Search Results (1,203)

Home energy management systems have emerged as a crucial solution for enhancing energy efficiency, reducing carbon emissions, and facilitating the integration of renewable energy sources into homes. To fully realize their potential, these systems’ performance must be optimized, which involves addressing multiple objectives, such as minimizing costs and environmental impact. The Pareto frontier is a tool widely adopted in multi-objective optimization within home energy management systems’ operation, where a range of optimal solutions are produced. This study uses the Pareto curve to optimize the operational performance of home energy management systems, considering the state health of the battery to determine the best answer among the optimal solutions in the curve. The main reason for considering the state of health is the effects of the battery’s operation on the performance of energy systems, especially for long-term optimization outcomes. In this study, the performance of the battery is measured through a physical model named PyBaMM that is tuned based on swarm intelligence techniques, including the Whale Optimization Algorithm, Grey Wolf Optimization, Particle Swarm Optimization, and the Gravitational Search Algorithm. The proposed framework automatically identifies the optimal solution out of the ones in the Pareto curve by comparing the performance of the battery through the tuned physical model. The effectiveness of the proposed algorithm is demonstrated for a home, including four distinct energy carriers along with a 12 V 128 Ah LFP chemistry Li-ion battery module, where the overall cost and carbon emissions are the metrics for comparisons. Implementation results show that tuning the physical model based on the Whale Optimization Algorithm reaches the highest accuracy compared to the other methods. Moreover, considering the state of health of the battery as the selecting criterion will improve home energy management systems’ performance, particularly in long-term operation models, because it guarantees a longer battery lifespan. Full article

Background: To meet the urgent need for novel antibacterial agents that are active also against worrying superbugs, natural pentacyclic triterpenoids, including totally inactive betulin (BET) and betulinic acid (BA), as well as ursolic acid (UA), active on Gram-positive bacteria, have been chemically modified, achieving compounds 1–7. Methods: Triterpenoid derivatives 1–7 and all synthetic intermediates were characterized by chemometric-assisted FTIR and NMR spectroscopy, as well as by other analytical techniques, which confirmed their structure and high purity. Minimum inhibitory concentration values (MICs) of 1–7, BET, BA and UA were determined by the broth dilution method, using a selection of Gram-positive and Gram-negative clinically isolated superbugs. Results: Performed experiments evidenced that compounds 4–7 had potent antibacterial effects against Gram-positive methicillin-resistant Staphylococcus aureus and S. epidermidis (MRSA and MRSE), as well as against vancomycin-resistant Enterococcus faecalis and E. faecium (VRE). The antibacterial effects of 4–7 were due to the insertion of a triphenyl phosphonium (TPP) group and were higher than those reported so far for other BET, BA and UA derivatives, especially considering the complex pattern of resistance of the isolates used here and their clinical source. Conclusions: For the first time, by inserting TPP, a real activity (MICs 2–16 µg/mL) was conferred to inactive BET and BA (MICs > 1024 and 256 µg/mL). Moreover, the antibacterial effects of UA were improved 16- and 32-fold against MRSE and MRSA (MICs = 2 vs. 32 and 64 μg/mL). Future Perspectives: Based on these very promising microbiologic results, new experiments are currently underway with the best-performing compounds 5 and 7 (MICs = 2 μg/mL) on an enlarged number of Gram-positive isolates, to confirm their MICs. Moreover, investigations about their possible antibiofilm activity, time-killing curves and cytotoxicity on eukaryotic cells will be carried out to define their pharmacological behavior and clinical potential. Full article

