Global Theory Research Articles

Partners in crime? Radical scepticism and malevolent global conspiracy theories

Although academic work on conspiracy theory has taken off in the last two decades, both in other disciplines as well as in epistemology, the similarities between global sceptical scenarios and global conspiracy theories have not been the focus of attention. The main reason for this lacuna probably stems from the fact that most philosophers take radical scepticism very seriously, while, for the most part, regarding ‘conspiracy thinking’ as epistemically defective. Defenders of conspiracy theory, on the other hand, tend not to be that interested in undermining radical scepticism, since their primary goal is to save conspiracy theories from the charges of irrationality. In this paper, I argue that radical sceptical scenarios and global conspiracy theories exhibit importantly similar features, which raises a serious dilemma for the ‘orthodox’ view that holds that while we must respond to radical scepticism, global conspiracy theories can just be dismissed. For, if, as I will show, both scenarios can be seen to be epistemically on a par, then either radical sceptical scenarios are as irrational as global conspiracy theories or neither type of scenario is intrinsically irrational. I argue for the first option by introducing a distinction between ‘local’ and ‘global’ sceptical scenarios and showing how this distinction maps onto contemporary debates concerning how best to understand the notion of a ‘conspiracy theory’. I demonstrate that, just as in the case of scepticism, ‘local’ conspiracies are, at least in principle, detectable and, hence, epistemically unproblematic, while global conspiracy theories, like radical scepticism, are essentially invulnerable to any potential counterevidence. This renders them theoretically vacuous and idle, as everything and nothing is compatible with what these ‘theories’ assert. I also show that radical sceptical scenarios and global conspiracy theories face the self-undermining problem: As soon as global unreliability is posited, the ensuing radical doubt swallows its children – the coherence of the sceptic’s proposal or the conspiracy theorist’s preferred conspiracy. I conclude that radical sceptical scenarios and global conspiracy theories are indeed partners in crime and should, therefore, be regarded as equally dubious.

Integrating self-attention mechanisms in deep learning: A novel dual-head ensemble transformer with its application to bearing fault diagnosis

In this paper, we propose a novel dual-head ensemble Transformer (DHET) algorithm for the classification of signals with time–frequency features such as bearing vibration signals. The DHET model employs a dual-input time–frequency architecture, integrating a 1D Transformer model and a 2D Vision Transformer model to capture the spatial and time–frequency features. By utilizing data from both the time and time–frequency domains, the proposed algorithm broadens its feature extraction capabilities and enhances the model’s capacity for generalization. In our DHET structure, the original Transformer model leverages self-attention mechanisms to consider relationships among signal input segmentations, which makes it effective at capturing long-range dependencies in signal data, while the Vision Transformer model takes 2D images as input and creates the image patches for embedding and each patch is linearly embedded into a flat vector and treated as a ‘token,’ then the ‘tokens’ are processed by the Transformer layers to learn global contextual representations, enabling the model to perform signal classification task. This integration notably enhances the performance and capability of the model. Our DHET is especially effective for rolling bearing fault diagnosis. The simulation results show that the proposed DHET has higher classification accuracy for bearing fault diagnosis and outperforms CNN-based methods.

Agent-SwinPyramidNet: an enhanced deep learning model with AMTCF-VMD for anomaly detection in oil and gas pipelines

PurposeThe anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.Design/methodology/approachFirst, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.FindingsIn the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.Originality/valueThis paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

Wavelet-fusion image super-resolution model with deep learning for downscaling remotely-sensed, multi-band spectral albedo imagery

