MDPI - Publisher of Open Access Journals

29 pages, 6672 KiB

Open AccessArticle

Discovery of a Novel Antimicrobial Peptide from Paenibacillus sp. Na14 with Potent Activity Against Gram-Negative Bacteria and Genomic Insights into Its Biosynthetic Pathway

by Nuttapon Songnaka, Adisorn Ratanaphan, Namfa Sermkaew, Somchai Sawatdee, Sucheewin Krobthong, Chanat Aonbangkhen, Yodying Yingchutrakul and Apichart Atipairin

Antibiotics 2025, 14(8), 805; https://doi.org/10.3390/antibiotics14080805 (registering DOI) - 6 Aug 2025

Abstract

Background/Objectives: Antimicrobial resistance (AMR) contributes to millions of deaths globally each year, creating an urgent need for new therapeutic agents. Antimicrobial peptides (AMPs) have emerged as promising candidates due to their potential to combat AMR pathogens. This study aimed to evaluate the antimicrobial [...] Read more.

Background/Objectives: Antimicrobial resistance (AMR) contributes to millions of deaths globally each year, creating an urgent need for new therapeutic agents. Antimicrobial peptides (AMPs) have emerged as promising candidates due to their potential to combat AMR pathogens. This study aimed to evaluate the antimicrobial activity of an AMP from a soil-derived bacterial isolate against Gram-negative bacteria. Method: Soil bacteria were isolated and screened for antimicrobial activity. The bioactive peptide was purified and determined its structure and antimicrobial efficacy. Genomic analysis was conducted to predict the biosynthetic gene clusters (BGCs) responsible for AMP production. Results: Genomic analysis identified the isolate as Paenibacillus sp. Na14, which exhibited low genomic similarity (61.0%) to other known Paenibacillus species, suggesting it may represent a novel species. The AMP from the Na14 strain exhibited heat stability up to 90 °C for 3 h and retained its activity across a broad pH range from 3 to 11. Structural analysis revealed that the Na14 peptide consisted of 14 amino acid residues, adopting an α-helical structure. This peptide exhibited bactericidal activity at concentrations of 2–4 µg/mL within 6–12 h, and its killing rate was concentration-dependent. The peptide was found to disrupt the bacterial membranes. The Na14 peptide shared 64.29% sequence similarity with brevibacillin 2V, an AMP from Brevibacillus sp., which also belongs to the Paenibacillaceae family. Genomic annotation identified BGCs associated with secondary metabolism, with a particular focus on non-ribosomal peptide synthetase (NRPS) gene clusters. Structural modeling of the predicted NRPS enzymes showed high similarity to known NRPS modules in Brevibacillus species. These genomic findings provide evidence supporting the similarity between the Na14 peptide and brevibacillin 2V. Conclusions: This study highlights the discovery of a novel AMP with potent activity against Gram-negative pathogens and provides new insight into conserved AMP biosynthetic enzymes within the Paenibacillaceae family. Full article

(This article belongs to the Special Issue Antimicrobial Peptides: An Emerging Hope in the Era of New Infections and Resistance, 2nd Edition)

► Show Figures

Graphical abstract

16 pages, 1185 KiB

Open AccessFeature PaperArticle

Hematological, Enzymatic, and Endocrine Response to Intense Exercise in Lidia Breed Cattle During the Roping Bull Bullfighting Celebration

by Julio Sedeño, Salvador Ruiz, Germán Martín and Juan Carlos Gardón

Animals 2025, 15(15), 2303; https://doi.org/10.3390/ani15152303 (registering DOI) - 6 Aug 2025

Abstract

The Lidia cattle breed is featured in several traditional popular bullfighting festivals throughout Spain, including the “Toro de Cuerda” event, in which the animals are subjected to intense physical exercise. However, the physiological impact and welfare implications of these activities remain poorly characterized. [...] Read more.

The Lidia cattle breed is featured in several traditional popular bullfighting festivals throughout Spain, including the “Toro de Cuerda” event, in which the animals are subjected to intense physical exercise. However, the physiological impact and welfare implications of these activities remain poorly characterized. This study aimed to evaluate the stress response and muscle damage in Lidia breed bulls during roping bull celebrations through comprehensive blood analysis. Blood samples were collected from 53 adult male Lidia bulls before and after a standardized 45 min continuous running exercise during traditional roping bull events in four Spanish autonomous regions. Hematological parameters, muscle enzymes (creatine kinase, lactate dehydrogenase, lactate), and stress hormones (cortisol and ACTH) were analyzed. Significant increases (p < 0.05) were observed in leukocytes, lymphocytes, monocytes, eosinophils, neutrophils, erythrocytes, hematocrit, hemoglobin, and post-exercise platelets. Muscle enzymes showed marked elevations, with creatine kinase increasing up to 10-fold above baseline values. Stress hormones, cortisol and ACTH, also demonstrated significant increases. Despite the magnitude of these changes, all parameters remained within established reference ranges for the bovine species. This study provides the first physiological assessment of Lidia cattle during popular bullfighting celebrations, establishing baseline data for evidence-based welfare evaluation and management protocols. Full article

(This article belongs to the Section Cattle)

► Show Figures

Figure 1

29 pages, 945 KiB

Open AccessArticle

Modeling Based on Machine Learning and Synthetic Generated Dataset for the Needs of Multi-Criteria Decision-Making Forensics

by Aleksandar Aleksić, Radovan Radovanović, Dušan Joksimović, Milan Ranđelović, Vladimir Vuković, Slaviša Ilić and Dragan Ranđelović

Symmetry 2025, 17(8), 1254; https://doi.org/10.3390/sym17081254 (registering DOI) - 6 Aug 2025

Abstract

Information is the primary driver of progress in today’s world, especially given the vast amounts of data available for extracting meaningful knowledge. The motivation for addressing the problem of forensic analysis—specifically the validity of decision making in multi-criteria contexts—stems from its limited coverage [...] Read more.

