Variable Climatic Conditions Research Articles

Mathematical Modeling of Host-Parasitoid Dynamics for Sustainable Pest Management

Aims: The management of agricultural pest populations represents a critical challenge in sustainable crop production, particularly for challenging species like the tomato fruit borer, Helicoverpa armigera. The research work aimed in developing and analysing a novel mathematical model describing the interaction between the tomato fruit borer and its egg parasitoid, Trichogramma chilonis. The main objective of this study to analyze the stability of the proposed system of differential equations. Model Formulation: The model incorporates stage-structured population dynamics and impulsive control measures, providing insights into biological pest control strategies. In this model the tomato borer is represented by the egg and larval stages, and the parasitoid is considered in terms of the parasitized eggs. This model assumes uniform spatial distribution of both host and parasitoid populations, constant environmental conditions affecting parameters like reproduction and mortality rates, and idealized parasitoid release conditions where the released amount p remains consistent across interventions. Methodology: We used existing secondary data from research articles' instead of conducting primary field experiments for parameter identification for this mathematical model. The use of secondary data from peer-reviewed literature not only provides validated parameter values that have been rigorously tested and verified by multiple researchers, but also allows for the incorporation of findings from diverse geographical locations and climatic conditions. Results: Stability analysis identified three equilibrium points: the trivial, host-only E0,coexistence equilibria E1, and E2. The coexistence equilibirium E2 found to be locally stable under optimal parasitoid release intervals and quantities. The equilibrium point E2= (9.14,0.02,5.71) represents a coexistence state in the host-parasitoid dynamics, where the tomato fruit borer's egg density stabilizes at 9.14, the density of parasitized eggs at 0.02, and the larval density stabilizes at 5.71. This equilibrium is analyzed through its associated eigenvalues. This stability provides a reliable basis for implementing successful biological pest control strategies in tomato cultivation, ensuring a resilient and sustainable ecosystem.

Large Losses of Pyrogenic Carbon (Biochar) and Native Soil Carbon During a 15-Month Field Study in North Florida, USA

Although the application of biochar to soils has been proposed as a method of carbon sequestration for climate mitigation while improving crop yields, losses of biochar carbon (BC) through mineralization may reduce these benefits. However, few field studies have been conducted that control for biochar migration so that the rates and processes of BC remineralization from soils, as well as the effects of biochar on native soil organic carbon, can be accurately determined. Here, biochar made from different biomass types (oak, pine wood, and grass) and temperatures (lightly charred at 250 °C and pyrolyzed at 400 and 650 °C) were added to fine sandy Entisol in an open agricultural field and Spodosol in a shaded forested site in North Central Florida. After 15 months, BC losses, determined by the Kurth–Mackenzie–Deluca chemical–thermal oxidation method, ranged from 17.5 to 93.3% y−1 (14.0–51.5% y−1 for the 650 °C biochar). These losses were correlated with but much greater than the 0.4–3% y−1 BC losses recorded in a one-year laboratory study using the same biochars and those of several previous field studies (1–14% y−1). The losses of non-BC native carbon stocks (i.e., positive priming) also varied with biochar and soil type and ranged from 1.5 to 15.8% y−1. The high BC losses observed in this study may be attributed to the subtropical and temporally variable climate conditions at the study site. Greater efforts should be made to base BC long-term stability estimates on field studies that monitor or control for biochar migration rather than reliance only upon laboratory incubations.

Comparative techno‐economic analysis of using multisource renewable energy with flexible storage systems for various environments

AbstractThis article presents a comprehensive techno‐economic analysis of integrating multisource renewable energy systems—solar panels, wind turbines, and flexible energy storage solutions (batteries, electrolyzers, and hydrogen tanks)—across various climatic regions in Iran. The study addresses the pressing need for transitioning from fossil fuels to renewables due to the environmental impacts of carbon emissions and rising global energy demands. Utilizing HOMER software for simulations, we evaluated the performance and economic viability of these renewable systems in 15 cities with diverse climatic conditions. Our findings highlight significant potential in regions like Zahedan, where favorable wind conditions enhance renewable viability, contrasted by challenges in Sari, which suffers from suboptimal solar and wind resources. The analysis demonstrates that a combination of renewable sources can effectively meet a daily electrical load of 5 MWh, contingent on careful consideration of component capacities and investment costs. Tailored solutions for different regions were emphasized to optimize energy production and reduce costs, advocating for strategic investments in renewable systems as a path to enhanced sustainability and economic viability in Iran. Moreover, the study identifies gaps in current literature, underscoring the need for more detailed investigations to further explore and optimize renewable energy systems countrywide. This research contributes to the broader discourse on sustainable energy transitions, providing valuable insights for policymakers and stakeholders aiming to invest in and develop Iran's renewable energy infrastructure.

