Search Results (5,726)

Harmful algal blooms (HABs) caused by toxic species such as Pseudo-nitzschia hasleana pose significant risks to marine ecosystems and human health. This study investigates the effects of heating rate on the fluorescence temperature curves (FTCs) of P. hasleana and compares them with non-toxic species (Phaeodactylum tricornutum and Picochlorum maculatum) to design a reliable detection method. An increasing heating rate leads to a change in the temperature spectrum of the fluorescence of the studied algae and to increasing differences between them. During the study, the FTCs were measured in the temperature range of 20–80 °C and at heating rates of 1, 2, 3, and 6°/min. The results showed that P. hasleana exhibited a distinct local fluorescence maximum at 45–55 °C when heated at a rate of 3 °C/min or more, which was absent in non-toxic species. Additionally, rapid heating (6 °C/min) preserved fluorescent pigment–protein complexes, yielding four-fold higher fluorescence intensity at 70–80 °C compared to slower rates. There were no such changes for the microalgae P. maculatum and P. tricornutum. The results of this study make it possible to increase the efficiency of detecting hazardous microalgae using non-invasive optical monitoring methods. These findings demonstrate that controlled heating protocols can enhance the species-specific identification of toxic microalgae, offering a practical tool for early HAB detection. Full article

To maintain product reliability and stabilize performance, it is essential to prioritize the identification and resolution of latent defects. Advanced products such as high-precision electronic devices and semiconductors are susceptible to performance degradation over time due to environmental factors and electrical stress. However, conventional performance testing methods typically evaluate products based solely on predefined acceptable ranges, making it difficult to predict long-term degradation, even for products that pass initial testing. In particular, products exhibiting borderline values close to the threshold during initial inspections are at a higher risk of exceeding permissible limits as time progresses. Therefore, to ensure long-term product stability and quality, a novel approach is required that enables the early prediction of potential defects based on test data. In this context, the present study proposes a machine learning-based framework for predicting latent defects in products that are initially classified as normal. Specifically, we introduce the Sigma Deviation Count Labeling (SDCL) method, which utilizes a Gaussian distribution-based approach. This method involves preprocessing the dataset consisting of initially passed test samples by removing redundant features and handling missing values, thereby constructing a more robust input for defect prediction models. Subsequently, outlier counting and labeling are performed based on statistical thresholds defined by 2σ and 3σ, which represent potential anomalies outside the critical boundaries. This process enables the identification of statistically significant outliers, which are then used for training machine learning models. The experiments were conducted using two distinct datasets. Although both datasets share fundamental information such as time, user data, and temperature, they differ in the specific characteristics of the test parameters. By utilizing these two distinct test datasets, the proposed method aims to validate its general applicability as a Predictive Anomaly Testing (PAT) approach. Experimental results demonstrate that most models achieved high accuracy and geometric mean (GM) at the 3σ level, with maximum values of 1.0 for both metrics. Among the tested models, the Support Vector Machine (SVM) exhibited the most stable classification performance. Moreover, the consistency of results across different models further supports the robustness of the proposed method. These findings suggest that the SDCL-based PAT approach is not only stable but also highly adaptable across various datasets and testing environments. Ultimately, the proposed framework offers a promising solution for enhancing product quality and reliability by enabling the early detection and prevention of latent defects. Full article

Aphid control often relies on synthetic pesticides, but their overuse has raised concerns about resistance development and negative impact on wildlife and human health. Consequently, the search for new biopesticide agents has gained significant attention. Nemertide alpha-1, a peptide toxin from the marine nemertean worm Lineus longissimus (Gunnerus, 1770), is known for its pesticide activity but has less documented biological safety. This study investigates the aphid feeding deterrence and biological safety of the experimental biopesticide nemertide alpha-1. Nemertide alpha-1 demonstrated a clear dose-dependent repellent effect on the penetration behaviour of the green peach aphid (Myzus persicae, Sulzer). It also demonstrates bacteriostatic and bactericidal effects in an MIC (Minimum Inhibitory Concentration) assay, respectively, on E. coli (MIC: 112.5 µM) and S. aureus (MIC: 28.4 µM). In a bacterial liposome leakage assay, nemertide alpha-1 exhibits a less pronounced effect than the melittin control (20% maximum leakage at 100 µM), strengthening the hypothesis on the specificity of its neurotoxic mode of action. It is not toxic to mammalian cell U-937 GTB with only a slight decline in the percentage of survival at the highest concentration tested (80 µM). Finally, nemertide alpha-1 displays thermal stability over time for four weeks in three different conditions: cold (6 °C), room temperature (20–24 °C), and physiological temperature (37 °C). Nemertide alpha-1 deters green peach aphid feeding in the low micromolar range and exhibits low antimicrobial properties and very low toxicity to human cells. Its potential utility is further underscored by thermal stability over time. Full article

