Applied Sciences

24 pages, 5307 KiB

Open AccessArticle

AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis

by Tahesin Samira Delwar, Mangal Singh, Sayak Mukhopadhyay, Akshay Kumar, Deepak Parashar, Yangwon Lee, Md Habibur Rahman, Mohammad Abrar Shakil Sejan and Jee Youl Ryu

Appl. Sci. 2025, 15(3), 1102; https://doi.org/10.3390/app15031102 (registering DOI) - 22 Jan 2025

Abstract

The significant number of road traffic accidents caused by fatigued drivers presents substantial risks to the public’s overall safety. In recent years, there has been a notable convergence of intelligent cameras and artificial intelligence (AI), leading to significant advancements in identifying driver drowsiness. [...] Read more.

The significant number of road traffic accidents caused by fatigued drivers presents substantial risks to the public’s overall safety. In recent years, there has been a notable convergence of intelligent cameras and artificial intelligence (AI), leading to significant advancements in identifying driver drowsiness. Advances in computer vision technology allow for the identification of driver drowsiness by monitoring facial expressions such as yawning, eye movements, and head movements. These physical indications, together with assessments of the driver’s physiological condition and behavior, aid in assessing fatigue and lowering the likelihood of drowsy driving-related incidents. This study presents an extensive variety of meticulously designed algorithms that were thoroughly analyzed to assess their effectiveness in detecting drowsiness. At the core of this attempt lay the essential concept of feature extraction, an efficient technique for isolating facial and ocular regions from a particular set of input images. Following this, various deep learning models, such as a traditional CNN, VGG16, and MobileNet, facilitated detecting drowsiness. Among these approaches, the MobileNet model was a valuable choice for drowsiness detection in drivers due to its real-time processing capability and suitability for deployment in resource-constrained environments, with the highest achieved accuracy of 92.75%. Full article

(This article belongs to the Special Issue Advancements in Deep Learning and Its Applications)

27 pages, 1483 KiB

Open AccessArticle

Carbon Emission Reduction in Traffic Control: A Signal Timing Optimization Method Based on Rainbow DQN

by Juan Lv, Zhaowei Wang and Jianxiao Ma

Appl. Sci. 2025, 15(3), 1101; https://doi.org/10.3390/app15031101 (registering DOI) - 22 Jan 2025

Abstract

To improve intersection traffic flow and reduce vehicle energy consumption and emissions at intersections, a signal optimization method based on deep reinforcement learning (DRL) is proposed. The algorithm uses Rainbow DQN as the core framework, incorporating vehicle position, speed, and acceleration information into [...] Read more.

To improve intersection traffic flow and reduce vehicle energy consumption and emissions at intersections, a signal optimization method based on deep reinforcement learning (DRL) is proposed. The algorithm uses Rainbow DQN as the core framework, incorporating vehicle position, speed, and acceleration information into the state space. The reward function simultaneously considers two objectives: reducing vehicle waiting times and minimizing carbon emissions, with the vehicle queue length as a weighted factor. Additionally, an ACmix module, which integrates self-attention mechanisms and convolutional layers, is introduced to enhance the model’s feature extraction and information representation capabilities, improving computational efficiency. The model is tested using an actual intersection as the study object, with a signal intersection simulation built in SUMO. The proposed approach is compared with traditional Webster signal timing, actuated signal timing, and control strategies based on DQN and D3QN models. The results show that the proposed strategy, through real-time signal timing adjustments, reduces the average vehicle waiting time by approximately 27.58% and the average CO₂ emissions by about 7.34% compared with the actuated signal timing method. A comparison with DQN and D3QN models further demonstrates the superiority of the proposed model, achieving a 15% reduction in average waiting time and a 6.5% reduction in CO₂ emissions. The model’s applicability is validated under various scenarios, including different proportions of electric vehicles and traffic volumes. This study aims to provide a flexible signal control strategy to enhance intersection vehicle flow and reduce carbon emissions. It offers a reference for the development of green, intelligent transportation systems and holds practical significance for promoting urban carbon reduction efforts. Full article

(This article belongs to the Section Transportation and Future Mobility)

33 pages, 3972 KiB

Open AccessArticle

Integrating Ground and UAV Mapping for GIS-Based Application of the Flash Flood Impact Severity Scale (FFISS) for the 2009 and 2020 Evia (Greece) Flash Floods

by Nafsika Ioanna Spyrou, Michalis Diakakis, Spyridon Mavroulis, Georgios Deligiannakis, Emmanuel Andreadakis, Christos Filis, Evelina Kotsi, Zacharias Antoniadis, Maria Melaki, Marilia Gogou, Katerina-Navsika Katsetsiadou, Eirini-Spyridoula Stanota, Emmanuel Skourtsos, Emmanuel Vassilakis and Efthymios Lekkas

