Integration Of Modules Research Articles

Exploring the Potential Impact of Training on Short-Term Quality of Life and Stress of Parents of Children with Autism: The Integrative Parents' Autism Training Module.

Parents of autistic children experience high levels of parental stress and low quality of life related to the demanding child caring burden they experience. Parent education and training programs are acknowledged to improve parental well-being and reduce parenting stress. In the framework of the Erasmus+ Integrative Autism Parents Training Project (IPAT), we developed the IPAT Training Module based on parents' expressed needs, in order to improve parental quality of life (QoL) and decrease their perceived stress. Sixty-two parents from four countries participated in the IPAT Module Training activity. We used WHOQOL-BREF and Perceived Stress Scale (PSS-10 version) for QoL and stress, respectively, before and after training and a study-specific questionnaire to assess participants' satisfaction. Parents' QoL improved significantly in the environment domain and specific items, while stress levels remained unmodified. Training appeared more advantageous for parents with lower initial QoL and those whose child had been enrolled in a special education program for an extended duration. Parents were quite satisfied, in particular those with lower initial social relationships QoL. Larger studies including a control group are necessary to support preliminary evidence provided by this study, identify additional effect moderators, and disentangle the contribution of different components of the training.

PIAENet: Pyramid integration and attention enhanced network for object detection

Object detection is a challenging task that requires a trade-off between accuracy and efficiency. Previous approaches have focused mainly on optimizing one aspect at the expense of the other, making them unsuitable for resource-constrained devices. To address this issue, we propose a new object detection network architecture, the Pyramid Integration and Attention Enhanced Network (PIAENet). PIAENet is a lightweight architecture that can achieve high accuracy and efficiency. We utilize a lightweight EfficientNet-B2 backbone for feature extraction to maintain accuracy while reducing computational overhead. The core components of PIAENet, the Pyramid Integration Module (PIM) and the Attention Enhanced Module (AEM), work together to improve the performance of object detection. PIM fuses multi-scale features using multiple branches to enhance the receptive field of the model, while AEM strengthens the fusion of features using two attention mechanisms to suppress the influence of irrelevant information. Our proposed method has been evaluated on the PASCAL VOC and KITTI datasets. The results have shown our method outperforms most of the existing state-of-the-art methods.

Acoustic stimulation of the human round window by laser-induced nonlinear optoacoustics.

The feasibility of low frequency pure tone generation in the inner ear by laser-induced nonlinear optoacoustic effect at the round window was demonstrated in three human cadaveric temporal bones (TB) using an integral pulse density modulation (IPDM). Nanosecond laser pulses with a wavelength in the near-infrared (NIR) region were delivered to the round window niche by an optical fiber with two spherical lenses glued to the end and a viscous gel at the site of the laser focus. Using IPDM, acoustic tones with frequencies between 20Hz and 1kHz were generated in the inner ear. The sound pressures in scala tympani and vestibuli were recorded and the intracochlear pressure difference (ICPD) was used to calculate the equivalent sound pressure level (eq. dB SPL) as an equivalent for perceived loudness. The results demonstrate that the optoacoustic effect produced sound pressure levels ranging from 140eq. dB SPL at low frequencies ≤ 200Hz to 90eq. dB SPL at 1kHz. Therefore, the produced sound pressure level is potentially sufficient for patients requiring acoustic low frequency stimulation. Hence, the presented method offers a potentially viable solution in the future to provide the acoustic stimulus component in combined electro-acoustic stimulation with a cochlear implant.

IBDTransDB: a manually curated transcriptomic database for inflammatory bowel disease.

Inflammatory Bowel Disease (IBD) therapies are ineffective in at least 40% patients, and transcriptomic datasets have been widely used to reveal the pathogenesis and to identify the novel drug targets for these patients. Although public IBD transcriptomic datasets are available from many web-based tools/databases, due to the unstructured metadata and data description of these public datasets, most of these tools/databases do not allow querying datasets based on multiple keywords (e.g. colon and infliximab). Furthermore, few tools/databases can compare and integrate the datasets from the query results. To fill these gaps, we have developed IBDTransDB (https://abbviegrc.shinyapps.io/ibdtransdb/), a manually curated transcriptomic database for IBD. IBDTransDB includes a manually curated database with 34 transcriptomic datasets (2932 samples, 122 differential comparisons) and a query system supporting 35 keywords from 5 attributes (e.g. tissue and treatment). IBDTransDB also provides three modules for data analyses and integration. IBDExplore allows interactive visualization of differential gene list, pathway enrichment, gene signature and cell deconvolution analyses from a single dataset. IBDCompare supports comparisons of selected genes or pathways from multiple datasets across different conditions. IBDIntegrate performs meta-analysis to prioritize a list of genes/pathways based on user-selected datasets and conditions. Using two case studies related to infliximab treatment, we demonstrated that IBDTransDB provides a unique platform for biologists and clinicians to reveal IBD pathogenesis and identify the novel targets by integrating with other omics data. Database URL: https://abbviegrc.shinyapps.io/ibdtransdb/.

The Impact of Treadmill Training on Tissue Integrity, Axon Growth, and Astrocyte Modulation.

Spinal cord injury (SCI) presents a complex challenge in neurorehabilitation, demanding innovative therapeutic strategies to facilitate functional recovery. This study investigates the effects of treadmill training on SCI recovery, emphasizing motor function enhancement, neural tissue preservation, and axonal growth. Our research, conducted on a rat model, demonstrates that controlled treadmill exercises significantly improve motor functions post-SCI, as evidenced by improved scores on the Basso, Beattie, and Bresnahan (BBB) locomotor rating scale and enhanced electromyography readings. Notably, the training facilitates the preservation of spinal cord tissue, effectively reducing secondary damage and promoting the maintenance of neural fibers in the injured area. A key finding is the significant stimulation of axonal growth around the injury epicenter in trained rats, marked by increased growth-associated protein 43 (GAP43) expression. Despite these advancements, the study notes a limited impact of treadmill training on motoneuron adaptation and highlights minimal changes in the astrocyte and neuron-glial antigen 2 (NG2) response. This suggests that, while treadmill training is instrumental in functional improvements post-SCI, its influence on certain neural cell types and glial populations is constrained.

An intelligent self‐powered life jacket system integrating multiple triboelectric fiber sensors for drowning rescue

AbstractThe inherent unpredictability of the maritime environment leads to low rates of survival during accidents. Life jackets serve as a crucial safety measure in underwater environments. Nonetheless, most conventional life jackets lack the capability to monitor the wearer's underwater body movements, impeding their effectiveness in rescue operations. Here, we present an intelligent self‐powered life jacket system (SPLJ) composed of a wireless body area sensing network, a set of deep learning analytics, and a human condition detection platform. Six coaxial core‐shell structure triboelectric fiber sensors with high sensitivity, stretchability, and flexibility are integrated into this system. Additionally, a portable integrated circuit module is incorporated into the SPLJ to facilitate real‐time monitoring of the wearer's movement. Moreover, by leveraging the deep‐learning‐assisted data analytics and establishing a robust correlation between the wearer's movements and condition, we have developed a comprehensive system for monitoring drowning individuals, achieving an outstanding recognition accuracy of 100%. This groundbreaking work introduces a fresh approach to underwater intelligent survival devices, offering promising prospects for advancing underwater smart wearable devices in rescue operations and the development of ocean industry.image

Numerical investigation of air flow characteristics for a compact 5 [formula omitted] 10 array tubular segmented-in-series solid oxide fuel cell stack

Tubular segmented-in-series solid oxide fuel cells (SIS-SOFCs) offer the advantages of high voltage, low current, easy sealing, and high thermal shock resistance. Tubular SIS-SOFCs can be assembled into a highly integrated power-generation stack module to increase the volumetric power density. This study investigated the air flow characteristics of a kilowatt-class tubular SIS-SOFC stack with a 5 × 10-cell array, each containing 59 segments. A three-dimensional stack model that considers practical stack structures, coupled electrochemical reactions, and the mass/momentum/heat transfer processes was developed. The results show that a large flow nonuniformity existed in the conventional tubular stack design, which resulted in a severe temperature distribution. Local hot spots at the marginal positions and circumferential temperature difference were found to be non-negligible. To improve the flow uniformity, an air distributor with optimized diameters at different positions was designed, and manifold parameters were adjusted to balance the flow resistance. The maximum temperature was reduced by 35 K and temperature uniformity was significantly improved.

Multi-View Dynamic Reflection Prior for Video Glass Surface Detection

Recent research has shown significant interest in image-based glass surface detection (GSD). However, detecting glass surfaces in dynamic scenes remains largely unexplored due to the lack of a high-quality dataset and an effective video glass surface detection (VGSD) method. In this paper, we propose the first VGSD approach. Our key observation is that reflections frequently appear on glass surfaces, but they change dynamically as the camera moves. Based on this observation, we propose to offset the excessive dependence on a single uncertainty reflection via joint modeling of temporal and spatial reflection cues. To this end, we propose the VGSD-Net with two novel modules: a Location-aware Reflection Extraction (LRE) module and a Context-enhanced Reflection Integration (CRI) module, for the position-aware reflection feature extraction and the spatial-temporal reflection cues integration, respectively. We have also created the first large-scale video glass surface dataset (VGSD-D), consisting of 19,166 image frames with accurately-annotated glass masks extracted from 297 videos. Extensive experiments demonstrate that VGSD-Net outperforms state-of-the-art approaches adapted from related fields. Code and dataset will be available at https://github.com/fawnliu/VGSD.

A Unified Self-Distillation Framework for Multimodal Sentiment Analysis with Uncertain Missing Modalities

Multimodal Sentiment Analysis (MSA) has attracted widespread research attention recently. Most MSA studies are based on the assumption of modality completeness. However, many inevitable factors in real-world scenarios lead to uncertain missing modalities, which invalidate the fixed multimodal fusion approaches. To this end, we propose a Unified multimodal Missing modality self-Distillation Framework (UMDF) to handle the problem of uncertain missing modalities in MSA. Specifically, a unified self-distillation mechanism in UMDF drives a single network to automatically learn robust inherent representations from the consistent distribution of multimodal data. Moreover, we present a multi-grained crossmodal interaction module to deeply mine the complementary semantics among modalities through coarse- and fine-grained crossmodal attention. Eventually, a dynamic feature integration module is introduced to enhance the beneficial semantics in incomplete modalities while filtering the redundant information therein to obtain a refined and robust multimodal representation. Comprehensive experiments on three datasets demonstrate that our framework significantly improves MSA performance under both uncertain missing-modality and complete-modality testing conditions.

ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field

Neural Radiance Fields (NeRF) have demonstrated impressive potential in synthesizing novel views from dense input, however, their effectiveness is challenged when dealing with sparse input. Existing approaches that incorporate additional depth or semantic supervision can alleviate this issue to an extent. However, the process of supervision collection is not only costly but also potentially inaccurate. In our work, we introduce a novel model: the Collaborative Neural Radiance Fields (ColNeRF) designed to work with sparse input. The collaboration in ColNeRF includes the cooperation among sparse input source images and the cooperation among the output of the NeRF. Through this, we construct a novel collaborative module that aligns information from various views and meanwhile imposes self-supervised constraints to ensure multi-view consistency in both geometry and appearance. A Collaborative Cross-View Volume Integration module (CCVI) is proposed to capture complex occlusions and implicitly infer the spatial location of objects. Moreover, we introduce self-supervision of target rays projected in multiple directions to ensure geometric and color consistency in adjacent regions. Benefiting from the collaboration at the input and output ends, ColNeRF is capable of capturing richer and more generalized scene representation, thereby facilitating higher-quality results of the novel view synthesis. Our extensive experimental results demonstrate that ColNeRF outperforms state-of-the-art sparse input generalizable NeRF methods. Furthermore, our approach exhibits superiority in fine-tuning towards adapting to new scenes, achieving competitive performance compared to per-scene optimized NeRF-based methods while significantly reducing computational costs. Our code is available at: https://github.com/eezkni/ColNeRF.

Research on the phase error of a Sagnac interferometer induced by modulation of a multifunctional integrated optical modulator.

High-precision IFOG requires an optical sensitivity of up to 10-8 rads-1 for interferometers; noise and error are two of the main reasons limiting its accuracy improvement. Any potential source of the signal error is worth being studied. This article introduces work on the modulation signal error caused by the mechanical vibration energy loss of MIOC crystals. This article theoretically derives and simulates the frequency spectrum of an energy loss from the perspective of electromechanical coupling and verifies it through experiments. This article also verifies the influence of MIOC mechanical loss on the output of a Sagnac interferometer through experiments. This study is an indispensable part of the bottleneck for improving the accuracy of ultrahigh-precision closed-loop IFOG and has potential engineering application value.

Frost matters: incorporating late-spring frost into a dynamic vegetation model regulates regional productivity dynamics in European beech forests

