Search Results (3,767)

Variations in the ecological roles of sea turtle species may lead to differentiations in ocular design and visual sensitivity to the colour spectrum. Behavioural colour preference studies in air and in water on hatchling and post-hatchling green turtles found evidence of a blue hue attractiveness when given a choice between blue, red, and yellow. This paper assessed and compared the colour preferences to singular colours via the behavioural responses of eleven hawksbill turtles and twelve green turtles at 15 months of age and at 22 months of age. Turtles were presented with one coloured water balloon per day (purple (400–450 nm), dark blue (450–490 nm), cyan (490–520 nm), green (520–560 nm), yellow (560–590 nm), orange (590–635 nm), and red (635–700 nm)). Time to contact balloons with beak and behaviours exhibited by turtles were recorded. Hawksbill turtles had the greatest level of interactions across both phases to shorter wavelengths with hue preference being between 450 and 490 nm. Green turtles consistently had the greatest level of interaction to longer wavelengths with a yellow (560–590 nm) hue preference. The results of this study support behavioural differences between two co-occurring turtle species that may reflect an adaptive preference for colour wavelengths associated with the optimal foraging niche for each. Full article

Objective: This review article aims to analyze the existing relevant literature comparing the clinical outcomes and underlining the most common complications associated with immediate, early, and delayed dental implant placement in order to determine the most favorable timing for achieving optimal functional and esthetic results for the patient. Methods: A comprehensive review of the literature was conducted using PubMed-MEDLINE and Cochrane Library and a number of keywords, including “dental implant placement timing”, “immediate implant”, “early implant”, “delayed implant”, “clinical outcomes”, “complications”, and “implant success”, focusing on studies comparing immediate, early, and delayed implant placement. The primary outcome variable was implant survival rate, while secondary outcome variables included implant success rate, complications, and patient-reported outcomes. Results: A total of 9774 articles were identified. The articles included a variety of studies, including randomized controlled trials, prospective cohort studies, and retrospective studies. Immediate implant placement was associated with a high survival rate (93.8–100%), but also with an increased risk of complications, such as gingival recession and implant exposure. Early implant placement (4–8 weeks or 12–16 weeks after extraction) showed similar survival rates (95–100%) and fewer complications compared with immediate placement. Delayed implant placement (more than 4 months after extraction) was the most commonly used protocol and demonstrated high survival rates (92–100%) with predictable outcomes. Implant success rates varied depending on the criteria used, but all types of placements showed acceptable success rates (83.3–100%). The choice of loading protocol (immediate, early, or conventional) also influences treatment outcomes. Conclusions: The timing of dental implant placement and loading should be individualized based on patient-specific factors, such as bone and soft tissue conditions, medical history, esthetic considerations, and patient preferences. Immediate placement can be successful in ideal conditions but requires careful patient selection and surgical expertise. Early and delayed placement offer more predictable outcomes and are suitable for a wider range of patients. Full article

Bio-based composites offer potential environmental benefits over fossil-based materials, but limited research exists on manufacturing processes with varying material combinations. This study performs a cradle-to-grave Life Cycle Assessment of five composite types to evaluate the role of fully and partially bio-based composites, focusing on the manufacturing stage. The composite materials include glass or flax fiber-based reinforcements embedded in polymer matrices based on a fossil epoxy, a partially bio-based epoxy, or epoxidized linseed oil, fabricated using vacuum-assisted resin infusion. Flax fibers in a partially bio-based epoxy achieve the lowest environmental impacts in most categories when assessed at equal geometry. Glass fiber composites exhibit a higher fiber volume content and material properties and thus demonstrate competitive environmental performance at equal absolute and normalized tensile strength. Composites using epoxidized linseed oil are the least advantageous, with the manufacturing stage contributing a majority of the environmental impacts due to their comparatively long curing times. These results are based on methodological choices and technical constraints which are discussed together with benchmarking against previous studies. While partially bio-based materials can provide a middle ground for enhancing composite environmental performance, the further optimization of bio-based material functionality regarding material properties and processability is pivotal to exploit the full potential of bio-based composites. Full article

