Search Results (41)

Industry 5.0 emphasizes human-centered integration of artificial intelligence in industrial contexts, yet successful adoption depends critically on workforce perception and acceptance. This research develops and validates a machine learning framework for predicting AI-related perceptions and expected impacts in the construction industry under small sample constraints typical of specialized industrial surveys. Specifically, the study aims to develop and empirically validate a predictive AI decision support model that estimates the expected impact of AI adoption in the construction sector based on digital competencies, ICT utilization, AI training and experience, and AI usage at both individual and organizational levels, operationalized through a composite AI Impact Index and two process-oriented outcomes (perceived task automation and perceived cost reduction). Using a dataset of 51 survey responses from Slovak construction professionals collected in 2025, we implement a methodologically rigorous approach specifically designed for limited-data regimes. The framework encompasses ordinal target simplification from five to three classes, dimensionality reduction through theoretically grounded composite indices reducing features from 15 to 7, exclusive deployment of low variance regularized models, and leave-one-out cross-validation for unbiased performance estimation. The optimal model (Lasso regression with recursive feature elimination) predicts cost reduction perception with R² = 0.501, MAE = 0.551, and RMSE = 0.709, while six classification targets achieve weighted F1 = 0.681, representing statistically optimal performance given sample constraints and perception measurement variability. Comparative evaluation confirms regularized models outperform high variance alternatives: random forest (R² = 0.412) and gradient boosting (R² = 0.292) exhibit substantially lower generalization performance, empirically validating the bias-variance trade-off rationale. Key methodological contributions include explicit bias-variance optimization preventing overfitting, feature selection via RFE reducing input space to six predictors (personal AI usage, AI impact on budgeting, ICT utilization, AI training, company size, and age), and demonstration that principled statistical approaches achieve meaningful predictions without requiring large-scale datasets or complex architectures. The framework provides a replicable blueprint for perception and impact prediction in data-constrained Industry 5.0 contexts, enabling targeted interventions, including customized training programs, strategic communication prioritization, and resource allocation for change management initiatives aligned with predicted adoption patterns. Full article

This study introduces SquareSwish, a smooth, self-gated activation $f\left(x\right)=x\sigma {\left(x\right)}^2$, and benchmarks it against ten established activations (ReLU, LeakyReLU, ELU, SELU, GELU, Snake, LearnSnake, Swish, Mish, Hard-Swish) across six CNN architectures (EfficientNet-B1/B4, EfficientNet-V2-M/S, ResNet-50, and Xception) under a uniform transfer-learning protocol. Two geographically grounded datasets are used in this study. FuelRiskMap-TR comprises 7686 satellite images of urban fuel stations in Türkiye, which is semantically enriched with the OpenStreetMap context and YOLOv8-Small rooftop segmentation (mAP@0.50 = 0.724) to support AI-enabled, ICT-integrated risk screening. In a similar fashion, FuelRiskMap-UK is collected, comprising 2374 images. Risk scores are normalized and thresholded to form balanced High/Low-Risk labels for supervised training. Across identical training settings, SquareSwish achieves a top-1 validation accuracy of 0.909 on EfficientNet-B1 for FuelRiskMap-TR and reaches 0.920 when combined with SELU in a simple softmax-probability ensemble, outperforming the other activations under the same protocol. By squaring the sigmoid gate, SquareSwish more strongly attenuates mildly negative activations while preserving smooth, non-vanishing gradients, tightening decision boundaries in noisy, semantically enriched Earth-observation settings. Beyond classification, the resulting city-scale risk layers provide actionable geospatial outputs that can support inspection prioritization and integration with municipal GIS, offering a reproducible and low-cost safety-planning approach built on openly available imagery and volunteered geographic information. Full article

Nitrogen-fused conjugated heterocycles have attracted growing interest owing to their tunable electronic properties and potential in organic optoelectronics. In this study, two centrosymmetric donor–acceptor-type emitters PP-6F and PPA-3F were designed by incorporating trifluorophenyl and anthracene acceptor units into a pyrrolo[3,2-b]pyrrole (PP) framework. The experimental and theoretical results reveal that subtle modulations in molecular conformation and π-conjugation pathways strongly affect the excited-state characteristics. PP-6F, featuring a nearly coplanar donor–acceptor configuration, exhibits efficient π-electron delocalization and a dominant local excitation (LE) emission with a large oscillator strength. In contrast, the bulky anthracene in PPA-3F increases the donor–acceptor dihedral angle, reduces conjugation coupling, and promotes orbital separation, leading to a hybrid intramolecular charge transfer and local excitation (ICT/LE) excited state. The rigid anthracene framework suppresses structural reorganization and nonradiative decay, allowing PPA-3F to retain a relatively high oscillator strength despite its charge-transfer nature. This work demonstrates that fine-tuning donor–acceptor dihedral angles and conjugation continuity within PP-based systems is an effective strategy for balancing LE and ICT emissions and developing high-efficiency nitrogen-fused organic emitters and scintillators. Full article

Photodynamic therapy (PDT) is a minimally invasive technique—used for the local eradication of neoplastic cells—that exploits the interaction of light, oxygen, and a photo-responsive drug called photosensitizer (PS) for the local generation of lethal ROS. Push-pull chromophores, that bear electron donor (D) and acceptor (A) groups linked through a π-electron bridge, are characterized by a non-homogeneous charge distribution in their excited state, with charge transfer from one extremity of the chain to the other one (Internal Charge Transfer—ICT). This phenomenon has a direct impact on the photophysical features of the push-pull compounds, as the bathochromic shift of the emission maxima and intersystem crossing (ISC) of the excited state are directly connected with the production of reactive oxygen species (ROS). In continuing our research regarding the synthesis and use of oligophenylene ethynylenes (OPEs) in PDT, two new push-pull glycosyl OPE-NOF and OPE-ONF—featuring electron-donor N,N-dimethylamino (N) and dimetoxyaryl (O) and acceptor tetrafluoroaryl (F) moieties on the OPE chain—have been efficiently prepared. The interchanged position of the D groups onto the conjugated skeleton was aimed to tune and optimize the push-pull effect, while the introduction of glucoside terminations was directed to give biocompatibility and bioaffinity to the chromophores. OPE-NOF, OPE-ONF, and the synthetic intermediates were fully characterized, and their photophysical properties were investigated by using UV-Vis absorption and emission spectroscopy. OPE-NOF showed a strong charge-transfer character and high PDT effect on HeLa cancer cells when irradiated with non-harmful blue light, causing massive cancer cell death. Full article

With the depletion of high-grade iron ore resources, the efficient utilization of low-grade iron ore has become a critical demand in the steel industry. Due to its uniform particle size and chemical composition, pelletized iron ore significantly enhances both the utilization rate of iron ore and the efficiency of metallurgical processes. This paper presents a deep learning model based on ResUNet, which integrates three-dimensional CT images obtained through industrial computed tomography (ICT) to precisely segment hematite, liquid phase, and porosity. By incorporating residual connections and batch normalization, the model enhances both robustness and segmentation accuracy, achieving F1 scores of 98.37%, 95.10%, and 83.87% for the hematite, pores, and liquid phase, respectively, on the test set. Through 3D reconstruction and quantitative analysis, the volume fractions and fractal dimensions of each component were computed, revealing the impact of the spatial distribution of different components on the physical properties of the pellets. Systematic evaluation of model robustness demonstrated varying sensitivity to different CT artifacts, with the strongest resistance to beam hardening and highest sensitivity to Gaussian noise. Multi-scale resolution analysis revealed that segmentation quality and fractal dimension estimates exhibit phase-dependent responses to resolution changes, with the liquid phase being the most sensitive. Despite these dependencies, the relative complexity relationships among phases remained consistent across resolutions, supporting the reliability of our qualitative conclusions. The study demonstrates that the deep learning-based image segmentation method effectively captures microstructural details, reduces human error, and enhances automation, providing a scientific foundation for optimizing pellet quality and improving metallurgical performance. It holds considerable potential for industrial applications. Full article

Non-specific low back pain (NSLBP) is a common musculoskeletal issue that can limit function and reduce the patient’s quality of life. Enhancing spinal stabilizer muscle activity through targeted exercises may help improve spinal alignment and alleviate NSLBP symptoms. This study aimed to investigate whether incorporating the abdominal drawing-in maneuver (ADIM) into selected low back exercises influences the electromyographic (EMG) activity of key spinal extensor muscles. Forty participants with NSLBP (n = 29 female and n = 11 male; mean age = 21.42 ± 1.07 years; BMI = 20.65 ± 2.08 kg/m²; 80% right-side dominant) performed three exercises, prone trunk extension, superman, and unstable superman, with and without the ADIM. The EMG amplitudes of the iliocostalis lumborum pars lumborum (ICL), iliocostalis lumborum pars thoracis (ICT), and longissimus thoracis (LT) were measured. A cross-sectional observational study design was employed. Significant main effects were observed for both exercise types and the ADIM on the EMG amplitudes of the ICL, ICT, and LT (ICL: F1,14 = 82.69–114.23, p < 0.001, η² ≥ 0.88; ICT: F1,14 = 100.69–117.13, p < 0.001, η² ≥ 0.90; LT: F1,14 = 62.69–74.88, p < 0.001, η² ≥ 0.81). Under ADIM conditions, the ICL activity decreased significantly (right ICL mean difference: 14.06–20.02; left ICL: 13.06–21.32; p < 0.001), while the ICT and LT activity increased (ICT mean difference: 6.45–8.89; LT: 9.37–12.13; p < 0.001). These changes were most pronounced during the unstable superman exercise (p < 0.01). Integrating the ADIM into specific low back exercises can differentially modulate spinal extensor muscle activity. In particular, the unstable superman exercise demonstrated the greatest changes in the EMG amplitudes. These findings support the inclusion of the ADIM in exercise programs aimed at improving spinal stability and may have implications for NSLBP management. Future research should examine the effects of the ADIM in populations with varying experience levels to enhance its generalizability and refine the clinical recommendations. Full article

Background: The purpose of this study is to find the effects of the international training program for enhancing intelligent capabilities through blended learning on computational thinking, artificial intelligence (AI) competency, and core competencies for the future society in graduated students enrolled in the Smart Information Communication Technology (SMART ICT) course. The teaching model followed the ADDIE framework. Methods: This study is a quasi-experimental study based on nonequivalent control group design. Study subjects were assigned to an experimental (n = 20) or control group (n = 20). The experimental group participated in the international training program in the blended learning form, real-time online classes (60 min per session for a week, six sessions) and face-to-face classes (4–8 h per session for 9 days, six sessions). The variables were measured with a self-report questionnaire and were evaluated before, right after, and in the 12th week of the program. Results: The AI competency of the experimental group was observed to be significantly changed at the points of time (F = 6.76, p = 0.002), and in comparison with that of a different group (F = 9.77, p = 0.003). Conclusions: This study suggests applying an international training program based on blended learning to strengthen intelligence capabilities such as artificial intelligence capabilities. Full article

Small and medium enterprises (SMEs) play a critical role in the economic growth of a nation, and their significance is increasingly acknowledged. More than 90% of commercial establishments, almost 70f% of jobs, and 55% of the GDP are held by SMEs in mature economies. Additionally, this sector accounts for 70% of employment possibilities and up to 40% of the GDP in developing countries. Technologically, the Internet of Things (IoT) enables multiple connected devices, i.e., “things”, to add value to businesses, as they can communicate and send messages or signals promptly. In this article, we investigate various challenges SMEs experience in IoT adoption to further their businesses. Amongst others, the challenges elicited include IoT considerations for SMEs, data, financial availability, and challenges related to the SME environment. Having analysed the challenges, a three-tiered solution framework coined the Secure IoT Control Framework (SIoTCF) to address the said challenges is developed and briefly validated through a theoretical analysis of the elements of the framework. It is hoped that the proposed framework will assist with aspects of design, governance, and maintenance in enhancing the security levels of IoT adoption and usage in SMEs, especially start-ups or less experienced SMEs. Future work in this area will involve surveying SME owners and ICT staff to validate the utility of the SIoTCF further. The study adds to the body of knowledge in general by developing a secure IoT control framework. In the field of ICT, this paradigm is expected to be useful for academics, researchers, and students. Full article

Background and Objectives: To our knowledge, this is the first study that investigated the prognostic value of radiomics features extracted from not only staging 18F-fluorodeoxyglucose positron emission tomography (FDG PET/CT) images, but also post-induction chemotherapy (ICT) PET/CT images. This study aimed to construct a training model based on radiomics features obtained from PET/CT in a cohort of patients with locally advanced head and neck squamous cell carcinoma treated with ICT, to predict locoregional recurrence, development of distant metastases, and the overall survival, and to extract the most significant radiomics features, which were included in the final model. Materials and Methods: This retrospective study analyzed data of 55 patients. All patients underwent PET/CT at the initial staging and after ICT. Along the classical set of 13 parameters, the original 52 parameters were extracted from each PET/CT study and an additional 52 parameters were generated as a difference between radiomics parameters before and after the ICT. Five machine learning algorithms were tested. Results: The Random Forest algorithm demonstrated the best performance (R² 0.963–0.998) in the majority of datasets. The strongest correlation in the classical dataset was between the time to disease progression and time to death (r = 0.89). Another strong correlation (r ≥ 0.8) was between higher-order texture indices GLRLM_GLNU, GLRLM_SZLGE, and GLRLM_ZLNU and standard PET parameters MTV, TLG, and SUVmax. Patients with a higher numerical expression of GLCM_ContrastVariance, extracted from the delta dataset, had a longer survival and longer time until progression (p = 0.001). Good correlations were observed between Discretized_SUVstd or Discretized_SUVSkewness and time until progression (p = 0.007). Conclusions: Radiomics features extracted from the delta dataset produced the most robust data. Most of the parameters had a positive impact on the prediction of the overall survival and the time until progression. The strongest single parameter was GLCM_ContrastVariance. Discretized_SUVstd or Discretized_SUVSkewness demonstrated a strong correlation with the time until progression. Full article

Modern terminals are developing toward intelligence and ubiquitous connection. Such ICTs (intelligent connected terminals) interact more frequently with the outside world and expose new attack surfaces. IDSs (intrusion detection systems) play a vital role in protecting ICT security. Multi-attack IDSs that can cover both intra-terminal and inter-terminal networks are a promising research direction for improving detection accuracy and the strength of security protection. However, a major challenge is the frequent dynamic signature updates across the network boundary, which cause significant computational overheads and result in losses in detection performance. In light of this, we propose a novel IDS framework based on OTA (over-the-air) signature updates to implement multi-attack detection. It updates the attack signatures of the target ICTs and adds the new attack signatures to the signature database in order to minimize the local memory storage and computing resources. It employs a CNN (convolutional neural network) based on an auto-encoder to achieve multi-attack detection, which can ensure the detection accuracy of multi-attacks with the multiple classification function. We evaluated our framework on four types of real-world ICT attack data, drawing comparisons with four widely used IDS schemes, and demonstrated the non-negligible superiority of our scheme over all benchmarks in terms of accuracy, recall, precision, and F1-score. Our work represents an important step toward an IDS that can detect multi-attacks in both intra-terminal and inter-terminal networks. Full article

Three new tetraphenylethene (TPE) push–pull chromophores exhibiting strong intramolecular charge transfer (ICT) are described. They were obtained via [2 + 2] cycloaddition–retroelectrocyclization (CA-RE) click reactions on an electron-rich alkyne-tetrafunctionalized TPE (TPE-alkyne) using both 1,1,2,2-tetracyanoethene (TCNE), 7,7,8,8-tetracyanoquinodimethane (TCNQ) and 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F₄-TCNQ) as electron-deficient alkenes. Only the starting TPE-alkyne displayed significant AIE behavior, whereas for TPE-TCNE, a faint effect was observed, and for TPE-TCNQ and TPE-F₄-TCNQ, no fluorescence was observed in any conditions. The main ICT bands that dominate the UV–Visible absorption spectra underwent a pronounced red-shift beyond the near-infrared (NIR) region for TPE-F₄-TCNQ. Based on TD-DFT calculations, it was shown that the ICT character shown by the compounds exclusively originated from the clicked moieties independently of the nature of the central molecular platform. Photothermal (PT) studies conducted on both TPE-TCNQ and TPE-F4-TCNQ in the solid state revealed excellent properties, especially for TPE-F4-TCNQ. These results indicated that CA-RE reaction of TCNQ or F₄-TCNQ with donor-substituted are promising candidates for PT applications. Full article