To address the high computational and communication overheads and the limited edge security found in many existing batch verification methods for Mobile Edge Computing (MEC), this paper presents a blockchain-based batch authentication and symmetric group key agreement protocol. A core feature of this protocol is the establishment of a shared symmetric key among all authenticated participants. This symmetry in key distribution is fundamental for enabling secure and efficient broadcast or multicast communication within the MEC group. The protocol introduces a chameleon hash function built on elliptic curves, allowing smart mobile devices (SMDs) to generate lightweight signatures. The edge server (ES) then performs efficient large-scale batch authentication using an aggregate signature technique. Considering the need for secure and independent communication between SMDs and ES, the protocol further establishes a one-to-one session key agreement mechanism and uses a Merkle tree to verify session key correctness. Formal verification with ProVerif2.05 tool confirms the protocol’s security and multiple protection properties. Experimental results show that, compared with the CPPBA, ECCAS, and LBVP schemes, the protocol improves computational efficiency of batch authentication by 0.94%, 67.20%, and 49.53%, respectively. For group key agreement, the protocol achieves a 35.26% improvement in computational efficiency over existing schemes. Full article

With the continuous development of cities and the increasing utilization of underground space, ambient noise seismic imaging has become an essential approach for exploring and monitoring the urban subsurface. The integration of Distributed Acoustic Sensing (DAS) with ambient noise imaging offers a more convenient and effective solution for investigating shallow subsurface structures in urban environments. To overcome the limitations of conventional ambient noise seismic nodes, which are costly and incapable of achieving high-density data acquisition, this work makes use of existing urban telecommunication fibers to record ambient noise and perform sliding-window cross-correlation on it. Then the Phase-Weighted Stack (PWS) technique is applied to enhance the quality and stability of the cross-correlation signals, and fundamental-mode Rayleigh wave dispersion curves are extracted from the cross-correlation functions through the High-Resolution Linear Radon Transform (HRLRT). In the inversion stage, an adaptive regularization strategy based on automatic L-curve corner detection is introduced, which, in combination with the Preconditioned Steepest Descent (PSD) method, enables efficient and automated dispersion inversion, resulting in a well-resolved near-surface S-wave velocity structure. The results indicate that the proposed workflow can extract useful surface-wave dispersion information under typical urban noise conditions, achieving a feasible level of subsurface velocity imaging and providing a practical technical means for urban underground space exploration and utilization. Full article

Based on comprehensive interpretation of three-dimensional seismic data and quantitative analysis of basin-boundary fault activity in the Nanpu Sag, this study employs subsidence history backstripping and equilibrium profile techniques to reconstruct the structural evolution of the main profile. The results indicate that the Cenozoic evolution of the Nanpu Sag can be divided into a syn-rift stage and a post-rift stage, with the syn-rift stage further subdivided into Rift I and Rift II episodes. During Rift I, tectonic activity was primarily controlled by the NE- and NEE-trending Xinanzhuang Fault, Shabei Fault, and No. 2 Fault Zone, which formed under a NW–SE extensional stress regime and governed the development of NE- or NEE-trending faults and associated sedimentary subsidence centers. In Rift II, tectonic activity was dominated by a southward-curved normal fault system, composed of the Xinanzhuang, Gaoliu, and Baigezhuang faults, as well as the Shabei Fault, reflecting the influence of a near N–S ex-tensional stress field. The progressive southward migration of the Sag’s subsidence center over time—from the Linque sub-sag in the third section of the Shahe Formation to the Caofeidian sub-sag in the Dongying Formation—and noting, coupled with the pronounced left-lateral strike-slip characteristics of the Baigezhuang Fault and No. 4 Fault, and regional tectonic evolution analysis of the Bohai Bay Basin, support the proposal that a strike-slip extension mechanism—characterized by lateral strike-slip and forward extension—constitutes the fundamental developmental model of the Nanpu Sag. This study deepens the understanding of the tectonic evolution of the Nanpu Sag and provides new insights in-to the dynamic mechanisms governing the formation of similar Sags in the Bohai Bay Basin. Full article

