Realistic Scenarios Research Articles

Deep Supramolecular Language Processing for Co-crystal Prediction.

Approximately 40% of marketed drugs exhibit suboptimal pharmacokinetic profiles. Co-crystallization, where pairs of molecules form a multicomponent crystal, constitutes a promising strategy to enhance physicochemical properties without compromising pharmacological activity. However, finding promising co-crystal pairs is resource-intensive, due to the large and diverse range of possible molecular combinations. We present DeepCocrystal, a novel deep learning approach designed to predict co-crystal formation by processing the "chemical language" from a supramolecular vantage point. Rigorous validation of DeepCocrystal showed a balanced accuracy of 78% in realistic scenarios, outperforming existing models. Explainable AI approaches uncovered the decision-making process of DeepCocrystal, showing its capability to learn chemically relevant aspects of the "supramolecular language" that match experimental co-crystallization patterns. By leveraging properties of molecular string representations, DeepCocrystal can also estimate the uncertainty of its predictions. We harness this capability in a challenging prospective study and successfully discovered two novel co-crystals of diflunisal, an anti-inflammatory drug. This study underscores the potential of deep learning - and in particular of chemical language processing - to accelerate co-crystallization and ultimately drug development, in both academic and industrial contexts.DeepCocrystal is available as an easy-to-use web application at https://deepcocrystal.streamlit.app/.

How are estimated cellular turnover rates influenced by the dynamics of a source population?

Estimating production and loss rates of cell populations is essential but difficult. The current state-of-the-art method to estimate these rates involves mathematical modelling of deuterium labelling experiments. Current models typically assume that the labelling in the precursors of the population of interest (POI) is proportional to the deuterium enrichment in body water/glucose. This assumption is not always true and it is known that this can have a significant effect on the rates estimated from labelling experiments. Here we quantify the effect that different turnover (replacement) rates of the precursors can have on the estimated proliferation and loss rates of a POI by explicitly modelling the dynamics of the precursors. We first confirm earlier results that the labelling curve of the POI only reflects its own turnover rate if either the turnover rate of the precursors is sufficiently fast, or the contribution from the precursors is sufficiently small. Next, we describe three realistic scenarios with a slowly turning over precursor population, and show how this changes the interpretation of the different parameter estimates. Our analyses underpin that uniquely identifying the turnover rate of a POI requires measurements (or knowledge) on the turnover of its immediate precursors.

Impact of the non-ideal condition in the analysis of high voltage gain switched impedance inverter with cost perspectives

Switched impedance inverters are a category of power electronic converters that can be applied across various applications through the implementation of appropriate control methods. This paper proposes a switched impedance inverter characterized by a high boost factor (B) at low duty cycles. The proposed structure is analyzed under both ideal and non-ideal conditions. The non-ideal analysis considers parasitic components. The voltage drop across each element is calculated. A detailed analysis of component voltage and current behavior is provided. Furthermore, a power loss analysis and design considerations are discussed, taking into account the influence of parasitic elements. A comprehensive comparison is conducted, evaluating various parameters such as voltage and current stress, boost factor, and efficiency. Additionally, a cost analysis comparing the proposed structure with conventional designs is provided under equivalent conditions. The experimental results obtained under different conditions are presented to validate the theoretical analysis, particularly in realistic scenarios.

Exploring the frontier of dental education: a cross-sectional study of VR simulation and manikin-based training at Ziauddin university