Appl. Sci. 2025, 15(3), 1100; https://doi.org/10.3390/app15031100 (registering DOI) - 22 Jan 2025

Abstract

Flash floods have been the cause of some of the most devastating events worldwide. The wide diversity of the effects, as well as the variety in the severity of the impacts, lead to major obstacles in obtaining a realistic understanding of the damages [...] Read more.

Flash floods have been the cause of some of the most devastating events worldwide. The wide diversity of the effects, as well as the variety in the severity of the impacts, lead to major obstacles in obtaining a realistic understanding of the damages caused by a flood event, thus hampering at the same time our ability to predict future impacts. In assessing flood impacts and their severity, most existing methods use a qualitative characterization (e.g., major, catastrophic, etc.) or view the impacts from a single viewpoint or discipline (e.g., economic losses). In this study, we apply the Flash Flood Impact Severity Scale (FFISS) to assess, map, and classify the impacts of two flash floods from the Lilas River in Greece in 2009 and 2020. This application aims to discuss the different severity levels in terms of how one flood can affect the impacts of the next event. The methodology encompasses comprehensive field research, including the collection of ground-based and aerial observations utilizing UAV technology to document the impacts. These observations are subsequently georeferenced, followed by application of the Flash Flood Impact Severity Scale (FFISS) and generation of detailed maps to assess and evaluate the severity of the impacts associated with the two flood events. The results show that despite the higher water stage of the second flood, the impacts in previously hit areas indicate lower severity values, attributed to the gradual adaptation of the community and its infrastructure, as well as significant local widening of the river channel. On the contrary, high severity remains an issue in newly flooded areas during the second event. Overall, the application of the FFISS can show the spatial patterns of severity impacts, providing insights into the nature of floods locally but also indicating a potential reduction in the overall risk in the post-flood period. Full article

(This article belongs to the Section Earth Sciences)

17 pages, 8636 KiB

Open AccessArticle

Image-Based Tactile Deformation Simulation and Pose Estimation for Robot Skill Learning

by Chenfeng Fu, Longnan Li, Yuan Gao, Weiwei Wan, Kensuke Harada, Zhenyu Lu and Chenguang Yang

Appl. Sci. 2025, 15(3), 1099; https://doi.org/10.3390/app15031099 - 22 Jan 2025

Abstract

The TacTip is a cost-effective, 3D-printed optical tactile sensor commonly used in deep learning and reinforcement learning for robotic manipulation. However, its specialized structure, which combines soft materials of varying hardnesses, makes it challenging to simulate the distribution of numerous printed markers on [...] Read more.

The TacTip is a cost-effective, 3D-printed optical tactile sensor commonly used in deep learning and reinforcement learning for robotic manipulation. However, its specialized structure, which combines soft materials of varying hardnesses, makes it challenging to simulate the distribution of numerous printed markers on pins. This paper aims to create an interpretable, AI-applicable simulation of the deformation of TacTip under varying pressures and interactions with different objects, addressing the black-box nature of learning and simulation in haptic manipulation. The research focuses on simulating the TacTip sensor’s shape using a fully tunable, chain-based mathematical model, refined through comparisons with real-world measurements. We integrated the WRS system with our theoretical model to evaluate its effectiveness in object pose estimation. The results demonstrated that the prediction accuracy for all markers across a variety of contact scenarios exceeded 92%. Full article

(This article belongs to the Special Issue Recent Advances in Autonomous Systems and Robotics, 2nd Edition)

24 pages, 1401 KiB

Open AccessArticle

Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region

by Wei Wan, Jingshan Bo and Da Peng

Appl. Sci. 2025, 15(3), 1098; https://doi.org/10.3390/app15031098 - 22 Jan 2025

Abstract

This study collects all strong motion records from 134 seismic events in the Loess Plateau region of China between 2020 and 2023. The corresponding ground motion attenuation dataset is constructed through data processing and screening, including 1149 soil horizontal strong ground motion records. [...] Read more.