Due to the growing impact of the information and communications technology (ICT) sector on electricity usage and greenhouse gas emissions, telecommunication networks require new solutions which will enable the improvement of the energy efficiency of networks. Access networks, which are responsible for the last mile of connectivity and also for one of the largest shares in network energy consumption, are viable candidates for the implementation of new protocols, models and methods which will contribute to the reduction of the energy consumption of such networks. Among the different types of access networks, hybrid fiber–wireless (FiWi) networks are a type of network that combines the capacity and reliability of optical networks with the flexibility and availability of wireless networks, and as such, FiWi networks have begun to be extensively used in modern access networks. However, due to the advent of high-bandwidth applications and Internet of Things networks, the increased energy consumption of FiWi networks has become one of the most concerning challenges required to be addressed. This paper provides a comprehensive overview of the progress in approaches for improving the energy efficiency (EE) of different types of FiWi networks, which include the radio-and-fiber (R&F) networks, the radio-over-fiber networks (RoF), the FiWi networks based on multi-access edge computing (MEC) and the software-defined network (SDN)-based FiWi networks. It also discusses future directions for improving the EE in the FiWi networks. Full article

Center of pressure refers to the centroid of the ground reaction force vector detected underneath the walking foot, which is a summary measure representing body segment movements during human locomotion. In this study, we developed a cost-effective, lightweight insole-type textile capacitive sensor (I-TCPs) to analyze plantar pressure (PP) distribution and center of pressure (COP) trajectory. To test the accuracy of I-TCPs, the measured pressure data was compared with that of F-scan. The sensor performance test was divided into a static baseline test and a dynamic gait experiment, both at two different gait speeds self-selected by the subjects. Static gait results showed that I-TCPs were capable of recognizing PP segments at different gait speeds. Dynamic gait results showed an average RMSE of 1.29 ± 0.47 mm in COPx (mediolateral shift) and 12.55 ± 5.08 mm in COPy (anteroposterior shift) at a comfortable gait speed. The COP correlation between I-TCPs and F-scan was 0.54 ± 0.09 in COPx and 0.92 ± 0.04 in COPy in comfortable gait speed conditions, in which COPy values presented a stronger correlation. RMSE and correlation in fast gait speed conditions also showed similar results. The findings of this study can be the basis for future research, including rehabilitation engineering, developing ICT devices, and creating smart wearable sensors to improve quality of life for patients and healthy individuals. Full article

Compliance with the basic conditions of quality in higher education implies the design of strategies to reduce student dropout, and Information and Communication Technologies (ICT) in the educational field have allowed directing, reinforcing, and consolidating the process of professional academic training. We propose an academic and emotional tracking model that uses data mining and machine learning to group university students according to their level of dropout risk. We worked with 670 students from a Peruvian public university, applied 5 valid and reliable psychological assessment questionnaires to them using a chatbot-based system, and then classified them using 3 density-based unsupervised learning algorithms, DBSCAN, K-Means, and HDBSCAN. The results showed that HDBSCAN was the most robust option, obtaining better validity levels in two of the three internal indices evaluated, where the performance of the Silhouette index was 0.6823, the performance of the Davies–Bouldin index was 0.6563, and the performance of the Calinski–Harabasz index was 369.6459. The best number of clusters produced by the internal indices was five. For the validation of external indices, with answers from mental health professionals, we obtained a high level of precision in the F-measure: 90.9%, purity: 94.5%, V-measure: 86.9%, and ARI: 86.5%, and this indicates the robustness of the proposed model that allows us to categorize university students into five levels according to the risk of dropping out. Full article

As industrial computerized tomography (ICT) is widely used in the non-destructive testing of a solid rocket motor (SRM), the problem of how to automatically discriminate defect types and measure defect sizes with high accuracy in ICT images of SRM grains needs to be urgently solved. To address the problems of low manual recognition efficiency and data utilization in the ICT image analysis of SRM grains, we proposed an automated defect analysis (ADA) system for ICT images of SRM grains based on the YOLO-V4 model. Using the region proposal of the YOLO-V4 model, a region growing algorithm with automatic selection of seed points was proposed to segment the defect areas of the ICT images of grains. Defect sizes were automatically measured based on the automatic determination of defect types by the YOLO-V4 model. In this paper, the image recognition performance of YOLO-V4, YOLO-V3, and Faster R-CNN models were compared. The results show that the average accuracy (mAP) of the YOLO-V4 model is more than 15% higher than that of the YOLO-V3 and Faster R-CNN models, the F1-score is 0.970, and the detection time per image is 0.152 s. The ADA system can measure defect sizes with an error of less than 10%. Tests show that the system proposed in this paper can automatically analyze the defects in ICT images of SRM grains and has certain application value. Full article