Search Results (7,231)

The emergence of generative artificial intelligence (GenAI) in higher education offers new opportunities for academic support while also raising complex ethical concerns. This study explores how university students ethically evaluate the use of GenAI in three academic contexts: improving essay writing, preparing for exams, and generating complete essays without personal input. Drawing on the Multidimensional Ethics Scale (MES), the research assesses five philosophical frameworks—moral equity, relativism, egoism, utilitarianism, and deontology—based on a survey conducted among undergraduate social sciences students in Spain. The findings reveal that students generally view GenAI use as ethically acceptable when used to improve or prepare content, but express stronger ethical concerns when authorship is replaced by automation. Gender and full-time employment status also influence ethical evaluations: women respond differently than men in utilitarian dimensions, while working students tend to adopt a more relativist stance and are more tolerant of full automation. These results highlight the importance of context, individual characteristics, and philosophical orientation in shaping ethical judgments about GenAI use in academia. Full article

Terahertz (THz) communication is regarded as a key technology for achieving high-speed data transmission and wireless communication due to its ultra-high frequency and large bandwidth characteristics. In this study, we focus on a single-carrier THz communication system and propose a two-stage deep learning-based time-domain equalization method, specifically designed to mitigate the nonlinear distortions in such systems, thereby enhancing communication reliability and performance. The method adopts a progressive learning strategy, whereby global characteristics are initially captured before progressing to local levels. This enables the effective identification and equalization of channel characteristics, particularly in the mitigation of nonlinear distortion and random interference, which can otherwise negatively impact communication quality. In an experimental setting at a frequency of 230 GHz and a channel distance of 2.1 m, this method demonstrated a substantial reduction in the system’s bit error rate (BER), exhibiting particularly noteworthy performance enhancements in comparison to before equalization. To validate the model’s generalization capability, data collection and testing were also conducted at a frequency of 310 GHz and a channel distance of 1.5 m. Experimental results show that the proposed time-domain equalizer, trained using the two-stage DL framework, achieved significant BER reductions of approximately 92.15% at 230 GHz (2.1 m) and 83.33% at 310 GHz (1.5 m), compared to the system’s performance prior to equalization. The method exhibits stable performance under varying conditions, supporting its use in future THz communication studies. Full article

The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. Accurate prediction of energy consumption and interpretation of the influencing factors are essential for improving operational efficiency, optimizing energy use, and reducing operating costs. Although existing studies have made progress in battery energy consumption prediction, challenges remain in achieving high-precision modeling and conducting a comprehensive analysis of the influencing features. To address these gaps, this study proposes a two-layer stacking framework for estimating the energy consumption of electric buses. The first layer integrates the strengths of three nonlinear regression models—RF (Random Forest), GBDT (Gradient Boosted Decision Trees), and CatBoost (Categorical Boosting)—to enhance the modeling capacity for complex feature relationships. The second layer employs a Linear Regression model as a meta-learner to aggregate the predictions from the base models and improve the overall predictive performance. The framework is trained on 2023 operational data from two electric bus routes (NO. 355 and NO. W188) in Changsha, China, incorporating battery system parameters, driving characteristics, and environmental variables as independent variables for model training and analysis. Comparative experiments with various ensemble models demonstrate that the proposed stacking framework exhibits superior performance in data fitting. Furthermore, XGBoost (Extreme Gradient Boosting) is introduced as a surrogate model to approximate the decision logic of the stacking framework, enabling SHAP (SHapley Additive exPlanations) analysis to quantify the contribution and marginal effects of influencing features. The proposed stacked and surrogate models achieved superior battery energy consumption prediction accuracy (lowest MSE, RMSE, and MAE), significantly outperforming benchmark models on real-world datasets. SHAP analysis quantified the overall contributions of feature categories (battery operation parameters: 56.5%; driving characteristics: 42.3%; environmental data: 1.2%), further revealing the specific contributions and nonlinear influence mechanisms of individual features. These quantitative findings offer specific guidance for optimizing battery system control and driving behavior. Full article

