Search Results (14,397)

Climate warming is anticipated to significantly alter the distribution and composition of plant species in the Arctic, thereby cascading through food webs and affecting both associated fauna and entire ecosystems. To elucidate the trend in plant distribution in response to climate change, we employed the MaxEnt model to project the future ranges of 25 representative Arctic and Circumpolar plant species (including grasses and shrubs). Species distribution data, in conjunction with bioclimatic variables derived from climate projections of three selected General Circulation Models (GCMs), ESM2, IPSl, and MPIE, were utilized to fit the MaxEnt models. Subsequently, we predicted the potential distributions of these species under three Shared Socioeconomic Pathways (SSPs)—SSP126, SSP245, and SSP585—across a timeline spanning 2010, 2050, 2100, 2200, 2250, and 2300 AD. Range shift indices were applied to quantify changes in plant distribution and range sizes. Our results show that the ranges of nearly all species are projected to diminish progressively over time, with a more pronounced rate of reduction under higher emission scenarios. The species are generally expected to shift northward, with the distances of these shifts positively correlated with both the time intervals from the current state and the intensity of thermal forcing associated with the SSPs. Arctic species (A_Spps) are anticipated to face higher extinction risks compared to Boreal–Arctic species (B_Spps). Additional indices, such as range gain, loss, and overlap, consistently corroborate these patterns. Notably, the peak range shift speeds differ markedly between SSP245 and SSP585, with the latter extending beyond 2100 AD. In conclusion, under all SSPs, A_Spps are generally expected to experience more significant range shifts than B_Spps. In the SSP585 scenario all species are projected to face substantial range reductions, with Arctic species being more severely affected and consequently facing the highest extinction risks. These findings provide valuable insights for developing conservation recommendations for polar plant species and have significant ecological and socioeconomic implications. Full article

This paper presents WaveCORAL-DCCA, an unsupervised domain adaptation (UDA) framework specifically developed to address data distribution shifts and operational variabilities (OVs) in rotor fault diagnosis. The framework introduces the novel integration of discrete wavelet transformation for robust time–frequency feature extraction and an enhanced deep canonical correlation analysis (DCCA) network with correlation alignment (CORAL) loss for superior domain-invariant representation learning. This combination enables more effective alignment of source and target feature distributions without requiring any labelled data from the target domain. Comprehensive validation on both experimental and numerically simulated rotor datasets across three health conditions—i.e., normal, unbalanced, and misaligned—demonstrates that WaveCORAL-DCCA achieves an average diagnostic accuracy of 95%. Notably, it outperforms established UDA benchmarks by at least 5–17% in cross-domain scenarios. These results confirm that WaveCORAL-DCCA provides robust generalisation across machines, fault severities, and operational conditions, even with scarce target domain samples, offering a scalable and practical solution for industrial rotor fault diagnosis. Full article

Underwater image enhancement is crucial for fields like marine exploration, underwater photography, and environmental monitoring, as underwater images often suffer from reduced visibility, color distortion, and contrast loss due to light absorption and scattering. Despite recent progress, existing methods struggle to generalize across diverse underwater conditions, such as varying turbidity levels and lighting. This paper proposes a novel hybrid UNet–Transformer architecture based on MaxViT blocks, which effectively combines local feature extraction with global contextual modeling to address challenges including low contrast, color distortion, and detail degradation. Extensive experiments on two benchmark datasets, UIEB and EUVP, demonstrate the superior performance of our method. On UIEB, our model achieves a PSNR of 22.91, SSIM of 0.9020, and CCF of 37.93—surpassing prior methods such as URSCT-SESR and PhISH-Net. On EUVP, it attains a PSNR of 26.12 and PCQI of 1.1203, outperforming several state-of-the-art baselines in both visual fidelity and perceptual quality. These results validate the effectiveness and robustness of our approach under complex underwater degradation, offering a reliable solution for real-world underwater image enhancement tasks. Full article

The evolutionary loss of ednrb2 in specific vertebrate lineages, such as mammals and cypriniform fish, raises fundamental questions about its functional necessity and potential redundancy or synergy with paralogous endothelin receptors in pigment cell development. In teleosts possessing both ednrb1a and ednrb2 (e.g., Nile tilapia), their respective and combined roles in regulating neural crest-derived pigment cell precursors remains unresolved. Using CRISPR/Cas9, we generated single and double ednrb mutants to dissect their functions. We demonstrated that ednrb1a and ednrb2 synergistically govern the differentiation and migration of iridophore precursors. While ednrb1a is broadly essential for iridophore development, ednrb2 plays a unique and indispensable role in the colonization of iridophores in the dorsal iris. Double mutants exhibit near-complete iridophore loss; severe depletion of melanophores, xanthophores, and erythrophores; and a striking, fertile, transparent phenotype. Crucially, this iridophore deficiency does not impair systemic guanine synthesis pathways. mRNA rescue experiments confirmed mitfa as a key downstream effector within the Ednrb signaling cascade. This work resolves the synergistic regulation of pigment cell fates by Ednrb receptors and establishes a mechanism for generating transparent ermplasm. Full article

