Search Results (402)

Considering the complexity and variability inherent in maritime environments, path planning algorithms for navigation have consistently been a subject of intense research interest. Nonetheless, single-algorithm approaches often exhibit inherent limitations. Consequently, this study introduces a path planning algorithm for autonomous surface vessels (ASVs) that integrates an improved fast marching method (FMM) with the dynamic window approach (DWA) for underactuated ASVs. The enhanced FMM improves the overall optimality and safety of the determined path in comparison to the conventional approach. Concurrently, it effectively merges the local planning strengths of the DWA algorithm, addressing the safety re-planning needs of the global path when encountering dynamic obstacles, thus augmenting path tracking accuracy and navigational stability. The efficient hybrid algorithm yields notable improvements in the path planning success rate, obstacle avoidance efficacy, and path smoothness compared with the isolated employment of either FMM or DWA, demonstrating superiority and practical applicability in maritime scenarios. Through a comprehensive analysis of its control output, the proposed integrated algorithm accomplishes efficient obstacle avoidance via agile control of angular velocity while preserving navigational stability and achieves path optimization through consistent acceleration adjustments, thereby asserting its superiority and practical worth in challenging maritime environments. Full article

With the current acceleration of climate change, there is a global demand for sustainable development and carbon emission reduction. As a major link in the global supply chain, the logistics industry’s green and low-carbon transformation has become a critical breakthrough in achieving the objective of reducing carbon emissions. This study develops a multidimensional assessment index method for the green logistics level. The study selects 51 major economies worldwide from 2000 to 2022 as research subjects. The cloud model–entropy value–TOPSIS method is applied to measure the green logistics level. The results of the green logistics level are analyzed from the perspectives of developed and developing countries, and their spatiotemporal evolution characteristics are explored. The study shows that (1) the green logistics level in developed countries is relatively high, mainly due to policy-driven, core technology advantages. However, they continue to encounter issues, such as regional imbalance and excessive green costs. (2) The green logistics level in developing countries is in the middle to lower level, limited by technological dependence, outdated infrastructure, and so on. They are generally caught in a “high-carbon lock-in” situation. (3) From the perspective of time, the global level of green logistics shows a rising trend year by year. The peak of the kernel density curve of the green logistics level is characterized by an “I” shape. There is a significant disparity in each country’s green logistics level, although it is narrowing every year. (4) From the spatial perspective, the green logistics level in each country shows a rising trend year by year vertically, while the horizontal disparity between countries is enormous. The development of the green logistics level between continents is unbalanced. The study presents several recommendations, including boosting technology transfer, giving financial support, strengthening international cooperation, and developing green infrastructure, to promote the global logistics industry’s green and low-carbon transformation to accomplish sustainable development goals. Full article

Collaborative digital games and interfaces are increasingly used in both research and commercial contexts, yet little is known about how the spatial arrangement and interface sharing affect the user experience in dyadic settings. Using a two-player iPad pong game, this study compared user experiences across three collaborative gaming scenarios: face-to-face single-screen (F2F-OneS), face-to-face dual-screen (F2F-DualS), and remote dual-screen (Rmt-DualS) scenarios. Eleven dyads participated in all conditions using a within-subject design. After each session, the participants completed a 21-item user experience questionnaire and took part in brief interviews. The results from a repeated-measure ANOVA and post hoc paired t-tests showed significant scenario effects for several experience items, with F2F-OneS yielding higher engagement, novelty, and accomplishment than remote play, and qualitative interviews supported the quantitative findings, revealing themes of social presence and interaction. These results highlight the importance of spatial and interface design in collaborative settings, suggesting that both technical and social factors should be considered in multi-user interface development. Full article