Assessing the role of genotype by environment interaction as determinants of maize grain yield and lodging resistance

BackgroundThe development of superior summer maize hybrids with high-yield potential and essential agronomic traits, such as resistance to lodging, is crucial for ensuring the sustainability of maize cultivation. However, the task of identifying and breeding genotypes that exhibit exceptional performance and stability across multiple environment conditions, while considering a wide range of traits, is challenging. Given the backdrop of global climate change, understanding which climate variables and soil properties most significantly impact environmental similarity is essential for selecting hybrids with improved adaptability to regions with diverse climatic and soil conditions. This study aimed to integrate envirotyping techniques (ETs) with a multi-trait selection approach to carry out a comprehensive evaluation of maize genotypes for performance and stability.ResultsThe grain yields of 13 maize hybrids, along with their four critical agronomic parameters, were assessed in the Huang-Huai-Hai Plain of China across 40 locations in eight provinces. By considering 20 years of climatic factors and soil covariates, these 40 locations were divided into six mega-environments (MEs) based on similar long-term weather patterns and soil characteristics. Additive main effects and multiplicative interaction (AMMI) analyses revealed that genotype (G), environment (E), and the GxE interaction had significant effects on all agronomic parameters (P < 0.001). The mean performance and stability of the genotypes in each mega-environment were assessed, allowing for the identification of superior hybrids using the multi-trait stability index (MTSI). In two of the MEs (ME2 and ME3), only two hybrids, HY321 and HY9112, were selected concurrently.ConclusionOverall, this study provides valuable insights into the effects of ETs on maize hybrids and enhances our understanding of GxE interactions in multi-environment trials. This understanding is essential for improving maize cultivation practices and breeding program in diverse environments.

Genetics of sorghum: grain quality, molecular aspects, and drought responses.

Sorghum kernel composition is a crucial characteristic that determines its functional qualities. The total protein content of sorghum grain increases under drought stress, but starch, protein digestibility, and micronutrient contents decrease. Sorghum (Sorghum bicolor L.) is a staple source of starch, protein, and micronutrients in Ethiopia, where it is a key ingredient in local foods like injera and traditional beverages such as tela and areke. It has adapted remarkably to the diverse climatic conditions and soils of both highland and lowland regions. However, grain quality is influenced by climate change, drought stress, and genotype-environment interactions. Under drought conditions, sorghum shows reduced starch content, protein digestibility, and micronutrient levels, as well as increased kernel hardness and total protein content. The genetic and geographic diversity of sorghum makes it an adaptable crop, essential for breeding and diversity studies. Genome-wide association studies (GWAS) have emerged as essential tools for identifying candidate genes linked to key traits, thereby advancing genetic improvement initiatives, particularly for Ethiopian sorghum landraces. Advances in genotyping techniques, particularly genotyping-by-sequencing (GBS) and association mapping, have facilitated the identification of quantitative trait loci (QTL) associated with grain quality, enhancing breeding efficiency and the development of resilient, high-quality sorghum varieties. This review explored the genetic and phenotypic diversity of sorghum, focusing on grain quality traits, molecular mechanisms, and responses to drought stress.

Enhanced flood prediction using LSTM and climate parameters: multi-station analysis of snowmelt-induced flooding in the Red River of the North

ABSTRACT Flooding in cold regions, particularly driven by snowmelt and climate variability, presents complex challenges for accurate prediction and effective risk management. The current study aims to bridge these gaps by integrating all three components – cold region river systems, long short-term memory (LSTM) modeling, and a broader set of climate variables. Specifically, we incorporate additional climate parameters, including air temperature, humidity, solar radiation, and wind speed, into the LSTM model. Using 12 years of hourly water level data (2007–2019) from three USGS stations along the Red River – Pembina, Drayton, and Grand Forks – the model predicts water levels at lead times of 6, 12 h, 1 day, 3 days, and 1 week. The incorporation of climate variables significantly improved short-term prediction accuracy, achieving R2 values of 0.999 for 6-h forecasts across all stations, demonstrating the potential for real-time flood warning systems. For 1-week predictions, R2 values were 0.778 for Grand Forks, 0.816 for Drayton, and 0.864 for Pembina, reflecting a decrease in accuracy with longer prediction horizons. These findings highlight the effectiveness of LSTM models for short-term flood forecasting in snowmelt-prone regions and underscore the need for further refinement to address long-term hydrological forecasting challenges, particularly under variable climatic conditions.

