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From lagging behind to going beyond: windows of opportunity and latecomers' catch-up strategies
PurposeTo answer the questions: what roles windows of opportunity act in the catchup process of latecomers, what strategies latecomer enterprises should adopt to size windows of opportunity to catch-up with incumbents even going beyond?Design/methodology/approachThis paper studies the catch-up history of the Chinese mobile phone industry and proposes a sectoral innovation system under scenario of technology paradigm shifts. Then a history-friendly simulation model and counterfactual analysis are conducted to learn how different windows of opportunity and catch-up strategies influence the catch-up performance of latecomers.FindingsResults show latecomers can catch up with technology ability by utilizing technology window and path-creating strategy. However, catching up with the market is not guaranteed. Demand window can help latecomers to catch up with market as it increases their survival rates, different sized windows benefit different strategies. However, it also enlarges incumbents' scale effect. Without technology window technology catch up is not guaranteed. Two windows have combination effects. Demand window affects the “degree” of change in survival rates, while the technology window affects the “speed” of change. Demand window provides security; technology window provides the possibility of a breakthrough for technology ability.Practical implicationsThe findings of this paper provide theoretical guidance for latecomer enterprises to choose appropriate catch-up strategies to seize different opportunity windows.Originality/valueThis paper emphasizes the abrupt change of industrial innovation system caused by technology paradigm shifts, which makes up for the shortcomings of previous researches on industrial innovation system which either studied the influence of static factors or based on the influence of continuous changes.
Catch, release and second chances: exploring the impact of angling on two coastal fish species
We addressed the impact of angling in two Mediterranean inshore sites by conducting a tag-recapture study on caught-and-released black scorpionfish (Scorpaena porcus) and giant goby (Gobius cobitis). We assessed the relationship between the fish vitality at release and the main factors affecting it, i.e. air exposure time, water temperature, fish length and handling. Then, we used conventional fish tags to study fish survivability to catch and release, growth rates, behaviour and site fidelity. Overall, 17 species (mainly gobids and sparids) were caught, with differences in species composition and abundance between the two sites, probably related to their different depth range. A total of 136 individuals of S. porcus and 38 of G. cobitis were caught, tagged and released. S. porcus had a better vitality than G. cobitis once released, which was negatively associated with an increase in air exposure time, although not significant. We recorded 34 recapture events, with a resulting recapture rate of 19.9% for S. porcus (without considering multiple recaptures) and 5.3% for G. cobitis. The length-weight relationship revealed an isometric growth in both species. The von Bertalanffy growth parameters (± standard error) estimated for S. porcus were L∞ = 26 cm ± 5.25 and k = 0.21 ± 0.09, with no significant differences detected in growth rate between immature and mature individuals. The species’ high site fidelity and resilience to catch-and-release indicate its potential susceptibility to repeated angling in confined coastal habitats. These results highlight the need to account for the cumulative ecological impacts of recreational fisheries in the management of coastal fish populations.
Catch Me If You Can! Keeping an Eye Out to Detect Unusual Malignancies Appearing in Cervical Pap Smear.
Background: Metastatic involvement of the uterine cervix by extrauterine non-gynecological malignancies is exceptionally rare due to the cervix's unique lymphatic and vascular characteristics. Detection of such unusual malignancies in cervical Papanicolaou (Pap) smears poses significant diagnostic challenges but can offer critical early clues. Objective: This study aimed to evaluate the spectrum and cytomorphological features of extrauterine nongynecological malignancies involving the cervix detected incidentally on routine cervical Pap smears. Materials and Methods: A retrospective analysis was conducted on 12,980 cervical Pap smears screened between January 2019 and December 2024 in a tertiary care center. Twenty-seven cases of extrauterine nongynecological malignancies were identified. Cytological findings were correlated with clinical, radiological, histopathological, and immunohistochemical data. Results: The mean patient age was 54 years (range: 22–84). The most common metastatic sites were the lower gastrointestinal tract (33.3%), breast (14.8%), vagina (22.2%), and other sites, including gallbladder, urinary bladder, retroperitoneum, and hematologic malignancies. In 33.3% of cases, the Pap smear provided the first diagnostic clue for an unknown malignancy. Cytological features varied across primary sites: gastrointestinal metastases showed tall columnar cells and signet-ring morphology; breast carcinoma displayed poorly differentiated cells; and melanomas exhibited pigmented cells with prominent nucleoli. Rare diagnoses included metastatic urothelial carcinoma, anaplastic large cell lymphoma, and retroperitoneal leiomyosarcoma. Conclusion: Although rare, extrauterine malignancies can be detected on cervical Pap smears and may even present as the first sign of disease. Awareness of subtle cytomorphological patterns, combined with clinical correlation and immunohistochemical studies, is essential to avoid misinterpretation and ensure accurate diagnosis and timely management. [ABSTRACT FROM AUTHOR]
Type-based assessment of aerosol direct radiative effects: A proof-of-concept using GEOS-Chem and CATCH
The radiative perturbation of the Earth's energy balance caused by all aerosols, the direct radiative effect (DRE), and anthropogenic aerosols, the direct radiative forcing (DRF), remain major sources of uncertainty in climate projections. Here we propose a method for determining DRE and DRF that makes use of the High Spectral Resolution Lidar (HSRL)-retrieved aerosol loading and derived aerosol types (i.e. dust, marine, urban, smoke, etc.) in combination with aerosol-type specific optical properties. As the global spatiotemporal distributions of HSRL-derived aerosol types are not currently available, the methodology is tested here using a global 3-D model of atmospheric chemistry (GEOS-Chem) along with Creating Aerosols from CHemistry (CATCH) algorithm-generated aerosol types analogous to ones derived by HSRL. In this method, the Rapid Radiative Transfer Model for General Circulation Models (RRTMG) is used to perform radiative transfer calculations with the single scattering albedo (SSA) and asymmetry parameter (g) of atmospheric particles assigned based on the aerosol type in each grid box. Average GEOS-Chem/CATCH-derived all-sky DRE and DRF across the North American domain are estimated to be −1.98 W/m² and − 0.77 W/m², respectively between mid-January and early February 2013 and − 4.20 W/m² and − 1.41 W/m² respectively between mid-July and early August 2014. Sensitivity studies revealed that the scheme may produce up to about ±0.42 W/m² and ± 0.21 W/m² uncertainty in DRE and DRF, respectively, related to variability in aerosol type-specific optical properties. This study presents a new way of determining DRE and DRF estimates once global retrievals of aerosol intensive parameters by HSRL become available.
