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Climate plays a major role in determining where species occur, and when they are active throughout the year. In the face of a changing climate, many species are shifting their ranges poleward. Many species are also shifting their emergence phenology. Wild bees in Great Britain are susceptible to changes in climatic conditions but little is known about historic or potential future spatio‐temporal trends of many species. This study utilized a sliding window approach to assess the impacts of climate on bee emergence dates, estimating the best temperature window for predicting emergence dates for 88 species of wild bees. Using a ‘middle‐of‐the‐road’ (RCP 4.5) and ‘worst‐case’ (RCP 8.5) climate scenario for the period 2070–2079, predictions of future emergence dates were made. In general, the best predicting climate window occurred in the 0–3 months preceding emergence. Across the 40 species that showed a shift in emergence dates in response to a climate window, the mean advance was 13.4 days under RCP 4.5 and 24.9 days under RCP 8.5. Species distribution models (SDMs) were used to predict suitable climate envelopes under historic (1980–1989), current (2010–2019) and future (2070–2079 under RCP 4.5 and RCP 8.5 scenarios) climate conditions. These models predict that the climate envelope for 92% of studied species has increased since the 1980s, and for 97% and 93% of species under RCP 4.5 and RCP 8.5 respectively, this is predicted to continue, due to extension of the northern range boundary. While any range changes will be moderated by habitat availability, it highlights that Great Britain will likely experience northward shifts of bee populations in the future. By combining spatial and temporal trends, this work provides an important step towards informing conservation measures suitable for future climates, directing how interventions can be provided in the right place at the right time. Climate change is driving spatio‐temporal shifts in many taxa, however the effects on British wild bees have not been quantified. We show that bees are emerging earlier in warmer years, and are predicted to continue to do so. Many bees have already shifted their distributions northwards, and are likley to continue to do so under future climate conditions.

Automatic anomaly detection is a crucial task in video surveillance system intensively used for public safety and others. The present system adopts a spatial branch and a temporal branch in a unified network that exploits both spatial and temporal information effectively. The network has a residual autoencoder architecture, consisting of a deep convolutional neural network-based encoder and a multi-stage channel attention-based decoder, trained in an unsupervised manner. The temporal shift method is used for exploiting the temporal feature, whereas the contextual dependency is extracted by channel attention modules. System performance is evaluated using three standard benchmark datasets. Result suggests that our network outperforms the state-of-the-art methods, achieving 97.4% for UCSD Ped2, 86.7% for CUHK Avenue, and 73.6% for ShanghaiTech dataset in term of Area Under Curve, respectively.

Aim: South-eastern Australia is a climate change hotspot with well-documented recent changes in its physical marine environment. The impact on and temporal responses of the biota to change are less well understood, but appear to be due to influences of climate, as well as the non-climate related past and continuing human impacts. We attempt to resolve the agents of change by examining major temporal and distributional shifts in the fish fauna and making a tentative attribution of causal factors. Location: Temperate seas of south-eastern Australia. Methods: Mixed data sources synthesized from published accounts, scientific surveys, spearfishing and angling competitions, commercial catches and underwater photographic records, from the 'late 1800s' to the ' present', were examined to determine shifts in coastal fish distributions. Results: Forty-five species, representing 27 families (about 30% of the inshore fish families occurring in the region), exhibited major distributional shifts thought to be climate related. These are distributed across the following categories: species previously rare or unlisted (12), with expanded ranges (23) and/or abundance increases (30), expanded populations in south-eastern Tasmania (16) and extralimital vagrants (4). Another 9 species, representing 7 families, experienced longerterm changes (since the 1800s) probably due to anthropogenic factors, such as habitat alteration and fishing pressure: species now extinct locally (3), recovering (3), threatened (2) or with remnant populations (1). One species is a temporary resident periodically recruited from New Zealand. Of fishes exhibiting an obvious poleward movement, most are reef dwellers from three Australian biogeographic categories: widespread southern, western warm temperate (Flindersian) or eastern warm temperate (Peronian) species. Main conclusions: Some of the region's largest predatory reef fishes have become extinct in Tasmanian seas since the ' late 1800s', most likely as a result of poor fishing practices. In more recent times, there have been major changes in the distribution patterns of Tasmanian fishes that correspond to dramatic warming observed in the local marine environment.

