High-power laser facilities necessitate predicting incremental damage to final optics to identify evolving damage trends. In this study, we propose a surface damage detection method utilizing image segmentation employing ResNet-18, and a damage area estimation network employing U-Net++. Paired sets of online and offline images of optics obtained from a large laser facility is used to train the network. The trends of varying damage could be identified by incorporating additional experimental parameters. A key advantage of the proposed method is that the network can be trained end-to-end on small samples, eliminating the need for manual labeling or feature extraction. The software developed based on these models can facilitate the daily inspection and maintenance of optics in large laser facilities.

In response to the issues of sensor noise and accumulated motion errors that lead to low positioning accuracy and insufficient robustness during large maneuvers or continuous flight of small unmanned aerial vehicles (UAVs) in complex environments, this paper proposes a multi-source and multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors including GPS (Global Positioning System), IMU (Inertial Measurement Unit), and visual sensors to complement the strengths and weaknesses of each hardware component, thereby eliminating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, the method introduces a digital twin UAV data source. By utilizing the Extended Kalman Filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multi-source multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency.

Deep learning classification is the state-of-the-art of machine learning approach. Earlier work proves that the deep convolutional neural network has successfully and brilliantly in different applications such as images or video data. Recognizing and clarifying the remote sensing aspect of the earth's surface and exploit land cover and land use (LCLU). First, this article summarized the remote sensing emerging application and challenges for deep learning methods. Second, we propose four approaches to learn efficient and effective CNNs to transfer image representation on the ImageNet dataset to recognize LCLU datasets. We use VGG16, Inception-ResNet-V2, Inception-V3, and DenseNet201 models to extract features from the EACC dataset. We use pre-trained CNNs on ImageNet to extract features. For feature selection we proposed principal component analysis (PCA) to improve accuracy and speed up the model. We train our model by multi-layer perceptron (MLP) as a classifier. Lastly, we apply the multi-granularity encoding ensemble model. We achieve an overall accuracy of 92.3% for the nine-class classification problem. This work will help remote sensing scientists understand deep learning tools and apply them in large-scale remote sensing challenges

This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLME-Resnet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial and spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3\%) and Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and adjustment of treatment, by providing physicians with insights into the underlying pathological organization of gliomas.

The advent of the era of big data will bring more convenience to people and greater development to society. But at the same time, it will also bring people the problem of 'information overload', i.e., when people are faced with huge information data, there are many redundant and worthless data. The redundant and worthless data information seriously interferes with the accurate selection of information data. Even though people can use Internet search engines to access information data, they cannot meet the individual needs of specific users in specific contexts. The personalized needs of a particular user in a particular context. Therefore, how to find useful and valuable information quickly has become one of the key issues in the development of big data. With the advent of the era of big data, recommendation systems, as an important technology to alleviate information overload, have been widely used in the field of e-commerce. Recommender systems suffer from a key problem: data sparsity. The sparsity of user history rating data causes insufficient training of collaborative filtering recommendation models, which leads to a significant decrease in the accuracy of recommendations. In fact, traditional recommendation systems tend to focus on scoring information and ignore the contextual context in which users interact. There are various contextual modal information in people's real life, which also plays an important role in the recommendation process. In this paper we achieve data degradation and feature extraction, solving the problem of sparse data in the recommendation process. An interaction context-aware sub-model is constructed based on a tensor decomposition model with interaction context information to model the specific influence of interaction context in the recommendation process. Then an attribute context-aware sub-model is constructed based on the matrix decomposition model and using attribute context information to model the influence of user attribute contexts and item attribute contexts on recommendations. In the process of building the model, the method not only utilizes the explicit feedback rating information of users in the original dataset, but also utilizes the interaction context and attribute context information of the implicit feedback and the unlabeled rating data. We evaluate our model by extensive experiments. The results illustrate the effectiveness of our recommender model.

A portable laser photoacoustic sensor based on a Field-Programmable Gate Array (FPGA) is reported for methane detection. A tunable DFB diode laser in the 1654 nm wavelength range is used as an excitation source. The photoacoustic signal processing was implemented by a FPGA device. A small resonant photoacoustic cell is designed. The minimum detection limit (1σ) of 10 ppm for methane (CH₄) is demonstrated.

The theoretical limit of link prediction is a fundamental problem in this field. Taking the network structure as object to research this problem is the mainstream method. This paper proposes a new viewpoint that link prediction methods can be divided into single or combination methods, based on the way they derive the similarity matrix, and investigates whether there a theoretical limit exists for combination methods. We propose and prove necessary and sufficient conditions for the combination method to reach the theoretical limit. The limit theorem reveals the essence of combination method that is to estimate probability density functions of existing links and nonexistent links. Based on limit theorem, a new combination method, theoretical limit fusion (TLF) method, is proposed. Simulations and experiments on real networks demonstrated that TLF method can achieve higher prediction accuracy.

The rapid urbanization in China has significantly contributed to the vast expansion of urban built-up areas. Precisely extracting and monitoring these areas is crucial for understanding and optimizing the developmental process and spatial attributes of smart, compact cities. However, most existing studies tend to focus narrowly on a single city or global scale with a single dimension, often ignoring mesoscale analysis across multiple urban agglomerations. In contrast, our study employs GIS and image processing techniques to integrate multi-source data for the identification of built-up areas. We specifically compare and analyze two representative urban agglomerations in China: the Yangtze River Delta (YRD) in the east and the Chengdu-Chongqing (CC) region in the west. We use different methods to extract built-up areas from socio-economic factors, natural surfaces, and traffic network dimensions. Additionally, we utilize a high-precision built-up area dataset of China as a reference for verification and comparison. Our findings reveal several significant insights: (1) The multi-source data fusion approach effectively enhances the extraction of built-up areas within urban agglomerations, achieving higher accuracy than previously employed methods. (2) Our research methodology performs particularly well in the CC urban agglomeration. The average precision rate in CC is 96.03%, while the average precision rate in YRD is lower at 80.33%. This study provides an objective and accurate assessment of distribution characteristics and internal spatial structure of built-up areas within urban agglomerations. This method offers a new perspective for identifying and monitoring built-up areas in Chinese urban agglomerations.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Dementia, primarily caused by neurodegenerative diseases like Alzheimer’s disease (AD), affects millions worldwide, making detection and monitoring crucial. To enable these tasks, we propose encoding in-text pauses and filler words (i.e., “uh” and “um”) in text-based language models, and thoroughly evaluate their effect in performance. Additionally, we suggest using contrastive learning to improve performance in a multi-task framework. Our results demonstrated the effectiveness of our approaches in enhancing the model’s performance, achieving 87% accuracy and an 86% F1-score. Compared to the state-of-the-art, our approach has similar performance despite having significantly fewer parameters. This highlights the importance of pause and filler word encoding on the detection of dementia.

With the development of remote sensing satellite technology for Earth observation, remote sensing stereo images have been used for three-dimensional reconstruction in various fields, such as urban planning and construction. However, remote sensing images often include noise, occluded regions, weakly textured areas and repeated textures, which can lead to reduced accuracy in stereo matching and affect the quality of the 3D reconstruction results. To reduce the impact of complex scenes in remote sensing images on stereo matching and to ensure both speed and accuracy, we propose a new end-to-end stereo matching network based on convolutional neural networks (CNNs). The proposed stereo matching network can learn features at different scales from the original images and construct cost volumes with varying scales to obtain richer scale information. Additionally, when constructing the cost volume, we introduce negative disparity to adapt to the common occurrence of both negative and nonnegative disparities in remote sensing stereo image pairs. For cost aggregation, we employ a 3D convolution-based encoder-decoder structure that allows the network to adaptively aggregate information. Before feature aggregation, we also introduce an attention module to retain more valuable feature information, enhance feature representation, and obtain a higher-quality disparity map. By training on the publicly available US3D dataset, we obtain the accuracy that 1.115 pixel in end-point error (EPE) and 5.32% in the error pixel ratio (D1) on the test dataset, and the inference speed is 92 ms. Comparing our model with existing state-of-the-art models, we achieve higher accuracy, and the network is beneficial for the three-dimensional reconstruction of remote sensing images.

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative, is to resort to weather-insensitive synthetic aperture radar (SAR) images. However, in many real-world applications, critical decisions are made based on some informative spectral features, such as water, vegetation or soil indices, which cannot be extracted from SAR images. In the absence of optical sources, these data must be estimated. The current practice is to perform linear interpolation between data available at temporally close time instants. In this work, we propose to estimate missing spectral features through data fusion and deep-learning. Several sources of information are taken into account - optical sequences, SAR sequences, DEM - so as to exploit both temporal and cross-sensor dependencies. Based on these data, and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May to November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baselines methods according to all performance indicators.

This paper aims at tackling with the task of fusion feature from images and its corresponding point clouds for 3D object detection in autonomous driving scenarios basing on AVOD, an Aggregate View Object Detection network. The proposed fusion algorithms fuse features targeted from Bird’s Eye View (BEV) LIDAR point clouds and its corresponding RGB images. Differs in existing fusion methods, which are simply the adoptions of concatenation module, element-wise sum module or element-wise mean module, our proposed fusion algorithms enhance the interaction between BEV feature maps and its corresponding images feature maps by designing a novel structure, where single level feature maps and another utilizes multilevel feature maps. Experiments show that our proposed fusion algorithm produces better results on 3D mAP and AHS with less speed loss comparing to existing fusion method used on the KITTI 3D object detection benchmark.

To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.

This study extends the CGAN-based MODIS cloud vertical-profile (64×64-scene, about 70km width×15km height) retrieval technique developed by Leinonen et al. (2019) [1] to construct seamless 3D cloud fields for the MODIS granules [2]. Firstly, the accuracy and spatial continuity of the Leinonen et al. [1] retrievals are statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bi-directional Ensemble Binning Probability Fusion (CGAN-BEBPF) technique are developed, which improves the CGAN retrieval accuracy and support to construct seamless 3D clouds. The CGAN-BEBPF technique involves three steps: cloud masking, intensity scaling, and optimal value selection. It ensures adequate coverage of the low reflectivity areas while preserving the high reflectivity cloud cores. The technique is applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system. The cloud structures of the CGAN-BEBPF results are highly consistent with the ground-based radar observations. It can retrieve weak clouds at the top levels of convective storms and hurricanes that were missed by ground-based radars and fill the gaps of the ground-based radar lower-level PPI scan. Overall, CGAN-BEBPF can be automated to retrieve the rich informative 3D structures of typhoons and severe convective systems over the oceans along the MODIS swath, which certainly presents great value for improving maritime and near-shore typhoons and convection prediction and other cloud-sensitive applications.

The proliferation of Unmanned Aerial Vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication, data integrity, and privacy within UAV networks’ highly dynamic and adaptable environments. Decentralized reputation systems emerge as a promising solution to enhance the reliability and trustworthiness of data and communications within these networks while safeguarding UAV security. This paper presents an exhaustive survey of trust and reputation systems, exploring existing frameworks and proposed innovations alongside their inherent challenges. It highlights the crucial role of reputation systems in strengthening trust, security, and privacy throughout these networks and discusses various strategies to mitigate existing vulnerabilities. As a useful resource for researchers and practitioners seeking to advance the state of the art in UAV network security, we hope this survey will spark further community discussion and stimulate innovative ideas in this burgeoning field.

Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on eight subjects while performing 5-minute sitting and walking tasks. Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Fringe projection profilometry(FPP) has been extensively applied in various fields for its superior fast speed, high accuracy and high data density. However, measuring some objects with high reflective surfaces or high dynamic range surfaces remains challenging for FPP. Some multiple exposure image fusion methods have been proposed and successfully improved the measurement performance for these kinds of objects. Normally, these methods have a relatively fixed sequence of exposure settings determined by artificial experience or trial and error experiments, which may decrease the efficiency of the entire measurement process and may have less robustness to various environmental lighting conditions and object reflective properties. In this paper, a novel self-adaptive multiple exposure image fusion method is proposed with two main aspects of improvement on adaptively optimizing the initial exposure and the exposure sequence. By introducing the theory of information entropy, combined with the analysis of the characterization of fringe image entropy, an adaptive initial exposure searching method is first proposed. Then, an exposure sequence generation method based on dichotomy is further described. On the base of these two improvements, a novel self-adaptive multiple exposure image fusion method for FPP as well as its detailed procedures are given. Experimental results validate the performance of the proposed self-adaptivity multiple exposure image fusion method by measuring the objects with different surface reflectivity and in different ambient lighting conditions.

In pattern recognition domain, deep architectures are widely used nowadays and they have achieved fine grades. However, these deep architectures need special demands, especially big datasets and GPU. Aiming to gain better grades without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size, therefore it can gain more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features’ dimensions are reduced by Principal Component Analysis( PCA) and we apply softmax to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames to compare with their neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. This makes the salient areas found from different subjects have the same size. Besides, gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database.

To address the problems of insufficient semantic fusion between text and questions and the lack of consideration of global semantic information encountered in machine reading comprehension models, we proposed a machine reading comprehension model called BERT_hybrid based on BERT and hybrid attention mechanism. In this model, BERT is utilized to separately map the text and questions into the feature space. Through the integration of Bi-LSTM, attention mechanism, and self-attention mechanism, the proposed model achieves comprehensive semantic fusion between text and questions. The probabilities distribution of answers is computed using Softmax. Experimental results on the public dataset DuReader demonstrate that the proposed model achieves improvements in BLEU-4 and ROUGE-L scores compared to existing models. Furthermore, to validate the effectiveness of the proposed model design, we analyze the factors influencing the model’s performance.

We propose a forward-only multi-layer encoding-decoding framework based on the principle of Maximal Coding Rate Reduction (MCR$^2$), an information-theoretic metric that measures a statistical distance between two sets of feature vectors up to the second moment. The encoder directly transforms data vectors themselves via gradient ascent to maximize the MCR$^2$ distance between different classes in the feature space, resulting in class-wise mutually orthogonal subspace representations. The decoder follows a process symmetric to the encoder, and transforms the subspace feature vectors via gradient descent to minimize the MCR$^2$ distance between the reconstructed data and the original data. We show that the encoder transforms data to linear discriminative representations without breaking the higher-order manifolds, and the decoder reconstructs the data with high fidelity.

The multi-modal data fusion method based on IA-net and CHMM technical proposed is designed to solve the problem that the incompleteness of target behavior information in complex family environment leads to the low accuracy of human behavior recognition.The two improved neural networks(STA-ResNet50、STA-GoogleNet)are combined with LSTM to form two IA-Nets respectively to extract RGB and skeleton modal behavior features in video. The two modal feature sequences are input CHMM to construct the probability fusion model of multi-modal behavior recognition.The experimental results show that the human behavior recognition model proposed in this paper has higher accuracy than the previous fusion methods on HMDB51 and UCF101 datasets. New contributions: attention mechanism is introduced to improve the efficiency of video target feature extraction and utilization. A skeleton based feature extraction framework is proposed, which can be used for human behavior recognition in complex environment. In the field of human behavior recognition, probability theory and neural network are cleverly combined and applied, which provides a new method for multi-modal information fusion.

The powder bed fusion (PBF) metal additive manufacturing (AM) method uses an energy source like a laser to melt the metal powders. The laser can locally melt the metal powders and creates a solid structure as it moves. The complexity of the heat distribution in laser PBF metal AM is one of the main features that need to be accurately addressed and understood to design and manage an optimized printing process. In this research, the dependency of local thermal rates and gradients on print after solidification (in the heat-affected zone) was numerically simulated and studied to provide information for designing the print process. The simulation results were validated by independent experimental results. The simulation shows that the local thermal rates are higher at higher laser power and scan speed. Also, the local thermal gradients increase if the laser power increases. The effect of scan speed on the thermal gradients is opposite during heating versus cooling times. Increasing the scan speed increases the local thermal gradients in the cooling times and decreases the local thermal gradients during the heating. In addition, these simulation results could be used in artificial intelligence (AI) and machine learning for developing digital additive manufacturing.

In this paper, we proposed the fusion of two projection based face recognition algorithms: local binary Patterns in DCT domain and singular value decomposition (SVD) characterized by its simplicity and efficiently. Standard databases ORL are used to test the experimental results which prove that proposed system achieves more accurate face recognition as compared to individual method.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

Gastric cancer is the fifth most common and fourth deadliest cancer worldwide, with a bleak 5-year survival rate of about 20%. Despite significant research into its pathobiology, prognostic predictability remains insufficient due to pathologists’ heavy workloads and the potential for diagnostic errors. Consequently, there is a pressing need for automated and precise histopathological diagnostic tools. This study leverages Machine Learning and Deep Learning techniques to classify histopathological images into healthy and cancerous categories. By utilizing both handcrafted and deep features and shallow learning classifiers on the GasHisSDB dataset, we conduct a comparative analysis to identify the most effective combinations of features and classifiers for differentiating normal from abnormal histopathological images without employing fine-tuning strategies. Our methodology achieves an accuracy of 95% with the SVM classifier, underscoring the effectiveness of feature fusion strategies. Additionally, cross-magnification experiments produced promising results with accuracies close to 80% and 90% when testing the models on unseen testing images with different resolutions.

Synthetic Aperture Radar (SAR) ship target detection has been extensively researched. However, most methods use the same dataset division for both training and validation. In practical applications, it is often necessary to quickly adapt to new loads, new modes, and new data to detect targets effectively. This presents a cross-domain detection problem that requires further study. This paper proposes a method for detecting SAR ships in complex backgrounds using fusion tensor and cross-domain adversarial learning. The method is designed to address the cross-domain detection problem of SAR ships with large differences between the training and test sets. Specifically, it can be used for the cross-domain detection task from the fully-polarised medium-resolution ship dataset (source domain) to the high-resolution single-polarised dataset (target domain). This method proposes a Channel Fusion Module (CFM) based on the YOLOV5s model. The CFM utilises the correlation between polarised channel images during training to enrich the feature information of single-polarised images extracted by the model during inference. The article proposes a module called Cross-Domain Adversarial Learning Module (CALM) to reduce overfitting and achieve adaptation between domains. Additionally, the paper introduces the Anti-Interference Head (AIH) which decouples the detection head to reduce the conflict of classification and localization problems. This improves the anti-interference and generalization ability in complex backgrounds. This paper conducts cross-domain experiments using the constructed medium-resolution SAR full polarization dataset (SFPD) as the source domain and the high-resolution single-polarised ship detection dataset (HRSID) as the target domain. Compared to the best-performing YOLOV8s model among typical mainstream models, this model improves Precision by 4.9%, Recall by 3.3%, AP by 2.4%, and F1 by 3.9%. This verifies the effectiveness of the method and provides a useful reference for improving cross-domain learning and model generalization capability in the field of target detection.

Hyperspectral images (HSI) contain abundant spectral information. Efficient extraction and utilization of this information for image classification remain prominent research topics. Previously, hyperspectral classification techniques primarily relied on statistical attributes and mathematical models of spectral data. Deep learning classification techniques have recently been extensively utilized for hyperspectral data classification, yielding promising outcomes. This study proposes a deep learning approach that uses polarization feature maps for classification. Initially, the polar coordinate transformation method is employed to convert the spectral information of all pixels in the image into spectral feature maps. Subsequently, the proposed Deep Context Feature Fusion Network (DCFF-NET) is utilized to classify these feature maps. The model is validated using three open-source hyperspectral datasets: Indian Pines, Pavia University, and Salinas. The experimental results indicated that DCFF-NET achieves excellent classification performance. Experimental results on three public HSI datasets demonstrate that the proposed method accurately recognizes different objects with an overall accuracy (OA) of 86.68%, 94.73%, and 95.14% based on the pixel method, and 98.15%, 99.86%, and 99.98% based on the pixel-patch method.

Ship detection from synthetic aperture radar (SAR) imagery is crucial for various fields in real-world applications. Numerous deep learning-based detectors have been investigated for SAR ship detection, which require a substantial amount of labeled data for training. However, SAR data annotation is time-consuming and demands specialized expertise, resulting in that deep learning-based SAR ship detectors struggle due to a lack of annotations. With limited labeled data, semi-supervised learning is a popular approach for boosting detection performance by excavating valuable information from unlabeled data. In this paper, a semi-supervised SAR ship detection network is proposed, termed as Multi-Teacher Dempster-Shafer Evidence Fusion Net-work (MTDSEFN). The MTDSEFN is an enhanced framework based on the basic teacher-student skeleton frame, comprising two branches: the Teacher Group (TG) and the Agency Teacher (AT). The TG utilizes multiple teachers to generate pseudo-labels for different augmentation versions of unlabeled samples, which are then refined to obtain high-quality pseudo-labels by using Dempster-Shafer (D-S) fusion. The AT not only serves to deliver weights of its own teacher to the TG at the end of each epoch, but also updates its own weights after each iteration, enabling the model to effectively learn rich information from unlabeled data. The combination of TG and AT guarantees both reliable pseudo-label generation and learning comprehensive diversity information from numerous unlabeled samples. Extensive experiments were performed on two public SAR ship datasets, and the results demonstrated the effectiveness and superiority of the proposed approach.

One of the potential sources of rare-earth elements (REEs) is the solid waste from alumina industry - bauxite residue, known as “red mud” (RM). The main REEs from the raw bauxite are concentrated in RM after the Bayer leaching process. The earlier worldwide studies were focused on the scandium (Sc) extraction from RM by concentrated acids to enhance the extraction degree. This leads to the dissolution of major oxides (Fe2O3 and Al2O3) from RM. This article studies the possibility of selective Sc extraction from alkali fusion red mud (RMF) by diluted nitric acid (HNO3) leaching at pH ≥ 2 to prevent co-dissolution of Fe2O3. RMF samples have been analyzed by X-ray fluorescence spectrometry (XRF), X-ray diffraction (XRD), electron probe microanalysis (EPMA), and inductively coupled plasma mass spectrometry (ICP-MS). Sc extraction has been found to be 71.2 % at RMF leaching by HNO3 at pH=2 and at 80 °C during 90 min. The kinetic analysis of experimental data by the shrinking core model has shown that Sc leaching process is limited by the interfacial diffusion and the diffusion through the product layer. The apparent activation energy (Ea) was 19.5 kJ/mol. We have established that according to EPMA of RMF, Sc is associated with iron minerals; it could act as the product layer. The linear dependence of Sc extraction of magnesium (Mg) extraction has been revealed. This fact indicates that Mg can act as a leaching agent of Sc presented in RMF by ion-exchangeable phase.