The transportation sector, as a major source of carbon emissions, plays a crucial role in the realization of dual carbon goals worldwide. In this study, an improved least absolute shrinkage and selection operator (LASSO) is used to identify six key factors affecting transportation carbon emissions (TCEs) in China. Aiming at the spatio-temporal characteristics of transportation carbon emissions, a CNN-BiLSTM neural network model is constructed for the first time for prediction, and an improved whale optimization algorithm (EWOA) is introduced for hyperparameter optimization, finding that the prediction model combining spatio-temporal characteristics has a more significant prediction accuracy, and scenario forecasting was carried out using the prediction model. Research indicates that over the past three decades, TCEs have demonstrated a rapid growth trend. Under the baseline, green, low-carbon, and high-carbon scenarios, peak carbon emissions are expected in 2035, 2031, 2030, and 2040. The adoption of a low-carbon scenario represents the most advantageous pathway for the sustainable progression of China’s transportation sector. Consequently, it is imperative for China to accelerate the formulation and implementation of low-carbon policies, promote the application of clean energy and facilitate the green transformation of the transportation sector. These efforts will contribute to the early realization of dual-carbon goals with a positive impact on global sustainable development. Full article

This study explores the transformative potential of blockchain technology in overhauling conventional housing rental systems. It specifically addresses persistent issues, such as information asymmetry, fraudulent listings, weak Rental Agreements, and data breaches. A comprehensive review of ten academic publications highlights the architectural frameworks, underlying technologies, and myriad benefits of decentralized rental platforms. The intrinsic characteristics of blockchain—immutability, transparency, and decentralization—are pivotal in enhancing the credibility of rental information and proactively preventing fraudulent activities. Smart contracts emerge as a key innovation, enabling the automated execution of Rental Agreements, thereby significantly boosting efficiency and minimizing reliance on intermediaries. Furthermore, Decentralized Identity (DID) solutions offer a robust mechanism for securely managing identities, effectively mitigating risks associated with data leakage, and fostering a more trustworthy environment. The suitability of platforms such as Hyperledger Fabric for developing such sophisticated rental systems is also critically evaluated. Blockchain-based systems promise to dramatically increase market transparency, bolster transaction security, and enhance fraud prevention. They also offer streamlined processes for dispute resolution. Despite these significant advantages, the widespread adoption of blockchain in the rental sector faces several challenges. These include inherent technological complexity, adoption barriers, the need for extensive legal and regulatory adaptation, and critical privacy concerns (e.g., ensuring compliance with GDPR). Furthermore, blockchain scalability limitations and the intricate balance between data immutability and the necessity for occasional data corrections present considerable hurdles. Future research should focus on developing user-friendly DID solutions, enhancing blockchain performance and cost-efficiency, strengthening smart contract security, optimizing the overall user experience, and exploring seamless integration with emerging technologies. While current challenges are undeniable, blockchain technology offers a powerful suite of tools for fundamentally improving the rental market’s efficiency, transparency, and security, exhibiting significant potential to reshape the entire rental ecosystem. Full article

Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing methods suffer from limitations in effectively integrating multi-scale features and adaptively modeling complex noise distributions, making it difficult to construct effective spatial–spectral joint representations. This often leads to issues like detail loss and spectral distortion, especially when dealing with complex mixed noise. To address these challenges, this paper proposes a multi-scale differentiated denoising network based on spatial–spectral cooperative attention (MDSSANet). The network first constructs a multi-scale image pyramid using three downsampling operations and independently models the features at each scale to better capture noise characteristics at different levels. Additionally, a spatial–spectral cooperative attention module (SSCA) and a differentiated multi-scale feature fusion module (DMF) are introduced. The SSCA module effectively captures cross-spectral dependencies and spatial feature interactions through parallel spectral channel and spatial attention mechanisms. The DMF module adopts a multi-branch parallel structure with differentiated processing to dynamically fuse multi-scale spatial–spectral features and incorporates a cross-scale feature compensation strategy to improve feature representation and mitigate information loss. The experimental results show that the proposed method outperforms state-of-the-art methods across several public datasets, exhibiting greater robustness and superior visual performance in tasks such as handling complex noise and recovering small targets. Full article

Robotic assembly of electrical connectors enables the automation of high-efficiency production of electronic products. A rigid gripper is adopted as the end-effector by the majority of existing works with a force–torque sensor installed at the wrist, which suffers from very limited perception capability of the manipulated objects. Moreover, the grasping and movement actions, as well as the inconsistency between the robot base and the end-effector frame, tend to result in angular misalignment, usually leading to assembly failure. Bio-inspired by the human finger, we designed a tactile finger in this paper with three characteristics: (1) Compliance: A soft ‘skin’ layer provides passive compliance for plenty of manipulation actions, thus increasing the tolerance for alignment errors. (2) Tactile Perception: Two types of sensing elements are embedded into the soft skin to tactilely sense the involved contact status. (3) Enhanced manipulation force: A rigid fingernail is designed to enhance the manipulation force and enable potential delicate operations. Moreover, a tactile-based alignment algorithm is proposed to search for the optimal orientation angle about the z axis. In the application of U-disk insertion, the three characteristics are validated and a success rate of 100% is achieved, whose generalization capability is also validated through the assembly of three types of electrical connectors. Full article

In this study, we consider monthly load forecasting, which is an essential decision for energy infrastructure planning and investment. This study focuses on the Texas power grid, where electricity consumption has surged due to rising industrial activity and the increased construction of data centers driven by growing demand for AI. Based on an extensive exploratory data analysis, we identify key characteristics of monthly electricity demand in Texas, including an accelerating upward trend, strong seasonality, and temperature sensitivity. In response, we propose a regression-based forecasting model that incorporates a carefully designed set of input features, including a nonlinear trend, lagged demand variables, a seasonality-adjusted month variable, average temperature of a representative area, and calendar-based proxies for industrial activity. We adopt a rolling forecasting approach, generating 12-month-ahead forecasts for both 2023 and 2024 using monthly data from 2013 onward. Comparative experiments against benchmarks including Holt–Winters, SARIMA, Prophet, RNN, LSTM, Transformer, Random Forest, LightGBM, and XGBoost show that the proposed model achieves superior performance with a mean absolute percentage error of approximately 2%. The results indicate that a well-designed regression approach can effectively outperform even the latest machine learning methods in monthly load forecasting. Full article

Job stress and burnout are major challenges in today’s workplaces. While most interventions adopt a clinical or deficit-based approach, this meta-analysis takes a positive perspective by examining the effectiveness of Positive Psychological Interventions (PPIs). A total of 24 studies conducted in workplace settings were analyzed to assess the impact of PPIs on psychological well-being, subjective well-being, and job performance. The results showed significant and sustained improvements across all three outcomes, with moderate effect sizes: subjective well-being (g = 0.50, 95% CI [0.18, 0.81]), psychological well-being (g = 0.46, 95% CI [0.15, 0.78]), and performance (g = 0.42, 95% CI [0.21, 0.62]). Higher effects were found for in-person interventions and those conducted in Western contexts. No significant moderation was observed for structural factors (e.g., implementation level: Individual, Group, Leader, or Organization [IGLO]) or sample characteristics (e.g., gender), among other variables examined. These findings highlight the relevance of PPIs for promoting well-being and sustaining performance, which may reflect the preservation of personal resources in the face of occupational stressors. Regardless of type, well-designed interventions may be key to fostering healthier workplace environments—especially when delivered face-to-face. Full article

In order to achieve 0.1 m resolution and fully polarimetric observation capabilities for airborne SAR systems, the adoption of stepped-frequency modulation waveform combined with the polarization time-division transmit/receive (T/R) technique proves to be an effective technical approach. Considering the issue of range resolution degradation and paired echoes caused by multichannel amplitude–phase mismatch in fully polarimetric airborne SAR with 0.1 m resolution, an amplitude–phase error estimation algorithm based on echo data is proposed in this paper. Firstly, the subband amplitude spectrum correction curve is obtained by the statistical average of the subband amplitude spectrum. Secondly, the paired-echo broadening function is obtained by selecting high-quality sample points after single-band imaging and the nonlinear phase error within the subbands is estimated via Sinusoidal Frequency Modulation Fourier Transform (SMFT). Thirdly, based on the minimum entropy criterion of the synthesized compressed pulse image, residual linear phase errors between subbands are quickly acquired. Finally, two-dimensional cross-correlation of the image slice is utilized to estimate the positional deviation between polarization channels. This method only requires high-quality data samples from the echo data, then rapidly estimates both intra-band and inter-band amplitude/phase errors by using SMFT and the minimum entropy criterion, respectively, with the characteristics of low computational complexity and fast convergence speed. The effectiveness of this method is verified by the imaging results of the experimental data. Full article

Digestate valorization is essential for sustainable waste management and circular economy strategies, yet large-scale adoption faces technical, economic, and environmental challenges. Beyond waste-to-energy conversion, digestate is a valuable soil amendment, enhancing soil structure and reducing reliance on synthetic fertilizers. However, its agronomic benefits depend on feedstock characteristics, treatment processes, and application methods. This study reviews digestate composition, treatment technologies, regulatory frameworks, and environmental impact assessment through Life Cycle Assessment. It analyzes the influence of functional unit selection and system boundary definitions on Life Cycle Assessment outcomes and the effects of feedstock selection, pretreatment, and post-processing on its environmental footprint and fertilization efficiency. A review of 28 JCR-indexed articles (2018–present) analyzed LCA studies on digestate, focusing on methodologies, system boundaries, and impact categories. The findings indicate that Life Cycle Assessment methodologies vary widely, complicating direct comparisons. Transportation distances, nutrient stability, and post-processing strategies significantly impact greenhouse gas emissions and nutrient retention efficiency. Techniques like solid–liquid separation and composting enhance digestate stability and agronomic performance. Digestate remains a promising alternative to synthetic fertilizers despite market uncertainty and regulatory inconsistencies. Standardized Life Cycle Assessment methodologies and policy incentives are needed to promote its adoption as a sustainable soil amendment within circular economy frameworks. Full article

The accurate perception of visual stimuli in human–machine systems is crucial for improving system safety, usability, and task performance. The widespread adoption of digital technology has significantly increased the importance of visual interfaces and information. Therefore, it is essential to design visual interfaces and information with user characteristics in mind to ensure accurate perception of visual information. This study employed the Cognitive Perceptual Assessment for Driving (CPAD) to evaluate and compare gender differences in the ability to perceive visual signals within complex visual stimuli. The experimental setup included a computer with CPAD installed, along with a touch monitor, mouse, joystick, and keyboard. The participants included 11 male and 20 female students, with an average age of 22 for males and 21 for females. Prior to the experiment, participants were instructed to determine whether a signal stimulus was present: if a square, presented as the signal, was included in the visual stimulus, they moved the joystick to the left; otherwise, they moved it to the right. Each participant performed a total of 40 trials. The entire experiment was recorded on video to measure overall response times. The experiment measured the number of correct detections of signal presence, response times, the number of misses (failing to detect the signal when present), and false alarms (detecting the signal when absent). The analysis of experimental data revealed no significant differences in perceptual ability or response times for visual stimuli between genders. However, males demonstrated slightly superior perceptual ability and marginally shorter response times compared to females. Analyses of sensitivity and response bias, based on signal detection theory, also indicated a slightly higher perceptual ability in males. In conclusion, although these differences were not statistically significant, males demonstrated a slightly better perception ability for visual stimuli. The findings of this study can inform the design of information, user interfaces, and visual displays in human–machine systems, particularly in light of the recent trend of increased female participation in the industrial sector. Future research will focus on diverse types of visual information to further validate these findings. Full article

Cutibacterium acnes is linked to the prevalent inflammatory skin disorder known as Acne Vulgaris (AV). Some topical agents exhibit unfavorable side effects like dryness and skin inflammation, and antimicrobial resistance (AMR) poses an increasing risk to effective AV management. This study develops and characterizes stable topical essential oil (EO)-loaded microemulgels with in vitro validated antimicrobial activities against C. acnes ATCC 6919, providing a solid scientific basis for their effectiveness. These microemulgels, with their potential to serve as an alternative to AMR-prone synthetic agents, could revolutionize the field of acne treatment. The MICs of the EOs (citronella, tea tree, and lemongrass) against C. acnes were determined. EO-loaded microemulgels were developed using a blend of microemulsion and carbopol/hyaluronic acid gel in a ratio of 1:1 and characterized, and their stability was observed over three months. The MICs of citronella, tea tree, and lemongrass EOs were 0.08, 0.16, and 0.62% v/v, respectively. The microemulgels were whitish and smooth, with characteristic EO odors. They demonstrated pH values ranging between 4.81 ± 0.20 and 5.00 ± 0.03, good homogeneity, a spreadability of 9.79 ± 0.6 and 12.76 ± 0.8 cm², a viscosity of 29,500 and 31,130 cP, and retained stability at 4, 25, and 40 °C. EO-loaded microemulgels were developed with the potential of C. acnes management. The formulation shows adequate potential for further pharmaceutical development towards translational adoption in acne management. Full article

Objectives: Anisakidosis is an emerging, cosmopolitan, and underdiagnosed parasitic disease caused by the accidental ingestion of third-stage anisakid larvae when consuming raw or improperly prepared seafood. Within hours to days of consuming infected raw seafood, patients may develop acute gastrointestinal symptoms including pain, nausea, vomiting, diarrhea and/or constipation, as live anisakid larvae attach to the gastric, or more rarely, the intestinal mucosa. Cases have been reported in which the nematodes succeed at migrating from the stomach upwards to the esophagus and then the oral cavity. Therefore, the purpose of the present literature review is to collect, analyze, summarize and present the relevant epidemiological, clinical, diagnostic, parasitological, therapeutic, and prognostic data concerning anisakidosis localized inside the oral cavity. Methods: An electronic search of the PubMed, Scopus, and Ovid databases was performed with them being accessed for the last time on 29 March 2025. Results: The present literature review identified 13 individual case reports of oral mucosa anisakidosis, which were published in the period 1971–2022. Conclusions: Our review aims to summarize the relevant epidemiological, clinical, diagnostic, parasitological, therapeutic, and prognostic data regarding the oral localization of anisakidosis, a helminthic infection caused by the accidental ingestion of live anisakid larvae and which manifests mainly with gastrointestinal symptoms. Its localization in the oral mucosa appears to be exceptionally rare and, in most cases, occurs with a characteristic clinical picture, defined by the onset of acute mouth or throat pain immediately after the consumption of raw seafood and by the observation of one or more larvae, either lying on or penetrating the oral mucosa. Despite its rarity, dental health professionals and other clinicians should be aware of this disease and the possibility of its intraoral localization, since environmental factors on the one hand, and the adoption of foreign dietary habits on the other, will likely make anisakidosis a much more common disease worldwide in the near future. Full article

This article analyzes the work of Fr. Elias Friedman, whose legacy of theological work on Jewish identity and Jewish conversion to Catholicism serves as the foundation of the Association of Hebrew Catholics, of which he is the founder. Friedman frames his work as a sensitive approach to Jewish identity and Catholic faith, but as this paper demonstrates, his work reveals a reiteration of some of the most entrenched and historically devastating tropes of Christian anti-Judaism, as well as racial antisemitism. This article presents three main arguments. First, it demonstrates that Friedman’s work evidences a theological anti-Judaism characteristic of Catholicism prior to the Second Vatican Council, which he maintained firmly even after the theological revision of Vatican II rejected such views; and furthermore, that his work also expresses an antisemitism that reflects the modern racial antisemitism adopted by the Nazi regime. Second, this article examines the positive reception of Friedman’s work, as evidenced not only in the revered position he holds within the Association for Hebrew Catholics, but also by the nihil obstat and imprimatur on both of Friedman’s monographs, that is, the official stamp of ecclesiastical approval within the Catholic Church, which declares that the work is “free of doctrinal and moral error.” It proposes that these factors evidence the uncritical reception of his work not only within the Association of Hebrew Catholics, but also on behalf of the institutional Catholic Church. Third, it raises the question of the extent to which Friedman’s identity as a Jewish convert to Catholicism is relevant in the analysis and reception of his work. It argues that his Jewish identity makes his concoction of religious anti-Judaism and racial antisemitism particularly potent, rendering anodyne even the most virulently antisemitic of his statements. Full article