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13 pages, 615 KB  
Article
Performance of Traditional Cardiovascular Risk Scores and Objective Optimization in Cancer Survivors
by Harsh A. Patel, Saifullah Syed, Pranathi Tella, Harshith Thyagaturu and Brijesh Patel
Curr. Oncol. 2026, 33(4), 230; https://doi.org/10.3390/curroncol33040230 (registering DOI) - 19 Apr 2026
Abstract
Introduction: Cardiovascular disease (CVD) is a leading cause of non-cancer death among cancer survivors, attributable to cardiotoxic therapies and cardiovascular risk factors. General population risk prediction tools, including ASCVD (Atherosclerotic cardiovascular disease), Framingham’s Score, and PREVENT (Predicting Risk of Cardiovascular Disease EVENTS), lack [...] Read more.
Introduction: Cardiovascular disease (CVD) is a leading cause of non-cancer death among cancer survivors, attributable to cardiotoxic therapies and cardiovascular risk factors. General population risk prediction tools, including ASCVD (Atherosclerotic cardiovascular disease), Framingham’s Score, and PREVENT (Predicting Risk of Cardiovascular Disease EVENTS), lack cancer-specific variables. We evaluated whether these models, even after statistical optimization, could predict cardiovascular mortality in cancer survivors. Methods: Using the National Health and Nutrition Examination Survey (NHANES) 2001–2018, linked with National Death Index (NDI) mortality data, we conducted a retrospective analysis of 634 and 429 cancer survivors, respectively, across model-specific cohorts free of baseline cardiovascular disease. Discrimination was assessed for ASCVD, Framingham Score, and PREVENT using standardized thresholds of 7.5% and 20%, as well as Youden-optimized cutoffs. Area under the curve (AUC) comparisons were performed using the DeLong non-parametric method. Results: Standard thresholds showed suboptimal discrimination across all models (AUCs: ASCVD 0.56, Framingham 0.53, PREVENT 0.64). In contrast, Youden-optimized AUCs (ASCVD: 0.68; PREVENT: 0.71; all p < 0.001, DeLong test). Optimization increased the “low-risk” group’s mortality rate from 2.8% to 4.1% (RR = 1.47), suggesting improved statistical fit came at the cost of overestimating the risk. Optimized thresholds outperformed conventional cutoffs, underscoring the necessity for recalibrated, cohort-specific risk stratification in cancer survivors. Conclusions: Standard risk scores have inadequate discrimination for cardiovascular mortality prediction in cancer survivors. Threshold recalibration improves statistical metrics but does not resolve the structural failure of these models to account for cardiotoxic exposure. Development of cardio-oncology-specific risk models incorporating oncologic exposures is therefore warranted. Full article
31 pages, 7683 KB  
Review
Prostate Cancer Diagnostics in Transition: A Review of Promising Biomarkers, Multiplex Biosensors, and Point-of-Care Diagnostic Strategies
by Sarra Takita, Alexei Nabok, Magdi H. Mussa, Abdalrahem Shtawa, Anna Lishchuk and David P. Smith
Chemosensors 2026, 14(4), 99; https://doi.org/10.3390/chemosensors14040099 (registering DOI) - 19 Apr 2026
Abstract
Prostate cancer (PCa) remains one of the most prevalent urological malignancies worldwide, with early and accurate diagnosis being critical for improving patient outcomes. Traditional screening approaches, such as digital rectal examination and prostate-specific antigen (PSA) testing, have long served as frontline tools; however, [...] Read more.