Insect pests cause severe damage and yield losses to many agricultural crops globally. The use of chemical pesticides on agricultural crops is not recommended because of their toxic effects on the environment and consumers. In addition, pesticide toxicity reduces soil fertility, poisons ground waters, and is hazardous to soil biota. Therefore, applications of entomopathogenic nematodes (EPNs) and plant growth-promoting rhizobacteria (PGPR) are an alternative, eco-friendly solution to chemical pesticides and mineral-based fertilizers to enhance plant health and promote sustainable food security. This review focuses on the biological and ecological aspects of these organisms while also highlighting the practical application of molecular communication approaches in developing a novel plant health product. This insight will support this innovative approach that combines PGPR and EPNs for sustainable crop production. Several studies have reported positive interactions between nematodes and bacteria. Although the combined presence of both organisms has been shown to promote plant growth, the molecular interactions between them are still under investigation. Integrating molecular communication studies in the development of a new product could help in understanding their relationships and, in turn, support the combination of these organisms into a single plant health product. Full article

The rapid proliferation of Internet of Things (IoT) devices has significantly increased vulnerability to Distributed Denial of Service (DDoS) attacks, which can severely disrupt network operations. DDoS attacks in IoT networks disrupt communication and compromise service availability, causing severe operational and economic losses. In this paper, we present a Deep Learning (DL)-based Intrusion Detection System (IDS) tailored for IoT environments. Our system employs three architectures—Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Transformer-based models—to perform binary, three-class, and 12-class classification tasks on the CiC IoT 2023 dataset. Data preprocessing includes log normalization to stabilize feature distributions and SMOTE-based oversampling to mitigate class imbalance. Experiments on the CIC-IoT 2023 dataset show that, in the binary classification task, the DNN achieved 99.2% accuracy, the CNN 99.0%, and the Transformer 98.8%. In three-class classification (benign, DDoS, and non-DDoS), all models attained near-perfect performance (approximately 99.9–100%). In the 12-class scenario (benign plus 12 attack types), the DNN, CNN, and Transformer reached 93.0%, 92.7%, and 92.5% accuracy, respectively. The high precision, recall, and ROC-AUC values corroborate the efficacy and generalizability of our approach for IoT DDoS detection. Comparative analysis indicates that our proposed IDS outperforms state-of-the-art methods in terms of detection accuracy and efficiency. These results underscore the potential of integrating advanced DL models into IDS frameworks, thereby providing a scalable and effective solution to secure IoT networks against evolving DDoS threats. Future work will explore further enhancements, including the use of deeper Transformer architectures and cross-dataset validation, to ensure robustness in real-world deployments. Full article

In the summer of 2022, central and eastern China experienced prolonged extreme high temperatures and severe drought, leading to significant economic losses. To gain a more profound understanding of this drought event and furnish a reference for forecasting similar events in the future, this study examines the circulation anomalies associated with the drought. Employing a diagnostic method focused on temperature and moisture anomalies, this study introduces a novel approach to quantify and compare the relative significance of moisture transport and warm air dynamics in contributing to the drought. This study examines the atmospheric circulation anomalies linked to the drought event and compares the relative contributions of water vapor transport and warm air activity in causing the drought, using two parameters defined in the paper. The results show the following: (1) The West Pacific Subtropical High (WPSH) was more intense than usual and extended westward, consistently controlling the Yangtze River Basin. Simultaneously, the polar vortex area was smaller and weaker, the South Asian High area was larger and stronger, and it shifted eastward. These factors collectively led to weakened water vapor transport conditions and prevailing subsiding air motions in the Yangtze River Basin, causing frequent high temperatures. (2) By defining I_q and I_t to represent the contributions of moisture and temperature to precipitation, we found that the drought event in the Yangtze River Basin was driven by both reduced moisture supplies in the lower troposphere and higher-than-normal temperatures, with temperature playing a dominant role. Full article