BackgroundDental education blends theoretical concepts with practical tasks, where preclinical simulations using manikins have long been integral. However, the limitations of manikin-based training, such as cost, material restrictions, and inter-rater reliability concerns, have led to the integration of emerging technologies like Virtual Reality (VR) to enhance learning. VR provides an immersive environment to practice clinical skills, offering potential flexibility, engagement, and tactile learning advantages. This study compares dental students’ perceptions of VR and manikin training at Ziauddin University, Karachi.MethodologyThis cross-sectional study was conducted at Ziauddin University College of Dentistry, Karachi, Pakistan, involving 229 dental students enrolled in various levels of the BDS program. A structured questionnaire assessed students’ experiences and perceptions of VR simulation and manikin training. Data was analyzed using SPSS version 22, with descriptive statistics and independent T-tests to evaluate differences in perception across student groups.ResultsBoth VR and manikin training were effective in improving learning. Manikins were preferred for realistic clinical scenarios, while VR was favored for engagement and tactile learning. 68.6% of students found both methods equally useful for reinforcing knowledge, 77.7% felt more confident after manikin training, and 97.4% found VR effective for understanding tooth textures.ConclusionThis study highlights the complementary strengths of VR and manikin-based training in dental education. Both methods should be integrated to provide a more effective and well-rounded learning experience. Further research is needed to explore VR’s cost-effectiveness and use in resource-limited settings.

Impact of Filtration Cycle Patterns on Both Water and Energy Footprints in Drip Irrigation Systems

Drip irrigation is a widely spreading technology, mainly due to its high water-use efficiency. This technique requires a filtration process that exhibits cyclical behavior where both filtration and backwashing modes repeat. In filtration, pressure increases with time due to the particle retention up to a preset value. In backwashing, the flow is reversed to clean the filter. Different design strategies to reduce energy and water consumption have been proposed, but their practical effects are not yet clear. Here, a global analysis method based on the classification of the time evolution of the pressure curve in filtration mode was developed. Energy and water use efficiency indices were defined and evaluated under different scenarios. More design options can be undertaken to reduce the consumption of energy than of water. The decrease in the pressure drop for clean filter conditions arose as the best option to increase energy efficiency (in a realistic scenario, a reduction of 20% in the pressure drop with tap water resulted in a reduction of 7.6% in the energy consumption per volume of filtered water). Precise backwashing times and flow rates were essential to improve water use efficiency (e.g., doubling the backwashing time led to a 4.5% decrease in water use efficiency).

Salience network resting-state functional connectivity predicts self-controlled decision-making

Salience network functional integration with the central executive network and the default mode network at rest has been shown to predict real-life self-control. It has been proposed that a network interaction index reflecting stronger functional integration of the salience network with the central executive network and reduced functional connectivity of the salience network with the default mode network represents a trait neural correlate of successful self-control exertion. Here, we attempted to replicate this result using data from our own study where 121 participants completed an fMRI self-control task comprising real-life scenarios and data from a second study (N = 79) retrieved from OpenNeuro (dataset ID: ds002643) where participants completed an fMRI food choice task. We could not replicate the proposed role of salience network resting-state functional connectivity in self-controlled decision-making in either of those data sets. Instead, we found evidence for the exact opposite effect, specifically a negative association between self-control performance and the network interaction index. The role of analysis pipelines, appropriate network ROIs, and the measurement of self-control are discussed in the context of our findings.

The Implementation of the District Heating Energy Security Index through a Legislation Framework

The resilience of district heating (DH) is a relevant research topic, as energy systems are frequently affected by natural and human-induced disruptions, such as floods and supply chain issues. A valuable tool for resilience assessment is the Energy Security Index (ESI). In the existing literature, it is mostly used for the evaluation of a country, but not as much for certain energy systems. In this study, a municipal DH system is studied through the lens of energy security. An ESI is used to better evaluate the state of the DH system during unplanned interruptions. It includes a set of indicators – social, economic, environmental, and technical – that form the basis of a composite ESI to evaluate the system’s status under current conditions. In this study, the previously developed methodology is applied to a case study from Latvia. Adjustments to the indicators are made, and three main risks – dependency on energy imports, affordability of DH, and diversity of heat sources – are identified to facilitate a more thorough analysis of the specific system. The results have been adjusted to align with the legislative framework in Latvia and the European Union concerning DH development. This adjustment aims to propose realistic scenarios for system improvement, considering its specific characteristics. These results can be used for decision-making at the municipal level, and the developed methodology can be applied to other cases.

A comprehensive evaluation of the transition to a biosimilar of adalimumab in rheumatoid arthritis and psoriatic arthritis: a single-center experience with a focus on imaging outcomes.