Performance Evaluation and Integration Strategies for Solar Façades in Diverse Climates: A State-of-the-Art Review

This review article discusses the performance evaluation and integration strategies for solar façades, focusing on photovoltaic (PV) façades in diverse climatic conditions. It examines recent technology developments and methodologies for performance assessment, highlighting the potential of solar façades to enhance energy performance through on-site electricity generation. This study offers novel insights into the economic viability of façade-mounted photovoltaics, highlighting their potential in urban areas with constrained rooftop availability. Additionally, it emphasizes their distinct advantages in cold climates, such as reduced maintenance requirements and extended operational lifespans. Additionally, it addresses challenges such as technical complexity, esthetic considerations, and market awareness, emphasizing the balance between efficiency and design. Novel findings underscore the promise of advanced materials like perovskites in improving the flexibility and performance, as well as strategies to optimize BIPV integration in different climate zones. For stakeholders, this research highlights the importance of supportive policies and innovative solutions to overcome barriers, while offering researchers valuable perspectives on the advancement of solar façades toward zero-energy and zero-carbon building targets.

The response of microbial communities to environmental factors in bank soil and river sediment: A case study along the mainstream of the Yangtze River.

Microbial communities, which are crucial for ecosystem function and sustainability, are under environmental pressure. Using phospholipid fatty acids (PLFAs) as a measure of microbial biomass and community structure, the responses of microorganisms to environmental drivers were studied in bank soil and sediment alongside the Yangtze River in China. Thirty-eight sites were investigated over a length of 5500km, ranging from the plateau to the estuary. Redundancy analysis revealed that microbial community composition in the bank soil was affected by MP (7.8%), geography (19.2%), and physicochemical properties (23.1%), while in the sediment, relevant factors were MP additives (12.8%), metals (21.1%), and physicochemical properties (23.3%). Variations in climate conditions along the course of the river had no effect on the microbial communities in the two habitats. Linear discriminant analysis of the PLFAs profiles showed changes in microbial community composition due to land use (forest, grass, cropland and built land), site class (up-, mid- and downstream) and MPs pollution level in both bank soil and sediment. The increased Gram-positive to negative bacteria (g+/g-) ratio and decreased iso-to anteiso-fatty acid (i/a) ratio indicated greater stress, such as caused by MP pollution (g+/g-: 12.6 to 19.3; i/a: 1.9 to 1.6). In bank soil, total microbial biomass was influenced by urbanization rate and nutrient availability. Specifically, total carbon (TC), total phosphorus (TP), and ammonium nitrogen (NH4+-N) had a positive impact, while inorganic phosphorus (IP), total potassium (TK) and nitrate nitrogen (NO3--N) had a negative impact. In contrast, in sediment only TC had a negative effect on biomass. This study applied PLFA to explore microbial communities and structures responses to environmental drivers in riverine habitats, revealing that anthropogenic factors (e.g. MP pollution and nutrient enrichment) alter microbial communities with urbanization.

Exploring natural ventilation strategies in Javanese vernacular houses for sustainable design

ABSTRACT This study investigates natural ventilation strategies in Javanese vernacular houses to enhance sustainable building design tailored to local climates and promote comfort and environmental sustainability in Javanese communities. It examines adaptations of these strategies in rural (Saradan) and urban (Laweyan) contexts using a comprehensive methodology comprising document analysis, literature review, stakeholder interviews, and case studies. Insights from classical texts and architectural records inform traditional values and principles related to energy sustainability, supplemented by perspectives gathered through interviews with homeowners, architects, and cultural experts on integrating local values into sustainable practices. The findings reveal that in Saradan’s rural areas, expansive layouts and natural elements effectively facilitate natural ventilation, utilizing large courtyards and strategically positioned windows to optimize airflow and thermal comfort. In contrast, Laweyan’s urban setting employs compact designs with smaller openings and internal courtyards to manage airflow and mitigate heat accumulation. The study illustrates how traditional Javanese architectural practices adapt to contemporary contexts, supporting energy efficiency and occupant well-being while reconciling technological advancements with cultural heritage preservation. It offers insights for architects, researchers, and policymakers to develop guidelines and innovations that optimize Javanese ventilation strategies across diverse climatic conditions and explore their socio-economic impacts.

