Search Results (1,161)

In the context of both domestic and international economic landscapes, regional policy has emerged as an increasingly influential factor shaping the developmental trajectories of Chinese enterprises. Despite its growing significance, the extant literature lacks a comprehensive and systematically visualized synthesis that encapsulates the scope and trends of research in this domain. This study addresses this critical gap by conducting an integrative bibliometric and qualitative review of the academic output related to regional policy and Chinese firm growth. Drawing on a final dataset comprising 3428 validated academic publications—selected from an initial pool of 3604 records retrieved from the Web of Science Core Collection between 1991 and 2022, the research employs a two-stage methodological framework. In the first phase, advanced bibliometric tools, and software applications, including RStudio, Bibliometrix, VOSviewer, and CitNetExplorer, are utilized to implement techniques such as keyword co-occurrence analysis, thematic clustering, and the tracing of thematic evolution over time. These methods facilitate rigorous data cleansing, breakpoint identification, and the visualization of intellectual structures and emerging research patterns. In the second phase, a targeted qualitative review is conducted to evaluate the influence of regional policies on Chinese firms across three critical stages of business development: start-up, expansion, and maturity. The findings reveal that regional policy interventions generally exert a positive influence on firm performance throughout all stages of development. Notably, a significant concentration of citation activity occurred prior to 2017; however, post-2017, the volume of scholarly publications, journal-level impact (as measured by h-index), and author-level influence experienced a marked increase. Among the 3428 analyzed publications, a substantial portion—2259 articles—originated from Chinese academic institutions, highlighting the strong domestic research interest in the subject. Furthermore, since 2015, there has been a discernible shift in keyword co-occurrence trends, with increasing scholarly attention directed towards sustainable development issues, particularly those related to carbon dioxide emissions and green innovation, reflecting evolving policy priorities and environmental imperatives. Full article

Ongoing changes in the distribution and abundance of several tick species of medical relevance in Canada have prompted the development of the eTick platform—an image-based crowd-sourcing public surveillance tool for Canada enabling rapid tick species identification by trained personnel, and public health guidance based on tick species and province of residence of the submitter. Considering that more than 100,000 images from over 73,500 identified records representing 25 tick species have been submitted to eTick since the public launch in 2018, a partial automation of the image processing workflow could save substantial human resources, especially as submission numbers have been steadily increasing since 2021. In this study, we evaluate an end-to-end artificial intelligence (AI) pipeline to support tick identification from eTick user-submitted images, characterized by heterogeneous quality and uncontrolled acquisition conditions. Our framework integrates (i) tick localization using a fine-tuned YOLOv7 object detection model, (ii) resolution enhancement of cropped images via super-resolution Generative Adversarial Networks (RealESRGAN and SwinIR), and (iii) image classification using deep convolutional (ResNet-50) and transformer-based (ViT) architectures across three datasets (12, 6, and 3 classes) of decreasing granularities in terms of taxonomic resolution, tick life stage, and specimen viewing angle. ViT consistently outperformed ResNet-50, especially in complex classification settings. The configuration yielding the best performance—relying on object detection without incorporating super-resolution—achieved a macro-averaged F1-score exceeding 86% in the 3-class model (Dermacentor sp., other species, bad images), with minimal critical misclassifications (0.7% of “other species” misclassified as Dermacentor). Given that Dermacentor ticks represent more than 60% of tick volume submitted on the eTick platform, the integration of a low granularity model in the processing workflow could save significant time while maintaining very high standards of identification accuracy. Our findings highlight the potential of combining modern AI methods to facilitate efficient and accurate tick image processing in community science platforms, while emphasizing the need to adapt model complexity and class resolution to task-specific constraints. Full article

Background/Objectives: Accurate diagnosis of brain tumors is one of the most important challenges in neuro-oncology since tumor classification and volumetric segmentation inform treatment planning. Two-dimensional classification and three-dimensional segmentation deep learning models can augment radiological workflows, particularly if paired with explainable AI techniques to improve model interpretability. The objective of this research was to develop a web-based brain tumor segmentation and classification diagnosis platform. Methods: A diagnosis system was developed combining 2D tumor classification and 3D volumetric segmentation. Classification employed a fine-tuned MobileNetV2 model trained on a glioma, meningioma, pituitary tumor, and normal control dataset. Segmentation employed a SegResNet model trained on BraTS multi-channel MRI with synthetic no-tumor data. A meta-classifier MLP was used for binary tumor detection from volumetric features. Explainability was offered using XRAI maps for 2D predictions and Gaussian overlays for 3D visualizations. The platform was incorporated into a web interface for clinical use. Results: MobileNetV2 2D model recorded 98.09% classification accuracy for tumor classification. 3D SegResNet obtained Dice coefficients around 68–70% for tumor segmentations. The MLP-based tumor detection module recorded 100% detection accuracy. Explainability modules could identify the area of the tumor, and saliency and overlay maps were consistent with real pathological features in both 2D and 3D. Conclusions: Deep learning diagnosis system possesses improved brain tumor classification and segmentation with interpretable outcomes by utilizing XAI techniques. Deployment as a web tool and a user-friendly interface made it suitable for clinical usage in radiology workflows. Full article

