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21 pages, 1366 KB

Open AccessArticle

Exploiting Exhausted Biomasses from Essential Oil Distillation in Animal Feeding: Chemical Characterisation and Volatile Profile of Laurel Bay (Laurus nobilis), Lavender (Lavandula angustifolia), Lavandin (L. angustifolia × L. latifolia), and Industrial Hemp (Cannabis sativa)

by Iolanda Altomonte, Roberta Ascrizzi, Maria Francesca Bozzini, Mina Martini, Federica Salari, Marco Ferrara, Filippo Fratini, Silvio Chericoni, Fabio Stefanelli, Alessandra Borghi, Roberta Paris, Massimo Montanari, Vilma Vilienė, Asta Raceviciute Stupeliene, Monika Nutautaite and Guido Flamini

Processes 2026, 14(17), 2779; https://doi.org/10.3390/pr14172779 (registering DOI) - 29 Aug 2026

Abstract

The essential oil (EO) industry generates large volumes of solid distillation residues that are commonly discarded as waste, despite their potential as a source of nutrients and bioactive compounds. This study evaluated the chemical quality and the volatile profile of four types of [...] Read more.

The essential oil (EO) industry generates large volumes of solid distillation residues that are commonly discarded as waste, despite their potential as a source of nutrients and bioactive compounds. This study evaluated the chemical quality and the volatile profile of four types of exhausted biomasses (EBs) obtained after steam distillation of EOs from bay laurel (Laurus nobilis), lavender (Lavandula angustifolia), lavandin (L. angustifolia × L. latifolia) and two industrial hemp specimens (Carifit1p, a breeding line, and Codimono, a cultivar), with a view to their potential valorisation in animal feeding. The nutritional composition of the distillation residues was determined according to AOAC methods, while the headspace volatile fraction was characterised by HS-SPME–GC/MS. The EBs were generally characterised by a high fibre content, with the highest crude fibre and NDF values in whole lavender and lavandin plants, whereas the industrial hemp line Carifit1p showed the highest crude protein (17.1% DM) and ash contents. The volatile profiles of the residues differed markedly from those typical of the corresponding fresh EOs: the bay laurel residue was dominated by monoterpene hydrocarbons (74.5%) with strongly depleted 1,8-cineole (1.9%), while lavender and lavandin retained high levels of β-caryophyllene (35.8% and 32.4%, respectively). Industrial hemp residues were enriched in oxygenated sesquiterpenes, with caryophyllene oxide reaching 21.2% in Carifit1p and exceeding β-caryophyllene. Overall, the results indicate that EO exhausted biomasses retain a species-specific profile of fibre, residual nutrients and less volatile, less water-soluble terpenoids, supporting their potential reuse as feed ingredients and contributing to a more sustainable, near-zero-waste distillation supply chain. Full article

(This article belongs to the Section Chemical Processes and Systems)

19 pages, 13050 KB

Open AccessArticle

Genome-Wide Identification of NLP Family Genes in Cultivated Strawberry (Fragaria × ananassa Duch.) and Analysis of Their Expression Under Heat and Botrytis cinerea Stresses

by Lijuan Chen, Ruyu He, Dong Wang, Qiao Xiao, Haiyan Deng and Hongwen Li

Int. J. Mol. Sci. 2026, 27(17), 7754; https://doi.org/10.3390/ijms27177754 (registering DOI) - 29 Aug 2026

Abstract

Nodule inception (NIN)-like proteins (NLPs) are plant-specific transcription factors regulating nutrient absorption, growth, and stress tolerance; however, their roles in stress responses remain largely uncharacterized. Cultivated strawberry (Fragaria × ananassa ‘Camarosa’) serves as an ideal model for dissecting the evolution and function [...] Read more.

Nodule inception (NIN)-like proteins (NLPs) are plant-specific transcription factors regulating nutrient absorption, growth, and stress tolerance; however, their roles in stress responses remain largely uncharacterized. Cultivated strawberry (Fragaria × ananassa ‘Camarosa’) serves as an ideal model for dissecting the evolution and function of NLP genes. In this study, 37 FaNLP genes were identified genome-wide. Phylogenetic analysis classified them into three subfamilies, which are evenly distributed across seven chromosomes. Divergent exon–intron structures and conserved motif compositions suggest functional differentiation among FaNLPs. Quantitative real-time PCR (qRT-PCR) revealed distinct expression profiles under heat stress and Botrytis cinerea infection. Notably, FaNLPs were significantly more upregulated in the cultivar ‘Shuxing’ than in ‘Benihoppe’. Heat stress inhibited photosynthesis and altered catalase activity (CAT), superoxide dismutase activities (SOD) and peroxidase activities (POD), whereas fungal infection enhanced chitinase activity in both cultivars. Comparative genomics with Arabidopsis and rice revealed strawberry-specific evolutionary patterns of NLPs. Subcellular localization prediction indicates that FaNLP proteins primarily localize to the nucleus, implying their potential roles as transcriptional regulators. This study links FaNLP sequence characteristics with stress response phenotypes, providing a foundation for elucidating NLP-mediated regulatory networks in strawberry. Future functional assays, including overexpression and knockout analyses, will further clarify the biological roles of FaNLPs. Full article

(This article belongs to the Special Issue Abiotic Stress Tolerance and Genetic Diversity in Plants, 3rd Edition)

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22 pages, 5347 KB

Open AccessArticle

Artificial Intelligence in Prehospital Tele-Emergency Medicine: A Survey of Acceptance and Attitudes

by Nadezhda Durdova, Mathias Schmidt, Hanna Schröder, Pia Thoma, Dominik Groß and Saskia Wilhelmy

Healthcare 2026, 14(17), 2753; https://doi.org/10.3390/healthcare14172753 (registering DOI) - 29 Aug 2026

Abstract

Background/Objectives: Artificial intelligence (AI) is increasingly being used in medicine to improve the efficiency, accuracy, and timeliness of patient care. In prehospital tele-emergency medicine, AI also has the potential to address various challenges and support tele-emergency physicians. This study focuses on a specific [...] Read more.

Background/Objectives: Artificial intelligence (AI) is increasingly being used in medicine to improve the efficiency, accuracy, and timeliness of patient care. In prehospital tele-emergency medicine, AI also has the potential to address various challenges and support tele-emergency physicians. This study focuses on a specific AI-based decision support system being developed for use by practitioners. While technical feasibility is a prerequisite for the successful implementation of this system, social, ethical, and patient-centered considerations are equally important. Methods: The acceptance and perceptions of the German public, as potential patients, regarding the implementation of a specific AI system for prehospital tele-emergency medicine were assessed through an online survey. The results provide qualitative and quantitative insights into participants’ attitudes and acceptance. Results: The perceived advantages and disadvantages of implementing AI in prehospital tele-emergency medicine, as seen by potential patients, were identified, along with moderate acceptance of the technology. Participants emphasized the importance of different factors, including time efficiency, safety, AI recommendation accuracy, responsible system use, data and legal protection, technical stability, and positive impact on physicians’ work and patient outcomes. Conclusions: The findings enhance understanding of public perceptions regarding the use of AI in prehospital tele-emergency medicine and provide a valuable bioethical foundation for guiding the responsible integration of AI into tele-emergency care. Full article

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28 pages, 9995 KB

Open AccessArticle

Non-Monotonic Embedment Response of a PRC Pipe Pile Retaining System Across a Silty Sand–Silty Clay Interface

by Weiyu Sun, Jiangang Han, Yuan Chen, Ping Lu, Houhai Yuan and Ying Wang

Appl. Sci. 2026, 16(17), 8619; https://doi.org/10.3390/app16178619 (registering DOI) - 29 Aug 2026

Abstract

This study investigates the non-monotonic embedment response of a PRC pipe pile retaining system in interbedded silty sand and silty clay using field monitoring and three-dimensional PLAXIS 3D analyses with the HSsmall model. Retaining structure deformation was jointly influenced by excavation depth H [...] Read more.

