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Recent Articles  

19 pages, 2153 KiB  
Article
Complex Network Method for Inferring Well Interconnectivity in Hydrocarbon Reservoirs
by M. Mayoral-Villa, F. A. Godínez, J. A. González-Guevara, J. Klapp and J. E. V. Guzmán
Fluids 2025, 10(4), 95; https://doi.org/10.3390/fluids10040095 (registering DOI) - 4 Apr 2025
Abstract
Reservoir management becomes increasingly critical as fields decline to a fully mature state. During this stage, engineers and managers must make decisions based on a limited set of field measurements (such as pressure and production rates). At the same time, up-to-date information concerning [...] Read more.
Reservoir management becomes increasingly critical as fields decline to a fully mature state. During this stage, engineers and managers must make decisions based on a limited set of field measurements (such as pressure and production rates). At the same time, up-to-date information concerning the reservoir’s geophysical characteristics and petrochemical properties may be unavailable. To aid in the expert’s appraisal of this production scenario, we present the results of applying a data-driven methodology based on visibility graph analysis (VGA) and multiplex visibility graphs (MVGs). It infers inter-well connectivities at the reservoir level and clarifies the degrees of mutual influence among wells. This parameter-free technique supersedes the limitations of traditional methods, such as the capacitance–resistance (CR) models and inter-well numerical simulation models (INSIMs) that rely heavily on geophysical data and are sensitive to porous datasets. We tested the method with actual data representing a field’s state over 62 years. The technique revealed short- and long-term dependencies between wells when applied to historical records of production rates (oil, water, and gas) and pressures (bottom and wellhead). The inferred connectivity aligned with documented operational trends and successfully identified stable connectivity structures. In addition, the interlayer mutual information (IMI) parameter exceeded 0.75 in most periods, confirming high temporal consistency. Moreover, validation by field experts confirmed that the inferred interconnectivity was consistent with the observed production. Full article
(This article belongs to the Special Issue Pipe Flow: Research and Applications, 2nd Edition)
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19 pages, 3230 KiB  
Review
Single-Nucleotide Polymorphisms Related to Multiple Myeloma Risk: A Systematic Review and Meta-Analysis
by Giovanna Gilioli da Costa Nunes, Francisco Cezar Aquino de Moraes, Aline Beatriz Carvalho de Almeida, Felipe Goes Costa, Luiz Fernando Duarte de Andrade Junior, Maria Vitória Sabino Hupp, Ruan Rotondano Assunção, Marianne Rodrigues Fernandes, Sidney Emanuel Batista dos Santos and Ney Pereira Carneiro dos Santos
Int. J. Mol. Sci. 2025, 26(7), 3369; https://doi.org/10.3390/ijms26073369 (registering DOI) - 4 Apr 2025
Abstract
Multiple myeloma ranks as the second most common hematopoietic malignancy in terms of both incidence and mortality. Prognostic stratification is critical for optimizing therapeutic strategies, as certain genetic alterations can significantly influence disease progression and treatment response. The meta-analysis analyzed data from 3421 [...] Read more.
Multiple myeloma ranks as the second most common hematopoietic malignancy in terms of both incidence and mortality. Prognostic stratification is critical for optimizing therapeutic strategies, as certain genetic alterations can significantly influence disease progression and treatment response. The meta-analysis analyzed data from 3421 multiple myeloma patients and 14,720 controls. PubMed, Web of Science, and Scopus were used as databases. Associations between the SNPs and multiple myeloma were calculated as a measure of pooled odds ratios (ORs) and 95% confidence intervals. Statistical analysis was performed using Review Manager (RevMan). DNAH11 rs4487645 A/C genotype (OR = 1.35; 95% CI: 1.24–1.46; p < 0.00001; I2 = 0%), ULK4 rs1052501 G/G genotype (OR = 1.21; 95% CI: 0.98–1.50; p = 0.08; I2 = 64%), ULK4 rs1052501 A/G genotype (OR = 1.23; 95% CI: 1.13–1.34; p < 0.00001; I2 = 0%), DTNB rs6746082 A/A genotype (OR = 1.10; 95% CI: 1.01–1.20; p = 0.03; I2 = 45%), and VDR rs1544410 A/G genotype (OR = 1.87; 95% CI: 1.04–3.36; p = 0.04; I2 = 0%) increased multiple myeloma risk. Our study concludes that DNAH11, ULK4, DTNB, and VDR may serve as predictive biomarkers for MM risk. Full article
(This article belongs to the Collection Feature Papers in Molecular Pathology, Diagnostics, and Therapeutics)
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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 (registering DOI) - 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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11 pages, 1254 KiB  
Article
Simultaneous Modified Tibial Plateau Leveling Osteotomy and Tibial Tuberosity Transposition for Grade IV Medial Patellar Luxation and Cranial Cruciate Ligament Disease in Small-Breed Dogs
by Changsu Jung and Byung-Jae Kang
Animals 2025, 15(7), 1042; https://doi.org/10.3390/ani15071042 (registering DOI) - 4 Apr 2025
Abstract
This study explored the complications and prognosis of modified tibial plateau leveling osteotomy with tibial tuberosity transposition (mTPLO-TTT) for simultaneously correcting high-grade medial patellar luxation (MPL) and cranial cruciate ligament disease (CCLD) in small-breed dogs. This retrospective study evaluated patient data, lameness scores, [...] Read more.