Information is the primary driver of progress in today’s world, especially given the vast amounts of data available for extracting meaningful knowledge. The motivation for addressing the problem of forensic analysis—specifically the validity of decision making in multi-criteria contexts—stems from its limited coverage in the existing literature. Methodologically, machine learning and ensemble models represent key trends in this domain. Datasets used for such purposes can be either real or synthetic, with synthetic data becoming particularly valuable when real data is unavailable, in line with the growing use of publicly available Internet data. The integration of these two premises forms the central challenge addressed in this paper. The proposed solution is a three-layer ensemble model: the first layer employs multi-criteria decision-making methods; the second layer implements multiple machine learning algorithms through an optimized asymmetric procedure; and the third layer applies a voting mechanism for final decision making. The model is applied and evaluated through a case study analyzing the U.S. Army’s decision to replace the Colt 1911 pistol with the Beretta 92. The results demonstrate superior performance compared to state-of-the-art models, offering a promising approach to forensic decision analysis, especially in data-scarce environments. Full article

(This article belongs to the Special Issue Symmetry or Asymmetry in Machine Learning)

► Show Figures

Figure 1

33 pages, 5098 KiB

Open AccessReview

Medicinal Plants for Skin Disorders: Phytochemistry and Pharmacological Insights

by Nazerke Bolatkyzy, Daniil Shepilov, Rakhymzhan Turmanov, Dmitriy Berillo, Tursunay Vassilina, Nailya Ibragimova, Gulzat Berganayeva and Moldyr Dyusebaeva

Molecules 2025, 30(15), 3281; https://doi.org/10.3390/molecules30153281 (registering DOI) - 6 Aug 2025

Abstract

Skin disorders are common and often chronic conditions with significant therapeutic challenges. Limitations of conventional treatments, such as adverse effects and antimicrobial resistance, have increased interest in plant-based alternatives. This article presents the phytochemical composition and pharmacological potential of several medicinal plants traditionally [...] Read more.

Skin disorders are common and often chronic conditions with significant therapeutic challenges. Limitations of conventional treatments, such as adverse effects and antimicrobial resistance, have increased interest in plant-based alternatives. This article presents the phytochemical composition and pharmacological potential of several medicinal plants traditionally used in the treatment of skin diseases, including Rubus vulgaris, Plantago major, Artemisia terrae-albae, and Eryngium planum. Based on an analysis of scientific literature, the presence of bioactive compounds—including flavonoids, anthocyanins, phenolic acids, tannins, and sesquiterpenes—is summarized, along with their antioxidant, anti-inflammatory, and antimicrobial effects. Emphasis is placed on the correlation between traditional ethnomedicinal applications and pharmacological mechanisms. The findings support the potential of these species as sources for dermatological phytotherapeutics. Further research is needed to standardize active constituents, assess safety, and conduct clinical validation. Full article

(This article belongs to the Special Issue Bioactive Molecules in Medicinal Plants)

► Show Figures

Figure 1

25 pages, 10639 KiB

Open AccessArticle

Sliding Mode Control of the MY-3 Omnidirectional Mobile Robot Based on RBF Neural Networks

by Huaiyong Li, Changlong Ye, Song Tian and Suyang Yu

Machines 2025, 13(8), 695; https://doi.org/10.3390/machines13080695 (registering DOI) - 6 Aug 2025

Abstract

Omnidirectional mobile robots have gained extensive application across diverse fields due to their exceptional maneuverability and adaptability in confined spaces. However, structural and systemic uncertainties significantly compromise motion accuracy. To enhance motion control precision, this paper proposes a sliding mode control (SMC) method [...] Read more.

Omnidirectional mobile robots have gained extensive application across diverse fields due to their exceptional maneuverability and adaptability in confined spaces. However, structural and systemic uncertainties significantly compromise motion accuracy. To enhance motion control precision, this paper proposes a sliding mode control (SMC) method integrated with a radial basis function (RBF) neural network. The approach aggregates model uncertainties, nonlinear dynamics, and unknown disturbances into a composite disturbance term. An RBF neural network is employed to approximate this disturbance, with compensation embedded within the SMC framework. An online adaptive law for neural network optimization is derived using the Lyapunov stability theorem, thereby improving the disturbance rejection capability. Comparative simulations and experiments validate the proposed method against modern control strategies. Results demonstrate superior tracking performance and robustness, significantly enhancing trajectory tracking accuracy for the MY3 wheeled omnidirectional mobile robot. Full article

(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)

► Show Figures

Figure 1

17 pages, 1097 KiB

Open AccessReview

Natural Feed Additives in Sub-Saharan Africa: A Systematic Review of Efficiency and Sustainability in Ruminant Production

by Zonaxolo Ntsongota, Olusegun Oyebade Ikusika and Thando Conference Mpendulo

Ruminants 2025, 5(3), 36; https://doi.org/10.3390/ruminants5030036 (registering DOI) - 6 Aug 2025

Abstract

Ruminant livestock production plays a crucial role in the agricultural systems of Sub-Saharan Africa, significantly supporting rural livelihoods through income generation, improved nutrition, and employment opportunities. Despite its importance, the sector continues to face substantial challenges, such as low feed quality, seasonal feed [...] Read more.

Ruminant livestock production plays a crucial role in the agricultural systems of Sub-Saharan Africa, significantly supporting rural livelihoods through income generation, improved nutrition, and employment opportunities. Despite its importance, the sector continues to face substantial challenges, such as low feed quality, seasonal feed shortages, and climate-related stresses, all of which limit productivity and sustainability. Considering these challenges, the adoption of natural feed additives has emerged as a promising strategy to enhance animal performance, optimise nutrient utilisation, and mitigate environmental impacts, including the reduction of enteric methane emissions. This review underscores the significant potential of natural feed additives such as plant extracts, essential oils, probiotics, and mineral-based supplements such as fossil shell flour as sustainable alternatives to conventional growth promoters in ruminant production systems across the region. All available documented evidence on the topic from 2000 to 2024 was collated and synthesised through standardised methods of systematic review protocol—PRISMA. Out of 319 research papers downloaded, six were included and analysed directly or indirectly in this study. The results show that the addition of feed additives to ruminant diets in all the studies reviewed significantly (p < 0.05) improved growth parameters such as average daily growth (ADG), feed intake, and feed conversion ratio (FCR) compared to the control group. However, no significant (p > 0.05) effect was found on cold carcass weight (CCW), meat percentage, fat percentage, bone percentage, or intramuscular fat (IMF%) compared to the control. The available evidence indicates that these additives can provide tangible benefits, including improved growth performance, better feed efficiency, enhanced immune responses, and superior meat quality, while also supporting environmental sustainability by reducing nitrogen excretion and decreasing dependence on antimicrobial agents. Full article