Prostate cancer (PCa) remains one of the most prevalent urological malignancies worldwide, with early and accurate diagnosis being critical for improving patient outcomes. Traditional screening approaches, such as digital rectal examination and prostate-specific antigen (PSA) testing, have long served as frontline tools; however, their limited specificity and sensitivity contribute to high rates of false positives, unnecessary biopsies, and overtreatment. Recent UK guidelines and international consensus increasingly question the role of PSA-based population screening, advocating for risk-stratified pathways and multiparametric MRI as first-line investigations. In parallel, advances in molecular biology have identified promising cancer-specific biomarkers, such as prostate cancer antigen 3 (PCA3) and transmembrane protease serine 2 (TMPRSS2:ERG), that outperform PSAs in terms of specificity and prognostic value. These developments have catalysed innovation in biosensor technologies, enabling rapid, cost-effective, and non-invasive detection of single and multiplex biomarkers in urine and serum. Electrochemical and optical affinity-based biosensors offer transformative potential for the development of personalised point-of-care platforms and diagnostics, reducing the reliance on invasive procedures and improving clinical decision-making. The latter can be augmented with artificial intelligence (AI) tools. This review critically examines the limitations of PSAs, synthesises evidence on novel biomarkers and imaging-led strategies, and evaluates the design, performance, and translational challenges of biosensor-based assays. Furthermore, it outlines future directions, including standardisation, large-scale clinical validation, and integration of multiplex biosensors with AI for precision diagnostics. By bridging molecular insights with engineering innovations, these approaches promise to redefine PCa screening and enable accurate, patient-centred care. Full article
(This article belongs to the Special Issue Electrochemical Biosensors for Global Health Challenges)
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31 pages, 1081 KB  
Perspective
Modeling of Biomechanical and Functional Parameters of Hydrogel–Cell Composites Fabricated by 3D Bioprinting Using AI-Supported Approach
by Izabela Rojek, Maciej Gniadek, Jakub Kopowski, Tomasz Kloskowski and Dariusz Mikołajewski
Materials 2026, 19(8), 1637; https://doi.org/10.3390/ma19081637 (registering DOI) - 19 Apr 2026
Abstract
3D bioprinting of hydrogel–cell composites requires simultaneous consideration of the biomechanical properties of the printed structures, the construct’s geometric stability, and conditions conducive to cell survival and function. Hydrogel cross-linking techniques and their kinetics play a key role in this process, determining the [...] Read more.
3D bioprinting of hydrogel–cell composites requires simultaneous consideration of the biomechanical properties of the printed structures, the construct’s geometric stability, and conditions conducive to cell survival and function. Hydrogel cross-linking techniques and their kinetics play a key role in this process, determining the time of shape fixation, the mechanical strength of the structures, and the mechanical environment in which the cells are located immediately after printing. The relationships between bioprinting parameters, material properties, cross-linking strategies, and the presence of cells are highly nonlinear and often investigated through trial and error, leading to significant time and material costs. This paper proposes an approach based on artificial intelligence-assisted simulation, focusing on computer modeling of the biomechanical and functional parameters of hydrogel–cell composites produced by 3D bioprinting. The methodology is based on data generated from computer simulations and allows for analysis of the impact of printing parameters and different cross-linking strategies on mechanical strength, time-dependent geometric stability, and limitations related to cellular function, including exposure time to non-cross-linked matrices. The use of artificial intelligence methods allows for the integration of simulation results and predictive assessment of material behavior, providing a basis for future optimization of bioprinting parameters and process costs prior to experimental validation. Full article
18 pages, 2476 KB  
Article
Structural Spillovers Among Bitcoin, Ethereum, Gold, and U.S. Equities: Evidence from the 2024 Spot ETF Institutionalization Regime
by Wisam Bukaita and Xinrui Li
Economies 2026, 14(4), 143; https://doi.org/10.3390/economies14040143 (registering DOI) - 19 Apr 2026
Abstract
This study examines dynamic interdependencies and risk transmission among major cryptocurrencies and traditional financial assets, including Bitcoin, Ethereum, U.S. equities, and gold, over the period 2017–2024. Particular attention is given to the structural shift associated with the 2024 U.S. spot Bitcoin exchange-traded fund [...] Read more.
This study examines dynamic interdependencies and risk transmission among major cryptocurrencies and traditional financial assets, including Bitcoin, Ethereum, U.S. equities, and gold, over the period 2017–2024. Particular attention is given to the structural shift associated with the 2024 U.S. spot Bitcoin exchange-traded fund (ETF) approval, which marked a significant milestone in the institutionalization of cryptocurrency markets. Using daily data, the analysis distinguishes volatility-driven co-movement from structural spillover effects across markets. Dependence structures are modeled using tail-sensitive Student-t copulas applied to GARCH-filtered returns to capture nonlinear and extreme co-movements, while a vector autoregressive framework combined with generalized impulse response functions and Diebold–Yilmaz connectedness measures is employed to evaluate order-invariant shock transmission dynamics across pre- and post-ETF regimes. The results reveal three main findings. First, cryptocurrencies display strong internal dependence and short-horizon contagion, with Bitcoin consistently acting as the dominant transmitter of shocks to Ethereum over an approximately three-day transmission window. Second, linkages between cryptocurrencies and equity markets remain moderate and largely regime-dependent rather than indicative of persistent structural spillovers. Third, gold remains weakly connected throughout the sample, maintaining its role as a diversification asset. Portfolio analysis further indicates that including Bitcoin can reduce portfolio variance by 4–7% and Value-at-Risk by up to 5%, although economic gains are sensitive to transaction costs. Overall, the findings suggest that cryptocurrencies function as a partially segmented asset class, offering conditional diversification benefits despite increasing institutional adoption. Full article
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27 pages, 1567 KB  
Article
Coordinated Dispatch Strategy of Flexible Resources in Distribution Networks for Temporary Loads
by Wenjia Sun and Bing Sun
Energies 2026, 19(8), 1976; https://doi.org/10.3390/en19081976 (registering DOI) - 19 Apr 2026
Abstract
Partial agricultural production loads exhibit significant temporality. The concentrated access of temporary loads can easily trigger operational challenges in distribution networks, such as heavy overload, terminal voltage violations, and increased network losses. To address these issues, this paper proposes a coordinated dispatch strategy [...] Read more.