The forests in China’s temperate semi-arid region play a significant role in water conservation, carbon storage, and biodiversity protection. An accurate estimation of their aboveground biomass (AGB) is crucial for assessing key ecological characteristics, such as forest carbon storage capacity, biodiversity, and ecological productivity. This provides a scientific basis for forest resource management and ecological conservation in this region. In this study, we extract 17 features related to the dominant species (Larix gmelinii and Betula platyphylla), including 7 vegetation indices derived from remote sensing data, 14 indices from 7 satellite bands, and 3 forest site characteristics. We then analyze the correlations between the AGB and these features. We compare the performance of AGB estimation models using linear regression (LR), polynomial regression (PR), ridge regression (RR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and random forest regression (RFR). The results show that for Larix gmelinii, the Landsat 8 bands TM4 and TM7 have a greater degree of correlation with the AGB than the other features, while for Betula platyphylla, bands TM3 and TM4 show a greater degree of correlation with the AGB, and elevation has a weaker correlation with the AGB. Although the linear regression (LR) demonstrates certain advantages for AGB estimation, particularly when the AGB values range from 40 to 70 t/ha, the RFR outperforms in overall performance, with estimation accuracies reaching 85% for Betula platyphylla and 89% for Larix gmelinii. This study reveals that both the species and environmental characteristics may significantly influence the selection of the remote sensing features for AGB estimation, and the choice of algorithm for model optimization is critical. This study innovatively extracts the features related to the dominant species in temperate forests, analyses their relationships with environmental factors, and optimizes the AGB estimation model using advanced regression techniques, offering a method that can be applied to other forest regions as well. Full article

Background: With the development of artificial intelligence, memristors have become an ideal choice to optimize new neural network architectures and improve computing efficiency and energy efficiency due to their combination of storage and computing power. In this context, spiking neural networks show the ability to resist Gaussian noise, spike interference, and AC electric field interference by adjusting synaptic plasticity. The anti-interference ability to spike neural networks has become an important direction of electromagnetic protection bionics research. Methods: Therefore, this research constructs two types of spiking neural network models with LIF model as nodes: VGG-SNN and FCNN-SNN, and combines pruning algorithm to simulate network connection damage during the training process. By comparing and analyzing the millimeter wave radar human motion dataset and MNIST dataset with traditional artificial neural networks, the anti-interference performance of spiking neural networks and traditional artificial neural networks under the same probability of edge loss was deeply explored. Results: The experimental results show that on the millimeter wave radar human motion dataset, the accuracy of the spiking neural network decreased by 5.83% at a sparsity of 30%, while the accuracy of the artificial neural network decreased by 18.71%. On the MNIST dataset, the accuracy of the spiking neural network decreased by 3.91% at a sparsity of 30%, while the artificial neural network decreased by 10.13%. Conclusions: Therefore, under the same network connection damage conditions, spiking neural networks exhibit unique anti-interference performance advantages. The performance of spiking neural networks in information processing and pattern recognition is relatively more stable and outstanding. Further analysis reveals that factors such as network structure, encoding method, and learning algorithm have a significant impact on the anti-interference performance of both. Full article

Sea anchors are crucial for stabilizing fishing vessels and improving operations, specifically for jigging vessels. Their effective performance depends on design and material choice, with the canopy material playing a key role. We compared the drainage and drying rates of sea anchor canopies made from polyamide (PA) fabric, polyester (PES) fabric, and canopies designed with alternating strips of PA and PES (PA-PES) fabric to improve sea anchor performance, work efficiency, safety, and the stability of fishing operations. PA fabric had a fast initial draining rate due to high seawater absorption but a slow drying rate, resulting in a heavy canopy. PES fabric showed optimal draining due to low seawater absorption and fast drying. PA-PES fabric showed intermediate performance. Statistical analyses revealed that Sample B performed significantly better than PA fabric and PA-PES fabric, which showed no significant differences in performance. The low absorption and fast drying properties of PES fabric enhance the handling and efficiency of sea anchors, reducing worker fatigue and improving safety. These characteristics make it an exceptional alternative to PA fabric for sea anchor canopies. Future studies should examine the roles of sea anchor canopy material and structure in fishing operation safety and efficiency. Full article

Water is one of humanity’s most fundamental needs. The demand for freshwater rises in tandem with population expansion. Only 0.01 percent of freshwater is available as surface water in lakes, wetlands, and rivers. As a result, the only choice is to extract water from the oceans. Desalination is an effective option for this. This study focused on the multi-objective design optimization, fabrication, and thermal evaluation of an integrated desalination system combining a solar still with a flat plate collector (SS-FPC). The study investigated the trade-off between two competing objectives: maximizing the efficiency of the SS-FPC system while minimizing its total cost. A numerical code is written in MATLAB to simulate the influence of changing design parameters (DPs) on the SS-FPC performances. The optimal SS-FPC design, offering low costs and a high thermal efficiency, was identified using the multi-objective colonial competitive algorithm (MOCCA). This design was subsequently fabricated and experimentally evaluated under the climatic conditions of Unaizah in Al Qassim, Saudi Arabia. The optimal numerical results were compared with both the literature values and experimental measurements. The comparison revealed strong agreement with the experimental data, with a maximum relative error of 4%. Moreover, the obtained results indicate that the optimized SS-FPC design is capable of achieving a 31% increase in efficiency and a 49% reduction in total cost relative to those reported in the literature. Full article

Soybean stands out for being the most economically important oilseed in the world. Remote sensing techniques and precision agriculture are being analyzed through research in different agricultural regions as a technological system aiming at productivity and possible low-cost reduction. Machine learning (ML) methods, together with the advent of demand for remotely piloted aircraft available on the market in the recent decade, have been conducive to remote sensing data processes. The objective of this work was to evaluate the best ML and input configurations in the classification of agronomic variables in different phenological stages. The spectral variables were obtained in three phenological stages of soybean genotypes: V8 (at 45 days after emergence—DAE), R1 (60 DAE), and R5 (80 DAE). A Sensefly eBee fixed-wing RPA equipped with the Parrot Sequoia multispectral sensor coupled to the RGB sensor was used. The Sequoia multispectral sensor with an RGB sensor acquired reflectance at wavelengths of blue (450 nm), green (550 nm), red (660 nm), near-infrared (735 nm), and infrared (790 nm). The following were used to evaluate the agronomic traits: days to maturity, number of branches, productivity, plant height, height of the first pod insertion and diameter of the main stem. The random forest (RF) model showed greater accuracy with data collected in the R5 stage, whose accuracies were close to 56 for the percentage of correct classifications (CC), close to 0.2 for Kappa, and above 0.55 for the F-score. Logistic regression (RL) and support vector machine (SVM) models showed better performance in the early reproductive stage R1, with accuracies above 55 for CC, close to 0.1 for Kappa, and close to 0.4 for the F-score. J48 performed better with data from the V8 stage, with accuracies above 50 for CC and close to 0.4 for the F-score. This reinforces that the use of different specific spectra for each model can enhance accuracy, optimizing the choice of model according to the phenological stage of the plants. Full article

The block-on-ring friction system is one of the more universal methods of measuring the tribological properties of materials, where we are able to compare the properties of different materials using a prescribed counter sample. When preparing for a test, we usually determine the parameters that appear to be optimal for testing a given material for its applications. The question remains whether and how such a choice affects the results obtained. In this paper, tests have been carried out on two commonly used plain bearing alloys, B83 and B89. The T-05 tester used for the tests allows the rotation of the counter sample to be adjusted, and, in this study, this was varied in the range of 50, 100, 150, 200 and 250 rpm. Another parameter variable in the tests was the friction distance, and two distances of 100 and 250 m were used. As the tests were carried out under technically dry friction conditions, it was possible to capture both the effect of the running-in period and the stable friction path. The tests showed that the alloys tested over the longer friction distance of 250 m responded differently to a change in parameters, with alloy B83 showing a sinusoidal variation in wear resistance with a maximum at medium speed, whereas alloy B89 is characterized by a continuous increase in wear with increasing speed. The coefficient of friction is more dependent on speed, and the basic conclusion can be drawn that increasing speed results in a lower coefficient of friction. This research has confirmed the need for the careful selection of test parameters and the difficulty of comparing results when there is even a slight difference in the test parameters used at different test centers. Full article

The equity of medical services is crucial for the quality of life of a population and the sustainable development of cities. Current research on optimizing the maximal equity of medical facilities has the following limitations: (1) In the accessibility calculation models for multiple transportation modes, the impact of factors such as public transport transfers and travel distance on the choice of transportation mode is often overlooked. (2) Existing spatial equity indicators are mostly derived from the overall study area, failing to fully consider the differences in population distribution and development gaps within different development zones inside the region. This study proposes a novel Incorporating Multiple Transportation Two-Step Floating Catchment Area (IMT-2SFCA) and a Hierarchical Theil-based Maximal Accessibility Equality model (HT-MAE) to optimize the equity of access to tuberculosis medical institutions in Wuhan. The findings reveal that, compared to single-mode transportation accessibility models, the multimodal accessibility model integrates the characteristics of four transportation modes, providing a more realistic reflection of residents’ access to medical services. The optimization results show a significant improvement in the equity of access to medical services across Wuhan and among different economic development zones, although the equity indicators in non-central urban areas have declined. The results of this study provide a theoretical basis and practical insights for alleviating the inequality of access to medical services across different urban layers. Full article

Planning in mass-customization supply and manufacturing processes is a complex process that requires continuous planning and optimization to minimize time and cost across a wide variety of choices in large production volumes. While soft computing techniques are widely used for optimizing mass-customization products, they face scalability issues when handling large datasets and rely heavily on manually defined rules, which are prone to errors. In contrast, machine learning techniques offer an opportunity to overcome these challenges by automating rule generation and improving scalability. However, their full potential has yet to be explored. This article proposes a machine learning-based approach to address this challenge, aiming to optimize both the supply and manufacturing planning phases as a practical solution for industry planning or optimization problems. The proposed approach examines supervised machine learning and deep learning techniques for manufacturing time and cost planning in various scenarios of a large-scale real-life pilot study in the bicycle manufacturing domain. This experimentation included K-Nearest Neighbors with regression and Random Forest from the machine learning family, as well as Neural Networks and Ensembles as deep learning approaches. Additionally, Reinforcement Learning was used in scenarios where real-world data or historical experiences were unavailable. The training performance of the pilot study was evaluated using cross-validation along with two statistical analysis methods: the t-test and the Wilcoxon test. These performance evaluation efforts revealed that machine learning techniques outperform deep learning methods and the reinforcement learning approach, with K-NN combined with regression yielding the best results. The proposed approach was validated by industry experts in bicycle manufacturing. It demonstrated up to a 37% reduction in both time and cost for orders compared to traditional expert estimates. Full article

This paper presents a strategic planning model aimed at optimizing the economic and environmental impacts of palm oil mill effluent (POME) treatment systems. The model determines the optimal selection of POME treatment systems to minimize the environmental impact, specifically focusing on three systems: an anaerobic digester tank system (ADT), a covered lagoon system (CL) with biogas capture, and an open pond system (OP). The model incorporates constraints related to fresh fruit bunch (FFB) production, POME generation, the biological oxygen demand (BOD), the chemical oxygen demand (COD), and carbon dioxide (CO₂) emissions. The optimization framework, formulated as a mixed-integer linear programming (MILP) model, is solved using the GAMS 40.1.0 software. Integer decision variables are used to represent the choice of POME treatment system that minimizes the environmental impact. The study specifically considers the ADT, CL, and OP systems, with the results indicating that the ADT system is the most effective in reducing the BOD, COD, and CO₂-equivalent emissions, thereby highlighting its environmental benefits. The model selects the ADT treatment system, which exhibits the lowest COD, BOD, and CO₂e emissions. Specifically, the COD registered an 85% reduction, from 84,830 mg/L to 12,725 mg/L. The BOD level was reduced by 88%, resulting in a BOD level of 41,208 mg/L to 4945 mg/L. The minimum CO₂e emissions that could be achieved was about 3173 t CO₂e per annum. This model provides a valuable tool for governmental agencies and policymakers to guide the private sector in developing environmentally sustainable POME treatment strategies. Full article

There is a growing need to predict the severity of vitamin D deficiency (VDD) through non-invasive methods due to its significant global health concerns. For vitamin D-level assessments, the 25-hydroxy vitamin D (25-OH-D) blood test is the standard, but it is often not a practical test. This study is focused on developing a machine learning (ML) model that is clinically acceptable for accurately detecting vitamin D status and eliminates the need for 25-OH-D determination while addressing overfitting. To enhance the capacity of the classification system to predict multiple classes, preprocessing procedures such as data reduction, cleaning, and transformation were used on the raw vitamin D dataset. The improved whale optimization (IWOA) algorithm was used for feature selection, which optimized weight functions to improve prediction accuracy. To gauge the effectiveness of the proposed IWOA algorithm, evaluation metrics like precision, accuracy, recall, and F1-score were used. The results showed a 99.4% accuracy, demonstrating that the proposed method outperformed the others. A comparative analysis demonstrated that the stacking classifier was the superior choice over the other classifiers, highlighting its effectiveness and robustness in detecting deficiencies. Incorporating advanced optimization techniques, the proposed method’s promise for generating accurate predictions is highlighted in the study. Full article

The combination of unmanned aerial vehicles and atomic magnetometers can be used for detection applications such as mineral resource exploration, environmental protection, and earthquake monitoring, as well as the detection of sunken ships and unexploded ordnance. A dark-resonance atomic magnetometer offers the significant advantages of a fully optical probe and omnidirectional measurement with no dead zones, making it an ideal choice for airborne applications on unmanned aerial vehicles. Enhancing the sensitivity of such atomic magnetometers is an essential task. In this study, we sought to enhance the sensitivity of a dark-state resonance atomic magnetometer. Initially, through theoretical analysis, we compared the excitation effects of coherent population trapping (CPT) resonance on the D₁ and D₂ transitions of ¹³³Cs thermal vapor. The results indicate that excitation via the D₁ line yields an increase in resonance contrast and a reduction in linewidth when compared with excitation through the D₂ line, aligning with theoretical predictions. Subsequently, considering the impact of various quantum system parameters on sensitivity, as well as their interdependent characteristics, two experimental setups were developed for empirical investigation. One setup focused on parameter optimization experiments, where we compared the linewidth and contrast of CPT resonances excited by both D₁ and D₂ transitions; this led to an optimization of atomic cell size, buffer gas pressure, and operating temperature, resulting in an ideal parameter range. The second setup was employed to validate these optimized parameters using a coupled dark-state atom magnetometer experiment, achieving approximately a 10-fold improvement in sensitivity. Full article

This study explores the optimization of rigid polyisocyanurate (PIR) foam formulations, focusing on foaming kinetics that significantly influence the foam’s microstructure and thermal insulation properties. By systematically altering components such as isocyanate, polyols, catalysts, blowing agents, and additives, this research investigates their effects on key characteristics including cell density, mechanical strength, and thermal conductivity. A statistical approach known as response surface modeling (RSM) was employed to identify relationships between formulation variables and performance metrics. The optimization aimed to enhance thermal insulation while ensuring feasibility for industrial-scale production, particularly for sandwich-type PIR panels. Two distinct formulations, with isocyanate indices of 335 and 400, were developed to assess the impact of various parameters on properties like foaming start time, gel time, and density. The results indicated that the choice of blowing agents and catalysts played a pivotal role in controlling foaming kinetics and final mechanical properties. The optimized formulations exhibited competitive thermal conductivity values (around 23.7 mW/(m·K)) and adequate compression strength (0.32 MPa), aligning closely with commercially available materials. These findings affirm the potential for enhancing production efficiency and performance consistency in the manufacturing of rigid PIR foams for insulation applications. Full article