► Show Figures

Figure 1

37 pages, 910 KiB

Open AccessReview

Invasive Candidiasis in Contexts of Armed Conflict, High Violence, and Forced Displacement in Latin America and the Caribbean (2005–2025)

by Pilar Rivas-Pinedo, Juan Camilo Motta and Jose Millan Onate Gutierrez

J. Fungi 2025, 11(8), 583; https://doi.org/10.3390/jof11080583 (registering DOI) - 6 Aug 2025

Abstract

Invasive candidiasis (IC), characterized by the most common clinical manifestation of candidemia, is a fungal infection with a high mortality rate and a significant impact on global public health. It is estimated that each year there are between 227,000 and 250,000 hospitalizations related [...] Read more.

Invasive candidiasis (IC), characterized by the most common clinical manifestation of candidemia, is a fungal infection with a high mortality rate and a significant impact on global public health. It is estimated that each year there are between 227,000 and 250,000 hospitalizations related to IC, with more than 100,000 associated deaths. In Latin America and the Caribbean (LA&C), the absence of a standardized surveillance system has led to multicenter studies documenting incidences ranging from 0.74 to 6.0 cases per 1000 hospital admissions, equivalent to 50,000–60,000 hospitalizations annually, with mortality rates of up to 60% in certain high-risk groups. Armed conflicts and structural violence in LA&C cause forced displacement, the collapse of health systems, and poor living conditions—such as overcrowding, malnutrition, and lack of sanitation—which increase vulnerability to opportunistic infections, such as IC. Insufficient specialized laboratories, diagnostic technology, and trained personnel impede pathogen identification and delay timely initiation of antifungal therapy. Furthermore, the empirical use of broad-spectrum antibiotics and the limited availability of echinocandins and lipid formulations of amphotericin B have promoted the emergence of resistant non-albicans strains, such as Candida tropicalis, Candida parapsilosis, and, in recent outbreaks, Candidozyma auris. Full article

(This article belongs to the Special Issue Candidiasis: Changes and Challenges in Its Epidemiology, Pathogenesis, Diagnosis, Treatment and Prevention)

► Show Figures

Figure 1

21 pages, 838 KiB

Open AccessSystematic Review

Systematic Review of Hip Fractures and Regional Anesthesia: Efficacy of the Main Blocks and Comparison for a Multidisciplinary and Effective Approach for Patients in the Hospital Setting of Anesthesiology and Resuscitation

by Enrique González Marcos, Inés Almagro Vidal, Rodrigo Arranz Pérez, Julio Morillas Martinez, Amalia Díaz Viudes, Ana Rodríguez Martín, Alberto José Gago Sánchez, Carmen García De Leániz and Daniela Rodriguez Marín

Surg. Tech. Dev. 2025, 14(3), 27; https://doi.org/10.3390/std14030027 (registering DOI) - 6 Aug 2025

Abstract

Background: Hip fractures represent a major clinical challenge, particularly in elderly and frail patients, where postoperative pain control must balance effective analgesia with motor preservation to facilitate early mobilization. Various regional anesthesia techniques are used in this setting, including the pericapsular nerve group [...] Read more.

Background: Hip fractures represent a major clinical challenge, particularly in elderly and frail patients, where postoperative pain control must balance effective analgesia with motor preservation to facilitate early mobilization. Various regional anesthesia techniques are used in this setting, including the pericapsular nerve group (PENG) block, fascia iliaca compartment block (FICB), femoral nerve block (FNB), and quadratus lumborum block (QLB), yet optimal strategies remain debated. Objectives: To systematically review the efficacy, safety, and clinical applicability of major regional anesthesia techniques for pain management in hip fractures, including considerations of fracture type, surgical approach, and functional outcomes. Methods: A systematic literature search was conducted following PRISMA 2020 guidelines in PubMed, Scopus, Web of Science, and the virtual library of the Hospital Central de la Defensa “Gómez Ulla” up to March 2025. Inclusion criteria were RCTs, systematic reviews, and meta-analyses evaluating regional anesthesia for hip surgery in adults. Risk of bias in RCTs was assessed using RoB 2.0, and certainty of evidence was evaluated using the GRADE approach. Results: Twenty-nine studies were included, comprising RCTs, systematic reviews, and meta-analyses. PENG block demonstrated superior motor preservation and reduced opioid consumption compared to FICB and FNB, particularly in intracapsular fractures and anterior surgical approaches. FICB and combination strategies (PENG+LFCN or sciatic block) may provide broader analgesic coverage in extracapsular fractures or posterior approaches. The overall risk of bias across RCTs was predominantly low, and certainty of evidence ranged from moderate to high for key outcomes. No significant safety concerns were identified across techniques, although reporting of adverse events was inconsistent. Conclusions: PENG block appears to offer a favorable balance of analgesia and motor preservation in hip fracture surgery, particularly for intracapsular fractures. For extracapsular fractures or posterior approaches, combination strategies may enhance analgesic coverage. Selection of block technique should be tailored to fracture type, surgical approach, and patient-specific functional goals. Full article

► Show Figures

Figure 1

32 pages, 1435 KiB

Open AccessReview

Smart Safety Helmets with Integrated Vision Systems for Industrial Infrastructure Inspection: A Comprehensive Review of VSLAM-Enabled Technologies

by Emmanuel A. Merchán-Cruz, Samuel Moveh, Oleksandr Pasha, Reinis Tocelovskis, Alexander Grakovski, Alexander Krainyukov, Nikita Ostrovenecs, Ivans Gercevs and Vladimirs Petrovs

Sensors 2025, 25(15), 4834; https://doi.org/10.3390/s25154834 (registering DOI) - 6 Aug 2025

Abstract

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused [...] Read more.