Drought stress is a major environmental constraint that significantly reduces wheat productivity worldwide. In this study, seventeen wheat genotypes were evaluated under well-watered and drought-stressed conditions across two consecutive years (2023–2024) in a controlled greenhouse experiment. Twenty morphological and agronomic traits were recorded, and their responses to prolonged water limitation were assessed using multivariate statistical methods, including three-way ANOVA, principal component analysis (PCA), and cluster analysis. Drought stress significantly decreased all traits except the harvest index (HI), with the most severe reductions observed in traits related to secondary spikes (e.g., grain weight reduced by 95%). The ANOVA results confirmed significant genotype × treatment (G × T) interactions for key agronomic traits, with the strongest effect observed for total grain weight (F = 7064.30, p < 0.001). A PCA reduced the 20 original variables to five principal components, explaining 87.2% of the total variance. These components reflected distinct trait groups associated with productivity, spike architecture, and development in phenology. Cluster analysis based on PCA scores grouped genotypes into three clusters with contrasting drought response profiles. A yield-based evaluation confirmed the cluster structure, distinguishing genotypes with a stable performance (average yield loss ~58%) from highly sensitive ones (~70% loss). Overall, the findings demonstrate that drought tolerance in wheat is governed by complex trait interactions. Integrating a trait-based multivariate analysis with a yield stability assessment enables the identification of genotypes with superior adaptation to water-limited environments, providing an excellent genotype background for future breeding efforts. Full article

Background: Meniere’s disease is characterized by a triad of vertigo episodes, fluctuating hearing loss, and tinnitus. The disease is followed by a loss of quality of life in patients, with the severity depending on the individual and the stage of the disease. Since there are no quantitatively validated tests that connect all elements of the disease, the only source of subjective data that can be analyzed is the disease diary and questionnaires, among which the MDPOSI (Meniere’s Disease Patient-Oriented Symptom-Severity Index) stands out as a designated quality-of-life assessment tool. This study aims to evaluate the differences in the questionnaire depending on the clinical characteristics of the disease. Methods: The study recruited 60 patients, with clinical variables including age, gender, disease laterality, caloric testing results, and PTA results, the presence of spontaneous nystagmus, pathological values of calorimetric testing, or rotatory chair testing abnormalities. Results: The appearance of spontaneous nystagmus showed a significant association with worse hearing threshold values at 500 Hz (p = 0.036, OR 4.416) and higher. Worse SRT scores correlated with Q1 (p = 0.011), Q2 (p = 0.028), Q4 (p = 0.045), Q5 (p = 0.013), and the total MDPOSI score (p = 0.008, 0.339). Multivariate analysis showed that a higher total value of the MDPOSI questionnaire was statistically significantly associated with older age (p = 0.042) and spontaneous nystagmus (p = 0.037). Conclusions: There is a correlation between the clinical characteristics of Meniere’s disease and the MDPOSI questionnaire, making it useful for assessing quality of life and disease progression. Full article

Research on commuting patterns has mainly focused on urban and metropolitan areas, and such studies are not typically applied to rural and small-town regions, where workers often face longer commutes due to limited job opportunities and inadequate public transportation. By using the Census Transportation Planning Package (CTPP) data, this research fills that gap by analyzing commuting behavior by ethnic group and industry in south-central Minnesota, which is a predominantly rural area of 13 counties in the United States. The results show that both white and minority groups in District 7 experienced an increase in excess commuting from 2006 to 2016, with the minority group in Nobles County showing a significantly higher rise. Analysis by industry reveals that excess commuting in the leisure and hospitality sector (including arts, entertainment, and food services) in Nobles County increased five-fold during this time, indicating a severe spatial mismatch between jobs and affordable housing. In contrast, manufacturing experienced a decline of 50%, possibly indicating better commuting efficiency or a loss of manufacturing jobs. These findings can help city and transportation planners conduct an in-depth analysis of rural-to-urban commuting patterns and develop potential solutions to improve rural transportation infrastructure and accessibility, such as promoting telecommuting and hybrid work options, expanding shuttle routes, and adding more on-demand transit services in rural areas. Full article

Pimelea is one of the highly toxic plants in Australia, particularly affecting cattle. It contains simplexin, a potent toxin that can cause Pimelea poisoning (St. George Disease) in livestock. A survey was conducted to assess the current impact of Pimelea on livestock production, pasture systems, and financial losses among agricultural producers. In addition, information was also sought about the environmental conditions that facilitate its growth and the effectiveness of existing management strategies. The survey responses were obtained from producers affected by Pimelea across nine different Local Government Areas, through three States, viz., Queensland, New South Wales, and South Australia. Pimelea was reported to significantly affect animal production, with 97% of producers surveyed acknowledging its detrimental effects. Among livestock, cattle were the most severely affected (94%), when compared to sheep (13%), goats (3%), and horses (3%). The presence of Pimelea was mostly observed in spring (65%) and winter (48%), although 29% of respondents indicated that it could be present all year-round under favorable rainfall conditions. Germination was associated with light to moderate rainfall (52%), while only 24% linked it to heavy rainfall. Pimelea simplex F. Muell. was the most frequently encountered species (71%), followed by Pimelea trichostachya Lindl. (26%). Infestations were reported to occur annually by 47% of producers, with 41% noting occurrences every 2 to 5 years. Financially, producers estimated average annual losses of AUD 67,000, with 50% reporting an average of 26 cattle deaths per year, reaching up to 105 deaths in severe years. Some producers were spending up to AUD 2100 per annum to manage Pimelea. While chemical and physical controls were commonly employed, integrating competitive pastures and alternative livestock, such as sheep and goats, was considered as a potential management strategy. This study reiterates the need for further research on sustainable pasture management practices to reduce Pimelea-related risks to livestock and agricultural production systems. Full article