This study investigates the non-monotonic embedment response of a PRC pipe pile retaining system in interbedded silty sand and silty clay using field monitoring and three-dimensional PLAXIS 3D analyses with the HSsmall model. Retaining structure deformation was jointly influenced by excavation depth H, relative embedment ratio λ, and three-dimensional corner restraint; the mean pile head displacement in the deep excavation zone was 1.59 times that in the shallow zone. During same-stratum embedment, increasing the retaining pile length L progressively reduced deformation and bending response. On the north side, increasing L from 18 to 23 m reduced the pile head, excavation base, and pile toe displacements by 29.54%, 32.92%, and 71.99%, respectively, while the maximum negative bending moment decreased by 14.34%. On the south side, increasing L from 20 to 23 m produced corresponding reductions of 25.86%, 28.43%, 59.18%, and 17.12%. After the pile toe entered the underlying silty clay layer, the calculated pile head and excavation base displacements and maximum negative bending moment increased again for L = 24–25 m, indicating a project-specific non-monotonic response associated with the change in pile toe stratigraphic condition. Retaining pile length affected ground settlement and basal heave magnitudes more strongly than the settlement trough location. For adjacent pipelines, large displacement and axial force responses occurred near the trough minimum, while appreciable bending remained within the high-gradient trough flank region; increasing burial depth generally reduced displacement and axial force, whereas bending moment varied non-monotonically. These findings provide case-specific guidance for embedment selection and buried utility protection in layered ground. The L = 24–25 m cross-stratum cases are numerical parametric predictions rather than independently field-validated configurations. Full article

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28 pages, 7665 KB

Open AccessArticle

Dynamic Modulus Prediction of Fiber-Reinforced Asphalt Mixtures Based on XGBoost Optimized by an Improved Black-Winged Kite Algorithm

by Xunqian Xu, Shuyong Pan, Cheng Zhou, Wenxuan Ge and Xu Wu

Materials 2026, 19(17), 3681; https://doi.org/10.3390/ma19173681 (registering DOI) - 29 Aug 2026

Abstract

Dynamic modulus is a key stiffness parameter in the mechanistic–empirical design of asphalt pavements. Traditional laboratory tests are time-consuming and costly, while conventional empirical models fail to characterize the nonlinear viscoelasticity introduced by fibers, and existing machine learning methods suffer from premature hyperparameter [...] Read more.

Dynamic modulus is a key stiffness parameter in the mechanistic–empirical design of asphalt pavements. Traditional laboratory tests are time-consuming and costly, while conventional empirical models fail to characterize the nonlinear viscoelasticity introduced by fibers, and existing machine learning methods suffer from premature hyperparameter convergence and limited interpretability. To address these issues, this study employs an improved black-winged kite algorithm (IBKA) to optimize eXtreme Gradient Boosting (XGBoost) for establishing a dynamic modulus prediction model. Gaussian chaotic mapping, guided pool strategy, and adaptive step size are introduced to enhance global hyperparameter optimization capability. A dataset of 288 samples involving temperature, frequency, strain, and fiber categories is compiled from multi-condition tests. Nested cross-validation and an independent test set are adopted for internal optimization and generalization assessment, with permutation testing (1000 Monte Carlo, p < 0.001) confirming the statistical reliability of the model. The results demonstrate that IBKA–XGBoost delivers excellent accuracy and robustness, achieving an RMSE of 355.1248 MPa and an R² of 0.9966 in NCV and 373.5450 MPa and 0.9955 on the independent test set. It outperforms BKA–XGBoost, four metaheuristic algorithms, and three conventional tuning strategies across nine evaluation metrics; compared with BKA–XGBoost, RMSE decreases by 23.9% and prediction uncertainty U₉₅ narrows by 23.7%. SHAP and PDP analyses identify temperature as the dominant factor, reveal fiber-type differentiation governed by modulus matching and interfacial compatibility, and confirm asymmetric temperature–frequency interactions consistent with the time–temperature superposition principle. The proposed framework facilitates fiber screening and the intelligent refined design of pavement materials. Full article

(This article belongs to the Section Construction and Building Materials)

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24 pages, 2004 KB

Open AccessArticle

Eye-Tracking Evidence for TACOM-Based Assessment of Procedural Task Complexity in a Nuclear Power Plant Full-Scope Simulator

by Huan Xiao, Pengcheng Li, Wenming Chen, Jiayuan He and Zetian Tao

Sensors 2026, 26(17), 5487; https://doi.org/10.3390/s26175487 (registering DOI) - 29 Aug 2026

Abstract

Emergency and operating procedures in nuclear power plants usually require operators to search for information, judge system states, and make control decisions across several linked interfaces. Conventional TACOM assessment quantifies the structural complexity of such procedure-guided tasks, but it does not directly show [...] Read more.

Emergency and operating procedures in nuclear power plants usually require operators to search for information, judge system states, and make control decisions across several linked interfaces. Conventional TACOM assessment quantifies the structural complexity of such procedure-guided tasks, but it does not directly show how operators visually process the task during execution. This study examined whether eye-tracking features can provide preliminary process-level evidence for TACOM-based assessment, rather than replace the TACOM framework. We extracted 25 eye-tracking features from 21 nuclear engineering graduate students while they completed 17 SGTR/SLOCA procedure fragments in an M310 full-scope simulator. A partial least-squares regression model predicted task-level TACOM scores, with RMSE = 0.357, MAE = 0.300, and R² = 0.538 under leave-one-task-out cross-validation. The RMSE corresponded to 18.3% of the observed TACOM range (2.070–4.017), and prediction was more strongly associated with observed task ranking (Spearman’s rho = 0.775) than with exact linear calibration (Pearson’s r = 0.750). Feature analyses suggested that fixation–duration variability, fixation dwell, pupil response, gaze dynamics, and spatial sampling jointly carried TACOM-related information. Exploratory subdimension analyses further indicated that task scope was most consistently associated with fixation dwell and fixation-time proportion, whereas task uncertainty was more closely associated with pupil variability and spatial entropy. These findings suggest that eye tracking may complement TACOM by describing execution-process demands, although the evidence remains correlational and limited by the small task set, graduate student sample, and task interface variability. Future studies should validate the signatures with licensed operators, larger multi-scenario task sets, independent TACOM scoring, and step-level AOI analyses. Full article

(This article belongs to the Section Industrial Sensors)

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20 pages, 2784 KB

Open AccessArticle

Genome-Wide Association Study Reveals Novel Loci and Candidate Genes of Vitamin E Content in Sesame (Sesamum indicum L.)

by Zishu Luo, Jianglong Zhou, Yijia Zhang, Huan Li, Rong Zhou, Ting Zhou, Yanxin Zhang, Jun You and Linhai Wang

Antioxidants 2026, 15(9), 1088; https://doi.org/10.3390/antiox15091088 (registering DOI) - 29 Aug 2026

Abstract

Sesame is a significant oilseed crop whose exceptional oxidative stability is closely associated with its abundant endogenous antioxidants. As one of the most predominant lipid-soluble antioxidants in sesame, vitamin E (VE) plays a critical role in scavenging lipid peroxyl radicals, terminating lipid peroxidation [...] Read more.