•We find that the cardiac hemodynamic phase function is time shifted locally.•We find that the respiratory hemodynamic phase function has single form across the brain.•We propose automatic physiologic signal detection without the external physiologic signal measures and its correction method in resting state-fMRI data.•We compare the efficacy of the proposed method to RETROICOR. Hemodynamic cardiac and respiratory-cycle fluctuations are a source of unwanted non-neuronal signal components, often called physiologic noise, in resting state (rs-) fMRI studies. Here, we use image-based retrospective correction of physiological motion (RETROICOR) with externally measured physiologic signals to investigate cardiac and respiratory hemodynamic phase functions reflected in rs-fMRI data. We find that the cardiac phase function is time shifted locally, while the respiratory phase function is described as single, fixed phase form across the brain. In light of these findings, we propose an update to Physiologic EStimation by Temporal ICA (PESTICA), our publically available software package that estimates physiologic signals when external physiologic measures are not available. This update incorporates: 1) auto-selection of slicewise physiologic regressors and generation of physiologic fixed phase regressors with total slices/TR sampling rate, 2) Fourier series expansion of the cardiac fixed phase regressor to account for time delayed cardiac noise 3) removal of cardiac and respiratory noise in imaging data. We compare the efficacy of the updated method to RETROICOR. [Display omitted]

Tobacco warehousing requires continuous surveillance to mitigate risks like unauthorized access, fire hazards, and moisture-induced decay. To address these challenges, this paper proposes an edge-cloud collaborative surveillance framework with adaptive deep learning, termed YOMO-TF (YOLO + MobileOne + Transformer + Federated self-distillation). The architecture consists of three layers: edge layer- employing lightweight models (YOLOv8-nano for real-time object detection and MobileOne-S for effective image classification) for performing fast, on-device video analytics without storing data in cloud. Next, the adaptive learning layer, where a federated self-distillation mechanism enables continuous knowledge refinement over distributed devices without centralized retraining; and the cloud layer, that leverages attention-based schemes like Temporal Shift Transformer (TST) for temporal anomaly detection. This hybrid model ensures high responsiveness, reduced bandwidth usage, with enhanced privacy. Experimental evaluations demonstrate that the proposed model attains 98.6% accuracy, 99.5% precision, 97.6% recall, and 98.5% F1-score, outperforming traditional schemes in both reliability and efficiency. These outcomes highlight the proposed framework’s potential as a scalable, privacy-preserving, and real-time solution for tobacco warehouse safety management, with broad applicability to other industrial safety domains.

This research proposes an innovative framework for mental health monitoring in 5G Edge-Enabled Cognitive internet of things (IoT) environments, integrating Stackelberg Game Theory and the Nomadic People Optimizer (NPO) algorithm. The temporal shift transformer is introduced as a key component for effective prediction of mental health. The Stackelberg Game Theory ensures strategic decision-making between the central authority and decentralized agents, optimizing resource allocation and enhancing the overall system’s performance. The Nomadic People Optimizer algorithm contributes to the efficiency of the decision-making process, providing an adaptive and dynamic solution for personalized mental health monitoring. The framework aims to address the challenges associated with nomadic lifestyles, leveraging 5G edge capabilities for real-time data processing and analysis. Personalized recommendations are provisioned based on the insights derived from cognitive processing, offering tailored interventions during critical mental health situations. According to experimental data, the suggested framework outperforms baseline models like CNN, GRU, and ResNet-50 + LSTM by achieving 96.38% accuracy, 96.2% F1 score, and 97.2% specificity. Additionally, real-time alert creation with an end-to-end latency of less than 46 ms is made possible by the integration of 5G edge computing, guaranteeing prompt mental health treatments. The proposed approach demonstrates promising results in terms of accuracy, adaptability, and scalability, showcasing its potential to revolutionize mental health care for nomadic populations within the evolving landscape of cognitive IoT and 5G technologies.

Peripheral Capillary Oxygen Saturation (SpO 2 ) has received increasing attention during the COVID-19 pandemic. Clinical investigations have demonstrated that individuals afflicted with COVID-19 exhibit notably reduced levels of SpO 2 before the deterioration of their health status. To cost-effectively enable individuals to monitor their SpO 2 , this paper proposes a novel neural network model named “ITSCAN” based on Temporal Shift Module. Benefiting from the widespread use of smartphones, this model can assess an individual’s SpO 2 in real time, utilizing standard facial video footage, with a temporal granularity of seconds. The model is interweaved by two distinct branches: the motion branch, responsible for extracting spatiotemporal data features and the appearance branch, focusing on the correlation between feature channels and the location information of feature map using coordinate attention mechanisms. Accordingly, the SpO 2 estimator generates the corresponding SpO 2 value. This paper summarizes for the first time 5 loss functions commonly used in the SpO 2 estimation model. Subsequently, a novel loss function has been contributed through the examination of various combinations and careful selection of hyperparameters. Comprehensive ablation experiments analyze the independent impact of each module on the overall model performance. Finally, the experimental results based on the public dataset (VIPL-HR) show that our model has obvious advantages in MAE (1.10%) and RMSE (1.19%) compared with related work, which implies more accuracy of the proposed method to contribute to public health.

