Search Results (11)

Interferon (IFN) signaling plays vital roles in host defense against viral infection. However, a variety of observations have been reported in the literature regarding the roles of IFN signaling in COVID-19. Thus, it would be important to reach a clearer picture regarding the activation or suppression of IFN signaling in COVID-19. In this work, regulation of marker genes for IFN signaling was examined in natural infection, viral challenge, and vaccination based on 13 public transcriptome datasets. Three subsets of interferon-stimulated genes (ISGs) were selected for detailed examination, including one set of marker genes for type I IFN signaling (ISGa) and two sets of marker genes for type II IFN signaling (IFN-γ signaling, GBPs for the GBP gene cluster, and HLAd for the HLA-D gene cluster). In natural infection, activation of ISGa and GBPs was accompanied by the suppression of HLAd in hospitalized patients. Suppression of GBPs was also observed in certain critical conditions. The scale of regulation was much greater for ISGa than that of GBPs and HLAd. In addition, the suppression of HLAd was correlated with disease severity, and it took much longer for HLAd to return to the level of healthy controls than that for ISGa and GBPs. Upon viral challenge, the activation of ISGa and GBPs was similar to that of natural infection, while the suppression of HLAd was not observed. Moreover, GBPs’ return to the pre-infection level was at a faster pace than that of ISGa. Upon COVID-19 vaccination, activation was observed for all of these three gene sets, and the scale of activation was comparable for ISGa and GBPs. Notably, it took a much shorter time for GBPs and ISGa to return to the level of healthy controls than that in COVID-19 infection. In addition, the baseline values and transient activation of these gene sets were also associated with subsequent vaccination response. The intricate balance of IFN signaling was demonstrated in mild breakthrough infection, where attenuated response was observed in people with prior vaccination compared to that in vaccine-naïve subjects. Overall, distinctive temporal profiles of IFN signaling were observed in natural infection, viral challenge, and vaccination. The features observed in this work may provide novel insights into the disease management and vaccine development. Full article

This study presents advanced control and energy management strategies for uncertain wind energy systems using a Takagi–Sugeno (T-S) fuzzy modeling framework. To address key challenges, such as system uncertainties, external disturbances, and input delays, the study integrates a fuzzy H∞ robust control approach with a neural network-based delay compensation mechanism. A fuzzy observer-based H∞ tracking controller is developed to enhance robustness and minimize the impact of disturbances. The stability conditions are rigorously derived using a quadratic Lyapunov function, H∞ performance criteria, and Young’s inequality and are expressed as Linear Matrix Inequalities (LMIs) for computational efficiency. In parallel, a neural network-based controller is employed to compensate for the input delays introduced by online learning processes. Furthermore, an energy management layer is incorporated to regulate the power flow and optimize energy utilization under varying operating conditions. The proposed framework effectively combines control and energy coordination to improve the systems’ performance. The simulation results confirm the effectiveness of the proposed strategies, demonstrating enhanced stability, robustness, delay tolerance, and energy efficiency in wind energy systems. Full article

This paper presents Feedback-Based Validation Learning (FBVL), a novel approach that transforms the role of validation datasets in deep learning. Unlike conventional methods that utilize validation datasets for performance evaluation post-training, FBVL integrates these datasets into the training process. It employs real-time feedback to optimize the model’s weight adjustments, enhancing prediction accuracy and overall model performance. Importantly, FBVL preserves the integrity of the validation process by using prediction outcomes on the validation dataset to guide training adjustments, without directly accessing the dataset. Our empirical study conducted using the Iris dataset demonstrated the effectiveness of FBVL. The Iris dataset, comprising 150 samples from three species of Iris flowers, each characterized by four features, served as an ideal testbed for demonstrating FBVL’s effectiveness. The implementation of FBVL led to substantial performance improvements, surpassing the accuracy of the previous best result by approximately 7.14% and achieving a loss reduction greater than the previous methods by approximately 49.18%. When FBVL was applied to the Multimodal EmotionLines Dataset (MELD), it showcased its wide applicability across various datasets and domains. The model achieved a test-set accuracy of 70.08%, surpassing the previous best-reported accuracy by approximately 3.12%. These remarkable results underscore FBVL’s ability to optimize performance on established datasets and its capacity to minimize loss. Using our FBVL method, we achieved a test set f1_score micro of 70.07%, which is higher than the previous best-reported value for f1_score micro of 67.59%. These results demonstrate that FBVL enhances classification accuracy and model generalization, particularly in scenarios involving small or imbalanced datasets, offering practical benefits for designing more efficient and robust neural network architectures. Full article

The development of emotionally intelligent computers depends on emotion recognition based on richer multimodal inputs, such as text, speech, and visual cues, as multiple modalities complement one another. The effectiveness of complex relationships between modalities for emotion recognition has been demonstrated, but these relationships are still largely unexplored. Various fusion mechanisms using simply concatenated information have been the mainstay of previous research in learning multimodal representations for emotion classification, rather than fully utilizing the benefits of deep learning. In this paper, a unique deep multimodal emotion model is proposed, which uses the meaningful neural network to learn meaningful multimodal representations while classifying data. Specifically, the proposed model concatenates multimodality inputs using a graph convolutional network to extract acoustic modality, a capsule network to generate the textual modality, and vision transformer to acquire the visual modality. Despite the effectiveness of MNN, we have used it as a methodological innovation that will be fed with the previously generated vector parameters to produce better predictive results. Our suggested approach for more accurate multimodal emotion recognition has been shown through extensive examinations, producing state-of-the-art results with accuracies of 69% and 56% on two public datasets, MELD and MOSEI, respectively. Full article

Background: Severe atopic dermatitis (AD) is a complex disease requiring systemic treatment. This study aimed to assess the effectiveness of combined therapy consisting of dupilumab and sublingual dust mite allergen immunotherapy (SLIT-HDM) in patients with severe AD and HDM allergies. Methods: Patients diagnosed with severe AD were included in this randomised, placebo-controlled, double-blind 12-month trial; they received SLIT for HDMs and/or dupilumab for 12 months and were compared with patients on cyclosporine. The primary outcomes for the treatment arms were changes in the Eczema Area and Severity Index (EASI), body surface area (%BSA), and Investigator Global Assessment (IsGA) over 12 months. The secondary outcomes were the proportion of patients who achieved IsGA success and reduced medication scores. Results: Significant improvements were observed in all analysed groups after 12 months of therapy based on the EASI, %BSA, and IsGA. However, the most substantial changes were observed in the groups treated with dupilumab or a combination of SLIT-HDM and dupilumab. Additionally, the proportion of patients who achieved an IsGA reduction was significantly greater in the group receiving combination therapy than in the other groups (9/14 [64% of the group receiving SLIT-HDM] vs. 11/14 [73% of the group receiving dupilumab] vs. 15/17 [88% of the group receiving dupilumab and SLIT-HDM] vs. 7/13 [53% of the group receiving cyclosporine]) (p < 0.05). Conclusions: In patients with severe AD and HDM allergies, combination treatment with dupilumab and allergen immunotherapy for HDMs may increase the therapeutic benefit over treatment with these methods separately. Full article

In cloud manufacturing environments, the scheduling of multi-user manufacturing tasks often fails to consider the impact of service supply on resource allocation. This study addresses this gap by proposing a bi-objective multi-user multi-task scheduling model aimed at simultaneously minimising workload and maximising customer satisfaction. To accurately capture customer satisfaction, a novel comprehensive rating index is introduced, integrating the actual completion cost, time, and processing quality against customer expectations. Furthermore, vehicle constraints are incorporated into the model to accommodate potential delays in transport vehicle availability, thereby enhancing its alignment with real-world manufacturing settings. The proposed mathematical model is solved using an improved three-stage genetic algorithm, which integrates the k-means algorithm and a real-time sequence scheduling strategy to optimise solution quality. Validation against alternative algorithms across various case scales demonstrates the efficacy of the approach in providing practical scheduling solutions for real-case scenarios. Full article

This work attempts to develop a novel framework to reveal the preferences of Chinese car users from online user-generated content (UGC) and guides automotive companies to allocate resources reasonably for sustainable design and improve existing product or service attributes. Specifically, a novel unsupervised word-boundary-identified algorithm for the Chinese language is used to extract domain professional feature words, and a set of sentiment scoring rules is constructed. By matching feature-sentiment word pairs, we calculate car users’ satisfaction with different attributes based on the rules and weigh the importance of attributes using the TF-IDF method, thus constructing an importance-satisfaction gap analysis (ISGA) model. Finally, a case study is used to realize the framework evaluation and analysis of the twenty top-mentioned attributes of a small-sized sedan, and the dynamic ISGA-time model is constructed to analyze the changing trend of the importance of user demand and satisfaction. The results show the priority of resource allocation/adjustment. Fuel consumption and driving experience urgently need resource input and management. Full article

This paper introduces a new tri-wideband fractal antenna for use in wireless communication applications. The fractal manufactured antenna developed has a Sierpinski hexagonal-shaped radiating element and a partial ground plane loaded with three rectangular stubs and three rectangular slits. The investigated antenna has a small footprint of 0.19λ₀ × 0.24 λ₀ × 0.0128 λ₀ and improved bandwidth and gain. According to the measurements, the designed antenna resonates throughout the frequency ranges of 2.19–4.43 GHz, 4.8–7.76 GHz, and 8.04–11.32 GHz. These frequency ranges are compatible with a variety of wireless technologies, including WLAN, WiMAX, ISM, LTE, RFID, Bluetooth, 5G spectrum band, C-band, and X-band. The investigated antenna exhibited good gain with almost omnidirectional radiation patterns. Utilizing CST MWS, the performance of the suggested Sierpinski hexagonal-shaped fractal antenna was achieved. The findings were then compared to the experimental results, which were found to be in strong agreement. Full article

Background and objectives: Allergen immunotherapy (AIT) is not a first-line therapy in atopic dermatitis (AD) and its effectiveness has been criticised. Objectives: The efficacy and safety of AIT in adult patients with AD and monosensitisation to house dust mites (HDMs) were investigated. Materials and Methods: A total of 37 patients were included in this double-blind, placebo-controlled study. Patients were eligible if they were diagnosed with AD; had moderate-to-severe AD according to the Eczema Area and Severity Index (EASI) with at least 7.1 points, the % BSA (body surface area) scale with at least 16 points, and the IsGA (investigator global assessment) scale with 3 points; had positive skin prick tests (SPTs); and were positive for the specific immunoglobulin E (sIgE) response to D. pteronyssinus and D. farinae extracts, as well as Der p 1 and Der f1. The patients received Purethal mites (20,000 AUeq/mL, HAL Allergy, Leiden, The Netherlands) with the extract allergens D. pteronyssinus and D. farinae (50/50%) or a placebo for 12 months. The primary outcomes included changes in EASI, % BSA, and IsGA due to SCIT between the start and after 12 months of therapy. Results: In the study group, significant improvement was observed in terms of the EASI score from 43 ± 8.2 to 21 ± 5.9 points, % BSA from 72 ± 18 to 28 ± 11 points, and IsGA from 4.5 ± 0.5 to 1.5 ± 0.5 points in comparison with the placebo after 1 year of AIT. Additionally, the proportion of patients who achieved success in the IsGA (IsGA < 2) was significantly better in comparison to the placebo with 13/20 (65%) vs. 4/14 (29%), respectively (p < 0.05). Conclusions: HDM-AIT effectively improved atopic dermatitis in patients that strictly qualified for desensitisation with a confirmed monovalent mite allergy. Full article

In the current complex financial world, paper currencies are vulnerable and unsustainable due to many factors such as current account deficit, gold reserves, dollar reserves, political stability, security, the presence of war in the region, etc. The vulnerabilities not limited to the above, result in fluctuation and instability in the currency values. Considering the devaluation of some Asian countries such as Pakistan, Sri Lanka, Türkiye, and Ukraine, there is a current tendency of some countries to look beyond the SWIFT system. It is not feasible to have reserves in only one currency, and thus, forex markets are likely to have significant growth in their volumes. In this research, we consider this challenge to work on having sustainable forex reserves in multiple world currencies. This research is aimed to overcome their vulnerabilities and, instead, exploit their volatile nature to attain sustainability in forex reserves. In this regard, we work to formulate this problem and propose a forex investment strategy inspired by gradient ascent optimization, a robust iterative optimization algorithm. The dynamic nature of the forex market led us to the formulation and development of the instantaneous stochastic gradient ascent method. Contrary to the conventional gradient ascent optimization, which considers the whole population or its sample, the proposed instantaneous stochastic gradient ascent (ISGA) optimization considers only the next time instance to update the investment strategy. We employed the proposed forex investment strategy on forex data containing one-year multiple currencies’ values, and the results are quite profitable as compared to the conventional investment strategies. Full article

This paper aims to design a compact broadband antenna for wireless local area network (WLAN) and worldwide interoperability for microwave access (WIMAX) applications. The suggested antenna consists of an octagonal radiator with Vicsek fractal slots and a partial ground plane, it is printed on FR-4 dielectric substrate, and its global dimension is 50 × 50 × 1.6 mm³. The antenna is designed and constructed using both CST MICROWAVE STUDIO^® and CADFEKO electromagnetic solver, and in order to validate the acquired simulation results, the antenna is manufactured and tested using vector network analyzer E5071C. The measurement results show that the designed antenna attains a broadband bandwidth (S₁₁ < −10 dB) from 2.48 to 6.7 GHz resonating at 3.6 and 5.3 GHz, respectively. The broadband bandwidth covers the two required bands: WiMAX at the frequencies 2.3/2.5/3.3/3.5/5/5.5 GHz and WLAN at the frequencies 3.6/2.4–2.5/4.9–5.9 GHz. In addition, the suggested antenna provides good gains of 2.78 dBi and 5.32 dBi, omnidirectional measured radiation patterns in the E-plane and the H-plane and high efficiencies of 88.5% and 84.6% at the resonant frequencies. A close agreement of about 90% between simulation and measurement results is noticed. Full article