Generating granular-scale surface albedo data is extremely important for solar photovoltaic site planning and to optimise renewable energy yield of bifacial panel installations. The albedo effect brings about a significant increase in power in bifacial photovoltaic systems, compared to their mono-facial counterparts, since the spectral response of bifacial solar panels correlates with the incident solar radiation wavelength on the back of the panel, to provide additional power generation capacity. Thus, harnessing the albedo data at relatively local scales is critical towards boosting solar power generation and providing greater power density in local electricity grids. This paper develops novel modelling approaches to produce high-resolution spectral albedo imagery across the Visible and Near Infrared (VNIR) bands, using the Wavelet-Fusion super-resolution model (i.e., Wavelet-FusionSR) trained with the Learned Gamma Correction approach by applying satellite image enhancement methodology. The proposed Wavelet-FusionSR model utilises the low-resolution moderate-resolution Imaging Spectroradiometer (MODIS) as well as high-resolution multi-spectral Advanced Space-borne Thermal Emission Reflection Radiometer (ASTER) satellite images, as critical inputs and ground-truth imagery, respectively, in order to perform sensor-to-sensor deep downscaling, without employing any synthetic or low-resolution satellite imagery data pairs. To augment the proposed deep learning algorithm across the decomposed sub-images of low-resolution inputs, we integrate local and global feature representation learning to train the proposed Wavelet-FusionSR model with Cauchy loss functions. In comparison with five competing benchmark models, the proposed Wavelet-FusionSR model demonstrates performance superiority using quantitative image downscaling metrics and visual assessments of the downscaled images for the visible band of solar radiation. The proposed Wavelet-FusionSR model yielded a Mean Square Error (MSE) of 0.00017, Signal-to-noise-ratio (PSNR) of 37.80, Structural Similarity Index (SSIM) of 0.999 and combined loss, MS-SSIMLoss, based on Multi Structural Similarity and Mean Absolute Error of 2.354 for the Visible Band images, and an MSE of 0.0014, PSNR of 28.43, SSIM of 0.999 and MS-SSIMLoss of 7.426 for the NIR spectral bands, demonstrating high efficacy of the proposed Wavelet-FusionSR method. The Wavelet-FusionSR method therefore attains high-resolution spectral albedo imagery outputs.

VSmTrans: A hybrid paradigm integrating self-attention and convolution for 3D medical image segmentation

PurposeVision Transformers recently achieved a competitive performance compared with CNNs due to their excellent capability of learning global representation. However, there are two major challenges when applying them to 3D image segmentation: i) Because of the large size of 3D medical images, comprehensive global information is hard to capture due to the enormous computational costs. ii) Insufficient local inductive bias in Transformers affects the ability to segment detailed features such as ambiguous and subtly defined boundaries. Hence, to apply the Vision Transformer mechanism in the medical image segmentation field, the above challenges need to be overcome adequately. MethodsWe propose a hybrid paradigm, called Variable-Shape Mixed Transformer (VSmTrans), that integrates self-attention and convolution and can enjoy the benefits of free learning of both complex relationships from the self-attention mechanism and the local prior knowledge from convolution. Specifically, we designed a Variable-Shape self-attention mechanism, which can rapidly expand the receptive field without extra computing cost and achieve a good trade-off between global awareness and local details. In addition, the parallel convolution paradigm introduces strong local inductive bias to facilitate the ability to excavate details. Meanwhile, a pair of learnable parameters can automatically adjust the importance of the above two paradigms. Extensive experiments were conducted on two public medical image datasets with different modalities: the AMOS CT dataset and the BraTS2021 MRI dataset. ResultsOur method achieves the best average Dice scores of 88.3 % and 89.7 % on these datasets, which are superior to the previous state-of-the-art Swin Transformer-based and CNN-based architectures. A series of ablation experiments were also conducted to verify the efficiency of the proposed hybrid mechanism and the components and explore the effectiveness of those key parameters in VSmTrans. ConclusionsThe proposed hybrid Transformer-based backbone network for 3D medical image segmentation can tightly integrate self-attention and convolution to exploit the advantages of these two paradigms. The experimental results demonstrate our method's superiority compared to other state-of-the-art methods. The hybrid paradigm seems to be most appropriate to the medical image segmentation field. The ablation experiments also demonstrate that the proposed hybrid mechanism can effectively balance large receptive fields with local inductive biases, resulting in highly accurate segmentation results, especially in capturing details. Our code is available at https://github.com/qingze-bai/VSmTrans.

