Search Results (205)

Lymantria xylina is a major pest in coastal casuarina shelterbelts and a species subject to quarantine regulations by countries to which it is non-native. Phototaxis is fundamental to the insect’s surveillance and risk assessment analysis, and it exhibits pronounced sexual dimorphism in compound eye structure. This dimorphism was investigated using scanning and transmission electron microscopy. Males displayed significantly larger compound eyes, characterized by greater ommatidial areas and a higher total number of facets per eye compared to females. From the distal to proximal end, the ommatidium consists of the cornea, primary and secondary pigment cells, crystalline cones, retinula cells, a rhabdom bundle, and basal retinal cells (in a “7 + 1” arrangement). The internal ultrastructure of the ommatidia is similar in both sexes. However, males possess significantly thinner cornea and extremely elongated crystalline cones. Based on external morphology, both sexes generally exhibit a parallel-symmetrical compound eye form, minimizing optical asymmetry to optimize nocturnal vision. These differences are attributed to the distinct visual demands of males for mate-searching in low-light environments, while females, being more stationary, have reduced visual needs. Paraffin sections of Lymantria xylina compound eyes further revealed that, during dark adaptation, pigment granules aggregated within the crystalline cone region to enhance low-light capture. Conversely, following intense light stimulation, these granules translocated to the perinuclear region of photoreceptor cells, forming a light-shielding configuration. Full article

The aim of this study was to assess the long-term adaptation and growth performance of 50 species introduced in 1991 on the island of Porto Santo, Madeira Archipelago, in order to guide afforestation and soil restoration under the island’s arid conditions, especially in biosphere reserves. The experiment was conducted in Alentejo, Pico Juliana and Matinho, three sites with different types of elevation, soil and exposure. A total of 502 experimental units (five plants each) were established with a completely randomized design in the three sites in 1991 to test the adaptation of 50 species from Mediterranean, African, Australian and American dry climates. Plants were grown in local nursery conditions and planted in rows with 1 × 4 m spacing. Soil properties were analyzed, and survival and growth (height and stem diameter) were monitored in 1991, 1992 and 2025. An analysis of variance was performed for the whole experiment, with the three sites showing significant differences in survival and height among species and sites thirty-four years after the planting. Some species showed high survival and growth, such as Pinus halepensis, Eucalyptus sideroxylon and Casuarina cunninghamiana. Others, like Schinus terebinthifolius and Thevetia neriifolia, showed good adaptation, and invasive behavior at the best sites, but their performance was strongly dependent on site conditions, with Alentejo being the most limiting site. This study demonstrates the long-term value of forest experiments and of long-term monitoring, providing rare data on species adaptation under semi-arid insular conditions. The findings support future afforestation strategies focusing on ecological suitability and invasiveness risk. Full article

Reliable spatio-temporal rainfall prediction is a key element in disaster mitigation and water resource management in dynamic tropical regions such as the Bengawan Solo River Watershed. However, high climate variability and data limitations often pose significant challenges to the accuracy of conventional prediction models. This study introduces an innovative approach by applying ensemble stacking, which combines machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Light Gradient-Boosting Machine (LGBM) and deep learning models like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Networks (TCN), Convolutional Neural Network (CNN), and Transformer architecture based on monthly Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data (1981–2024). The novelty of this research lies in the systematic exploration of various model combination scenarios—both classical and deep learning and the evaluation of their performance in projecting rainfall for 2025–2030. All base models were trained on the 1981–2019 period and validated with data from the 2020–2024 period, while ensemble stacking was developed using a linear regression meta-learner. The results show that the optimal ensemble scenario reduces the MAE to 53.735 mm, the RMSE to 69.242 mm, and increases the R² to 0.795826—better than all individual models. Spatial and temporal analyses also indicate consistent model performance at most locations and times. Annual rainfall projections for 2025–2030 were then interpolated using IDW to generate a spatio-temporal rainfall distribution map. The improved accuracy provides a strong scientific basis for disaster preparedness, flood and drought management, and sustainable water planning in the Bengawan Solo River Watershed. Beyond this case, the approach demonstrates significant transferability to other climate-sensitive and data-scarce regions. Full article

Background/Objectives: Cancer is a leading cause of death for Aboriginal and Torres Strait Islander people, with remoteness increasing the risk for poorer outcomes. Primary health care (PHC) clinics have an important role in cancer screening, diagnosis, and post-discharge cancer care, particularly in remote communities, so accurate, timely communication between hospitals, specialists and PHC clinics is vital. This paper analyses the perspectives of Northern Territory health care professionals on communication between PHC and hospital services related to providing care for Aboriginal people with cancer and recommends strategies for improving communication between services. Methods: A qualitative study was undertaken in which semi-structured interviews were conducted with fifty staff from 15 health services (8 regional, remote, and very remote PHC clinics; 3 hospitals; one cancer centre and 3 cancer support services) between 2016 and 2019. Transcripts were thematically analysed, with findings categorized into barriers and enablers to communication. Results: Deficiencies in communication impeded patient care and support. A major barrier was fragmented, inefficient information systems; IT systems across health services were unable to interface, resulting in delayed/missing patient information that impacted discharge and follow up. Other barriers included PHC staff with limited knowledge of cancer, high turnover of PHC staff and tertiary hospital staff with limited understanding of remote health care challenges. Individuals used workarounds to overcome system failures and made substantial efforts around individual patients to improve communication. Specific roles and the use of telehealth between services and centralised cancer care services supported better between-service communication. Conclusions: Communication between hospital services and remote PHC clinics is essential to care for Aboriginal cancer patients; our research identified communication as inadequate in terms of consistency and timeliness. Commitment to more timely communication, health care IT systems that facilitate sharing information, designated staff in PHC clinics to support patients with cancer, dedicated Aboriginal cancer roles and additional resourcing to coordinate telehealth appointments could improve communication and sharing of patient information between services. Full article

Earthquakes remain among the most destructive natural hazards, causing severe loss of life and property in seismically active regions such as Nepal. Major events such as the 1934 Nepal–Bihar earthquake (M_w 8.2), the 2015 Gorkha earthquake (M_w 7.8), and the 2023 Jajarkot earthquake (M_L 6.4) have repeatedly exposed the vulnerability of Nepal’s built environment. In response, the Ready-to-Use Detailing (RUD) guideline (NBC 205:2024) was introduced to provide standardized structural detailing for low-rise reinforced concrete buildings without masonry infill, particularly for use in areas where access to professional engineering services is limited. This study was motivated by the need to critically assess the structural performance of buildings designed according to such rule-of-thumb detailing, which is widely applied through owner–builder practices. Nonlinear pushover analyses were carried out using finite element modelling for typical configurations on soil types C and D, under peak ground accelerations of 0.25 g, 0.30 g, 0.35 g, and 0.40 g. The response spectrum from NBC 105:2020 was adopted to determine performance points. The analysis focused on global response, capacity curves, storey drift, and hinge formation to evaluate structural resilience. The maximum story drift for the linear static analysis is found to be 0.56% and 0.86% for peak ground acceleration of 0.40 g, for both three and four-storied buildings. Also, from non-linear static analysis, it is found that almost all hinges formed in the beams and columns are in the Immediate Occupancy (IO) level. The findings suggest that the RUD guidelines are capable of providing adequate seismic performance for low-rise reinforced concrete buildings, given that the recommended material quality and construction standards are satisfied. Full article

Typhoon disturbances significantly influence forest carbon cycling by altering both physical structures and biogeochemical processes. Typhoon-induced fluctuations in soil respiration can substantially affect the carbon balance in forest ecosystems. In this study, we conducted a comparative investigation of soil respiration in plantations of Casuarina equisetifolia L. that were either affected or unaffected by the severe Typhoon Yagi, which ravaged Hainan Island, China, in 2024. The soil respiration and its components in Casuarina equisetifolia L. plantations in the coastal areas of Hainan, China, as well as their responses to environmental factors before and after typhoon disturbance, were investigated based on total soil respiration rate (R_s), heterotrophic respiration rate (R_h), 5 cm soil temperature (T₅), and 10 cm soil moisture (W₁₀) to support the carbon emission estimation in coastal sandy land plantations. The mean R_s and R_h in the typhoon-disturbed plots were (1.82 ± 0.16) and (1.19 ± 0.26) μmol·m⁻²·s⁻¹, respectively, while those in the control plots were (2.62 ± 1.08) and (1.41 ± 0.23) μmol·m⁻²·s⁻¹, respectively, with statistically significant differences (p < 0.05). In both plots, R_s exhibited a significant positive correlation with T₅ (p < 0.01). The T₅ correlation and Q₁₀ values for soil respiration were significantly higher in the typhoon-disturbed plots than in the control plots (p < 0.05). W₁₀ of the soil exhibited significant negative correlations with R_s and R_h in typhoon disturbance plots (p < 0.05). Consequently, typhoon disturbance markedly inhibited soil respiration and its components in the Casuarina equisetifolia L. plantations, indicating substantial impacts of typhoons on soil respiration processes and carbon cycling within the forest ecosystem. This study provides key parameters and empirical evidence to improve the accuracy of soil carbon emission estimates in Casuarina equisetifolia L. plantations on coastal sandy soils affected by typhoon events. Full article

This study aims to develop a climate-resilient landscape design framework for coastal healthcare facilities by integrating Artificial Intelligence (AI)-generated design prompts with Computational Fluid Dynamics (CFD) simulations and on-site validation. Focusing on a coastal hospital in Penghu, Taiwan—a region vulnerable to strong winds, salt spray, and extreme weather—the research proposes a climate-adaptive, microclimate-responsive, and resilient design framework. Key findings demonstrate that the optimized design reduced average winter wind speed from 12 m/s to 4.5 m/s (a 62.5% reduction) and increased the three-year survival rate of salt-tolerant plant species (e.g., Pittosporum tobira, Casuarina) to 92%, significantly outperforming conventional planting strategies. The combination of water features and evapotranspiration planting reduced summer temperatures by 2.3 °C and increased humidity to 75%, with the PMV comfort index improving from +1.5 to +0.5. The program also resulted in a 15% increase in biodiversity, a 20% reduction in soil erosion, and a 40% improvement in users’ perceived aesthetic value of outdoor spaces. Furthermore, AI-based analyses to determine foundational depth led to a reduction in structural failure rates—from 40% to 5%—substantially elevating the safety and long-term durability of outdoor infrastructures. This study demonstrates that integrating AI with CFD is both feasible and highly effective for addressing complex coastal climate challenges in landscape architecture. The developed framework is parametric, evidence-based, and tailored to site-specific requirements, enabling the formulation of intelligent, climate-responsive landscape solutions for future healthcare environments in vulnerable coastal areas. Full article

In the agricultural domain, the availability of labelled data for disease recognition tasks is often limited due to the cost and expertise required for annotation. In this paper, a novel semi-supervised learning framework named TOM-SSL is proposed for automatic tomato leaf disease recognition using pseudo-labelling. TOM-SSL effectively addresses the challenge of limited labelled data by leveraging a small labelled subset and confidently pseudo-labelled samples from a large pool of unlabelled data to improve classification performance. Utilising only 10% of the labelled data, the proposed framework with a MobileNetV3-Small backbone achieves the best accuracy at 72.51% on the tomato subset of the PlantVillage dataset and 70.87% on the Taiwan tomato leaf disease dataset across 10 disease categories in PlantVillage and 6 in the Taiwan dataset. While achieving recognition performance on par with current state-of-the-art supervised methods, notably, the proposed approach offers a tenfold enhancement in label efficiency. Full article

Despite the well-documented health risks of noise pollution, its impact remains overlooked mainly in life cycle assessment (LCA). This study introduces a methodological innovation by integrating both traffic and construction noise into the LCA framework for concrete construction, providing a more holistic and realistic evaluation of environmental and health impacts. By combining building information modeling (BIM) with LCA, the method automates material quantification and assesses both environmental and noise-related health burdens. A key advancement is the inclusion of health-based indicators, such as annoyance and sleep disturbance, quantified through disability-adjusted life years (DALYs). Two scenarios are examined: (1) a comparative analysis of concrete versus timber flooring and (2) end-of-life options (reuse vs. landfill). The results reveal that concrete has up to 7.4 times greater environmental impact than timber, except in land use. When noise is included, its contribution ranges from 7–33% in low-density regions (Darwin) and 62–92% in high-density areas (NSW), underscoring the critical role of local context. Traffic noise emerged as the dominant source, while equipment-related noise was minimal (0.3–1.5% of total DALYs). Timber slightly reduced annoyance but showed similar sleep disturbance levels. Material reuse reduced midpoint environmental impacts by 67–99.78%. Sensitivity analysis confirmed that mitigation measures like double glazing can cut noise-related impacts by 2–10% in low-density settings and 31–45% in high-density settings, validating the robustness of this framework. Overall, this study establishes a foundation for integrating noise into LCA, supporting sustainable material choices, environmentally responsible construction, and health-centered policymaking, particularly in noise-sensitive urban development. Full article

Nitrogen is an essential element for plant growth, and both insufficient and excessive use of nitrogen have been shown to negatively affect sweet potato production. Nitrogen supply can affect carbon metabolism in plant storage organs; however, limited studies have examined its effects on the accumulation of non-structural carbohydrates (soluble sugar and starch) during the formation of sweet potato storage roots. Two pot trials were conducted to evaluate the effects of different nitrogen application levels and timings on the accumulation of non-structural carbohydrates during the formation of sweet potato storage roots. In the first experiment, plants were supplied with 0, 50, 100, or 200 mg/L of nitrogen. In the second experiment, the optimum nitrogen rate (100 mg/L) for storage root formation from the previous experiment was applied at five different times: nil N supply and nitrogen applied at planting or 3, 7, or 14 days after planting. A significant highest starch accumulation in roots during the first 35 days after transplanting was recorded in the 100 mg/L treatment. However, sweet potato required more nitrogen after storage root formation, as indicated by higher non-structural carbohydrate accumulation in roots (1905 mg/plant) in the 200 mg/L treatment at 49 days after planting. Earlier nitrogen applications promoted soluble sugar and starch accumulation in plants during storage root formation, with up to 5697 mg of non-structural carbohydrate accumulated in a plant. The study provided agronomic indicators that moderate nitrogen should be available in soil before or on planting day. Full article

Land degradation from trace metal pollution in North Africa severely compromises soil fertility. This study investigates the synergistic remediation potential of Eucalyptus biochar (EuB) and Casuarina glauca in iron mine soil contaminated with Fe, Zn, Mn, Pb, Cd, and As. Seedlings were grown for six months in: non-mining soil (NMS), contaminated soil (CS), and CS amended with 5% EuB (CS + EuB). Comprehensive ecophysiological assessments evaluated growth, water relations, gas exchange, chlorophyll fluorescence, oxidative stress, and metal accumulation. EuB significantly enhanced C. glauca tolerance to multi-trace metal stress. Compared to CS, CS + EuB increased total dry biomass by 14% and net photosynthetic rate by 22%, while improving predawn water potential (from −1.8 to −1.3 MPa) and water-use efficiency (18%). Oxidative damage was mitigated. EuB reduced soluble Fe by 71% but increased Zn, Mn, Pb, and Cd mobility. C. glauca exhibited hyperaccumulation of Fe, Zn, As, Pb, and Cd across treatments, with pronounced Fe accumulation under CS + EuB. EuB enhanced nodule development and amplified trace metals sequestration within nodules (Zn: +1.4×, Mn: +2.4×, Pb: +1.5×, Cd: +2.0×). The EuB-C. glauca synergy enhances stress resilience, optimizes rhizosphere trace metals bioavailability, and leverages nodule-mediated accumulation, establishing a sustainable platform for restoring contaminated lands. Full article

Temperature increases due to climatic changes have been increasingly recognized as posing significant public health challenges, with wide-ranging socio-economic implications. This scoping review examines the relationship between high temperatures and violence against women (VAW) globally. Nine studies from both high-income and low- and middle-income countries were included in this review. The findings suggest an overall positive association between high temperatures and rates of VAW. Theoretical frameworks, including the temperature–aggression hypothesis and routine activity theory, offer insights into the mechanisms driving this relationship. Key risk factors such as socioeconomic status, urban heat island effects, rurality, patriarchal norms, and alcohol consumption were considered to be risk factors affecting rates of VAW. Despite growing evidence, research gaps persist, particularly in regions with high rates of VAW and in the form of qualitative studies that capture women’s lived experiences. The positive associations between temperature and VAW underscore the urgency of integrating gender-sensitive strategies into climate adaptation policies to mitigate the compounding risks of climate change and gender-based violence. Full article

Vision transformers (ViTs) have recently gained traction in plant disease classification due to their strong performance in visual recognition tasks. However, their application to tomato leaf disease recognition remains challenged by two factors, namely the need for models that can generalise across diverse disease conditions and the absence of a unified framework for systematic comparison. Existing ViT-based approaches often yield inconsistent results across datasets and disease types, limiting their reliability and practical deployment. To address these limitations, this study proposes the ViT-Based Robust Framework (ViT-RoT), a novel benchmarking framework designed to systematically evaluate the performance of various ViT architectures in tomato leaf disease classification. The framework facilitates the systematic classification of state-of-the-art ViT variants into high-, moderate-, and low-performing groups for tomato leaf disease recognition. A thorough empirical analysis is conducted using one publicly available benchmark dataset, assessed through standard evaluation metrics. Results demonstrate that the ConvNeXt-Small and Swin-Small models consistently achieve superior accuracy and robustness across all datasets. Beyond identifying the most effective ViT variant, the study highlights critical considerations for designing ViT-based models that are not only accurate but also efficient and adaptable to real-world agricultural applications. This study contributes a structured foundation for future research and development in vision-based plant disease diagnosis. Full article

Annually, a considerable amount of agricultural waste is produced leading to serious environmental pollution if not managed effectively. A wide range of bio-decomposers, including fungi, bacteria, and actinomycetes may break down the complex agro-residues in an eco-friendly way through secreting many cellulolytic and amylolytic enzymes. The present study aimed at exploring the ability of Frankia to degrade cellulose and starch and identifying the cellulase and α-amylase genes in Frankia genomes for potential agricultural waste degradation. Frankia alni ACN14a and Frankia casuarinae CcI₃ produced clear zones around growing hyphae on carboxymethyl cellulose (CMC) and starch substrates. The hydrolytic index (HI) ranged from 1 to 2.14 reflecting variation in their degradation efficacy. Quantification of CMCase (carboxymethyl cellulase) production in strain ACN14a presented the maximum activity (0.504 U/mL) under 1% CMC after 16 days whereas strain CcI₃ produced a weak activity after 6 days from incubation. Besides, amylase activity in strain ACN14a reached the highest value (3.215 U/mL) after 4 days of growing with 1% starch, while strain CcI₃ had the superior production (3.04 U/mL) after 12 days from 1% starch condition. Data mining and genome blasting led to the identification of multiple genes related to cellulose and starch degradation. Two endoglucanases (celA1, FRAAL4955 and celA2, FRAAL4956), two glycosyl hydrolase family 16 (FRAAL6120 and FRAAL2663), and one glycosyl hydrolase family 16 (Francci3_3843) were predicted in the two genomes. Likewise, the α-amylase genes (FRAAL5900) from Frankia alni ACN14a and (Francci3_3679) from strain CcI₃ were identified. The gene expression of endo-1, 4-beta-glucanase (celA2, FRAAL4956) revealed the maximum increment in its mRNA abundance under 0.25% CMC exposure and showed a 3.3-fold increase. Frankia capability to degrade cellulose and starch represents a critical process in nutrient cycling and environment protection. Full article