This study collects all strong motion records from 134 seismic events in the Loess Plateau region of China between 2020 and 2023. The corresponding ground motion attenuation dataset is constructed through data processing and screening, including 1149 soil horizontal strong ground motion records. Based on this dataset, two ground motion attenuation models are developed. Specifically, through magnitude grouping and weighted processing, two-step fitting methods are used to establish prediction equations for the peak ground acceleration (PGA), effective peak acceleration (EPA), and peak ground velocity (PGV) at the horizontal soil layers. These equations can estimate ground motion parameters with magnitudes ranging from M_S 3.0 to M_S 6.5 and epicentral distances between 0 and 100 km. Furthermore, attenuation curves are plotted based on the prediction equations. A comparison between the actual observed values and the predicted curves and an evaluation of the fitting residuals assess our models’ fitting performance. The attenuation characteristics of horizontal ground motion parameters in the Loess Plateau region are systematically summarized. Full article

(This article belongs to the Section Earth Sciences)

23 pages, 752 KiB

Open AccessArticle

Distributed Inference Models and Algorithms for Heterogeneous Edge Systems Using Deep Learning

by Qingqing Yuan and Zhihua Li

Appl. Sci. 2025, 15(3), 1097; https://doi.org/10.3390/app15031097 - 22 Jan 2025

Abstract

Computations performed by using convolutional layers in deep learning require significant resources; thus, their scope of applicability is limited. When deep neural network models are employed in an edge-computing system, the limited computational power and storage resources of edge devices can degrade inference [...] Read more.

Computations performed by using convolutional layers in deep learning require significant resources; thus, their scope of applicability is limited. When deep neural network models are employed in an edge-computing system, the limited computational power and storage resources of edge devices can degrade inference performance, require a considerable amount of computation time, and result in increased energy consumption. To address these issues, this study presents a convolutional-layer partitioning model, based on the fused tile partitioning (FTP) algorithm, for enhancing the distributed inference capabilities of edge devices. First, a resource-adaptive workload-partitioning optimization model is designed to promote load balancing across heterogeneous edge systems. Next, the FTP algorithm is improved, leading to a new layer-fused partitioning method that is used to solve the optimization model. The results of simulation experiments show that the proposed convolutional-layer partitioning method effectively improves the inference performance of edge devices. When five edge devices are used, the speed of the proposed method becomes 1.65–3.48 times those of existing algorithms. Full article

(This article belongs to the Special Issue Edge Intelligence and Green Communication Networks for IoT)

21 pages, 1231 KiB

Open AccessArticle

Impact of Arch Dam Cracking on Monitoring Data

by André Conde, Miguel Á. Toledo and Eduardo Salete

Appl. Sci. 2025, 15(3), 1096; https://doi.org/10.3390/app15031096 - 22 Jan 2025

Abstract

It is well established that the proper functioning of dams plays an important role in society. Therefore, as a fundamental part of their safety, the identification of anomalies by monitoring their deformations must be given special consideration. The ability to detect cracks based [...] Read more.

It is well established that the proper functioning of dams plays an important role in society. Therefore, as a fundamental part of their safety, the identification of anomalies by monitoring their deformations must be given special consideration. The ability to detect cracks based on monitoring device records depends largely on the number of devices included in the dam’s original design, their arrangement, and their accuracy. This paper aims to help determine the appropriate spacing and accuracy of the devices for detecting a given crack. For this purpose, numerical simulations of cracks that match the most likely cracks in arch dams are performed based on a real dam, ensuring that such cracks result in an opening wide enough to be of concern. Afterward, the study analyzes the impact of the length and depth of the crack and the accuracy and position of the monitoring device on the ability of the system to detect the crack. Full article

21 pages, 6218 KiB

Open AccessArticle

Multi-GPU Acceleration for Finite Element Analysis in Structural Mechanics

by David Herrero-Pérez and Humberto Martínez-Barberá

Appl. Sci. 2025, 15(3), 1095; https://doi.org/10.3390/app15031095 - 22 Jan 2025

Abstract

This work evaluates the computing performance of finite element analysis in structural mechanics using modern multi-GPU systems. We can avoid the usual memory limitations when using one GPU device for many-core computing using multiple GPUs for scientific computing. We use a GPU-awareness MPI [...] Read more.