Indocyanine green near-infrared fluorescence (ICG-NIRF) imaging is widely used to assess tissue perfusion, yet its subjective interpretation limits correlation with postoperative parathyroid function. To address this, the Workflow model for ICG-angiography integrating Standardization and Quantification (WISQ) was developed. This exploratory prospective multicenter study evaluated the reproducibility of WISQ in adults undergoing total thyroidectomy at two Dutch university centres. Patients with contraindications to ICG or prior neck surgery were excluded. Intraoperative imaging used standardized camera settings with blood volume-adjusted ICG dosing, and perfusion curves were analyzed using predefined regions of interest. Eighty patients were included. Significant inter-centre variability was observed in maximum fluorescence intensity, inflow slope, and outflow slope (n = 30). At the lead centre, outflow was the most promising predictor of postoperative hypoparathyroidism (HPT) (median −0.33 [IQR −0.49–−0.15] a.f.u./s for HPT vs. −0.68 [−0.91–−0.41], n = 17, p = 0.08), although no parameter significantly predicted HPT. Repeated ICG injections consistently produced lower maximal intensities irrespective of injection rate, and reproducible curves were achieved only when ICG was freshly dissolved at 0.5 mg/mL instead of 2.5 mg/mL. These findings indicate that ICG concentration and injection technique influence perfusion kinetics and underscore the need to update WISQ with standardized injection dilution to improve its clinical utility. Full article

With the rapid advancement of virtual human technology and live-streaming e-commerce, virtual anchors have increasingly become key interactive entities in the digital economy. However, emerging issues such as fake reviews, abnormal tipping, and illegal transactions pose significant threats to platform financial security and user privacy. To address these challenges, a multimodal emotion–finance fusion security recognition framework (MSF-Net) is proposed, which integrates visual, audio, textual, and financial transaction signals to achieve cross-modal feature alignment and multi-signal risk modeling. The framework consists of three core modules: the multimodal alignment transformer (MAT), the fake review detection (FRD) module, and the multi-signal fusion decision module (MSFDM), enabling deep integration of semantic consistency modeling and emotion–behavior collaborative recognition. Experimental results demonstrate that MSF-Net achieves superior performance in virtual live-streaming financial security detection, reaching a precision of 0.932, a recall of 0.924, an F1-score of 0.928, an accuracy of 0.931, and an area under curve (AUC) of 0.956, while maintaining a real-time inference speed of 60.7 FPS, indicating outstanding precision and responsiveness. The ablation experiments further verify the necessity of each module, as the removal of any component leads to an F1-score decrease exceeding 4%, confirming the structural validity of the model’s hierarchical fusion design. In addition, a lightweight version of MSF-Net was developed through parameter distillation and quantization pruning techniques, achieving real-time deployment on mobile devices with an average latency of only 19.4 milliseconds while maintaining an F1-score of 0.923 and an AUC of 0.947. The results indicate that MSF-Net exhibits both innovation and practicality in multimodal deep fusion and security risk recognition, offering a scalable solution for intelligent risk control in data-driven artificial intelligence applications across financial and virtual interaction domains. Full article

As technology advances, developers continually create innovative solutions to enhance smartphone security. However, the rapid spread of Android malware poses significant threats to devices and sensitive data. The Android Operating System (OS)’s open-source nature and Software Development Kit (SDK) availability mainly contribute to this alarming growth. Conventional malware detection methods, such as signature-based, static, and dynamic analysis, face challenges in detecting obfuscated techniques, including encryption, packing, and compression, in malware. Although developers have created several visualization techniques for malware detection using deep learning (DL), they often fail to accurately identify the critical malicious features of malware. This research introduces MalVis, a unified visualization framework that integrates entropy and N-gram analysis to emphasize meaningful structural and anomalous operational patterns within the malware bytecode. By addressing significant limitations of existing visualization methods, such as insufficient feature representation, limited interpretability, small dataset sizes, and restricted data access, MalVis delivers enhanced detection capabilities, particularly for obfuscated and previously unseen (zero-day) malware. The framework leverages the MalVis dataset introduced in this work, a publicly available large-scale dataset comprising more than 1.3 million visual representations in nine malware classes and one benign class. A comprehensive comparative evaluation was performed against existing state-of-the-art visualization techniques using leading convolutional neural network (CNN) architectures, MobileNet-V2, DenseNet201, ResNet50, VGG16, and Inception-V3. To further boost classification performance and mitigate overfitting, the outputs of these models were combined using eight distinct ensemble strategies. To address the issue of imbalanced class distribution in the multiclass dataset, we employed an undersampling technique to ensure balanced learning across all types of malware. MalVis achieved superior results, with 95% accuracy, 90% F1-score, 92% precision, 89% recall, 87% Matthews Correlation Coefficient (MCC), and 98% Receiver Operating Characteristic Area Under Curve (ROC-AUC). These findings highlight the effectiveness of MalVis in providing interpretable and accurate representation features for malware detection and classification, making it valuable for research and real-world security applications. Full article