Abstract. Late-spring frost (LSF) is a critical factor influencing the functioning of temperate forest ecosystems. Frost damage in the form of canopy defoliation impedes the ability of trees to effectively photosynthesize, thereby reducing tree productivity. In recent decades, LSF frequency has increased across Europe, likely intensified by the effects of climate change. With increasing warming, many deciduous tree species have shifted towards earlier budburst and leaf development. The earlier start of the growing season not only facilitates forest productivity but also lengthens the period during which trees are most susceptible to LSF. Moreover, recent forest transformation efforts in Europe intended to increase forest resilience to climate change have focused on increasing the share of deciduous species in forests. To assess the ability of forests to remain productive under climate change, dynamic vegetation models (DVMs) have proven to be useful tools. Currently, however, most state-of-the-art DVMs do not model processes related to LSF and the associated impacts. Here, we present a novel LSF module for integration with the dynamic vegetation model Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS). This new model implementation, termed LPJ-GUESS-FROST, provides the ability to directly attribute simulated impacts on forest productivity dynamics to LSF. We use the example of European beech, one of the dominant deciduous species in central Europe, to demonstrate the functioning of our novel LSF module. Using a network of tree-ring observations from past frost events, we show that LPJ-GUESS-FROST can reproduce productivity reductions caused by LSF. Further, to exemplify the effects of including LSF dynamics in DVMs, we run LPJ-GUESS-FROST for a study region in southern Germany for which high-resolution climate observations are available. Here, we show that modeled LSF plays a substantial role in regulating regional net primary production (NPP) and biomass dynamics, emphasizing the need for LSF to be more widely accounted for in DVMs.

Multi-Modal Attentive Prompt Learning for Few-shot Emotion Recognition in Conversations

Emotion recognition in conversations (ERC) has emerged as an important research area in Natural Language Processing and Affective Computing, focusing on accurately identifying emotions within the conversational utterance. Conventional approaches typically rely on labeled training samples for fine-tuning pre-trained language models (PLMs) to enhance classification performance. However, the limited availability of labeled data in real-world scenarios poses a significant challenge, potentially resulting in diminished model performance. In response to this challenge, we present the Multi-modal Attentive Prompt (MAP) learning framework, tailored specifically for few-shot emotion recognition in conversations. The MAP framework consists of four integral modules: multi-modal feature extraction for the sequential embedding of text, visual, and acoustic inputs; a multi-modal prompt generation module that creates six manually-designed multi-modal prompts; an attention mechanism for prompt aggregation; and an emotion inference module for emotion prediction. To evaluate our proposed model’s efficacy, we conducted extensive experiments on two widely recognized benchmark datasets, MELD and IEMOCAP. Our results demonstrate that the MAP framework outperforms state-of-the-art ERC models, yielding notable improvements of 3.5% and 0.4% in micro F1 scores. These findings highlight the MAP learning framework’s ability to effectively address the challenge of limited labeled data in emotion recognition, offering a promising strategy for improving ERC model performance.

Towards hybrid self-learning ontologies: A new Python module for closed-loop integration of decision trees and OWL

Ontologies are a valuable tool for organising and representing knowledge. Decision trees (DTs) are a machine learning (ML) technique that can be used to learn from data. The integration of DTs and ontologies has the potential to improve the performance of both technologies. This paper proposes a novel approach to integrating DTs and ontologies. For that purpose, a new Python module is developed and validated to demonstrate this approach. The dt2swrl module has been evaluated against two research questions (RQs). RQ1 asked whether existing Python packages are capable of integrating DT rules with ontologies. The investigation showed that none of the existing packages can achieve the defined goals. RQ2 asked whether a generic module can be developed to automatically integrate DT rules with ontologies. The dt2swrl module has been developed to address this gap and it has been shown to be effective in achieving the desired goals. It can be used to develop hybrid ontologies that seamlessly integrate expert-based and data-based rules, and self-learning ontologies that can automatically maintain their rules based on new data. The paper concludes by discussing the limitations of the dt2swrl module and the implications of the research for future work.

Integrated disease management modules for rice-bacterial leaf blight ecosystem under rice: Wheat cropping system

Integrated disease management modules for rice-bacterial leaf blight ecosystem under rice: Wheat cropping system

Fluoroscopy integration module guides successful ablation in atrial fibrillation with coronary-pulmonary artery fistula

Fluoroscopy integration module guides successful ablation in atrial fibrillation with coronary-pulmonary artery fistula

Long‐term outdoor study of an organic photovoltaic module for building integration

AbstractOrganic photovoltaics (OPV) has attracted tremendous attention as a promising alternative to silicon wafer‐based technologies for building integration. While significant progress has been achieved on the power conversion efficiency of OPV technologies, their field stability is rarely studied. This work investigates the field performance and reliability of a large‐area OPV module designed for building integration in tropical Singapore for 4.5 years. The device suffered more than 14% degradation in power at the standard testing conditions from the initial performance, largely due to losses in fill factor (−12% relative). During the monitoring period, it exhibited comparable performance to more conventional silicon PV technologies, with an average specific energy yield of about 4 kWh/kWp/day and an average performance ratio of 0.96. Excellent performance at low light conditions was also observed. However, its field performance was heavily impacted by soiling, which typically led to a 5 to 10% loss in the current output after several months. Further, the device's outdoor performance also showed a three‐stage degradation process, including (1) an initial slow degradation in the first 2 years (about −1%/year), (2) a stable period with negligible performance loss from Years 2 to 3.5, and (3) a rapid degradation in the last year (about −5%/year).

InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and cross-modal generation

BackgroundWith the development of single-cell technology, many cell traits can be measured. Furthermore, the multi-omics profiling technology could jointly measure two or more traits in a single cell simultaneously. In order to process the various data accumulated rapidly, computational methods for multimodal data integration are needed.ResultsHere, we present inClust+, a deep generative framework for the multi-omics. It’s built on previous inClust that is specific for transcriptome data, and augmented with two mask modules designed for multimodal data processing: an input-mask module in front of the encoder and an output-mask module behind the decoder. InClust+ was first used to integrate scRNA-seq and MERFISH data from similar cell populations, and to impute MERFISH data based on scRNA-seq data. Then, inClust+ was shown to have the capability to integrate the multimodal data (e.g. tri-modal data with gene expression, chromatin accessibility and protein abundance) with batch effect. Finally, inClust+ was used to integrate an unlabeled monomodal scRNA-seq dataset and two labeled multimodal CITE-seq datasets, transfer labels from CITE-seq datasets to scRNA-seq dataset, and generate the missing modality of protein abundance in monomodal scRNA-seq data. In the above examples, the performance of inClust+ is better than or comparable to the most recent tools in the corresponding task.ConclusionsThe inClust+ is a suitable framework for handling multimodal data. Meanwhile, the successful implementation of mask in inClust+ means that it can be applied to other deep learning methods with similar encoder-decoder architecture to broaden the application scope of these models.

Molecular mechanism for combined toxicity of micro(nano)plastics and carbon nanofibers to freshwater microalgae Chlorella pyrenoidosa

The understanding of the environmental consequences resulting from the presence of micro(nano)plastics and carbon nanofibers (CNFs) in aquatic ecosystems is currently limited. This research endeavor sought to investigate the underlying molecular mechanisms by which engineered polystyrene-based microplastics (MPs)/nanoplastics (NPs) and CNFs, both individually and in combination, elicit toxic effects on an algal species Chlorella pyrenoidosa. The findings revealed that the combined toxicity of MPs/NPs and CNFs depended on the concentration of the mixture. As the concentration increased, the combined toxicity of MPs/NPs and CNFs was significantly greater than the toxicity of each component on its own. Furthermore, the combined toxicity of NPs and CNFs was higher than that of MPs and CNFs. The study integrated data on cell membrane integrity, oxidative stress, and antioxidant modulation to create an Integrated Biomarker Response index, which demonstrated that the co-exposure of algae to NPs and CNFs resulted in more severe cellular stress compared to exposure to NPs alone. Similarly, the combination of NPs and CNFs caused greater cellular stress than the combination of MPs and CNFs. Additionally, significant changes in the expression of stress-related genes caused by MPs/NPs alone and in combination with CNFs indicated that oxidative stress response, glucose metabolism, and energy metabolism played critical roles in particle-induced toxicity. Overall, this study provides the first insight into the toxicological mechanism of MPs/NPs and CNFs mixtures at the molecular level in freshwater microalgae.

Strategic outsourcing in reverse logistics: Neutrosophic integrated approach with a hierarchical and interactive quality function deployment

The efficiency of strategic outsourcing in reverse logistics (RL) depends on stakeholder requirements (SRs) directing organization development and experts’ knowledge decomposing and reconstructing strategic decision, which urges the adoption of hierarchical and interactive quality function deployment (QFD). Considering the uncertainty within a QFD, single valued neutrosophic number (SVNN) is introduced to capture both indeterminacy and inconsistency hidden in quantitive votes and qualitative judgments of stakeholders and experts. With unknown weight assignments, two maximum deviation models are constructed to obtain the priority of SRs and Shapley weights of experts. An SVNN-enabled grey relational analysis is extended to identify the interdependence priority of engineer characterizers (ECs). Due to the interactions among components such as experts in a group or ECs in QFD, two information aggregation tools under neutrosophic environments are presented based on fuzzy integral delineating the positive or negative effect. Built on these, we propose an integrated neutrosophic approach composed of a transformation module, weight module, integration module and QFD analysis module to support the outsourcing decision. Finally, an illustration example is used to confirm the practicality of the proposed approach. Sensitivity analysis and comparative analysis with multiple views have been conducted to show the flexibility and superiority of the given decision.