Limited data in Latin America exists regarding the efficacy of switches from original biologicals to biosimilars in real-life scenarios. Currently, no studies assess this switch using imaging. The objective of this study was to evaluate clinical, functional, ultrasonographic, and radiological responses in a group of patients with rheumatoid arthritis (RA) and psoriatic arthritis (PsA) switched from original adalimumab (oADA) to biosimilar (bADA) (GP2017). A prospective cohort study included diagnosed RA and PsA patients undergoing oADA treatment. At the baseline visit, blood analysis, X-rays, ultrasound, and an interview for sociodemographic and clinical data were conducted. Evaluators were unaware of each other's data. Patients switched to bADA during follow-up and were assessed in the same program within 3 to 12 months post-switch (only including patients with all evaluations). Out of 270 RA cohort patients, 35 met the criteria for complete pre-and-post control post-switch to bADA (GP2017), along with 15 PsA patients. The mean time between the switch and the second evaluation was 4.1 months (interquartile range 7). No statistical differences were observed in disease activity or functional capacity. Regarding imaging, no difference was found in X-ray erosion number; however, ultrasonography revealed decreased power Doppler (PD) activity, but not grayscale findings. No differences in acute phase reactants, joint count, or patient visual analog scale were observed between controls. In this analysis of the switch between oADA and bADA, no differences were found in disease activity, functional capacity, or radiographic progression. Ultrasonography indicated improvement of PD findings.

Research on channel estimation based on joint perception and deep enhancement learning in complex communication scenarios

In contemporary wireless communication systems, channel estimation and optimization have become increasingly pivotal with the growing number and complexity of devices. Communication systems frequently encounter multiple challenges, such as multipath propagation, signal fading, and interference, which may result in the degradation of communication quality, a reduction in data transmission rates, and even communication interruptions. Therefore, effective estimation and optimization of channels in complex communication environments are of paramount importance to ensure communication quality and enhance system performance. In this article, we address the intelligent, reflective surface (IRS)-assisted channel estimation problem and propose an intelligent channel estimation model based on the fusion of convolutional neural network (CNN) and gated recurrent unit (GRU) row features, utilizing the reinforcement learning Deep Deterministic Policy Gradient (DDPG) strategy for Channel Reconstruction Prediction and Generation Network (CRPG-Net). The framework initially acquires the received signal by converting the guide-frequency symbols at the transmitter into time-domain sequences to be transmitted, and after propagating through the direct channel and the IRS reflection channel, processes the data at the receiver. Subsequently, the spatial and temporal features in the received signal are extracted using the CRPG-Net model, with the adaptive optimization capability of the model enhanced by deep reinforcement learning. The introduction of reinforcement learning enables the model to continuously optimize decisions in dynamic channel environments, improve the robustness of channel estimation, and quickly adjust the IRS reflection parameters when the channel state changes to adapt to complex communication conditions. Experimental results demonstrate that the framework achieves significant channel estimation accuracy and robustness across several public datasets and real test scenarios, with the channel estimation error markedly smaller than that of traditional least squares (LS) and linear minimum mean square error (LMMSE) methods. This method introduces innovative techniques for channel estimation in intelligent communication systems, playing a crucial role in enhancing communication quality and overall system performance.

KDM-OPTICS based vehicle clustering algorithm for traffic millimeter-wave radar

Abstract Traffic millimeter-wave radar suffers from environmental noise, dynamic changes in the number of vehicles, and feature similarity leading to degradation of point-trace clustering accuracy when measuring moving vehicle targets. We propose a new density-based clustering algorithm to address this problem. Unlike traditional datasets, the radar measurement dataset used in this algorithm contains specific multidimensional information against a moving vehicle target, such as target distance, normal angle, relative velocity, and reflection intensity. In order to eliminate the correlation interference between the parameters as well as the influence of measurement units, the normalized Mahalanobis distance method is introduced to generate the target spot trace distance matrix. Subsequently, based on the target point trace distance matrix, the reachable distance map is quickly generated by KD tree nearest neighbor search. At the same time, the dynamic adjustment of neighborhood radius of OPTICS is introduced to enhance the contrast of the target point traces in the reachable distance matrix, to obtain the point trace segmentation data of similar vehicles,and to realize the point trace clustering of vehicle targets in the distance and speed dimensions. Finally,the target ranking recognition clustering structure algorithm (KDM-OPTICS) based on KD tree and normalized martens distance is implemented.In this study,experimental validation based on vehicle target data from real urban traffic scenarios is carried out.The results show that enhancing the target information by local power-law transformation can improve the stability and reliability of distinguishing vehicles with similar distance speed. In addition,the KDM-OPTICS algorithm has higher recognition accuracy and lower computational speed compared with other algorithms.