In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19–50 Hz, 14–38 Hz, 9–26 Hz, and 3–14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP. Full article

Background/Objectives: The 2019 WHO classification redefined high-grade gastrointestinal neuroendocrine neoplasms (GI NENs), encompassing not only poorly differentiated neuroendocrine carcinomas (NECs), but also well-differentiated grade 3 neuroendocrine tumors (NETs G3). Since both subtypes share a Ki-67 index > 20%, distinguishing them based solely on morphology is challenging. Prior studies have shown TP53 alterations in NECs but not in NETs. This study aimed to evaluate clinico-morphological parameters and the immunohistochemical (IHC) expression of p53 in high-grade GI NENs to identify relevant correlations. Methods: Tumors were stratified by Ki-67 index into two groups: >20–50% and >50%. p53 IHC expression was assessed as “wild-type” (1–20% positive tumor cells) or “non-wild-type” (absence or >20% positivity). Correlations were analyzed between Ki-67, p53 status, and various pathological features. Results: Significant correlations were found between the Ki-67 index and maximum tumor size, pT stage, lymphovascular invasion, perineural infiltration, and diagnostic classification. Similarly, p53 immunohistochemical status was significantly associated with lymphovascular invasion, lymph node metastasis, and tumor classification (NET G3 versus NEC, including NEC components of MiNENs). Conclusions: The findings support the value of Ki-67 and p53 as complementary biomarkers in the pathological evaluation of high-grade GI NENs. Their significant associations with key morphological parameters support their utility in differentiating NETs G3 from NECs, particularly in cases showing overlapping histological features. The immunohistochemical profile of p53 may serve as a useful diagnostic adjunct in routine practice. Full article

Anti-neutrophil cytoplasmic antibodies (ANCA)-associated vasculitis (AAV) is a heterogeneous group of small-vessel vasculitides, characterized by the presence of antibodies binding to myeloperoxidase (MPO) and proteinase-3 (PR3) found in neutrophil granules. Apart from being the target of ANCA, neutrophils actively contribute to the vicious cycle of inflammation and vascular damage in AAV. On the other hand, platelets have recently been recognized as essential for thrombosis and as inflammatory effectors that collaborate with neutrophils, reinforcing the generation of reactive oxygen species (ROS) and the formation of neutrophil extracellular traps (NETs) in those diseases. Neutrophils exhibit morphological and functional heterogeneity in AAV, reflecting the complexity of their contribution to disease pathogenesis. Since long-term immunosuppression may be related to serious infections and malignancies, there is an urgent need for reliable biomarkers of disease activity to optimize the management of AAV. This review summarizes the current understanding of the role of neutrophils and platelets in the pathogenesis of granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA), focusing on their crosstalk, and highlights the potential for identifying novel biomarkers relevant for predicting the disease course and its relapses. Full article

Forest fire detection is an essential application in environmental surveillance since wildfires cause devastating damage to ecosystems, human life, and property every year. The effective and accurate detection of fire is necessary to allow for timely response and efficient management of disasters. Traditional techniques for fire detection often experience false alarms and delayed responses in various environmental situations. Therefore, developing robust, intelligent, and real-time detection systems has emerged as a central challenge in remote sensing and computer vision research communities. Despite recent achievements in deep learning, current forest fire detection models still face issues with generalizability, lightweight deployment, and accuracy trade-offs. In order to overcome these limitations, we introduce a novel technique (FireNet-KD) that makes use of knowledge distillation, a method that maps the learning of hard models (teachers) to a light and efficient model (student). We specifically utilize two opposing teacher networks: a Vision Transformer (ViT), which is popular for its global attention and contextual learning ability, and a Convolutional Neural Network (CNN), which is esteemed for its spatial locality and inductive biases. These teacher models instruct the learning of a Swin Transformer-based student model that provides hierarchical feature extraction and computational efficiency through shifted window self-attention, and is thus particularly well suited for scalable forest fire detection. By combining the strengths of ViT and CNN with distillation into the Swin Transformer, the FireNet-KD model outperforms state-of-the-art methods with significant improvements. Experimental results show that the FireNet-KD model obtains a precision of 95.16%, recall of 99.61%, F1-score of 97.34%, and mAP@50 of 97.31%, outperforming the existing models. These results prove the effectiveness of FireNet-KD in improving both detection accuracy and model efficiency for forest fire detection. Full article