Sesame is a significant oilseed crop whose exceptional oxidative stability is closely associated with its abundant endogenous antioxidants. As one of the most predominant lipid-soluble antioxidants in sesame, vitamin E (VE) plays a critical role in scavenging lipid peroxyl radicals, terminating lipid peroxidation chain reactions, and protecting cellular membranes from oxidative damage, thereby maintaining intracellular redox homeostasis and attenuating the development of oxidative stress-related disorders. Although VE is a potent natural antioxidant with significant pharmacological activities in mitigating oxidative stress-related disorders, the genetic mechanisms underlying the natural variation in VE content in sesame remain incompletely understood. Here, variation in VE content was evaluated across 400 sesame accessions grown in two environments. Ultra-high-performance liquid chromatography (UHPLC) analysis revealed that only γ-tocopherol was detected in sesame seeds, with concentrations between 169.33 and 463.31 mg/kg, averaging 316.28 mg/kg. The newly acquired SNP and InDel data from whole-genome resequencing were associated with the phenotypic data, leading to the identification of five significant loci associated with VE content. Comparative transcriptomic profiling of two sesame accessions with distinct VE contents revealed the differential expression of key biosynthetic enzyme genes (such as GGDR, PDS1, VTE2, and VTE4) between the two accessions. By integrating a genome-wide association study with transcriptomic data, two primary candidate effector genes, SINPZ1100015/SiNST1 and SINPZ0901927, were identified, with SiNST1 being particularly prominent. Functional validation further showed that overexpression of SiNST1 in the hairy root of sesame significantly decreased VE content. This investigation advances the comprehension of variations in VE content and the regulatory mechanisms governing its biosynthetic metabolism in sesame, supporting the development of functional sesame varieties for dietary antioxidant supplementation. Full article

(This article belongs to the Section Natural and Synthetic Antioxidants)

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13 pages, 3363 KB

Open AccessArticle

An Optimal Receiver Deployment Method for Achieving Robust Localization Performance in Practical Environments

by Bosung Park, Jongho Keun and Hosung Choo

Sensors 2026, 26(17), 5486; https://doi.org/10.3390/s26175486 (registering DOI) - 29 Aug 2026

Abstract

In this paper, we propose a receiver deployment optimization method that ensures robust localization performance under Direction of Arrival (DoA) estimation errors arising in practical environments. The proposed method accounts for DoA estimation errors and optimizes receiver deployments by incorporating Position Dilution of [...] Read more.

In this paper, we propose a receiver deployment optimization method that ensures robust localization performance under Direction of Arrival (DoA) estimation errors arising in practical environments. The proposed method accounts for DoA estimation errors and optimizes receiver deployments by incorporating Position Dilution of Precision (PDoP) to mitigate their amplification into localization errors. To accurately characterize DoA estimation errors under realistic propagation and installation constraints, a ray-tracing-based Wireless InSite simulator is employed to model environments including actual terrain and buildings. Based on this model, a cost function incorporating both the DoA estimation errors at each receiver and the PDoP is defined, and a Genetic Algorithm (GA) is applied to minimize this cost function and determine the optimal receiver deployment. The optimal deployment achieves low localization RMSE and maintains high robustness against environmental and system uncertainties, particularly when the standard deviation of the DoA estimation error exceeds approximately 8°. These results demonstrate that the proposed deployment method provides reliable localization performance in realistic propagation environments and remains robust against increasing DoA estimation errors. Full article

(This article belongs to the Section Communications)

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20 pages, 5983 KB

Open AccessArticle

Spark Plasma Sintered La_0.8Ce_0.2Fe_9.2Co_0.6Si_1.2/Fe Composites with Superior Properties for Near-Room-Temperature Magnetocaloric Applications

by Xichun Zhong, Zhongyuan Hao, Xuan Huang, Dongling Jiao, Cuilan Liu, Juan Cheng and Raju V. Ramanujan

Magnetochemistry 2026, 12(9), 94; https://doi.org/10.3390/magnetochemistry12090094 (registering DOI) - 29 Aug 2026

Abstract

La_0.8Ce_0.2Fe_9.2Co_0.6Si_1.2/Fe bulk composites were fabricated via spark plasma sintering (SPS), and the effects of Fe powder content on the phase composition, microstructure, magnetic properties, mechanical properties, and thermal conductivity of the composites were [...] Read more.

La_0.8Ce_0.2Fe_9.2Co_0.6Si_1.2/Fe bulk composites were fabricated via spark plasma sintering (SPS), and the effects of Fe powder content on the phase composition, microstructure, magnetic properties, mechanical properties, and thermal conductivity of the composites were investigated. The Fe powder content alters the α-Fe phase content in the composites. During SPS, atomic diffusion occurs between the Fe powder and the La_0.8Ce_0.2Fe_9.2Co_0.6Si_1.2 matrix, which reduces the compositional homogeneity of the desired 1:13 phase and induces the formation of thermal decomposition (TD) structures in particles adjacent to the Fe powder. As Fe powder content increases from 0 wt% to 15 wt%, the maximum magnetic entropy change ((−ΔS_M)^max) of the composites decreases from 8.11 to 5.78 J∙kg⁻¹∙K⁻¹ under 2 T. Interestingly, the α-Fe phase significantly enhances the mechanical strength and thermal conductivity (λ) of the composites. The composite with 15 wt% Fe (S15) forms a continuous α-Fe network structure and possesses the best mechanical and thermal properties: the (σ_bc)^max reaches 1463 MPa, and the λ at 300 K is 20 W·m⁻¹·K⁻¹. Owing to their balanced magnetic, mechanical, and thermal performances, the La_0.8Ce_0.2Fe_9.2Co_0.6Si_1.2/Fe composites exhibit distinctive and balanced properties, making them promising candidates for near-room-temperature magnetic refrigeration applications. Full article

(This article belongs to the Section Applications of Magnetism and Magnetic Materials)

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27 pages, 31966 KB

Open AccessArticle

Surface Energy Partitioning and Its Relation to Environmental Factors in Alpine Shrubland and Meadow Ecosystems on the Northeastern Qinghai–Tibet Plateau, China

by Yongxin Tian, Aihua Long, Zhangwen Liu, Yaping Zhou, Rensheng Chen, Chuntan Han and Xinmao Ao

Atmosphere 2026, 17(9), 852; https://doi.org/10.3390/atmos17090852 (registering DOI) - 29 Aug 2026

Abstract

Surface energy partitioning regulates heat and water exchange between land and atmosphere and reflects alpine ecosystem responses to meteorological variation. Using radiation and meteorological data from November 2022 to October 2023, we compared adjacent alpine shrubland (Hulu 1) and alpine meadow (Hulu 2) [...] Read more.

Surface energy partitioning regulates heat and water exchange between land and atmosphere and reflects alpine ecosystem responses to meteorological variation. Using radiation and meteorological data from November 2022 to October 2023, we compared adjacent alpine shrubland (Hulu 1) and alpine meadow (Hulu 2) ecosystems in the Qilian Mountains. Surface energy fluxes were estimated with a combined method based on surface energy balance, then evaluated with eddy covariance measurements. Path models examined direct and indirect environmental effects on turbulent fluxes. Standardized sensitivity coefficients based on evaporative fraction (EF) assessed seasonal responses of energy partitioning to environmental variation. Both ecosystems showed similar seasonal patterns, although flux magnitudes differed. Net radiation (R_n) followed a unimodal annual cycle and averaged 107.69 W m⁻² in the meadow and 89.36 W m⁻² in the shrubland. Sensible heat flux (H) peaked in May, with annual means of 60.22 and 51.68 W m m⁻². Latent heat flux (LE) peaked in July and averaged 49.12 and 38.58 W m m⁻². Soil heat flux (G) varied least, averaging −21.64 and −0.89 W m⁻². Path analysis identified R_n as the strongest control on turbulent fluxes. Its effect on H was weaker in the shrubland (0.92) than in the meadow (0.97), whereas its effect on LE was stronger in the shrubland (0.94) than in the meadow (0.71). Wind speed was positively related to H but negatively related to LE, with a stronger effect on H in the shrubland. Vapor pressure deficit (VPD) was negatively related to H but positively related to LE. Soil water content (SWC) had limited direct effects on turbulent fluxes at both sites. Sensitivity analysis showed higher overall EF sensitivity to environmental variation in the meadow during the growing season (0.510 vs. 0.228). Meadow EF was more sensitive to soil temperature (T_s) and SWC, whereas shrubland EF responded more strongly to VPD. Over the whole period, overall EF sensitivity was higher in the shrubland than in the meadow (0.439 vs. 0.353). These findings show that vegetation type and local environmental conditions jointly shape surface energy balance and energy partitioning in alpine ecosystems. Full article

(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)

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45 pages, 16025 KB

Open AccessArticle

Fault Diagnosis of Cascaded NPC Inverter Based on Single Sensor

by Chao Wu, Yihao Wang, Pengcheng Han and Jiahui Lv

Machines 2026, 14(9), 986; https://doi.org/10.3390/machines14090986 (registering DOI) - 29 Aug 2026

Abstract

Accurate and low-cost fault diagnosis is essential for improving the reliability of cascaded neutral-point-clamped (NPC) inverters. This paper proposes a single-sensor fault diagnosis method for a single-phase three-module cascaded NPC inverter. Only one DC-side current sensor is required for the diagnostic algorithm, while [...] Read more.