Quercus cerris Leaf Functional Traits to Assess Urban Forest Health Status for Expeditious Analysis in a Mediterranean European Context.

In the Mediterranean basin, urban forests are widely recognized as essential landscape components, playing a key role in nature-based solutions by enhancing environmental quality and providing a range of ecosystem services. The selection of woody plant species for afforestation and reforestation should prioritize native species that align with the biogeographical and ecological characteristics of the planting sites. Among these, Quercus cerris L. (Turkey oak) is considered a promising candidate for urban reforestation. However, its fitness within urban forest environments remains poorly understood. This study aimed to identify suitable leaf functional traits for assessing the response of Q. cerris in urban forests and to analyze the main climatic variables influencing its performance in urban contexts. We also proposed practical, rapid monitoring tools to compare urban and natural forests across different seasons. The results demonstrated that Q. cerris experiences significant water stress in urban forests due to the combined effects of drought and high temperatures. To find the tools to mitigate this stress, the differences between leaf traits such as specific leaf area, thickness, and the contents of chlorophyll, anthocyanins, and flavonols in urban and natural forests were analyzed. Our findings underscore the high adaptability of Q. cerris to varied climatic and environmental conditions. This study provides a practical method for rapidly assessing the responses of tree species to climate change. In the future, this approach will be tested on other native species that are characteristic of Mediterranean forest ecosystems to help with choosing afforestation and reforestation strategies.

Assessment of genetic diversity by multivariate analysis in Turkish Hawthorn (Crataegus azarolus) genetic resources

Abstract Due to its diverse climatic conditions, Türkiye provides a suitable environment for the cultivation of many fruit species. This study focused on natural hawthorn (Crataegus azarolus), one of these fruit species, to select 94 different hawthorn genotypes collected from natural populations in Türkiye and determine their molecular and morphological characteristics. A significant variation was observed among the hawthorn genotypes in terms of the analyzed traits. According to the results of the molecular study using the inter simple sequence repeat (ISSR) marker system, 178 polymorphic bands were obtained from 12 different primers (Invitrogen Life Technologies, USA), and the polymorphism rate was determined to be 100%. The similarity index for the genotypes ranged from 0.40 to 0.93, and the genotypes were completely differentiated from one another. In terms of fruit characteristics, there was considerable variation among the genotypes: fruit width ranged from 8.96 mm (genotype 72) to 29.75 mm (genotype 101), fruit length ranged from 9.19 mm (genotype 72) to 24.86 mm (genotype 101), and fruit weight ranged from 0.45 to 12.42 g. This study provides a foundation for developing hawthorn genotypes that will offer high productivity while conserving water and soil resources in arid and semi-arid regions by revealing the degree of genetic variation and the morphological characteristics of hawthorn genetic resources adapted to different ecological conditions in Türkiye. Furthermore, the study demonstrates that genotypes with superior traits can be utilized as genetic material in hawthorn breeding programs, medical research, and the development of new cultivars.

Time-series machine learning for predictive optimisation of a highly efficient evaporative cooling system

As data centres become integral to modern infrastructure, their energy consumption, particularly in cooling systems, presents a critical challenge for sustainability. This paper addresses this issue by applying time-series machine learning models to forecast the performance of a highly efficient 100 kW evaporative cooling system applied in a real-world data centre. Using a dataset spanning 4 months, we developed and optimised two predictive models based on XGBoost and Random Forest, to estimate cooling capacity and Coefficient of Performance (COP). Initial results showed suboptimal performance, with the XGBoost model achieving a Mean Absolute Error (MAE) of 1.34 for cooling capacity and 6.50 for COP, alongside a negative R-squared, indicating poor fit. However, after hyperparameter tuning, the Random Forest model significantly improved the predictions, achieving an MAE of 0.39 and an R-squared of 0.85 for cooling capacity, and an MAE of 2.21 and an R-squared of 0.54 for COP. These findings underscore the potential of these models to optimise cooling efficiency, offering valuable insights for reducing energy consumption and operational costs in data centre operations. This research paves the way for more sustainable data centre designs and operations across diverse climatic conditions. Practical application The predictive models developed in this study enable building environment professionals to optimise data centre cooling systems. By accurately forecasting cooling capacity and Coefficient of Performance (COP) under varying environmental conditions, these models allow for proactive adjustments to cooling strategies, ensuring efficient operation and minimising energy waste. This research provides a practical tool for enhancing the sustainability of data centres, directly supporting industry efforts to meet stringent energy efficiency targets and reduce the carbon footprint of critical infrastructure.

Tea Farmers' Perceptions of the Influence of Climate Variability on Dodder Occurrence in Nandi County, Kenya

Climate variability continues to adversely impact agricultural systems globally. From the literature, it is reported that there has been an unprecedented emergence of pests and diseases associated with climate change and variability among other factors. In Kenya, crop cultivation and yield are affected primarily by changing climatic conditions, pests, and diseases. Of major concern in counties of Kenya, are the impacts of climate variability and parasitic dodder (Cuscuta spp.) on tea cultivation and production. From the literature, little is reported on the continuing invasion and effects of parasitic dodder on tea farming. This study aimed to assess tea farmer perceptions on the role of climate variability on the occurrence of dodder in Nandi. An exploratory survey design was utilized for this study. Using the Yamane Formula, a sample size of 392 was drawn from the Kenya Tea Development Agency tea farmers to represent the study. Primary data obtained from the administered questionnaires was extracted, collated, classified, and analysed (p≤0.05) with the aid of the Statistical Package of Social Sciences. Rainfall and temperature data for the County was obtained from the Kenya Meteorological Department from 1992 to 2022. Mann-Kendall Trend Analysis showed a significant increase in temperature denoted by a Sen’s slope= 0.031 and a strong positive correlation of Kendall’s Tau 0.554 indicated an increase at p&lt;0.000. Rainfall showed an increasing trend Sen’s slope= 5.618 was not statistically significant at p= 0.341 with a weak correlation of Kendall’s Tau= 0.123. On the influence of rainfall and temperature on dodder occurrence, 95.6%, and 68.9% of the respondents perceived that long rainy (March to May) and cold (June to August) seasons increased its intensity. While 69.8% of the respondents perceived a moderate intensity of dodder during the hot season (December to February). In conclusion, climate variability has influenced the spread of dodder on tea farms. The study recommends further research to examine the effectiveness of different Integrated Pest Management strategies under varying climate conditions.

Assessing solar absorption chillers for data center cooling across diverse climatic conditions

Assessing solar absorption chillers for data center cooling across diverse climatic conditions

Carboxymethyl Cellulose‐Based Composite Hydrogels for Sustainable Agriculture Under Drought Stress

ABSTRACTThis study investigates the synthesis and characterization of a DAP‐loaded composite hydrogel comprising carboxymethyl cellulose‐g‐poly(acrylic acid‐acrylamide)/diatomite (CMC‐g‐P(AA‐AAm)/diatomite/DAP) using chemical crosslinking. The incorporation of diatomite enhanced the equilibrium water absorbency of the hydrogel by 54.48% in distilled water and 34.02% in tap water. The hydrogel demonstrated excellent reswelling capacity, maintaining 105.8 g/g and 36.13 g/g in distilled and tap water, respectively, after 10 cycles. Soil treated with the hydrogel retained the highest water at a 0.3% concentration, showing a 291% increase at 35°C compared to the control. Composite hydrogel treatments improved soil physiochemical properties, including bulk density, porosity, organic carbon, water‐holding capacity, and microbial counts while reducing soil pH. The hydrogel facilitated a controlled release of nitrogen and phosphorus, following Fickian diffusion. Enhanced germination rates and improved mustard plant growth under drought conditions were observed, indicating the potential of hydrogel in sustainable agriculture. Further research and field trials are required to evaluate its performance under diverse soil and climate conditions, paving the way for its widespread adoption.

Design of a photovoltaic performance prediction model using meteorological models and machine learning techniques

This study presents an advanced predictive model to estimate daily photovoltaic energy production using machine learning techniques. Artificial neural networks, support vector machines, and random forests were employed to capture complex relationships among meteorological variables such as solar radiation and temperature. The models were trained and evaluated using historical data, employing metrics like mean squared error and cross-validation techniques to ensure robustness and generalization of the results. Findings indicate that the implemented models effectively predict the performance of photovoltaic systems under diverse climatic conditions in the eastern region of Michoacán. This model not only enhances operational planning of solar installations but also facilitates more efficient integration of renewable energy into power grids. The results underscore the potential of photovoltaic technologies and the crucial role of artificial intelligence in transitioning towards more sustainable energy systems.

