Search Results (7,331)

In our current study, we developed a focused series of original ((benzyloxy)benzyl)propanamide derivatives that demonstrated potent activity across in vivo mouse seizure models, specifically, maximal electroshock (MES) and 6 Hz (32 mA) seizures. Among these derivatives, compound 5 emerged as a lead molecule, exhibiting robust protection following intraperitoneal (i.p.) injection, as follows: ED₅₀ = 48.0 mg/kg in the MES test, ED₅₀ = 45.2 mg/kg in the 6 Hz (32 mA) test, and ED₅₀ = 201.3 mg/kg in the 6 Hz (44 mA) model. Additionally, compound 5 displayed low potential for inducing motor impairment in the rotarod test (TD₅₀ > 300 mg/kg), indicating a potentially favorable therapeutic window. In vitro toxicity assays further supported its promising safety profile. We also attempted to identify a plausible mechanism of action of compound 5 by applying both binding and functional in vitro studies. Overall, the data obtained for this lead molecule justifies the more comprehensive preclinical development of compound 5 as a candidate for a potentially broad-spectrum and safe anticonvulsant. Full article

Mortality in children accounts for 15% of all AIDS-related deaths globally, with a higher burden among Cameroonian children (25%), likely driven by poor virological response. We sought to evaluate viral suppression (VS) and its determinants in a nationally representative paediatric and young adult population receiving antiretroviral therapy (ART). A cross-sectional and multicentric study was conducted among Cameroonian children (<10 years), adolescents (10–19 years) and young adults (20–24 years). Data were collected from the databases of nine reference laboratories from December 2023 to March 2024. A conditional backward stepwise regression model was built to assess the predictors of VS, defined as a viral load (VL) <1000 HIV-RNA copies/mL. Overall, 7558 individuals (females: 73.2%) were analysed. Regarding the ART regimen, 17% of children, 80% of adolescents and 83% of young adults transitioned to dolutegravir (DTG)-based regimens. Overall VS was 82.3%, with 67.3% (<10 years), 80.5% (10–19 years) and 86.5% (20–24 years), and p < 0.001. VS was 85.1% on a DTG-based regimen versus 80.0% on efavirenz/nevirapine and 65.6% on lopinavir/ritonavir or atazanavir/ritonavir. VS was higher in females versus males (85.8% versus 78.2%, p < 0.001). The VS rate remained stable around 85% at 12 and 24 months but dropped to about 80% at 36 months after ART initiation, p < 0.009. Independent predictors of non-VS were younger age, longer ART duration (>36 months), backbone drug (non-TDF/3TC) and anchor drug (non-DTG based). In this Cameroonian paediatric population with varying levels of transition to DTG, overall VS remains below the 95% targets. Predictors of non-VS are younger age, non-TDF/3TC- and non-DTG-based regimens. Thus, efforts toward eliminating paediatric AIDS should prioritise the transition to a DTG-based regimen in this new ART era. Full article

In the rapidly advancing domain of Intelligent Connected Vehicles (ICVs), multimodal Federated Learning (FL) presents a powerful methodology to harness diverse data sources, such as sensors, cameras, and Vehicle-to-Everything (V2X) communications, without compromising data privacy. Despite its potential, the presence of Byzantine adversaries–malicious participants who contribute incorrect or misleading updates–poses a significant challenge to the robustness and reliability of the FL process. This paper proposes a Byzantine-robust multimodal FL framework specifically designed for ICVs. Our framework integrates a robust aggregation mechanism to mitigate the influence of adversarial updates, a multimodal fusion strategy to effectively manage and combine heterogeneous input data, and a global optimization objective that accommodates the presence of Byzantine clients. The theoretical foundation of the framework is established through formal definitions and equations, demonstrating its ability to maintain reliable and accurate learning outcomes despite adversarial disruptions. Extensive experiments highlight the framework’s efficacy in preserving model performance and resilience in real-world ICV environments. Full article