Bridging Theory and Practice for Enhanced Cybersecurity Awareness in Critical Infrastructures

Developing advanced cybersecurity training programs is crucial for increasing cybersecurity awareness, especially when dealing with Critical Infrastructures (CIs). There is a significant gap in addressing both theoretical and practical activities to build a comprehensive and effective program that matches real-life cyberattacks on critical systems. This paper presents the efforts undertaken within the EU project CyberSecPro to bridge this gap by providing advanced training materials that align theoretical insights with practical activities to handle real scenarios.

Morphological, Physico‐Chemical, and Thermal Characterization of Non‐Reactive Protective Materials for Steel Structures

ABSTRACTThis study addresses the critical issue of fire safety in densely populated urban areas and focuses on the resilience of new and existing buildings, with an emphasis on passive fire protection materials for steel structures. Conventional fire codes are considered potentially restrictive, which has led to research in the field of performance‐based fire safety engineering (FSE). This research focuses specifically on non‐reactive passive fire protection materials, which are known to protect steel elements from high fire temperatures. Two kinds of materials, calcium silicate‐based cement (CSC) and gypsum (GP), are investigated using morphological, physicochemical, and thermal analyses in more realistic fire scenarios. Unlike standard fire curves, such as ISO 834, lower heating rates (up to 100°C/min) allowed for a more realistic assessment of the material effectiveness in protecting steel structures from fire. CSC releases only free water molecules within 150°C, resulting in a lower weight loss up to 1000°C, with endothermic transformations totaling 270 J/g. GP releases both free and bound water molecules at different temperatures and triggers several endothermic reactions (with a higher total amount of heat removed from the fire 670 J/g), which increases fire resistance. This mechanism uses the external heat generated by the fire to vaporise water, which increases the fire resistance of the material. This study links the chemical and thermal properties of passive fire protection materials to their fire performance, showing that materials with similar compositions can behave differently. This highlights the need for a new classification system based on material‐specific properties.

Efficient Prompt Optimization for Relevance Evaluation via LLM-Based Confusion Matrix Feedback

Evaluating query-passage relevance is a crucial task in information retrieval (IR), where the performance of large language models (LLMs) greatly depends on the quality of prompts. Current prompt optimization methods typically require multiple candidate generations or iterative refinements, resulting in significant computational overhead and limited practical applicability. In this paper, we propose a novel prompt optimization method that leverages LLM-based confusion matrix feedback to efficiently optimize prompts for the relevance evaluation task. Unlike previous approaches, our method systematically analyzes LLM predictions—both correct and incorrect—using a confusion matrix, enabling prompt refinement through a single-step update. Our experiments in realistic IR scenarios demonstrate that our method achieves competitive or superior performance compared to existing methods while drastically reducing computational costs, highlighting its potential as a practical and scalable solution.

Method to Use Transport Microsimulation Models to Create Synthetic Distributed Acoustic Sensing Datasets