This study explored the complications and prognosis of modified tibial plateau leveling osteotomy with tibial tuberosity transposition (mTPLO-TTT) for simultaneously correcting high-grade medial patellar luxation (MPL) and cranial cruciate ligament disease (CCLD) in small-breed dogs. This retrospective study evaluated patient data, lameness scores, radiographic outcomes, and complications over a median follow-up period of 10 weeks. Additionally, an owner interview was conducted 6 months postoperatively. Nine stifles from seven dogs were included in this study. All cases showed satisfactory patellar alignment and stability after surgery, with no major complications or reluxations. The lameness scores improved, and radiographic assessments confirmed implant stability and appropriate bone healing. Owner-reported outcomes at 6 months were also favorable. These findings suggest that simultaneous mTPLO-TTT is an effective surgical option for small-breed dogs with concurrent CCLD and Grade IV MPL. Full article
(This article belongs to the Section Veterinary Clinical Studies)
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7 pages, 2585 KiB  
Case Report
Successful Treatment of Acanthamoeba Keratitis According to New Protocol with Polihexanide 0.08% Therapy: Case Report
by Tomislav Kuzman, Suzana Matić, Ivan Gabrić, Antonela Geber and Ana Meter
Reports 2025, 8(2), 44; https://doi.org/10.3390/reports8020044 (registering DOI) - 4 Apr 2025
Abstract
Background and Clinical Significance: Acanthamoeba keratitis (AK) is a rare but serious corneal infection that can lead to severe visual impairment or blindness if not promptly treated. The condition is primarily associated with contact lens use but can also occur due to ocular [...] Read more.
Background and Clinical Significance: Acanthamoeba keratitis (AK) is a rare but serious corneal infection that can lead to severe visual impairment or blindness if not promptly treated. The condition is primarily associated with contact lens use but can also occur due to ocular trauma or environmental contamination. The most frequently used treatment options include biguanides and diamidines, though dosing protocols remain empirical and vary widely among clinicians. Recent research has explored a new standardized protocol with 0.08% polihexanide (polyhexamethylene biguanide, PHMB) as a monotherapy for AK, offering improved efficacy and better corneal penetration. Case Presentation: This case report describes a 35-year-old female contact lens wearer who presented with redness, pain, photophobia, and vision loss in her right eye. Upon referral, a slit-lamp examination revealed stromal infiltrates and perineural involvement, with in vivo confocal microscopy (IVCM) confirming Acanthamoeba cysts. The patient was treated with a new standardized intensive regimen of polihexanide 0.08% monotherapy, leading to rapid clinical improvement. Corneal infiltrates were significantly reduced, and the best-corrected visual acuity (BCVA) improved from 0.4 logMAR to 0.15 logMAR. Resolution with only discrete stromal haze was achieved over the following months, without recurrence. Conclusions: This case highlights the potential of polihexanide 0.08% monotherapy as an effective treatment for AK in a new standardized treatment protocol. Full article
(This article belongs to the Section Ophthalmology)
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19 pages, 15989 KiB  
Article
A Harmonic Suppression Method for the Single Phase PWM Rectifier in the Hydrogen Production Power Supply
by Li Lun, Siming Chen, Yihe Zhan, Hui Yang and Jianyong Zhu
Appl. Sci. 2025, 15(7), 3978; https://doi.org/10.3390/app15073978 (registering DOI) - 4 Apr 2025
Abstract
In renewable and sustainable hydrogen production energy systems (RSHPES), the presence of harmonics gives rise to fluctuations in the voltage and current of the electrolysis cell (EC). This, in turn, results in an unstable electrolysis process, a reduction in hydrogen production efficiency, and [...] Read more.