Accurate and low-cost fault diagnosis is essential for improving the reliability of cascaded neutral-point-clamped (NPC) inverters. This paper proposes a single-sensor fault diagnosis method for a single-phase three-module cascaded NPC inverter. Only one DC-side current sensor is required for the diagnostic algorithm, while the voltage sensor used in the outer voltage-control loop is not involved in fault-feature extraction. The measured DC-side current is decomposed via Fourier analysis, and a low-dimensional feature vector is constructed using the amplitudes of the zeroth, 2nd, 3rd, and 4th harmonics together with the phases of the 1st and 3rd harmonics. The six Fourier features are normalized using feature-wise Min–max parameters determined exclusively from the training data. A back-propagation (BP) neural network is then adopted to identify and locate 24 single-switch open-circuit faults in the three-module system. The investigated inverter produces 13 output-voltage levels under healthy operation, and the BP network converges after 5835 training iterations to an error threshold of 1 × 10⁻⁶. An adaptive confirmation criterion based on consecutive diagnosis-code consistency and inter-window feature convergence is introduced. For the nominal 25-class simulation test set, the accuracy, macro-precision, macro-recall, and macro-F1-score are all 100%. In addition, 134 of the 136 dynamic-condition simulation runs are correctly diagnosed, corresponding to an overall robustness-test accuracy of 98.53%. One confirmed, but incorrect final code occurs under the load disturbance applied at 90° of the output-voltage fundamental, and another occurs at an SNR of 20 dB, while no unconfirmed run is observed. Under the severe RL-load condition with τ/T₀ = 1, the mean and maximum diagnostic delays are 41.7 ms and 52 ms, respectively. Full article

(This article belongs to the Special Issue Research Progress and Prospects of Multi-Level Converters)

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17 pages, 1990 KB

Open AccessArticle

Research on the Angle Measurement Accuracy of Laser Seekers Based on Electrowetting Dual-Liquid Dynamic Zoom Systems

by Yingqi Yao, Ru Zheng, Lingyun Wang and Jiayi Qiao

Sensors 2026, 26(17), 5484; https://doi.org/10.3390/s26175484 (registering DOI) - 29 Aug 2026

Abstract

Traditional zoom semi-active laser seekers cannot adaptively adjust the spot size while maintaining a compact structure, which degrades angle measurement accuracy. This paper proposes a method based on aberration theory to analyze angle measurement designs an electrowetting dual-liquid zoom optical system that meets [...] Read more.

Traditional zoom semi-active laser seekers cannot adaptively adjust the spot size while maintaining a compact structure, which degrades angle measurement accuracy. This paper proposes a method based on aberration theory to analyze angle measurement designs an electrowetting dual-liquid zoom optical system that meets compactness constraints. The dynamic curvature control architecture replaces traditional mechanical zoom to achieve optimal detection spot for four-quadrant detectors throughout the entire trajectory. The Gaussian bracket method is employed to calculate and distribute the total optical power of the system, and a dynamic zoom optical system meeting compactness requirements is designed and simulated. Quantitative equations relating the spatial position of the liquid lens to aberrations are derived. A global optimization of the liquid lens zoom optical system for the seeker is performed on the ZEMAX 2024 platform, enabling the system to meet the detection requirements of the entire trajectory through voltage control. Design results show a total system length of 61.6 mm, with distortion controlled within 0.1% during continuous focallength adjustment from 30 to 57 mm. A quantitative evaluation model for aberration-induced angle measurement error is established, and calculations indicate a 62.8% reduction in the RMS angle measurement error over the full zoom range. Full article

(This article belongs to the Special Issue High-Power Laser Systems and Coherent Detection: Sensing Technologies and Applications)

25 pages, 1770 KB

Open AccessArticle

An Enhanced Hybrid SVD-DBO-VMD Framework for Rolling Bearing Fault Feature Extraction Using Vibration Data

by Chen Zhang, Luyan Xu, Xiansong He, Zhibin Zhao and Xiaoli Zhao

Sensors 2026, 26(17), 5485; https://doi.org/10.3390/s26175485 (registering DOI) - 29 Aug 2026

Abstract

To address the challenging problem of extracting fault information from rolling bearings, this study proposes a novel hybrid method that integrates singular value decomposition (SVD), Dung Beetle Optimization (DBO), and Variational Mode Decomposition (VMD) for enhanced fault feature extraction. The method consists of [...] Read more.

To address the challenging problem of extracting fault information from rolling bearings, this study proposes a novel hybrid method that integrates singular value decomposition (SVD), Dung Beetle Optimization (DBO), and Variational Mode Decomposition (VMD) for enhanced fault feature extraction. The method consists of three key steps: (1) adaptive SVD denoising via singular-value difference spectrum, where the collected signal is first reconstructed into a phase-space Hankel matrix, and the maximum extremum point of its singular value difference spectrum is identified as the optimal rank order for SVD denoising; (2) DBO of VMD parameters using minimum envelope entropy—to prevent over-decomposition, where DBO is employed to automatically determine the optimal number of modes K by minimizing the envelope entropy, and the signal is decomposed into a set of Intrinsic Mode Functions (IMFs); (3) kurtosis-peak-to-peak joint screening of sensitive IMFs, in which a joint criterion based on kurtosis and peak-to-peak values is applied to select the IMF that contains the richest fault information. This sensitive IMF is then subjected to Hilbert envelope spectrum analysis to accurately extract the fault characteristic frequency of rolling bearings. Experimental results from two different test rigs demonstrate that the proposed method can more effectively highlight periodic fault impulses and identify fault types, offering a reliable approach for rolling bearing fault feature extraction. Full article

(This article belongs to the Special Issue Intelligent Sensors for Condition Monitoring, Diagnosis, and Prognostics, 2nd Edition)

27 pages, 6197 KB

Open AccessArticle

Edge-Based Facial Emotion Recognition for Nurse-Assistive Robots Using a Compact CNN

by Quoc-Cuong Pham, Thanh-Long Le, Huy-Hoang Pham and Huu-Dung Nguyen

Technologies 2026, 14(9), 535; https://doi.org/10.3390/technologies14090535 (registering DOI) - 29 Aug 2026

Abstract

Facial emotion recognition (FER) can provide supplementary affective information for human–robot interaction, but deployment on resource-constrained assistive robots requires a balance between recognition performance and computational efficiency. This study presents an edge-based FER framework using a compact CNN operating on 48 × 48 [...] Read more.