This research introduces a new method for creating synthetic Distributed Acoustic Sensing (DAS) datasets from transport microsimulation models. The process involves modeling detailed vehicle interactions, trajectories, and characteristics from the PTV VISSIM transport microsimulation tool. It then applies the Flamant–Boussinesq approximation to simulate the resulting ground deformation detected by virtual fiber-optic cables. These synthetic DAS signals serve as large-scale, scenario-controlled, labeled datasets on training machine learning models for various transport applications. We demonstrate this by training several U-Net convolutional neural networks to enhance spatial resolution (reducing it to half the original gauge length), filtering traffic signals by vehicle direction, and simulating the effects of alternative cable layouts. The methodology is tested using simulations of real road scenarios, featuring a fiber-optic cable buried along the westbound shoulder with sections deviating from the roadside. The U-Net models, trained solely on synthetic data, showed promising performance (e.g., validation MSE down to 0.0015 for directional filtering) and improved the detectability of faint signals, like bicycles among heavy vehicles, when applied to real DAS measurements from the test site. This framework uniquely integrates detailed traffic modeling with DAS physics, providing a novel tool to develop and evaluate DAS signal processing techniques, optimize cable layout deployments, and advance DAS applications in complex transportation monitoring scenarios. Creating such a procedure offers significant potential for advancing the application of DAS in transportation monitoring and smart city initiatives.

Facing Climate Change: How to Manage the Arising New Crises?

The increasing frequency and intensity of climate change-related disasters highlight the need to develop adaptive crisis management approaches. This paper explores the integration of R-IO Suite, a knowledge-based decision support system, with GAMA, a multi-agent simulation platform, to improve situational awareness and crisis response in a changing climate. This approach enables decision-makers and local communities to better understand, anticipate, and mitigate crisis impacts by combining real-time data modelling and predictive simulation. Although the proposed framework has been conceptually defined, its implementation and validation remain open key challenges. The French ANR-funded ATEsT project aims to overcome major technical obstacles— interoperability, semantic alignment, and temporal synchronisation—through concrete use cases. Future work will focus on implementing and testing this integration in realistic crisis scenarios, such as mega-fires and flash floods across different territories, to assess its effectiveness in improving decision-making processes and citizen engagement.

The use of deep learning and artificial intelligence-based digital technologies in art education

In order to explore the application of deep learning (DL) and artificial intelligence (AI) technologies in art education, this work proposes and optimizes an innovative art creation system—Creative Intelligence Cloud (CIC). The system combines a deep generative adversarial network and convolutional neural network, aiming to enhance the automation level, consistency of artistic styles, and creation efficiency in art creation. This work first analyzes existing art creation methods. It points out the shortcomings of traditional systems in terms of image quality, style transfer, and computational performance, especially the application limitations in real teaching scenarios. Therefore, this work designs an art creation model optimized by DL and validates and evaluates it through extensive experiments. The experimental results show that CIC outperforms existing mainstream models in multiple dimensions, including image quality, computational performance, user experience, and style creation. For example, in image quality evaluation, CIC achieves high scores in clarity (0.89), detail performance (0.85), style consistency (0.87), and color accuracy (0.91). In terms of computational performance and resource consumption, CIC shows its superiority, with a training time of only 1500 s, memory consumption of 4.9GB, and a Graphics Processing Unit resource usage rate of 70%. Compared to models such as the Visual Perception Generative Adversarial Network and Artistic Recognition and Transfer Style Convolutional Neural Network, CIC is more efficient and consumes fewer resources. Furthermore, CIC’s scores in user experience and style transfer capability are significantly higher than those of other models, providing smoother and more creatively rich art creation tools for art education. Therefore, this work offers new ideas and methods for the application of DL and AI technologies in art creation and art education, and promotes the practical use of AI in art education. The work has certain academic contributions and practical value.

Two-qubit gate protocols with microwave-dressed Rydberg ions in a linear Paul trap

Abstract Ultracold trapped atomic ions excited into highly energetic Rydberg states constitute a promising platform for scalable quantum information processing. Elementary building blocks for such tasks are high-fidelity and sufficiently fast entangling two-qubit gates, which can be achieved via strong dipole-dipole interactions between microwave-dressed Rydberg ions, as recently demonstrated experimentally [1]. We theoretically investigate the performance of three protocols leading to controlled-phase gate operations. Starting from a microscopic description of Rydberg ions in a linear Paul trap, we derive an effective Hamiltonian that faithfully captures the essential dynamics underlying the gate protocols. We then use an optimization scheme to fine-tune experimentally controllable parameters like laser detuning and Rabi frequency to yield maximal gate fidelity under each studied protocol. We show how non-adiabatic transitions resulting from fast laser driving relative to the characteristic time scales of the system detrimentally affect the fidelity. Despite this, we demonstrate that in the realistic scenario of Rydberg ions with finite radiative lifetimes, optimizing the best found gate protocol enables achievement of fidelities as high as 99.25 % for a gate time of 0.2 μs. This considerably undercuts entangling gate durations between ground state ions, for which gate times are typically limited by the comparably slower time scales of vibrational modes. Overall, this places trapped Rydberg ions into the regime where fast high-accuracy quantum computing and eventually quantum error correction become possible.