Shuffle-fusion pyramid network for bearing fault diagnosis under noisy environments

Abstract Recent advancements in deep learning have propelled the exploration of big data-driven fault diagnosis techniques. Nevertheless, traditional models often suffer from prohibitive computational demands, rendering them impractical for on-site deployment in rolling bearing fault diagnosis. To address this challenge, this paper introduces a novel lightweight fault diagnosis model with the pyramid architecture, named Shuffle-Fusion Pyramid Network (Shuffle-FPN). The model heralds several innovations: (1) A pyramid structure is designed to amalgamate fault signals across various scales, enlarging the network’s breadth and curtailing its depth. (2) Depth wise separable convolutions are adopted to streamline network parameters, thus achieving a lightweight model, and channel shuffling to ensure thorough information fusion across convolutional channels. (3) A global representation module is employed to offset the loss of global context, which accompanies increased convolutional depth. Collectively, these innovations enable Shuffle-FPN to extract nuanced fault features amidst noise and to operate on devices with limited memory, ensuring real-time fault diagnosis even in complex environments. Rigorous experiments on public datasets from Paderborn University (PU), supplemented by validations with our research group’s experiment data, reveal that Shuffle-FPN excels in fault identification under severe noise conditions and reduces memory footprint, which solidifies the superiority of Shuffle-FPN for practical fault diagnosis.

Health information systems data for decision-making: case study in three cities on current practices and opportunities

A functional and reliable Health Information System (HIS) is vital for data-based decision-making in public health. Here we describe the assessment of data processes and general HIS principles by adapting a global approach to three cities. The assessments supported the data strategy of the CARDIO4cities initiative in each city aiming to improve urban population health by increasing the use of cardiovascular disease (CVD) data to inform decision-making. We aimed to explore data collection processes and entities, data availability and quality as well as data ownership and sharing regarding a set of identified key performance indicators (KPIs). KPIs were based on a global theory of change (ToC) and a global evaluation and indicator framework and were tailored to each location. By first assessing existing sources and processes regarding data, recommendations for changes and improvements are sure to build on current circumstances. To map the data, existing data collection, analysis and storage processes were investigated. A flow chart was created to visualize the data pathways and challenges for each system and findings were compared across cities to document differences and similarities. Data quality and interoperability of various separate systems were the most prominent challenges for all HISs. The observed dvata quality issues originated from incorrect, missing and incomplete data and were connected to the misunderstanding of indicators, incomplete data input forms or the lack of a systematic data routine in the workflow. Harmonization of the HISs to ensure interoperability can facilitate data collection and analysis of health data and can provide a solid basis for health management decision-making. Based on the presented HIS cases, we recommend to examine, map and verify current processes when conducting a HIS assessment, to visualize findings and to gauge the interest of government entities to ensure political support.

Advanced Global Prototypical Segmentation Framework for Few-Shot Hyperspectral Image Classification.

With the advancement of deep learning, related networks have shown strong performance for Hyperspectral Image (HSI) classification. However, these methods face two main challenges in HSI classification: (1) the inability to capture global information of HSI due to the restriction of patch input and (2) insufficient utilization of information from limited labeled samples. To overcome these challenges, we propose an Advanced Global Prototypical Segmentation (AGPS) framework. Within the AGPS framework, we design a patch-free feature extractor segmentation network (SegNet) based on a fully convolutional network (FCN), which processes the entire HSI to capture global information. To enrich the global information extracted by SegNet, we propose a Fusion of Lateral Connection (FLC) structure that fuses the low-level detailed features of the encoder output with the high-level features of the decoder output. Additionally, we propose an Atrous Spatial Pyramid Pooling-Position Attention (ASPP-PA) module to capture multi-scale spatial positional information. Finally, to explore more valuable information from limited labeled samples, we propose an advanced global prototypical representation learning strategy. Building upon the dual constraints of the global prototypical representation learning strategy, we introduce supervised contrastive learning (CL), which optimizes our network with three different constraints. The experimental results of three public datasets demonstrate that our method outperforms the existing state-of-the-art methods.

EEG-Based Seizure Prediction Using Hybrid DenseNet-ViT Network with Attention Fusion.