Facial emotion recognition (FER) can provide supplementary affective information for human–robot interaction, but deployment on resource-constrained assistive robots requires a balance between recognition performance and computational efficiency. This study presents an edge-based FER framework using a compact CNN operating on 48 × 48 grayscale facial images and retaining all seven FER-2013 expression categories. Square-root-smoothed inverse-frequency weighting is employed to mitigate class imbalance without excessively emphasizing rare classes. On the held-out FER-2013 test set, the proposed model achieves 63.78% Accuracy and 59.32% Macro-F1, achieving higher Accuracy and Macro-F1 than the evaluated ImageNet-pretrained MobileNetV2 and MobileNetV3-Small baselines. INT8 post-training quantization reduces model size by 74.13% relative to FP32, with decreases of only 0.91 and 0.50 percentage points in Accuracy and Macro-F1, respectively. On a Raspberry Pi 3 Model B+, INT8 achieves a mean model-only inference latency of 19.30 ms and a model-only throughput of 51.82 FPS. Using an actor-disjoint RAVDESS protocol comprising 416 videos, EMA stabilization reduces prediction switching by 54.86% on the held-out test actors. These results support the feasibility of compact edge-based FER for assistive robotic interaction while emphasizing that the framework provides supplementary affective cues rather than clinical diagnosis or autonomous decision-making. Full article

(This article belongs to the Special Issue Advances in Automatics, Robotics & Artificial Intelligence)

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22 pages, 817 KB

Open AccessArticle

A Multi-Distance Ensemble of Multi-Criteria Decision Making for Ontology Ranking

by Ameeth Sooklall and Jean Vincent Fonou-Dombeu

Future Internet 2026, 18(9), 464; https://doi.org/10.3390/fi18090464 (registering DOI) - 29 Aug 2026

Abstract

Due to the increase in the number of ontologies in various domains, ranking them to facilitate their selection for reuse is an important task in ontology engineering to date. To assess the multi-faceted quality configurations of candidate ontologies, Multi-Criteria Decision Making (MCDM) frameworks [...] Read more.

Due to the increase in the number of ontologies in various domains, ranking them to facilitate their selection for reuse is an important task in ontology engineering to date. To assess the multi-faceted quality configurations of candidate ontologies, Multi-Criteria Decision Making (MCDM) frameworks are used. In particular, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an MCDM method that is widely adopted for the task of ontology ranking. However, traditional TOPSIS implementations rely almost exclusively on the Euclidean distance metric. This introduces severe rank volatilities and systematic biases when evaluating heterogeneous ontology metadata. To address these limitations, this paper introduces a novel Multi-Distance Ensemble TOPSIS (Ensemble-TOPSIS) method for robust ontology ranking. Rather than forcing a localized geometric choice, the proposed Ensemble-TOPSIS method simultaneously projects alternative ontologies through a multi-distance ensemble composed of Euclidean, Chebyshev, cosine, and Mahalanobis configurations. The Ensemble-TOPSIS method was applied to three datasets of ontologies from the artificial intelligence, agricultural, and biological domains to test its scalability and multi-domain applicability. The experimental results reveal that all the ontologies from the three domains were successfully ranked by the proposed Ensemble-TOPSIS method. Furthermore, the statistical rank correlation using Spearman’s

ρ

, Kendall’s

τ

, and the

W S

rank similarity coefficients was calculated between the TOPSIS variants, and the proposed Ensemble-TOPSIS method achieved the highest correlation in the majority of cases. Moreover, a comprehensive Monte Carlo simulation across 1200 stochastically generated, non-linear, and skewed multicollinear decision domains established the asymptotic stability of the proposed Ensemble-TOPSIS method, which achieved the highest global mean performance (

\bar{ρ} = 0.88

,

\bar{τ} = 0.75

,

\bar{W S} = 0.94

), minimized rank variance (

σ^{2} (W S) = 0.0006

), and optimally maximized the lower-bound worst-case performance profile (

ρ = 0.67

) compared to individual baseline formulations. Full article

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18 pages, 4680 KB

Open AccessArticle

Proof-of-Concept Beam-Position-Resolved Backscatter Measurements of Three Preserved Cultured-Fish Specimens Using Calibrated High-Frequency Narrow-Beam Broadband Acoustics

by Shujie Wan, Jing Cheng, Zhijun Wang and Guodong Li

Fishes 2026, 11(9), 510; https://doi.org/10.3390/fishes11090510 (registering DOI) - 29 Aug 2026

Abstract

High-frequency broadband acoustics can provide fine spatial resolution for near-range fish measurements, but the performance and limitations of beam-position-resolved backscatter measurements require careful evaluation. This proof-of-concept study examined one commercially sourced, dead, previously frozen specimen of each of three cultured fishes: golden pompano [...] Read more.

High-frequency broadband acoustics can provide fine spatial resolution for near-range fish measurements, but the performance and limitations of beam-position-resolved backscatter measurements require careful evaluation. This proof-of-concept study examined one commercially sourced, dead, previously frozen specimen of each of three cultured fishes: golden pompano (Trachinotus ovatus; 24.4 cm), mandarin fish (Siniperca chuatsi; 29.1 cm), and large yellow croaker (Larimichthys crocea; 31.2 cm). A 650–750 kHz narrow-beam system was referenced to a 10.3 mm tungsten-carbide sphere, and matched-filter pulse compression and 1° stepwise scanning were used to estimate a beam-position-resolved backscatter metric along each body. At broadside incidence, the section-summed backscatter indices were −33.61, −22.45, and −33.07 dB for the T. ovatus, S. chuatsi, and L. crocea specimens, respectively. The section-summed abdominal backscatter index, in the region occupied by the swimbladder in the post-thaw X-ray images, exceeded the arithmetic mean of the head and tail group indices by 9.71 ± 1.84 dB (range: 8.27–11.86 dB). Tailward beam positions fell below the noise floor at approximately 12.5% of the expected scan positions for S. chuatsi and 22.2% for L. crocea. A 15° departure from broadside reduced the section-summed index by 3.73–11.43 dB. Kirchhoff-ray-mode (KRM) simulations based on post-thaw dual-view X-ray geometry were broadly consistent with the specimen-level contrast observed among the preserved specimens, but were 0.92–2.74 dB lower than the corresponding broadside section-summed measurement indices at 700 kHz. These differences are not a quantitative validation because the measured and modeled estimators, frequency weighting, geometry, and tissue parameters were not equivalent. These results demonstrate the feasibility of a calibrated beam-position workflow for preserved specimens, while not establishing live-fish target strength, species benchmarks, or biomass-estimation performance. Full article

(This article belongs to the Special Issue Applications of Hydroacoustic Technology to Marine Ecology and Fisheries)

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34 pages, 1591 KB

Open AccessArticle

First-Order Wall-Slip-Induced Departure from Inverse-Cubic Scaling of Levitation-Pressure RMS in a Push–Pull Levitation Stage

by Eisuke Umesaki, Hiroki Suzuki, Junji Sakamoto and Toshinori Kouchi

Fluids 2026, 11(9), 218; https://doi.org/10.3390/fluids11090218 (registering DOI) - 29 Aug 2026

Abstract

This study clarifies how rarefaction-induced wall slip in a thin gas film affects the established inverse-cubic scaling law for the spatial nonuniformity of levitation pressure in a push–pull levitation stage. The levitation force in this stage is generated by the combined action of [...] Read more.