This work evaluates the computing performance of finite element analysis in structural mechanics using modern multi-GPU systems. We can avoid the usual memory limitations when using one GPU device for many-core computing using multiple GPUs for scientific computing. We use a GPU-awareness MPI approach implementing a suitable smoothed aggregation multigrid for preconditioning an iterative distributed conjugate gradient solver for GPU computing. We evaluate the performance and scalability of different models, problem sizes, and computing resources. We take an efficient multi-core implementation as the reference to assess the computing performance of the numerical results. The numerical results show the advantages and limitations of using distributed many-core architectures to address structural mechanics problems. Full article

(This article belongs to the Special Issue Data Structures for Graphics Processing Units (GPUs))

► Show Figures

Figure 1

15 pages, 6661 KiB

Open AccessArticle

The Development of a Novel Aluminosilicate Catalyst Fabricated via a 3D Printing Mold for Biodiesel Production at Room Temperature

by Selene Díaz-González, Karina Elvira Rodríguez and Laura Díaz

Appl. Sci. 2025, 15(3), 1094; https://doi.org/10.3390/app15031094 - 22 Jan 2025

Abstract

Biodiesel production has gained attention as a sustainable alternative to fossil fuels, but challenges related to catalyst recovery and energy consumption remain. In this study, a novel lithium-impregnated aluminosilicate catalyst (LiSA) was developed using a 3D-printed mold, providing precise control over its structure [...] Read more.

Biodiesel production has gained attention as a sustainable alternative to fossil fuels, but challenges related to catalyst recovery and energy consumption remain. In this study, a novel lithium-impregnated aluminosilicate catalyst (LiSA) was developed using a 3D-printed mold, providing precise control over its structure to optimize performance. The structured catalyst featured a cylindrical shape with multiple circular channels, enhancing fluid dynamics and reactant interaction in a fixed-bed reactor. Catalyst characterization by SEM, TGA, XRD, and ICP-MS confirmed high thermal stability and uniform pore distribution. Jatropha curcas oil was used as feedstock, with diethyl ether (DEE) acting as a cosolvent to improve methanol solubility and enable transesterification at room temperature. The process achieved a high fatty acid methyl ester (FAME) yield, averaging 97.1% over 508 min of continuous operation, demonstrating the catalyst’s stability and sustained activity. By reducing mass transfer limitations and energy demands, this approach highlights the potential of 3D-printed catalysts to advance sustainable biodiesel production, offering a scalable and efficient pathway for green energy technologies. Full article

(This article belongs to the Special Issue Industrial Chemical Engineering and Organic Chemical Technology)

21 pages, 1868 KiB

Open AccessArticle

Regulated Power Supply with High Power Factor for Hyperspectral Imaging Applications

by Jose M. Cabrera-Peña, Raquel Leon, Samuel Ortega, Himar Fabelo, Eduardo Quevedo and Gustavo M. Callico

Appl. Sci. 2025, 15(3), 1093; https://doi.org/10.3390/app15031093 - 22 Jan 2025

Abstract

Illumination is a crucial factor in hyperspectral imaging systems. In this respect, this work is focused on analyzing the influence of the light power source in acquiring hyperspectral images. To this end, a custom regulated power supply was designed and developed. This power [...] Read more.

Illumination is a crucial factor in hyperspectral imaging systems. In this respect, this work is focused on analyzing the influence of the light power source in acquiring hyperspectral images. To this end, a custom regulated power supply was designed and developed. This power supply was then integrated into a hyperspectral acquisition system, and several light stability measurements were conducted. Finally, several parameters related to the stability of the light produced by those systems were extracted using image analysis techniques, and a statistical comparison among the different power supplies was performed. Two commercial power supplies were also analyzed under the same experimental conditions and compared with the proposed power supply. The hyperspectral measurements were conducted using light transmission and reflectance. The results indicate that the proposed power supply performs better than or at least as well as commercial power supplies in terms of light stability. Additionally, this study shows the impact of power supply design on the stability and quality of hyperspectral illumination, especially concerning the signal-to-noise ratio (SNR) across different spectral bands. It is shown that optimizing the design of the power supply could improve light stability in hyperspectral imaging applications. Full article

(This article belongs to the Special Issue Advanced Power Electronics Converters: Design, Control and Applications)

14 pages, 727 KiB

Open AccessArticle

Intake of Phytoestrogens and Estrogenic Effect of the Diet of Female University Students in Mexico

by Diana Espino-Rosales, Leticia Heras-Gonzalez, Maria J. Jimenez-Casquet, Nicolás Olea, Fátima Olea-Serrano and Miguel Mariscal-Arcas

Appl. Sci. 2025, 15(3), 1092; https://doi.org/10.3390/app15031092 - 22 Jan 2025

Abstract

Phytoestrogens are components naturally occurring in plants and include many foods that are part of the regular diet of animals and humans. Phytoestrogens are xenoestrogens of plant origin that are not produced in the endocrine system. Phytoestrogens can act as either agonists or [...] Read more.