Degenerative disc disease (DDD) is a major cause of chronic low back pain (LBP), yet the molecular mechanisms driving disc degeneration and pain remain poorly understood. This study analyzed intervertebral disc (IVD) tissue from 36 young patients (median age = 36.00 [31.00, 42.50] years) with herniated discs and LBP, alongside healthy controls, to investigate changes in the extracellular matrix (ECM) and neurochemical alterations. Disc degeneration was assessed using MRI (Pfirrmann grading) and histology (Sive’s criteria). Histochemical and immunohistochemical methods were used to evaluate aggrecan content, calcification, and the expression of nerve growth factor (NGF), substance P (SP), and S-100 protein. MRI findings included Pfirrmann grades V (30.55%), IV (61.11%), III (5.56%), and II (2.78%). Severe histological degeneration (10–12 points) was observed in three patients. Aggrecan depletion correlated with longer pain duration (r = 0.449, p = 0.031). NGF expression was significantly elevated in degenerated discs (p = 0.0287) and strongly correlated with SP (r = 0.785, p = 5.268 × 10⁻⁹). Free nerve endings were identified in 5 cases. ECM calcification, present in 36.1% of patients, was significantly associated with radiculopathy (r = 0.664, p = 0.005). The observed co-localization of NGF and SP suggests a synergistic role in pain development. These results indicate that in young individuals, aggrecan loss, neurochemical imbalance, and ECM calcification are key contributors to DDD and chronic LBP. Full article

Distributed generation strengthens distribution network reliability by placing generators close to load centers. The integration of electric vehicle charging stations (EVCSs) with PV systems mitigates the effects of EV charging burden. In this research, the objective is to combineEVCSs with distributed generation (DG) units in the Puducherry smart grid system to obtain optimized locations and enhance their reliability. To determine the right nodes for DGs and EVCSs in an uneven distribution network, the modified decision-making (MDM) algorithm and the model predictive control (MPC) approach are used. The Indian utility 29-node distribution network (IN29NDN), which is an unbalanced network, is used for testing. The effects of PV systems and EVCS units are studied in several settings and at various saturation levels. This study validates the correctness of its findings by evaluating the outcomes of proposed methodological approaches. DIgSILENT Power Factory is used to conduct the simulation experiments. The results show that optimizing the location of the DG unit and the size of the PV system can significantly minimize power losses and make a distribution network (DN) more reliable. Full article

While existing studies document physical and economic impacts of new-build gentrification, the temporally protracted trauma of indirect displacement in communities adjacent to redeveloped areas remains understudied. Employing constructivist grounded theory, this study asks the following question: how do residents experience place attachment erosion during prolonged policy suspension in Shanghai’s new-build gentrification? Through iterative analysis of 25 interviews, we reveal a temporal vicious cycle of waiting triggered by uneven redevelopment and policy inertia. This cycle systematically dismantles belonging through several mechanisms: (1) chronic place-identity deterioration; (2) progressive social network fragmentation; (3) the collapse of imagined futures; and (4) the ambiguous loss of place attachment—where physical presence coexists with psychological disengagement. Crucially, we redefine indirect displacement as a temporal erosion of place identity and attachment, revealing a paradoxical state of physical presence coexisting with psychological disengagement. This paper provides a new perspective for better understanding the different dimensions of indirect displacement in new-build gentrification, which will help inform equitable development efforts that are more inclusive and just. Full article

Heavy metal contamination in freshwater ecosystems poses persistent threats to aquatic organisms and public health. This study evaluates the transgenerational toxicity of cadmium chloride and copper sulfate on Culex pipiens, focusing on development, reproduction, and midgut histopathology over two successive generations. Larval bioassays showed cadmium chloride to be more toxic than copper sulfate, with early instars exhibiting higher sensitivity (LC₅₀ = 8.66 μg/L for Cd; 175.63 μg/L for Cu). Both metals significantly delayed larval and pupal development, reduced fecundity, and decreased egg hatchability in a dose-dependent manner. Histopathological examination revealed midgut epithelial degeneration, vacuolation, and brush border loss, with copper sulfate inducing more severe cytotoxicity. These findings confirm that sublethal, chronic metal exposure can impair physiological and reproductive traits across generations. Moreover, this study highlights the utility of mosquitoes as sensitive bioindicators of aquatic pollution, and underscores the long-term ecological implications of heavy metal contamination on vector dynamics and disease transmission. Full article