This study clarifies how rarefaction-induced wall slip in a thin gas film affects the established inverse-cubic scaling law for the spatial nonuniformity of levitation pressure in a push–pull levitation stage. The levitation force in this stage is generated by the combined action of blowing and suction. The continuum-based stress description is retained, and only the wall boundary condition is changed from the no-slip condition to a first-order slip condition. Three-dimensional incompressible unsteady Stokes simulations in a thin-gap unit-cell model are combined with a lubrication-theory scaling analysis. The effects of levitation height, blowing-velocity distribution, port-radius ratio, and computational-domain width are examined systematically. The results show that wall slip has only a minor influence at relatively large levitation heights. As the levitation height decreases, however, rarefaction-induced slip increases the flow-rate conductance. Consequently, the intensity of the spatial variation in levitation pressure is systematically reduced below that predicted by the no-slip inverse-cubic scaling law. This departure is well described by a correction relation derived from the lubrication approximation and remains robust against changes in the prescribed blowing-velocity distribution and geometric conditions. These findings provide an incompressible baseline for predicting levitation-pressure nonuniformity within the nominal gap-based Knudsen-number range examined here. Full article

(This article belongs to the Special Issue 10th Anniversary of Fluids—Recent Advances in Fluid Mechanics)

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24 pages, 5337 KB

Open AccessArticle

Ancient Herb, Modern Metabolomics: Primary and Secondary Metabolite Profiling of Wild Fennel (Foeniculum vulgare Mill.) Accessions Under Drought

by Anja Batel, Nikola Major, Marta Anđelini, Nina Išić, Tvrtko Karlo Kovačević, Dean Ban, Igor Pasković and Smiljana Goreta Ban

Plants 2026, 15(17), 2652; https://doi.org/10.3390/plants15172652 (registering DOI) - 29 Aug 2026

Abstract

Fennel (Foeniculum vulgare Mill.) is an aromatic species of culinary and medicinal value, but the metabolic basis of its drought response and the extent of variation among wild populations remain poorly characterized. We investigated eleven wild fennel accessions from the Mediterranean region [...] Read more.

Fennel (Foeniculum vulgare Mill.) is an aromatic species of culinary and medicinal value, but the metabolic basis of its drought response and the extent of variation among wild populations remain poorly characterized. We investigated eleven wild fennel accessions from the Mediterranean region of Croatia under control and drought conditions, combining morphological measurements with targeted profiling of 78 primary and 55 secondary metabolites. Drought significantly reduced biomass, leaf area, length, and width, while leaf dry matter content increased. Primary metabolism shifted markedly: amino acids accumulated, with asparagine and arginine increasing by over 20-fold, whereas TCA-cycle (tricarboxylic acid cycle) organic acids declined. Pathway analysis identified Alanine, aspartate, and glutamate metabolism as most strongly affected by drought treatment. A decline in the GSH/GSSG ratio indicated oxidative stress under water shortage. Among secondary metabolites, free hydroxycinnamic acids and flavonoid aglycones accumulated while most glycosylated flavonoids declined, and eight compounds showed genotype-dependent responses in drought conditions. These results reveal accession-specific metabolic adjustments to drought and characterize Croatian wild fennel as a genetic resource with distinct phytochemical profiles relevant to future conservation and breeding. Full article

(This article belongs to the Section Plant Physiology and Metabolism)

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21 pages, 1563 KB

Open AccessReview

Trimetazidine in Chronic Coronary Syndrome and Coronary Artery Disease: A Narrative Review of Clinical Efficacy, Safety, and Current Guideline Position

by Daniel Miron Brie, Cristian Mornoș, Alexandru Tîrziu, Roxana Popescu and Alina Diduța Brie

Pharmaceuticals 2026, 19(9), 1370; https://doi.org/10.3390/ph19091370 (registering DOI) - 29 Aug 2026

Abstract

Background: Chronic coronary syndrome (CCS) is a major source of morbidity and healthcare utilization, and a substantial proportion of patients continue to experience angina despite contemporary revascularization and guideline-directed medical therapy. Trimetazidine, a metabolic anti-ischemic agent that does not affect heart rate or [...] Read more.

Background: Chronic coronary syndrome (CCS) is a major source of morbidity and healthcare utilization, and a substantial proportion of patients continue to experience angina despite contemporary revascularization and guideline-directed medical therapy. Trimetazidine, a metabolic anti-ischemic agent that does not affect heart rate or blood pressure, has been used for decades as a non-hemodynamic add-on. Its position in international guidelines, however, has shifted. We aimed to synthesize current evidence on the clinical efficacy, safety, and guideline status of trimetazidine in CCS and coronary artery disease (CAD) and to delineate clinical circumstances in which a symptom-directed trial may be considered. Methods: We performed a structured narrative review of randomized controlled trials, network and conventional meta-analyses, and large observational studies indexed in PubMed/MEDLINE, Embase, and the Cochrane Library between January 1990 and March 2026. Reference lists of eligible articles, international clinical practice guidelines, and regulatory communications from European and national agencies were also examined. Results: Across pooled analyses, trimetazidine modestly but reproducibly improved exercise tolerance and reduced angina frequency and short-acting nitrate use, with efficacy comparable to other non–heart-rate-lowering antianginal agents. Improvements in left ventricular function were reported in small, single-center studies in ischemic cardiomyopathy and selected high-risk subgroups; however, these findings have not been validated in contemporary large-scale trials, and their clinical significance against modern guideline-directed therapy is unknown. The ATPCI trial confirmed safety but showed no reduction in major adverse cardiovascular events after percutaneous coronary intervention. Rare extrapyramidal effects prompted the 2012 European Medicines Agency referral and several prescribing restrictions. Conclusions: Trimetazidine remains a reasonable symptom-directed adjunct for residual angina in carefully selected CCS patients, particularly those in whom further hemodynamic up-titration is limited or poorly tolerated. The 2024 ESC class IIb recommendation may partly reflect a broader emphasis on prognostic benefit, although the neutral post-PCI outcome evidence and regulatory safety restrictions also support a more cautious position. Full article

(This article belongs to the Special Issue Management and Treatment of Cardiometabolic Disorders Across the Lifespan)

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23 pages, 1079 KB

Open AccessArticle

Deep Reinforcement Learning-Based Energy-Efficient Resource Allocation and Scheduling in 6G-Enabled UAV-Assisted IoT Wireless Networks

by Ali Nauman and Sung Won Kim

Sensors 2026, 26(17), 5483; https://doi.org/10.3390/s26175483 (registering DOI) - 29 Aug 2026

Abstract

Unmanned Aerial Vehicles (UAVs) have emerged as a flexible, cost-effective solution for connecting Internet of Things (IoT) devices where traditional infrastructure falls short. However, managing their limited energy alongside the diverse demands of densely deployed devices makes resource allocation a genuinely hard problem. [...] Read more.

Unmanned Aerial Vehicles (UAVs) have emerged as a flexible, cost-effective solution for connecting Internet of Things (IoT) devices where traditional infrastructure falls short. However, managing their limited energy alongside the diverse demands of densely deployed devices makes resource allocation a genuinely hard problem. This paper presents a Deep Reinforcement Learning (DRL) framework that jointly optimizes user scheduling, IoT device transmit power, bandwidth, and UAV movement in a 6G-enabled UAV-relay uplink network, using a deterministic large-scale air-to-ground path-loss channel model. The UAV acts as an aerial decode-and-forward relay between IoT devices and a Base Station (BS), with a Deep Q-Network (DQN) making decisions based on queue backlogs, channel conditions, UAV position, and remaining battery. The reward function balances Energy Efficiency (EE), queue stability, fairness, and battery longevity. We benchmark the DQN against six baselines; Round Robin (RR), Random Allocation (RA), the Single-to-Noise Ratio (Max-SNR), Proportional Fair (PF), a Lyapunov heuristic, and a GreedyEE scheme; across a range of device counts, traffic loads, battery budgets, and flight altitudes. Simulations consistently show that the DQN outperforms all baselines, including a RA baseline with equal access to UAV mobility; in EE, throughput, delay, and fairness, confirming that the gain stems from the learned joint control policy rather than from UAV mobility being available. Full article

(This article belongs to the Special Issue Edge Computing for Resource Sharing and Sensing in IoT Systems)

27 pages, 1104 KB

Open AccessReview

Mapping the Biotechnological Applications of Green-Synthesized Nanomaterials

by Sofia Genoves, Gabriel Omar Ostapchuk, Exequiel Giorgi, Fresia Melina Silva Sofrás, Sofia Municoy, Pablo Edmundo Antezana, Rajshree Jotania, Ratiram Gomaji Chaudhary, Paolo Nicolas Catalano, Mauricio César De Marzi, Pablo Luis Santo-Orihuela and Martín Federico Desimone

J. Pharm. BioTech Ind. 2026, 3(3), 20; https://doi.org/10.3390/jpbi3030020 (registering DOI) - 29 Aug 2026

Abstract

Green nanotechnology is now well established, showcasing how natural precursors can replace hazardous synthesis routes. This review analyzes the diverse biotechnological applications of biogenic nanomaterials. In the biomedical field, they have demonstrated significant efficacy as antimicrobial agents, targeted drug delivery vehicles, wound healers, [...] Read more.

Green nanotechnology is now well established, showcasing how natural precursors can replace hazardous synthesis routes. This review analyzes the diverse biotechnological applications of biogenic nanomaterials. In the biomedical field, they have demonstrated significant efficacy as antimicrobial agents, targeted drug delivery vehicles, wound healers, and theragnostic platforms. They also enhance food packaging security, optimize nano-fertilizers, and control insect pests. Additionally, their unique surface properties and catalytic activity make them key candidates for pollutant remediation and advanced chemical sensors. Ultimately, while green nanoparticles offer clear advantages over traditional chemical methods, key bottlenecks, like batch-to-batch reproducibility, industrial scalability, and long-term ecotoxicological and multigenerational impacts, still need to be addressed. Full article

(This article belongs to the Special Issue In Honor of Prof. Dr. Osvaldo Cascone: From His Biotechnology Lessons to Industrial Application)

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34 pages, 742 KB

Open AccessArticle

Frequency-Selective Station Diagnostics for Diffuse Volcanic Degassing: Integrating Soil-Gas Flux with Paired-Depth Pressure and Temperature Measurements

by Sebastiano Ettore Spoto

Appl. Sci. 2026, 16(17), 8618; https://doi.org/10.3390/app16178618 (registering DOI) - 29 Aug 2026

Abstract

Diffuse volcanic-degassing stations record environmental and instrument-driven variability across minutes to seasons. A flux record alone does not identify which forcing components reach the monitored layer or whether the acquisition chain alters them. We present a local three-channel diagnostic integrating chamber-based soil-gas flux [...] Read more.

Diffuse volcanic-degassing stations record environmental and instrument-driven variability across minutes to seasons. A flux record alone does not identify which forcing components reach the monitored layer or whether the acquisition chain alters them. We present a local three-channel diagnostic integrating chamber-based soil-gas flux with paired-depth pressure and temperature measurements. The model distinguishes natural, measurement-conditioned, and reported flux; derives frequency-dependent penetration, finite-spacing response, detectability, and sampling effects; and treats pressure-to-flux transfer through a calibratable depth-weighting closure. A reproducible synthetic benchmark uses separate fit, event-free calibration, and held-out test intervals, unequal sensor chains, dry–wet state changes, chamber artifacts, input-measurement stress, and independent environmental and supply perturbations. Across 100 stochastic forcing-and-noise realizations, median quiet-interval

R^{2}

was 0.96 for distributed-lag ridge and 0.94 for the frequency-domain flux-row transfer, versus 0.33 for instantaneous regression; full-test medians were 0.36, 0.26, and −0.16. Full-diagnostic median offline retrospective event-window precision/recall were 0.80/1.00; median false supply-candidate and false-any-candidate time fractions were 0 and 0.077. Of seven active transfer components audited at four representative periods, pressure-to-flux, pressure-to-pressure-gradient, and temperature-to-temperature-gradient were robustly recovered; temperature-to-flux recovery was moderate, whereas wind-pressure components were non-separable. These results demonstrate controlled internal consistency, not field validation or source reconstruction. Full article

(This article belongs to the Special Issue Applied Geophysics and Volcanology: Tools for Exploring the Dynamic Earth)

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26 pages, 4342 KB

Open AccessSystematic Review

CT-Based Peritumoral and Perirenal Fat Radiomics in Renal Cell Carcinoma: A Systematic Review and Meta-Analysis of Grade, Stage, and Adherent Perinephric Fat Prediction

by Abdulrahman Al Mopti, Ali H. D. Alshehri and Abdulsalam Alqahtani

J. Clin. Med. 2026, 15(17), 6720; https://doi.org/10.3390/jcm15176720 (registering DOI) - 29 Aug 2026

Abstract

Background/Objectives: Preoperative risk stratification in renal cell carcinoma (RCC) remains challenging for tumor aggressiveness and for adherent perinephric fat (APF) relevant to surgical planning. Although intratumoral radiomics are established, the peritumoral and perirenal fat microenvironment is biologically active and may encode imaging [...] Read more.

Background/Objectives: Preoperative risk stratification in renal cell carcinoma (RCC) remains challenging for tumor aggressiveness and for adherent perinephric fat (APF) relevant to surgical planning. Although intratumoral radiomics are established, the peritumoral and perirenal fat microenvironment is biologically active and may encode imaging biomarkers. This systematic review and meta-analysis evaluated CT-based peritumoral and perirenal fat radiomics for preoperative grading, staging, and APF prediction and examined whether fat-derived features add incremental value beyond intratumoral models. Methods: Following PRISMA 2020 and a registered protocol (PROSPERO CRD420251150155), databases were searched through March 2026, with documented verification searches through August 2026. Eligible studies extracted CT radiomics from peritumoral or perirenal fat in adults with RCC and reported discrimination metrics. Random-effects meta-analyses (restricted maximum likelihood with Hartung–Knapp intervals) were performed; incremental value was estimated from nested within-study AUC differences under predefined rules; and methodological quality was assessed with PROBAST, RQS, METRICS, and TRIPOD, and certainty with an adapted GRADE framework. Results: Twenty-five retrospective studies (10,761 patients) were included. Fat radiomics were most consistent for APF prediction (pooled AUC 0.848, 95% CI 0.738–0.917; I² = 0%). Pooled AUCs were 0.772 (0.599–0.884; I² = 93%; 95% prediction interval 0.298–0.964) for grade and 0.813 (0.681–0.899; I² = 72%) for stage. Combined fat-plus-tumor models were numerically higher than tumor-only models in 12 of 15 studies (exploratory p = 0.022), but the formally estimable nested increment (stage family, five studies) was small (delta-AUC +0.017; governing modified Hartung–Knapp 95% CI −0.008 to +0.042) and statistically compatible with no true difference; fat-only models showed no evidence of differing from tumor-only models. PROBAST rated 72% of studies at high risk of bias, and 88% originated from China. Certainty ranged from VERY LOW (grade prediction; grade-family fat-versus-tumor comparison) to LOW (all remaining outcomes). Conclusions: CT-based peritumoral and perirenal fat radiomics show the most consistent signal for APF prediction, while the measurable increment from adding fat features to tumor models is small and of unestablished clinical utility. Standardized fat segmentation, calibration and decision-curve reporting and prospective multicenter validation are required before clinical translation. Full article

(This article belongs to the Section Oncology)

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22 pages, 2900 KB

Open AccessArticle

Ternary Mixture Design of Soy Protein, Apple Fiber, and Corn Starch: Compositional, Color, and Techno-Functional Behavior

by Betsabé Hernández-Santos, Jesús Rodríguez-Miranda, Erick A. Juárez-Arellano, Juan G. Torruco-Uco, José M. Juárez-Barrientos, Enrique Ramírez-Figueroa and Athziri R. Terán-Antonio

Processes 2026, 14(17), 2777; https://doi.org/10.3390/pr14172777 (registering DOI) - 29 Aug 2026

Abstract

Soy protein, apple fiber, and corn starch are widely used functional ingredients, yet their combined effects on food matrix properties remain poorly characterized. A D-optimal mixture design (16 runs) was used to evaluate how these three components, individually and in binary and ternary [...] Read more.

Soy protein, apple fiber, and corn starch are widely used functional ingredients, yet their combined effects on food matrix properties remain poorly characterized. A D-optimal mixture design (16 runs) was used to evaluate how these three components, individually and in binary and ternary combinations, determine the proximate composition, CIELab color parameters, and techno-functional properties (water and oil absorption and solubility, pH, apparent density, emulsifying and foaming capacity, least gelation concentration, and foam stability over 120 min) of model blends. Response surface models were statistically significant for 19 of the 21 responses evaluated (R² = 0.70–1.00, p ≤ 0.05); water and oil absorption capacity did not reach significance (R² = 0.66–0.72, p > 0.05). Formulation was the main driver of the system’s behavior. Starch increased moisture, carbohydrate content, and lightness; protein determined ash, protein content, and water solubility; and fiber dominated crude fiber, lipid content, and red–yellow chromaticity, producing the greatest total color difference relative to pure starch. Unlike composition and color, which followed largely additive, single-ingredient-driven trends, interfacial functionality was governed by strong binary interactions: a synergistic protein–fiber interaction dominated emulsifying capacity (positive) and foaming capacity (strongly negative, almost completely suppressing foam formation even at protein levels comparable to the pure-protein vertex), while a synergistic protein–starch interaction enhanced foam stability. Water and oil absorption capacities varied within narrow, statistically non-significant ranges, indicating structural rather than compositional control. These findings show that mixture design and response surface methodology can quantitatively predict and tune the physicochemical, color, and functional performance of protein–fiber–starch blends, providing a practical framework for designing plant-based functional ingredients with targeted nutritional and technological profiles. Full article

(This article belongs to the Special Issue Advanced Processing and Integration of Functional Ingredients in Sustainable Food Systems)

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13 pages, 883 KB

Open AccessArticle

Nocturnal Autonomic Dysregulation and Admission-Window Clinical Suicide-Risk Assessment in Hospitalized Children and Adolescents

by Qiyuan Cao, Jiaqi Xu, Wenjing Li, Yan Zhang, Xuehua Huang, Kexin Zhou, Jinquan Zhang and Lijun Jiang

J. Clin. Med. 2026, 15(17), 6719; https://doi.org/10.3390/jcm15176719 (registering DOI) - 29 Aug 2026

Abstract

Background/Objectives: Suicide-risk assessment during child and adolescent psychiatric hospitalization draws on patient report, clinical history, and professional observation. Whether nocturnal autonomic physiology is concurrently associated with a structured admission-window assessment after accounting for depressive symptoms and self-reported suicidal ideation remains uncertain. Methods [...] Read more.

Background/Objectives: Suicide-risk assessment during child and adolescent psychiatric hospitalization draws on patient report, clinical history, and professional observation. Whether nocturnal autonomic physiology is concurrently associated with a structured admission-window assessment after accounting for depressive symptoms and self-reported suicidal ideation remains uncertain. Methods: We analyzed 212 hospitalized children and adolescents receiving inpatient care for a major depressive episode. All had a Nurses’ Global Assessment of Suicide Risk (NGASR) rating, self-report measures, covariates, and first admission-night non-contact autonomic data. Principal component analysis was used to derive a nocturnal autonomic dysregulation factor from heart-rate and heart-rate-variability (HRV) summaries. Ordinary least squares regression with HC3 robust standard errors estimated its concurrent association with NGASR after adjustment for age, sex, BMI, Beck Depression Inventory score, and Chinese Beck Scale for Suicide Ideation score. Score-appropriate sensitivity analyses used Poisson, negative-binomial, and predefined NGASR-category models. Results: Higher autonomic dysregulation was associated with higher NGASR after adjustment for depressive symptoms and self-reported suicidal ideation (standardized beta = 0.182, 95% CI 0.067 to 0.297; p = 0.002; q = 0.005), accounting for an additional 3.2 percentage points of explained variance. The association was similar in a robust Poisson model (incidence-rate ratio = 1.059, 95% CI 1.022 to 1.097; p = 0.001) and remained stable after adjustment for subjective sleep quality, AHI, sleep efficiency, and monitoring timing. Separate component models showed larger associations for SDNN and RMSSD than for heart rate or LF/HF, but did not decompose the composite effect. Conclusions: Admission-window nocturnal autonomic dysregulation showed a modest concurrent association with clinical suicide-risk assessment after adjustment for depressive symptoms and self-reported suicidal ideation. The finding does not establish disclosure-independent assessment, temporal improvement of admission assessment, or prediction of future suicidal behavior. Full article

(This article belongs to the Special Issue Children and Adolescent Mood Disorders: Risks and Treatment)

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37 pages, 1213 KB

Open AccessArticle

Healthcare-Associated Infections After Aneurysmal Subarachnoid Hemorrhage: A Retrospective Single-Center Cohort Study

by Aleksandra Kosikowska, Aleksandra Tołkacz, Zuzanna Nowak, Adrianna Lebiedzińska, Jarosław Kędziora, Waldemar Goździk, Jowita Woźniak and Małgorzata Burzyńska

J. Clin. Med. 2026, 15(17), 6718; https://doi.org/10.3390/jcm15176718 (registering DOI) - 29 Aug 2026

Abstract

Background/Objectives: Healthcare-associated infections (HAIs) frequently complicate aneurysmal subarachnoid hemorrhage (aSAH) requiring neurocritical care. We assessed their incidence, timing, microbiology, associated factors, and outcomes. Methods: This retrospective, single-center cohort study included 106 consecutive adults with acute aSAH admitted to a neurocritical care unit during [...] Read more.

Background/Objectives: Healthcare-associated infections (HAIs) frequently complicate aneurysmal subarachnoid hemorrhage (aSAH) requiring neurocritical care. We assessed their incidence, timing, microbiology, associated factors, and outcomes. Methods: This retrospective, single-center cohort study included 106 consecutive adults with acute aSAH admitted to a neurocritical care unit during 2019–2024. Time to first HAI was analyzed using cause-specific Cox regression with competing risks; to limit immortal-time bias, HAI was modeled as a time-dependent exposure in outcome analyses. Results: HAIs occurred in 47 patients (44.3%; 95% CI 35.2–53.8); median onset was 10 days (IQR 8–12). Seventy-six episodes were recorded; site-specific figures denote affected patients, with no recurrent same-site episodes: ventilator-associated pneumonia, 30 (28.3%); catheter-associated urinary tract infection, 24 (22.6%); cerebrospinal fluid infections, 13 (12.3%); and central line-associated bloodstream infection, 9 (8.5%). Acinetobacter baumannii predominated, accounting for all extensively drug-resistant isolates. High World Federation of Neurosurgical Societies grade was associated with the first HAI (HR 4.168; 95% CI 2.072–8.384), as was higher modified Fisher grade in sensitivity analysis (HR 2.886; 95% CI 1.237–6.735). Time-dependent HAI was associated with a lower hazard of live discharge (HR 0.295; 95% CI 0.164–0.529) but not with in-hospital mortality (HR 1.178; 95% CI 0.478–2.902). Its association with the discharge Glasgow Outcome Scale (GOS) was threshold-dependent (GOS ≤ 1: OR 0.715, 95% CI 0.264–1.933; GOS ≤ 3: OR 7.939, 95% CI 2.580–24.436). Conclusions: HAIs affected nearly half of this cohort. Greater initial severity was associated with a higher hazard of first HAI; HAI, in turn, was associated with prolonged hospitalization and unfavorable functional outcome, but not with mortality. These associations are observational, not causal. Surveillance, timely diagnosis, and infection prevention remain integral to neurocritical care in aSAH. Full article

(This article belongs to the Section Intensive Care)

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