This study examined the heterogeneity in temporal shifts of unrealistic optimism (UO) by analyzing students’ grade expectation throughout a semester. UO was defined as the gap between students’ estimated and current course grades, with a larger gap indicating higher UO. Final course grades were viewed as the outcome of UO. A total of 206 participants completed baseline measures of personal factors during the first week of the semester and repeated measurements at five subsequent time points. Using person-centered trajectory analysis (e.g., latent growth mixture models), we identified three distinct UO trajectories: UO-Persistent (6.8%; consistently high UO), UO-Decreasing (26.2%; diminishing UO), and Realistic (67.0%; consistently low UO). High perfectionistic standards and dysphoria predicted UO-Persistent group membership, while perfectionistic discrepancy, self-efficacy, and belief in optimism's power did not. The Realistic group achieved higher final grades than the UO-Decreasing group. Unexpectedly, no difference was found in final grades between the strongest UO group (i.e., UO-Persistent) and other two groups. These findings suggest that UO's temporal shift is not a unitary construct, and distinct UO patterns may be associated with different academic outcomes. This study underscores the significance of comprehending temporal shifts and employing person-centered analysis in UO related to academic achievement. The discussion addresses both research and practical implications.

(1) Background: Ultra-short-term photovoltaic (PV) power prediction is crucial for optimizing grid scheduling and enhancing energy utilization efficiency. Existing prediction methods face challenges of missing data, noise interference, and insufficient accuracy. (2) Methods: This study proposes a single-step hybrid neural network model integrating Temporal Convolutional Network (TCN), Temporal Shift Transformer (TST), and Bidirectional Gated Recurrent Unit (BiGRU) to achieve high-precision 15-minute-ahead PV power prediction, with a design aligned with symmetry principles. Data preprocessing uses Variational Mode Decomposition (VMD) and random forest interpolation to suppress noise and repair missing values. A symmetric parallel dual-branch feature extraction module is built: TCN-TST extracts local dynamics and long-term dependencies, while BiGRU captures global features. This symmetric structure matches the intra-day periodic symmetry of PV power (e.g., symmetric irradiance patterns around noon) and avoids bias from single-branch models. Tensor concatenation and an adaptive attention mechanism realize feature fusion and dynamic weighted output. (3) Results: Experiments on real data from a Xinjiang PV power station, with hyperparameter optimization (BiGRU units, activation function, TCN kernels, TST parameters), show that the model outperforms comparative models in MAE and R2—e.g., the MAE is 26.53% and 18.41% lower than that of TCN and Transforme. (4) Conclusions: The proposed method achieves a balance between accuracy and computational efficiency. It provides references for PV station operation, system scheduling, and grid stability.

Background Aedes albopictus and Culex pipiens larvae reared in the same breeding site compete for resources, with an asymmetrical outcome that disadvantages only the latter species. The impact of these interactions on the overall ecology of these two species has not yet been assessed in the natural environment. In the present study, the temporal patterns of adult female mosquitoes from both species were analysed in north-eastern Italy, and substantial temporal shifts between abundance curves of Cx. pipiens and Ae. albopictus were observed in several sites. To understand which factors can drive the observed temporal shifts, we developed a mechanistic model that takes explicitly into account the effect of temperature on the development and survival of all mosquito stages. We also included into the model the effect of asymmetric interspecific competition, by adding a mortality term for Cx. pipiens larvae proportional to the larval abundance of Ae. albopictus within the same breeding site. Model calibration was performed through a Markov Chain Monte Carlo approach using weekly capture data collected in our study sites during 2014 and 2015. Results In almost half of observation sites, temporal shifts were due to competition, with an early decline of Cx. pipiens caused by the concurrent rise in abundance of its competitor, and this effect was enhanced by higher abundance of both species. We estimate that competition may reduce Cx. pipiens abundance in some sites by up to about 70%. However, in some cases temporal shifts can also be explained in the absence of competition between species resulting from a “temporal niche” effect, when the optimal fitness to environmental conditions for the two species are reached at different times of the year. Conclusions Our findings demonstrate the importance of considering ecological interactions and, in particular, competition between mosquito species in temperate climates, with important implications for risk assessment of mosquito transmitted pathogens, as well as the implementation of effective control measures.