Conventional ground reaction force (GRF) and load rate (LR) analyses may overlook temporal and waveform characteristics that reflect injury status and acuity. This study used an alternative GRF processing methodology to characterize GRF waveforms among runners with symptomatic medial tibial stress fractures (MTSS) and those recovering from tibial stress fractures (TSF; both unilateral [UL] and bilateral [BL]). This cross-sectional analysis of runners (n = 66) included four groups: symptomatic MTSS, recovering from UL or BL TSF, or uninjured case-matched controls. Participants ran at self-selected speed on an instrumented treadmill. Kinematics were collected with a 3D optical motion analysis system. Double-Gaussian models described the biphasic loading pattern of running gait (initial impact, active phases). Gaussian parameters described relative differences in the GRF waveform by injury condition. LR was calculated using the central difference numerical derivative of the raw normalized net force data. During the impact phase (0–20% of stance), controls and BL TSF produced higher GRF amplitudes than UL TSF and MTSS (p < 0.05). BL TSF and controls had greater maximal positive LR and minimum LR than UL TSF and MTSS. Peak medial GRF was 18–43% higher in the BL TSF group than in MTSS and UL TSF (p < 0.05). Correlations existed between tibial pain severity and early stance net GRF (r = 0.512; p = 0.016) and between pain severity and the duration since diagnosis for LR values during the impact phase (r values = 0.389–0.522; all p < 0.05). Collectively, these data suggest that this waveform modeling approach can differentiate injury status and pain acuity in runners. Early stance GRF and LR may offer novel insight into the management of running-related injuries. Full article

Background/Objectives: Maternal exposures are known to influence the risk of isolated cleft lip with or without cleft palate (CL/P)—a common and highly heritable birth defect with a multifactorial etiology. Methods: To identify new risk loci, we conducted a genome-wide gene–environment interaction (GEI) analysis of CL/P with maternal smoking and vitamin use in Filipinos (N_cases = 540, N_controls = 260). Since GEI analyses are typically low in power and the results can be difficult to interpret, we applied multiple testing frameworks to evaluate potential GEI effects: a one degree-of-freedom (1df) GxE test, the 3df joint test, and the two-step EDGE approach. Results: While no genome-wide significant interactions were detected, we identified 11 suggestive GEIs with smoking and 24 with vitamin use. Several implicated loci contain biologically plausible genes. Notable interactions with smoking include loci near FEZF1, TWIST2, and NET1. While FEZF1 is involved in early neuronal development, TWIST2 and NET1 regulate epithelial–mesenchymal transition, which is required for proper lip and palate fusion. Interactions with vitamins encompass CECR2—a chromatin remodeling protein required for neural tube closure—and FURIN, a critical protease during early embryogenesis that activates various growth factors and extracellular matrix proteins. The activity of both proteins is influenced by folic acid. Conclusions: Our findings highlight the critical role of maternal exposures in identifying genes associated with structural birth defects such as CL/P and provide new paths to explore for CL/P genetics. Full article

Based on the experience gained worldwide from potential solutions to the fouling problem of fisheries and aquaculture infrastructure, we attempted to design, construct and test the antifouling efficiency of a new hybrid filament created from non-laminated copper wire braided with synthetic fibers made of Dyneema. The design involved the creation of a hybrid twine substituting a percentage of the synthetic fibers with 0.1–0.15 mm diameter copper wire at 5%, 10%, 20% and 40% levels. There is limited information in the international literature for comparison with our results, since there has never been any attempt to create such a hybrid net. The results showed that for the 6 mm mesh, the maximum openness obtained after the 8-month experimental period was 8.72%, with Cu wire substitution at 35%. For the 12 mm mesh, these values were 27.07% at 26%, and for the 20 mm mesh, they were 33.68% at 28%. A conservative average independent from mesh size to achieve optimum openness in the long term is 30 ± 4.73% Cu wire substitution. In addition, we found that both the mesh size (mm) and the copper substitution percentage affected the fouling process during the experimental period, which lasted 8 months. Full article

With water scarcity becoming worse, and demand increasing, the urgency for the water industry to hit net-zero carbon is accelerating. Even as a multitude of utilities have pledged to reach net-zero by 2050, advancing beyond the energy–water nexus remains a heavy lift. This paper, using a systematic literature review that complies with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA), aims to propose sustainable water resource management (SWRM) strategies that may assist water utilities in decarbonizing their value chains and achieving net-zero carbon. In total, 31 articles were included from SCOPUS, ResearchGate, ScienceDirect, and Springer. The findings show that water utilities are responsible for 3% of global greenhouse gas emissions and could reduce these emissions by more than 45% by employing a few strategies, including the electrification of transport fleets, the use of renewables, advanced oxidation processes (AOPs) and energy-efficient technologies. A broad-based case study from Scottish Water shows a 254,000-ton CO₂ reduction in the period since 2007, indicative of the potential of these measures. The review concludes that net-zero carbon is feasible through a mix of decarbonization, wastewater reuse, smart systems and policy-led innovation, especially if customized to both large and small utilities. To facilitate a wider and a more scalable transition, research needs to focus on development of low-cost and flexible strategies for underserved utilities. Full article