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

► Show Figures

Figure 1

21 pages, 11484 KiB

Open AccessArticle

Analytical Investigation of Primary Waveform Distortion Effect on Magnetic Flux Density in the Magnetic Core of Inductive Current Transformer and Its Transformation Accuracy

by Michal Kaczmarek and Kacper Blus

Sensors 2025, 25(15), 4837; https://doi.org/10.3390/s25154837 (registering DOI) - 6 Aug 2025

Abstract

This paper analyzes how distortion in the primary current waveform affects the magnetic flux density in the magnetic core of an inductive current transformer and its transformation accuracy. Keeping the primary current’s RMS value constant, it studies the impact of changes in the [...] Read more.

This paper analyzes how distortion in the primary current waveform affects the magnetic flux density in the magnetic core of an inductive current transformer and its transformation accuracy. Keeping the primary current’s RMS value constant, it studies the impact of changes in the RMS values and phase angles of low-order harmonics on the core’s flux density and the values of current error and phase displacement of their transformation. The distorted current waveforms, resulting flux density, and hysteresis loops are examined to identify the operating conditions of the inductive current transformer. This study also highlights the strong influence of low-order harmonics and the diminishing effect of higher-frequency harmonics on the magnetic flux density in its magnetic core, e.g., third, fifth, and seventh higher harmonics may cause an increase in magnetic flux density in the magnetic core of the inductive current transformer in relation to that obtained for a sinusoidal current with a frequency of 50 Hz by about 8.5%, while with additional second, fourth, and sixth harmonics, the increase may reach about 23%. Therefore, the testing procedure should consider not only the load impedance and the RMS values of the primary current but also its harmonic content, including the RMS values of individual harmonics and their phase angles. Full article

(This article belongs to the Special Issue Condition Monitoring of Electrical Equipment Within Power Systems)

► Show Figures

Figure 1

23 pages, 723 KiB

Open AccessArticle

Multivariate Modeling of Some Datasets in Continuous Space and Discrete Time

by Rigele Te and Juan Du

Entropy 2025, 27(8), 837; https://doi.org/10.3390/e27080837 (registering DOI) - 6 Aug 2025

Abstract

Multivariate space–time datasets are often collected at discrete, regularly monitored time intervals and are typically treated as components of time series in environmental science and other applied fields. To effectively characterize such data in geostatistical frameworks, valid and practical covariance models are essential. [...] Read more.

Multivariate space–time datasets are often collected at discrete, regularly monitored time intervals and are typically treated as components of time series in environmental science and other applied fields. To effectively characterize such data in geostatistical frameworks, valid and practical covariance models are essential. In this work, we propose several classes of multivariate spatio-temporal covariance matrix functions to model underlying stochastic processes whose discrete temporal margins correspond to well-known autoregressive and moving average (ARMA) models. We derive sufficient and/or necessary conditions under which these functions yield valid covariance matrices. By leveraging established methodologies from time series analysis and spatial statistics, the proposed models are straightforward to identify and fit in practice. Finally, we demonstrate the utility of these multivariate covariance functions through an application to Kansas weather data, using co-kriging for prediction and comparing the results to those obtained from traditional spatio-temporal models. Full article

(This article belongs to the Special Issue Statistical Methods for Modeling High-Dimensional and Complex Data: Second Edition)

► Show Figures

Figure 1

15 pages, 425 KiB

Open AccessArticle

Game-Optimization Modeling of Shadow Carbon Pricing and Low-Carbon Transition in the Power Sector

by Guangzeng Sun, Bo Yuan, Han Zhang, Peng Xia, Cong Wu and Yichun Gong

Energies 2025, 18(15), 4173; https://doi.org/10.3390/en18154173 (registering DOI) - 6 Aug 2025

Abstract

Under China’s ‘Dual Carbon’ strategy, the power sector plays a central role in achieving carbon neutrality. This study develops a bi-level game-optimization model involving the government, power producers, and technology suppliers to explore the dynamic coordination between shadow carbon pricing and emission trajectories. [...] Read more.

Under China’s ‘Dual Carbon’ strategy, the power sector plays a central role in achieving carbon neutrality. This study develops a bi-level game-optimization model involving the government, power producers, and technology suppliers to explore the dynamic coordination between shadow carbon pricing and emission trajectories. The upper-level model, guided by the government, focuses on minimizing total costs, including emission reduction costs, technological investments, and operational costs, by dynamically adjusting emission targets and shadow carbon prices. The lower-level model employs evolutionary game theory to simulate the adaptive behaviors and strategic interactions among power producers, regulatory authorities, and technology suppliers. Three representative uncertainty scenarios, disruptive technological breakthroughs, major policy interventions, and international geopolitical shifts, are incorporated to evaluate system robustness. Simulation results indicate that an optimistic scenario is characterized by rapid technological advancement and strong policy incentives. Conversely, under a pessimistic scenario with sluggish technology development and weak regulatory frameworks, there are substantially higher transition costs. This research uniquely contributes by explicitly modeling dynamic feedback between policy and stakeholder behavior under multiple uncertainties, highlighting the critical roles of innovation-driven strategies and proactive policy interventions in shaping effective, resilient, and cost-efficient carbon pricing and low-carbon transition pathways in the power sector. Full article

► Show Figures

Figure 1

24 pages, 62899 KiB

Open AccessEssay

Monitoring and Historical Spatio-Temporal Analysis of Arable Land Non-Agriculturalization in Dachang County, Eastern China Based on Time-Series Remote Sensing Imagery

by Boyuan Li, Na Lin, Xian Zhang, Chun Wang, Kai Yang, Kai Ding and Bin Wang

Earth 2025, 6(3), 91; https://doi.org/10.3390/earth6030091 (registering DOI) - 6 Aug 2025

Abstract

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of [...] Read more.