Background/Objectives: We examined the feasibility, acceptability, and potential mental health impact of a digital peer support intervention involving videoconferencing and text-based support for parents of school-aged children living with T1D and analyzed posts exchanged by parents on a texting platform. Methods: Eighteen parents were recruited for Huddle4Parents, a 4-month digital intervention that involved four synchronous group-based Zoom sessions coupled with an asynchronous 24/7 peer support texting room. Primary outcomes were feasibility (i.e., ability to recruit n = 20 parents and retain at least 75%) and acceptability (i.e., satisfaction ratings of “good” to “very good”). Baseline and 4-month assessments also measured diabetes distress, quality of life, and perceived support. A content analysis of text exchanges was also performed. Results: All 15 parents who completed the intervention attended at least one Huddle and posted at least one message on the 24/7 peer support room. The retention rate was 83%, with 100% indicating that they would “definitely” or “probably yes” recommend both platforms to other parents. They also rated the topics, facilitator, and overall Huddles as “good” to “excellent.” No changes were observed for psychosocial endpoints. Of the 1084 texts posted, core support themes included the following: (1) dealing with technology and devices; (2) seeking and providing emotional support; (3) managing T1D in the school setting; and (4) exchanging tips and strategies. Conclusions: Huddle4Parents, a digital T1D caregiver intervention offering synchronous and asynchronous support, is feasible based on recruitment, participation, and attrition rates and acceptable as demonstrated by engagement and satisfaction ratings for the Huddles and 24/7 peer support room. Full article

Large language models (LLMs) are widely integrated into autonomous driving systems to enhance their operational intelligence and responsiveness and improve self-driving vehicles’ overall performance. Despite these advances, LLMs still struggle between hallucinations—when models either misinterpret the environment or generate imaginary parts for downstream use cases—and taxing computational overhead that relegates their performance to strictly non-real-time operations. These are essential problems to solve to make autonomous driving as safe and efficient as possible. This work is thus focused on symmetrical trade-offs between the reduction of hallucination and optimization, leading to a framework for these two combined and at least specifically motivated by these limitations. This framework intends to generate a symmetry of mapping between real and virtual worlds. It helps in minimizing hallucinations and optimizing computational resource consumption reasonably. In autonomous driving tasks, we use multimodal LLMs that combine an image-encoding Visual Transformer (ViT) and a decoding GPT-2 with responses generated by the powerful new sequence generator from OpenAI known as GPT4. Our hallucination reduction and optimization framework leverages iterative refinement loops, RLHF—reinforcement learning from human feedback (RLHF)—along with symmetric performance metrics, e.g., BLEU, ROUGE, and CIDEr similarity scores between machine-generated answers specific to other human reference answers. This ensures that improvements in model accuracy are not overused to the detriment of increased computational overhead. Experimental results show a twofold improvement in decision-maker error rate and processing efficiency, resulting in an overall decrease of 30% for the model and a 25% improvement in processing efficiency across diverse driving scenarios. Not only does this symmetrical approach reduce hallucination, but it also better aligns the virtual and real-world representations. Full article

This study deeply integrates multimodal data analysis and big data technology, proposing a multimodal learning framework that consolidates various information sources, such as user geographic location, behavior data, and product attributes, to achieve a more comprehensive understanding and prediction of consumer behavior. By comparing the performance of unimodal and multimodal approaches in handling complex cross-border e-commerce data, it was found that multimodal learning models using the Adam optimizer significantly outperformed traditional unimodal learning models in terms of prediction accuracy and loss rate. The improvements were particularly notable in training loss and testing accuracy. This demonstrates the efficiency and superiority of multimodal methods in capturing and analyzing heterogeneous data. Furthermore, the study explores and validates the potential of big data and multimodal learning methods to enhance customer satisfaction in the cross-border e-commerce environment. Based on the core findings, specific applications of big data technology in cross-border e-commerce operations were further explored. A series of innovative strategies aimed at improving operational efficiency, enhancing consumer satisfaction, and increasing global market competitiveness were proposed. Full article

The identification of gas leakages is a significant factor to be taken into consideration in various industries such as coal mines, chemical industries, etc., as well as in residential applications. In order to reduce damage to the environment as well as human lives, early detection and gas type identification are necessary. The main focus of this paper is multimodal gas data that were obtained simultaneously by using multiple sensors for gas detection and a thermal imaging camera. As the reliability and sensitivity of low-cost sensors are less, they are not suitable for gas detection over long distances. In order to overcome the drawbacks of relying just on sensors to identify gases, a thermal camera capable of detecting temperature changes is also used in the collection of the current multimodal dataset The multimodal dataset comprises 6400 samples, including smoke, perfume, a combination of both, and neutral environments. In this paper, convolutional neural networks (CNNs) are trained on thermal image data, utilizing variants such as bidirectional long–short-term memory (Bi-LSTM), dense LSTM, and a fusion of both datasets to effectively classify comma separated value (CSV) data from gas sensors. The dataset can be used as a valuable source for research scholars and system developers to improvise their artificial intelligence (AI) models used for gas leakage detection. Furthermore, in order to ensure the privacy of the client’s data, this paper explores the implementation of federated learning for privacy-protected gas leakage classification, demonstrating comparable accuracy to traditional deep learning approaches. Full article