With the growing demand for positioning services, angle-of-arrival (AoA) estimation or direction-finding (DF) has been widely investigated for applications in fifth-generation (5G) technologies. Many existing AoA estimation algorithms only require the measurement of the direction of the incident wave at the transmitter to obtain correct results. However, for most of the cellular systems such as Bluetooth indoor positioning system due to multipath and non-line-of-sight (NLOS) propagation, the indoor positioning accuracy is severely affected. In this paper a comprehensive algorithm is investigated that combines radio measurements from Bluetooth AoA local navigation systems with indoor position estimates obtained using particle filtering, which allows us to explore new optimised methods to reduce estimation errors in indoor positioning. First, particle filtering is used to predict the rough position of a moving target. Then, an algorithm with robust beam weighting is used to estimate the AoA of the multipath components. Based on this, a system of pseudo-linear equations for target positioning based on the probabilistic framework of PF and AoA measurement is derived. Theoretical analysis and simulation results show that the algorithm can improve the positioning accuracy by about 25.7% on average.

Global optimization is widely adopted nowadays in a variety of practical and scientific problems. In this context, a group of techniques that is widely used is that of evolutionary techniques. A relatively new evolutionary technique in this direction is that of Giant Armadillo Optimization, which is based on the hunting strategy of giant armadillos. In this paper, a number of modifications to this technique are proposed, such as the periodic application of a local minimization method as well as the use of modern termination techniques based on statistical observations. The proposed modifications have been tested on a wide - series test functions, available from the relevant literature and it was compared against other evolutionary methods.

Laser-targeted weeding methods further enhance the sustainable development of green agriculture, with one key technology being the improvement of weed localization accuracy. Here, we propose an improved YOLOv8 instance segmentation based on bidirectional feature fusion and deformable convolution (BFFDC-YOLOv8-seg) to address the challenges of insufficient weed localization accuracy in complex environments with resource-limited laser weeding devices. Initially, by training on extensive datasets of plant images, the most appropriate model scale and training weights are determined, facilitating the development of a lightweight network. Subsequently, the introduction of the Bidirectional Feature Pyramid Network (BiFPN) during feature fusion effectively prevents the omission of weeds. Lastly, the use of Dynamic Snake Convolution (DSConv) to replace some convolutional kernels enhances flexibility, benefiting the segmentation of weeds with elongated stems and irregular edges. Experimental results indicate that the BFFDC-YOLOv8-seg model achieves a 4.9% increase in precision, an 8.1% increase in recall rate, and a 2.8% increase in mAP50 value to 98.8% on a vegetable weed dataset compared to the original model. It also shows improved mAP50 over other typical segmentation models such as Mask R-CNN, YOLOv5-seg, and YOLOv7-seg by 10.8%, 13.4%, and 1.8%, respectively. Furthermore, the model achieves a detection speed of 24.8 FPS on the Jetson Orin nano standalone device, with a model size of 6.8MB that balances between size and accuracy. The model meets the requirements for real-time precise weed segmentation, suitable for complex vegetable field environments and resource-limited laser weeding devices.

Sleep deficiency impacts the quality of life and may have serious health consequences in the long run. Questionnaire-based subjective assessment of sleep deficiency has many limitations. On the other hand, objective assessment of sleep deficiency is challenging. In this study, we propose a polysomnography-based mathematical model for computing baseline sleep deficiency severity score and then investigated the estimation of sleep deficiency severity using features available only from wearable sensor data including heart rate variability and single-channel electroen-cephalography for a dataset of 500 subjects. We used Monte-Carlo Feature Selection (MCFS) and inter-dependency discovery for selecting the best features and removing multi-collinearity. For developing the Regression model we investigated both the frequentist and the Bayesian ap-proaches. An Artificial Neural Network achieved the best performance of RMSE = 5.47 and an R-squared value of 0.67 for sleep deficiency severity estimation. The developed method is com-parable to conventional methods of Functional Outcome of Sleep Questionnaire and Epworth Sleepiness Scale for assessing the impact of sleep apnea on sleep deficiency. Moreover, the results pave the way for reliable and interpretable sleep deficiency severity estimation using a wearable device in Smart Health.

This research refers to the creation of a method of medical technology such as a device that can collect, encode, temporarily store, and transfer electroencephalographic signals from epileptic patients for study and evaluation. For applying the specific apparatus related to neurostimulation technologies such as Deep Brain Stimulation (DBS) and Responsive Neurostimulation (RNS) the approaches followed concern three basic levels. The first level is concerned with initial modelling and creating the template for the following two stages. Further on, the second level is based on developing code for programming integrated circuits and simulation with the appropriate software. The third and final stage is the most substantial, as the transmitter’s construction is attempted at the evaluation level. In particular, more than one software and device are involved in this phase for the most realistic performance of the desired result. However, it is worth noting that the principal goal of this study is to achieve low consumption rates for the device’s complete and smooth operation. As a result, this research aims to evolve this device so that it can send data wirelessly and simultaneously provide energy efficiency.

Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC) and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA) and variable combination population analysis and iterative retained information variable algorithm (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC+VCPA-IRIV+SVR (R2C=0.995, R2P=0.986, RPD=8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil.

Digital Elevation Models (DEMs) are widely used in geographic and environmental studies. In the current work, the fusion of multi-source DEMs is investigated to improve the overall accuracy of public domain DEMs. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods – the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD) and nonlinear adaptive multi-scale decomposition (N-AMD) - are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D – 30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the ice cloud and land elevation satellite (ICESat) global land surface altimetry data as a reference. The vertical accuracy of the fused DEM shows significant improvements of 29.6% and 19.3% in a mountainous region and 27.4% and 15.5% in a low-relief region, compared to the SRTM-1 and AW3D30 respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM.

The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, the online model identification is scrutinized to achieve high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation alertness and numerical stability, so as to achieve accurate online adaption of model parameters. Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of LIB. Simulation and experimental studies are performed to evaluate the performance of the proposed data-model fusion method. Results suggest that the proposed method can effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high accuracy and high stability.

Ultraviolet light incident on organic material can initiate its spontaneous dissipative structuring into chromophores which can catalyze their own replication. This may have been the case for one of the most ancient of all chromophores dissipating the Archean UVC photon flux, the nucleic acids. Oligos of nucleic acids with affinity to particular amino acids which foment UVC photon dissipation would have been selected through non-equilibrium thermodynamic imperatives which favor entropy production. Indeed, we show here that those amino acids with characteristics most relevant to fomenting UVC photon dissipation are precisely those with greatest chemical affinity to their codons or anticodons. Entropy production could thus provide an explanation for the accumulation of information in nucleic acids relevant to the dissipation of the externally imposed thermodynamic potentials. The accumulation of information in this manner provides a link between evolution and entropy production.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

In order to perform multi-degree-of-freedom motion measurements of marine machinery such as ship-borne mechanical platform in an absolute environment without a reference, absolute measurement methods using acceleration sensors and tilt gyroscopes are typically employed. However, due to the influence of wave forces on ship-borne mechanical platform, the coupling between different degrees of freedom can cause mutual interference, resulting in significant sensor measurement disturbances that make efficient computation and real-time analysis challenging. Specifically, when ship-borne mechanical platform swings, the proof mass of the acceleration sensor produces an undesirable signal output in the vertical direction, which leads to imprecise acceleration integration, thereby affecting accurate motion collection and posture estimation. To address these challenges, by analyzing the influence of the inclination angle of the ship-borne mechanical platform on the sensor measurement, and using the working principle of the acceleration sensor and angle sensor, a correction method for the motion measurement of the ship-borne mechanical platform based on multi-sensor fusion is proposed. In this article, we first analyze the influence of inclination angle on the integral effect in the heave direction. Then the configuration using four groups of acceleration sensors to correct the integral effect is proposed. Finally, the optimal inclination angle is determined through Kalman filtering based on the measured value of angle sensors and estimated values from the acceleration sensor sets. Experiments have proved that the average error of the corrected heave displacement signal is 25.34 mm, which is better than the integral displacement signal of a single acceleration sensor. At the same time, the acceleration sensor is used to calculate the roll angle and pitch angle of the ship-borne mechanical platform, and combined with the angle sensor signal to perform Kalman filtering, which filters out the errors caused by the shaking and instability of the mechanical platform, and can more accurately estimate the true inclination of the platform. Therefore, this method can enhance the precision and accuracy of the ship-borne mechanical platform motion signal acquisition, providing more valuable experimental data for research in marine engineering and related fields.

Although Eulerian approaches are standard in computational acoustics, they are less effective for certain classes of problems like bubble acoustics and combustion noise. A different approach for solving acoustic problems is to compute with individual particles following particle motion. In this paper, a Lagrangian approach to model sound propagation in moving fluid is presented and implemented numerically, using three meshfree methods to solve the Lagrangian acoustic perturbation equations (LAPE) in the time domain. The LAPE split the fluid dynamic equations into a set of hydrodynamic equations for the motion of fluid particles and perturbation equations for the acoustic quantities corresponding to each fluid particle. Then, three meshfree methods, the smoothed particle hydrodynamics (SPH) method, the corrective smoothed particle (CSP) method, and the generalized finite difference (GFD) method, are introduced to solve the LAPE and the linearized LAPE (LLAPE). The SPH and CSP methods are widely used meshfree methods, while the GFD method based on the Taylor series expansion can be easily extended to higher orders. Applications to modeling sound propagation in steady or unsteady fluids in motion are outlined, treating a number of different cases in one and two space dimensions. A comparison of the LAPE and the LLAPE using the three meshfree methods is also presented. The Lagrangian approach shows good agreement with exact solutions. The comparison indicates that the CSP and GFD method exhibit convergence in cases with different background flow. The GFD method is more accurate, while the CSP method can handle higher Courant numbers.

The paper describes the new method for image compression based on coordinated group signal transformation. The algorithm is a type of difference coding. Coordinated processing significantly simplifies the difference signal conversion scheme using a single group codec for all signals. The described method considers color channels as correlated signals of a multi-channel communication system. The specifics of the processed data required modification of the coordinated group processing algorithm. First, we changed how to calculate the difference signals to prevent data loss. Secondly, the group codec was supplemented with a neural network to improve the quality of reconstructed images. We considered the following types of neural networks: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, it is best to use fully connected neural networks if the goal is to minimize processing delay time. These networks have a response time of 13 ms. On the other hand, if the priority is to improve quality in cases where delays are not critical, then convolution neural networks in the Fourier space should be used. These networks have a response time of 140 ms and are of significant interest.

