ratio of 1,23, indicating that the business remains financially feasible with moderate efficiency. Most farmers were classified into feasible categories with limited profit margins. This condition is associated with high stocking density combined with reliance on natura feed, and low input fond management, resulting in suboptimal biological productivity but reduced production costs. These findings highlight the role of traditional milkfish farming as a relatively stoble income source for coastal households and emphasize the need for improved simple technical management to enhance production performance and economic and efficiency.

Due to the increased interest in vanadium cobaltite (VCo 2 O 4 ) as a significant component of various catalysts, we have decided to investigate how its primary surfaces respond to oxidation and reduction processes. Our study employed computational modelling based on density functional theory to assess various surface types of geometries and surface free energies. This includes the stoichiometric plane and those containing either insufficient or excessive amounts of oxygen atoms. The most stable surface in the crystal is the (001) orientation. The crystal has an equilibrium morphology that resembles a cube with rounded corners. In our analysis, we identified the surface free energies of the most stable VCo 2 O 4 (001) surface when oxygen atoms are adsorbed and reduced. The adsorption of oxygen atoms ensures the stability of the system, while their reduction causes it to become unstable. We analysed the oxygen adsorption (Γ= +1, +2) and vacancy formation energies (Γ= −1, −2); however, upon adsorption, we noticed the exothermic behaviour with decreasing adsorption energies. Conversely, the vacancy formation demonstrates an endothermic behaviour with increasing energies as oxygen atoms are reduced. The Bader charge provides insights into the interactions between atoms within a system. The reduction and adsorption of oxygen atoms result in minimal changes in the charge of the V and Co atoms, whether they are oxidized or reduced, compared to their original state. The interplanar distances indicate that the introduction of an oxygen atom leads to an expansion of the system, while its removal causes the system to contract. Understanding the work function aids in determining the system's level of reactivity. The presence of oxygen atoms reduces the system's reactivity, while their absence enhances it. We investigated and described the changes in the magnetic moment as the surface coverage increased. The findings will assist us in identifying a catalyst that can enhance the performance of the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER), ultimately improving the efficiency of Zn-air batteries.

Abstract Agricultural marketing in India has traditionally been characterized by physical mandis, multiple intermediaries, and limited price transparency, which often reduced farmers’ share in the final consumer price. In recent years, digital transformation—driven by government initiatives such as e-NAM and Digital India, along with the emergence of agritech platforms—has begun to reshape agricultural marketing systems. However, the adoption of digital platforms remains uneven, largely due to behavioural, socio-economic, and infrastructural constraints.The present study examines the impact of digital transformation on agricultural marketing practices and analyses the behavioural responses of farmers in Yavatmal district of Maharashtra. A mixed-method research design was adopted using primary data collected from 150 farmers through structured questionnaires, personal interviews, and focus group discussions. Descriptive statistics, correlation analysis, and hypothesis testing were employed to analyze the data.The findings reveal that digital platforms have improved price transparency, market access, and transaction efficiency for farmers who adopted them. Education level, digital literacy, and trust in online systems emerged as significant determinants of digital adoption. However, challenges such as poor internet connectivity, fear of digital fraud, and low awareness continue to hinder widespread adoption, particularly among small and marginal farmers. The study highlights the need for targeted policy interventions, digital capacity-building programs, and farmer-centric platform design to ensure inclusive and sustainable digital transformation in agricultural marketing. Keywords Digital Transformation, Agricultural Marketing, Farmer Behaviour, e-NAM, Digital Adoption, Indiaold, Investment Preferences, Comparative Analysis, Investor Behavior, Risk Perception.

Artificial intelligence (AI) has been a fascinating concept of science fiction for decades, but many researchers think AI is getting closer to reality. AI perceives its environment and takes actions that maximize its chance of successfully achieving its goals. The term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". Artificial intelligence was founded as an academic discipline in 1956. In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas. 2015 was a landmark year for artificial intelligence. In the year 2017,capabilities generally classified under AI were understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomous cars, intelligent routing in content delivery network and military simulations. This study makes an attempt to know fields in which AI is currently applied. It also talks about the future of AI and what it will/ should mean to humanity.

The paper is a comprehensive survey of adversarial attacks on file fragment classification (FFC) models -a relatively unexplored area in digital forensics, given the increasing application of machine learning techniques. Unlike image or text classification adversarial attacks, adversarial attacks on FFC exploit statistical and structural properties at the byte level in systems that lack semantic or perceptual knowledge. Such properties necessitate the use of domain-specific defense strategies, as the defense strategies adopted from other domains are typically not effective for the problems of FFC. The survey comprehensively evaluates attack mechanisms relevant to FFC, including evasion and poisoning attacks, and discusses their impact on forensic reliability. It highlights the absence of domain-specific benchmarks, robust evaluation protocols, and systematic research on the adversarial robustness of FFC. The paper also discusses the different types of byte level perturbations that can happen in fragment data, and it sets specific research priorities for raising the reliability of machine learning-based digital evidence recovery and security. The paper provides building blocks for future work, offering practical insights for development in ensuring file fragment classification systems utilized in forensics are secure.

IntroductionIntrusion detection in Internet of Things networks continuously encounters issues with high-dimensional traffic data, class imbalance, and patterns of attacks.MethodsThis paper proposes a hybrid intrusion detection framework which combines a novel bio-inspired Ant-Baby Optimizer for feature selection with a Bidirectional Long Short-Term Memory neural network for classification. The Ant-Baby Optimizer algorithm selects small, informative subsets of features using mutual information to replace features guaranteed to be low-relevance, thereby enhancing computational efficiency and interpretability. The selected features are re-shaped into pseudo-sequential order to allow, via the Bidirectional Long Short-Term Memory, to capture temporal dependencies in both directions.ResultsThe results on the CICIoT2023 dataset demonstrate superior detection accuracy (97.32%) balanced per-class precision, recall, and F1-scores (macro F1 = 0.9732), with an inference latency of 0.219 ms per sample and process time of over 4,500 samples per second.DiscussionExperiments and verification against state-of-the-art metaheuristic and deep learning approaches support the proposed framework as effective. The interdependence of computational complexity and dataset limitation cannot be overstated. In fact, these four qualities of performance, efficiency, interpretability, and adaptiveness, when considered as one, will pave the way for the proposed framework to perform at a high level in the real-world IoT intrusion detection systems. Moreover, future works will involve cross-domain validation, attention-based mechanisms, and online learning for zero-day attack handling.