Epilepsy seizure prediction is vital for enhancing the quality of life for individuals with epilepsy. In this study, we introduce a novel hybrid deep learning architecture, merging DenseNet and Vision Transformer (ViT) with an attention fusion layer for seizure prediction. DenseNet captures hierarchical features and ensures efficient parameter usage, while ViT offers self-attention mechanisms and global feature representation. The attention fusion layer effectively amalgamates features from both networks, guaranteeing the most relevant information is harnessed for seizure prediction. The raw EEG signals were preprocessed using the short-time Fourier transform (STFT) to implement time-frequency analysis and convert EEG signals into time-frequency matrices. Then, they were fed into the proposed hybrid DenseNet-ViT network model to achieve end-to-end seizure prediction. The CHB-MIT dataset, including data from 24 patients, was used for evaluation and the leave-one-out cross-validation method was utilized to evaluate the performance of the proposed model. Our results demonstrate superior performance in seizure prediction, exhibiting high accuracy and low redundancy, which suggests that combining DenseNet, ViT, and the attention mechanism can significantly enhance prediction capabilities and facilitate more precise therapeutic interventions.

DAT-Net: Deep Aggregation Transformer Network for automatic nuclear segmentation

Detection and segmentation of cell nuclei in hematoxylin and eosin-stained tissue images pose critical clinical challenges, including intricate backgrounds, variable nuclear appearances, overlapping nuclei, and unclear boundaries. These complexities render automated instance segmentation a difficult research area. Thus, this paper proposes a deep aggregation transformer network for automatic nucleus instance segmentation. The method employs a robust Vision Transformer-based backbone to extract informative features from images. To fuse multi-level information, we propose the group aggregation multi-level feature fusion module, which merges low-level features for shape representation and high-level features for pixel edge segmentation, enhancing overall model performance. The depthwise integrated multilayer perceptron module enhances feature learning by combining the global context-dependent representation of MLP and the local representation of convolution through channel separation. This fosters feature learning across different modes, thereby improving network representation. The bidirectional path interaction feature processing module is introduced for multi-scale information extraction and fusion. This module enhances the entire feature pyramid hierarchy by transferring deep semantic information to shallow layers with robust spatial information via bidirectional paths. The Large kernel Attention module at each layer extensively improves feature extraction through a large receptive field. Our approach outperforms state-of-the-art models, including StarDIST, CPP-Net, TSFD-Net and TransNuSeg, by 7.8%, 6.3%, 1.5% and 3.5% in average panoptic quality for different cell nuclei segmentation categories across tissues of the Pannuke dataset. Furthermore, DAT-Net outperforms all other state-of-the-art models in terms of the F1 score for the five classes of cell nuclei in the PanNuke dataset.

Unilateral global structure for discrete Dirichlet problem with Minkowski-curvature operator

In this paper, we are concerned with the unilateral global bifurcation results for the Minkowski-curvature discrete problem { − ∇ [ ϕ ( Δu ( t ) ) ] = λa ( t ) u ( t ) + f ( t , u ( t ) , λ ) , t ∈ [ 1 , T ] Z , u ( 0 ) = u ( T + 1 ) = 0 , where λ ∈ R is a parameter, a : [ 1 , T ] Z → ( 0 , ∞ ) , f ∈ C ( [ 1 , T ] Z × R 2 , R ) , Δ is the forward difference operator with Δ u ( t ) = u ( t + 1 ) − u ( t ) and ∇ is the backward difference operator with ∇ u ( t ) = u ( t ) − u ( t − 1 ) , ϕ ( s ) = s 1 − s 2 is a ϕ-Laplacian operator. We shall show that there are two distinct unbounded continua C + and C − , consisting of the bifurcation branch C if f is not necessarily differentiable at the origin with respect to u . As the applications of the above result, we shall investigate the existence and asymptotic behaviours of one-sign solutions for the half-quasilinear discrete problem { − ∇ [ ϕ ( Δu ( t ) ) ] = α ( t ) u + ( t ) + β ( t ) u − ( t ) + μa ( t ) F ( u ( t ) ) , t ∈ [ 1 , T ] Z , u ( 0 ) = u ( T + 1 ) = 0 , where μ ≠ 0 is a parameter, a : [ 1 , T ] Z → ( 0 , + ∞ ) , α , β : [ 1 , T ] Z → R , u + = max { u , 0 } , u − = − min { u , 0 } , F ∈ C ( R , R ) satisfies sF ( s ) > 0 for s ≠ 0 . We will use the obtained unilateral global bifurcation theory and the approximation of connected components to prove main results.