The main focus of the in vitro toxicity assessment methods is on the viability of the cells, which is usually based on metabolism changes. Yet, when exposed to toxic substances, the cell triggers multiple signals in response. With this in mind, we have developed a promising cell-based toxicity method that observes various cell responses when exposed to toxic substances (either death, division or remaining viable). Based on the collective cell response, we observed and predicted the dynamics of the cell population to determine the toxicity of the toxicant. The method was tested with two different conformations: In the first conformation, we exposed a monoculture model of blood macrophages to UV light, hydrogen peroxide, nutrient deprivation, tetrabromobisphenol A, fatty acids and 5-fluorouracil. In the second, we exposed a coculture liver model consisting of hepatocytes, hepatic stellate cells, Kupffer cells and liver sinusoidal endothelial cells to rifampicin, ibuprofen and 5-fluorouracil. The method showed good accuracy compared to established toxicity assessment methods. In addition, this approach provided more representative information on the toxic effects of the compounds as it considers the different cellular responses induced by toxic agents.

Object detection algorithms for open water aerial images present challenges such as small object size, unsatisfactory detection accuracy, numerous network parameters, and enormous computational demands. Current detection algorithms struggle to meet the accuracy and speed requirements while being deployable on small mobile devices. This paper proposes DFLM-YOLO, a lightweight small-object detection network based on the YOLOv8 algorithm with multiscale feature fusion. Firstly, to solve the class imbalance problem of the SeaDroneSee dataset, we propose a data augmentation algorithm called Small Object Multiplication (SOM). SOM enhance dataset balance by increasing the number of objects in specific categories, thereby improving model accuracy and generalization capabilities. Secondly, we optimize the backbone network structure by implementing Depthwise Separable Convolution (DSConv) and the newly designed FC-C2f, which reduces the model's parameters and inference time. Finally, we design the Lightweight Multiscale Feature Fusion Network (LMFN) to address the challenges of multiscale variations by gradually fusing the four feature layers extracted from the backbone network in three stages. In addition, LMFN incorporates the Dilated Re-param Block structure to increase the effective receptive field and improve the model's classification ability and detection accuracy. Experimental results on the SeaDroneSea dataset indicate that DFLM-YOLO improves mAP by 12.4% compared to the original YOLOv8s, while reducing parameters by 67.2%. This achievement provides a new solution for UAVs to conduct object detection missions in open water efficiently.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the corporate distribution image, and further reduce corporate operating costs and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identified the locations of primary and secondary logistics nodes but also provided a new perspective for constructing the urban last-mile logistics system, enriching the academic research related to logistics node construction. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

Proton Exchange Membrane Fuel Cell (PEMFC) is a fast-developing battery technology, and the key to its reliability and lifespan improvement lies in the accurate assessment of durability. However, the degradation mechanism of PEMFC is hard to determine and its internal parameters are highly coupled. Thus, the development of the more accurate life prediction model that meets the actual scenarios is urgent to investigate. To solve this problem, a multi-feature fusion life prediction method based on Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) is proposed. A TCN algorithm is used as the prediction base model, and two GRU modules are included to the model to strengthen the model expression ability and improve its predictive accuracy. Two widely recognized datasets and two operating conditions are utilized for model training and prediction, respectively. Comparisons are made with single-feature parameter models in terms of Root Mean Square Error (RMSE) and Determination Coefficient (R2). The results show that the prediction accuracy of the TCN-GRU multi-feature fusion model is higher than that of the single-feature models in terms of stability and anti-interference under both operating conditions. Furthermore, with the increase of input feature parameters, the TCN-GRU model is closer to the real value, which proves once again that the proposed model can meet the accuracy requirements of the life prediction of PEMFC.

Age-related Macular Degeneration (AMD) is the major cause of elders’ vision loss, early screening and treatment are the most efficient way to reduce the rate of blindness. AI-based methods based on ophthalmic images play great potential for AMD diagnosis. However, low levels of accuracy, robustness, and explainability are challenges for AI approaches applied in clinics. Thus, this study proposed a multi-type of data source fusion method and a multi-model fusion approach for AMD detection. Typical unsupervised (Hierarchical Clustering and K-Means), typical supervised (SVM, VGG-16, and ResNet) methods, and proposed methods (multi-source data fusion-based method and multi-model fusion-based approach) are compared based on Optical Coherence Tomography (OCT), Fundus Autofluorescence (FAF), regular color fundus photography (CFP) and Ultra-Wide field Fundus (UWF) images. Data preprocessing and enhancements of each type of data are discussed. A feature extraction based on unsupervised ML models, feature combination and normalization, and multi-layer perception (MLP) algorithm are involved in the proposed multi-source data fusion-based method. Supervised ML and DL models and a voting mechanism are involved in the multi-model fusion-based approach. Findings show that the proposed methods present a high performance of accuracy and robustness. A real-world UWF database is involved from Shenzhen Aier Hospital. Practical and theoretical contributions are delivered. A reference value for medical diagnosis based on multiple digital images is contributed.

Fault line detection timely and accurately when single-phase-to-earth fault occurs in resonant grounding system is still a focus of research. This paper presents a new approach for fault detection based on data fusion and it has non-artificial setting. Firstly, the fault criterion for interphase difference energy ratio and time-frequency correlation coefficient of each line is proposed. Subsequently, the paper establish a coordinate system with the interphase difference energy ratio as X axis and the time-frequency correlation coefficient as Y axis, and it uses the Euclidean distance algorithm to get the characteristic distance of each line by fusing two-dimensional information. Finally, comparing the sound distance and the fault distance of each line to discriminate the fault line. Electromagnetic Transients Program (EMTP) simulation results and adaptability analysis have confirmed the effectiveness and reliability of the proposed scheme.

Image compression is a top priority today due to the need for faster encoding and decoding. To achieve this, the present study has proposed the use of Canonical Huffman Coding (CHC) as an entropy coder, which has a lower decoding time complexity than binary Huffman codes. For image compression, combining of the Discrete Wavelet Transform (DWT) and CHC with Principal Component Analysis (PCA) has been recommended. The lossy method has been introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed image has been found to have a better peak signal-to-noise ratio (PSNR) value. This study has developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for efficient use of storage space. The proposed technique has achieved up to 60% compression while maintaining high visual quality. This method has also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach has been tested on various color images, including Peppers 512×512 and Couple 256×256, showing improvement by 17 dB and 22 dB, respectively, while reducing bpp by 0.56 and 0.10, respectively. For grayscale images, i.e., Lena 512×512 and Boat 256×256, the proposed method has shown an improvement by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases.

The emergence of carbapenemase-producing Enterobacteriaceae has limited therapeutic options and presents a major public health problem. Resistances against carbapenems in this bacterial family are mostly conveyed by metallo-beta-lactamases (MBL) including VIM, which are often encoded on resistance plasmids. We characterized four VIM-positive isolates that were obtained as part of a routine diagnostic screening from two laboratories in north-eastern Germany be-tween June and August 2020. Whole-genome sequences were generated to address a) their phylo-genetic properties, b) their plasmid content, and c) the underlying mechanisms of resistance and virulence. In addition, we performed phenotypic antibiotic and mercury resistance analyses. The genomic analysis revealed three different bacterial species including C. freundii, E. coli and K. oxytoca with four different sequence types. All isolates were geno- and phenotypically multi-drug-resistant (MDR). Three isolates of four carried identical VIM-1-resistance plasmids, which – in addition – encoded for a mercury resistance operon and showed some similarity to two ex-ternal plasmid sequences. The distribution of virulence features among the genomes was limited. Our results highlight the circulation of an identical VIM-1-resistance plasmid in three of four bacterial isolates of different Enterobacteriaceae in north-eastern Germany.

Polarimetric Synthetic Aperture Radar (PolSAR) images encompass valuable information that can facilitate extensive land cover interpretation and generate diverse output products. Extracting meaningful features from PolSAR data poses challenges distinct from those encountered in optical imagery. Deep Learning (DL) methods offer effective solutions for overcoming these challenges in PolSAR feature extraction. Convolutional Neural Networks (CNNs) play a crucial role in capturing PolSAR image characteristics by leveraging kernel capabilities to consider local information and the complex-valued nature of PolSAR data. In this study, a novel three-branch fusion of complex-valued CNN named Shallow to Deep Feature Fusion Network (SDF2Net) is proposed for PolSAR image classification. To validate the performance of the proposed method, classification results are compared against multiple state-of-the-art approaches using the Airborne Synthetic Aperture Radar (AIRSAR) datasets of Flevoland, San Francisco, and ESAR Oberpfaffenhofen dataset. The results indicate that the proposed approach demonstrates improvements in OA, with 1.3% and 0.8% enhancement for the AIRSAR datasets and 0.5% improvement for the ESAR dataset. The analyses conducted on the Flevoland data underscore the effectiveness of the SDF2Net model, revealing a promising OA of 96.01% even with only 1% sampling ratio.

We propose a neural-network-based watermarking method that introduces the quantized activation function that approximates the quantization of JPEG compression. Many neural-network-based watermarking methods have been proposed. Conventional methods have acquired robustness against various attacks by introducing an attack simulation layer between the embedding network and the extraction network. The quantization process of JPEG compression was replaced by the noise addition process in the attack layer of the conventional methods. In this paper, we propose a quantized activation function that can simulate the JPEG quantization standard as it is in order to improve the robustness against the JPEG compression. Our quantized activation function consists of several hyperbolic tangent functions and is applied as an activation function for neural networks. Our network was introduced in the attack layer of ReDMark proposed by Ahmadi et al. to compare it with their method. That is, the embedding and extraction networks had the same structure. We compared the usual JPEG compressed images and the images applying the quantized activation function. The results showed that a network with quantized activation functions can approximate JPEG compression with high accuracy. We also compared the bit error rate (BER) of estimated watermarks generated by our network with those generated by ReDMark. We found that our network was able to produce estimated watermarks with lower BERs than those of ReDMark. Therefore, our network outperformed the conventional method with respect to image quality and BER.

The branch prediction units (BPUs) generally have security vulnerabilities, which can be used by attackers to obtain the execution status, jump directions, and jump address of conditional branches, and the existing protection methods cannot defend against these attacks. Therefore, this article proposes a hardware security protection method for conditional branches of embedded systems. This method calculates the number of updates to the branch target buffer (BTB). When it exceeds the threshold, BTB is locked to prevent attackers from analyzing the execution status of branches based on the time difference of whether BTB is updated. Moreover, the hybrid physical unclonable function (PUF) circuit is designed to provide confidentiality protection for the jump directions, jump addresses, and their indexes, preventing attackers from stealing these critical data. If these mechanism fails and attackers successfully tamper with conditional branches, this paper proposes a control flow integrity (CFI) protection mechanism based on branch labels to timely detect tampering with instruction codes, jump addresses, and jump directions. The proposed method is implemented and tested on FPGA. The experimental results show that this method can achieve fine-grained security protection for conditional branches, with about 5.4% resource overhead and less than 5.5% performance overhead.

