Search Results (13,461)

Amplified spontaneous emission of CdTe and CdTe-Ag quantum dot composites were compared for increasing the optical stripe length, whereby optical gain coefficients for various emission wavelengths were obtained. In the case of CdTe-Ag nanoparticle composites, we observed that plasmonic coupling causes both optical enhancement and quenching at different wavelengths, where the amplified spontaneous emission intensity becomes enhanced at short wavelengths but suppressed at long wavelengths (>600 nm). To analyze the logistic stripe length dependence of amplified spontaneous emission intensity, we used a differential method to obtain the gain coefficient beyond the amplification range. This analysis enabled us to find the limit of the commonly used fitting method in terms of a threshold length and a saturation length, where amplification begins and saturation ends, respectively. Full article

A surface coating of polymer particles of different hydrophobicity and wide-ranged size is helpful for the surface modification of materials such as woody thin board (WTB) derived from biomass. A preparation method for polymer particles was, in this study, proposed using a capillary-type flow system. Under hydrothermal conditions, the refinement of dispersed oil droplets in water (O/W emulsions) and the polymerization reaction could be simultaneously advanced, and polymer particles of polystyrene (PS), polyvinyl alcohol (PVA), polymethyl methacrylate (PMMA), and poly-L-lactic acid (PLLA) with a particle size of about 100 nm could be synthesized. The coating of polymer particles gave an improved effect on the water repellency of WTBs due to the hydrophobicity of polymer particles and an alteration of surface roughness, and it also provided long-term stability (more than 6 years). Full article

Cellulose nanomaterials have been demonstrated to be excellent barriers against grease, oxygen, and other vapors, but their implementation in packaging materials is challenging because of numerous technical and practical challenges. In this work, the oxygen, air, grease, and heptane barrier performance of copy papers coated with cellulose nanocrystals (CNCs), oxidized cellulose nanofibrils (TOCNs), and carboxymethyl cellulose (CMC) weas examined. The effects of different materials and processing conditions were evaluated for their impacts on the resulting barrier properties. TOCN coatings demonstrated significantly better barrier properties than CNC and CMC coatings due to the long-range networked structure of TOCN suspensions eliciting enhanced film formation at the paper surface. Neat coatings of nanocellulose did not readily result in strong oxygen barriers, but the addition of CMC and/or an additional waterborne water barrier coating was found to result in oxygen barriers suitable for packaging applications (1 cm³/m²·day transmission at low humidity with a 10 g/m² coating). Cast films and thick coatings of CMC were good barriers to oxygen, grease, and air, and its addition to cellulose nanomaterial suspensions aided the coating process and reduced coating defects. In all cases, the incorporation of additional processing aids or coatings was necessary to achieve suitable barrier properties. However, maintaining the strong barrier properties of nanocellulose coatings after creasing remains challenging. Full article

With the increasing use of lithium-ion (Li-ion) batteries in electric vehicles (EVs), accurately measuring the state of charge (SoC) has become crucial for ensuring battery reliability, performance, and safety. In addition, EVs operate in different environmental conditions with different driving styles, which also cause inaccurate SoC estimation resulting in reduced reliability and performance of battery management systems (BMSs). To address this issue, this work proposes a new hybrid method that integrates a gated recurrent unit (GRU), temporal convolution network (TCN), and attention mechanism. The TCN and GRU capture both long-term and short-term dependencies and the attention mechanism focuses on important features within input sequences, improving model efficiency. With inputs of voltage, current, and temperature, along with their moving average, the hybrid GRU-TCN-Attention (GTA) model is trained and tested in a range of operating cycles and temperatures. Performance metrics, including average RMSE (root mean squared error), MAE (mean absolute error), MaxE (maximum error), and $R^2$ score indicates the model is performing well, with average values of 0.512%, 0.354%, 1.98%, and 99.94%, respectively. The proposed model performs well under both high and low noise conditions, with an RMSE of less than 2.18%. The proposed hybrid approach is consistently found to be superior when compared against traditional baseline models. This work offers a potential method for accurate SoC estimation in Li-ion batteries, which has an important impact on clean energy integration and battery management systems in EVs. Full article

Video prediction, which is the task of predicting future video frames based on past observations, remains a challenging problem because of the complexity and high dimensionality of spatiotemporal dynamics. To address the problems associated with spatiotemporal prediction, which is an important decision-making tool in various fields, several deep learning models have been proposed. Convolutional long short-term memory (ConvLSTM) can capture space and time simultaneously and has shown excellent performance in various applications, such as image and video prediction, object detection, and semantic segmentation. However, ConvLSTM has limitations in capturing long-term temporal dependencies. To solve this problem, this study proposes an encoder–decoder structure using self-attention ConvLSTM (SA-ConvLSTM), which retains the advantages of ConvLSTM and effectively captures the long-range dependencies through the self-attention mechanism. The effectiveness of the encoder–decoder structure using SA-ConvLSTM was validated through experiments on the MovingMNIST, KTH dataset. Full article

The safe operation of aero-engines is crucial for ensuring flight safety, and effective fault detection methods are fundamental to achieving this objective. In this paper, we propose a novel approach that integrates an auto-encoder with long short-term memory (LSTM) networks and a self-attention mechanism for the anomaly detection of aero-engine time-series data. The dataset utilized in this study was simulated from real data and injected with fault information. A fault detection model is developed utilizing normal data samples for training and faulty data samples for testing. The LSTM auto-encoder processes the time-series data through an encoder–decoder architecture, extracting latent representations and reconstructing the original inputs. Furthermore, the self-attention mechanism captures long-range dependencies and significant features within the sequences, thereby enhancing the detection accuracy of the model. Comparative analyses with the traditional LSTM auto-encoder, as well as one-class support vector machines (OC-SVM) and isolation forests (IF), reveal that the experimental results substantiate the feasibility and effectiveness of the proposed method, highlighting its potential value in engineering applications. Full article

Antibiotics, widely used medications that have significantly increased life expectancy, possess a broad range of effects beyond their primary antibacterial activity. While some are recognized as adverse events, others have demonstrated unexpected benefits. These adjunctive effects, which have been defined as “pleiotropic” in the case of other pharmacological classes, include immunomodulatory properties and the modulation of the microbiota. Specifically, macrolides, tetracyclines, and fluoroquinolones have been shown to modulate the immune system in both acute and chronic conditions, including autoimmune disorders (e.g., rheumatoid arthritis, spondyloarthritis) and chronic inflammatory pulmonary diseases (e.g., asthma, chronic obstructive pulmonary disease). Azithromycin, in particular, is recommended for the long-term treatment of chronic inflammatory pulmonary diseases due to its well-established immunomodulatory effects. Furthermore, antibiotics influence the human microbiota. Rifaximin, for example, exerts a eubiotic effect that enhances the balance between the gut microbiota and the host immune cells and epithelial cells. These pleiotropic effects offer new therapeutic opportunities by interacting with human cells, signaling molecules, and bacteria involved in non-infectious diseases like spondyloarthritis and inflammatory bowel diseases. The aim of this review is to explore the pleiotropic potential of antibiotics, from molecular and cellular evidence to their clinical application, in order to optimize their use. Understanding these effects is essential to ensure careful use, particularly in consideration of the threat of antimicrobial resistance. Full article

Internet of Things (IoT) devices are ubiquitous in various applications, such as smart homes, asset and people tracking, and city management systems. However, their deployment in adverse conditions, including unstable internet connectivity and power sources, present new cybersecurity challenges through new attack vectors. The LoRaWAN protocol, with its open and distributed network architecture, has gained prominence as a leading LPWAN solution, presenting novel security challenges. This paper proposes the implementation of machine learning algorithms, specifically the K-Nearest Neighbours (KNN) algorithm, within an Intrusion Detection System (IDS) for LoRaWAN networks. Through behavioural analysis based on previously observed packet patterns, the system can detect potential intrusions that may disrupt critical tracking services. Initial simulated packet classification attained over 90% accuracy. By integrating the Suricata IDS and extending it through a custom toolset, sophisticated rule sets are incorporated to generate confidence metrics to classify packets as either presenting an abnormal or normal behaviour. The current work uses third-party multi-vendor sensor data obtained in the city of Lisbon for training and validating the models. The results show the efficacy of the proposed technique in evaluating received packets, logging relevant parameters in the database, and accurately identifying intrusions or expected device behaviours. We considered two use cases for evaluating our work: one with a more traditional approach where the devices and network are static, and another where we assume that both the devices and the network are mobile; for example, when we need to report data back from sensors on a rail infrastructure to a mobile LoRaWAN gateway onboard a train. Full article

Through long-form, creative, high-quality stories, slow journalism seeks to counteract the effects of speed and immediacy in news production and consumption primarily driven by technological advancements. The advantages of artificial intelligence (AI) in journalism include generating and enhancing content, reducing workloads, and consequently giving journalists more time for non-routine and creative tasks. This raises the question of where AI fits into slow journalism. Twenty-one semi-structured interviews were conducted with practitioners of slow journalism in Spain to explore their use, attitudes, and perceptions of AI in their work. The findings indicate that the interviewees make rudimentary use of AI tools, and their attitudes range from a slight lack of interest to a willingness to learn more about them, alongside concerns regarding ethical boundaries and the potential for job losses. They assert that they have a moral and human responsibility when producing stories that AI cannot enhance in terms of quality, creativity, and emotional depth. It can be concluded that AI offers little to ‘slow’ journalists due to the significant limitations in enhancing long-form reporting. At most, it may enable them to streamline repetitive and non-creative work, thereby allowing the depth required in slow journalism, at least in its current state of development. Full article