Current functional safety mechanisms mainly control the access points and perimeters of manufacturing cells without guaranteeing the integrity of their internal components or the absence of unauthorized humans or objects. In this work, we present a novel deep learning (DL)-based safety system that enhances the safety circuit designed according to functional safety principles, detecting, with great reliability, the presence of persons within the cell and, with high precision, anomalous elements of any kind. Our approach follows a two-stage DL methodology that combines contrastive learning with Bayesian clustering. First, a supervised contrastive scheme learns the characteristics of safe scenarios and distinguishes them from unsafe ones caused by workers remaining inside the cell. Next, a Bayesian mixture models the latent space of safe scenarios, quantifying deviations and enabling the detection of previously unseen anomalous objects without any specific fine-tuning. To further improve robustness, we introduce an ensemble-based hybrid latent-space methodology that maximizes performance regardless of the underlying encoders’ characteristics. The experiments are conducted on a real dataset captured in a belt-picking cell in production. The proposed system achieves 100% accuracy in distinguishing safe scenarios from those with the presence of workers, even in partially occluded cases, and an average area-under-the-curve of 0.9984 across seven types of anomalous objects commonly found in manufacturing environments. Finally, for interpretability analysis, we design a patch-based feature-ablation framework that demonstrates the model’s reliability under uncertainty and the absence of learning biases. The proposed technique enables the deployment of an innovative high-performance safety system that, to our knowledge, does not exist in the industry. Full article

Carbon Fiber-Reinforced Polymers (CFRPs) are gaining popularity as a reliable strengthening technique for reinforced concrete (RC) columns. Several efficient models were developed to predict the stress–strain (σ-ε) curve of CFRP-confined concrete based on experiment findings. The ultimate strength is a crucial parameter for accurate (σ-ε) behavior prediction, since it constitutes an initial step in estimating the corresponding axial strain, as it provides a direct indication of the desired increase in strength. Literature analytical models often produce inconsistent results due to errors in estimating the confinement pressure or effectively confined area or the lack of a strong and stable correlation between ultimate strength and confinement parameters. This study looked at a large collection of experimental results from existing research. It used a statistical method (Pearson’s coefficient) to see how well ultimate strength correlated with various confinement factors. For normal-strength concrete columns with circular sections, there was a strong linear correlation between ultimate strength and the thickness of the CFRP jacket. This correlation weakened for high-strength concrete (HSC) and for rectangular columns. A sensitivity analysis was performed to identify the most influential confinement parameters, showing that the number of CFRP layers (n $\times$ t) is the most dominant factor, particularly with normal-strength concrete (NSC) in circular columns, accounting for the vast majority of the variance in ultimate strength. Using multiple linear regression equations to predict ultimate strength was also explored; this method demonstrated the best performance with HSC in circular sections, but the results were less promising with NSC. Artificial Neural Networks (ANNs) were developed and trained on the built database, and four statistical metrics were computed for evaluation (R², RMSE, MAE, MRAE), proving highly accurate and superior to linear regression equations, with mean relative absolute errors MRAEs between 2.4–7.2% for ultimate strength prediction, opening new avenues for optimizing CFRP-strengthened element designs. Full article