Background/Objectives:The first attempts of posterior lumbar interbody fusion can be traced back to the 1940s, where their performance was marked by animosity due to high rates of failure. Nowadays, the use of interbody fusion involves a much more complicated consideration of factors such as global alignment, disc height, fusion rates, and operative risk, all combined at once.Our study aims to examine all these effects using Flarehawk 9 as a means of interbody fusion.Materials and methods: A total of 58 patients underwent open posterior lumbar surgery using Flarehawk 9 between September 2021 and February 2023 with a mean follow up of 1 year.The mean age was 59.8 years old ( range between 33 and 79 years old), 36 of them are males while 22 of them are females. The diagnostic groups included spinal canal stenosis (n=40), failed back surgery syndrome-revision surgery (n=5),spondylolisthesis with slip percentage 25% (n=4),recurrent herniated disc (n=7) and adjacent segment disease (n=2). Results: The majority of patients demonstrated remarkable improvement based on ODI scores.The rate of union varied based on the fused levels and whether the patients had previous surgery or not.The amount of lordosis postoperatively is increased by 2±0.4 degrees.Conclusions: Even though anterior approaches become more and more popular and advanced nowadays, posterior fusion with interbody cage still remains a safe and effective method to treat various spine surgical pathologies with optimal results.

Flower color is an important ornamental feature, often modulated by the contents of flavonoid. Chalcone synthase is the first key enzyme in the biosynthesis of flavonoid, but little is known about the role of R. delavayi CHS in flavonoid biosynthesis. In this paper, three CHS genes (RdCHS1-3) were successfully cloned from R. delavayi flowers. According to multiple sequence alignment and phylogenetic analysis, only RdCHS1 contained all the highly conserved and important residues, and classified into the cluster of bona fide CHSs. Thus RdCHS1 was then subjected for further functional analysis. Real-Time PCR analysis revealed that transcripts of RdCHS1 was the highest in leaves and lowest in root, did not match the anthocyanin accumulation patterns during flower development. Biochemical characterization displayed that RdCHS1 could catalyze p-coumaroyl-CoA and malonyl-CoA molecules to produce naringenin chalcone. Physiological function of RdCHS1 was checked in Arabidopsis mutant and tobacco, the results showed RdCHS1 transgenes could recover the color phenotypes of tt4 mutant and made tobacco flower color from pink to dark pink through modulating the expression of endogenous structural and regulatory genes in tobacco. All these results demonstrated that RdCHS1 is a bona fide CHS and contribute to flavonoid biosynthesis in R. delavayi.

Based on orbital angular momentum (OAM) properties of Laguerre Gaussian beams LG(p,ℓ), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on intensity profile generated by a coherent superposition of two OAM-carrying Laguerre-Gaussian modes and Machine-learning detection method. In the encoding process, the intensity profile for data encoding is generated based on selection of p and ℓ indices, while the decoding process is performed using support vector machine (SVM) algorithm. Two different decoding models based on SVM algorithm are tested to verify the robustness of optical encoding model, finding a BER =10−9 for 10.2dB of signal-to-noise ratio in one of the SVM models.

The segmentation of abnormal regions is vital in smart manufacturing. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby impeding the rapid production of industrial assembly lines. To alleviate this issue, we propose the two-stage Illumination-aware Sauce-packet Leakage Segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: Illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose an image enhancement to relieve the impact of uneven illumination, enhancing the texture details of ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the Multi-scale Feature Fusion Module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by adaptive weighting of spatial and channel dimensions. Furthermore, we generated a self-built dataset of sauce-packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. Our code is available at https://github.com/LSJ5106/SauceDetect.

Brand trust holds significant importance in the Mongolian cashmere industry. This study examines the primary predictors of customer brand trust among Mongolian cashmere users. Our research includes a survey with 800 participants and 20 individual interviews conducted among both Mongolian cashmere users and non-users. We utilized an online platform to reach participants, employing convenience sampling. The study investigates the relationship between various factors and brand trust within the context of Mongolian cashmere brands. Through a combination of in-dividual interviews and statistical analysis, we test six hypotheses. The findings indicate that higher brand quality, a strong reputation, greater transparency, effective issue resolution, commitment to environmental and social responsibility, and pre-purchase research on such commitments positively impact brand trust. Key insights gleaned from the interviews emphasize the importance of quality, reputation, transparency, customer service, and environmental responsibility in shaping consumer trust. These results offer valuable implications for cashmere brands aiming to bolster consumer trust and foster loyalty.

Strip steel surface defect recognition research has important research significance in industrial production. Aiming at the problems of defect feature extraction difficulty, slow detection speed and insufficient dataset, YOLOv5 is improved on the basis of YOLOv5, and YOLO-LFPD (Lightweight fine particle detection) model is proposed. By introducing the RepVGG (Re-param VGG) module, the robustness of the model to the surface defect features of strip steel is enhanced, and the expressive ability of the model is improved. FasterNet is used to replace the backbone network, which ensures the accuracy and accelerates the inference speed, making the model more suitable for real-time monitoring. The use of pruning, GA genetic algorithm with OTA loss function method further reduces the model size while better learning the strip steel defect feature information, thus improving the generalisation ability and accuracy of the model. The experimental results show that the introduction of the RepVGG module and the use of FasterNet can well improve the model performance, with a reduction of 48% in the number of parameters, a reduction of 13% in the number of GFLOPs, an inference time of 77% of the original, and an optimal accuracy compared with the network models in recent years. The experimental results on the NEU-DET dataset show that the accuracy of YOLO-LFPD is improved by 3% to 81.2%, which is better than other models, and provides new ideas and references for the lightweight strip steel surface defect detection scenarios and application deployment.

In emergency situations, ensuring standardized Cardiopulmonary Resuscitation (CPR) actions is crucial. However, current Automated External Defibrillators (AEDs) lack methods to determine whether CPR actions are performed correctly, leading to inconsistent CPR quality. To address this issue, we introduce a novel method called Deep Learning-based CPR Action Standardization (DLCAS). This method involves three parts. First, it detects correct posture using OpenPose to recognize skeletal points. Second, it identifies a marker wristband with our CPR-Detection algorithm and measures compression depth, count and frequency using a depth algorithm. Finally, we optimize the algorithm for edge devices to enhance real-time processing speed. Extensive experiments on our custom dataset have shown that the CPR-Detection algorithm achieves a mAP0.5 of 97.04%, while reducing parameters to 0.20M and FLOPs to 132.15K. In a complete CPR operation procedure, the depth measurement solution achieves an accuracy of 90% with a margin of error less than 1 cm, while the count and frequency measurements achieve 98% accuracy with a margin of error less than 2 counts. Our method meets the real-time requirements in medical scenarios, and the processing speed on edge devices have increased from 8fps to 25fps.

Persons with stroke-induced aphasia (PWAs) are often impaired in tense/time reference (TR) production. It is not clear, however, whether PWAs’ impaired TR production is due to TR-related encoding or retrieval deficits. This study aims at disentangling TR-related encoding deficits from TR-related retrieval deficits in aphasia. Two sentence completion tasks (SCTs) tapping production of past and future reference were administered to eight Greek-speaking PWAs, eight Russian-speaking PWAs, six Italian-speaking PWAs, seven English-speaking PWAs and four groups of language-, age- and education-matched healthy controls. SCT 1 tapped TR-related encoding processes and TR-related retrieval processes to a similar extent. SCT 2 predominantly tapped TR-related retrieval processes. All four control groups performed at ceiling. Three Greek-speaking PWAs, one Russian-speaking PWA, three Italian-speaking PWAs and two English-speaking PWAs showed between-task dissociations. A double dissociation emerged, as some Greek-, Russian- and English-speaking PWAs performed better on SCT 1 than on SCT 2, whereas other Greek- and Italian-speaking PWAs performed worse on SCT 1 than on SCT 2. It will be shown that the experimental design employed here has the potential to disentangle TR-related encoding deficits from TR-related retrieval deficits, as both PWAs with selective TR-related encoding deficits and PWAs with selective TR-related retrieval deficits were identified.

Abstract In order to solve the problem that it is difficult to obtain the accurate trigger signal moment when using the time difference of UAV flight acoustic signals collected by acoustic sensors arranged in advance to locate the position of UAVs when they cannot be tracked by radar and are difficult to be observed by the human eye, an acoustic source localisation method with improved empirical modal decomposition (REMD) under an adaptive frequency window is proposed. Firstly, the collected UAV flight signals are smoothed and filtered, then the robust empirical modal decomposition (REMD) is used for IMF modal decomposition and spectral analysis of the collected signals, and a panning frequency window with variable bandwidth is introduced for corresponding frequency locking and extracting the IMFs; the GWO optimisation is used and the sliding metrics are set to determine the position of the panning frequency window automatically; finally, the extracted Finally, the extracted IMF components are reconstructed according to the criterion, and the trigger signal moments in the reconstructed IMF components are resolved and recorded, and the algorithm is used to calculate the difference between the reception moments of different sensors; for the traditional sound source localisation algorithms with a small detection range, which are prone to fall into the no-solution or false-solution, i.e., unable to achieve the localisation, a weighted least-squares-based Chan-Taylor algorithm is proposed for the localisation computation, and a weighted least-squares algorithm is proposed for the localisation computation, through the Inputting the sensor delay parameter and solving the linear equation system to get the target position information; finally, this paper verifies the robustness and performance of the design method with simulated and actual signals. The results show that the maximum positioning error is no more than 5% in an area with a side length of 15m in the measurement area. With the improved accuracy of time delay estimation, the positioning simulation results meet the positioning requirements even though the positioning error is further expanded when the measurement area is further expanded.

Rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can obtain images with centimeter or even subcentimeter ground resolution, which provides a new efficient and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we propose a real-time edge reconstruction crack detection network (ERNet) which adopts multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and background. To capture global dependencies across spatial and channel levels, we propose an efficient bilateral decomposed convolutional attention module (BDAM) that combines depth separable convolution and dilated convolution to capture global dependencies across spatial and channel levels. To enhance the accuracy of crack detection, we use a coordinate-based fusion module that integrates spatial, semantic, and edge reconstruction information. In addition, we propose an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. Experimental results demonstrate that our network achieves the best balance between accuracy and inference speed.

Introduction: Anterior cervical discectomy and fusion (ACDF) for cervical disc herniation (CDH) is commonly performed. Specific postoperative complications include dysphagia, dysphonia, cervicalgia, adjacent segment disorder, cage subsidence, and infections. However, interscapular pain is commonly reported by these patients after surgery, although its mechanisms have been not clarified yet. Methods: This retrospective series of 31 patients undergoing ACDF for CDH at a single Academic Hospital. Baseline and postoperative clinical, radiological, and surgical data were analyzed. The linear regression analysis was conducted to identify any factor independently influencing the occurrence rate of postoperative interscapular pain. Results: The mean age was 57.6±10.8 years, and the M:F ratio was 2.1. The preoperative mean VAS-arm was 7.15±0.81 among the 20 patients reporting brachialgia, and the mean VAS-neck was 4.36±1.43 among those 9 patients reporting cervicalgia. At 1 month interscapular pain was still reported by 8 out of the 17 patients who experienced it postoperatively, and it was recovered in all patients after 2 months. The regression analysis showed how the interscapular pain was not directly associated to age(p=0,74), gender(p=0,46), smoking status(p=0,44), diabetes(0,42), preoperative brachialgia(p=0,21) or cervicalgia(p=0,48), symptoms duration(p=0,13), baseline VAS-arm(p=0,11), VAS-neck(p=0,93), or mJOA(p=0,63) scores, or disc height modification(p=0,90). However, the postoperative increase of the mean zygapophyseal joint rims distance was identified as an independent factor in determining interscapular pain (p=0,02). Conclusions: Our study revealed that the onset of interscapular pain following ACDF may be determined by over-distraction of the zygapophyseal joints rim. Then, proper sizing of prosthetic implants could reduce this painful complication.