As the application of the Global Navigation Satellite System (GNSS) continues to expand, its stability and safety issues are receiving more and more attention, especially the interference problem. Interference reduces the signal reception quality of ground terminals and may even lead to the paralysis of GNSS function in severe cases. In recent years, Low Earth Orbit (LEO) satellites have been highly emphasized for their unique advantages in GNSS interference detection, and related commercial and academic activities have increased rapidly. In this context, based on the signal-to-noise ratio (SNR) and radio-frequency interference (RFI) measurements data from COSMIC-2 satellites, this paper explores a method of predicting RFI measurements using SNR correlation variations in different GNSS signal channels for application to the detection and localization of civil terrestrial GNSS interference signals. Research shows that the SNR in different GNSS signal channels shows a correlated change under the influence of RFI. To this end, a CNN-BiLSTM-Attention model combining a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and attention mechanism is proposed in this paper, and the model takes the multi-channel SNR time series of the GNSS as the input and outputs the maximum measured value of RFI in the multi-channels. The experimental results show that compared with the traditional band-pass filtering inter-correlation method and other deep learning models, the model in this paper has a root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R²) of 1.0185, 1.8567, and 0.9693, respectively, in RFI prediction, which demonstrates a higher RFI detection accuracy and a wide range of rough localization capabilities, showing significant competitiveness. Since the correlation changes in the SNR can be processed to decouple the signal strength, this model is also suitable for future GNSS-RO missions (such as COSMIC-1, CHAMP, GRACE, and Spire) for which no RFI measurements have yet been made. Full article

Background: The optimal antimicrobial treatment duration for diabetes-related foot osteomyelitis (DFO) currently needs to be determined. We systematically reviewed the effects of short and long treatment durations on outcomes of DFO. Methods: We performed a systematic review searching Cochrane, CENTRAL, MEDLINE, Embase, and CINAHL Plus from inception up to 19 January 2024. Two independent reviewers screened the titles and abstracts of the studies. Studies comparing short (<6 weeks) and long (>6 weeks) treatment durations for DFO were included. The primary outcome was amputation; the secondary outcomes were remission, mortality, costs, quality of life, and adverse events. Risk of bias and GRADE were assessed. Results: We identified 2708 references, of which 2173 remained after removing duplicates. Two studies were included. Differences in methodology precluded a meta-analysis. The primary outcome, major amputation, was reported in one study, with a rate of 10% in both the intervention and comparison groups (p = 1.00), regardless of treatment duration. For the secondary outcome, remission rates, the first study reported 60% in the intervention group versus 70% in the comparison group (p = 0.50). In the second study, remission rates were 84% in the intervention group versus 78% in the comparison group (p = 0.55). Data for the outcomes mortality, costs, and quality of life were not available. Short treatment duration may lead to fewer adverse events. The risk of bias was assessed as low to moderate, and the level of evidence ranged from very low to moderate. Conclusions: Our findings suggest that for DFO, there is no difference between a shorter and more prolonged duration of antimicrobial treatment regarding amputation and remission, with potentially fewer adverse events with shorter treatment durations. However, the uncertainty stems from limited, heterogeneous studies and generally low-quality evidence marred by moderate biases, imprecision, and indirectness. More high-quality studies are needed to substantiate these findings. Full article

Indoor air quality and energy efficiency are instrumental aspects of school facility design and construction, as they directly affect the physical well-being, comfort, and academic output of both pupils and staff. The challenge of balancing the need for adequate ventilation to enhance indoor air quality with the goal of reducing energy consumption has long been a topic of debate. The implementation of mixed-mode ventilation systems with automated controls presents a promising solution to address this issue. However, a comprehensive literature review on this subject is still missing. To address this gap, this review examines the potential application of mixed-mode ventilation systems as a solution to attaining improved energy savings without compromising indoor air quality and thermal comfort in educational environments. Mixed-mode ventilation systems, which combine natural ventilation and mechanical ventilation, provide the versatility to alternate between or merge both methods based on real-time indoor and outdoor environmental conditions. By analyzing empirical studies, case studies, and theoretical models, this review investigates the efficacy of mixed-mode ventilation systems in minimizing energy use and enhancing indoor air quality. Essential elements such as operable windows, sensors, and sophisticated control technologies are evaluated to illustrate how mixed-mode ventilation systems dynamically optimize ventilation to sustain comfortable and healthy indoor climates. This paper further addresses the challenges linked to the design and implementation of mixed-mode ventilation systems, including complexities in control and the necessity for climate-adaptive strategies. The findings suggest that mixed-mode ventilation systems can considerably lower heating, ventilation, and air conditioning energy usage, with energy savings ranging from 20% to 60% across various climate zones, while also enhancing indoor air quality with advanced control systems and data-driven control strategies. In conclusion, mixed-mode ventilation systems offer a promising approach for school buildings to achieve energy efficiency and effective ventilation without sacrificing indoor environment quality. Full article

Indoor point clouds often present significant challenges due to the complexity and variety of structures and high object similarity. The local geometric structure helps the model learn the shape features of objects at the detail level, while the global context provides overall scene semantics and spatial relationship information between objects. To address these challenges, we propose a novel network architecture, PointMSGT, which includes a multi-scale geometric feature extraction (MSGFE) module and a global Transformer (GT) module. The MSGFE module consists of a geometric feature extraction (GFE) module and a multi-scale attention (MSA) module. The GFE module reconstructs triangles through each point’s two neighbors and extracts detailed local geometric relationships by the triangle’s centroid, normal vector, and plane constant. The MSA module extracts features through multi-scale convolutions and adaptively aggregates features, focusing on both local geometric details and global semantic information at different scale levels, enhancing the understanding of complex scenes. The global Transformer employs a self-attention mechanism to capture long-range dependencies across the entire point cloud. The proposed method demonstrates competitive performance in real-world indoor scenarios, with a mIoU of 68.6% in semantic segmentation on S3DIS and OA of 86.4% in classification on ScanObjectNN. Full article

Nowadays, predicting customer churn is essential for the success of any company. Loyal customers generate continuous revenue streams, resulting in long-term success and growth. Moreover, companies are increasingly prioritizing the retention of existing customers due to the higher costs associated with attracting new ones. Consequently, there has been a growing demand for advanced methods aimed at enhancing customer loyalty and satisfaction, as well as predicting churners. In our work, we focused on building a robust churn prediction model for the telecommunications industry based on large embeddings from large language models and logistic regression to accurately identify churners. We conducted extensive experiments using a range of embedding techniques, including OpenAI Text-embedding, Google Gemini Text Embedding, bidirectional encoder representations from transformers (BERT), Sentence-Transformers, Sent2vec, and Doc2vec, to extract meaningful features. Additionally, we tested various classifiers, including logistic regression, support vector machine, random forest, K-nearest neighbors, multilayer perceptron, naive Bayes, decision tree, and zero-shot classification, to build a robust model capable of making accurate predictions. The best-performing model in our experiments is the logistic regression classifier, which we trained using the extracted feature from the OpenAI Text-embedding-ada-002 model, achieving an accuracy of 89%. The proposed model demonstrates a high discriminative ability between churning and loyal customers. Full article

Atmospheric pollution caused by aerosols deteriorates air quality, increasing public health risks. Anthropogenic aerosols are usually located within the atmospheric boundary layer (ABL), which presents a daytime evolution that determines the air pollutants’ vertical mixing of those produced near the surface and, therefore, their ground-level concentration from local sources. Precise and complete characterization of the mixing layer is of crucial importance for numerical weather forecasting and climate models, but traditional methods such as radiosounding present some spatial and temporal limitations. Better resolutions have been obtained using lidar, which provides the aerosol vertical distribution. A particular type of lidar, the ceilometer, has demonstrated continuous measurement capabilities, providing vertical profiles with sub-minute time resolution and several-meter spatial resolution. Advanced methods, such as the recently developed STRATfinder algorithm, are required to estimate the ABL height in the presence of residual layers. More complex situations occur due to the advection of aerosols (e.g., due to long-range transport of desert dust, volcanic eruptions, or pyrocloud convection), producing a lofted layer in the free troposphere that may remain decoupled from the local ABL but can also be mixed. Aerosol-based methods for determination of the ABL height are challenging in those situations. The main objective of this research is the assessment of the impact of Saharan dust intrusions on the ABL using ceilometer signals, over a period of four years, 2020–2023. The ABL height database, obtained from ceilometer measurements every hour, is analyzed based on the most frequent synoptic patterns. A reduction in the ABL height was obtained from high dust load days (1576 ± 876 m) with respect to low dust load days (1857 ± 914 m), although it was still higher than clean days (1423 ± 772 m). This behavior is further studied discriminating by season and synoptic patterns. These results are relevant for health advice during Saharan dust intrusion days. Full article