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article

(This article belongs to the Topic Global Farmland Protection, Food Security and Land Use Planning)

► Show Figures

Figure 1

25 pages, 426 KiB

Open AccessReview

Survey on the Application of Robotics in Archaeology

by Panagiota Kyriakoulia, Anastasios Kazolias, Dimitrios Konidaris and Panagiotis Kokkinos

Sensors 2025, 25(15), 4836; https://doi.org/10.3390/s25154836 (registering DOI) - 6 Aug 2025

Abstract

This work explores the application of robotic systems in archaeology, highlighting their transformative role in excavation, documentation, and the preservation of cultural heritage. By combining technologies such as LiDAR, GIS, 3D modeling, sonar, and other sensors with autonomous and semi-autonomous platforms, archaeologists can [...] Read more.

This work explores the application of robotic systems in archaeology, highlighting their transformative role in excavation, documentation, and the preservation of cultural heritage. By combining technologies such as LiDAR, GIS, 3D modeling, sonar, and other sensors with autonomous and semi-autonomous platforms, archaeologists can now reach inaccessible sites, automate artifact analysis, and reconstruct fragmented remains with greater precision. The study provides a systematic overview of underwater, aerial, terrestrial, and other robotic systems, drawing on scientific literature that showcases their innovative use in both fieldwork and museum settings. Selected examples illustrate how robotics is being applied to solve key archaeological challenges in new and effective ways. While the paper emphasizes the potential of these technologies, it also addresses their technical, economic, and ethical limitations, concluding that successful adoption depends on interdisciplinary collaboration, careful implementation, and a balanced respect for cultural integrity. Full article

(This article belongs to the Special Issue Advanced Intelligent Robotics and Autonomous Control Systems—Celebrating the 75th Birthday of Professor Imre J. Rudas)

► Show Figures

Figure 1

33 pages, 905 KiB

Open AccessArticle

by Edwin Kipruto and Willi Sauerbrei

Stats 2025, 8(3), 70; https://doi.org/10.3390/stats8030070 (registering DOI) - 6 Aug 2025

Abstract

Penalized regression methods are widely used for variable selection. Non-negative garrote (NNG) was one of the earliest methods to combine variable selection with shrinkage of regression coefficients, followed by lasso. About a decade after the introduction of lasso, adaptive lasso (ALASSO) was proposed [...] Read more.

Penalized regression methods are widely used for variable selection. Non-negative garrote (NNG) was one of the earliest methods to combine variable selection with shrinkage of regression coefficients, followed by lasso. About a decade after the introduction of lasso, adaptive lasso (ALASSO) was proposed to address lasso’s limitations. ALASSO has two tuning parameters (

λ

and

γ

), and its penalty resembles that of NNG when

γ = 1

, though NNG imposes additional constraints. Given ALASSO’s greater flexibility, which may increase instability, this study investigates whether NNG provides any practical benefit or can be replaced by ALASSO. We conducted simulations in both low- and high-dimensional settings to compare selected variables, coefficient estimates, and prediction accuracy. Ordinary least squares and ridge estimates were used as initial estimates. NNG and ALASSO (

γ = 1

) showed similar performance in low-dimensional settings with low correlation, large samples, and moderate to high

R^{2}

. However, under high correlation, small samples, and low

R^{2}

, their selected variables and estimates differed, though prediction accuracy remained comparable. When

γ \neq 1

, the differences between NNG and ALASSO became more pronounced, with ALASSO generally performing better. Assuming linear relationships between predictors and the outcome, the results suggest that NNG may offer no practical advantage over ALASSO. The

γ

parameter in ALASSO allows for adaptability to model complexity, making ALASSO a more flexible and practical alternative to NNG. Full article

(This article belongs to the Section Statistical Methods)

► Show Figures

Figure 1

38 pages, 10941 KiB

Open AccessReview

Recent Advances in Numerical Modeling of Aqueous Redox Flow Batteries

by Yongfu Liu and Yi He

Energies 2025, 18(15), 4170; https://doi.org/10.3390/en18154170 (registering DOI) - 6 Aug 2025

Abstract

Aqueous redox flow batteries (ARFBs) have attracted significant attention in the field of electrochemical energy storage due to their high intrinsic safety, low cost, and flexible system configuration. However, the advancement of this technology is still hindered by several critical challenges, including capacity [...] Read more.

Aqueous redox flow batteries (ARFBs) have attracted significant attention in the field of electrochemical energy storage due to their high intrinsic safety, low cost, and flexible system configuration. However, the advancement of this technology is still hindered by several critical challenges, including capacity decay, structural optimization, and the design and application of key materials as well as their performance within battery systems. Addressing these issues requires systematic theoretical foundations and scientific guidance. Numerical modeling has emerged as a powerful tool for investigating the complex physical and electrochemical processes within flow batteries across multiple spatial and temporal scales. It also enables predictive performance analysis and cost-effective optimization at both the component and system levels, thus accelerating research and development. This review provides a comprehensive overview of recent progress in the modeling of ARFBs. Taking the all-vanadium redox flow battery as a representative example, we summarize the key multiphysics phenomena involved and introduce corresponding multi-scale modeling strategies. Furthermore, specific modeling considerations are discussed for phase-change ARFBs, such as zinc-based ones involving solid–liquid phase transition, and hydrogen–bromine systems characterized by gas–liquid two-phase flow, highlighting their distinctive features compared to vanadium systems. Finally, this paper explores the major challenges and potential opportunities in the modeling of representative ARFB systems, aiming to provide theoretical guidance and technical support for the continued development and practical application of ARFB technology. Full article

(This article belongs to the Special Issue Advanced Energy Storage Technologies)

► Show Figures

Figure 1

19 pages, 3691 KiB

Open AccessArticle

Assessing CFTR Function and Epithelial Morphology in Human Nasal Respiratory Cell Cultures: A Combined Immunofluorescence and Electrophysiological Study

by Roshani Narayan Singh, Vanessa Mete, Willy van Driessche, Heymut Omran, Wolf-Michael Weber and Jörg Grosse-Onnebrink

Int. J. Mol. Sci. 2025, 26(15), 7618; https://doi.org/10.3390/ijms26157618 (registering DOI) - 6 Aug 2025

Abstract

Cystic fibrosis (CF), the most common hereditary lung disease in Caucasians, is caused by dysfunction of the cystic fibrosis transmembrane conductance regulator (CFTR). We evaluated CFTR function using a newly developed Ussing chamber system, the Multi Trans Epithelial Current Clamp (MTECC), in an [...] Read more.