Chromium (Cr) metal has garnered significant attention in alloy systems owing to its exceptional properties, such as high melting point, low density, and superior oxidation and corrosion resistance. However, its processing capabilities are hindered by the high Ductile-Brittle Transition Temperature (DBTT). Recently, powder bed fusion-laser beam for metals (PBF-LB/M) has emerged as a promising technique, offering the fabrication of net shapes and precise control over crystallographic texture. Nevertheless, research investigating the mechanism underlying crystallographic texture development in pure Cr via PBF-LB/M still needs to be conducted. This study explores the impact of scan speed on relative density and crystallographic texture. At the optimal scan speed, an increase in grain size attributed to epitaxial growth was observed, resulting in the formation of cubic texture. Consequently, a reduction in High Angle Grain Boundaries (HAGB) was achieved, suppressing defects such as cracks and enhancing relative density up to 98.1%. Furthermore, with increasing densification, Vickers hardness also exhibited a corresponding increase. These findings underscore the efficacy of PBF-LB/M for processing metals with high DBTT properties.

The monocular structured light measurement system has low cost, simple algorithms, and a wide measurement range, making it widely applicable in multiple fields. However, in practical applications, the system may be affected by various factors including noise, non-linear intensity, changes in the reflectivity of the object being measured, and the calibration method and accuracy of the system, thereby reducing the measurement accuracy. In order to solve these issues, this paper proposes a new monocular structured light measurement method, mainly comprises the following two points: (a) A denoising algorithm for phase images based on DAE is proposed, which calculates the wrapped phase of the phase-shifted fringes after initial denoising by DAE. Then, a new phase-shifted fringe pattern is generated and input into DAE for iterative denoising, achieving high-performance image denoising and high-precision wrapped phase solution. (b) A new absolute phase height calibration algorithm is proposed, which introduces camera internal and external parameters and uses a two-layer feedforward network to directly establish the relationship between phase and the 3D coordinate system. Without the need for high-precision motion platforms, high-precision phase height calibration is attainable. In general, compared to conventional methods, the experimental results indicate the effectiveness of the proposed method for low-quality phase-shifted fringe 3D reconstruction. In addition, the measurement method described in this article also demonstrates effectiveness in high-dynamic scenes.

As argued by ‘Declaration of Concrete Environment (2010)’ of Korea and ‘Declaration of Asian Concrete Environment (2011)’ of six Asian countries, concrete as a single material has lately shown extremely large impact on environmental issues such as climate change. Assessment of environmental impact from concrete material and production has considerable importance. Concrete is a major material used in the construction industry that emits a large amount of substances with environmental impacts during its life cycle. Accordingly, technologies for the reduction in and assessment of the environmental impact of concrete from the perspective of Life Cycle Assessment must be developed. At present, the studies in relation to greenhouse gas emission from concrete are being carried out globally as a countermeasure against climate change. In this study, a sustainable concrete mix design algorithm was designed using correlation analyses, and its carbon emission and cost reduction performances were assessed. Using correlation analyses, the concrete strength, w/b and s/a ratios, and CO₂ emissions were identified as major variables of concrete mix design that influenced other variables. Also, this study aims to evaluate the CO₂ emission reduction performance of the algorithm-deduced sustainable concrete mix design, and therefore, the CO₂ emissions of the sustainable concrete mix design are compared with those of the actual concrete mix design applied to the construction of the office building A in South Korea.

Dissimilar welds between ferritic and austenitic steels represent a good solution for exploiting the best performance of stainless steels at high and low temperatures and in aggressive environments, while minimizing costs. Therefore, they are widely used in nuclear and petrochemical plants, however, due to the different properties of the steels involved, the welding process can be challenging. Fusion welding can be specifically addressed to connect low-carbon, or low-alloy steels, with high-alloy steels, which have similar melting point. Welding of thick plates can be performed with electric arc in multiple passes or in a single pass by means of a laser beam equipment. Since microstructure and consequently mechanical properties of the weld are closely related to composition, the choice of the filler metal and processing parameters, which in turn affects the dilution rate, plays a fundamental role. Numerous technical solutions were proposed for welding dissimilar steels and much research was developed on the weldment metallurgy; therefore, this article is aimed at a review of the most recent scientific literature on the issues relating to the fusion welding of ferritic / austenitic steels. Two specific sections are dedicated respectively to electric arc and laser beam welding; finally, metallurgical issues, related to dilution and thermal field are debated in the discussion section.

Tuberculosis (TB) is an infectious disease declared a global emergency by the World Health Organization and continues as one of the world’s top ten causes of death. TB diagnosis is more critical in developing countries where demanded infrastructure for detection, and treatment complicates the efforts against the disease. These aspects related to limited resources are significant, especially in areas away from the main cities, with few mechanisms to make a timely diagnosis that contributes to successfully addressing the possible patients. Artificial intelligence has begun to be essential in providing additional strategies in the diagnosis processes for health professionals’ support. This paper uses natural language processing (NLP) and machine learning (ML) techniques to create models that can supply TB diagnosis when the needed infrastructure is unavailable. Two different sources were explored: text extracted from electronic medical records (EMR) and patient clinical data (CD). Four proposals using five different machine learning models were implemented. The first two models employed ML and each data source independently. Then, two additional approaches developed a data fusion from both sources. This strategy’s employment was analyzed with physicians according to their pertinence in the process and understanding of the EMR. Finally, the results of the data fusion were compared to each source, obtaining better performance at using only the CD, where an area under the ROC curve of 69.9±2.3% was obtained. However, the advantage of analyzing physician’s reports is the availability of this information contrasted to clinical-specific data, which can be more useful in places far from the main cities without enough basic structure for its obtaining.

The accompanying mechanical behavior during the flow of ore-rock dispersion is an important factor leading to the instability and failure of the orepass in the ore storage section. It is of great significance to accurately understand the instability characteristics of the well wall in the ore section, during the ore drawing process by understanding the flow characteristics and internal mechanical transfer mechanism of ore-rock dispersion. Using the discrete element method to analyze the flow characteristics, contact compactness, stress distribution characteristics and contact force probability distribution of the ore-rock dispersion, it realizes the quantitative characterization of the damage degree of ore-rock flow, and reveals the damage mechanism of mine storage wall. The results show that: (1) The friction between ore-rock particles, between ore-rock and well wall is an important reason for the change of the macroscopic flow form of ore-rock from “one” to “V” type. (2) The compactness and stress concentration of ores are increasing with the increase of storage depth, which is shown that with the increase of storage depth, the greater the probability of well wall instability. (3) The probability distribution of contact force strength between particles decreases exponentially in the whole process of ore drawing, in which the strong chain plays a major role in the stability of the dispersion system. (4) The number of overpressure and the overpressure coefficient can be used to quantitatively characterize the wall damage degree under the action of ore-rock flow. The dynamic side pressure decreases periodically in the form of exponential fluctuations, and the dynamic load formed by the ore-rock flow mainly played a role in the lower area of the ore storage section.

The wellness economy has developed rapidly recently, particularly in wellness tourism. Wellness tourism transforms sustainable strategy through the availability of extended multi-actor engagement in service innovation to further the tourism experience. Therefore, when operating a wellness tourism destination, it is necessary to encourage multi-actor engagement in service innovation to achieve sustainable development. This paper introduces a mixed research method with the SWOT-AHP-QSPM, as it qualitatively analyzes the advantages, disadvantages, opportunities, and threats, constructs an evaluation index system, conducts quantitative analysis using the analytic hierarchy process, determines the index weight and weighted score, and calculate the sustainable development strategy, which is appropriate for wellness development of Chongqing Tea Mountain and Bamboo Forest National Forest Park. Purposive field surveys and personal interviews were conducted in Chongqing forest tourism destinations, involving 356 effective respondents. The research reveals that multi-actor engagement significantly enhances sustainable development. These findings bridge service innovation knowledge gaps and provide valuable insights for forest tourism destination management and sustainable policy strategies to elevate multi-actor engagement in forest sites.

Tack is one of the most important factors affecting the operability of prepregs, but it is still very difficult to obtain the change of prepreg tack with time at different temperatures. In this paper, a newly designed T-peel device is used to test the tack decay rate of prepregs at different temperatures. The obtained decay rate deviates less than 8.9% from the manufacturer's data. The maximum deviation of the ratio of tack decay speeds calculated by the Arrhenius equation from the experiments is not higher than -9.7%. It is proved that the present method can obtain the decay rate of tack of prepregs at different temperatures. This method is also applicable to the other two types of prepregs. The article proposes the handling life unit, an operational life measurement unit for prepregs, and establishes that the relationship between tack and remaining shelf life reduces prepreg management costs.

The development of computational acoustics allows simulation of sound generation and propagation in complex environment. In particular, meshfree methods are widely used to solve acoustics problems through arbitrarily distributed field points and approximation smoothness flexibility. As a Lagrangian meshfree method, smoothed particle hydrodynamics (SPH) method reduce the difficulty in solving problems with deformable boundaries, complex topologies, or multiphase medium. The traditional SPH method has been applied in acoustic simulation. This study presents the corrective smoothed particle method (CSPM), which is a combination of SPH kernel estimate and Taylor series expansion. The CSPM is introduced as a Lagrangian approach to improve accuracy in solving acoustic wave equations in the time domain. Moreover, a boundary treatment technique based on the hybrid meshfree and finite difference time domain (FDTD) method is proposed to represent different acoustic boundaries with particles. To model sound propagation in pipes with different boundaries, soft, rigid, and absorbing boundary conditions are built with this technique. Numerical results show that the CSPM algorithm is consistent and demonstrates convergence with exact solutions. Main computational parameters are discussed, and different boundary conditions are validated to be effective for benchmark problems in computational acoustics.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

Positioning based on wireless signals such as mobile communication networks has become an important means to provide high-precision location services in environments where satellite signals are blocked. In complex environments such as indoor and underground, wireless signal propagation is obstructed and non-line-of-sight (NLOS) phenomena appears due to serious occlusion and reflection. The time delay caused by NLOS effects has little impact on communication system but can significantly increase positioning errors in positioning systems. Therefore, effective suppression of NLOS errors is crucial to improving 5G positioning accuracy. To address the insufficient feature extraction of existing NLOS error suppression methods, the neglect of residual NLOS measurement errors, and poor stability of position estimation results, this paper innovatively proposes a NLOS mitigation and location estimation method for 5G positioning terminals. Simulation and experimental test results demonstrate that the proposed method outperforms the comparative methods both theoretically and practically, achieving an average positioning accuracy of 1.85 meters in complex indoor NLOS environments. The method proposed in this paper provides a new strategy for NLOS error suppression in indoor 5G positioning, which can significantly contribute to high-precision location services based on commercial 5G networks.

By repeated unloading and loading of the excavation dynamic and static loads, repeated train dynamic and static loads of multi-factors, the underpass tunnel, cross tunnel engineering influence area rock layer, the existing tunnel structure of the three constitutes a multifaceted structure, multi-factor interaction system. Existing tunnels into the prediction theory model of the new tunnel excavation process of dynamic and static loads, dynamic and static loads of existing tunnels are mostly analyzed individually, the results of synergistic analysis needs to be improved. Based on the theory of the current layer method, the paper comprehensively considers the stiffness of the existing tunnel, the stiffness of the new tunnel, the excavation method load and the existing tunnel load, coupled with Peck's empirical formula, the theoretical model of deformation prediction for excavation of a two-lane highway tunnel under the tunnel through the existing railroad is proposed, and verified and optimized by numerical calculations and monitoring data, which proves the applicability and reliability of the theoretical model in the similar strata and the project promotion.

Vibration problems have become one of the most important factors affecting robot performance. To this end, a terminal residual vibration suppression method based on joint trajectory optimization is proposed to improve the accuracy and stability of robot motion. Firstly, based on the characteristics of the friction nonlinearity due to joint coupling and physical feasibility of dynamic parameters, a semidefinite programming method is used to identify dynamic parameters with actual physical meaning, thereby obtaining an accurate dynamic model. Then, based on the result of the residual vibration time domain analysis, a joint trajectory optimization model with the goal of minimizing joint tracking error is established. The Chebyshev collocation method is used to discretize the optimization model. The dynamic model is used as the optimization constraint, and barycentric interpolation is used to obtain the optimized joint motion trajectory. Finally, industrial robot experiments prove that the vibration suppression method proposed in this article can reduce the maximum acceleration amplitude of residual vibration by 62% and the vibration duration by 71%. Compared with the input shaping method, the method proposed in this paper can reduce the terminal residual vibration more effectively and ensure the consistency of running time and trajectory.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

In recent years, the rapid development of computer-aided planning and design technology has provided a new perspective for the study of complex problems such as the generation of architectural complex forms. This study focuses on how to apply parameterization technology to analyze and articulate traditional spatial form composition rules, aiming to minimize reliance on subjective human judgment in the protection and renewal design of the historical style of traditional villages. It aims to establish digital generative design tools to address the challenges of accurately inheriting and innovatively utilizing historical and cultural information in traditional settlements. It introduces how to rely on parameterization technology to analyze the spatial form composition rules, parameter extraction rules, and spatial reconstruction rules of traditional villages, facilitating the complete process from spatial features to parameterization rules, and then to the application of computational methods to deduce spatial features. It also includes case studies demonstrating the application of parameterization technology tools for village protection and explores the role of generative design tools in preserving the spatial style of these settlements.

Metachromatic leukodystrophy (MLD) is a hereditary neurodegenerative disease characterized by demye-lination and motor and cognitive impairment due to the deficiency of the lysosomal enzyme arylsulfatase A (ARSA) or the saposin B activator protein (SapB). Current treatments are limited; however, gene therapy using adeno-associated virus (AAV) vectors for ARSA delivery has shown promising results. The main challenges for MLD gene therapy include optimizing AAV dosage, selecting the most effective serotype, and determining the best route of administration for ARSA delivery into the central nervous system. This study aims to evaluate the safety and efficacy of AAV serotype 9 encoding ARSA (AAV9-ARSA) gene therapy when administered intrave-nously or intrathecally in minipigs, a large animal model with anatomical and physiological similarities to humans. By comparing these two administration methods, this study contributes to the understanding of how to improve the effectiveness of MLD gene therapy and offers valuable insights for future clinical applications.

Nuclear fusion is essential for the sexual reproduction of various organisms, including plants, animals, and fungi. During the life cycle of flowering plants, nuclear fusion occurs three times: once during female gametogenesis and twice during double fertilization, when two sperm cells fertilize the egg and the central cell. Haploid nuclei migrate in an actin filament-dependent manner to become in close contact, then two nuclei fuse. The nuclear fusion process in plant reproduction is achieved by the sequential nuclear membrane fusion events. Recent molecular genetic analyses using Arabidopsis thaliana showed the conservation of nuclear membrane fusion machinery between plants and the budding yeast Saccharomyces cerevisiae. These include the heat shock protein 70 in the endoplasmic reticulum and conserved nuclear membrane proteins. Analyses of A. thaliana mutants of these components show that completion of the sperm nuclear fusion at fertilization is essential for proper embryo and endosperm development.

Nanocomposites of TiO₂ Degussa P25 nanoparticles/activated carbon (TiO₂/AC ) were prepared at various mass ratios of (4:1), (3:2), (2:3), and (1:4) by a facile process. The effects of TiO₂/AC mass ratios on the structural, morphological, and photocatalytic properties were systematically studied in comparison with bare TiO₂ and bare AC. TiO₂ nanoparticles exhibited dominant anatase and minor rutile phases, and a crystallite size of approximately 21 nm; while AC had XRD peaks of graphite and carbon, and a crystallite size of 49 nm. The composites exhibited tight decoration of TiO₂ nanoparticles on micron-/submicron AC particles, and uniform TiO₂/AC composites were obtained as evidenced by the uniform distribution of Ti, O, and C in an EDS mapping. Moreover, Raman spectra show the typical vibration modes of anatase TiO₂ (e.g., E_1g⁽¹⁾, B_1g⁽¹⁾, E_g⁽³⁾) and carbon materials with D and G bands. The TiO₂/AC with (4:1), (3:2), and (2:3) possessed higher reaction rate constants (k) in photocatalytic degradation of methylene blue (MB) than that of either TiO₂ or AC. Among the investigated materials, TiO₂/AC = 4:1 achieved the highest photocatalytic activity with a high k of 55.2×10^-3 min^-1 and an MB removal efficiency of 96.6% after 30 min treatment under UV-Vis irradiation (120 mW/cm²). The enhanced photocatalytic activity for TiO₂/AC is due to the synergistic effect of the high adsorption capability of AC and the high photocatalytic activity of TiO₂. Furthermore, TiO₂/AC promotes the separation of photoexcited electron/hole (e^-/h⁺) pairs to reduce their recombination rate and thus enhance photocatalytic activity. The optimal TiO₂/AC composite in this study can be used for treating industrial or household wastewater with organic pollutants.

Pancreatic cancer (PC) is one of the most lethal forms of cancers with 5-year mean survival rate of less than 10%. Most of the PC associated deaths are due to metastasis to secondary sites. Calcium (Ca²⁺) signaling plays a critical role in regulating hallmarks of cancer progression including cell proliferation, migration and apoptotic resistance. Store operated Ca²⁺ entry (SOCE) mediated by Orai1/2/3 channels is a highly regulated and ubiquitous pathway responsible for Ca²⁺ influx into non-excitable cells. In this study, we performed extensive bioinformatic analysis of publicly available datasets and observed that Orai3 expression is inversely associated with the mean survival time of PC patients. Orai3 expression analysis in a battery of PC cell lines corroborated its differential expression profile. We then carried out thorough Ca²⁺ imaging experiments in 6 PC cell lines and found that Orai3 forms a functional SOCE in PC cells. Our in vitro functional assays show that Orai3 regulates PC cell cycle progression, apoptosis and migration. Most importantly, our in vivo xenograft studies demonstrate a critical role of Orai3 in PC tumor growth and secondary metastasis. Mechanistically, Orai3 controls G₁ phase progression, matrix metalloproteinase expression and epithelial-mesenchymal transition in PC cells. Taken together, this study for the first time reports that Orai3 drives aggressive phenotypes of PC cells i.e. migration in vitro and metastasis in vivo. Considering that Orai3 expression is inversely associated with the PC patients survival time, it appears to be a highly attractive therapeutic target.

The proposal that the genetic code was formed on the basis of protein synthesis directed by tRNA dimers is reviewed and updated. The tRNAs paired through the anticodon loops are an indication on the process. Dimers are considered mimics of the ribosomes – structures that hold tRNAs together and facilitate the transferase reaction, and of the translation process – anticodons are at the same time codons for each other. The primitive protein synthesis system gets stabilized when the product peptides are able to bind the producers therewith establishing a self-stimulating production cycle. The chronology of amino acid encoding starts with Glycine and Serine, indicating the metabolic support of the Glycine-Serine C1-assimilation pathway, which is also consistent with evidence on origins of bioenergetics mechanisms. Since it is not possible to reach for substrates simpler than C1 and compounds in the identified pathway are apt for generating the other central metabolic routes, it is considered that protein synthesis is the beginning and center of a succession of sink-effective mechanisms that drive the formation and evolution of the metabolic flow system. Plasticity and diversification of proteins construct the cellular system following the orientation given by the flow and implementing it. Nucleic acid monomers participate in bioenergetics and the polymers are conservative memory systems for the synthesis of proteins. Protoplasmic fission is the final sink-effective mechanism, part of cell reproduction, guaranteeing that proteins don’t accumulate to saturation, which would trigger inhibition.

As the natural resources are getting exhausted, the concept of sustainable development of region has received increasing attentions, especially for resource-dependent cities. In this paper an innovative method that emergy analysis and IPAT (Human Impact Population Affluence Technology) model were combined in order to analyze the quantitative relationship of economic growth and energy consumption and further to evaluate its overall sustainability level. Taiyuan, a traditional, resource-dependent city in China, is selected as the case study region. The main results show that total emergy of Taiyuan increased from 9.023× 1023 sej in 2007 to 9.116× 1023sej in 2014, with 38% reduction on non-renewable emergy and 125% growth on imported emergy. Regional emergy money ratio (RMB) was reduced by 48% from 5.31× 1013sej/$ in 2007 to 2.74× 1013sej/$ in 2014, indicating that the increasing speed of consuming resources and energy was faster than the increase of GDP, and that Taiyuan’s money purchasing power declined. The lower emergy sustainability index (ESI) indicates that Taiyuan was explored and produced large quantities of mineral resources, which puts more stress on the environment as a consequence, and that this is not sustainable in the long run. The IPAT analysis demonstrates that Taiyuan sticks to the efforts of energy conservation and environmental protection, in order to promote regional sustainable development, it is necessary to take an integrated effort. Policy insights suggest that resourceful regions should improve include energy and resource efficiency, optimizing energy and resourceful structure.

The Internet of Things (IoT) is a concept based on the integration of various devices and methods of communication to exchange information in order to contribute to the improvement of everyday life, business and environment. This paper focuses on the establishment of a modern Internet of Things platform to ensure secure collection, processing and storage of data, under unexpected events. This platform establishes reliable connectivity which enhance system uptime; the key to success when implementing an IoT strategy. For this purpose, we proposed a hybrid approach combining different connectivity technologies to ensure the availability and smooth operation of the system and the prevention of data loss in unforeseen cases.

The detection and quantification of protein–protein interactions (PPIs) is a crucial technique that often involves the use of recombinant proteins with fusion-protein tags, such as maltose-binding protein (MBP) and glutathione-S-transferase (GST). In this study, we improved the cohesive and sticky properties of gelatinized starch by supplementing it with agarose, resulting in a harder gel that could coat the bottom of a microtiter plate. The resulting gelatinized starch/agarose mixture allowed for the efficient immobilization of MBP-tagged proteins on the coated plates, enabling the use of indirect ELISA-like PPI assays. By using the enzymatic activity of GST as an indicator, we succeeded in determining the dissociation constants between MBP-tagged and GST-tagged proteins on 96-well microtiter plates and a microplate reader without any expensive specialized equipment.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

Demand for environmentally friendly types of energy is growing all over the world, which naturally increases the intensity of work on the production of hydrogen-containing fuel mixtures. Steam reforming of methanol is one of the most effective areas of research, as it is a process with low energy consumption. The results of steam reforming of methanol on the catalysts prepared by solution combustion synthesis (SCS) and impregnation by moisture capacity are presented. Investigation of the activity of Cu-Ce-Al catalysts of different composition was carried out for production of hydrogen and comparison of SCS with traditional impregnation method. Methanol conversion reaches 99%, while the selectivity of H2 was 88%, at 500 °C. It was found that CuAl2O4 spinel formation is due to substitution of Al3+ with Cu2+ Ce3+ cations. A consequence CuAl2O4 and CeAlO3 lattice parameters increase because of difference in ionic radius of Al3+ (0.53 Å) and Cu2+ (0.73 Å), Ce3+ (1.07 Å). Advantages of SCS catalysts in steam reforming of methanol were shown. The aim of this research is to develop a new catalyst for conversion of methanol into hydrogen containing fuel mixtures, which will further promote the organization of a new era of environmentally friendly energy-saving production methods.

Mass hauling operations play central roles in construction projects. They typically use many haulers that consume large amounts of energy and emit significant quantities of CO₂. However, practical methods for estimating the energy consumption and CO₂ emissions of such operations during project planning are lacking. This paper presents a detailed model for estimating the energy consumption and CO₂ emissions of mass haulers that integrates the mass hauling plan with a set of predictive equations. The mass hauling plan is generated using a planning program such as DynaRoad in conjunction with data on the productivity of selected haulers and the amount of material to be hauled during cutting, filling, borrowing, and disposal operations. This plan is then used as input for estimating the energy consumption and CO₂ emissions of the selected hauling fleet. The proposed model will help planners to assess the energy and environmental performance of mass hauling plans, and to select hauler and fleet configurations that will minimize these quantities. The model was applied in a case study, demonstrating that it can reliably predict energy consumption, CO₂ emissions, and hauler productivity as functions of the hauling distance for individual haulers and entire hauling fleets.

Pedestrian Dead Reckoning (PDR) is a promising algorithm for indoor positioning. However, the accuracy of PDR degrades due to the accumulated error, especially in multi-floor buildings. This paper introduces a three-dimensional (3D) positioning method based on terrain feature matching to reduce the influence of accumulated error in multi-floor scene. The proposed method involves two steps: motion pattern recognition and position matching calibration. The motion pattern recognition aims to detect different motion states, i.e., taking the stairs or horizontal walking, from the streaming data. Then, stair entrances and corridor corners are matched with transition points of motion states and pedestrian turning points, respectively. After matching, calibration is performed to eliminate the accumulated errors. By carrying out experiments on a two-floor closed-loop path with a walking distance about 145 m, it is shown that this method can effectively reduce the accumulated error of PDR , achieving accurate 3D positioning . The average error is reduced from 6.60 m to 1.83 m.

Atmospheric pollens were investigated using the Lanzoni VPSS 2000 device for 2 years between 2012 and 2013 in the Sarıkamış district of Kars province, one of the highest regions of Türkiye. A total of 37909 pollen grains were collected; 15,298 pollen grains in 2012 and 22611 pollen grains in 2013. Out of 43 identified taxa, 21 were arboreal and 22 were non arboreal. Pollen from arboreal plants accounted for 36.34% (13778 pollen) of the total, while pollen from non arboreal plants accounted for 63.56% (24095 pollen). The most frequent arboreal plants pollen in the Sarıkamış atmosphere were Pinaceae (29.79%), Cupressaceae/Taxaceae (2.54%), and Morus (1.30%). The main non arboreal plants pollen in the atmosphere of Sarıkamış were Poaceae (44.60%), Artemisia (2.98%), Amaranthaceae (2.79%), Rumex (2.41%), Urticaceae (2.33%), Plantago (2.19%), and Boraginaceae (1.40%). The maximum pollen concentration was observed in June.

Omicron SARS-CoV-2 variants have dominated the strains of COVID-19 circulating worldwide for the last two years. Although the mortality rate of the COVID-19 Omicron variants is only approximately half that of previous variants, vaccines effective against Omicron are needed. Demonstrating that a SARS-CoV-2 vaccine can protect against Omicron in an in vivo infection model, particularly in that of Syrian hamsters, is important for the preclinical characterization of SARS-CoV-2 vaccines. Here, we report on the evaluation of W-PreS-O for its ability to protect Syrian hamsters against infection by Omicron. W-PreS-O is a chimeric vaccine based on a single fusion protein (W-PreS-O), combining RBDs from Wuhan hu-1 wild-type and Omicron adsorbed to aluminum hydroxide. The Syrian hamsters were immunized three times at three-week intervals with W-PreS-O or with aluminum hydroxide (placebo), and then they were infected with Omicron BA.1. Non-infected and non-vaccinated animals served as controls. Neutralizing antibody (nAB) titers, weight, lung symptoms (i.e., edema and pneumonia index), and viral loads, as measured using RT-PCR in the upper and lower respiratory tracts, were determined. In addition, infectious virus titers from the lungs were measured using a plaque-forming assay. W-PreS-O-vaccinated hamsters developed robust nABs against Omicron, showed almost no development of pneumonia, and had significantly reduced infectious virus titers in the lungs. Importantly, the viral loads in the nasal cavities of W-PreS-O-vaccinated hamsters were close to or above the PCR cycle threshold considered to be non-infectious. Our data provide compelling evidence demonstrating that the W-PreS-O vaccine has protective effect against Omicron in a Syrian hamster in vivo infection model. W-PreS-O is therefore a highly promising candidate vaccine for Omicron infections, and it should be further evaluated in clinical studies.

In recent decades, with the ever-growing scale of video data, near-duplicate videos continue to emerge. Data quality issue caused by near-duplicate videos is becoming more and more prominent, which has affected the application of normal videos. Although current studies on near-duplicate video detection can be helpful to uncover data quality issues for videos, they still lack a process of automatic merging for the video data represented by high-dimensional features, which are difficult to automatically clean the near-duplicate videos to improve data quality for video datasets. At present, there are few studies on near-duplicate video data cleaning. The existing studies have the sensitive problems of video data orderliness and clustering initial center under a condition that prior distribution is unknown, which seriously affect the accuracy of near-duplicate video data cleaning. To address the above issues, an automatic near-duplicate video data cleaning method based on a consistent feature hash ring is proposed in this paper. First, a residual network with convolutional block attention modules, a long short-term memory deep network, and an attention model are integrated to construct an RCLA deep network with the multi-headed attention mechanism to extract spatiotemporal features of video data. Then, a consistent feature hash ring is constructed, which can effectively alleviate the sensitivity of video data orderliness while providing a condition of near-duplicate video merging. To reduce the sensitivity of the initial cluster centers to results of near-duplicate video cleansing, an optimized feature distance-means clustering algorithm is constructed by utilizing a mountain peak function on a consistent feature hash ring, which can implement automatic cleaning of near-duplicate video data. Finally, experiments are conducted based on a commonly used dataset named CC_WEB_VIDEO and a coal mining video dataset. Compared with some existing works, simulation results demonstrate that the performance of the proposed method.

This study proposes an innovative pharmacovigilance approach, VIGI-4, leveraging ChatGPT Teams Pro version. Our method involves aggregating data from all phase 4 studies published in February 2024 to identify adverse reactions. These results are categorized by disease state alphabetically, demonstrating the method's capacity to generate structured and easily navigable outputs. The results section is tailored to exemplify the valuable, actionable insights attainable through our workflow. This research emphasizes the game-changing potential of integrating advanced technologies such as ChatGPT into pharmacovigilance, signaling a new era in safeguarding drug safety.

Live attenuated influenza virus (LAIV) vaccines elicit a combination of systemic and mucosal immunity by mimicking a natural infection. To further enhance protective mucosal responses, we incorporated the gene encoding the IgA-inducing protein (IGIP) into the LAIV genomes of the cold-adapted A/Leningrad/134/17/57 (H2N2) strain (caLen) and the experimental attenuated backbone A/turkey/Ohio/313053/04 (H3N2) (OH/04att). Incorporation of IGIP into the caLen background led to a virus that grew poorly in prototypical substrates. In contrast, IGIP in the OH/04att background (IGIP-H1att) virus grew to titers comparable to the isogenic backbone H1att (H1N1) without IGIP. IGIP-H1att- and H1caLen-vaccinated mice were protected against lethal challenge with a homologous virus. The IGIP-H1att vaccine generated robust serum HAI responses in naïve mice against the homologous virus, equal or better than those obtained with the H1caLen vaccine. Analyses of IgG and IgA responses using a protein microarray revealed qualitative differences in humoral and mucosal responses between vaccine groups. Overall, serum and bronchoalveolar lavage samples from the IGIP-H1att group showed trends towards increased stimulation of IgG and IgA responses compared to H1caLen samples. In summary, introduction of genes encoding immunomodulatory functions into a candidate LAIV that can serve as natural adjuvants to improve overall vaccine safety and efficacy.

The increasing prevalence of spinal disorders and the subsequent rise in spinal fusion surgeries stress the necessity for optimizing spinal fusion cages, crucial for stabilizing the spine and supporting osseointegration. Despite the widespread attempts at material modifications and surface enhancements to improve these devices, failure rates and complications remain significant. This research explores an imaginitive approach to enhancing spinal fusion cages through the application of geometric tiling designs, a method scarcely employed . The study systematically investigates the potential of triangular, diamond, and hexagonal tiling patterns to improve the structural integrity and capability for osseointegration of spinal fusion cages. Through a finite element analysis simulation, each design's ability to withstand multi-directional loads and effectively distribute stress was tested under simulated conditions that mimic the complexities of spinal motion. Preliminary findings in fields of engineering indicate that geometric tiling can significantly enhance the load-bearing capabilities and stress distribution of spinal fusion cages, potentially reducing the likelihood of implant subsidence and failure. The triangular and hexagonal designs, in particular, demonstrate superior performance in maintaining structural stability under axial and anterior compressive forces compared to traditional cage designs. This research opens a new pathway for the development of more durable and efficient spinal fusion cages. By leveraging the structural advantages of geometric tiling, future spinal implants could achieve better clinical outcomes, leading to improved patient recovery and reduced rates of complications.