Partial agricultural production loads exhibit significant temporality. The concentrated access of temporary loads can easily trigger operational challenges in distribution networks, such as heavy overload, terminal voltage violations, and increased network losses. To address these issues, this paper proposes a coordinated dispatch strategy for multiple flexible resources to cope with temporary loads. First, combining the operational characteristics of motor-pumped well loads, a refined model for motor-pumped well loads is constructed to fully exploit their regulation potential as flexible loads. Second, considering the supporting role of mobile energy storage systems (MESS) for heavy overload distribution networks, a spatiotemporal dispatch model for MESS is established. Then, aiming to minimize the total system operating cost, an economic dispatch model coordinating multiple flexible resources, including MESS, distributed generators (DG), and flexible loads, is developed. The original non-convex problem is transformed into a mixed-integer second-order cone programming problem using Second-Order Cone Relaxation (SOCR) method for efficient solution. Finally, the effectiveness of the proposed strategy is verified on an improved IEEE 33-bus system. Full article
(This article belongs to the Special Issue Advances in Renewable Energy Integration in Power System)
42 pages, 4403 KB  
Review
A Review of Catalysts for Hydrogen Production from Methanol
by Eun Duck Park
Molecules 2026, 31(8), 1345; https://doi.org/10.3390/molecules31081345 (registering DOI) - 19 Apr 2026
Abstract
Methanol is the simplest C1 oxygenated compound possessing the highest hydrogen-to-carbon ratio and can therefore be used as an effective hydrogen carrier. Furthermore, it can be easily transported by land and sea because it is liquid at room temperature and atmospheric pressure. Methanol [...] Read more.
Methanol is the simplest C1 oxygenated compound possessing the highest hydrogen-to-carbon ratio and can therefore be used as an effective hydrogen carrier. Furthermore, it can be easily transported by land and sea because it is liquid at room temperature and atmospheric pressure. Methanol can be converted into hydrogen via methanol steam reforming (MSR), aqueous-phase reforming of methanol (APRM), or aqueous methanol dehydrogenation (AMDH). In this review, various catalysts for MSR, APRM, and AMDH are summarized. Highly active and stable catalysts that can operate under low steam-to-methanol ratios are needed to increase the economics of the MSR process. Compared with the MSR process, the APRM process is rather simple because the water–gas shift reaction can occur simultaneously; however, more constraints exist in the selection of active metals and supports to ensure high activity and stability under APRM conditions. The inherently low reaction rate compared to MSR and the structural vulnerability of the catalyst under severe hydrothermal conditions are obstacles that the APRM catalysts must overcome. The low intrinsic catalytic activity and the high cost of homogeneous catalysts represent fundamental limitations inherent to AMDH catalysts. Based on a literature survey of MSR, APRM, and AMDH catalysts, some future research directions are also discussed. Full article
(This article belongs to the Special Issue Advances in Heterogeneous Catalysis for Green Chemistry)
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24 pages, 45558 KB  
Article
Pose- and Direction-Dependent Modulation and Accuracy in Robotic Milling
by Chandan, Daksh Singh Chauhan, Nalli Gnaneswara Rao, Ranjeet Kumar, Sajan Kapil and Mohit Law
J. Manuf. Mater. Process. 2026, 10(4), 137; https://doi.org/10.3390/jmmp10040137 (registering DOI) - 19 Apr 2026
Abstract
Robotic milling offers flexibility and lower capital cost than conventional CNC machining but is limited by low, pose-dependent structural stiffness. This study experimentally investigates how pose, cutting orientation, and engagement conditions govern dynamic response and machining accuracy, benchmarked against a CNC machine under [...] Read more.
Robotic milling offers flexibility and lower capital cost than conventional CNC machining but is limited by low, pose-dependent structural stiffness. This study experimentally investigates how pose, cutting orientation, and engagement conditions govern dynamic response and machining accuracy, benchmarked against a CNC machine under matched conditions. Tool-point frequency response functions show that the robot exhibits dominant low-frequency structural modes at 8–15 Hz with compliances on the order of 10−5 m/N, one to two orders of magnitude more flexible than higher-frequency tool–holder modes (~10−6 m/N). In contrast, the CNC system is dominated by a stiff mode near 600 Hz (~2 × 10−7 m/N) with negligible low-frequency compliance. During cutting, the response is not resonance-driven; instead, low-frequency compliance induces modulation of spindle-synchronous vibrations, resulting in broadband spectral spreading and cycle-to-cycle variability. Poincaré analysis captures this modulation, which increases with spindle speed and depth of cut. Orientation-dependent alignment with compliant directions amplifies vibration and cross-axis coupling. Regression analysis shows a significant association between Z-direction vibration and depth-of-cut deviation (R = 0.739 locally; R = 0.363 globally). The results establish a framework linking compliance, modulation, and machining performance in robotic milling. Full article
(This article belongs to the Special Issue New Trends in Precision Machining Processes)
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17 pages, 10905 KB  
Article
Tailoring Pore Size and Surface Charge of Polyamide Reverse Osmosis Membranes via Alkaline Post-Treatment for Brackish Water Desalination
by Ying Li, Renzhong Wang, Zheng Liu, Yang Zhao, Long Li, Qian Cao and Feng Shao
Polymers 2026, 18(8), 995; https://doi.org/10.3390/polym18080995 (registering DOI) - 19 Apr 2026
Abstract
Overcoming the inherent permeability–selectivity trade−off is essential to broaden the practical application of polyamide (PA) reverse osmosis (RO) membranes in brackish water desalination. In this study, we developed a facile and cost-effective alkaline (NaOH) post-treatment method to fabricate high−performance loose-structured RO membranes. The [...] Read more.
Overcoming the inherent permeability–selectivity trade−off is essential to broaden the practical application of polyamide (PA) reverse osmosis (RO) membranes in brackish water desalination. In this study, we developed a facile and cost-effective alkaline (NaOH) post-treatment method to fabricate high−performance loose-structured RO membranes. The NaOH post−treatment hydrolyzed part of the amide bonds within the membrane, converting them to negatively charged carboxyl groups. This process led to a slight increase in pore size and the formation of a looser structure. Molecular weight cut−off (MWCO) measurements confirmed that the pore size slightly increased from 0.19 nm to 0.21 nm, while X−ray photoelectron spectroscopy (XPS) and zeta potential measurements confirmed the conversion of amide bonds to carboxyl groups, which further enhanced the surface electronegativity. The synergistic effects of pore size enlargement and surface charge modification were elucidated as the key mechanisms for performance enhancement. The TPA membrane exhibited a 2−fold increase in water permeance (from 1.05 to 3.21 L m−2 h−1 bar−1), while the enhanced surface negative charge contributed to maintaining a high NaCl rejection of 98.5%. Additionally, the membrane also exhibited excellent pH stability as well as long-term stability over 100 h of continuous operation. This easily scalable post−treatment strategy offers a low−cost route to fabricate loose-structured membranes, with significant potential to enhance efficiency and reduce costs in brackish water desalination. Full article
(This article belongs to the Special Issue Polymer Composites for Smart and Eco-Friendly Systems)
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16 pages, 1224 KB  
Review
Securing the Achilles’ Heel of Esophagectomy: An Updated Evidence-Based Roadmap for Anastomotic Leak Prevention
by Lorenzo Viggiani d’Avalos, Marcel A. Schneider, Diana Vetter, Pascal Burri, Daniel Gerö and Christian A. Gutschow
Cancers 2026, 18(8), 1294; https://doi.org/10.3390/cancers18081294 (registering DOI) - 19 Apr 2026
Abstract
Background: Esophagectomy remains the definitive curative treatment for esophageal cancer but is historically burdened by significant procedure-related morbidity. Anastomotic leakage (AL) is still the “Achilles’ heel” of esophageal surgery, serving as a primary benchmark for surgical quality due to its profound impact [...] Read more.
Background: Esophagectomy remains the definitive curative treatment for esophageal cancer but is historically burdened by significant procedure-related morbidity. Anastomotic leakage (AL) is still the “Achilles’ heel” of esophageal surgery, serving as a primary benchmark for surgical quality due to its profound impact on patient recovery, healthcare costs, and long-term oncological outcomes. While surgical expertise and perioperative care have matured, reported AL rates remain persistently high. This necessitates a shift in focus from purely technical modifications toward integrated, data-driven preventive strategies. Purpose: Five years after our initial review, this update synthesizes the rapid evolution in AL prevention. We evaluate the transition from empirical surgical pragmatism to evidence-based protocols, integrating recent breakthroughs in real-time perfusion monitoring, prophylactic endoluminal technologies, and multidisciplinary patient optimization. This work provides a contemporary “roadmap” for navigating the complexities of esophageal reconstruction. Conclusions: The prevention of AL has evolved into a multimodal “bundle” that begins well before the index operation. This review highlights the critical shift toward quantitative perfusion assessment via indocyanine green fluorescence angiography, which is increasingly replacing subjective visual inspection as the standard for anastomotic site selection. We discuss the emerging role of gastric ischemic preconditioning as a biological strategy to enhance conduit vascularity, alongside the paradigm of proactive management using preemptive endoluminal vacuum therapy to mitigate septic sequelae in high-risk cases. Furthermore, we examine technical refinements in conduit construction and conditioning—focusing on the ‘tension-perfusion’ relationship—and the essential role of structured prehabilitation within enhanced recovery after surgery frameworks. While the quality of evidence remains heterogeneous, the move toward standardized reporting and objective monitoring marks a new era of precision in esophageal surgery. Full article
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19 pages, 2881 KB  
Article
Numerical Simulation of Photocatalytic NO Removal and Sustainable Coating Strategy Optimization for Tunnel Pavement and Wall Surfaces
by Ruibin Li, Mingjian Yin, Xiaofeng Chen, Sitian Wu, Dong Ye, Ke Wu and Kai Zhu
Sustainability 2026, 18(8), 4058; https://doi.org/10.3390/su18084058 (registering DOI) - 19 Apr 2026
Abstract
Motor vehicle exhaust in urban tunnels can cause nitric oxide (NO) to accumulate, severely degrading air quality both inside the tunnel and in the surrounding environment. Photocatalytic technology is an efficient, secondary-pollution-free approach with clear potential for treating tunnel exhaust; however, parametric analyses [...] Read more.
Motor vehicle exhaust in urban tunnels can cause nitric oxide (NO) to accumulate, severely degrading air quality both inside the tunnel and in the surrounding environment. Photocatalytic technology is an efficient, secondary-pollution-free approach with clear potential for treating tunnel exhaust; however, parametric analyses for practical tunnel engineering applications remain limited. Using computational fluid dynamics (CFD), this study developed a numerical model to simulate photocatalytic NO degradation in a congested tunnel and examined how the surface reaction rate, coating extent, and longitudinal coated section affect NO reduction performance. The results show that NO reduction efficiency increased with the surface reaction rate; however, once the surface reaction rate constant exceeded 2.11 × 10−4 m/s, further gains diminished and the efficiency approached a plateau due to mass-transfer limitations. With respect to the coating extent, full four-wall coating (sidewalls, ceiling, and road surface) provided the best performance, followed by three-wall coating (excluding the ceiling). Moreover, because the road surface lies in a region of high pollutant concentration and low air velocity, coating on the road surface achieved a markedly stronger reduction effect than coating on the sidewalls or the ceiling. In the simulated 500 m tunnel, the downstream coated section achieved a markedly higher NO reduction efficiency in the ambient environment outside the tunnel (5.9%) than the upstream coated section (1.0%), approaching that of the full-length (500 m) coated section (6.6%). Therefore, in practical engineering applications, priority should be given to coating strategies targeting the downstream section and the road surface in order to balance NO reduction performance and economic cost. Such a strategy is beneficial not only for improving tunnel air quality, but also for promoting sustainable pavement and tunnel-surface engineering by reducing unnecessary coating area and enabling a more resource-efficient and cost-effective use of photocatalytic materials. These findings provide theoretical and methodological support for the sustainable design and application of photocatalytic coating systems in urban tunnels. Full article
(This article belongs to the Special Issue New Materials and Sustainable Development in Pavement Engineering)
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28 pages, 2196 KB  
Article
Parameter Sensitivity Analysis of Generators and Grid-Connected Constraints in Hybrid Microgrids Using Deep Reinforcement Learning
by Inoussa Legrene, Tony Wong and Louis-A. Dessaint
Appl. Sci. 2026, 16(8), 3969; https://doi.org/10.3390/app16083969 (registering DOI) - 19 Apr 2026
Abstract
Hybrid renewable energy systems, which combine photovoltaic panels, wind turbines, batteries, generators, and grid connections, require careful sizing to balance economic performance, renewable integration, and supply reliability. In this context, this study proposes a deep reinforcement learning (DRL)-based sensitivity analysis framework in which [...] Read more.
Hybrid renewable energy systems, which combine photovoltaic panels, wind turbines, batteries, generators, and grid connections, require careful sizing to balance economic performance, renewable integration, and supply reliability. In this context, this study proposes a deep reinforcement learning (DRL)-based sensitivity analysis framework in which the admissible energy contributions from the diesel generator and the grid are treated as explicit design-control parameters. The objective is to simultaneously minimize the levelized cost of energy, minimize the loss of power supply probability, and maximize the renewable energy fraction. A sensitivity analysis was conducted across different HRES configurations, load profiles, and tau/gamma values. The performance of the DRL approach was compared with that of multi-objective particle swarm optimization and the non-dominated sorting genetic algorithm II under the same study setting. The results indicate that DRL can identify competitive trade-offs, especially under standard load conditions, while also providing insight into how admissible backup-energy constraints reshape techno-economic and reliability compromises. The best trade-offs were observed around intermediate tau and gamma values, suggesting that moderate backup-energy margins are more favorable than extreme values. These findings should be interpreted within the scope of a simulation-based study and provide comparative design-oriented evidence rather than universally transferable design rules. Full article
(This article belongs to the Special Issue Holistic Approaches in Artificial Intelligence and Renewable Energy)
18 pages, 2432 KB  
Article
Precision Without Complexity: A Comparative Study of YOLO26 Pose Variants for Distal Arm Landmark Detection
by Prathiksha Padmanabha, H. M. K. K. M. B. Herath, Nuwan Madusanka, Hi-Joon Park, Chang-Su Na, Myunggi Yi and Byeong-il Lee
Appl. Sci. 2026, 16(8), 3968; https://doi.org/10.3390/app16083968 (registering DOI) - 19 Apr 2026
Abstract
Accurate anatomical landmark localization in clinical images requires millimeter-level spatial precision, yet whether increasing model scale improves such precision in structured medical imaging tasks remains unclear. Five YOLO26 pose-estimation variants (N, S, M, L, and X) were evaluated on 3679 RGB distal-arm images [...] Read more.
Accurate anatomical landmark localization in clinical images requires millimeter-level spatial precision, yet whether increasing model scale improves such precision in structured medical imaging tasks remains unclear. Five YOLO26 pose-estimation variants (N, S, M, L, and X) were evaluated on 3679 RGB distal-arm images from 262 participants under a standardized overhead imaging protocol, with five anatomical landmarks annotated across the proximal forearm, mid-forearm, and hand. Localization error was quantified in millimeters using ArUco-marker-based pixel-to-millimeter calibration; all models were initialized from COCO-pretrained weights, fine-tuned under identical conditions, and assessed using COCO-style detection metrics and physically grounded localization error. Detection performance saturated across all scales (mAP@0.5 = 99.5%), while localization performance differed substantially; YOLO26N achieved the lowest mean error (2.76 ± 0.96 mm) and the highest proportion of predictions within 4 mm (88.0%), whereas YOLO26X produced the highest mean error (4.08 ± 2.59 mm) despite a 26.9× higher computational cost. Landmark-wise analysis revealed a consistent proximal-to-distal error gradient, with the largest degradation at anatomically ambiguous proximal landmarks in larger models. These findings suggest that increasing model capacity does not improve clinically meaningful localization precision in structured distal-arm imaging, and lightweight models may offer the most favorable accuracy-efficiency trade-off in resource-constrained clinical settings. Full article
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22 pages, 1755 KB  
Article
Process Engineering Evaluation of Plant-Based Corrosion Inhibitors: Case Study of Citrus limon and Eucalyptus globulus
by Sadjia Bertouche, Souhila Kadem, Sabrina Koribeche, Khalida Allaoui, Fatima Zahra Aougabi, Lilia Farah, Nour El Houda Laoufi, Dounia Lezar, Nassila Sabba and Seif El Islam Lebouachera
Processes 2026, 14(8), 1304; https://doi.org/10.3390/pr14081304 (registering DOI) - 19 Apr 2026
Abstract
Corrosion continues to be a major concern in industrial systems, causing material degradation and raising maintenance costs. In recent years, plant-derived corrosion inhibitors have gained interest as environmentally friendly alternatives to conventional chemical treatments. In this work, ethanolic extracts from the leaves of [...] Read more.
Corrosion continues to be a major concern in industrial systems, causing material degradation and raising maintenance costs. In recent years, plant-derived corrosion inhibitors have gained interest as environmentally friendly alternatives to conventional chemical treatments. In this work, ethanolic extracts from the leaves of Citrus limon (L.) Osbeck and Eucalyptus globulus Labill. were evaluated as green corrosion inhibitors for C45 carbon steel in 1 M HCl solution. The extracts were prepared by continuous Soxhlet extraction and characterized through antioxidant activity measurements using the 2,2-diphenyl-1-picrylhydrazyl DPPH radical scavenging method, gravimetric (weight loss) tests, and electrochemical techniques including potentiodynamic polarization. In addition, the extraction parameters were optimized using a face-centered central composite design (CCD) within a response surface methodology (RSM) framework, and the resulting models were analyzed by analysis of variance (ANOVA). The effects of inhibitor concentration and temperature on corrosion inhibition performance were systematically examined. The antioxidant assay indicated that E. globulus extract reached a scavenging activity above 95% at 1000 mg/L, while C. limon extract showed moderate activity around 71%. Gravimetric tests revealed that both extracts reduced the corrosion rate, with optimal inhibition efficiencies of approximately 67% for C. limon (at 0.3 g/100 mL) and 82% for E. globulus (at 1.0 g/100 mL). Beyond these optimal concentrations, a decline in performance was observed, suggesting surface saturation. The statistical optimization showed that the C. limon response model was solvent-driven (R2 = 92.05%), whereas the E. globulus model was curvature-driven (R2 = 95.45%), with contrasting response surface topographies. Electrochemical measurements confirmed that both extracts acted as mixed-type inhibitors, shifting the corrosion potential toward less negative values and reducing the corrosion current density. Overall, E. globulus extract demonstrated superior performance across all methods, and both extracts represent promising candidates for sustainable corrosion protection in acidic industrial environments. Full article
(This article belongs to the Section Catalysis Enhanced Processes)
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28 pages, 899 KB  
Review
The Hydrogen Economy: Progress and Challenges to Future Growth
by Ifeanyi Oramulu and Vincent P. Paglioni
Hydrogen 2026, 7(2), 51; https://doi.org/10.3390/hydrogen7020051 (registering DOI) - 19 Apr 2026
Abstract
The rally to mitigate growing carbon emissions and climate change necessitates decarbonization strategies, with hydrogen emerging as a key candidate option across multiple sectors. This review examines the current state of the hydrogen economy, including production, implementation, and associated risks. Hydrogen’s versatility in [...] Read more.
The rally to mitigate growing carbon emissions and climate change necessitates decarbonization strategies, with hydrogen emerging as a key candidate option across multiple sectors. This review examines the current state of the hydrogen economy, including production, implementation, and associated risks. Hydrogen’s versatility in industry, transportation, and energy storage is highlighted, alongside the challenges of transitioning from fossil fuel-based production. It explores the current state of hydrogen technologies, differentiating between green, blue, and gray hydrogen production methods, and highlights advancements in production techniques like thermochemical water splitting. Key findings show that while green hydrogen offers the cleanest pathway, high production costs and infrastructure limitations remain significant barriers to widespread adoption. This study also addresses safety concerns and public perception, emphasizing the need for robust risk assessment methodologies and management approaches. Furthermore, this paper underscores the importance of technological innovations, such as high-temperature electrolysis and synergies with renewable energy sources, to enhance efficiency and sustainability. Policy recommendations include financial incentives, regulatory frameworks, and international cooperation to accelerate hydrogen adoption and balance its development with other low-carbon solutions. Full article
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