Global Trial Representation and Availability of Tyrosine Kinase Inhibitors for Treatment of Chronic Myeloid Leukemia.

Background: Evaluating clinical trial representation for countries with different socio-demographic index (SDI) and tyrosine kinase inhibitor (TKI) availability for chronic myeloid leukemia (CML). Methods: CML incidence rates (IRs) and disability-adjusted life years (DALYs) (1999-2019) from the Institute of Health Metrics and Evaluation were analyzed. Trials investigating TKI use in CML were obtained from ClinicalTrials.gov. Site data for eligible trials (N = 30) and DALYs were analyzed. TKI approvals, DALYs, and IRs were summarized by SDI. Results: North America (NA) had significant decreases in annual percent change (APC) in DALYs and incidence rates from 1999 to 2004. IRs were highest in Europe and Central Asia (ECA) and NA, while DALYs were highest in South Asia (SAsia) and Sub-Saharan Africa (SSA). Countries in the high-SDI quintile were likely to have lower DALYs than lower-SDI quintiles. Differences in regional DALYs vs. sites in TKI trials were significant for SAsia, SSA, and ECA. High-SDI countries were included in all 30 trials, and TKI approvals were prominent in high-SDI (142) vs. low-SDI (14) countries. Conclusions: The inclusion of disproportionately affected countries during the design of and recruitment into clinical trials should occur, as should TKI availability. The lack of representation demonstrates healthcare disparities.

Granformer: A granular transformer net with linear complexity

Recently, transformer models have demonstrated excellent performance across various intelligent applications owing to their ability to understand global context through self-attention mechanism. However, the extensively investigated multiplicative-based attention mechanism is inadequate for capturing relationship-based feature representations, as the dot product cannot depict the intricate semantic information between objects. Moreover, it has a great computational burden with a complexity of O(n2), due to the global feature representation capability achieved by calculating the relationship between each token within the entire feature sequence. To solve the current problem, this paper proposes a granular transformer framework with linear complexity, wherein diverse granulation functions can be employed to supersede the prevailing multiplicative relationships, and an innovative linearization methodology in the form of matrix factorization is designed to reduce the computational burden. Relying on the intricate semantics information embedded within granular structures, the capacity for feature extraction is significantly more comprehensive. Then, a novel matrix factorization methodology is developed for the linearity of granulation-based attention, accomplished by implementing separate deformable convolution sampling and using an approximate iterative algorithm based on cubic equations to calculate the Moore–Penrose inverse. The mathematical proof that our method is approximate with the complete granulation-based attention matrix is investigated in detail. Finally, the performance of Granformer, an innovative reconfiguration of plug-and-play transformer block, is evaluated on representative intelligent applications, including 3D point cloud classification, emotion recognition and sentiment analysis, and object detection. The experimental results suggest that our methodologies outperform the state-of-the-art models.

Multiscale dynamically parallel shrinkage network for fault diagnosis of aviation hydraulic pump and its generalizable applications

Aiming to address the multiscale characteristics and noise corruption problems in the vibration signals of aviation hydraulic pumps, this article develops a novel Multiscale Dynamically Parallel Shrinkage Network (MDPSN) to learn complementary and rich fault-related multiscale features, with the ultimate goal of yielding higher diagnostic accuracy. One significant property is the development of a novel dynamically parallel shrinking module (DPSM) that adaptively generates independent soft thresholds for different scales, effectively shrinking noise-related features to zeros. On one hand, DPSM aggregates and interacts with features at all scales to construct a global feature representation containing richer fault-related information, which is served as the foundation for soft thresholding generation, significantly improving the accuracy and rationality of the generated thresholds. On the other hand, DPSM can adaptively generate individual soft threshold for each scale, allowing each scale to use an independent threshold tailored to its own characteristic to eliminate noise-related information. This avoids the issues of over-denoising or under-denoising caused by the uniform application of thresholds across all scales. Finally, the effectiveness of MDPSN is validated by a series of experiment comparisons on an aviation hydraulic pump dataset and two bearing datasets with various types of noise. The experimental results demonstrate that MDPSN achieves superior diagnostic accuracy compared to five other comparison methods.