Catch Weight Prediction for Multi-Species Fishing using Artificial Neural Networks
Due to the increasing demand for fish consumption, sustainable fishery become more and more challenging. To prevent from overfishing, massive data in open sea fishing have been collected and analyzed to achieve efficient management of fishery. Still, it is extremely difficult for fishers and fishery managers to exploit available data for accurate prediction, because of their limited data processing capacities, and the overall lack of adequate database systems [1].The goal of this work is therefore to analyze the relationship between data collected from all sensors installed on-board fishing vessels and catch weight, to better support generating a map showing likely fishing effort allocation. To do so, we train neural networks to predict catch weight using all available data from sensors on fishing vessels. The raw data are pre-processed using random sampling techniques to be fed into a neural network for training. A multi-layer perceptron (MLP) neural network is proposed as the baseline. We propose a data augmentation method and a training strategy in order to optimize the prediction accuracy of the model. Our data augmentation method conducts random sampling of the original data multiple times, which reduces the root mean square error (RMSE) by 15.8%, as compared with the results obtained by the model trained without data augmentation. Our training strategy works well to further optimize the prediction accuracy of the model trained with an augmented dataset, which significantly decreased the RMSE by 11. 2%. To the best of our knowledge, this is the first study on the catch weight prediction using neural networks.
Higher comorbidities associated with less improvement in disease activity in early RA: results from CATCH cohort.
Objectives Comorbidities negatively influence remission rates in RA. This study estimated the effects of comorbidities on components of disease activity in early RA (ERA). Methods Using the Rheumatic Disease Comorbidity Index (RDCI), the influence of comorbidities on trajectories of components of the SDAI (Simple Disease Activity Index) was assessed in participants with ERA enrolled in Canadian Early Arthritis Cohort (CATCH) over the first year of treatment. Adjusted effects of RDCI scores categorized 0, 1, 2 and ≥3 on SDAI trajectories over time were analysed using multivariable generalized estimating equations (GEEs) models adjusted for multiple confounders. Results ERA participants (N  = 2248) had a mean (S.D.) symptom duration of 5.7 (3) months; mean age 55 (15) years and 72% were female. Baseline SDAI was 29 (15), with 90% having moderate-high SDAI. Baseline RDCI scores were 0 in 888 (40%), 1 in 547 (24%), 2 in 451 (20%) and ≥3 in 362 (16%). While baseline disease activity was similar across comorbidity groups, patients with higher RDCI scores showed worse SDAI trajectories over the first year of RA treatment. Higher RDCI scores were independently associated with pain, patient and physician global assessments over time. Conclusion This large real-world analysis of ERA patients seen in routine rheumatology practice across Canada showed that while RA disease activity across comorbidity groups at diagnosis was similar, higher comorbidity was associated with slower improvement in RA disease activity over the first year of treatment, likely driven by independent associations with patient and physician global assessments and pain.
Localization of try block and generation of catch block to handle exception using an improved LSTM
Several contemporary programming languages, including Java, have exception management as a crucial built-in feature. By employing try-catch blocks, it enables developers to handle unusual or unexpected conditions that might arise at runtime beforehand. If exception management is neglected or applied improperly, it may result in serious incidents like equipment failure. Exception handling mechanisms are difficult to implement and time expensive with the preceding methodologies. This research introduces an efficient Long Short Term Memory (LSTM) technique for handling the exceptions automatically, which can identify the locations of the try blocks and automatically create the catch blocks. Bulky java code is collected from GitHub and splitted into several different fragments. For localization of the try block, Bidirectional LSTM (BiLSTM) is used initially as a token level encoder and then as a statement-level encoder. Then, the Support Vector Machine (SVM) is used to predict the try block present in the given source code. For generating a catch block, BiLSTM is initially used as an encoder, and LSTM is used as a decoder. Then, SVM is used here to predict the noisy tokens. The loss functions of this encoder-decoder model have been trained to be as small as possible. The trained model then uses the black widow method to forecast the following tokens one by one and then generates the entire catch block. The proposed work reaches 85% accuracy for try block localization and 50% accuracy for catch block generation. An improved LSTM with an attention mechanism method produces an optimal solution compared to the existing techniques. Thus the proposed method is the best choice for handling the exceptions.