This study investigates the impact of remittances on income inequality in Lebanon using annual time-series data for the years 2000–2023. Applying Johansen’s cointegration test, with financial development (FD), GDP, and household consumption expenditure (HCE) as the control variables, the study examines the long-run and short-run relationship between remittances and inequality. The study also considers the moderating impacts of FD and HCE to account for their indirect role in the remittance–inequality relationship. Dynamic relations are also examined by using impulse response functions (IRFs) and Forecast Error Variance Decomposition (FEVD) analyses. The long-run model estimates validate that remittances and income inequality are significantly and negatively related, i.e., increased remittance receipts serve to reduce income inequality in Lebanon. Remittance effects, however, are statistically insignificant in the short run. Interestingly, the results reveal that financial development weakens the remittances’ inequality-reducing effect, dampening their impact. Contrarily, a higher household consumption expenditure slightly strengthens the inequality-reducing effect of remittances. A comparison between the pre- and post-2019 periods reveals that the explanatory strength of remittances weakened during times of economic crisis, since the function of remittances was different during times of economic distress. Based on these findings, this study recommends that Lebanon not only promote financial development but also focus on financial inclusion, improve social safety nets, and provide inclusive economic growth to maximize remittance inflow benefits and efficiently reduce inequality. Full article

Intelligent fault diagnosis for rotary machinery often suffers performance degradation under domain shifts between training and deployment environments. To address this, we propose a robust fault diagnosis framework incorporating three key components: (1) an order-frequency-based preprocessing method to normalize rotational variations, (2) a U-Net variational autoencoder (U-NetVAE) to enhance adaptation through reconstruction learning, and (3) a test-time training (TTT) strategy enabling unsupervised target domain adaptation without access to source data. Since existing works rarely evaluate under true domain shift conditions, we first construct a unified cross-domain benchmark by integrating four public datasets with consistent class and sensor settings. The experimental results show that our method outperforms conventional machine learning and deep learning models in both F1-score and recall across domains. Notably, our approach maintains an F1-score of 0.47 and recall of 0.51 in the target domain, outperforming others under identical conditions. Ablation studies further confirm the contribution of each component to adaptation performance. This study highlights the effectiveness of combining mechanical priors, self-supervised learning, and lightweight adaptation strategies for robust fault diagnosis in the practical domain. Full article

Global biodiversity is in crisis, with wildlife populations declining 69% since 1970 (WWF). Preserving and restoring ecosystems is essential for sustaining life on Earth. However, many countries rely on market-based instruments like biodiversity offsets, despite little evidence of their effectiveness. This study critically examines biodiversity offsets, identifying institutional, data, ecological, economic, and social failures that undermine their success. Using Indonesia, a global biodiversity hotspot, as a case study, we develop an econometric model to analyze key drivers of deforestation. The findings reveal that biodiversity offset schemes are fundamentally flawed: they lack scientific credibility, rely on arbitrary ratios, lack auditing and transparency, create value conflicts, and fail to achieve “No Net Loss” even over a 100-year timeframe. Offsets do not compensate for lost biodiversity, especially for affected communities, and are rarely supported by ecosystem mapping or robust valuation metrics. Without major reforms, they cannot halt or reverse biodiversity loss. A stronger, evidence-based approach is urgently needed. Rather than relying on ineffective offset schemes, the global community must prioritize genuine ecosystem restoration and sustainable conservation strategies to protect biodiversity for future generations. Full article

Federated learning allows models to be trained on edge devices with local data, eliminating the need to share data with a central server. This significantly reduces the amount of data transferred from edge devices to central servers, which is particularly important in rural areas with limited bandwidth resources. Despite the potential of federated learning to fine-tune deep learning models using data collected from edge devices in low-resource environments, its application in the field of bird monitoring remains underexplored. This study proposes a federated learning pipeline tailored for bird species classification in wetlands. The proposed approach is based on lightweight convolutional neural networks optimized for use on resource-constrained devices. Since the performance of federated learning is strongly influenced by the models used and the experimental setting, this study conducts a comprehensive comparison of well-known lightweight models such as WideResNet, EfficientNetV2, MNASNet, GoogLeNet and ResNet in different training settings. The results demonstrate the importance of the training setting in federated learning architectures and the suitability of the different models for bird species recognition. This work contributes to the wider application of federated learning in ecological monitoring and highlights its potential to overcome challenges such as bandwidth limitations. Full article