In lithium-ion batteries, the fracture of active particles that are under stress is a key cause of battery aging, which leads to a reduction in active materials, an increase in internal resistance, and a decay in battery capacity. A coupled electrochemical–thermal–mechanical model was established to study the concentration and stress distributions of negative electrode particles under different charging rates and ambient temperatures. The results show that during charging, the maximum lithium-ion concentration occurs on the particle surface, while the minimum concentration appears at the particle center. Moreover, as the temperature decreases, the concentration distribution of negative electrode active particles becomes more uneven. Stress analysis indicates that when charging at a rate of 1C and 0 °C, the maximum stress of particles at the negative electrode–separator interface reaches 123.7 MPa, while when charging at 30 °C, the maximum particle stress is 24.3 MPa. The maximum shear stress occurs at the particle center, presenting a tensile stress state, while the minimum shear stress is located on the particle surface, showing a compressive stress state. Finally, to manage the stress of active materials in lithium-ion batteries while charging for health maintenance, this study uses a DNN (Deep Neural Network) to predict the maximum shear stress of particles based on simulation results. The predicted indicators, MAE (Mean Absolute Error) and RMSE (Root Mean Square Error), are 0.034 and 0.046, respectively. This research is helpful for optimizing charging strategies based on the stress of active materials in lithium-ion batteries during charging, inhibiting battery aging and improving safety performance. Full article

Tulipa auliekolica and Tulipa turgaica have been recently described as endangered species endemic to Kazakhstan, which require urgent conservation amid rising human impact and climate change. Biotechnology offers effective tools for conserving such rare species; however, species-specific in vitro protocols tailored to their biological traits remain largely unreported. This study aimed to develop an in vitro propagation protocol for these rare Tulipa species by investigating the effects of different temperature regimes and phytohormone treatments. We conducted a study on the in vitro propagation of two recently described species, T. auliekolica and T. turgaica. Species-specific temperature regimes for seed stratification were established. Maximum germination of T. auliekolica was achieved at alternating temperatures of 4/10 °C, and of T. turgaica at 10/20 °C. No seed germination from either species occurred at a constant temperature of 20 °C. Bulbs cultured on Murashige & Skoog (MS) medium supplemented with 90 g/L sucrose and the growth regulators mT (meta-topolin) and BAP (6-benzylaminopurine) were effective in stimulating the formation of up to 4–7 microbulbs. Cultivation on a medium supplemented with 0.5 mg L⁻¹ IBA (indole-3-butyric acid) resulted in the formation of mature bulbs covered with scales. These results can be successfully used in biodiversity conservation programs for the endemic Tulipa species. In addition, they provide a valuable basis for future biotechnological research, including microclonal propagation, the establishment of gene banks, and the development of reintroduction methods for Kazakh endemic Tulipa species. Full article

Tetranychus mexicanus (McGregor, 1950) (Tetranychidae) is considered one of the primary phytosanitary problems in passion fruit crops, resulting in significant production losses. Understanding the impact of this mite species’ activity on the physiology of passion fruit plants can serve as a basis for developing sustainable management strategies. With this in mind, this research sought to analyze, using supervised and unsupervised machine learning models, how T. mexicanus mite infestation influences gas exchange, chlorophyll “a” and chlorophyll “b” levels, fluorescence, and thermal response of passion fruit plants. We tested the hypothesis that juvenile and adult mites alter the physiological and thermal response patterns of plants. Only the variables related to the fluorescent response (Fo, Fm, and Fv) had a significant relationship with mite infestation. In the joint comparison of multiple fluorescent variables, there were differences between the treatments of plants infested and not infested by T. mexicanus. The variables’ initial fluorescence (Fo), maximum fluorescence (Fm), and variable fluorescence (Fv) of chlorophyll a had a direct negative impact on both reproductive activity, as measured by the number of eggs and nymphs produced, and the total number of mites found. The unsupervised model based on multidimensional scaling with the k-means algorithm revealed a clear separation between the groups of infested passion fruit plants (Group 1) and healthy plants (Group 2). The Fo response was described with high accuracy for the reproductive rate (75%) and total infestation of eggs, nymphs, and adults of the mites (99.99%). Kappa values were moderate (Kappa = 0.50) and high (Kappa = 0.99) for reproductive and total rates of T. mexicanus, respectively. Additionally, the thermal response revealed that the infested passion fruit plants had a median temperature of 25.1 °C, compared to a median temperature of 25.7 °C, with notable differences between these medians. Therefore, the T. mexicanus mite altered both the fluorescent and thermal patterns of passion fruit plants. Our findings have implications for the development of early detection tools and the generation of future resistance breeding. Full article

A greenhouse with properly managed temperature can provide 5 to 10 times greater yield than conventional methods for crops such as blueberries, cucumbers, and tomatoes; the yield is also of higher quality. However, existing designs in Australia often follow practices developed for cooler regions, making them less effective under local high-radiation conditions. To determine the design parameters for the local condition, this study develops and validates a numerical model of a commercial blueberry greenhouse, applying it to examine how structural parameters, including overall height, arch height, and number of spans, influence indoor temperature distribution in multi-span hoop structures. Results show that increasing greenhouse height by 0.40 m reduced average temperature by up to 0.62%, whereas raising arch height by the same increment led to a marginal increase of 0.15%. In contrast, expanding span numbers from 2 to 12 resulted in a maximum temperature difference of 6 °C (approximately 20% above ambient temperature) across the structure, posing significant risks to plant growth. These findings provide a theoretical basis for optimising design parameters that minimise heat stress while reducing reliance on fossil-fuel-based cooling. The study highlights how tailoring greenhouse design to local conditions can improve productivity and support both environmental and economic sustainability. Full article

Considering the effects of increasing heat waves already underway, especially in several areas of the Mediterranean region, the study of the effect of temperature on the qualitative yield of hemp oil becomes necessary. Given this, an experiment was conducted in order to evaluate the effect of temperature during the grain-filling period on fatty acid accumulation and composition in hemp seed, comparing two locations with different temperature regimes, two years, two sowing times and two monoecious hemp varieties, characterized by different earliness. The accumulation of different fatty acids in hemp seeds at maturity seems to depend on the genetic background of the two genotypes studied. However, high temperatures also affect the activity of desaturase Δ12 and Δ15, which are responsible for the production of polyunsaturated fatty acids, in particular if greater than an 18 °C minimum night temperature and 30 °C maximum daily temperature, respectively. This result makes it possible to orient, even if partially, the qualitative characteristics of hemp oil for different uses, by identifying the suitable cultivation environment. Considering the Mediterranean area, hilly and foothill environments would favor the percentage of polyunsaturated fatty acid in the oil, with an improvement of the n-6/n-3 ratio, while the plain and warmer area, characterized by heat stress during the grain-filling period, would give an oil with an increased percentage of monounsaturated acids to the detriment of polyunsaturated fatty acid. Full article

Lakes account for approximately 87% of the Earth’s surface water resources and serve as sensitive indicators of climate and environmental change. Understanding how lake areas respond to climate change across different elevation gradients is crucial for guiding sustainable water resource management in Xinjiang. We utilized Landsat series remote sensing imagery (1990–2023) on the Google Earth Engine (GEE) platform to extract the temporal dynamics of natural lakes larger than 10 km² in Xinjiang, China (excluding reservoirs). We analyzed the relationships between lake area dynamics, climatic factors, and human activities to assess the sensitivity of lakes at different altitudinal zones to environmental change. The results showed that (1) the total area of Xinjiang lakes increased by 1188.36 km² over the past 34 years, with an average annual area of 5998.54 km²; (2) plain lakes experienced fluctuations, reaching their maximum in 2000 and their minimum in 2015, alpine lakes peaked in 2016, and plateau lakes continued to expand, with the maximum recorded in 2020 and the minimum in 1995; and (3) human activities such as urban and agricultural water use were the primary causes of shrinking plain lakes, while an increased PET accelerates evaporation, alpine lakes were influenced by both climate variability and human disturbance, and plateau lakes were highly sensitive to climate change, with rising temperatures increasing snowmelt and glacial runoff into lakes, which were the main drivers of their expansion. These findings highlight the importance of incorporating elevation-specific lake responses into climate adaptation strategies and sustainable water management policies in arid regions. Full article

Lithium iron phosphate (LFP) batteries have become a popular choice for energy storage, electrified mobility, and plants. All lithium-based batteries produce flammable vent gas as a result of failure through thermal runaway. LFP cells produce less gas by volume than nickel-based cells, but the composition of this gas most often contains less carbon dioxide and more hydrogen. However, when LFP cells fail, they generate lower temperatures, so the vent gas is rarely ignited. Therefore, the hazard presented by a LFP cell in thermal runaway is less of a direct battery fire hazard but more of a flammable gas source hazard. This research identified the constituents and components of the vent gas for different sized LFP prismatic cells when overcharged to failure. This data was used to calculate the maximum homogenous concentration of gas that would be released into a 1.73 m³ test rig and the percentage of the lower explosive limit (LEL). Overcharge experiments were conducted using the same type of cells in the test rig in the presence of remote ignition sources. Ignition and deflagration of the vent gas were possible at concentrations below the theoretical LEL of the vent gas if it was homogeneously mixed. Full article

In deep-sea oil and gas development scenarios, deep-sea dual-channel connectors often face the risk of seal failure due to internal and external temperature difference loads. To address this issue, this paper systematically establishes equivalent heat transfer models for the key parts of the connector based on the third-type boundary condition. On this basis, the quantitative correlation between the equivalent thermal conductivity, composite heat transfer coefficient and temperature of each part is explored. Using the finite element numerical simulation method, the transient temperature field of the connector under three working conditions (heating, cooling and temperature shock) is simulated and analyzed, revealing the temperature distribution characteristics and temperature change trends of the maximum temperature difference of each key component of the connector; combined with thermal–structural coupling simulation, the temperature field is converted into static load, to determine the behavior of the contact stress on the sealing surface under different temperature–pressure coupling working conditions; in addition, by placing the test prototype in a high-low temperature cycle chamber, the seal performance tests under pressurized and non-pressurized working conditions are carried out to verify the reliable sealing performance of the connector under variable temperature conditions. The results of this paper provide comprehensive theoretical support and an experimental basis for the thermodynamic optimization design of deep-sea connectors and the improvement of the reliability of the sealing system. Full article

In this research article, a fast and efficient hybrid Maximum Power Point Tracking (MPPT) control technique is proposed for photovoltaic (PV) systems. The method combines two phases—offline and online—to estimate the appropriate duty cycle for operating the converter at the maximum power point (MPP). In the offline phase, temperature and irradiance inputs are used to compute the real-time reference peak power voltage through an Adaptive Neuro-Fuzzy Inference System (ANFIS). This estimated reference is then utilized in the online phase, where the Robust Backstepping Super-Twisting (RBST) controller treats it as a set-point to generate the control signal and continuously adjust the converter’s duty cycle, driving the PV system to operate near the MPP. The proposed RBST control scheme offers a fast transient response, reduced rise and settling times, low tracking error, enhanced voltage stability, and quick adaptation to changing environmental conditions. The technique is tested in MATLAB/Simulink under three different scenarios: continuous variation in meteorological parameters, sudden step changes, and partial shading. To demonstrate the superiority of the RBST method, its performance is compared with classical backstepping and integral backstepping controllers. The results show that the RBST-based MPPT controller achieves the minimum rise time of $0.018$$\mathrm{s}$, the lowest squared error of $0.3015$$\mathrm{V}$, the minimum steady-state error of $0.29$%, and the highest efficiency of $99.16$%. Full article