Initiatives to the Global Representation of Scientific Collections

Ensuring that all global scientific collections become accessible online is a challenge. The Global Biodiversity Information Facility (GBIF) maintains a Global Registry of Scientific Collections (GRSciColl) but as of May 2023, 58% of the collections included in the registry were from institutions based in Europe or North America. This shows a low representation of organizations from other regions. GBIF is addressing the bias with the help of its participant network and regional support teams. The GBIF participant network is actively encouraging institutions in its regions to register and describe their collection data through GBIF and GRSciColl. To complement this effort, the regional support teams based in Africa, Asia, Latin America and the Caribbean, and Europe are engaged in mobilizing new collections and updating existing ones (Fig. 1). In addition, a community of editors review changes proposed via a user suggestion system and keeps the entries in GRSciColl updated. The work of the regional support teams involves reviewing institutional web pages or other available online sources to update GRSciColl entries. In cases where the data are not accessible on the web, contacts from the institutions are invited to suggest changes to their GRSciColl pages. As a next step, institutions are contacted to inquire about their interest and plan for sharing open access data. If the response is positive, technical support is offered to help the institutions mobilize and publish the collection data through GBIF. The regional support teams have contacted a number of institutions and several institutions have shown interest in publishing collections on GBIF. Some organizations have already started digitizing and publishing their collection(s). With the training and technical support from the regional support teams, the following institutions have started to share data on GBIF: Instituto de Biodiversidad de Venezuela - INBIO has published data on 23,414 herbarium specimens. Natural History Museum, Tribhuvan University, Nepal has started digitizing its collections and has published eight datasets constituting 1,776 specimens. National Museum of Natural History at the Bulgarian Academy of Sciences has started digitizing 31 out of its 32 collections, of which they have published two herbarium collections represented as two datasets consisting of over 6,357 specimens. Instituto de Biodiversidad de Venezuela - INBIO has published data on 23,414 herbarium specimens. Natural History Museum, Tribhuvan University, Nepal has started digitizing its collections and has published eight datasets constituting 1,776 specimens. National Museum of Natural History at the Bulgarian Academy of Sciences has started digitizing 31 out of its 32 collections, of which they have published two herbarium collections represented as two datasets consisting of over 6,357 specimens. This presentation will highlight the data mobilization activities of the institutions, the progress of the GBIF regional support teams (Fig. 2), and the opportunities and challenges experienced in the mobilization effort.

Global Representation on the ACS Council

Global Representation on the ACS Council

Sustainable Waste Management and Service Quality Delivery in Nigeria

Waste disposal, collection and treatment is an indispensable facet in the developmental phases of rural and urban cities across the globe. The accumulation, disposal and treatment of waste in urban centers have become a predicament to both private and public stakeholders in Uyo metropolis, Akwa Ibom State, Nigeria. The objective of this study is to examine sustainable waste management and service quality delivery in Nigeria. The issues to be addressed by this study include; attitude of residents towards waste disposal, non-conformity with modern waste collection/treatment processes, and the waste management infrastructural gaps. The study adopted the 9 Development Band global theory of waste and development - using Integrated Sustainable Waste Management – ISWM approaches, with a model elucidating the effects of waste management on environmental footprint. Descriptive survey technique was adopted in which, questionnaire was administered to 384 respondents in the metropolis, as well as the conduct of in-depth interview to obtain additional information on issues that were not clarified in the structured questionnaire. Data collected from the field were collated and analyzed using Pearson Correlations Analysis Technique, to ascertain the relationship between the dependent and independent variables. The findings of the study revealed unwholesome and unsustainable approaches in waste disposal/management, and suggest effective service delivery approaches in line with the 9DB model ISWM. The study recommends that residents should dispose waste in line with established waste disposal procedures, waste management authorities should adopt efficient and sustainable waste collection and treatment approaches. If these suggestions are implemented; it could reduce the metropolis environmental footprint, mitigate on the current global climate change challenges, and promote the UN-SDG - 11 which anchors on the need for sustainable cities and communities.