This study investigates the use of Si and CdTe-based Timepix3 detectors for photovoltaic energy conversion using solar X-rays and other high-energy electromagnetic radiation in space. As space missions increasingly rely on miniaturized platforms like CubeSats, power generation in compact and radiation-prone environments remains a critical challenge. Conventional solar panels are limited by size and spectral sensitivity, prompting the need for alternative energy harvesting solutions—particularly in the high-energy X-ray domain. A novel CubeSat-compatible payload design incorporates a UV-visible filter to isolate incoming X-rays, which are then absorbed by semiconductor detectors to generate electric current through ionization. Laboratory calibration was performed using Fe-55, Ba-133, and Am-241 sources to compare spectral response and clustering behaviour. CdTe consistently outperformed Si in detection efficiency, spectral resolution, and cluster density due to its higher atomic number and material density. Equalization techniques further improved pixel threshold uniformity, enhancing spectroscopic reliability. In addition to experimental validation, simulations were conducted to quantify the expected energy conversion performance under orbital conditions. Under quiet-Sun conditions at 500 km LEO, CdTe absorbed up to 1.59 µW/cm² compared to 0.69 µW/cm² for Si, with spectral power density peaking between 10 and 20 keV. The photon absorption efficiency curves confirmed CdTe’s superior stopping power across the 1–100 keV range. Under solar flare conditions, absorbed power increased dramatically, up to 159 µW/cm² for X-class and 15.9 µW/cm² for C-class flares with CdTe sensors. A time-based energy model showed that a 10 min X-class flare could yield nearly 1 mJ/cm² of harvested energy. These results validate the concept of a compact photovoltaic payload capable of converting high-energy solar radiation into electrical power, with dual-use potential for both energy harvesting and radiation monitoring aboard small satellite platforms. Full article

Partially frozen soil (PFS) is composed of coexisting unfrozen water and ice within its pores at subzero temperatures. This review paper examines how unfrozen water content (UWC) and pore ice content interact during phase changes under near-freezing conditions, governed by microscopic thermodynamic equilibrium. We present key theories describing why UWC persists (premelting, disjoining pressure) and the soil freezing characteristic curve (SFCC), along with measurement techniques, including the gravimetric approach to advanced nuclear magnetic resonance for characterization of water content. The influence of the water–ice phase composition on mechanical behavior is discussed, signifying pore pressure and effective stress. Various modelling approaches categorized into empirical SFCC, physio-empirical estimations, and emerging machine learning and molecular simulations are evaluated for capturing predictions in PFS behavior. The relevance of PFS to infrastructural foundations, tailing dams, permafrost slope stability, and climate change’s impacts on cold regions’ environmental geotechnics is also highlighted as a challenge in practical application. Hence, understanding pore pressure dynamics and effective stress in PFS is critical when assessing frost heave, thaw weakening, and the overall performance of geotechnical structures in cold regions. By combining micro-scale phase interaction mechanisms and macro-scale engineering observations, this review paper provides a theoretical understanding of the underlying concepts vital for future research and practical engineering in cold regions. Full article

This study extends the Environmental Kuznets Curve (EKC) framework to analyze the growth–emissions nexus in twelve post-socialist European countries by integrating agricultural development, regulatory quality, renewable energy, and transport dynamics. Employing advanced panel econometric techniques—FMOLS, DOLS, and PARDL—and treating regulatory quality (REGURAQUAL) as an exogenous determinant, the analysis identifies the structural and institutional factors shaping carbon intensity (CI). The results indicate that regulatory quality, transport efficiency, and long-run emissions trajectories significantly reduce carbon intensity, while the independent contribution of renewable energy is comparatively weaker. Agricultural productivity exhibits a nonlinear relationship with emissions, validating the EKC hypothesis: emissions increase during early growth but decline beyond a threshold as modernization and climate-smart practices enhance efficiency. The study’s scientific value lies in its integrated approach, combining economic, institutional, and sectoral dimensions to explain long-run decarbonization in transitional economies. By focusing on post-socialist Europe, it advances EKC research beyond income-based models and underscores the importance of governance and structural transformation. Limitations include data coverage and cross-country heterogeneity, suggesting future work should adopt spatial and nonlinear frameworks and include adaptation and resilience metrics. Overall, robust governance and technological innovation can guide post-socialist economies toward sustainable, low-carbon growth. Full article

Background/Objectives: Pulmonary arterial hypertension (PAH) is a complex cardiovascular disease with a high burden of morbidity and mortality. Although several risk prediction models have been proposed, the exact significance of distinct clinical parameters in predicting survival in PAH remains unclear. It is important to emphasize that this study does not aim to validate or contradict existing clinical risk assessment calculators provided by the ESC or other scientific societies. Instead, the goal of this research is to identify and rank clinical parameters according to their importance in predicting mortality in PAH patients using machine learning techniques. Methods: Using the Database of Pulmonary Hypertension in the Polish population (BNP-PL) registry, 1755 adult patients with PAH were selected. Feature engineering was conducted using domain knowledge, guided by European Society of Cardiology (ESC) recommendations. Features were reduced using LASSO regression and sequential feature elimination algorithms. A classification model was built using the XGBoost algorithm, utilizing 17 features. The model was tested on a preselected subset of the BNP-PL data. The Shapley Additive Explanations (SHAP) method was used to explain the model’s predictions and to rank feature importance. Results: The model achieved satisfactory results across evaluated metrics, including an area under the curve of 0.767, accuracy of 0.738, specificity of 0.733, and sensitivity of 0.800. SHAP values effectively ranked the features, corroborating the significance of parameters present in the ESC risk stratification tables. Furthermore, local interpretation of results using SHAP enabled individualized assessment of feature importance, enhancing clinical applicability. Conclusions: The proposed artificial intelligence-based model demonstrates satisfactory predictive capability, highlighting the potential of machine learning techniques to support more personalized approaches to the management of PAH patients. This approach offers complementary insights into traditional risk assessment methods, providing clinicians with a novel tool for individualized risk evaluation and decision-making. Full article

Corrosion in metallic materials is a critical challenge in maintenance and safety, and traditional visual inspection methods are often time-consuming, labor-intensive, and dependent on human expertise, highlighting the need for more efficient and reliable solutions. Deep learning, particularly convolutional neural networks (CNNs), provides a promising approach by enabling automated and accurate image-based classification. This study investigates binary image classification of corrosion using four pre-trained CNN architectures, namely ResNet50, MobileNetV2, NASNetMobile, and EfficientNetV2B0, and integrates explainable artificial intelligence (XAI) techniques to provide interpretability and insight into each model’s decision-making process. A curated dataset of 4012 images, divided between corroded and non-corroded surfaces, was pre-processed, and augmented images resulted in a total of 9636 images used to train and evaluate the models. Performance was assessed through accuracy, confusion matrices, computational timing, receiver operating characteristic curves, precision–recall curves, and Cohen’s Kappa. In this paper, Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations are incorporated as an XAI technique to provide interpretable insight into the model’s reasoning process, enabling clear identification of corrosion regions and offering justification for each prediction produced by the system. A key contribution of this work is the integration of Grad-CAM to enhance explainability. The results showed that EfficientNetV2B0 demonstrates stable training with minimal sign overfitting compared to other models. MobileNetV2 achieved the lowest time to train with the datasets given, and ResNet50 achieved the highest classification performance in terms of confusion matrix, with an accuracy of 96.58%. Through Grad-CAM reasoning, EfficientNetV2B0 shows a specific high activation towards corroded regions compared to the other three models that were evaluated. Full article