In renewable and sustainable hydrogen production energy systems (RSHPES), the presence of harmonics gives rise to fluctuations in the voltage and current of the electrolysis cell (EC). This, in turn, results in an unstable electrolysis process, a reduction in hydrogen production efficiency, and an escalation in electrode corrosion. This paper puts forward a novel harmonic suppression control method (HSCM), which is devised for the single phase PWM rectifier in hydrogen production rectifiers (HPR) with the aim of alleviating the adverse impacts caused by harmonics. Initially, a highly meticulous harmonic model is constructed, which lays solid groundwork for understanding the existing problems. Subsequently, a comprehensive and detailed explanation of the HSCM is provided, accentuating its novel and inventive strategy for harmonic suppression. Thereafter, a comparison is drawn between the HSCM and traditional methods, thereby manifesting its enhanced suitability and superiority within the context of RSHPES. In conclusion, the simulation and experimental results vividly demonstrate the advantages, effectiveness, and practicality of HSCM under four conditions of power grids containing integer multiples of harmonics, interharmonics, ultraharmonics, and voltage disturbances. Full article
(This article belongs to the Special Issue Power Systems and Renewable Energy Systems: Technologies and Applications)
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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 (registering DOI) - 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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26 pages, 22584 KiB  
Article
Expansion of Output Spatial Extent in the Wavenumber Domain Algorithms for Near-Field 3-D MIMO Radar Imaging
by Yifan Gong, Limin Zhai, Yan Jia, Yongqing Liu and Xiangkun Zhang
Remote Sens. 2025, 17(7), 1287; https://doi.org/10.3390/rs17071287 (registering DOI) - 4 Apr 2025
Abstract
Microwave camera provides 3-D high-resolution radar images at video frame rates, enabling the capture of dynamic target features. Multiple-input–multiple-output (MIMO) array-based 3-D radar imaging system requires fewer antennas, which effectively reduces hardware costs. Due to the limited computational resources of the miniaturized MIMO [...] Read more.
Microwave camera provides 3-D high-resolution radar images at video frame rates, enabling the capture of dynamic target features. Multiple-input–multiple-output (MIMO) array-based 3-D radar imaging system requires fewer antennas, which effectively reduces hardware costs. Due to the limited computational resources of the miniaturized MIMO microwave camera, real-time processing of a large amount of 3-D echo data requires an imaging algorithm that has both real-time performance and large output spatial extent. This paper presents the limited output spatial extent and spatial aliasing in existing MIMO wavenumber domain algorithms through theoretical derivation and simulation. To suppress aliasing while expanding the output spatial extent, an optimization approach for the wavenumber domain algorithms is proposed. The improved wavenumber domain algorithms divide the target area into multiple sub-blocks, and a broader range of imaging results is obtained through independent imaging of the sub-blocks and a spatial aliasing suppression filter. Simulation results show that the improved wavenumber domain algorithms effectively suppress the aliasing energy of each sub-block while maintaining the advantage of low time complexity. Expansion of output spatial extent in existing MIMO wavenumber domain algorithms is achieved. Full article
(This article belongs to the Special Issue Array and Signal Processing for Radar)
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19 pages, 1118 KiB  
Review
Lactylation in Glioblastoma: A Novel Epigenetic Modifier Bridging Epigenetic Plasticity and Metabolic Reprogramming
by Qingya Qiu, Hui Deng, Ping Song, Yushu Liu and Mengxian Zhang
Int. J. Mol. Sci. 2025, 26(7), 3368; https://doi.org/10.3390/ijms26073368 (registering DOI) - 4 Apr 2025
Abstract
Glioblastoma, the most common and aggressive primary malignant brain tumor, is characterized by a high rate of recurrence, disability, and lethality. Therefore, there is a pressing need to develop more effective prognostic biomarkers and treatment approaches for glioblastoma. Lactylation, an emerging form of [...] Read more.
Glioblastoma, the most common and aggressive primary malignant brain tumor, is characterized by a high rate of recurrence, disability, and lethality. Therefore, there is a pressing need to develop more effective prognostic biomarkers and treatment approaches for glioblastoma. Lactylation, an emerging form of protein post-translational modification, has been closely associated with lactate, a metabolite of glycolysis. Since the initial identification of lactylation sites in core histones in 2019, accumulating evidence has shown the critical role that lactylation plays in glioblastoma development, assessment of poor clinical prognosis, and immunosuppression, which provides a fresh angle for investigating the connection between metabolic reprogramming and epigenetic plasticity in glioblastoma cells. The objective of this paper is to present an overview of the metabolic and epigenetic roles of lactylation in the expanding field of glioblastoma research and explore the practical value of developing novel treatment plans combining targeted therapy and immunotherapy. Full article
(This article belongs to the Section Molecular Oncology)
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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 (registering DOI) - 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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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 (registering DOI) - 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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15 pages, 21805 KiB  
Article
Case Study on the Rupture Morphology of a Copper Tube in an Air Conditioner Condenser After Fire
by Yunlong Ou, Ming Fu, Jing Zhang, Wenzhong Mi, Changzheng Li, Shouhai Chen and Shoulei Zheng
Fire 2025, 8(4), 145; https://doi.org/10.3390/fire8040145 (registering DOI) - 4 Apr 2025
Abstract
The new eco-friendly flammable refrigerant in air conditioners has resulted in an annual increase in fire incidents associated with these units. Fire investigators face significant challenges in identifying the causes of these fires. In this study, copper tube samples were extracted from various [...] Read more.
