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Recent Articles  

11 pages, 2759 KiB  
Technical Note
User-Friendly Tool for Expedited Ground Vibration Assessment Induced by Impact Pile Driving
by Ahmed M. Abouelmaty, Aires Colaço and Pedro Alves Costa
Vibration 2025, 8(2), 17; https://doi.org/10.3390/vibration8020017 - 4 Apr 2025
Abstract
Driven piles are a common geotechnical solution for foundations in weak soil profiles. However, hammer impacts during the driving process can generate excessive levels of ground vibration, which, in extreme cases, can affect nearby structures and people. Due to the complexity of wave [...] Read more.
Driven piles are a common geotechnical solution for foundations in weak soil profiles. However, hammer impacts during the driving process can generate excessive levels of ground vibration, which, in extreme cases, can affect nearby structures and people. Due to the complexity of wave propagation in soils, the accurate prediction of these vibrations typically requires advanced numerical modeling approaches. To address this challenge, a surrogate modeling framework was developed by integrating Artificial Neural Networks (ANNs) and Extreme Gradient Boosting (XGBoost), trained on a synthetic dataset generated from an experimentally validated numerical model. The proposed surrogate model enables the rapid prediction of ground vibration characteristics, including peak particle velocity (PPV) and frequency content, across a broad range of soil, pile, and hammer conditions. In addition to its predictive capabilities, the tool allows users to design a specific mitigation measure (open trench) and compare the vibration levels with international standards. Experimental validation confirmed the model’s ability to replicate field measurements with acceptable accuracy. The expedited prediction tool is available as supplemental data and can be used by other researchers and technicians for quick and accurate ground vibration predictions. Full article
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18 pages, 6451 KiB  
Article
Social Network Analysis Reveals Spatiotemporal Patterns of Green Space Recreational Walking Between Workdays and Rest Days
by Jiali Zhang and Zhaocheng Bai
Urban Sci. 2025, 9(4), 111; https://doi.org/10.3390/urbansci9040111 - 4 Apr 2025
Abstract
Growing concerns about the negative impacts of high-density built environments on residents’ physical and mental health have made optimizing recreational walking networks in green spaces a crucial issue for improving urban public health service efficiency. While previous studies have largely focused on static [...] Read more.
Growing concerns about the negative impacts of high-density built environments on residents’ physical and mental health have made optimizing recreational walking networks in green spaces a crucial issue for improving urban public health service efficiency. While previous studies have largely focused on static accessibility measures, these methods cannot capture actual human recreational behaviors and temporal variations in green space usage. Our research introduces a novel social network analysis methodology using GPS trajectory data from Shanghai’s Inner Ring Area to construct and compare recreational walking networks during workdays and rest days, revealing dynamic spatiotemporal patterns that traditional methods miss. Key findings include: (1) At the node level, green spaces of different sizes play differentiated roles in the network, with large-scale spaces serving as destination hubs while pocket green spaces function as critical connecting points; (2) At the regional level, workday networks show more dispersed spatial distribution patterns with higher modularity, while rest day networks form high-density clusters in the central urban area; (3) At the overall network level, rest day networks demonstrate higher density and diversity, reflecting residents’ expanded spatial activity range and diverse recreational preferences. Green space management should focus on the social value of urban green networks. These findings provide theoretical and methodological support for transitioning from “static equity” to “dynamic justice” in green space system planning, contributing to the development of more inclusive and resilient urban green space networks. Full article
(This article belongs to the Special Issue Assessing Urban Ecological Environment Protection)
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19 pages, 835 KiB  
Review
Current Challenges and Issues in the Application of Astaxanthin
by Limin Peng, Zhiqiang Zhang, Qing Li and Hui Yang
Fishes 2025, 10(4), 159; https://doi.org/10.3390/fishes10040159 - 4 Apr 2025
Abstract
Astaxanthin, a xanthophyll carotenoid, exhibits potent biological functions, including antioxidant, immune regulation, growth promotion, improved reproductive capacity, and enhancement of the body color of aquatic animals. In recent years, with the rapid development of the aquaculture industry, the application of astaxanthin in aquaculture [...] Read more.
Astaxanthin, a xanthophyll carotenoid, exhibits potent biological functions, including antioxidant, immune regulation, growth promotion, improved reproductive capacity, and enhancement of the body color of aquatic animals. In recent years, with the rapid development of the aquaculture industry, the application of astaxanthin in aquaculture has garnered increasing attention. Studies have demonstrated that astaxanthin significantly enhances the antioxidant capacity of aquatic animals, reduces oxidative damage, and regulates the expression of immune-related genes, thereby improving immunity and disease resistance. Moreover, astaxanthin promotes growth and reproductive performance, particularly in high-value aquaculture species, where it also serves as a natural pigment to increase market competitiveness. However, the low bioavailability and high production costs of astaxanthin remain major constraints to its widespread use in aquaculture. To address these limitations, various strategies—such as microencapsulation, liposomal delivery, and nanotechnology—have been explored to improve its stability and water solubility. Additionally, expanding astaxanthin sources and optimizing production processes are effective approaches to reducing costs. This review summarizes recent advances in astaxanthin research within aquaculture, highlights its multifunctional roles in promoting the health and production efficiency of aquatic animals, and discusses the current challenges and future research directions. Full article
(This article belongs to the Section Welfare, Health and Disease)
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20 pages, 7686 KiB  
Review
Learning from Octopuses: Cutting-Edge Developments and Future Directions
by Jinjie Duan, Yuning Lei, Jie Fang, Qi Qi, Zhiming Zhan and Yuxiang Wu
Biomimetics 2025, 10(4), 224; https://doi.org/10.3390/biomimetics10040224 - 4 Apr 2025
Abstract
This paper reviews the research progress of bionic soft robot technology learned from octopuses. The number of related research papers increased from 760 in 2021 to 1170 in 2024 (Google Scholar query), with a growth rate of 53.95% in the past five years. [...] Read more.
This paper reviews the research progress of bionic soft robot technology learned from octopuses. The number of related research papers increased from 760 in 2021 to 1170 in 2024 (Google Scholar query), with a growth rate of 53.95% in the past five years. These studies mainly explore how humans can learn from the physiological characteristics of octopuses for sensor design, actuator development, processor architecture optimization, and intelligent optimization algorithms. The tentacle structure and nervous system of octopus have high flexibility and distributed control capabilities, which is an important reference for the design of soft robots. In terms of sensor technology, flexible strain sensors and suction cup sensors inspired by octopuses achieve accurate environmental perception and interaction. Actuator design uses octopus muscle fibers and movement patterns to develop various driving methods, including pneumatic, hydraulic and electric systems, which greatly improves the robot’s motion performance. In addition, the distributed nervous system of octopuses inspires multi-processor architecture and intelligent optimization algorithms. This paper also introduces the concept of expected functional safety for the first time to explore the safe design of soft robots in failure or unknown situations. Currently, there are more and more bionic soft robot technologies that draw on octopuses, and their application areas are constantly expanding. In the future, with further research on the physiological characteristics of octopuses and the integration of artificial intelligence and materials science, octopus soft robots are expected to show greater potential in adapting to complex environments, human–computer interaction, and medical applications. Full article
(This article belongs to the Special Issue Bio-Inspired Soft Robotics: Design, Fabrication and Applications)
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26 pages, 18086 KiB  
Article
Interconnected Histories: Searching for Jacob Gens’ Grave and Instead Finding a Forgotten Early 18th Century Cemetery
by Philip Reeder, Harry Jol, Alastair McClymont, Paul Bauman and Mantas Daubaras
Histories 2025, 5(2), 17; https://doi.org/10.3390/histories5020017 - 4 Apr 2025
Abstract
Jacob Gens, the head of the Vilnius Ghetto Police Force, and eventually the entire Ghetto during the Holocaust, was murdered on 14 September 1943 by the head of the Vilnius Gestapo. Historical documents and Holocaust survivor testimonies indicate that he was killed at [...] Read more.
Jacob Gens, the head of the Vilnius Ghetto Police Force, and eventually the entire Ghetto during the Holocaust, was murdered on 14 September 1943 by the head of the Vilnius Gestapo. Historical documents and Holocaust survivor testimonies indicate that he was killed at a site that became known as the Rasu Street Prison, and not the Gestapo Headquarters, as it is widely believed. In 2016, research was completed at the Rasu Street Prison site using ground penetrating radar (GPR) and electrical resistivity tomography (ERT) to locate subsurface reflection patterns that possibly indicate the location of where Jacob Gens is buried. Intersecting GPR and ERT reflection patterns were discovered and a plan was put in place to excavate that location. The excavation revealed the presence of human remains at 1.45 m below the surface. A skull and upper torso were exposed, and two teeth were collected for DNA and radiocarbon analysis. The DNA from the tooth was compared to Jacob Gens’ daughter’s DNA, and this comparison yielded a negative result, so the human remains were not those of Jacob Gens. The radiocarbon analysis provided a date between 1685 and 1735. In 1705, a plot of land was donated to the Visitation Monastery, which used this plot, and which coincides with the location of the Rasu Street Prison, as a cemetery for the poor. In 1709 and 1710, a plague epidemic was prevalent in Vilnius, as was turmoil and famine associated with the Great Northern War (1700 to 1721). Based on these discoveries, rather than finding the remains of Jacob Gens, it is likely that we found human remains that are part of a forgotten 18th century cemetery associated with the Visitation Monastery. Full article
(This article belongs to the Section Cultural History)
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23 pages, 352 KiB  
Article
Unmasking Delistings: A Multifactorial Analysis of Financial, Non-Financial, and Macroeconomic Variables
by Peter Lansdell, Ilse Botha and Ben Marx
J. Risk Financial Manag. 2025, 18(4), 194; https://doi.org/10.3390/jrfm18040194 - 4 Apr 2025
Abstract
The stability of financial markets is influenced by the strength and transparency of companies listed on stock exchanges. This paper explores how financial, non-financial, and macroeconomic factors influence delisting likelihood among companies listed on the Johannesburg Stock Exchange (JSE), addressing a limitation in [...] Read more.
The stability of financial markets is influenced by the strength and transparency of companies listed on stock exchanges. This paper explores how financial, non-financial, and macroeconomic factors influence delisting likelihood among companies listed on the Johannesburg Stock Exchange (JSE), addressing a limitation in the current body of knowledge that often overlooks the combination of these factors, especially within the context of developing economies. Using a sample of 302 companies delisted between 2010 and 2023 and 302 as a control group, we analyzed 72 variables through a multivariate panel probit regression model. Our findings reveal that delisting decisions are driven by a complex interplay of financial health, governance practices, and macroeconomic conditions. Financial health, including liquidity and market valuation, is crucial in mitigating delisting risk. Non-financial factors, such as corporate governance and shareholder composition, further reduce the likelihood of delisting. Macroeconomic conditions, including inflation and interest rates, introduce significant external pressures. This study is especially relevant in developing economies like South Africa, where economic volatility adds risks for listed companies. The results provide insights for companies, investors, regulators, and policymakers to ensure a stable and robust stock market and financial system and identify early warning signals for delisting. Full article
(This article belongs to the Section Applied Economics and Finance)
14 pages, 502 KiB  
Article
Gender Differences in Objective and Subjective Social Isolation and Self-Reported Hypertension in Older Adults
by Tyrone C. Hamler, Ann W. Nguyen, Harry Owen Taylor, Weidi Qin and Fei Wang
J. Cardiovasc. Dev. Dis. 2025, 12(4), 136; https://doi.org/10.3390/jcdd12040136 - 4 Apr 2025
Abstract
Hypertension is a major public health concern, especially in older adults, and gender differences are a factor in terms of its prevalence. Social connections benefit health, while social isolation is linked to negative outcomes. Prior studies suggest social isolation and connectedness vary by [...] Read more.
Hypertension is a major public health concern, especially in older adults, and gender differences are a factor in terms of its prevalence. Social connections benefit health, while social isolation is linked to negative outcomes. Prior studies suggest social isolation and connectedness vary by gender, but few have explored this relationship with hypertension. This study examined gender differences in the association between social isolation and hypertension in older adults using data from the National Survey of American Life (1280 adults aged ≥55). Weighted logistic regressions tested gender differences in objective and subjective social isolation and hypertension. Both men and women who were objectively isolated from family and friends, or only friends, were less likely to have hypertension than those not isolated. However, when accounting for subjective isolation, only isolation from family predicted hypertension. Gender moderated this relationship—men isolated from family and friends had a higher likelihood of hypertension, while no such association was found for women. Findings suggest that preventing objective isolation, particularly from family, may help reduce hypertension risk in older adults. This study highlights the need to further investigate social isolation’s impact on health and its underlying mechanisms among older adults in the U.S. Full article
(This article belongs to the Section Epidemiology, Lifestyle, and Cardiovascular Health)
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17 pages, 5491 KiB  
Article
Dynamics of the Diphtheria Epidemic in Nigeria: Insights from the Kano State Outbreak Data
by Sani Musa, Salisu Usaini, Idris Ahmed, Chanakarn Kiataramkul and Jessada Tariboon
Mathematics 2025, 13(7), 1189; https://doi.org/10.3390/math13071189 - 4 Apr 2025
Abstract
Diphtheria is a severely infectious and deadly bacterial disease with Corynebacterium diphtheriae as the causative agent. Since the COVID-19 pandemic, contagious diseases such as diphtheria have re-emerged due to disruptions in routine childhood immunization programs worldwide. Nigeria is witnessing a significant increase in [...] Read more.
Diphtheria is a severely infectious and deadly bacterial disease with Corynebacterium diphtheriae as the causative agent. Since the COVID-19 pandemic, contagious diseases such as diphtheria have re-emerged due to disruptions in routine childhood immunization programs worldwide. Nigeria is witnessing a significant increase in diphtheria outbreaks likely due to an inadequate health care system and insufficient public enlightenment campaign. This paper presents a mathematical epidemic diphtheria model in Nigeria, which includes a public enlightenment campaign to assess its positive impact on the prevalence of the disease. The mathematical analysis of the model reveals two equilibrium points: the diphtheria infection-free equilibrium and the endemic equilibrium. These equilibrium points are shown to be stable globally asymptotically if Rc<1 and Rc>1, respectively. The model was fit using the confirmed diphtheria cases data of Kano State from January to December 2023. Sensitivity analysis indicates that the transmission rate and recovery rate of asymptomatic peopleare crucial parameters to be considered in developing effective strategies for diphtheria control and prevention. This analysis also reveals that the implementation of a high-level public enlightenment campaign and its high efficacy effectively reduce the prevalence of diphtheria. Finally, numerical simulations show that combining the public enlightenment campaign and isolating infected individuals is the best strategy to contain the spread of diphtheria. Full article
(This article belongs to the Special Issue Mathematical Modeling of Disease Dynamics)
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16 pages, 7032 KiB  
Article
I-NeRV: A Single-Network Implicit Neural Representation for Efficient Video Inpainting
by Jie Ji, Shuxuan Fu and Jiaju Man
Mathematics 2025, 13(7), 1188; https://doi.org/10.3390/math13071188 - 4 Apr 2025
Abstract
Deep learning methods based on implicit neural representations offer an efficient and automated solution for video inpainting by leveraging the inherent characteristics of video data. However, the limited size of the video embedding (e.g., 16×2×4) generated by the [...] Read more.
Deep learning methods based on implicit neural representations offer an efficient and automated solution for video inpainting by leveraging the inherent characteristics of video data. However, the limited size of the video embedding (e.g., 16×2×4) generated by the encoder restricts the available feature information for the decoder, which, in turn, constrains the model’s representational capacity and degrades inpainting performance. While implicit neural representations have shown promise for video inpainting, most of the existing research still revolves around image inpainting and does not fully account for the spatiotemporal continuity and relationships present in videos. This gap highlights the need for more advanced techniques capable of capturing and exploiting the spatiotemporal dynamics of video data to further improve inpainting results. To address this issue, we introduce I-NeRV, the first implicit neural-representation-based design specifically tailored for video inpainting. By embedding spatial features and modeling the spatiotemporal continuity between frames, I-NeRV significantly enhances inpainting performance, especially for videos with missing regions. To further boost the quality of inpainting, we propose an adaptive embedding size design and a weighted loss function. We also explore strategies for balancing model size and computational efficiency, such as fine-tuning the embedding size and customizing convolution kernels to accommodate various resource constraints. Extensive experiments on benchmark datasets demonstrate that our approach substantially outperforms state-of-the-art methods in video inpainting, achieving an average of 3.47 PSNR improvement in quality metrics. Full article
(This article belongs to the Special Issue New Trends in Computer Vision, Deep Learning and Artificial Intelligence)
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21 pages, 915 KiB  
Article
Access to Livelihood Assets and Vulnerability to Lower Levels of Well-Being in Kakuma Refugee Camp, Kenya
by Mary Nyambura Kinyanjui
Economies 2025, 13(4), 103; https://doi.org/10.3390/economies13040103 - 4 Apr 2025
Abstract
This paper investigates the role that access to livelihood assets plays in reducing vulnerability to lower levels of well-being, especially for camp-based refugees. We develop the multidimensional vulnerability index using the 2019 Kakuma socioeconomic survey to provide a comprehensive and holistic approach to [...] Read more.
This paper investigates the role that access to livelihood assets plays in reducing vulnerability to lower levels of well-being, especially for camp-based refugees. We develop the multidimensional vulnerability index using the 2019 Kakuma socioeconomic survey to provide a comprehensive and holistic approach to measuring vulnerability. The fractional regression results suggest that the household head’s age and education level determine the vulnerability of refugees to lower levels of well-being. In addition, access to finance and employment substantially reduces refugees’ vulnerability. Although remittances from abroad are a prevalent source of finance among refugees, we find that remittances from abroad only lessen the prevalence of vulnerability by 1.1%. Therefore, we recommend camp refugees adopt more self-reliant ways of accessing sustainable finance. The multidimensional vulnerability index reveals a high level of food insecurity in camps caused by the influx of refugees over the years. We recommend the inclusion of refugees in farming and training on climate change to provide sustainable solutions around food security to them and the host community. Full article
(This article belongs to the Special Issue Human Capital Development in Africa)
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25 pages, 13176 KiB  
Article
Deep Object Occlusion Relationship Detection Based on Associative Embedding Clustering
by Peiyong Gong, Kai Zheng, Ting Liu, Yi Jiang and Huixuan Zhao
Technologies 2025, 13(4), 143; https://doi.org/10.3390/technologies13040143 - 4 Apr 2025
Abstract
Visual relationship detection is crucial for understanding scenes depicted in images when aiming to detect objects within the image and recognize the visual relationships between each pair of objects. Nevertheless, profound occlusion, as a typical visual relationship existing between objects and constituting a [...] Read more.
Visual relationship detection is crucial for understanding scenes depicted in images when aiming to detect objects within the image and recognize the visual relationships between each pair of objects. Nevertheless, profound occlusion, as a typical visual relationship existing between objects and constituting a pivotal semantic feature, has regrettably been subjected to insufficient scrutiny. To address this issue, we propose a pioneering approach termed DOORD-AEC, which is specifically designed for detecting occlusion spatial relationships among targets. DOORD-AEC introduces associative embedding clustering to supervise a convolutional neural network with two branches, enabling it to take in an input image and produce a triplet set representing occlusion spatial relationships. The network learns to simultaneously identify all of the targets and occlusions that make up the triplet set and group them together using associative embedding clustering. Additionally, we contribute the KORD dataset, which is a novel and challenging dataset for occlusion spatial relationships among targets. We demonstrate the effectiveness of our DOORD-AEC method using this dataset. Full article
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10 pages, 208 KiB  
Article
Leveraging Artificial Intelligence for Personalized Rehabilitation Programs for Head and Neck Surgery Patients
by Gianluca Marcaccini, Ishith Seth, Jennifer Novo, Vicki McClure, Brett Sacks, Kaiyang Lim, Sally Kiu-Huen Ng, Roberto Cuomo and Warren M. Rozen
Technologies 2025, 13(4), 142; https://doi.org/10.3390/technologies13040142 - 4 Apr 2025
Abstract
Background: Artificial intelligence (AI) and large language models (LLMs) are increasingly used in healthcare, with applications in clinical decision-making and workflow optimization. In head and neck surgery, postoperative rehabilitation is a complex, multidisciplinary process requiring personalized care. This study evaluates the feasibility of [...] Read more.
Background: Artificial intelligence (AI) and large language models (LLMs) are increasingly used in healthcare, with applications in clinical decision-making and workflow optimization. In head and neck surgery, postoperative rehabilitation is a complex, multidisciplinary process requiring personalized care. This study evaluates the feasibility of using LLMs to generate tailored rehabilitation programs for patients undergoing major head and neck surgical procedures. Methods: Ten hypothetical head and neck surgical clinical scenarios were developed, representing oncologic resections with complex reconstructions. Four LLMs, ChatGPT-4o, DeepSeek V3, Gemini 2, and Copilot, were prompted with identical queries to generate rehabilitation plans. Three senior clinicians independently assessed their quality, accuracy, and clinical relevance using a five-point Likert scale. Readability and quality metrics, including the DISCERN score, Flesch Reading Ease, Flesch–Kincaid Grade Level, and Coleman–Liau Index, were applied. Results: ChatGPT-4o achieved the highest clinical relevance (Likert mean of 4.90 ± 0.32), followed by DeepSeek V3 (4.00 ± 0.82) and Gemini 2 (3.90 ± 0.74), while Copilot underperformed (2.70 ± 0.82). Gemini 2 produced the most readable content. A statistical analysis confirmed significant differences across the models (p < 0.001). Conclusions: LLMs can generate rehabilitation programs with varying quality and readability. ChatGPT-4o produced the most clinically relevant plans, while Gemini 2 generated more readable content. AI-generated rehabilitation plans may complement existing protocols, but further clinical validation is necessary to assess their impact on patient outcomes. Full article
(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives)
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Graphical abstract

18 pages, 3425 KiB  
Article
Improvement of Bank Fraud Detection Through Synthetic Data Generation with Gaussian Noise
by Fray L. Becerra-Suarez, Halyn Alvarez-Vasquez and Manuel G. Forero
Technologies 2025, 13(4), 141; https://doi.org/10.3390/technologies13040141 - 4 Apr 2025
Abstract
Bank fraud detection faces critical challenges in imbalanced datasets, where fraudulent transactions are rare, severely impairing model generalization. This study proposes a Gaussian noise-based augmentation method to address class imbalance, contrasting it with SMOTE and ADASYN. By injecting controlled perturbations into the minority [...] Read more.
Bank fraud detection faces critical challenges in imbalanced datasets, where fraudulent transactions are rare, severely impairing model generalization. This study proposes a Gaussian noise-based augmentation method to address class imbalance, contrasting it with SMOTE and ADASYN. By injecting controlled perturbations into the minority class, our approach mitigates overfitting risks inherent in interpolation-based techniques. Five classifiers, including XGBoost and a convolutional neural network (CNN), were evaluated on augmented datasets. XGBoost achieved superior performance with Gaussian noise-augmented data (accuracy: 0.999507, AUC: 0.999506), outperforming SMOTE and ADASYN. These results underscore Gaussian noise’s efficacy in enhancing fraud detection accuracy, offering a robust alternative to conventional oversampling methods. Our findings emphasize the pivotal role of augmentation strategies in optimizing classifier performance for imbalanced financial data. Full article
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28 pages, 3613 KiB  
Article
Chatbot Based on Large Language Model to Improve Adherence to Exercise-Based Treatment in People with Knee Osteoarthritis: System Development
by Humberto Farías, Joaquín González Aroca and Daniel Ortiz
Technologies 2025, 13(4), 140; https://doi.org/10.3390/technologies13040140 - 4 Apr 2025
Abstract
Knee osteoarthritis (KOA) is a prevalent condition globally, leading to significant pain and disability, particularly in individuals over the age of 40. While exercise has been shown to reduce symptoms and improve physical function and quality of life in patients with KOA, long-term [...] Read more.
Knee osteoarthritis (KOA) is a prevalent condition globally, leading to significant pain and disability, particularly in individuals over the age of 40. While exercise has been shown to reduce symptoms and improve physical function and quality of life in patients with KOA, long-term adherence to exercise programs remains a challenge due to the lack of ongoing support. To address this, a chatbot was developed using large language models (LLMs) to provide evidence-based guidance and promote adherence to treatment. A systematic review conducted under the PRISMA framework identified relevant clinical guidelines that served as the foundational knowledge base for the chatbot. The Mistral 7B model, optimized with Parameter-Efficient Fine-Tuning (PEFT) and Mixture-of-Experts (MoE) techniques, was integrated to ensure computational efficiency and mitigate hallucinations, a critical concern in medical applications. Additionally, the chatbot employs Self-Reflective Retrieval-Augmented Generation (SELF-RAG) combined with Chain of Thought (CoT) reasoning, enabling dynamic query reformulation and the generation of accurate, evidence-based responses tailored to patient needs. The chatbot was evaluated by comparing pre- and post-improvement versions and against a reference model (ChatGPT), using metrics of accuracy, relevance, and consistency. The results demonstrated significant improvements in response quality and conversational coherence, emphasizing the potential of integrating advanced LLMs with retrieval and reasoning methods to address critical challenges in healthcare. This approach not only enhances treatment adherence but also strengthens patient–provider interactions in managing chronic conditions like KOA. Full article
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)
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14 pages, 263 KiB  
Review
The Multi-Pistil Phenomenon in Higher Plants
by Liang Chai, Cheng Cui, Benchuan Zheng, Ka Zhang, Yanling Li, Tongyun Zhang, Yongchun Zhou, Jun Jiang, Haojie Li, Jinfang Zhang and Liangcai Jiang
Plants 2025, 14(7), 1125; https://doi.org/10.3390/plants14071125 - 4 Apr 2025
Abstract
Correct floral morphology determines the accuracy of fruit formation, which is crucial for reproductive success in higher plants. Despite this, an abnormal, multi-pistil phenotype has been observed in the flowers of many plants. In this review, we gather information on the multi-pistil phenomenon [...] Read more.
Correct floral morphology determines the accuracy of fruit formation, which is crucial for reproductive success in higher plants. Despite this, an abnormal, multi-pistil phenotype has been observed in the flowers of many plants. In this review, we gather information on the multi-pistil phenomenon in various species and highlight potential causes, as well as possible consequences, of the trait. Our assessment of the reported multi-pistil phenotype in rice (Oryza sativa L.), wheat (Triticum aestivum L.), tomato (Solanum lycopersicum L.), Medicago, sweet cherry (Prunus avium L.), rye (Secale cereale L.), and rapeseed (Brassica napus L. and B. campestris L.) leads us to conclude that hybridization and mutation are the main factors that give rise to this phenotype. We also delve into the inheritance patterns of the multi-pistil phenotype and factors that influence this trait, such as nuclear–cytoplasmic interactions, temperature conditions, and shading. Finally, we discuss the effects of multi-pistil flowers on the yield of these plants. This analysis increases our understanding of floral development and lays the foundation for the potential utilization of the multi-pistil trait to increase seed production in crops. Full article
(This article belongs to the Section Plant Development and Morphogenesis)
19 pages, 16309 KiB  
Article
Nutrient Uptake of Two Semidomesticated Jaltomata Schltdl. Species for Their Cultivation
by Ignacio Darío Flores-Sánchez, Manuel Sandoval-Villa and Ebandro Uscanga-Mortera
Plants 2025, 14(7), 1124; https://doi.org/10.3390/plants14071124 - 4 Apr 2025
Abstract
The nutrient uptake of a species under cultivated conditions is important for program fertilization. The Jaltomata genus has two semidomesticated species, J. procumbens and J. tlaxcala, used as food and considered with potential for their study in controlled environments. The objective of [...] Read more.
The nutrient uptake of a species under cultivated conditions is important for program fertilization. The Jaltomata genus has two semidomesticated species, J. procumbens and J. tlaxcala, used as food and considered with potential for their study in controlled environments. The objective of this research was to determine nutrient uptake curves of these species in a greenhouse and using hydroponics. The research was carried out at the Colegio de Postgraduados, Campus Montecillo, Texcoco, State of Mexico, from August to November 2020. The treatments included the following: two species and three electrical conductivity levels: 1, 2, and 3 dS m−1. Nutrients in leaf and total dry matter (TDM) were determined. Variability between species and phenological stages on the nutrient concentration and accumulation of TDM was observed. For macronutrients, J. procumbens concentrated in descending order more P from the vegetative stage (4.21–2.43 g kg−1 dry matter), and Mg until fructification (4.92–3.26 g kg−1 dry matter), for K it was higher at vegetative (52.29 g kg−1 dry matter) and harvesting stages (26.05 g kg−1 dry matter), and N (23.92 g kg−1 dry matter) at flowering; J. tlaxcala concentrated more Ca from fructification (10.10–13.85 g kg−1 dry matter). For micronutrients, J. tlaxcala concentrated more Fe from the vegetative stage (157.7–207.5 mg kg−1 dry matter), B and Zn at 23.3–38.4 and 26.04–28.45 mg kg−1 dry matter, respectively, from flowering, and Mn (108.4–232.28 mg kg−1 dry matter) from fructification. The main structures of TDM accumulation by vegetative stage in J. procumbens were the leaf and root (vegetative and flowering), root and stem (fructification), and reproductive structures and root (harvesting); in J. tlaxcala, the main structures were the leaf and root (vegetative), root and leaf (flowering and fructification), and root and reproductive structures (harvesting). Due to this variability, specific fertilization programs are required for each species. Full article
(This article belongs to the Special Issue Nutrient Requirements and Fertilizer Management Strategies in Plant Cultivation)
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12 pages, 2679 KiB  
Article
In Vitro Propagation of Clausena lenis Drake
by Pajaree Sathuphan, Srunya Vajrodaya, Nuttha Sanevas and Narong Wongkantrakorn
Plants 2025, 14(7), 1123; https://doi.org/10.3390/plants14071123 - 4 Apr 2025
Abstract
Clausena lenis Drake, a valuable medicinal plant in the Rutaceae family, faces threats from wildlife predation, overharvesting, and climate change. In the wild, C. lenis primarily propagates through seeds; however, their rapid loss of viability poses challenges for long-term storage and germplasm conservation. [...] Read more.
Clausena lenis Drake, a valuable medicinal plant in the Rutaceae family, faces threats from wildlife predation, overharvesting, and climate change. In the wild, C. lenis primarily propagates through seeds; however, their rapid loss of viability poses challenges for long-term storage and germplasm conservation. Plant tissue culture offers a practical solution for both its conservation and large-scale production. This study examines seed sterilization, callus induction, shoot multiplication, and root induction protocols for C. lenis. Seeds attained a 100% sterilization rate using 0.2% (w/v) HgCl2 for 20 min without compromising germination. When cultured on MS medium containing 0.5 mg/L 2,4-D, seed, stem-node, and 1-week-old seedling explants produced abundant callus. A 2.0 mg/L BA treatment achieved 100% shoot induction, with stem-node explants yielding the highest shoot proliferation (3.90 ± 0.31 shoots/explant), followed by 1-week-old seedlings (2.30 ± 0.21 shoots/explant) and seed explants (1.60 ± 0.16 shoots/explant). Rooting was most effective on half-strength MS medium supplemented with 20.0 mg/L IBA, producing an average of 4.30 ± 0.83 roots per shoot in shoot-tip-deprived explants. The rooted plantlets successfully acclimatized, attaining a 100% survival rate in a 1:1:1 mixture of sterile soil, cocopeat, and vermiculite. These findings provide a robust platform for the sustainable propagation and conservation of C. lenis in response to its growing vulnerabilities. Full article
(This article belongs to the Section Horticultural Science and Ornamental Plants)
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15 pages, 1945 KiB  
Review
Effects of Freeze–Thaw Cycles on Uptake Preferences of Plants for Nutrient: A Review
by Fang Liu, Wei Zhang and Siqi Li
Plants 2025, 14(7), 1122; https://doi.org/10.3390/plants14071122 - 4 Apr 2025
Abstract
Freeze–thawing is an abiotic climatic force prevalent at mid-to-high latitudes or high altitudes, significantly impacting ecosystem nitrogen (N) and phosphorus (P) cycling, which is receiving increasing attention due to ongoing global warming. The N and P nutrients are essential for plant growth and [...] Read more.
Freeze–thawing is an abiotic climatic force prevalent at mid-to-high latitudes or high altitudes, significantly impacting ecosystem nitrogen (N) and phosphorus (P) cycling, which is receiving increasing attention due to ongoing global warming. The N and P nutrients are essential for plant growth and development, and the uptake and utilization of these nutrients by plants are closely linked to external environmental conditions. Additionally, the availability of N and P nutrients influences the ecological adaptability of plants. Adapting plants to diverse external environments for the efficient uptake and utilization of N and P nutrients represents a main focus in contemporary ecological research on plant nutrient utilization in the ecosystems of mid-to-high latitudes or high altitudes. Through a comprehensive analysis of the experimental results regarding plant nutrient uptake and utilization in mid-to-high-latitude or high-altitude ecosystems, this paper discussed the processes of soil N and P cycling and the different utilization strategies of nutrient forms employed by plants during freezing and thawing. Freeze–thaw cycles affect the availability of N and P in the soil. Under freeze–thaw conditions, plants preferentially take up readily available N sources (e.g., nitrate (NO3−-N) or ammonium (NH4+-N)) and adjust their root growth and timing of N uptake, developing specific physiological and biochemical adaptations to meet their growth needs. When nutrient conditions are poor or N sources are limited, plants may rely more on low-molecular-weight organic nitrogen (e.g., amino acids) as N sources. Plants adapt to changes in their environment by adjusting root growth, making changes in root secretions, and utilizing microbial communities associated with the P cycle to support more efficient P utilization. Future research should (i) enhance the monitoring of plant roots and nutrient dynamics in the subterranean layers of the soil; (ii) incorporate a broader range of nutrients; (iii) examine specific freeze–thaw landscape types, along with the spatial and temporal heterogeneity of climate change within seasons, which is essential for minimizing uncertainty in our understanding of plant nutrient utilization strategies. Full article
(This article belongs to the Section Plant Nutrition)
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13 pages, 885 KiB  
Article
Machine Learning to Simulate Quantum Computing System Errors from Physical Observations
by Jie Feng, Xingchen Zhang, Guanhao Feng and Hong-Hao Zhang
Universe 2025, 11(4), 120; https://doi.org/10.3390/universe11040120 - 4 Apr 2025
Abstract
In the context of quantum computing, error correction remains a pivotal challenge, primarily due to imperfect gate operations and environmental interactions. This study introduces a machine learning-based method to simulate and analyze these errors. Utilizing a minimal scalable 2-Majorana-zero-mode (2-MZM) island model within [...] Read more.
In the context of quantum computing, error correction remains a pivotal challenge, primarily due to imperfect gate operations and environmental interactions. This study introduces a machine learning-based method to simulate and analyze these errors. Utilizing a minimal scalable 2-Majorana-zero-mode (2-MZM) island model within a one-dimensional p-wave topological superconductor, this research employs a detailed Hamiltonian approach combined with a bosonic thermal bath interaction. The analysis is conducted using the Pauli master equation and Monte Carlo simulations. This work’s novel contribution lies in applying Boosted Decision Tree with Gradient boosting (BDTG) and Multi-Layer Perceptron (MLP) machine learning techniques. These methods, trained on Monte Carlo simulation data, showed proficiency in predicting the evolution of error probabilities in the quantum system. The results indicate a significant potential for machine learning to offer a more efficient alternative for simulating quantum computing errors, thereby contributing to developing more robust quantum computing systems. Full article
(This article belongs to the Section Foundations of Quantum Mechanics and Quantum Gravity)
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11 pages, 432 KiB  
Article
Inclusive Neutrino and Antineutrino Scattering on the 12C Nucleus Within the Coherent Density Fluctuation Model
by Martin V. Ivanov and Anton N. Antonov
Universe 2025, 11(4), 119; https://doi.org/10.3390/universe11040119 - 4 Apr 2025
Abstract
We investigate quasielastic (anti)neutrino scattering on the 12C nucleus utilizing a novel scaling variable, ψ*. This variable is derived from the interacting relativistic Fermi gas model, which incorporates both scalar and vector interactions, leading to a relativistic effective mass for [...] Read more.
We investigate quasielastic (anti)neutrino scattering on the 12C nucleus utilizing a novel scaling variable, ψ*. This variable is derived from the interacting relativistic Fermi gas model, which incorporates both scalar and vector interactions, leading to a relativistic effective mass for the interacting nucleons. For inclusive lepton scattering from nuclei, we develop a new scaling function, denoted as fQE(ψ*), based on the coherent density fluctuation model (CDFM). This model serves as a natural extension of the relativistic Fermi gas (RFG) model applicable to finite nuclei. In this study, we compute theoretical predictions and compare them with experimental data from Minerνa and T2K for inclusive (anti)neutrino cross-sections. The scaling function is derived within the CDFM framework, employing a relativistic effective mass of mN*=0.8mN. The findings demonstrate a high degree of consistency with experimental data across all (anti)neutrino energy ranges. Full article
(This article belongs to the Special Issue Neutrino Insights: Peering into the Subatomic Universe)
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24 pages, 11440 KiB  
Article
Research on Estimation Optimization of State of Charge of Lithium-Ion Batteries Based on Kalman Filter Algorithm
by Tian Xia, Xiangyang Xia, Jiahui Yue, Yu Gong, Jianguo Tan and Lixing Wen
Electronics 2025, 14(7), 1462; https://doi.org/10.3390/electronics14071462 - 4 Apr 2025
Abstract
Accurate prediction of the State of Charge (SOC) of lithium-ion batteries is the foundation for the stable and efficient operation of battery management systems. This paper proposes a lithium-ion battery SOC estimation method based on the Dung Beetle Optimizer (DBO), optimizing the second-order [...] Read more.
Accurate prediction of the State of Charge (SOC) of lithium-ion batteries is the foundation for the stable and efficient operation of battery management systems. This paper proposes a lithium-ion battery SOC estimation method based on the Dung Beetle Optimizer (DBO), optimizing the second-order Kalman filter algorithm (DBO-DKF). Leveraging the DBO’s fast convergence speed and strong global search capability, this method optimizes the Kalman filter algorithm in the parameter identification stage and the extended Kalman filter algorithm in the SOC estimation stage to address the issue of insufficient estimation accuracy caused by noise covariance matrices of input current and voltage measurements. Through the discharge of current tests under complex conditions, as well as comparing and analyzing credibility indicators such as MAE, RMSE, and MSE as measures of estimation accuracy, it can be verified that the proposed method effectively enhances SOC estimation accuracy. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Energy Conversion Systems)
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14 pages, 16149 KiB  
Article
Modeling and Optimization of Structural Tuning in Bandgap-Engineered Tunneling Oxide for 3D NAND Flash Application
by Zhihong Xu, Shibo Xie, Zhijun Ying, Wenlong Zhang and Liming Gao
Electronics 2025, 14(7), 1461; https://doi.org/10.3390/electronics14071461 - 4 Apr 2025
Abstract
The bandgap-engineered tunneling oxide (BE-TOX) structure has been proposed to address the incompatibility between erase efficiency and retention performance in NAND flash memory. Previous studies have primarily focused on single flash memory cells, whose architecture significantly differs from that of 3D NAND flash [...] Read more.
The bandgap-engineered tunneling oxide (BE-TOX) structure has been proposed to address the incompatibility between erase efficiency and retention performance in NAND flash memory. Previous studies have primarily focused on single flash memory cells, whose architecture significantly differs from that of 3D NAND flash memory. Thus, the BE-TOX structure requires further research and optimization to improve device performance. In this study, the impact of varying proportions of the SiO2/SiOxNy/SiO2 (O1/N/O2) structure on performance is investigated using Technology Computer-Aided Design (TCAD) simulations. The results indicate that as the thickness of the N layer increases, the program/erase (P/E) speed improves, but reliability deteriorates. By adjusting the ratio of the O1 and O2 layers, the P/E speed can be optimized, and an optimal thickness can be identified. The simulation results demonstrate that the phenomenon is attributed to the combined effects of different barrier heights for charge tunneling and variations in band bending across the material layers. This study paves the way for further designing BE-TOX structures with balanced P/E performance and reliability. Full article
(This article belongs to the Section Electronic Materials, Devices and Applications)
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18 pages, 5124 KiB  
Article
Influence of Electro-Optical Characteristics on Color Boundaries
by Jingxu Li, Xifeng Zheng, Deju Huang, Fengxia Liu, Junchang Chen, Yufeng Chen, Hui Cao and Yu Chen
Electronics 2025, 14(7), 1460; https://doi.org/10.3390/electronics14071460 - 4 Apr 2025
Abstract
This paper presents a comprehensive investigation into the phenomenon of gamut boundary distortion that occurs during the gamut conversion process in LED full-color display systems. This phenomenon is influenced by the electro-optical transfer function. First, a CIE-xyY colorimetric framework specifically designed for LEDs [...] Read more.
This paper presents a comprehensive investigation into the phenomenon of gamut boundary distortion that occurs during the gamut conversion process in LED full-color display systems. This phenomenon is influenced by the electro-optical transfer function. First, a CIE-xyY colorimetric framework specifically designed for LEDs is developed and established as the foundation for gamut conversion in LED applications. Next, the principles of gamut conversion based on this model are detailed. Additionally, a set of indices, including the Laplacian operator, entropy function, and magnitude of deviation of distorted color points, is integrated to form a comprehensive descriptive methodology. This methodology enables a thorough quantification of distribution patterns and effectively illustrates the outcomes of distortion. The findings of this research are significant for improving color conversion strategies and enhancing the color performance of display devices, making meaningful contributions to related fields. Full article
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24 pages, 2024 KiB  
Article
An IoT Featureless Vulnerability Detection and Mitigation Platform
by Sarah Bin Hulayyil and Shancang Li
Electronics 2025, 14(7), 1459; https://doi.org/10.3390/electronics14071459 - 4 Apr 2025
Abstract
With the increase in ownership of Internet of Things (IoT) devices, there is a bigger demand for stronger implementation of security mechanisms and addressing zero-day vulnerabilities. This work is the first to provide a platform that combines featureless approaches with artificial intelligence (AI) [...] Read more.
With the increase in ownership of Internet of Things (IoT) devices, there is a bigger demand for stronger implementation of security mechanisms and addressing zero-day vulnerabilities. This work is the first to provide a platform that combines featureless approaches with artificial intelligence (AI) algorithms, which are deep learning and large language models, to uncover IoT security vulnerabilities based on network traffic data directly without manual feature selection. The platform correctly identifies vulnerable and secure IoT devices just by raw network traffic! Experimental results show that the proposed study detects vulnerability with great accuracy by using pre-trained deep learning and LLM models, which facilitates direct extraction of vulnerability features from the dataset and therefore helps speed up the identification process. In addition, the design of the platform ensures that the models are accessible and can be easily applied by users with a user-friendly interface. Furthermore, the models with small sizes, 277.5 MB and 334 MB for the deep learning model and the LLM model, respectively, illustrated the potential use of the detection tool in practical settings. The ability to defend large-scale, diversified IoT ecosystems efficiently and in a scalable way by installing thousands of software manifestations quickly while exposing new applications to growing cyber threats is made possible by this significant advancement in the field of IoT security. Full article
(This article belongs to the Special Issue New Insights in Information Security and Data Privacy: Challenges and Solutions)
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17 pages, 3315 KiB  
Review
Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability
by Claudio Urrea
Actuators 2025, 14(4), 177; https://doi.org/10.3390/act14040177 - 4 Apr 2025
Abstract
Cooperative robotics relies on robust fault-tolerant control (FTC) to maintain resilience in dynamic environments, where actuators are pivotal to system reliability. This review synthesizes advancements in hybrid FTC, integrating mechanical redundancy with electronic adaptability and learning-based techniques like deep reinforcement learning and swarm-optimized [...] Read more.
Cooperative robotics relies on robust fault-tolerant control (FTC) to maintain resilience in dynamic environments, where actuators are pivotal to system reliability. This review synthesizes advancements in hybrid FTC, integrating mechanical redundancy with electronic adaptability and learning-based techniques like deep reinforcement learning and swarm-optimized control, drawing from interdisciplinary evidence across manufacturing, healthcare, agriculture, space exploration, and underwater robotics. It examines how these approaches enhance uptime, precision, and coordination in multi-robot systems, reporting significant improvements despite physical validation being limited to approximately one-quarter of strategies. Addressing gaps in prior work by overcoming limitations of traditional methods, it extends to Construction 5.0, supporting human–robot collaboration (HRC) through scalability and adaptability. Future efforts will prioritize broader testing, standardized benchmarks, safety considerations, and optimization under uncertainty to align theoretical gains with practical outcomes, enhancing resilient automation across domains. Full article
(This article belongs to the Section Actuators for Robotics)
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