Phytoestrogens are components naturally occurring in plants and include many foods that are part of the regular diet of animals and humans. Phytoestrogens are xenoestrogens of plant origin that are not produced in the endocrine system. Phytoestrogens can act as either agonists or antagonists, depending on their tissue concentrations and the levels of endogenous estrogens at various life stages. The aim was to evaluate the intake of phytoestrogens and the estrogenic effect of the diet of women at university in Chihuahua (Mexico). In total, 400 female university students individually filled out a food frequency questionnaire (FFQ) that included 120 foods. Estimates of the intake of phytoestrogen (genistein, daidzein, biochanin A, formononetin, matairesinol, coumestrol, enterolactone, secoisoresinol, enterodiol) in the subjects’ daily diet were based on published reports. Quantification of phytoestrogens was expressed in µg day⁻¹. The estrogenic effect of those compound identified according to the foods consumed was estimated using the in vitro E-SCREN test. SPSS v.22.0 (IBM, Chicago, IL, USA) was applied for statistical analysis following descriptive analysis and stepwise regression. p < 0.050 was taken as significant. The results of intake show that the majority of isoflavones are formononetin (median 110.60 (μg day⁻¹) and their estrogenic activity is 4.11 Eq. E2 (pmol day⁻¹); the majority of lignans are enterolactone (median 147.24 (μg day⁻¹), and their estrogenic activity is 4.94 Eq. E2 (pmol day⁻¹). The total phytoestrogen estrogenic effect is measured in pM of E2, with a mean of 28.28 (SD = 23.97) and median of 21.50. The mean consumption of phytoestrogens in Mexican university students is similar to the consumption found in similar studies in the United States, England, Germany, and Spain (<1 mg day⁻¹). Phytoestrogens can be beneficial in adult women during perimenopause and menopause due to their estrogenic effects, but they are less recommended for women in the fertile stage, as, for example, in the study presented here, because they could function as endocrine disruptors. They are not recommended as dietary supplements for young women or pregnant women. Full article

(This article belongs to the Section Applied Biosciences and Bioengineering)

► Show Figures

Figure 1

12 pages, 1218 KiB

Open AccessArticle

Sugar Maple and Red Maple Face-Off: Which Produces More and Sweeter Sap?

by Aya Garfa, Roberto Silvestro, Sara Yumi Sassamoto Kurokawa, Sergio Rossi, Annie Deslauriers and Serge Lavoie

Appl. Sci. 2025, 15(3), 1091; https://doi.org/10.3390/app15031091 - 22 Jan 2025

Abstract

Among the species used for syrup production, sugar maple (Acer saccharum Marsh.) is preferred by producers, while red maple (Acer rubrum L.) is considered less productive in terms of sap yield and sugar content. This study aims to measure the volume [...] Read more.

Among the species used for syrup production, sugar maple (Acer saccharum Marsh.) is preferred by producers, while red maple (Acer rubrum L.) is considered less productive in terms of sap yield and sugar content. This study aims to measure the volume and physicochemical characteristics of the sap produced from two red maples and two sugar maples during the 2023 sugar season in a commercial sugarbush in Laterrière (QC, Canada). Sap exudation was measured continuously with the gravity method using automatic rain gauges. Sap production was discontinuous and heterogeneous, reaching 2.6 L during the most productive day. No significant difference was found in the daily production between species, but we observed a difference in the cumulative sap production (7 L in red maple vs. 13.5 L in sugar maple) due to a longer period of sap exudation in the latter. Despite daily variations in pH, Brix values, sucrose concentration, osmolality, and conductivity, no differences in physicochemical characteristics were detected between species. Full article

(This article belongs to the Section Ecology Science and Engineering)

24 pages, 3186 KiB

Open AccessArticle

Evaluation of Ototoxic Effects of Cisplatin in a Rat Model: A Dose–Response Study

by Filippo Hellies, Silvia Fracaro, Giovanni Pintus, Edi Simoni, Erica Gentilin, Gino Marioni, Alessandro Martini, Piero Nicolai, Elisabetta Zanoletti, Giovanna Albertin and Laura Astolfi

Appl. Sci. 2025, 15(3), 1090; https://doi.org/10.3390/app15031090 - 22 Jan 2025

Abstract

Cisplatin (cis-diclorodiamminoplatin, CDDP) is a common chemotherapeutic agent for solid tumors, but its use is limited by severe side effects such as ototoxicity and nephrotoxicity. Variability in CDDP dosing and administration, along with high mortality and sensitivity in animal models, complicates experimental approaches. [...] Read more.