Cystic fibrosis (CF), the most common hereditary lung disease in Caucasians, is caused by dysfunction of the cystic fibrosis transmembrane conductance regulator (CFTR). We evaluated CFTR function using a newly developed Ussing chamber system, the Multi Trans Epithelial Current Clamp (MTECC), in an in vitro model of human airway epithelia. Air–liquid interface (ALI) cultures were established from nasal brushings of healthy controls (HC) and CF patients with biallelic CFTR variants. ALI layer thickness was similar between groups (HC: 62 ± 13 µm; CF: 55 ± 9 µm). Immunofluorescence showed apical CFTR expression in HC, but reduced or absent signal in CF cultures. MTECC enabled continuous measurement of transepithelial resistance (Rt), potential difference (PD), and conductance (G_t). G_t was significantly reduced in CF cultures compared to HC (0.825 ± 0.024 vs. −0.054 ± 0.016 mS/cm²), indicating impaired cAMP-inducible ion transport by CFTR. Treatment of CF cultures with elexacaftor, tezacaftor, and ivacaftor (Trikafta^®) increased G_t, reflecting partial restoration of CFTR function. These findings demonstrate the utility of MTECC in detecting functional differences in CFTR activity and support its use as a platform for evaluating CFTR-modulating therapies. Our model may contribute to the development of personalized treatment strategies for CF patients. Full article

(This article belongs to the Special Issue Molecular Mechanisms and Pathophysiology of Cystic Fibrosis)

15 pages, 1952 KiB

Open AccessArticle

Processing of Secondary Raw Materials from Ferrochrome Production via Agglomeration and Study of Their Mechanical Properties

by Yerlan Zhumagaliyev, Yerbol Shabanov, Maral Almagambetov, Maulen Jundibayev, Nursultan Ulmaganbetov, Salamat Laikhan, Akgul Jundibayeva, Aigerim Abilberikova, Nurbala Ubaidulayeva and Rysgul Adaibayeva

Metals 2025, 15(8), 878; https://doi.org/10.3390/met15080878 (registering DOI) - 6 Aug 2025

Abstract

In the process of producing ferroalloys, a large amount of secondary raw materials is formed, including slag, aspiration dusts and sludge. The recycling of secondary raw materials can create resources and bring environmental and economic benefits. Wet secondary raw materials (WSRMs) are characterized [...] Read more.

In the process of producing ferroalloys, a large amount of secondary raw materials is formed, including slag, aspiration dusts and sludge. The recycling of secondary raw materials can create resources and bring environmental and economic benefits. Wet secondary raw materials (WSRMs) are characterized by a high chromium oxide content (averaging 24%), but due to their high moisture levels, they cannot be directly used in arc furnaces. As a strategic approach, mixing WSRMs with drier, more chromium-rich dusts (up to 45% Cr₂O₃) has been proposed. This not only reduces the overall moisture content of the mixture but also enhances the metallurgical value of the charge material. This paper presents the results of laboratory studies on the agglomeration of secondary wet raw materials using briquetting, extrusion and pelletizing methods. The main factors influencing the quality of the resulting product were analyzed, including the method of agglomeration, the composition of the mixture, as well as the type and dosage of the binder component. The strength characteristics of the finished agglomerated samples were evaluated in terms of resistance to splitting, impact loads and falling. Notably, the selected binders are organic and polymer substances capable of complete combustion under metallurgical smelting conditions. Full article

► Show Figures

Figure 1

13 pages, 2220 KiB

Open AccessCommunication

Feminization of the Blood–Brain Barrier Changes the Brain Transcriptome of Drosophila melanogaster Males

by Danyel S. Davis, Warda Hashem, Chamala Lama, Joseph L. Reeve and Brigitte Dauwalder

Curr. Issues Mol. Biol. 2025, 47(8), 626; https://doi.org/10.3390/cimb47080626 (registering DOI) - 6 Aug 2025

Abstract

Beyond its crucial role as a tight barrier to protect the nervous system, the Blood–Brain Barrier (BBB) is increasingly being recognized for its physiological processes that affect brain function and behavior. In Drosophila melanogaster, the BBB expresses sex-specific transcripts, and a change in [...] Read more.

Beyond its crucial role as a tight barrier to protect the nervous system, the Blood–Brain Barrier (BBB) is increasingly being recognized for its physiological processes that affect brain function and behavior. In Drosophila melanogaster, the BBB expresses sex-specific transcripts, and a change in the sexual identity of adult BBB cells results in a significant reduction in male courtship behavior. The molecular nature of this BBB/brain interaction and the molecules that mediate it are unknown. Here we feminize BBB cells by targeted expression of the Drosophila female-specific master regulator TraF in otherwise normal males. We examined the effect on RNA expression in dissected brains by RNA sequenc-ing. We find that 283 transcripts change in comparison to normal control males. Tran-scripts representing cell signaling processes and synaptic communication are enriched, as are hormonal mediators. These transcripts provide a valuable resource for addressing questions about BBB and brain interaction. Full article

(This article belongs to the Section Biochemistry, Molecular and Cellular Biology)

15 pages, 4886 KiB

Open AccessArticle

Fabrication of Diffractive Optical Elements to Generate Square Focal Spots via Direct Laser Lithography and Machine Learning

by Hieu Tran Doan Trung, Young-Sik Ghim and Hyug-Gyo Rhee

Photonics 2025, 12(8), 794; https://doi.org/10.3390/photonics12080794 (registering DOI) - 6 Aug 2025

Abstract

Recently, diffractive optics systems have garnered increasing attention due to their myriad benefits in various applications, such as creating vortex beams, Bessel beams, or optical traps, while refractive optics systems still exhibit some disadvantages related to materials, substrates, and intensity shapes. The manufacturing [...] Read more.