A critical element of neurological function is eye–hand coordination: the ability of our vision system to coordinate the information received through the eyes to control, guide, and direct the hands to accomplish a task. Recent evidence shows that this ability can be disturbed by strokes or other neurological disorders, with critical consequences for motor behaviour. This paper presents a system based on serious games and multimodal devices aimed at improving the assessment of eye–hand coordination. The system implements gameplay that involves drawing specific patterns (labyrinths) to capture hand trajectories. The user can draw the path using multimodal devices such as a mouse, a stylus with a tablet, or robotic devices. Multimodal input devices can allow for the evaluation of complex coordinated movements of the upper limb that involve the synergistic motion of arm joints, depending on the device. A preliminary test of technological validation with healthy volunteers was conducted in the laboratory. The Dynamic Time Warping (DTW) index was used to compare hand trajectories without considering time-series lag. The results suggest that this multimodal framework allows for measuring differences between fine and gross motor skills. Moreover, the results support the viability of this system for developing a high-resolution metric for measuring eye–hand coordination in neurorehabilitation. Full article

Well placement optimization is one of the most important means to control the decline of oilfields and improve the recovery rate in the development process of deep and heterogeneous reservoirs, such as deep buried carbonate oil reservoirs. However, the mapping relationship from deployed well positions to actual profits is non-linear and multi-modal. At the same time, the injection and production relationship of new wells also affects the contribution of well positions to final profits. Currently, common algorithms include gradient-based and heuristic non-gradient algorithms, which have advantages, but face problems of high computational complexity, slow optimization speed, and difficulty in convergence. We propose an evolutionary algorithm for well placement optimization in carbonate reservoirs. This algorithm improves well placement optimization and computational speed by constraining the sampling process to effective sampling spaces, integrating prior knowledge to enhance sampling efficiency, strengthening local optima exploration, and utilizing parallel computing. Additionally, it refines the optimized variable content based on actual control factors, enhancing the algorithm’s robustness in practical applications. A case study from a carbonate reservoir in northwestern China demonstrated that this algorithm not only improved the performance by 50% compared to the classic DE algorithm but also achieved 15% higher optimization effectiveness than the current state-of-the-art algorithms. Full article

Intramedullary spinal cord glioblastoma (ISCG) is lethal due to lack of effective treatment. We previously established a rat C6-ISCG model and the antitumor effect of F3.CD-TK, an hNSC line expressing CD and TK, via producing cytocidal 5FU and GCV-TP. However, the neurotherapeutic potential of this hNSC approach has remained uninvestigated. Here for the first time, cultured F3.CD-TK cells were found to have a markedly higher oncolytic effect, which was GJIC-dependent, and BDNF expression but less VEGF secretion than F3.CD. In Rowett athymic rats, F3.CD-TK (1.5 × 10⁶ cells/10 µL × 2), injected near C6-ISCG (G55 seeding 7 days earlier: 10 K/each) and followed by q.d. (×5/each repeat; i.p.) of 5FC (500 mg/kg/5 mL/day) and GCV (25 mg/kg/1 mL/day), robustly mitigated cardiorespiratory, locomotor, and sensory deficits to improve neurofunction and overall survival compared to animals receiving either F3.CD or F3.CD-TK+F3.CD debris formula. The F3.CD-TK regimen exerted greater tumor penetration and neural inflammation/immune modulation, reshaped C6-ISCG topology to increase the tumor’s surface area/volume ratio to spare/repair host axons (e.g., vGlut1+ neurites), and had higher post-prodrug donor self-clearance. The multimodal data and mechanistic leads from this proof-of-principle study suggest that the overall stronger anti-ISCG benefit of our hNSC-based GDEPT is derived from its concurrent oncolytic and neurotherapeutic effects. Full article

Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as images and videos are now shared on media platforms, aiding in-depth analysis of social sensing systems. This study proposed an analytical framework to extract disaster-related spatiotemporal information from multimodal social media data. Using a pre-trained multimodal neural network and a location entity recognition model, the framework integrates disaster semantics with spatiotemporal information, enhancing situational awareness. A case study of the April 2024 heavy rain event in Guangdong, China, using Weibo data, demonstrates that multimodal content correlates more strongly with rainfall patterns than textual data alone, offering a dynamic perception of disasters. These findings confirm the utility of multimodal social media data and offer a foundation for future research. The proposed framework offers valuable applications for emergency response, disaster relief, risk assessment, and witness discovery, and presents a viable approach for safety risk monitoring and early warning systems. Full article