Optimizing Crown and Trunk Heights in Rigid Vegetation Model for Effective mitigation of Flood Damages

The effectiveness of flow energy reduction and flow characteristics through emergent vegetation have been studied assuming only a trunk portion of the vegetated corridor to the flow direction. However, in real scenarios, riparian areas, whether natural or restored, typically feature trees positioned in both the upper canopy (crown) and lower trunk regions, relative to the direction of flood flow. Therefore, the present study involves an experimental investigation into the impact of a tree’s vegetative structure, encompassing both its canopy and trunk, on the flow direction considering the open channel flow. A sequence of experimental trials was conducted, involving the variation of Froude numbers ([Formula: see text], variations in crown height ratio above the ground surface ([Formula: see text], and a case with no crown (NC), which was included for the purpose of comparison. The results showed that the backwater rise was increased with the increase in Froude number and decrease in crown height ratio. A steep slope was generated within the vegetation region when the crown height was minimum ([Formula: see text] as compared to NC case due to the high blockage ratio generated by the crown portion the trees. Maximum energy loss ([Formula: see text] was generated when the crown part of the trees ([Formula: see text] were close to the ground surface as compared to NC ([Formula: see text]. Similarly, energy loss was maximum with the increase in Froude number. Therefore, the optimum outcome produced in terms of energy loss was when the tree consisted of both trunk and crown portions considerably close to the ground surface in order to reduce the impacts of floods. The findings of this study enhance flood mitigation by strategically optimizing crown and trunk heights in rigid vegetation to improve flow resistance and energy dissipation.

An online 11 kv distribution system insulator defect detection approach with modified YOLOv11 and mobileNetV3

With the advent of smart distribution grids, detection of defects in insulators with unmanned aerial vehicles as a part of distribution automation system (DAS) has attained a widespread attention. The defects are essential to detect to avoid damaging the service life of distribution lines, serious power loss and cascading power outages in extreme conditions. The intricate background, limited image dataset and small-scale object makes the problem of detection more complex. Owing to the exponential advancement in deep learning, deep learning-based insulator defect detection is gradually attaining a foothold in the research domain. This paper presents a novel approach for detecting insulator defects in an 11 kV distribution system using a modified version of You Only Look Once (YOLO V11) and the MobileNetV3 model. Data augmentation was applied as part of the preprocessing phase to train the proposed model. The model’s performance was compared with earlier versions of YOLO and other existing methods to demonstrate its effectiveness. Additionally, multiple case studies were conducted to validate the method’s robustness and reliability for insulator defect detection. This paper incorporates a modified version of YOLOv11 architecture using the constituent C3K2, SPFF and C2PSA algorithmic blocks, mounted with a MobileNetV3 classifier to allow lightweight framework in DAS based devices. Studies involving various real-life scenarios show the efficacy and applicability of the proposed algorithmic pipeline.

Smart Protection System for Women Using IOT

Abstract - In the modern world, ensuring women's safety is a crucial issue, particularly with the increasing incidents of harassment and violence. This research paper presents a Smart Women Protection System leveraging IoT technologies. The system is designed to be a compact, wearable device that enables real-time location tracking and emergency alerting. It incorporates an ESP8266 microcontroller, NEO-6M GPS module, GSM module, buzzer, and a 3.3V power supply to ensure timely assistance during distress. The device sends the victim's location to pre-defined contacts and emits a buzzer to attract attention. This paper outlines the system's design, implementation, and potential applications in real-life scenarios. Key Words: Women safety, IoT, ESP8266, GPS, GSM, Emergency Alert System