Efficient image super-resolution based on transformer with bidirectional interaction

In single-image super-resolution (SISR) tasks, many methods benefit from the local and global contexts of the image. Despite that, no methods use the bidirectional interaction between these two contexts. So, we were inspired by the fully adaptive Transformer for high-level vision. We propose a fully adaptive Transformer super-resolution (FATSRN) for SISR. The model uses local and global information and their bidirectional interaction in a context-aware manner. The model is based on fully adaptive self-attention (FASA) as the main block, which uses self-modulated convolutions to extract local representation adaptively. Also, the FASA uses self-attention in down-sampled space to extract global representation. In addition, this FASA uses a bidirectional adaptation process between local and global representation to model the interaction. Moreover, a fine-grained downsampling strategy is used to improve the down-sampled self-attention mechanism. Based on the FASA, we built a fully adaptive self-attention block (FASAB) as the main block of our model. Then, the fully adaptive self-attention group (FASAG) is used as the backbone for our FATSRN. Extensive experiments are done to show the efficiency of the model against the state-of-the-art methods. For example, our model improved the PSNR from 27.69 to 27.73 compared to the SwinIR-light for the B100 dataset at the scale of × 4. In addition, our model achieved 0.04 dB better PSNR compared to the state-of-the-art STSN model for the Set5 dataset at the scale of × 2 with 64% and 48% fewer parameters and Mult-adds.

Self-supervised visual–textual prompt learning for few-shot grading of gastric intestinal metaplasia

Gastric intestinal metaplasia (GIM) is a frequent lesion of the gastrointestinal tract, and its clinical grading can significantly reduce the risk of gastric cancer. Pre-trained vision–language models have demonstrated impressive generalizability in many domains. However, they suffer from the following limitations when applied to GIM grading: 1) Small GIM lesions of endoscopic images and their colour features are difficult to represent with global representations alone, 2) medical concepts are highly specialized, and their dependencies and semantics cannot be precisely represented and 3) Because of the scarcity of medical prior knowledge, multi-modal prompts are over-reliant on random initialization and fail to maintain semantic consistency. To address the above challenges, we propose a self-supervised visual–textual prompt (SVTP) learning strategy for few-shot GIM grading. In particular, image and colour prompts are simultaneously configured in the visual branch to use the colour information in lesion areas. Meanwhile, a coupling function is introduced to implement the exchange of high-dimensional information. Then, to enhance the encoding of complex information and model the dependencies between medical concepts, text prompts and an adapter are integrated into the textual branch. Finally, a three-dimensional contrastive learning (3DCL) strategy is proposed to enrich medical prior knowledge of prompts and maintain their semantic consistency, where three-modal prompts are projected into a unified 3D space for contrastive learning. We verified SVTP on a public endoscopic image dataset and a private GIM grading dataset. Experimental results demonstrate that SVTP has achieved SOTA grading performance on all datasets.

Dynamics of Local Politics in the Digital Era Using Bibliometric and Qualitative Analysis

<em>This comprehensive analysis explores the multifaceted landscape of academic research through a systematic examination of high-impact publishers, author contributions, global academic representation, and keyword emphasis. Fig. 2 illustrates the prominence of publishers such as "Energy Research and Social Science" and "Ambio," showcasing their substantial impact in respective fields, while also recognizing the noteworthy contributions of journals with varying h-index values. The dataset spans diverse topics from energy research to social sciences, emphasizing the dynamic nature of academic contributions over different years. This study utilizes bibliometric analysis and qualitative research.This comprehensive analysis serves as a guiding framework for scholars, researchers, and policymakers, offering insights into the dynamics of high-impact publishers, the diversity of author contributions, global academic landscapes, and the nuanced aspects of local politics. The interplay of these elements shapes the rich tapestry of academic exploration, inviting continual inquiry and collaboration. In the digital era, there has been a shift in the dynamics of local politics in Makassar, increasing political awareness and public participation through social media while presenting challenges such as opinion polarization and misinformation. This study emphasizes the importance of utilizing digital technology strategies to strengthen interethnic dialogue and advance a participatory and inclusive democracy in Makassar.</em>