The new eco-friendly flammable refrigerant in air conditioners has resulted in an annual increase in fire incidents associated with these units. Fire investigators face significant challenges in identifying the causes of these fires. In this study, copper tube samples were extracted from various locations of air conditioner condenser debris post fire. The morphology characteristics of the ruptured copper tubes formed by a high-temperature flame in fire and that formed by corrosion were analyzed, respectively. The findings indicate that the ruptures in the copper tubes of air conditioners may be classified into two types based on their origins: ruptures resulting from fire and ruptures resulting from corrosion. The ruptures in the copper tubes resulting from fire are associated with the presence of aluminum alloy fins. At elevated temperatures, the copper and aluminum atoms persist in diffusing and fracturing. A significant quantity of silver-white aluminum is present surrounding the ruptures, and distinct elemental layers may be seen in the cross-section. The corrosion-induced ruptures in the copper tubes are associated with ant nest corrosion. Despite the influence of high-temperature flame melting on surface corrosion pits, they will not entirely obscure the pits and the cross-section continues to exhibit the bifurcated structure characteristic of ant nest corrosion. This investigation demonstrates that corrosion of ant nests is the root cause of copper tube breakage obscured by flames. An investigation method for the refrigerant leakage air conditioning fire is proposed. The above findings can provide proof and method for air conditioning fire investigation. Full article
(This article belongs to the Special Issue Fire Detection and Public Safety, 2nd Edition)
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18 pages, 5895 KiB  
Article
Numerical Simulation and Optimization of a Chevron-Type Corrugated Solar Air Heater
by Umar Fahed Alqsair
Energies 2025, 18(7), 1821; https://doi.org/10.3390/en18071821 (registering DOI) - 4 Apr 2025
Abstract
In the present study, a numerical simulation and optimization combined approach is applied to investigate the thermal performance of a solar air heater (SAH). Numerical simulation of the solar air heater is performed based on computational fluid dynamics (CFDs) via ANSYS Fluent 2023R1 [...] Read more.
In the present study, a numerical simulation and optimization combined approach is applied to investigate the thermal performance of a solar air heater (SAH). Numerical simulation of the solar air heater is performed based on computational fluid dynamics (CFDs) via ANSYS Fluent 2023R1 software. The solar air heater includes a corrugated absorber plate with a Chevron-type design. Present study was conducted in Al-Kharj, Saudi Arabia on August 15. The optimization process is used to enhance the thermal efficiency of the solar system. In the optimization process, several geometric parameters of the solar air heater, including the wave height and pitch length of the corrugated absorber plate and the height of the airflow channel under the absorber plate, have been evaluated. The wave height is between 10 and 20 mm, the pitch length is between 50 and 90 mm, and the channel height is between 70 and 90 mm. Therefore, the design of experiment (DOE) and response surface methodology (RSM) are utilized to estimate temperature rise and thermal efficiency. The thermal analysis shows that increasing the wave height, decreasing the pitch length, and shortening the channel height enhances both the temperature rise coefficient and the thermal efficiency. Full article
(This article belongs to the Section A: Sustainable Energy)
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22 pages, 40986 KiB  
Article
Modeling Short-Term Drought for SPEI in Mainland China Using the XGBoost Model
by Fanchao Zeng, Qing Gao, Lifeng Wu, Zhilong Rao, Zihan Wang, Xinjian Zhang, Fuqi Yao and Jinwei Sun
Atmosphere 2025, 16(4), 419; https://doi.org/10.3390/atmos16040419 (registering DOI) - 4 Apr 2025
Abstract
Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework for short-term drought forecasting using SPEI time series (1979–2020) and evaluates three predictive models: (1) a baseline XGBoost model (XGBoost1), [...] Read more.
Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework for short-term drought forecasting using SPEI time series (1979–2020) and evaluates three predictive models: (1) a baseline XGBoost model (XGBoost1), (2) a feature-optimized XGBoost variant incorporating Pearson correlation analysis (XGBoost2), and (3) an enhanced CPSO-XGBoost model integrating hybrid particle swarm optimization with dual mechanisms of binary feature selection and parameter tuning. Key findings reveal spatiotemporal prediction patterns: temporal-scale dependencies show all models exhibit limited capability at SPEI-1 (R2: 0.32–0.41, RMSE: 0.68–0.79) but achieve progressive accuracy improvement, peaking at SPEI-12 where CPSO-XGBoost attains optimal performance (R2: 0.85–0.90, RMSE: 0.33–0.43) with 18.7–23.4% error reduction versus baselines. Regionally, humid zones (South China/Central-Southern) demonstrate peak accuracy at SPEI-12 (R2 ≈ 0.90, RMSE < 0.35), while arid regions (Northwest Desert/Qinghai-Tibet Plateau) show dramatic improvement from SPEI-1 (R2 < 0.35, RMSE > 1.0) to SPEI-12 (R2 > 0.85, RMSE reduction > 52%). Multivariate probability density analysis confirms the model’s robustness through enhanced capture of nonlinear atmospheric-land interactions and reduced parameterization uncertainties via swarm intelligence optimization. The CPSO-XGBoost’s superiority stems from synergistic optimization: binary particle swarm feature selection enhances input relevance while adaptive parameter tuning improves computational efficiency, collectively addressing climate variability challenges across diverse terrains. These findings establish an advanced computational framework for drought early warning systems, providing critical support for climate-resilient water management and agricultural risk mitigation through spatiotemporally adaptive predictions. Full article
(This article belongs to the Special Issue Advances in Methods for the Investigation of the Atmospheric Water Cycle)
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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 (registering DOI) - 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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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 (registering DOI) - 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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26 pages, 5156 KiB  
Article
Integrative Assessment of Surface Water Contamination Using GIS, WQI, and Machine Learning in Urban–Industrial Confluence Zones Surrounding the National Capital Territory of the Republic of India
by Bishnu Kant Shukla, Lokesh Gupta, Bhupender Parashar, Pushpendra Kumar Sharma, Parveen Sihag and Anoop Kumar Shukla
Water 2025, 17(7), 1076; https://doi.org/10.3390/w17071076 (registering DOI) - 4 Apr 2025
Abstract
This study proposes an innovative framework integrating geographic information systems (GISs), water quality index (WQI) analysis, and advanced machine learning (ML) models to evaluate the prevalence and impact of organic and inorganic pollutants across the urban–industrial confluence zones (UICZ) surrounding the National Capital [...] Read more.
This study proposes an innovative framework integrating geographic information systems (GISs), water quality index (WQI) analysis, and advanced machine learning (ML) models to evaluate the prevalence and impact of organic and inorganic pollutants across the urban–industrial confluence zones (UICZ) surrounding the National Capital Territory (NCT) of India. Surface water samples (n = 118) were systematically collected from the Gautam Buddha Nagar, Ghaziabad, Faridabad, Sonipat, Gurugram, Jhajjar, and Baghpat districts to assess physical, chemical, and microbiological parameters. The application of spatial interpolation techniques, such as kriging and inverse distance weighting (IDW), enhances WQI estimation in unmonitored areas, improving regional water quality assessments and remediation planning. GIS mapping highlighted stark spatial disparities, with industrial hubs, like Faridabad and Gurugram, exhibiting WQI values exceeding 600 due to untreated industrial discharges and wastewater, while rural regions, such as Jhajjar and Baghpat, recorded values below 200, reflecting minimal anthropogenic pressures. The study employed four ML models—linear regression (LR), random forest (RF), Gaussian process regression (GPR_PUK), and support vector machines (SVM_Poly)—to predict WQI with high precision. SVM_Poly emerged as the most effective model, achieving testing CC, RMSE, and MAE values of 0.9997, 11.4158, and 5.6085, respectively, outperforming RF (0.9925, 29.8107, 21.7398) and GPR_PUK (0.9811, 68.4466, 54.0376). By leveraging machine learning models, this study enhances WQI prediction beyond conventional computation, enabling spatial extrapolation and early contamination detection in data-scarce regions. Sensitivity analysis identified total suspended solids as the most critical predictor influencing WQI, underscoring its relevance in monitoring programs. This research uniquely integrates ML algorithms with spatial analytics, providing a novel methodological contribution to water quality assessment. The findings emphasize the urgency of mitigating the fate and transport of organic and inorganic pollutants to protect Delhi’s hydrological ecosystems, presenting a robust decision-support system for policymakers and environmental managers. Full article
(This article belongs to the Special Issue Environmental Fate and Transport of Organic Pollutants in Water)
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18 pages, 1525 KiB  
Article
A Pilot 24-Week ‘Bulk and Cut’ Dietary Protocol Combined with Resistance Training Is Feasible and Improves Body Composition and TNF-α Concentrations in Untrained Adult Males
by Anthony J. Giannopoulos, Steve Kottaras, Bryan Allanigue, Jeremia M. Coish, David S. Ditor, Val A. Fajardo and Panagiota Klentrou
Nutrients 2025, 17(7), 1265; https://doi.org/10.3390/nu17071265 (registering DOI) - 4 Apr 2025
Abstract
Background/Objectives: This study piloted a 24-week bodybuilding program combining resistance training (RT) with a dietary bulk-and-cut protocol in middle-aged adult males. Methods: Seven untrained males (33 ± 3.0 years; BMI = 35.0 ± 4.6 kg/m2; body fat = 36 [...] Read more.
Background/Objectives: This study piloted a 24-week bodybuilding program combining resistance training (RT) with a dietary bulk-and-cut protocol in middle-aged adult males. Methods: Seven untrained males (33 ± 3.0 years; BMI = 35.0 ± 4.6 kg/m2; body fat = 36 ± 5%) completed a 24-week intervention combining RT with a dietary protocol consisting of 12-week cycles of caloric bulking (0–12 weeks) and cutting (12–24 weeks). The participant retention rate was 64%, while compliance with training was 96.7%, and adherence to dietary cycles was over 93%. To assess the preliminary efficacy of the intervention, venous blood samples and measurements of body composition (BodPod), muscle strength, and VO2max (cycle ergometer) were collected at baseline (week 0) and following the bulking (week 12) and cutting (week 24) cycles. Circulating lipids (triglycerides, total, low-density, and high-density cholesterol), C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-10 (IL-10) were measured in serum. Results: The training led to significant increases in muscle strength, especially in the deadlift (+46%, p < 0.001) and squat (+65%, p < 0.001). Improvements in body composition were characterized by an increase in fat-free mass and a decrease in body fat percentage over the 24-week intervention (+3% and −6%, respectively, p < 0.05). Lipids, CRP, IL-6, and IL-10 did not change significantly, but there was a notable reduction in TNF-α (time effect p = 0.05, pη2 = 0.39), with 15% lower concentrations at week 24 compared to baseline, indicating reduced inflammation. Conclusions: Overall, the pilot intervention achieved high compliance and adherence rates, leading to improvements in body composition and lower resting TNF-α concentrations in a group of middle-aged males with obesity. Full article
(This article belongs to the Section Sports Nutrition)
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14 pages, 3049 KiB  
Article
Optimized Adaptive Fuzzy Synergetic Controller for Suspended Cable-Driven Parallel Robots
by Yasser Hatim Alwan, Ahmed A. Oglah and Muayad Sadik Croock
Automation 2025, 6(2), 15; https://doi.org/10.3390/automation6020015 (registering DOI) - 4 Apr 2025
Abstract
A suspended cable-driven parallel robot is a type of lightweight large-span parallel robot. The stability and control of this multi-input multi-output robot are studied in this work to overcome its inherited vulnerability to disturbance. An adaptive fuzzy synergetic controller is proposed to overcome [...] Read more.
A suspended cable-driven parallel robot is a type of lightweight large-span parallel robot. The stability and control of this multi-input multi-output robot are studied in this work to overcome its inherited vulnerability to disturbance. An adaptive fuzzy synergetic controller is proposed to overcome these issues, combining synergetic control theory with adaptive fuzzy logic to ensure robust trajectory tracking. The parameters of the controller are optimized using the Dragonfly Algorithm, a metaheuristic technique known for its simplicity and fast convergence. The adaptive fuzzy synergetic controller is tested on a suspended cable-driven parallel robot model under both disturbance-free and disturbed conditions, demonstrating global asymptotic stability and superior tracking accuracy compared to existing controllers. Simulation results show the proposed controller achieves minimal tracking error and improved robustness in the presence of dynamic uncertainties, validating its practical applicability in industrial scenarios. The findings highlight the effectiveness of integrating synergetic control, fuzzy logic adaptation, and optimization for enhancing the performance and reliability of suspended cable-driven parallel robots. Full article
(This article belongs to the Collection Smart Robotics for Automation)
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19 pages, 17937 KiB  
Article
The Importance of Dunnian Runoff in Atlantic Forest Remnants: An Integrated Analysis Between Machine Learning and Spectral Indices
by Alarcon Matos de Oliveira, Mara Rojane Barros de Matos, Marcos Batista Figueiredo and Lusanira Nogueira Aragão de Oliveira
Appl. Sci. 2025, 15(7), 3977; https://doi.org/10.3390/app15073977 (registering DOI) - 4 Apr 2025
Abstract
This study investigated Dunnian runoff in the Sauípe River basin, Bahia, Brazil, analyzing the relationship between soil moisture, terrain slope, and land use. It utilized Landsat satellite images, annual water balance data, and rainfall data from the last 10 days. The Normalized Difference [...] Read more.
This study investigated Dunnian runoff in the Sauípe River basin, Bahia, Brazil, analyzing the relationship between soil moisture, terrain slope, and land use. It utilized Landsat satellite images, annual water balance data, and rainfall data from the last 10 days. The Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) were calculated, along with image classification using the Random Forest machine learning algorithm. (1) Saturated zones with potential for Dunnian runoff were identified, especially on steeper slopes, with a notable negative influence of eucalyptus on soil moisture, except in areas with steeper slopes. (2) Dunnian runoff was predominantly observed from the middle course to the mouth, following the east-west direction of the watershed. (3) Higher areas exhibited Dunnian runoff with high soil moisture values, while areas with less steep slopes showed low moisture levels. (4) The results indicate a positive correlation between steeper slopes and Dunnian runoff and a negative correlation between eucalyptus plantations and soil moisture. (5) Forest fragments exhibited high NDVI and NDWI values, suggesting dense forests with high moisture, especially in areas with steep slopes. This suggests that forest fragments are in good moisture conditions, acting to delay Dunnian runoff. (6) In areas with savannization or without vegetation, significant moisture content was not observed, indicating the absence of intense rainfall in the last ten days of image acquisition. This confirms the importance of this runoff for forest remnants. Full article
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8 pages, 186 KiB  
Opinion
Evidence for Cognitive Spatial Models from Ancient Roman Land-Measurement
by Andrew M. Riggsby
Brain Sci. 2025, 15(4), 376; https://doi.org/10.3390/brainsci15040376 (registering DOI) - 4 Apr 2025
Abstract
Influential studies in the history of cartography have argued that map-like representations of space were (virtually) unknown in the Classical Mediterranean world and that the cause of this was an absence of underlying cognitive maps. That is, persons in that time/place purportedly had [...] Read more.
Influential studies in the history of cartography have argued that map-like representations of space were (virtually) unknown in the Classical Mediterranean world and that the cause of this was an absence of underlying cognitive maps. That is, persons in that time/place purportedly had only route/egocentric-type mental representations, not survey/allocentric ones. The present study challenges that cognitive claim by examining the verbal descriptions of plots of land produced by ancient Roman land-measurers. Despite the prescription of a route-based form, actual representations persistently show a variety of features which suggest the existence of underlying survey-type mental models and the integration of those with the route-type ones. This fits better with current views on interaction between types of spatial representation and of cultural difference in this area. The evidence also suggests a linkage between the two kinds of representations. Full article
(This article belongs to the Special Issue Latest Research Progress of Human Navigation in Physical and Mental Space)
10 pages, 6501 KiB  
Communication
Phase Disturbance Compensation for Quantitative Imaging in Off-Axis Digital Holographic Microscopy
by Ying Li, Wenlong Shao, Lijie Hou and Changxi Xue
Photonics 2025, 12(4), 345; https://doi.org/10.3390/photonics12040345 (registering DOI) - 4 Apr 2025
Abstract
Holographic detection technology has found extensive applications in biomedical imaging, surface profilometry, vibration monitoring, and defect inspection due to its unique phase detection capability. However, the accuracy of quantitative holographic phase imaging is significantly affected by the interference from direct current and twin [...] Read more.
Holographic detection technology has found extensive applications in biomedical imaging, surface profilometry, vibration monitoring, and defect inspection due to its unique phase detection capability. However, the accuracy of quantitative holographic phase imaging is significantly affected by the interference from direct current and twin image terms. Traditional methods, such as multi-exposure phase shifting and off-axis holography, have been employed to mitigate these interferences. While off-axis holography separates spectral components by introducing a tilted reference beam, it inevitably induces phase disturbances that compromise measurement accuracy. This study provides a computational explanation for the incomplete phase compensation issue in existing algorithms and establishes precision criteria for phase compensation based on theoretical formulations. We propose two novel phase compensation methods—the non-iterative compensation approach and the multi-iteration compensation technique. The principles and applicable conditions of these methods are thoroughly elucidated, and their superiority is demonstrated through comparative experiments. The results indicate that the proposed methods effectively compensate for phase disturbances induced by the tilted reference beam, offering enhanced precision and reliability in quantitative holographic phase measurements. Full article
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11 pages, 5939 KiB  
Article
Pulsed Electromagnetic Field Therapy Alters the Genomic Profile of Bladder Cancer Cell Line HT-1197
by Maxwell Sandberg, Wyatt Whitman, Randall Bissette, Christina Ross, Matvey Tsivian and Stephen J. Walker
J. Pers. Med. 2025, 15(4), 143; https://doi.org/10.3390/jpm15040143 (registering DOI) - 4 Apr 2025
Abstract
Background/Objectives: Pulsed electromagnetic field (PEMF) therapy involves the use of magnetic waveform energy for targeted treatment delivery. This technique has shown promising results in the treatment of various cancers. Currently, treatment of bladder cancer is highly invasive, involving intravesical chemotherapy or radical cystectomy. [...] Read more.
Background/Objectives: Pulsed electromagnetic field (PEMF) therapy involves the use of magnetic waveform energy for targeted treatment delivery. This technique has shown promising results in the treatment of various cancers. Currently, treatment of bladder cancer is highly invasive, involving intravesical chemotherapy or radical cystectomy. The potential therapeutic effects of PEMF therapy on bladder cancer are a relatively new and understudied area; therefore, the goal of this investigation was to gain mechanistic insight by examining the effects of PEMF therapy on a bladder cancer cell line in vitro. Methods: Cells from the bladder cancer cell line HT-1197 were cultured and incubated with (treatment group) or without (control group) PEMF therapy for one hour each day for five days. Cell counts were compared using Incucyte® data to determine proliferation rates. At days 1 and 5, total RNA was isolated from cells, and following quantity and quality checks, gene expression was compared between the two groups. Proliferation rates from cell line HT-1197 were compared to prior published results on the bladder cancer cell line HT-1376. Results: HT-1197 cells treated with PEMF therapy had slower proliferation rates compared to controls (p < 0.05), but HT-1376 cells did not (p > 0.05). Principal component analysis showed complete separation of treated and untreated cells, with PEMF treatment accounting for 76% of the variation between the groups. Expression of numerous genes and cancer-related pathways was altered in the treated cells relative to the controls. Conclusions: Bladder cancer HT-1197 cells treated with PEMF therapy had slower proliferation and corresponding changes in gene expression. Several cancer-relevant pathways were differentially regulated following PEMF treatment. The conclusions are limited by the lack of a control healthy urothelial cell line in the experiments. Despite this shortcoming, our results suggest that PEMF therapy may be a promising avenue for further research in the treatment of bladder cancer. Full article
(This article belongs to the Special Issue Novel Diagnostic and Therapeutic Approaches to Urologic Oncology)
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16 pages, 11833 KiB  
Article
Distinction Between Interturn Short-Circuit Faults and Unbalanced Load in Transformers
by Raul A. Ortiz-Medina, David A. Aragon-Verduzco, Victor A. Maldonado-Ruelas, Juan C. Olivares-Galvan and Rafael Escalera-Perez
Appl. Syst. Innov. 2025, 8(2), 50; https://doi.org/10.3390/asi8020050 (registering DOI) - 4 Apr 2025
Abstract
Transformers are essential in electrical networks, and their failure can lead to the shutdown of a section or the entire grid. This study proposes a combination of techniques for early fault detection, distinguishing between small load imbalances and incipient interturn short circuits. An [...] Read more.
Transformers are essential in electrical networks, and their failure can lead to the shutdown of a section or the entire grid. This study proposes a combination of techniques for early fault detection, distinguishing between small load imbalances and incipient interturn short circuits. An experimental setup was developed using a three-phase transformer bank with three single-phase dry-type transformers. One transformer was modified to create controlled short circuits of two and four turns and to simulate a load imbalance by reducing the winding by four turns. The main contribution of this research is the development of a combined diagnostic approach using instantaneous space phasor (ISP) spectral analysis and infrared thermal imaging to differentiate between load imbalances and incipient interturn short circuits in transformers. This method enhances early fault detection by identifying distinctive electrical and thermal signatures associated with each condition. The results could improve transformer monitoring, reducing the risk of failure and enhancing grid reliability. Full article
(This article belongs to the Section Control and Systems Engineering)
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27 pages, 16777 KiB  
Article
Reinforcement Learning Approach to Optimizing Profilometric Sensor Trajectories for Surface Inspection
by Sara Roos-Hoefgeest, Mario Roos-Hoefgeest, Ignacio Álvarez and Rafael C. González
Sensors 2025, 25(7), 2271; https://doi.org/10.3390/s25072271 (registering DOI) - 3 Apr 2025
Abstract
High-precision surface defect detection in manufacturing often relies on laser triangulation profilometric sensors for detailed surface measurements, providing detailed and accurate surface measurements over a line. Accurate motion between the sensor and workpiece, usually managed by robotic systems, is critical for maintaining optimal [...] Read more.
High-precision surface defect detection in manufacturing often relies on laser triangulation profilometric sensors for detailed surface measurements, providing detailed and accurate surface measurements over a line. Accurate motion between the sensor and workpiece, usually managed by robotic systems, is critical for maintaining optimal distance and orientation. This paper introduces a novel Reinforcement Learning (RL) approach to optimize inspection trajectories for profilometric sensors based on the boustrophedon scanning method. The RL model dynamically adjusts sensor position and tilt to ensure consistent profile distribution and high-quality scanning. We use a simulated environment replicating real-world conditions, including sensor noise and surface irregularities, to plan trajectories offline using CAD models. Key contributions include designing a state space, action space, and reward function tailored for profilometric sensor inspection. The Proximal Policy Optimization (PPO) algorithm trains the RL agent to optimize these trajectories effectively. Validation involves testing the model on various parts in simulation and performing real-world inspection with a UR3e robotic arm, demonstrating the approach’s practicality and effectiveness. Full article
(This article belongs to the Special Issue Applications of Manufacturing and Measurement Sensors: 2nd Edition)
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