Lithium Niobate-Based MMI-MZI Electro-optic Switches with Lower Phase Shift Voltage

Outcome of very high-risk patients treated by Sotrovimab for mild-to-moderate COVID-19 Omicron, a prospective cohort study (the ANRS 0003S COCOPREV study)

Increasing vehicle manufacturers has led to a considerable rise in the automotive industry's competitiveness. Various research projects are being developed to devise process optimization strategies using virtual simulation. The paper discusses the importance of applying modeling and simulation strategies to optimize automotive manufacturing processes to meet the demands of a constantly evolving market. The aim is to demonstrate a strategy divided into a few stages to optimize the process of validating new projects applied in the automotive industry. The methodology employed was computer modeling and simulation, which enabled identifying and eliminating risks inherent in the validation process, thereby reducing the time required to complete new projects. The results demonstrated that implementing this strategy facilitated an efficient transition of the validation process, maintaining the quality and safety standards required in the cell. Consequently, it can be posited that the methodology developed is an efficacious instrument for expediting the implementation of novel processes in automotive manufacturing, thereby contributing to the competitiveness and innovation of the industry in the automotive segment. Received: 24 October 2024 | Revised: 10 December 2024 | Accepted: 9 January 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement William Aparecido Celestino Lopes: Conceptualization, Methodology, Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Adilson Cunha Rusteiko: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Cleiton Rodrigues Mendes: Conceptualization, Methodology, Software, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization. Nicolas Vinicius Cruz Honório: Conceptualization, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization. Marcelo Tsuguio Okano: Validation, Formal analysis, Data curation, Supervision, Project administration.

Unemployment is a significant macroeconomic issue that affects not only economic development but also the social and psychological well-being of individuals and families. In Vietnam, growing attention has been given to the potential disparity in unemployment rates between men and women, as such differences may reflect structural inequalities in the labor market. This study applies the ARIMA (AutoRegressive Integrated Moving Average) model to forecast gender-specific unemployment rates using official data from 2011 to 2023. The results reveal that although there are fluctuations in unemployment rates for both genders throughout the observed period, the differences are relatively small and not statistically significant. Nonetheless, even minor disparities may suggest the existence of unequal access to employment opportunities, particularly for women in certain regions or sectors. Based on these findings, the report proposes several policy recommendations aimed at fostering a more inclusive labor market. These include expanding access to vocational education and skills development programs for women, implementing gender-sensitive employment policies, and encouraging both public and private sectors to adopt equitable recruitment practices. Furthermore, the enhancement of labor market monitoring systems and the continued use of forecasting tools like ARIMA can support policymakers in anticipating changes and designing timely, data-driven responses. Overall, this study contributes to a deeper understanding of gender-related employment trends in Vietnam and provides practical insights for government agencies, researchers, and policy planners working to promote equal labor market participation and reduce unemployment across all segments of the population. Received: 23 September 2024 | Revised: 18 March 2025 | Accepted: 4 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Quang Phung Duy: Conceptualization, Methodology, Writing — review & editing, Supervision, Project administration. Huyen Linh Nguyen: Methodology, Formal analysis, Investigation, Data curation, Writing — original draft, Writing — review & editing, Project administration. Tra My Dinh Thi: Software, Validation, Formal analysis, Data curation, Writing — original draft, Writing — review & editing, Project administration. Ha Anh Ta Thi: Investigation, Writing — original draft, Writing — review & editing, Visualization, Project administration. Phuong Thuy Nguyen: Investigation, Resources, Data curation, Writing — original draft, Writing — review & editing, Project administration. Trung Kien Hoang: Investigation, Resources, Writing — original draft, Writing — review & editing, Project administration.