Recently, diffractive optics systems have garnered increasing attention due to their myriad benefits in various applications, such as creating vortex beams, Bessel beams, or optical traps, while refractive optics systems still exhibit some disadvantages related to materials, substrates, and intensity shapes. The manufacturing of diffractive optical elements has become easier due to the development of lithography techniques such as direct laser writing, photo lithography, and electron beam lithography. In this paper, we improve the results from previous research and propose a new methodology to design and fabricate advanced binary diffractive optical elements that achieve a square focal spot independently, reducing reliance on additional components. By integrating a binary square zone plate with an axicon zone plate of the same scale, we employ machine learning for laser path optimization and direct laser lithography for manufacturing. This streamlined approach enhances simplicity, accuracy, efficiency, and cost effectiveness. Our upgraded binary diffractive optical elements are ready for real-world applications, marking a significant improvement in optical capabilities. Full article

(This article belongs to the Section Lasers, Light Sources and Sensors)

► Show Figures

Figure 1

18 pages, 5831 KiB

Open AccessArticle

Cure Kinetics-Driven Compression Molding of CFRP for Fast and Low-Cost Manufacturing

by Xintong Wu, Ming Zhang, Zhongling Liu, Xin Fu, Haonan Liu, Yuchen Zhang and Xiaobo Yang

Polymers 2025, 17(15), 2154; https://doi.org/10.3390/polym17152154 (registering DOI) - 6 Aug 2025

Abstract

Carbon fiber-reinforced polymer (CFRP) composites are widely used in aerospace due to their excellent strength-to-weight ratio and tailorable properties. However, these properties critically depend on the CFRP curing cycle. The commonly adopted manufacturer-recommended curing cycle (MRCC), designed to accommodate the most conservative conditions, [...] Read more.

Carbon fiber-reinforced polymer (CFRP) composites are widely used in aerospace due to their excellent strength-to-weight ratio and tailorable properties. However, these properties critically depend on the CFRP curing cycle. The commonly adopted manufacturer-recommended curing cycle (MRCC), designed to accommodate the most conservative conditions, involves prolonged curing times and high energy consumption. To overcome these limitations, this study proposes an efficient and adaptable method to determine the optimal curing cycle. The effects of varying heating rates on resin dynamic and isothermal–exothermic behavior were characterized via reaction kinetics analysis using differential scanning calorimetry (DSC) and rheological measurements. The activation energy of the reaction system was substituted into the modified Sun–Gang model, and the parameters were estimated using a particle swarm optimization algorithm. Based on the curing kinetic behavior of the resin, CFRP compression molding process orthogonal experiments were conducted. A weighted scoring system incorporating strength, energy consumption, and cycle time enabled multidimensional evaluation of optimized solutions. Applying this curing cycle optimization method to a commercial epoxy resin increased efficiency by 247.22% and reduced energy consumption by 35.7% while meeting general product performance requirements. These results confirm the method’s reliability and its significance for improving production efficiency. Full article

(This article belongs to the Special Issue Advances in High-Performance Polymer Materials, 2nd Edition)

► Show Figures

Figure 1

50 pages, 6488 KiB

Open AccessArticle

A Bio-Inspired Adaptive Probability IVYPSO Algorithm with Adaptive Strategy for Backpropagation Neural Network Optimization in Predicting High-Performance Concrete Strength

by Kaifan Zhang, Xiangyu Li, Songsong Zhang and Shuo Zhang

Biomimetics 2025, 10(8), 515; https://doi.org/10.3390/biomimetics10080515 (registering DOI) - 6 Aug 2025

Abstract

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant [...] Read more.

Accurately predicting the compressive strength of high-performance concrete (HPC) is critical for ensuring structural integrity and promoting sustainable construction practices. However, HPC exhibits highly complex, nonlinear, and multi-factorial interactions among its constituents (such as cement, aggregates, admixtures, and curing conditions), which pose significant challenges to conventional predictive models. Traditional approaches often fail to adequately capture these intricate relationships, resulting in limited prediction accuracy and poor generalization. Moreover, the high dimensionality and noisy nature of HPC mix data increase the risk of model overfitting and convergence to local optima during optimization. To address these challenges, this study proposes a novel bio-inspired hybrid optimization model, AP-IVYPSO-BP, which is specifically designed to handle the nonlinear and complex nature of HPC strength prediction. The model integrates the ivy algorithm (IVYA) with particle swarm optimization (PSO) and incorporates an adaptive probability strategy based on fitness improvement to dynamically balance global exploration and local exploitation. This design effectively mitigates common issues such as premature convergence, slow convergence speed, and weak robustness in traditional metaheuristic algorithms when applied to complex engineering data. The AP-IVYPSO is employed to optimize the weights and biases of a backpropagation neural network (BPNN), thereby enhancing its predictive accuracy and robustness. The model was trained and validated on a dataset comprising 1,030 HPC mix samples. Experimental results show that AP-IVYPSO-BP significantly outperforms traditional BPNN, PSO-BP, GA-BP, and IVY-BP models across multiple evaluation metrics. Specifically, it achieved an R² of 0.9542, MAE of 3.0404, and RMSE of 3.7991 on the test set, demonstrating its high accuracy and reliability. These results confirm the potential of the proposed bio-inspired model in the prediction and optimization of concrete strength, offering practical value in civil engineering and materials design. Full article

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)

15 pages, 284 KiB

Open AccessArticle

Co-Use of Alcohol and Cannabis During COVID-19: Associations Between Sociodemographic Factors and Self-Reported Mental Health Symptoms and Heavy Episodic Drinking in Canadian Adults

by Nibene H. Somé, Sameer Imtiaz, Yeshambel T. Nigatu, Samantha Wells, Claire de Oliveira, Shehzad Ali, Tara Elton-Marshall, Jürgen Rehm, Kevin D. Shield and Hayley A. Hamilton

Psychoactives 2025, 4(3), 27; https://doi.org/10.3390/psychoactives4030027 (registering DOI) - 6 Aug 2025

Abstract

This study estimates the prevalence of co-use of alcohol and cannabis, assesses the sociodemographic risk factors of co-use, and examines the associations between mental health and heavy episodic drinking (HED) and alcohol–cannabis co-use in Canada during the early years of the COVID-19 pandemic. [...] Read more.

This study estimates the prevalence of co-use of alcohol and cannabis, assesses the sociodemographic risk factors of co-use, and examines the associations between mental health and heavy episodic drinking (HED) and alcohol–cannabis co-use in Canada during the early years of the COVID-19 pandemic. Nine successive cross-sectional surveys, held from May 2020 to January 2022, of adults (aged ≥18 years) living in Canada were pooled for 9011 participants. The prevalence of co-use was calculated across sociodemographic groups. Logistic regressions were used to assess associations. Alcohol–cannabis co-use was associated with a greater likelihood of engaging in HED and experiencing symptoms of anxiety, depression, and loneliness. The prevalence of co-use of alcohol was different across sociodemographic groups. The highest prevalence was among TGD people (35.5%), followed by individuals aged 18–39 years (14.5%). Additionally, being TGD (aOR = 3.61, 95% CI 2.09–6.25), separated/divorced/widowed (aOR = 1.60, 95% CI 1.23–2.07), living in an urban area (aOR = 1.26, 95% CI 1.07–1.56), and having a high household income (aOR = 1.41, 95% CI 1.09–1.82) increased the likelihood of reporting alcohol–cannabis co-use. These findings underscore the fact that developing public health and clinical interventions for preventing and treating excessive alcohol or cannabis use must consider both alcohol and cannabis use patterns and should be tailored to the highest-risk TGD and young adults. Full article

21 pages, 826 KiB

Open AccessArticle

Socio-Economic and Environmental Trade-Offs of Sustainable Energy Transition in Kentucky

by Sydney Oluoch, Nirmal Pandit and Cecelia Harner

Sustainability 2025, 17(15), 7133; https://doi.org/10.3390/su17157133 (registering DOI) - 6 Aug 2025

Abstract

A just and sustainable energy transition in historically coal-dependent regions like Kentucky requires more than the adoption of new technologies and market-based solutions. This study uses a stated preferences approach to evaluate public support for various attributes of energy transition programs, revealing broad [...] Read more.

A just and sustainable energy transition in historically coal-dependent regions like Kentucky requires more than the adoption of new technologies and market-based solutions. This study uses a stated preferences approach to evaluate public support for various attributes of energy transition programs, revealing broad backing for moving away from coal, as indicated by a negative willingness to pay (WTP) for the status quo (–USD 4.63). Key findings show strong bipartisan support for solar energy, with Democrats showing the highest WTP at USD 8.29, followed closely by Independents/Others at USD 8.22, and Republicans at USD 8.08. Wind energy also garnered support, particularly among Republicans (USD 4.04), who may view it as more industry-compatible and less ideologically polarizing. Job creation was a dominant priority across political affiliations, especially for Independents (USD 9.07), indicating a preference for tangible, near-term economic benefits. Similarly, preserving cultural values tied to coal received support among Independents/Others (USD 4.98), emphasizing the importance of place-based identity in shaping preferences. In contrast, social support programs (e.g., job retraining) and certain post-mining land uses (e.g., recreation and conservation) were less favored, possibly due to their abstract nature, delayed benefits, and political framing. Findings from Kentucky offer insights for other coal-reliant states like Wyoming, West Virginia, Pennsylvania, Indiana, and Illinois. Ultimately, equitable transitions must integrate local voices, address cultural and economic realities, and ensure community-driven planning and investment. Full article

(This article belongs to the Special Issue Energy, Environmental Policy and Sustainable Development)

22 pages, 639 KiB

Open AccessArticle

Variations on the Theme “Definition of the Orthodrome”

by Miljenko Lapaine

ISPRS Int. J. Geo-Inf. 2025, 14(8), 306; https://doi.org/10.3390/ijgi14080306 (registering DOI) - 6 Aug 2025

Abstract

A geodesic or geodetic line on a sphere is called the orthodrome. Research has shown that the orthodrome can be defined in a large number of ways. This article provides an overview of various definitions of the orthodrome. We recall the definitions of [...] Read more.

A geodesic or geodetic line on a sphere is called the orthodrome. Research has shown that the orthodrome can be defined in a large number of ways. This article provides an overview of various definitions of the orthodrome. We recall the definitions of the orthodrome according to the greats of geodesy, such as Bessel and Helmert. We derive the equation of the orthodrome in the geographic coordinate system and in the Cartesian spatial coordinate system. A geodesic on a surface is a curve for which the geodetic curvature is zero at every point. Equivalent expressions of this statement are that at every point of this curve, the principal normal vector is collinear with the normal to the surface, i.e., it is a curve whose binormal at every point is perpendicular to the normal to the surface, and that it is a curve whose osculation plane contains the normal to the surface at every point. In this case, the well-known Clairaut equation of the geodesic in geodesy appears naturally. It is found that this equation can be written in several different forms. Although differential equations for geodesics can be found in the literature, they are solved in this article, first, by taking the sphere as a special case of any surface, and then as a special case of a surface of rotation. At the end of this article, we apply calculus of variations to determine the equation of the orthodrome on the sphere, first in the Bessel way, and then by applying the Euler–Lagrange equation. Overall, this paper elaborates a dozen different approaches to orthodrome definitions. Full article

► Show Figures

Figure 1

19 pages, 2135 KiB

Open AccessArticle

Development of an Automotive Electronics Internship Assistance System Using a Fine-Tuned Llama 3 Large Language Model

by Ying-Chia Huang, Hsin-Jung Tsai, Hui-Ting Liang, Bo-Siang Chen, Tzu-Hsin Chu, Wei-Sho Ho, Wei-Lun Huang and Ying-Ju Tseng

Systems 2025, 13(8), 668; https://doi.org/10.3390/systems13080668 (registering DOI) - 6 Aug 2025

Abstract

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited [...] Read more.

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10⁻⁵ across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ² = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills. Full article

(This article belongs to the Special Issue The Application of a Large Language Model (LLM) in Education Reform and Innovation)

► Show Figures

Figure 1

Recent Articles

Open Access Journals

Highly Accessed Articles

Latest Books

News

Blog Posts

Topics

Selected Special Issues

Selected Collections

Journals by Subject

Further Information

Guidelines

MDPI Initiatives

Follow MDPI