Cisplatin (cis-diclorodiamminoplatin, CDDP) is a common chemotherapeutic agent for solid tumors, but its use is limited by severe side effects such as ototoxicity and nephrotoxicity. Variability in CDDP dosing and administration, along with high mortality and sensitivity in animal models, complicates experimental approaches. This study aimed to evaluate ototoxic damage in rats by comparing a single bolus versus three divided CDDP injections, also considering nephrotoxic effects. Twenty-four Sprague-Dawley rats were divided into three groups: eight received a single intraperitoneal injection of CDDP (14 mg/kg), eight received three injections (4.6 mg/kg/day), and eight were untreated controls. All CDDP-treated rats showed significant high-frequency hearing loss and morphological damage, including cochlear outer hair cell loss and renal glomerular atrophy with proximal tubule necrosis. Oxidative stress markers (nitrotyrosine and SOD1 expression) confirmed cochlear and renal alterations. Notably, the single bolus group had a 25% mortality rate and significant weight loss among survivors, unlike the other groups. This study introduces the novel finding that cumulative administration of three doses reduces mortality and weight loss while maintaining similar ototoxic and nephrotoxic effects. Therefore, cumulative administration is recommended for future studies to mitigate ototoxic and nephrotoxic damage, offering a potentially improved protocol for the administration of cisplatin. Full article

(This article belongs to the Section Applied Biosciences and Bioengineering)

11 pages, 1123 KiB

Open AccessArticle

Relationship Between Isometric Mid-Thigh Pull Force, Sprint Acceleration Mechanics and Performance in National-Level Track and Field Athletes

by Ioannis Stavridis, Maria Zisi, Gavriil G. Arsoniadis, Gerasimos Terzis, Charilaos Tsolakis and Giorgos P. Paradisis

Appl. Sci. 2025, 15(3), 1089; https://doi.org/10.3390/app15031089 - 22 Jan 2025

Abstract

This study aimed to examine the relationships between isometric mid-thigh pull maximal force (IMTPF), sprint mechanics, and performance. Fifteen national-level track and field athletes (sprinters and hurdlers) performed three maximal-effort isometric mid-thigh pulls on a force plate and two 30 m sprints. The [...] Read more.

This study aimed to examine the relationships between isometric mid-thigh pull maximal force (IMTPF), sprint mechanics, and performance. Fifteen national-level track and field athletes (sprinters and hurdlers) performed three maximal-effort isometric mid-thigh pulls on a force plate and two 30 m sprints. The IMTPF, the sprint mechanical variables (theoretical maximum horizontal force (F₀), velocity (v₀), and power (P_max)), as well as the sprint performance data at 5 m distance intervals, were collected. Pearson’s product–moment correlation analysis revealed large linear associations between IMTPF and v₀ (r = 0.65, R² = 0.42, p = 0.009), as well as negative linear relationships between IMTPF and sprint times of 15 m (r = −0.53, R² = 0.28, p = 0.043), 20 m (r = −0.55, R² = 0.30, p = 0.033), 25 m (r = −0.57, R² = 0.33, p = 0.025), and 30 m (r = −0.60, R² = 0.36, p = 0.019). The F₀, P_max, and sprint times to 5 m and 10 m were not significantly correlated with the IMTPF (p < 0.05). The study results highlight that during the late acceleration phase (>15 m), the capacity to generate horizontal force at high running velocities is related to the ability to develop maximal force during isometric contractions. Full article

(This article belongs to the Special Issue Innovative Technologies for and Approaches to Sports Performance)

19 pages, 762 KiB

Open AccessArticle

Enhancing In-Context Learning of Large Language Models for Knowledge Graph Reasoning via Rule-and-Reinforce Selected Triples

by Shaofei Wang

Appl. Sci. 2025, 15(3), 1088; https://doi.org/10.3390/app15031088 - 22 Jan 2025

Abstract

Knowledge graph (KG) reasoning aims to obtain new knowledge based on existing data. Utilizing large language models (LLMs) through in-context learning for KG reasoning has become a significant direction. However, existing methods mainly extract in-context triples by manually defined standards (such as the [...] Read more.