We appreciate the attention to our article and the thoughtful comments provided. This study was intended as a first step toward exploring a novel approach to keloid treatment rather than relying solely on conventional conservative options. Regarding the small sample size, our aim was to assess feasibility and preliminary efficacy in a pilot setting. At the time, there was limited scientific background supporting this combination, and based on prior experience, we anticipated challenges in convincing patients to try the novel procedure. For this reason, we preferred to conduct a smaller study rather than abandon the idea, viewing it as a starting point for future work. The limited sample size was clearly acknowledged in our discussion. We agree that reporting effect sizes would have strengthened interpretation, and this is an important consideration for subsequent studies. When designing the trial, the literature on hyaluronidase in keloid treatment was minimal. To our knowledge, the only relevant research was by Goyal et al. [1], which evaluated the triple combination of steroid, 5-FU, and hyaluronidase. We adopted this as the reference for our comparison. We acknowledge that this design does not allow isolation of hyaluronidase's independent effect and explicitly noted this in our discussion. As for blinding, participants became aware of their allocation because of the presence or absence of lidocaine in the injections. Before enrollment, they were informed of the components in each group. Since lidocaine substantially reduces procedural pain, its absence or presence inevitably revealed the group. We prioritized minimizing patient discomfort over complete blinding. To reduce bias in objective measures, a blinded dermatologist documented outcomes. Nevertheless, we recognize that subjective measures such as pain and pruritus may be influenced by perception, and some degree of bias is inevitable. In future studies, standardizing the amount or route of anesthetic administration may help minimize this effect. The short follow-up period was another deliberate choice. In interventional scar studies, long-term follow-up is crucial; however, with a small cohort, the risk of missing data from patient dropout was considerable. We therefore opted for a shorter follow-up to ensure complete data collection, which we achieved. We recognize the limitations noted in the letter—including small sample size, short follow-up, partial blinding, and potential confounding from multiple agents in the intervention group—and we reflected these in our discussion. Our aim was not to draw definitive conclusions, but to highlight the possible promising role of hyaluronidase in keloid management. We hope this pilot study will encourage larger, more rigorous trials to validate and expand on our findings, and we appreciate the shared commitment to advancing research in this field. Ala Ehsani: data curation, investigation, formal analysis. Fatemeh Lotfi: investigation, formal analysis, data curation, writing – original draft, writing – review and editing. Alireza Firooz: conceptualization, supervision, project administration, writing – review and editing. Amirhoushang Ehsani: conceptualization, writing – review and editing, project administration, supervision. Zahra Razavi: methodology, formal analysis, supervision, validation, writing – original draft. Mahshid Sadat Ansari: methodology, validation, formal analysis, supervision, writing – review and editing. Pedram Nourmohammadpour: conceptualization, supervision, writing – review and editing. Fatemeh Sima: data curation, formal analysis, methodology. Mina Koohian Mohammadabadi: data curation, methodology, writing – original draft. Amirhossein Rahimnia: conceptualization, investigation, methodology, project administration, writing – original draft, writing – review and editing. All authors have read and approved the final version of the manuscript. The authors received no specific funding for this work. The authors declare no conflicts of interest. The lead author Amirhossein Rahimnia affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. The data that support the findings of this study are available from the corresponding author upon reasonable request. The corresponding author had full access to all of the data in the original study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.

This study investigates the effectiveness of embedding fiber Bragg grating (FBG) sensors in power transmission towers to assess the remaining service life of the structures following impacts from strong winds and hurricanes. FBG sensors monitor the structural integrity of the tower using online measurement of strain variations at critical structural points. The novelty of this work lies in employing a compact long short-term memory (LSTM) framework to estimate the remaining useful lifetime (RUL) from real-time FBG sensor data under both stable and fluctuating wind conditions. To estimate RUL of the tower, LSTM neural network has been implemented, providing predictive insights for proactive maintenance and risk mitigation. A prototype transmission tower was built and experimentally evaluated in a wind tunnel to assess the effectiveness and performance of the proposed RUL model. To simulate different hurricane categories, the experiment was conducted across wind speeds between 0 and 150 mph. FBG sensors installed at critical locations continuously captured real-time strain data, which was transmitted via a low-power micro FBG interrogator to a computer for input into the RUL prediction model. The proposed three-layer LSTM converges rapidly, reducing training and validation loss by nearly two orders of magnitude within 40 epochs, and achieves robust RUL predictions with an average bias of about 50 s on the test set. To quantify structural health, a mathematical health indicator was formulated based on the observed strain responses. The FBG sensors demonstrated high effectiveness in accurately detecting strain variations and monitoring the tower's dynamic behavior under extreme wind loads. The findings support the implementation of condition-based maintenance strategies, enhance safety assessments, and enable early failure detection. This approach not only improves operational reliability but also facilitates timely intervention and maintenance during critical events. Received: 26 June 2025 | Revised: 12 September 2025 | Accepted: 11 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Yu Shi: Methodology, software, formal analysis, data curation, writing – original draft, writing – review & editing, visualization, supervision, and project administration. Abolghassem Zabihollah: Conceptualization, methodology, software, validation, investigation, resources, data curation, writing – original draft, writing – review & editing, visualization, supervision, and project administration. Yao-Chi Yu: Methodology, software, formal analysis, data curation, and writing – original draft. Arunima Pathak: Investigation, writing – original draft, and visualization. Oluwaseyi Oyetunji: Investigation.

Tomatoes are one of the most widely grown horticultural crops but are highly susceptible to leaf diseases that could significantly affect yield and quality. Early and accurate disease detection is necessary to enhance crop productivity and food security. The present study presents a deep learning system for automatic tomato leaf disease classification using a weighted ensemble of EfficientNetV2 models. Seven variants of EfficientNetV2 (B0-B3, S, M, and L) were fine-tuned on an augmented version of the filtered PlantVillage dataset. To improve feature visibility under varying lighting conditions, images were preprocessed using contrast-limited adaptive histogram equalization. Five optimization algorithms, which include Adam, Adamax, AdamW, Nadam, and RMSProp, were tested to assess their impact on model convergence and generalization. Among individual models, Adam-optimized EfficientNetV2L performed the best in accuracy with a measure of 99.50%. For classifiability resilience improvement, 16 ensemble configurations (eight unweighted and eight weighted ensembles using different combinations of EfficientNetV2-S, M, and L variants and five optimizers) were explored, with the best weights discovered using brute-force grid search. The best-performing weighted ensemble achieved 99.89% in accuracy, precision, recall, and F1-score, which was evaluated on a held-out test set not used during training or validation, demonstrating its strong generalizability. The proposed framework offers a scalable and reliable solution for early tomato disease detection with significant potential for real-time plant health monitoring in precision agriculture. Received: 10 September 2025 | Revised: 22 December 2025 | Accepted: 6 February 2026 Conflicts of InterestThe authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Aliyu Tetengi Ibrahim: Conceptualization, Methodology, Formal analysis, Writing – original draft. Ibrahim Hayatu Hassan: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – original draft. Mohammed Abdullahi: Validation, Formal analysis, Resources, Data curation, Writing – review & editing, Visualization. Abeer Rashad Mirdad: Conceptualization, Software, Validation, Investigation, Writing – original draft, Writing – review & editing, Supervision, Project administration. Muhammad I. Khan: Methodology, Validation, Investigation, Resources, Data curation, Writing – review & editing, Visualization, Project administration. Saeed Ali Bahaj: Conceptualization, Software, Writing – original draft. Fatima Khan Nayer: Investigation, Visualization, Supervision, Project administration.

Malware forensic investigation plays a critical role in cybersecurity, aiming to unveil malicious activities, decipher their tactics, and bolster defense mechanisms. This article introduces an innovative approach to malware forensic investigation, harnessing the capabilities of the Gray Wolf Optimization (GWO) algorithm in conjunction with a range of machine learning classifiers. These classifiers include naive Bayes, random forests (RF), decision trees, support vector machines, and K-nearest neighbors. The study leverages the CIC-MalMem-2022 dataset, which comprises memory-based data, and employs the Python programming language for model development. Research findings highlight the superiority of the RF classifier, achieving an impressive 75.6% accuracy in a multiclass classification scenario involving 16 classes. Notably, our proposed approach consistently exhibits higher accuracy when compared to existing models applied to different datasets, reaching 99.2% in binary classification. Furthermore, on the same dataset, our model outperforms the competition by achieving 86.34% and 75.64% accuracy in multiclass classification scenarios involving four classes and 16 classes, respectively. These results underscore the promising potential of our proposed model in the domain of malware forensic investigation, particularly when analyzing data extracted from memory. By combining the strength of the GWO algorithm with RF, this study aids in the progression of robust and accurate malware forensic investigation methods, thereby enhancing cybersecurity efforts in an ever-evolving threat landscape. Received: 20 December 2024 | Revised: 10 March 2025 | Accepted: 3 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Mosleh Mohammd Abualhaj: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing, Supervision. Sumaya Al-Khatib: Conceptualization, Software, Validation, Resources, Data curation, Writing – original draft, Visualization. Nida Al Shafi: Software, Validation, Resources, Data curation, Writing – review & editing. Iyas Qaddara: Methodology, Formal analysis, Writing – original draft, Visualization. Abdallah Hyassat: Methodology, Formal analysis.

In modern life, the easy access to the internet and social media has become almost essential. Although online social media channels are good means of communication, their frequency of cyberbullying and harassment creates problems as well. On these sites, cyberbullying takes many forms that cause targeted victims to experience anxiety, guilt, and despair. With an eye on demographic factors and personal experiences of victimizing, this study explores the degree and impressions of cyberbullying within a Thai sample community. By means of a thorough questionnaire survey, important new perspectives on respondents’ awareness of cyberbullying, its underlying causes, and coping mechanisms for such events were obtained. Analyzing demographic data in tandem with cyberbullying experiences found a clear correlation between social media use length and the likelihood of running into cyberbullying. Moreover, the work suggests the creation of a customized natural language processing system specifically designed to identify Thai language cyberbullying events, therefore attaining an interesting accuracy rate of 84.23%. The study also points out the possible market chances for commercializing this method, implying it may be a profitable investment. These results provide a fresh understanding of language processing strategies for efficient content analysis on several internet platforms. Received: 16 September 2024 |Revised: 12 November 2024 | Accepted: 20 December 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Bello Musa Yakubu: Conceptualization, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization, Funding acquisition. Worapong Bumrungsri: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Pattarasinee Bhattarakosol: Conceptualization, Methodology, Formal analysis, Resources, Data curation, Writing – review & editing, Supervision, Project administration, Funding acquisition.

The increasing demand for compact, high-performance antennas capable of supporting multiple wireless communication standards has driven the development of multiband microstrip antennas. This research presents the design, simulation, and optimization of a multiband microstrip patch antenna operating at 2.4 GHz, 3.5 GHz, and 5.3 GHz, targeting applications in Wi-Fi and WLAN systems. The antenna structure is designed and analyzed using CST Microwave Studio, leveraging its full-wave 3D electromagnetic solver to evaluate key performance metrics including reflection coefficient (S11), gain, bandwidth, and radiation characteristics. To enhance the antenna's performance and reduce the design iteration cycle, support vector regression (SVR), a supervised machine learning technique, is employed. SVR models the nonlinear relationship between the antenna's geometric parameters and its performance outcomes, enabling efficient prediction and optimization. A dataset of 1844 samples is generated through parametric simulations in CST, and the SVR model—using a radial basis function kernel with C = 300, ε = 0.00000000025, and γ = 0.5—is trained to predict return loss and gain across the three target frequencies. The optimized antenna design achieves improved impedance matching, gain enhancement, and bandwidth control at all three frequency bands. Power transfer efficiency exceeds 96% in each band. The results demonstrate that the integration of SVR into the antenna design workflow provides a robust, data-driven approach to achieving multiband performance with high efficiency, making it suitable for next-generation wireless communication systems. Received: 11 August 2025 | Revised: 15 October 2025 | Accepted: 27 November 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Nejat Abdulwahid Hassen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Visualization, Supervision. Adomeas Asfaw Tafere: Conceptualization, Methodology, Software, Validation, Resources, Data curation,Writing - review & editing, Visualization,Supervision. Murad Ridwan Hassen: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Writing - original draft, Visualization, Supervision, Project administration. Tsega Asresa Mengistu: Conceptualization, Software, Validation, Resources, Writing - review & editing, Visualization, Supervision. Mekete Asmare Huluka: Conceptualization, Software, Validation, Resources, Writing - review & editing, Visualization, Supervision. Amsalu Tessema Adgeh: Conceptualization, Software, Formal analysis, Resources, Writing - review & editing, Visualization.

The promotion of low-carbon and ecologically friendly economic growth is widely accepted worldwide. The low-carbon city pilot policy was introduced by the Chinese government in three batches between 2010 and 2017 to address climate change. We use panel data from 277 Chinese cities from 2009 to 2019 to investigate the link between urban carbon emissions and the low-carbon city pilot policy. To alleviate the endogeneity brought on by sample selection bias, we utilize a combination of propensity score matching and the difference-in-difference model in our causal inference technique. The results demonstrate a noteworthy decrease in urban carbon emissions subsequent to the implementation of the policy; Through optimizing industrial structure, substituting clean energy, and innovating green technologies, this approach lowers urban carbon emissions; In addition, heterogeneity analysis results indicate that the carbon reduction effects in eastern and central cities are more significant than that in western cities. Finally, we provide policy recommendations on how to reduce urban carbon emissions. Received: 4 March 2024 | Revised: 7 June 2024 | Accepted: 12 July 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Xinyu Liu: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Visualization, Project administration. Mengya Li: Conceptualization, Software, Formal analysis, Investigation, Resources, Writing - original draft, Visualization. Chengjing Wang: Conceptualization, Validation, Formal analysis, Investigation, Resources, Writing - original draft, Visualization. Ping Lu: Writing - review & editing, Supervision, Project administration, Funding acquisition.

In this paper we study the personalized book recommender system in a child–robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. The feedback is processed by converting the user's reaction into accept or reject. According to children's fuzzy language input, the proposed method gives the predicted interests, which is based on a common set of interests related to children's literature. Thirdly, the domain specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Additional phrases are extended to broaden the user's search range. Experimental results show that the proposed recommender system has an improved performance compared with the baseline results. Due to the algorithm efficiency design, it can operate on embedded consumer devices with limited computational resources. Received: 15 March 2023 | Revised: 11 April 2023 | Accepted: 28 April 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are not publicly available due to involvement with commercial products. Author Contribution Statement Yun Liu: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Tianmeng Gao: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Baolin Song: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Chengwei Huang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.

For usage in solid-state white light-emitting device applications, dysprosium-activated calcium aluminum barium sodium borate glass was synthesized. The detailed understanding of physical, optical, structural, and photoluminescence characteristics was examined and compared. To comprehend the oscillator strength of the ligands in the prepared glasses, the Judd–Ofelt intensity characteristics were assessed. The Ω2 greater relative oscillator strength value shows highest for the prepared glasses suggesting their cause due to hypersensitive transitions. In comparison to other glasses in the research, the potential radiative transition probabilities, branching ratios, and stimulated emission cross-section for 575 nm emission all exhibited higher values. The stimulated emission cross-section shows highest for 0.3 mol% Dy2O3 content in the present glasses. The asymmetry of synthesized glass sample ratio has been evaluated and compared using yellow to blue (Y/B) emission ratio. Study of the emission spectra and assessment of co-related color temperature and DUV values for the glasses reveal emission in the CIE diagram's white light zone. Received: 8 December 2023 | Revised: 1 March 2024 | Accepted: 4 March 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Basavaraj Gurav: Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. G. B. Devidas: Validation, Project administration. Ashok Dinkar: Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing. Darya Pavlovna Surzhikova: Software, Investigation. R. Rajaramakrishna: Conceptualization, Methodology, Validation, Writing – review & editing, Supervision, Project administration.

This study investigates electron-atom scattering within linearly polarized laser field, employing the first-order Born approximation and perturbation theory for target dressing. The Hamiltonian integrates both unbound electron and atomic target in the laser field, with the electron's wave functions described by Gordon-Volkov waves. Elastic scattering for hydrogen atoms in various states (1s, 2s, 3s) is examined to determine differential cross-sections (DCS) and energy shifts. To study the nature of DCS and energy shifting, electric field strengths (1 to 4 a.u.), Bessel function orders (1 to 4), scattering angles (0 to 30 degrees), and laser energy (1.17 eV) are used in this research. Results indicate higher laser field strengths correlate with lower energy shifts, significantly impacting scattering dynamics, while DCS values decrease with increased scattering angles and higher Bessel function orders. The transitions involving higher orbital states show broader momentum ranges and smoother DCS decay. These findings elucidate the interplay between atomic structure, incident energy, and scattering characteristics, offering valuable insights for applications in plasma physics and materials science by aiding precise modeling of atomic interactions under varying conditions. Received: 29 July 2024 | Revised: 13 September 2024 | Accepted: 13 December 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Raju Bohora: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Visualization. Kishori Yadav: Validation, Writing - review & editing, Supervision, Project administration. Saddam Husain Dhobi: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Visualization.

The transition from product-centric to service-driven paradigms in cloud computing has enabled the on-demand provisioning of scalable resources. However, the extensive operational complexity and interdependent infrastructure of cloud environments make them highly susceptible to failures, posing significant challenges to ensuring robust fault tolerance in dynamic and distributed systems. This study introduces a novel Hybrid Value-Policy Double Deep Q-Learning (DDQL) Scheduling approach, integrating value-based DDQL with policy-based reinforcement learning techniques, which are Proximal Policy Optimization and Soft Actor-Critic, to enhance fault-tolerant scheduling dynamically. Combining the advantages of both learning paradigms, the proposed method improves decision-making efficiency, enhances system resilience against failures, and optimizes scheduling performance in real-time cloud environments. Improved mean time between failures, mean time to recovery, failure recovery time, and fault tolerance success rate are guaranteed by the Hybrid DDQL scheduler's dynamic adjustments to scheduling and resource allocation algorithms. Also, it reduces the ratio of task deadline miss, energy consumption, and response time and maximizes the throughput. These results confirm the flexibility and strength of the model in dynamic and unreliable real-time cloud computing systems. Received: 27 May 2025 | Revised: 30 September 2025 | Accepted: 16 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Chetankumar Kalaskar: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Thangam Somasundaram: Methodology, Validation, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.

This study investigates the optical and dielectric properties of nitrile butadiene rubber (NBR) reinforced with varying concentrations of N220 carbon black (CB). Ultraviolet (UV)–visible spectroscopy, in the range of 200–800 nm, was used. The optical spectrum showed a high absorption peak at approximately 220 nm, indicating that the material is suitable as a strong absorber of ultraviolet radiation. The direct optical band gap decreases slightly from 5.55 eV to 5.34 eV as CB content increases. The Urbach energy values, around 1.5 eV, suggest the formation of structural disorder and band tail states induced by CB–rubber interfacial interactions. The optical parameters such as extinction coefficient and refractive index exhibit characteristic variations around 220 nm, corresponding to strong absorption and high photon–carrier coupling. Dispersion behavior, interpreted through the Wemple–DiDomenico and Sellmeier models, reveals consistent variations in the effective oscillator energy, dispersion energy, and oscillator length strength. Dielectric analysis demonstrates that both the static and high-frequency dielectric constants are sensitive to CB loading, while the real part of the dielectric constant dominates over the imaginary component across the spectrum. Generally, the optical and dielectric findings demonstrate that the incorporation of N220 CB substantially modifies the energy band structure, optical dispersion, and charge carrier dynamics of NBR composites. These characteristics suggest their promising potential for optoelectronic and UV-sensing applications, where tunable optical absorption and dielectric response are critical. Received: 24 August 2025 | Revised: 10 November 2025 | Accepted: 23 December 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement K. Alfaramawi: Conceptualization, Formal analysis, Resources, Writing – review & editing. S. Abboudy: Formal analysis, Resources, Data curation, Writing – review & editing, Visualization, Supervision, Project administration. L. Abulnasr: Formal analysis, Investigation, Data curation, Writing – review & editing. S. Ebrahim: Investigation, Writing – review & editing. Ahlam K. Yousif: Methodology, Data curation, Writing – original draft.