This paper advances two interrelated theses. As for the first thesis, it distinguishes well-being, on the one hand, from happiness, on the other hand. As for the second thesis, it differentiates between two important facets of happiness: what this paper calls “happiness-as-tranquility” and “happiness-as-aspiration”. Actually, in order to differentiate the two facets of happiness, we first need to distinguish happiness from well-being. This is the case because happiness, after all, is a by-product of reflecting upon and ruminating over well-being. Given it is the same well-being, how could it give rise to different facets of happiness? It can only do so if we stop conflating happiness with well-being. This entails taking to task the widely accepted concept of “subjective wellbeing”. Such concept is expressly designed to obfuscate the difference between well-being and happiness. As for the two facets of happiness (the second thesis), this paper relies upon the contrast of two famous works of literature: the story of Job and the story of Faust. The contrast uncovers the criticality of the temporal dimension in the acts of reflection upon and rumination over well-being. If people reflect on past accomplishments, they experience backward-looking happiness along the Job story—i.e., happiness-as-tranquility. If people reflect on desire, they experience forward-looking happiness along the Faust story—i.e., happiness-as-aspiration. While the two facets of happiness seem contradictory, they are indeed complementary if we recognize the temporal element when one reflects upon and ruminates over well-being. Full article

In the present work, we address the theory of the lattice-gas model to the study of intercalation materials by using a novel kinetic Monte Carlo (kMC) algorithm for the simulation of an electrochemical method of everyday use in R&D laboratories: constant-current chrono-potentiometric measurements. The main aim of the present approach is to show how to use these atomistic simulations to study intercalation materials used as electrodes in alkali-ion batteries under galvanostatic conditions. The framework can be applied to related areas. To accomplish this, we explain the electrochemical background, linking the continuum scale with the microscopic events of discrete simulations. A comprehensive theoretical approach developed in a previous work is used as a reference for this aim. The galvanostatic kMC algorithm proposed is explained in detail and is subject to validation tests. The present work may serve as a basis for future implementations of kMC under galvanostatic conditions to study phenomena beyond the applicability of simulations on the continuum scale. Full article

This study aimed to produce invertase with characteristics comparable to commercial formulations using a low-cost raw material, employing environmentally friendly extraction and refinement methods. Sifted yeast, the residual baker’s yeast in industrial production, was used as raw material, and invertase in the yeast cell was extracted by ultrasonication and purified by micro- and ultra-filtration (MF and UF) methods. Subjecting the crude enzyme extract to MF followed by UF resulted in increased activity and specific activity. Through this process, the enzyme activity increased from 153 IU/mL to 691 IU/mL. The purified enzyme was lyophilized and the production of invertase at the laboratory scale was accomplished. The obtained enzyme was found to be stable at pH 5 and within the temperature range of 30–40 °C. It retained its activity for 60 days at −18 °C, whereas a 20% loss in activity was observed at +4 °C over the same period. As a result, it was demonstrated that invertase enzyme can be produced from a low-cost raw material which is considered as waste in the industry. To pass to the commercial production stage, processing of higher amounts of raw material by preventing foaming and heating problems in ultrasonication and scale-up studies testing the permeability properties of different membrane systems at a pilot-scale are necessary. Full article

Background: Traumatic optic neuropathy (TON) represents a major cause of vision loss worldwide, and treatment options are limited. Here, we study whether methylene blue (MB), a free radical scavenger, is able to prevent morphological and electrophysiological hallmarks of neuropathy in an animal model of TON. Methods: The left eyes of Wistar rats were subjected to intraorbital nerve crush (IONC) while the right ones were sham operated. The group of rats treated with MB (n = 16) received five intraperitoneal injections with 2.0 mg/kg MB in the 24 h following IONC while the control group (n = 16) received just vehicle (PBS) as a control. Twenty-one days after surgery, scotopic full field (scERG), scotopic oscillatory potentials (OP), photopic full field (phERG) and pattern (PERG) electroretinography were performed for retinal function assessment. Furthermore, the number of cell nuclei in the ganglion cell layer (GCL) was recorded in post mortem histological sections. Results: IONC induced very significant reductions in electrophysiological parameters including scotopic a- and b-wave, OPs, photopic b-wave, PhNR amplitude and N2 amplitude. In addition, it also generated a significant prolongation of the N2 implicit time, indicating a profound impact on retinal function. This was further corroborated by a very significant reduction in the number of neuronal nuclei in the GCL, suggesting an intense loss and functional impairment of retinal ganglion cells. MB treatment was able to prevent, partially or completely, all those parameters, indicating the efficiency of such approach. Conclusions: Since MB is already approved for clinical use and presents a high safety profile, it could be repurposed as a neuroprotective drug for ophthalmological applications once proper phase 2 clinical trials are accomplished. Full article

Background: Around 1 in 59 individuals is diagnosed with Autism Spectrum Disorder (ASD), according to CDS statistics. Conventionally, ASD has been diagnosed using functional brain regions, regions of interest, or multi-tissue-based training in artificial intelligence models. The objective of the exhibit study is to develop an efficient deep learning network for identifying ASD using structural magnetic resonance imaging (MRI)-based brain scans. Methods: In this work, we developed a VGG-based deep learning network capable of diagnosing autism using whole brain gray matter (GM) tissues. We trained our deep network with 132 MRI T1 images from normal controls and 140 MRI T1 images from ASD patients sourced from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Results: The number of participants in both ASD and normal control (CN) subject groups was not statistically different (p = 0.23). The mean age of the CN subject group was 14.62 years (standard deviation: 4.34), and the ASD group had mean age of 14.89 years (standard deviation: 4.29). Our deep learning model accomplished a training accuracy of 97% and a validation accuracy of 96% over 50 epochs without overfitting. Conclusions: To the best of our knowledge, this is the first study to use GM tissue alone for diagnosing ASD using VGG-Net. Full article

Because of their distinctive toroidal blade configuration, toroidal propellers can improve propulsion efficiency, reduce underwater noise, and enhance blade stability and strength. In recent years, they have emerged as an extremely promising novel underwater propulsion technology. To investigate their working mechanism, a geometric model of the toroidal propeller was initially established, and an unsteady numerical calculation model was constructed based on the sliding mesh technique. Subsequently, with the E779A conventional propeller as the research subject, the numerical model was verified, and a grid independence test was accomplished. Thereafter, the hydrodynamic performance of the toroidal propeller under diverse advance coefficients was analyzed based on the numerical model, and open water characteristic curves were established. Eventually, the surface pressure distribution, velocity field, and vorticity field of the toroidal propeller under various working conditions were studied. The outcomes demonstrate that the toroidal propeller attains the maximum propulsion efficiency at high advance coefficients, possesses a broad range of working condition adaptability, and is more applicable to high-speed vessels. At low advance coefficients, the toroidal propeller exhibits a relatively strong thrust performance, with the thrust generated by the front propeller being greater than that generated by the rear propeller, and the pressure peak emerges at the leading edge of the transition section of the front blade. The analysis of the velocity field indicates that its acceleration effect is superior to that of the conventional propeller. The analysis of the vorticity field reveals that the trailing vortices shed from the leading edge of the transition section of the front propeller merge and develop with the tip vortices, resulting in a more complex vortex structure. This research clarifies the working mechanism of the toroidal propeller through numerical simulation methods, providing an important basis for its performance optimization. Full article

Bird strikes pose a significant threat to aviation safety, particularly affecting the wing structures of aircraft. This research aims to design and analyze the impact of bird strikes on wing structures using response surface method and metaheuristics (MHs), which are used to explore various risk minimization and damage mitigation techniques. The optimization problem is the minimization of the maximum von Mises stress of aircraft wing structure against bird strike that is subject to displacement and stress constraints. The design variables include skin and rib thickness, as well as sweep angle. Difficulty due to embedded bird strike simulation and optimization design can be alleviated using a response surface method (RSM). The regression technique in the RSM of the data can reach our goal of model fitting with a higher R² until 0.9951 and 0.9919 are obtained for the displacement and von Mises stress model, respectively. The response surface function of the displacement and von Mises stress are related to skin thickness, while sweep angles rather than rib thickness have a greater impact on both design variables. The optimized design of the design variables is performed using MHs, which are TLBO, JADE, and PBIL. The comparative result of MHs can conclude that the PBIL outperformed others in all descriptive statistics. The optimized design results revealed that the optimum solution can release better energy due to bird strike with the highest limit of skin thickness, moderate rib thickness, and less than half of the sweep angle. The results are in accordance with the response surface function analysis. In conclusion, the optimized design of the aircraft wing structure against bird strike can be accomplished with our proposed technique. Full article

The present work aims to verify whether the incremental hole-drilling technique (IHD), a widely accepted technique, is suitable for determining residual stresses in AISI 316L samples obtained by selective laser melting (SLM). The thermo-mechanical effects of cutting during the application of this technique can induce unwanted residual stresses due to the relatively low thermal conductivity of this material, leading to erroneous results. To accomplish this aim, a hybrid experimental-numerical method was implemented to analyze the ability of IHD to determine an imposed stress state. Experimentally, samples were subjected to a tensile calibration stress using a horizontal tensile test machine. To eliminate pre-existing residual stress, the samples were subjected to differential loads, instead of absolute ones. In this way, experimental strain-depth relaxation curves related to the imposed calibration stress were obtained. Based on the experimental data, IHD was numerically simulated using the finite element method. Numerical strain-depth relaxation curves, related to the same calibration stress used in the experimental study, were obtained. The comparison between the experimental and numerical strain-depth relaxation curves, as well as the stresses calculated using the so-called integral method for determining stresses via IHD, shows that IHD is a suitable technique for measuring residual stresses in additively manufactured AISI 316L samples. Full article

This article outlines the method of creating electrodes for electrochemical sensors using hybrid nanostructures composed of graphene and conducting polymers with insertion of gold nanoparticles. The technology employed for graphene dispersion and support stabilization was based on the chemical vapor deposition technique followed by electrochemical delamination. The method used to obtain hybrid nanostructures from graphene and conductive polymers was drop-casting, utilizing solutions of P3HT, PANI-EB, and F8T2. Additionally, the insertion of gold nanoparticles utilized an innovative dip-coating technique, with the graphene-conducting polymer frameworks submerged in a HAuCl₄/2-propanol solution and subsequently subjected to controlled heating. The integration of gold nanoparticles differs notably, with P3HT showing the least adhesion of gold nanoparticles, while PANI-EB exhibits the highest. An inkjet printer was employed to create electrodes with metallization accomplished through the use of commercial silver ink. Notable variations in roughness (grain size) result in unique behaviors of these structures, and therefore, any potential differences in the sensitivity of the generated sensing structures can be more thoroughly understood through this spatial arrangement. The electrochemical experiments utilized a diluted sulfuric acid solution at three different scan rates. The oxidation and reduction potentials of the structures seem fairly alike. Nevertheless, a notable difference is seen in the anodic and cathodic current densities, which appear to be largely influenced by the active surface of gold nanoparticles linked to the polymeric grains. The graphene–PANI-EB structure with Au nanoparticles showed the highest responsiveness and will be further evaluated for biomedical applications. Full article

The recent improvements in streamflow measurement approaches have boosted the reliability and accuracy of river flow measurement. In this study, long-term measurements of river discharge in the Tokoro River, Japan, were conducted. The key objective of this work is to investigate the extent of river flow measurement in a very shallow and narrow silt stream using the fluvial acoustic tomography system (FAT). Despite the preliminary nature of the measurement results, the recorded data were subject to analysis from three different outlooks. First, examinations were performed under very shallow and high-water conditions. Second, examinations were performed using double acoustic frequency. Third, examinations were performed using multiple independent flow datasets. As a new achievement in terms of advanced monitoring capabilities, it was documented that the measurement by the FAT was possible even in extremely shallow conditions. However, the minimum water depth along the measured cross-section must be ≥9 cm. Moreover, the FAT system demonstrated its capability to monitor streamflow in high water levels. In addition, it was found that using high transmission frequency can provide shorter wavelengths, permitting better spatial resolution and higher velocity resolutions and hence desirable measurement accuracy. Nevertheless, measurements in the presence of high suspended sediment particles were lacking. Alternatively, a lower transmission frequency offers a longer wavelength, which might be less sensitive to small-scale variations and results in an imprecise degree of measurements. Nonetheless, measurements can be accomplished even during the mobilization of a high concentration of suspended sediment matters. Finally, using multiple independent streamflow measurement records, the results proved that the flow measured by the FAT system was in very good agreement with the records acquired using sophisticated measurement approaches such as HADCP and STIV with a very low range of uncertainty. Full article

Background/Objectives: Burnout is a critical concern among healthcare professionals, particularly during crises such as the COVID-19 pandemic. This study investigated burnout levels among forensic medicine and pathology personnel at three distinct phases: the early pandemic period (Phase 1—September 2020), the peak of the pandemic (Phase 2—October 2021), and the post-pandemic period (Phase 3—October 2024). Methods: A total of 37 participants employed in forensic medicine and pathology departments completed the Maslach Burnout Inventory (MBI). A one-way repeated measures ANOVA was conducted to assess within-subject differences over time. Normality and sphericity were tested using the Shapiro–Wilk test and Mauchly’s test, with the Greenhouse-Geisser correction. Post hoc Bonferroni-adjusted comparisons identified significant differences, and partial eta squared (η²) was reported for effect sizes. Results: Results showed significant fluctuations in burnout levels across the three phases. Emotional exhaustion and low personal accomplishment peaked during Phase 2, with slight reductions observed in Phase 3. Gender differences were evident, with females reporting higher EE levels and males exhibiting higher depersonalization across all phases. Marital and parental status also influenced burnout levels, with unmarried individuals and those without children showing higher burnout scores. Medical doctors experienced the highest burnout levels among professional roles, while auxiliary staff showed significant challenges in the PA subscale. Conclusions: The COVID-19 pandemic was pivotal in exacerbating burnout levels due to increased workload, crisis decision-making, and emotional toll. Although the sample size is limited, these findings underscore the importance of implementing targeted interventions to mitigate burnout among forensic and pathology personnel, especially during healthcare emergencies. Gender-based differences in burnout suggest the necessity of specific workplace well-being strategies, while the protective role of family status demonstrates the importance of work-life balance policies. The persistence of psychological distress after a medical crisis calls for long-term monitoring and support programs. There is a need for improved workload distribution, peer support networks, and mental health training to build resilience among forensic and pathology personnel. Full article

With the rapid development of Internet of Things (IoT) technology, the number of devices connected to the network is exploding. How to improve the performance of edge devices has become an important challenge. Research on quality evaluation algorithms for brain tumor images remains scarce within symmetry edge intelligence systems. Additionally, the data volume in brain tumor datasets is frequently inadequate to support the training of neural network models. Most existing non-reference image quality assessment methods are based on natural statistical laws or construct a single-network model without considering visual perception characteristics, resulting in significant differences between the final evaluation results and subjective perception. To address these issues, we propose the AM-VGG-IQA (Attention Module Visual Geometry Group Image Quality Assessment) algorithm and extend the brain tumor MRI dataset. Visual saliency features with attention mechanism modules are integrated into AM-VGG-IQA. The integration of visual saliency features brings the evaluation outcomes of the model more in line with human perception. Meanwhile, the attention mechanism module cuts down on network parameters and expedites the training speed. For the brain tumor MRI dataset, our model achieves 85% accuracy, enabling it to effectively accomplish the task of evaluating brain tumor images in edge intelligence systems. Additionally, we carry out cross-dataset experiments. It is worth noting that, under varying training and testing ratios, the performance of AM-VGG-IQA remains relatively stable, which effectively demonstrates its remarkable robustness for edge applications. Full article