This work presents an analytical 2D model for estimating eddy current losses in the permanent magnets (PMs) of a coaxial magnetic gear (CMG), with a focus on loss minimization through magnet segmentation. The model is applied under various operating conditions, including different rotational speeds, load levels, and segmentation configurations, to derive empirical expressions for eddy current losses in both the inner and outer rotors. A 1D lumped-parameter thermal model is then used to predict the steady-state temperature of the PMs, incorporating empirical correlations for the thermal convection coefficient. Both models are validated against finite element analysis (FEA) simulations. The analytical eddy current loss model exhibits excellent agreement, with a maximum error of 2%, while the thermal model shows good consistency, with a maximum temperature deviation of 5%. The results confirm that eddy current losses increase with rotational speed but can be significantly reduced through magnet segmentation. However, achieving an acceptable thermal performance at high speeds may require a large number of segments, particularly in the outer rotor, which could influence the manufacturing cost and complexity. The proposed models offer a fast and accurate tool for the design and thermal analysis of CMGs, enabling early-stage optimization with minimal computational effort. Full article

The appearance of spin-induced ferroelectric polarization in the so-called type-II multiferroic materials has received a lot of attention. The nature and mechanisms of such polarization were intensively studied using perovskite rare-earth manganites, RMnO₃, as model systems. Later, multiferroic properties were discovered in some RFeO₃ perovskites and possibly in some RCrO₃ perovskites. However, R₂NiMnO₆ double perovskites have ferromagnetic structures that do not break the inversion symmetry. It was found recently that more complex magnetic structures are realized in A-site-ordered quadruple perovskites, RMn₃Ni₂Mn₂O₁₂. Therefore, they have the potential to be multiferroics. In this work, dielectric properties in magnetic fields up to 9 T were investigated for such perovskites as RMn₃Ni₂Mn₂O₁₂ with R = Ce to Ho and for BiMn₃Ni₂Mn₂O₁₂. The samples with R = Bi, Ce, and Nd showed no dielectric anomalies at all magnetic fields, and the dielectric constant decreases with decreasing temperature. The samples with R = Sm to Ho showed qualitatively different behavior when the dielectric constant started increasing with decreasing temperature below certain temperatures close to the magnetic ordering temperatures, T_N. This difference could suggest different magnetic ground states. The samples with R = Eu, Dy, and Ho still showed no anomalies on the dielectric constant. On the other hand, peaks emerged at T_N on the dielectric constant in the R = Sm sample from about 2 T up to the maximum available field of 9 T. The Gd sample showed peaks on dielectric constant at T_N between about 1 T and 7 T. Transition temperatures increase with increasing magnetic fields for R = Sm and decrease for R = Gd. These findings suggest the presence of magnetic-field-induced multiferroic states in the R = Sm and Gd samples with intermediate ionic radii. Dielectric properties at different magnetic fields are also reported for Lu₂NiMnO₆ for comparison. Full article

Conogethes punctiferalis (Guenée, 1854) is a polyphagous pest with a wide host range and strong reproductive ability, and its potential threat to agricultural production cannot be ignored. Based on the optimized maximum entropy niche model, this study evaluated potential suitable habitats for C. punctiferalis in China and their dynamic changes under current conditions (Model 1: bioclimatic factors + elevation; Model 2: bioclimatic factors + elevation + human activity) and four different future climate scenarios (Model 3: bioclimatic factors + elevation + human activity). The results suggest that the potential suitable habitats for C. punctiferalis are mainly driven by a combination of temperature, precipitation, elevation, and human activity. Under current conditions, suitable habitats are mainly concentrated in southern Northeast China, North China, the Yangtze River Basin, and its south regions; highly suitable areas are primarily located in the main maize-producing regions of the Huang-Huai-Hai Plain. The area of suitable habitats predicted by Model 2 is smaller than that predicted by Model 1. Under future climate scenarios, the potential distribution range of C. punctiferalis will show an expanding trend, with the expanded area larger than the contracted area. Compared with Model 2, the suitable areas are expected to increase under Model 3 by approximately 91,799 km² to 723,711 km². This study provides an important basis for assessing the potential hazard risk of C. punctiferalis and is of major significance in guiding the formulation of targeted integrated pest management strategies and protecting the safety of agricultural production. Full article