Wastewater management by using effective phytoremediator duckweed

Water pollution and the increasing discharge of untreated wastewater into natural water bodies are intensifying the pressure on aquatic environments. Sustainable and eco-friendly methods are essential for restoring water quality and mitigating pollution. One such approach is water phytoremediation, which involves the use of aquatic plants to remove pollutants from water bodies and ecosystems. This study explored the removal of contaminants from municipal wastewater using floating aquatic plants such as Lemna minor, which have proven highly effective for phytoremediation due to their low cost, high pollutant uptake capacity, minimal generation of chemical and biological sludge, ease of transport, adaptability to diverse climatic conditions, and rapid reproduction rates. Notable findings from the research include reductions in chemical oxygen demand (COD) by 82.2%, biological oxygen demand (BOD) by 84.3%, phosphates by 63.8%, ammonium nitrogen by 75.4%, and nitrates by 79.1% for Lemna minor. Maximum plant growth occurred during the experiment at pH levels between 7 and 8. The study demonstrated that large-scale remediation could be effectively achieved using Lemna minor as a phytoremediation agent within 17 days. This method offers a cost-effective, efficient tertiary treatment for heavily polluted wastewater. Additionally, it can serve as an independent treatment solution when integrated with preliminary wastewater treatment systems in smaller communities.

Value Addition in Green Peas by Drying under Fluidized Bed Drying

India's diverse climatic conditions provide advantages for the production of fruits and vegetables, ensuring year-round availability of various fresh produce. Pisum sativum (peas), a significant vegetable crop in India, contributes approximately 2.5% (3.86 million MT) to total vegetable production and accounts for 4.6% of the vegetable-growing area (NHB, 2014). However, due to their high moisture content, peas cannot be stored for extended periods and require preservation methods such as freezing, canning, cold storage, or drying (Chauhan and Srivastava, 2009). This study evaluates fluidized bed drying of green peas as a preservation method to enhance farmers' income, particularly in Gujarat. The investigation included two pre-treatments (unblanched and blanched), three bed heights (20, 40, and 60 mm), and three drying air temperatures (45°C, 55°C, and 65°C). Mature, fresh green peas were manually deshelled, blanched at 85°C for 1 minute, cooled under running water, and surface moisture was removed before drying. Results revealed that drying occurred entirely in the falling rate period, with the overall drying rate ranging from 0.0053 to 0.0158 kg water/kg dry matter/min. The maximum drying rate (0.0158 kg water/kg dry matter/min) was observed with the blanched sample at a bed height of 20 mm and 65°C. The highest protein content (17.87 g/100g) was recorded at 45°C, similar to 55°C (17.85 g/100g). The highest chlorophyll content (106.98 mg/100g) was observed in blanched samples dried at 40 mm bed height and 45°C, comparable to 55°C (106.68 mg/100g). The highest rehydration ratio (3.53) was achieved in blanched samples at 40 mm bed height and 55°C. It is concluded that blanching green peas at 85°C for 1 minute, followed by fluidized bed drying at 40 mm bed height and dry air temperature at 55°C, produces high-quality dried peas with good qualities.

Study on Reliability of Field-Aged Photovoltaic Connectors

The paper presents the reliability study on field-aged photovoltaic connectors of different makes characterized by diverse climatic conditions with 2 to 10 years of operational life. A series of tests conforming to IEC 62852 conducted on field-aged connectors and control samples. The objective is to elucidate the mechanisms of aging and evaluate reliability of connectors, identify failure modes, potential hazards, changes in electrical and mechanical performance because of longstanding exposure to environmental and operational stresses. Field survey and testing reveal lack of awareness of workmanship and maintenance. Laboratory testing reveals connector less than 6 years of life shows low contact resistance and remain stable or have marginal linear increase after thermal and damp heat testing. Samples with life of 6 years or more from extreme climatic regions show high contact resistance with nonlinear pattern increasing to 300% in the thermal cycle and up to 600% in damp heat test. In SEM fretting was observed with high contact resistance with marginal polymer deterioration

Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree

Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens.