Search Results (37)

Future $e^{+}{e}^{-}$ colliders are expected to play a fundamental role in measuring Standard Model (SM) parameters with unprecedented precision and in probing physics beyond the SM (BSM). This study investigates two-particle angular correlation distributions involving final-state SM charged hadrons. Unexpected correlation structures in these distributions is considered to be a hint for new physics perturbing the QCD partonic cascade and thereby modifying azimuthal and (pseudo)rapidity correlations. Using Pythia8 Monte Carlo generator and fast simulation, including selection cuts and detector effects, we study potential structures in the two-particle angular correlation function. We adopt the QCD-like Hidden Valley (HV) scenario as implemented in Pythia8 generator, with relatively light HV v-quarks (below about 100 GeV), to illustrate the potential of this method. Full article

Structural magnetic resonance imaging (sMRI) is a vital tool for diagnosing neurological brain diseases. However, sMRI scans often show significant structural changes only in limited brain regions due to localised atrophy, making the identification of discriminative features a key challenge. Importantly, the human brain exhibits inherent bilateral symmetry, and deviations from this symmetry—such as asymmetric atrophy—are strong indicators of early Alzheimer’s disease (AD). Patch-based methods help capture local brain changes for early AD diagnosis, but they often struggle with fixed-size limitations, potentially missing subtle asymmetries or broader contextual cues. To address these limitations, we propose a novel augmented reality (AR)-enhanced patch-level explainable deep learning (ARE-PaLED) system. It includes an adaptive multi-scale patch extraction network (AMPEN) to adjust patch sizes based on anatomical characteristics and spatial context, as well as an informative patch selection algorithm (IPSA) to identify discriminative patches, including those reflecting asymmetry patterns associated with AD; additionally, an AR module is proposed for future immersive explainability, complementing the patch-level interpretation framework. Evaluated on 1862 subjects from the ADNI and AIBL datasets, the framework achieved an accuracy of 92.5% (AD vs. NC) and 85.9% (AD vs. MCI). The proposed ARE-PaLED demonstrates potential as an interpretable and immersive diagnostic aid for sMRI-based AD diagnosis, supporting the interpretation of model predictions for AD diagnosis. Full article

This paper introduces a novel end-to-end fault diagnosis framework that integrates Bidirectional Long Short-Term Memory (BiLSTM) networks with Time-Domain Reflectometry (TDR) for the detection, characterization, and localization of wiring faults. The method is designed to operate directly on TDR signals, requiring no manual feature extraction or preprocessing. A forward model is used to simulate TDR responses across various fault scenarios and topologies, serving as the basis for supervised learning. The proposed BiLSTM-based model is trained and validated on common wiring network topologies, demonstrating high diagnostic performance. Experimental results show a diagnostic accuracy of 98.97% and a macro-average sensitivity exceeding 98%, outperforming conventional machine learning techniques. In addition to technical performance, the proposed approach supports sustainable and predictive maintenance strategies by reducing manual inspection efforts and enabling real-time automated diagnostics. Full article

Background: This retrospective study evaluated and compared oncological outcomes in patients with localized prostate cancer treated either by laparoscopic radical prostatectomy (LRP) or by external beam radiotherapy (EBRT) combined with androgen deprivation therapy (ADT). The primary aim was to identify predictors of biochemical recurrence (BCR) and to assess recurrence-free survival. Subjects and methods: A total of 107 patients diagnosed with localized prostate cancer and treated between 2016 and 2023 were included in the analysis. Of these, 61 patients underwent LRP, and 46 patients received EBRT+ADT. The median follow-up period was 60 months for the LRP group (IQR 24–72) and 66 months for the EBRT group (IQR 49.5–72). Biochemical recurrence (BCR) was defined as a PSA level > 0.2 ng/mL after LRP or an increase > 2 ng/mL above nadir following EBRT. Kaplan–Meier survival curves, log-rank tests, Pearson’s chi-square, and Cox regression models were used to evaluate outcomes and identify predictors of recurrence, with significance set at p < 0.05. Results: Biochemical recurrence occurred in 21 (34.4%) of LRP patients and 10 (21.7%) of EBRT patients. The five-year BCR-free survival was 40 (65.6%) patients in the LRP group and 33 (71.7%) for EBRT, with a trend toward improved outcomes in the EBRT group that approached statistical significance (log-rank p = 0.089). Median time to recurrence was 30 months for LRP (IQR 12.75–60) and 48 months for EBRT (IQR 30–60). Predictive analysis revealed that in the LRP group, higher ISUP grade at biopsy (p = 0.001), advanced pathological stage (p < 0.001), positive surgical margins (p < 0.001), and intermediate initial PSA levels (10–20 ng/mL; p = 0.080) were associated with increased risk of BCR. No independent predictors of recurrence were identified in the EBRT group. Conclusions: Both LRP and EBRT+ADT provide effective cancer control with similar five-year BCR-free survival. However, LRP was associated with a higher recurrence rate, particularly among patients with intermediate-risk features such as iPSA 10–20 ng/mL, high ISUP grade, advanced pathological stage, or positive surgical margins. These findings highlight the need for risk-adapted follow-up and timely salvage treatment in high-risk LRP patients to improve long-term outcomes. Full article

The identification and resolution of faults, along with the proactive maintenance of wiring networks, are essential for ensuring the reliable, safe, and energy-efficient operation of industrial systems. Research in this domain advances fault detection and prevention, thereby enhancing overall safety, reliability, efficiency, and cost-effectiveness. Time-domain reflectometry (TDR) responses are extensively utilized for this purpose; however, their inherent nonlinearity and complexity pose significant challenges in interpretation. We propose an innovative solution to this problem that is aimed at diagnosing the state of the wiring network: integrating TDR responses with twin support vector machines (TWSVMs) by utilizing kernel functions. The effectiveness and feasibility of the TDR and TWSVM-based fault diagnosis methodology are substantiated through its application to two prevalent wiring network configurations, demonstrating superior performance compared to other fault diagnosis techniques. Full article

We present a theoretical framework that allows one to make an explicit connection between the phenomenology of QCD, namely the properties of the gluon correlator and Wilson loops, and a particular relativistic model for the description of nuclear matter and neutron stars: the chiral confining model. Starting with the field correlator method, which explicitly and simultaneously incorporates confinement and chiral symmetry breaking, we describe how to obtain the response of the composite nucleon to the nuclear scalar field, as well as the relative role of confinement and chiral symmetry breaking in in-medium nucleon mass evolution, thereby generating the three-body forces needed for the saturation mechanism. Full article

This study examines the impact that International Public Sector Accounting Standards adoption might have on governance quality and corruption control in Spain, Portugal, and Italy. IPSAS was designed to globally enhance public transparency and accountability thanks to accrual accounting. However, its effectiveness in fighting corruption and steering better governance has varied across institutional contexts and implementation phases. This paper examines, using partial least squares structural equation modeling (PLS-SEM) and comparative analysis, how legal systems, political stability, and anti-corruption measures mediate the relationship. The results indicate that full IPSAS adoption, as in the case of Spain, significantly enhances governance if the institutional framework is solid and, by extension, reduces corruption. Partial adoption, such as that by Portugal, exposes moderate improvements, but Italy, still in the preparation of the process, shows the poorest result. The study identifies that the legal system, along with complementary reforms like capacity building and political stability, is a very crucial factor in enhancing the IPSAS impact. This covers the evidential gaps and provides actionable insights for policymakers, while at the same time underlining institutional strength as a key driver for IPSAS adoption, contributing to broader discussions on advancing public sector accounting reforms. Full article

To address the vulnerability of rolling bearings to noise interference in industrial settings, along with the problems of weak noise resilience and inadequate generalization in conventional residual network frameworks, this study introduces an adaptive denoising residual network (AD-ResNet) for diagnosing rolling bearing faults. Initially, the sensors collect the bearing vibration signals, which are then converted into two-dimensional grayscale images through the application of a continuous wavelet transform. Then, a spatial adaptive denoising network (SADNet) architecture is incorporated to comprehensively extract multi-scale information from noisy images. By exploiting the improved pyramid squeeze attention (IPSA) module, which excels in extracting representative features from channel attention vectors, this unit substitutes the standard convolutional layers present in typical residual networks. Ultimately, this model was validated through experiments using publicly available bearing datasets from CWRU and HUST. The findings suggest that with −6 dB Gaussian white noise, the average accuracy of recognition achieves a rate of 90.96%. In scenarios of fluctuating speeds accompanied by strong noise, the recognition accuracy can reach 89.54%, while the training time per cycle averages merely 3.65 s. When compared to other widely utilized fault diagnosis techniques, the approach described in this paper exhibits enhanced noise resistance and better generalization capabilities. Full article

As a range-free localization algorithm, DV-Hop has gained widespread attention due to its advantages of simplicity and ease of implementation. However, this algorithm also has some defects, such as poor localization accuracy and vulnerability to network topology. This paper presents a comprehensive analysis of the factors contributing to the inaccuracy of the DV-Hop algorithm. An improved proportional integral derivative (PID) search algorithm (PSA) DV-Hop hybrid localization algorithm based on weighted hyperbola (IPSA-DV-Hop) is proposed. Firstly, the first hop distance refinement is employed to rectify the received signal strength indicator (RSSI). In order to replace the original least squares solution, a weighted hyperbolic algorithm based on the degree of covariance is adopted. Secondly, the localization error is further reduced by employing the improved PSA. In addition, the selection process of the node set is optimized using progressive sample consensus (PROSAC) followed by a 3D hyperbolic algorithm based on coplanarity. This approach effectively reduces the computational error associated with the hopping distance of the beacon nodes in the 3D scenarios. Finally, the simulation experiments demonstrate that the proposed algorithm can markedly enhance the localization precision in both isotropic and anisotropic networks and reduce the localization error by a minimum of 30% in comparison to the classical DV-Hop. Additionally, it also exhibits stability under the influence of a radio irregular model (RIM). Full article

Adopting Wireless Power Transfer (WPT) technology to an Unmanned Aerial Vehicle (UAV) involves adding extra components, which may impact the drone’s overall weight and performance. This paper aims to enhance UAV performance by designing a lightweight WPT system through a parametric design approach. This method explores novel perspectives by identifying the most suitable combination of parameters in terms of efficiency, weight, and feasibility. Various parameters such as the compensation topology, number of turns of coils, and frequency were studied. The system was analyzed through a coupled simulation approach, where electromagnetic modeling of the coupler using the finite element method (FEM) was combined with electrical circuit simulations, providing a more accurate assessment of the overall system efficiency and behavior considering variations in the coupling factor due to misalignment. A prototype of the resulting configuration was designed and tested experimentally versus misalignment at reduced power using a specific test bench. The results show a 70% efficiency level with SP compensation that was improved to 80% with SS compensation. Full article

We interpret the recent MicroBooNE data on neutral-current single ${\pi }^0$ production on argon with the hypothesis that this process occurs via Delta excitation. We calculate the flux-integrated total cross section with our RPA-based model which allows for a simultaneous description of Delta-mediated resonant and coherent pion production. We also discuss the ratio between the two exclusive measurements with one proton and zero protons in the final state. Full article

For intensified global environmental and societal challenges, the Sustainable Development Goals (SDGs) have come to the forefront as a pivotal framework for corresponding action. For SDGs, the role of commercial spatial design and planning gains unique prominence. Using importance–performance analysis (IPA) with importance–performance–scope analysis (IPSA), we explore the SDGs’ deployment and ramifications in commercial spatial planning in this study. By spotlighting pivotal SDG indicators, the study results underscore commercial spaces’ performance in the environmental, social, and economic sectors. The combined analytical approaches were used to elucidate the present-day salience and efficacy of these attributes and highlight prospective trajectories for future evolution. The results offer a blueprint to empower designers and planners in the commercial realm to assimilate the SDGs effectively and design with environmental stewardship, social responsibility, and economic viability. The industry can have pragmatic strategic guidance aligned with global sustainable development imperatives. Full article

The current power load exhibits strong nonlinear and stochastic characteristics, increasing the difficulty of short-term prediction. To more accurately capture data features and enhance prediction accuracy and generalization ability, in this paper, we propose an efficient approach for short-term electric load forecasting that is grounded in a synergistic strategy of feature optimization and hyperparameter tuning. Firstly, a dynamic adjustment strategy based on the rate of the change of historical optimal values is introduced to enhance the PID-based Search Algorithm (PSA), enabling the real-time adjustment and optimization of the search process. Subsequently, the proposed Improved Population-based Search Algorithm (IPSA) is employed to achieve the optimal adaptive variational mode decomposition of the load sequence, thereby reducing data volatility. Next, for each load component, a Bi-directional Gated Recurrent Unit network with an attention mechanism (BiGRU-Attention) is established. By leveraging the interdependence between feature selection and hyperparameter optimization, we propose a synergistic optimization strategy based on the Improved Population-based Search Algorithm (IPSA). This approach ensures that the input features and hyperparameters for each component’s predictive model achieve an optimal combination, thereby enhancing prediction performance. Finally, the optimal parameter prediction model is used for multi-step rolling forecasting, with the final prediction values obtained through superposition and reconstruction. The case study results indicate that this method can achieve an adaptive optimization of hybrid prediction model parameters, providing superior prediction accuracy compared to the commonly used methods. Additionally, the method demonstrates robust adaptability to load forecasting across various day types and seasons. Consequently, this approach enhances the accuracy of short-term load forecasting, thereby supporting more efficient power scheduling and resource allocation. Full article

This paper presents an in-depth analysis of the Kopčić House, a significant example of modernist architecture in Sarajevo, Bosnia and Herzegovina, focusing on its structural-specific features and seismic performance. The Kopčić House embodies a confined masonry structure with innovative construction features, combining load-bearing masonry walls with reinforced concrete elements. This architectural approach was pioneering for its time, combining traditional construction methods with innovative materials and techniques. Detailed analysis using numerical modeling techniques, specifically 3D modeling with the 3Muri software (Vers.14.2.0.4), was conducted to assess the seismic resilience of the structure. The analysis considered different load distributions and eccentricities to comprehensively evaluate the building’s response to lateral forces. The findings of this research reveal the structural capacity and potential vulnerabilities of the Kopčić House when subjected to seismic events. While the building demonstrates inherent strength due to its confined masonry design, areas requiring structural strengthening were identified through numerical simulations. This study contributes to the broader understanding of confined masonry construction within the context of modernist architecture. By integrating historical research with advanced structural analysis, this work aims to bridge the gap between architectural heritage and contemporary engineering practices. Full article

To examine the impact of ultra-high iPSA levels of >50 ng/mL (uhPSA) after modern radiotherapy, we compared outcomes of 214 patients with uhPSA levels to 1161 other high-risk patients. Radiotherapy included brachytherapy ± external beam radiotherapy (EBRT) and EBRT alone (intensity-modulated radiotherapy or stereotactic body radiotherapy). The biochemical disease-free survival rate (bDFS), the distant metastasis-free survival rate (DMFS), local control, and pelvic lymph node control were analyzed. Patients with uhPSA levels had an inferior bDFS (84.8% at 5 years) and DMFS (93.9% at 5 years) compared to other high-risk patients (92.7% and 97.2%, both p < 0.001). The uhPSA group showed more distant metastases than the non-uhPSA group; however, the frequencies of local failure and pelvic lymph node recurrence were similar. The uhPSA group demonstrated hazard ratios (HRs) of 2.74 for bDFS and 2.71 for DMFS, similar to those of T3b–4 (HR 2.805 and 2.678 for bDFS and DMFS) and GS 9–10 (HR 2.280 and 2.743 for bDFS and DMFS). An uhPSA level could be a candidate for a single VHR factor to identify high-risk patients who require intensified treatment. Full article