Knowledge graph (KG) reasoning aims to obtain new knowledge based on existing data. Utilizing large language models (LLMs) through in-context learning for KG reasoning has become a significant direction. However, existing methods mainly extract in-context triples by manually defined standards (such as the neighbors that are directly linked with the query triple), without considering whether they are useful for LLM reasoning. Furthermore, the triples beyond the neighbors can also provide important clues for reasoning. Therefore, it is necessary to extract more useful in-context triples of LLMs for KG reasoning. This paper proposes a rule-and-reinforce in-context triple extraction method to enhance the in-context learning of LLMs for KG reasoning. First, we collect the in-context triples specific to each query triple with the guidance of logical rules, and a neural extractor is pre-trained by the collected triples. Subsequently, the feedback of LLMs is collected as rewards to further optimize the extractor, where the policy gradient is utilized to encourage the extractor to explore more useful triples that yield higher rewards. The experimental results on five different knowledge graphs demonstrate that the proposed method can effectively improve the reasoning performance of LLMs. Compared to the traditional reasoning method AnyBURL, the greatest improvement is 0.147 on Hits@10, FB15k-237. Full article

► Show Figures

Figure 1

27 pages, 8341 KiB

Open AccessArticle

Mixed Convection Heat Transfer and Fluid Flow of Nanofluid/Porous Medium Under Magnetic Field Influence

by Rehab N. Al-Kaby, Samer M. Abdulhaleem, Rafel H. Hameed and Ahmed Yasiry

Appl. Sci. 2025, 15(3), 1087; https://doi.org/10.3390/app15031087 - 22 Jan 2025

Abstract

This study aims to investigate the effect of a constant magnetic field on heat transfer, flow of fluid, and entropy generation of mixed convection in a lid-driven porous medium enclosure filled with nanofluids (TiO₂-water). Uniform constant heat fluxes are partially applied [...] Read more.

This study aims to investigate the effect of a constant magnetic field on heat transfer, flow of fluid, and entropy generation of mixed convection in a lid-driven porous medium enclosure filled with nanofluids (TiO₂-water). Uniform constant heat fluxes are partially applied to the bottom wall of the enclosure, while the remaining parts of the bottom wall are considered to be adiabatic. The vertical walls are maintained at a constant cold temperature and move with a fixed velocity. A sinusoidal wall is assumed to be fixed and kept adiabatic at the top enclosure. Three scenarios are considered corresponding to different directions of the moving isothermal vertical wall (±1). The influence of pertinent parameters on the heat transfer, flow of fluid, and entropy generation in an enclosure are deliberated. The parameters are the Richardson number (

{\tilde{R}}_{i}

= 1, 10, and 100), the Hartmann number (0 ≤

\tilde{H}

a ≤ 75 with a 25 step), and the solid volume fraction of nanoparticles (0 ≤

\tilde{Φ}

≤ 0.15 with a 0.05 step). The Grashof and Darcy numbers are assumed to be constant at 10⁴ and 10⁻³, respectively. The finite element method, utilizing the variational formulation/weak form, is applied to discretize the main governor equations. Triangular elements have been employed within the studied envelope, with the elements adapting as needed. The results showed that the streamfunction and fluid temperature decreased as the solid volume fraction increased. The local

\tilde{N} u

number increased by more than 50% at low values of

\tilde{Φ}

(up to 0.1). This percentage decreases between 25% and 40% when

\tilde{Φ}

is in the range of 0.1 to 0.15. As

\tilde{H}

a increases from 0 to 75, these percentages increase at low values of the value of

\tilde{R} i = 1

and 10. These variations are primarily dependent on the value of the Richardson number. Full article

(This article belongs to the Special Issue Research on Heat Transfer Analysis in Fluid Dynamics)

► Show Figures

Figure 1

16 pages, 1061 KiB

Open AccessArticle

Topology Control of Low-Connection UAV Laser Network with Virtual Nodes

by Pengyu Wang, Qingkui Li and Junkai Yi

Appl. Sci. 2025, 15(3), 1086; https://doi.org/10.3390/app15031086 - 22 Jan 2025

Abstract

Space laser communication technology has advantages such as high bandwidth and low interference, but the limitation of the load capacity of an unmanned aerial vehicle (UAV) makes it difficult for UAV clusters to have sufficient communication links to build a stable communication network. [...] Read more.