A novel polishing method is proposed to increase material removal rates through the acceleration of abrasive movements using micro-jets formed by spontaneous collapses of bubbles due to the cavitation in a special-shaped Venturi tube. The Venturi structure is optimized by numerical simulations. Process-related parameters for the optimal cavitation ratio are investigated for achieving maximum adaptation to polishing flat workpieces. Furthermore, this novel approach enhances processing efficiency by approximately 60% compared to traditional abrasive flow polishing. The processing method that employs cavitation bubbles within a special-shaped Venturi tube to augment the flow of abrasive particles holds significant potential for material polishing applications. Full article

Large Language Models (LLMs) combined with visual foundation models have demonstrated significant advancements, achieving intelligence levels comparable to human capabilities. This study analyzes the latest Multimodal LLMs (MLLMs), including Multimodal-GPT, GPT-4 Vision, Gemini, and LLaVa, with a focus on hydrological applications such as flood management, water level monitoring, agricultural water discharge, and water pollution management. We evaluated these MLLMs on hydrology-specific tasks, testing their response generation and real-time suitability in complex real-world scenarios. Prompts were designed to enhance the models’ visual inference capabilities and contextual comprehension from images. Our findings reveal that GPT-4 Vision demonstrated exceptional proficiency in interpreting visual data, providing accurate assessments of flood severity and water quality. Additionally, MLLMs showed potential in various hydrological applications, including drought prediction, streamflow forecasting, groundwater management, and wetland conservation. These models can optimize water resource management by predicting rainfall, evaporation rates, and soil moisture levels, thereby promoting sustainable agricultural practices. This research provides valuable insights into the potential applications of advanced AI models in addressing complex hydrological challenges and improving real-time decision-making in water resource management Full article

Background: Symmetry is a special kind of balance. This study aims to systematically explore and apply the role of balanced composition in aesthetic judgments by focusing on balanced composition features and employing research methods from computational aesthetics and neuroaesthetics. Methods: First, experimental materials were classified by quantifying balanced composition using several indices, including symmetry, center of gravity, and negative space. An EEG experiment was conducted with 18 participants, who were asked to respond dichotomously to the same stimuli under different judgment tasks (balance and aesthetics), with both behavioral and EEG data being recorded and analyzed. Subsequently, participants’ data were combined with balanced composition indices to construct and analyze various SVM classification models. Results: Participants largely used balanced composition as a criterion for aesthetic evaluation. ERP data indicated that from 300–500 ms post-stimulus, brain activation was more significant in the aesthetic task, with unbeautiful and imbalanced stimuli eliciting larger frontal negative waves and occipital positive waves. From 600–1000 ms, beautiful stimuli caused smaller negative waves in the PZ channel. The results of the SVM models indicated that the model incorporating aesthetic subject data (ACC = 0.9989) outperforms the model using only balanced composition parameters of the aesthetic object (ACC = 0.7074). Conclusions: Balanced composition is a crucial indicator in aesthetics, with similar early processing stages in both balance and aesthetic judgments. Multi-modal data models validated the advantage of including human factors in aesthetic evaluation systems. This interdisciplinary approach not only enhances our understanding of the cognitive and emotional processes involved in aesthetic judgments but also enables the construction of more reasonable machine learning models to simulate and predict human aesthetic preferences. Full article

Our research area concerns the development of an intelligent e-service system to help manage multimodal transportation processes. To better respond to the requirements of sustainable development, we encourage the development of multimodal cargo transportation. Therefore, it is important to ensure that the dissemination and management of information in multimodal transportation requires more accurate information transmission and implementation for better coordination of these processes with the interaction of all process participants. Also, contextual data integration into the e-service provision processes is important for more adequate real cargo transportation management. The transition to multimodal freight transport and the increase in its activity directly impact the sustainable development of this sector as transport flows are removed from ground roads and distributed more evenly to load more railways and sea vessels. This research aims to develop an approach to developing the infrastructure of an e-service system with the ability to integrate contextual data and influence the management of multimodal transportation. The methodological approach is based on methods of conceptual representation of information and methods for recognizing the flow of needful information during multimodal freight transportation according to adaptable management processes. The e-service provision system creates benefits for cargo drivers and delivery managers with more accurate information implementation and more adequate coordination of processes under real conditions by helping them make the right decisions. Full article