Space laser communication technology has advantages such as high bandwidth and low interference, but the limitation of the load capacity of an unmanned aerial vehicle (UAV) makes it difficult for UAV clusters to have sufficient communication links to build a stable communication network. Aiming to address the problem of the low upper limit of the node degree of the self-organized network after the introduction of space laser communication technology for UAVs, a virtual node topology control (VNTC) algorithm is proposed for constructing an efficient and stable communication network structure. The algorithm is based on the unit disc graph model and achieves a lower-node-degree upper bound while maintaining connectivity through node pairing and virtual graph construction. The theoretical analysis and experimental results of the algorithm show that the algorithm can effectively deal with the topology control problem in UAV laser networks. In contrast to the XTC algorithm (exceptional topology control), the VNTC algorithm maintains the connectivity and most of the planarizability of the network, and, at the same time, it performs better in reducing the node degree and number of edges, thus providing an effective solution for low-connectivity topology control. Full article

► Show Figures

Figure 1

23 pages, 3232 KiB

Open AccessArticle

Comparative Analysis of LiDAR and Photogrammetry for 3D Crime Scene Reconstruction

by Fatemah M. Sheshtar, Wajd M. Alhatlani, Michael Moulden and Jong Hyuk Kim

Appl. Sci. 2025, 15(3), 1085; https://doi.org/10.3390/app15031085 - 22 Jan 2025

Abstract

Accurate and fast 3D mapping of crime scenes is crucial in law enforcement, and first responders often need to document scenes in detail under challenging conditions and within a limited time. Traditional methods often fail to capture the details required to understand these [...] Read more.

Accurate and fast 3D mapping of crime scenes is crucial in law enforcement, and first responders often need to document scenes in detail under challenging conditions and within a limited time. Traditional methods often fail to capture the details required to understand these scenes comprehensively. This study investigates the effectiveness of recent mobile phone-based mapping technologies equipped with a LiDAR (Light Detection and Ranging) sensor. The performance of LiDAR and pure photogrammetry is evaluated under different illumination (day and night) and scanning conditions (slow and fast scanning) in a mock-up crime scene. The results reveal that the mapping using an iPhone LIDAR in daylight conditions with 5 min of fast scanning shows the best results, yielding 0.1084 m of error. Also, the cloud-to-cloud distance showed that 90% of the point clouds exhibited under 0.1224 m of error, demonstrating the utility of these tools for rapid and portable scanning in crime scenes. Full article

(This article belongs to the Special Issue Current Advances in 3D Scene Classification and Object Recognition)

► Show Figures

Figure 1

16 pages, 13626 KiB

Open AccessArticle

Metabolomic Insights into the Potential of Chestnut Biochar as a Functional Feed Ingredient

by Serena Reggi, Sara Frazzini, Simone Pedrazzi, Martina Ghidoli, Maria Claudia Torresani, Marco Puglia, Nicolò Morselli, Marianna Guagliano, Cinzia Cristiani, Salvatore Roberto Pilu, Elisabetta Onelli, Alessandra Moscatelli and Luciana Rossi

Appl. Sci. 2025, 15(3), 1084; https://doi.org/10.3390/app15031084 - 22 Jan 2025

Abstract

Biochar is potentially a functional ingredient in animal nutrition that offers health benefits such as detoxification, while also promoting environmental sustainability through carbon sequestration, emission reduction, and its circular production. However, the heterogeneity of commercially available biochar products requires a detailed assessment of [...] Read more.

Biochar is potentially a functional ingredient in animal nutrition that offers health benefits such as detoxification, while also promoting environmental sustainability through carbon sequestration, emission reduction, and its circular production. However, the heterogeneity of commercially available biochar products requires a detailed assessment of their functional properties for applications in animal feed. This study evaluates chestnut biochar from morphological, chemical, and metabolomic perspectives and assesses its functional properties. Metabolomic analysis of a water extract using QTOF HPLC-MS/MS confirmed the presence of bioactive compounds, such as hydroxybenzoic and succinic acids, highlighting its potential as a functional feed ingredient. The chestnut biochar inhibited the growth of the pathogenic E. coli strains F18+ and F4+, with maximum inhibition rates of 15.8% and 28.6%, respectively, after three hours of incubation. The downregulation of genes associated with quorum sensing (MotA, FliA, FtsE, and HflX, involved in biofilm formation and cellular division) suggests that biochar interferes with several aspects of the pathogenic process. Importantly, biochar was not found to adversely affect beneficial probiotic bacteria, such as Lactiplantibacillus plantarum and Limosilactobacillus reuteri. These findings support the potential of chestnut biochar as a versatile ingredient for sustainable animal nutrition, thus promoting animal welfare while offering environmental benefits. Full article

(This article belongs to the Special Issue Extraction of Functional Ingredients and Their Application)

► Show Figures

Figure 1

Journal Description

Applied Sciences

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI