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22 pages, 4154 KB  
Article
Evaluating the Performance of 3D-Printed Stab-Resistant Body Armor Using the Taguchi Method and Artificial Neural Networks
by Umur Cicek
Polymers 2025, 17(19), 2699; https://doi.org/10.3390/polym17192699 - 7 Oct 2025
Abstract
Additive manufacturing has promising potential for the development of 3D-printed protective structures such as stab-resistant body armor. However, no research to date has examined the impact of 3D printing parameters on the protective performance of such 3D-printed structures manufactured using fused filament fabrication [...] Read more.
Additive manufacturing has promising potential for the development of 3D-printed protective structures such as stab-resistant body armor. However, no research to date has examined the impact of 3D printing parameters on the protective performance of such 3D-printed structures manufactured using fused filament fabrication technology. This study, therefore, investigates the effects of five key printing parameters: layer thickness, print speed, print temperature, infill density (Id), and layer width, on the mechanical and protective performance of 3D-printed polycarbonate (PC) armor. A Taguchi L27 matrix was employed to systematically analyze these parameters, with toughness, stab penetration depth, and armor panel weight as the primary responses. ANOVA results, along with the Taguchi approach, demonstrated that Id was the most influential factor across all print parameters. This is because a higher Id led to denser structures, reduced voids and porosities, and enhanced energy absorption, significantly increasing toughness while reducing penetration depth. Morphological analysis supported the statistical findings regarding the role of Id on the performance of such structures. With optimized printing parameters, no penetration to the armor panels was recorded, outperforming the UK body armor standard of a maximum permitted knife penetration depth of 8 mm. Moreover, an artificial neural network (ANN) utilizing the 5-14-12-3 topology was created to predict the toughness, stab penetration depth, and armor panel weight of 3D-printed armors. The ANN model demonstrated better prediction performance for stab penetration depth compared to the Taguchi method, confirming the successful application of such an approach. These findings provide a critical foundation for the development of high-performance 3D-printed protective structures. Full article
(This article belongs to the Section Polymer Analysis and Characterization)
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14 pages, 3486 KB  
Article
Asiatic Acid from Centella asiatica as a Potent EGFR Tyrosine Kinase Inhibitor with Anticancer Activity in NSCLC Cells Harboring Wild-Type and T790M-Mutated EGFR
by Chaiwat Monmai, Sahachai Sabuakham, Wachirachai Pabuprapap, Waraluck Chaichompoo, Apichart Suksamrarn and Panupong Mahalapbutr
Biomolecules 2025, 15(10), 1410; https://doi.org/10.3390/biom15101410 - 3 Oct 2025
Viewed by 268
Abstract
Lung cancer is a leading cause of cancer mortality worldwide. Targeted therapies with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) represent a significant advance in the management of lung cancer. However, their long-term efficacy is often limited by acquired resistance, particularly [...] Read more.
Lung cancer is a leading cause of cancer mortality worldwide. Targeted therapies with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) represent a significant advance in the management of lung cancer. However, their long-term efficacy is often limited by acquired resistance, particularly due to the T790M mutation, highlighting the need for novel EGFR-TKIs. Although compounds derived from Centella asiatica have demonstrated anticancer potential, their role in EGFR inhibition has not yet been reported. In this study, we investigated the inhibitory activity of two primary constituents, asiaticoside and asiatic acid, against wild-type and double-mutant (L858R/T790M) EGFR, as well as the anticancer effects of the more potent compound in lung cancer cells. A kinase activity assay revealed that asiatic acid potently inhibited both wild-type and double-mutant EGFR, whereas asiaticoside showed minimal inhibitory activity. Molecular docking demonstrated that asiatic acid bound to the ATP-binding pocket of both EGFR forms with binding energies superior to those of erlotinib and osimertinib. Treatment with asiatic acid significantly (i) reduced viability of A549 and H1975 cells while remaining non-toxic to BEAS-2B normal lung cells, (ii) enhanced cancer cell apoptosis, (iii) suppressed extracellular signal-regulated kinase (ERK) and protein kinase B (Akt) signaling pathways, and (iv) inhibited EGFR activation in A549 and H1975 cells. These results suggest that asiatic acid is a promising lead compound for anticancer drug development. Full article
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22 pages, 8178 KB  
Article
Vibration Control and Energy Harvesting of a Two-Degree-of-Freedom Nonlinear Energy Sink to Primary Structure Under Transient Excitation
by Xiqi Lin, Xiaochun Nie, Junjie Fu, Yangdong Qin, Lingzhi Wang and Zhitao Yan
Buildings 2025, 15(19), 3561; https://doi.org/10.3390/buildings15193561 - 2 Oct 2025
Viewed by 159
Abstract
Environmental vibrations may affect the functional use of engineering structures and even lead to disastrous consequences. Vibration suppression and energy harvesting based on Nonlinear Energy Sink (NES) and the piezoelectric effect have gained significant attention in recent years. The harvested electrical energy can [...] Read more.
Environmental vibrations may affect the functional use of engineering structures and even lead to disastrous consequences. Vibration suppression and energy harvesting based on Nonlinear Energy Sink (NES) and the piezoelectric effect have gained significant attention in recent years. The harvested electrical energy can supply power to the structural health monitoring sensor device. In this work, the electromechanical-coupled governing equations of the primary structure coupled with the series-connected 2-degree-of-freedom NES (2-DOF NES) integrated by a piezoelectric energy harvester are derived. The absorption and dissipation performances of the system under varying transient excitation intensities are investigated. Additionally, the targeted energy transfer mechanism between the primary structure and the two NESs oscillators is investigated using the wavelet analysis. The reduced slow flow of the dynamical system is explored through the complex-variable averaging method, and the primary factors for triggering the target energy transfer phenomenon are revealed. Furthermore, a comparison is made between the vibration suppression performance of the single-degree-of-freedom NES (S-DOF NES) system and the 2-DOF NES system as a function of external excitation velocity. The results indicate that the vibration suppression performance of the first-level NES (NES1) oscillator is first stimulated. As the external excitation intensity gradually increases, the vibration suppression performance of the second-level NES (NES2) oscillator is also triggered. The 1:1:1, high-frequency, and low-frequency transient resonance captures are observed between the primary structure and NES1 and NES2 oscillators over a wide frequency range. The 2-DOF NES demonstrates superior efficiency in suppressing vibrations of the primary structure and exhibits enhanced robustness to varying external excitation intensities. This provides a new strategy for structural vibration suppression and online power supply for health monitoring devices. Full article
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25 pages, 2480 KB  
Article
Decentralized Renewable Energy and Socioeconomic Disparities
by Yuval Dagan Chudner, Ram Fishman and Ravit Hananel
Urban Sci. 2025, 9(10), 403; https://doi.org/10.3390/urbansci9100403 - 1 Oct 2025
Viewed by 262
Abstract
Decentralized renewable energy (DRE) has emerged as a key tool for global energy transition and emissions reduction. While DRE has the potential to democratize energy production, evidence suggests it may cause unequal benefit distribution across population groups. This study provides the first comprehensive [...] Read more.
Decentralized renewable energy (DRE) has emerged as a key tool for global energy transition and emissions reduction. While DRE has the potential to democratize energy production, evidence suggests it may cause unequal benefit distribution across population groups. This study provides the first comprehensive empirical analysis of DRE distribution patterns across all Israeli municipalities, examining policy implications for equitable energy transitions. We analyzed 16,998 rooftop solar installations across 232 municipalities between 2017 and 2022, categorized as residential and commercial installations. Using regression analysis, we examined how geographic, socioeconomic, and demographic factors associate with installation adoption rates. Findings reveal divergent patterns between installation types. For residential installations, socioeconomic status emerged as the primary determinant, with adoption rates increasing linearly with municipal wealth. These disparities widened significantly over time, contradicting expectations that declining costs would democratize access. For commercial installations, the urban–rural divide proved dominant, with rural areas showing substantially higher adoption rates. Our analysis reveals important policy implications and recommendations for global DRE deployment, emphasizing the need to integrate equity considerations into renewable energy policy design to accelerate the transition to renewable energy while minimizing socioeconomic disparities. Full article
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37 pages, 905 KB  
Review
Application of Fuzzy Logic Techniques in Solar Energy Systems: A Review
by Siviwe Maqekeni, KeChrist Obileke, Odilo Ndiweni and Patrick Mukumba
Appl. Syst. Innov. 2025, 8(5), 144; https://doi.org/10.3390/asi8050144 - 30 Sep 2025
Viewed by 276
Abstract
Fuzzy logic has been applied to a wide range of problems, including process control, object recognition, image and signal processing, prediction, classification, decision-making, optimization, and time series analysis. These apply to solar energy systems. Though experts in renewable energy prefer fuzzy logic techniques, [...] Read more.
Fuzzy logic has been applied to a wide range of problems, including process control, object recognition, image and signal processing, prediction, classification, decision-making, optimization, and time series analysis. These apply to solar energy systems. Though experts in renewable energy prefer fuzzy logic techniques, their contribution to the decision-making process of solar energy systems lies in the possibility of illustrating risk factors and introducing the concepts of linguistic variables of data from solar energy applications. In solar energy systems, the primary beneficiaries and audience of the fuzzy logic techniques are solar energy policy makers, as it concerns decision-making models, ranking of criteria or weights, and assessment of the potential location of the installation of solar energy plants, depending on the case. In a real-world scenario, fuzzy logic allows easy and efficient controller configuration in a non-linear control system, such as a solar panel. This study attempts to review the role and contribution of fuzzy logic in solar energy based on its applications. The findings from the review revealed that the fuzzy logic application identifies and detects faults in solar energy systems as well as in the optimization of energy output and the location of solar energy plants. In addition, fuzzy model (predicting), hybrid model (simulating performance), and multi-criteria decision-making (MCDM) are components of fuzzy logic techniques. As the review indicated, these are useful as a solution to the challenges of solar energy systems. Importantly, the integration and incorporation of fuzzy logic and neural networks should be recommended for the efficient and effective performance of solar energy systems. Full article
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21 pages, 6905 KB  
Article
Simulation and Experimental Study on Abrasive–Tool Interaction in Drag Finishing Edge Preparation
by Julong Yuan, Yuhong Yan, Youzhi Fu, Li Zhou and Xu Wang
Micromachines 2025, 16(10), 1113; https://doi.org/10.3390/mi16101113 - 29 Sep 2025
Viewed by 347
Abstract
Tool edge preparation is the process aimed at eliminating edge defects and optimizing the micro-geometric parameters of cutting tools. Drag finishing, the primary engineering method, subjects tools to planetary motion (simultaneous revolution and rotation) within abrasive media to remove burrs and micro-chips, thereby [...] Read more.
Tool edge preparation is the process aimed at eliminating edge defects and optimizing the micro-geometric parameters of cutting tools. Drag finishing, the primary engineering method, subjects tools to planetary motion (simultaneous revolution and rotation) within abrasive media to remove burrs and micro-chips, thereby improving cutting performance and extending tool life. A discrete element method (DEM) model of drag finishing edge preparation was developed to investigate the effects of processing time, tool rotational speed, and rotation direction on abrasive-mediated tool wear behavior. The model was validated through milling cutter edge preparation experiments. Simulation results show that increasing the processing time causes fluctuating changes in average abrasive velocity and contact forces, while cumulative energy and tool wear increase progressively. Elevating tool rotational speed increases average abrasive velocity, contact forces, cumulative energy, and tool wear. Rotation direction significantly impacts tool wear: after 2 s of clockwise (CW) rotation, wear reached 1.45 × 10−8 mm; after 1 s of CW followed by 1 s of counterclockwise (CCW) rotation, wear was 1.25 × 10−8 mm; and after 2 s of CCW rotation, wear decreased to 1.02 × 10−8 mm. Experiments, designed based on simulation trends, confirm that edge radius increases with time and tool rotational speed. After 30 min of processing at 60, 90, and 120 rpm, average edge radius increased to 22.5 μm, 28 μm, and 30 μm, respectively. CW rotation increased the edge shape factor K, while CCW rotation decreased it. The close agreement between experimental and simulation results confirms the model’s effectiveness in predicting the impact of edge preparation parameters on tool geometry. Rotational speed control optimizes edge preparation efficiency, the predominant tangential cumulative energy reveals abrasive wear as the primary material removal mechanism, and rotation direction modulates the shape factor K, enabling symmetric edge preparation. Full article
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17 pages, 916 KB  
Article
Medical Nutrition Therapy Adherence and Lifestyle in Stage 5 CKD: Challenges and Insights
by Patrizia Palumbo, Gaetano Alfano, Francesca Cavani, Rossella Giannini, Roberto Angelo Pulizzi, Silvia Gabriele, Niccolò Morisi, Floriana Cannito, Renata Menozzi and Gabriele Donati
Nutrients 2025, 17(19), 3091; https://doi.org/10.3390/nu17193091 - 28 Sep 2025
Viewed by 379
Abstract
Background: Adherence to Medical Nutrition Therapy (MNT) is a key determinant of therapy success, particularly in chronic diseases like chronic kidney disease (CKD). MNT in CKD requires significant changes in patient’s dietary habits, which can affect long-term adherence. This study aims to evaluate [...] Read more.
Background: Adherence to Medical Nutrition Therapy (MNT) is a key determinant of therapy success, particularly in chronic diseases like chronic kidney disease (CKD). MNT in CKD requires significant changes in patient’s dietary habits, which can affect long-term adherence. This study aims to evaluate the adherence to MNT in stage 5 CKD patients undergoing conservative kidney management (CKM), identifying potential challenges and strengths of nutritional intervention. Methods: We enrolled in 94 stage 5 CKD patients undergoing CKM at the University Hospital of Modena, Italy. We collect clinical data from medical and nutrition records. The inclusion criteria comprised patients of all genders, ages, and ethnicity with stage 5 chronic kidney disease (CKD), in pre-dialysis, enrolled in the nephrology and dietetics program, who had access to 24-h urine tests, anthropometric measurements, and dietary history records. Exclusion criteria included patients with CKD stages lower than 5, those who had not undergone at least one nutritional assessment, or lacked accessible 24-h urine data. The study utilized medical and dietary records from September 2017 to March 2025. The primary outcome was the assessment of adherence to medical nutrition therapy (MNT), comparing prescribed protein intake with actual intake, estimated from dietary history (DH). Protein intake was compared with normalized protein nitrogen appearance (nPNA) as stated by recent guidelines. Additional factors influencing adherence, such as age, gender, comorbidities, physical activity, and prior dietary interventions, were also evaluated. Anthropometric measurements and biochemical tests were collected, and dietary intake was assessed using a seven-day DH. Results: Data were analyzed using descriptive statistics, linear correlation models, univariate logistic regression, t-tests, paired t-tests, and chi-square tests, with significance set at p < 0.05. Most of the patients follow suggested energy and protein intakes limits; however, substantial individual variability emerged Bland–Altman analysis indicated a moderate bias and wide limits of agreement for energy intake (+116 kcal; limits of agreement –518.8 to +751.3 kcal), revealing frequent overestimation in self-reports. Protein intake showed less systematic error, but discrepancies between dietary recall and biochemical markers persisted. Protein intake decreased significantly over time (p < 0.001), while correlation with nPNA did not reach statistical significance (ρ = 0.224, p = 0.051). No significant associations were identified between adherence and most clinical or lifestyle factors, although diabetes was significantly associated with lower adherence to protein intake (p = 0.042) and a predominantly sedentary lifestyle showed a borderline association with energy intake adherence (p = 0.076), warranting further investigation. Longitudinal analysis found stable BMI and body weight, alongside notable reductions in sodium (p = 0.018), potassium (p = 0.045), and phosphorus intake (p < 0.001) over time. Conclusions: Assessing dietary adherence in CKD remains complex due to inconsistencies between self-reported and biochemical estimates. These findings highlight the need for more objective dietary assessment tools and ongoing, tailored nutritional support. Multifaceted interventions—combining education, personalized planning, regular monitoring, and promotion of physical activity—are recommended to enhance adherence and improve clinical outcomes in this vulnerable population. Full article
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24 pages, 3089 KB  
Article
Optimal Sizing of a Wind-Powered Green Ammonia Plant for Maritime Fuel Supply—A Case in the Greater Bay Area
by Yimiao Gu and Weihao Lan
Energies 2025, 18(19), 5157; https://doi.org/10.3390/en18195157 - 28 Sep 2025
Viewed by 367
Abstract
Green ammonia has emerged as a promising alternative fuel for maritime decarbonization, owing to its carbon-free combustion, favorable volumetric energy density, and well-established logistics infrastructure compared to other alternatives. However, critical gaps persist in the development of an integrated fuel supply framework, which [...] Read more.
Green ammonia has emerged as a promising alternative fuel for maritime decarbonization, owing to its carbon-free combustion, favorable volumetric energy density, and well-established logistics infrastructure compared to other alternatives. However, critical gaps persist in the development of an integrated fuel supply framework, which hinders the large-scale adoption of ammonia-fueled vessels. Therefore, this paper proposes an onshore wind-powered green ammonia plant located along the Gaolan–Yangpu feeder route. The plant comprises PEM electrolysis, nitrogen separation, Haber–Bosch synthesis, and storage facilities. An optimal plant configuration is subsequently derived through hourly simulations based on wind power generation and a priority-based capacity expansion algorithm. Key findings indicate that a stable ammonia supply—synchronized with monsoon wind patterns and capable of fueling vessels with 10 MW propulsion systems consuming around 680 tons per fortnight—requires a 72 MW onshore wind farm, a 63 MW PEM electrolyzer, 3.6 MW of synthesis facility, and 3205 tons of storage. This configuration yields a levelized cost of ammonia (LCOA) of approximately USD 700/ton, with wind turbines and electrolyzers (including replacement costs) accounting for over 70% of the total cost. Sensitivity analysis further shows that wind turbine and electrolyzer prices are the primary factors affecting ammonia costs. Although variations in operational parameters may significantly alter final configuration, they cause only minor (±1%) fluctuations in the levelized cost without significantly altering its overall trend. Full article
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16 pages, 2181 KB  
Article
Continuous Separation of Lithium Iron Phosphate and Graphite Microparticles via Coupled Electric and Magnetic Fields
by Wenbo Liu, Xiaolei Chen, Pengfei Qi, Xiaomin Liu and Yan Wang
Micromachines 2025, 16(10), 1094; https://doi.org/10.3390/mi16101094 - 26 Sep 2025
Viewed by 278
Abstract
Driven by the growing demand for sustainable resource utilization, the recovery of valuable constituents from spent lithium-ion batteries (LIBs) has attracted considerable attention, whereas conventional recycling processes remain energy-intensive, inefficient, and environmentally detrimental. Herein, an efficient and environmentally benign separation strategy integrating dielectrophoresis [...] Read more.
Driven by the growing demand for sustainable resource utilization, the recovery of valuable constituents from spent lithium-ion batteries (LIBs) has attracted considerable attention, whereas conventional recycling processes remain energy-intensive, inefficient, and environmentally detrimental. Herein, an efficient and environmentally benign separation strategy integrating dielectrophoresis (DEP) and magnetophoresis (MAP) is proposed for isolating the primary components of “black mass” from spent LIBs, i.e., lithium iron phosphate (LFP) and graphite microparticles. A coupled electric–magnetic–fluid dynamic model is established to predict particle motion behavior, and a custom-designed microparticle separator is developed for continuous LFP–graphite separation. Numerical simulations are performed to analyze microparticle trajectories under mutual effects of DEP and MAP and to evaluate the feasibility of binary separation. Structural optimization revealed that the optimal separator configuration comprised an electrode spacing of 2 mm and a ferromagnetic body length of 5 mm with 3 mm spacing. Additionally, a numerical study also found that an auxiliary flow velocity ratio of 3 resulted in the best particle focusing effect. Furthermore, the effects of key operational parameters, including electric and magnetic field strengths and flow velocity, on particle migration were systematically investigated. The findings revealed that these factors significantly enhanced the lateral migration disparity between LFP and graphite within the separation channel, thereby enabling complete separation of LFP particles with high purity and recovery under optimized conditions. Overall, this study provides a theoretical foundation for the development of high-performance and environmentally sustainable LIBs recovery technologies. Full article
(This article belongs to the Collection Micro/Nanoscale Electrokinetics)
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15 pages, 1698 KB  
Article
AI-Driven Energy-Efficient Data Aggregation and Routing Protocol Modeling to Maximize Network Lifetime in Wireless Sensor Networks
by R. Arun Chakravarthy, C. Sureshkumar, M. Arun and M. Bhuvaneswari
NDT 2025, 3(4), 22; https://doi.org/10.3390/ndt3040022 - 25 Sep 2025
Viewed by 239
Abstract
The research work presents an artificial intelligence-driven, energy-aware data aggregation and routing protocol for wireless sensor networks (WSNs) with the primary objective of extending overall network lifetime. The proposed scheme leverages reinforcement learning in conjunction with deep Q-networks (DQNs) to adaptively optimize both [...] Read more.
The research work presents an artificial intelligence-driven, energy-aware data aggregation and routing protocol for wireless sensor networks (WSNs) with the primary objective of extending overall network lifetime. The proposed scheme leverages reinforcement learning in conjunction with deep Q-networks (DQNs) to adaptively optimize both Cluster Head (CH) selection and routing decisions. An adaptive clustering mechanism is introduced wherein factors such as residual node energy, spatial proximity, and traffic load are jointly considered to elect suitable CHs. This approach mitigates premature energy depletion at individual nodes and promotes balanced energy consumption across the network, thereby enhancing node sustainability. For data forwarding, the routing component employs a DQN-based strategy to dynamically identify energy-efficient transmission paths, ensuring reduced communication overhead and reliable sink connectivity. Performance evaluation, conducted through extensive simulations, utilizes key metrics including network lifetime, total energy consumption, packet delivery ratio (PDR), latency, and load distribution. Comparative analysis with baseline protocols such as LEACH, PEGASIS, and HEED demonstrates that the proposed protocol achieves superior energy efficiency, higher packet delivery reliability, and lower packet losses, while adapting effectively to varying network dynamics. The experimental outcomes highlight the scalability and robustness of the protocol, underscoring its suitability for diverse WSN applications including environmental monitoring, surveillance, and Internet of Things (IoT)-oriented deployments. Full article
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22 pages, 3499 KB  
Article
Zinc Promotes Mitochondrial Health Through PGC-1alpha Enhancing Bacterial Clearance in Macrophages Infected with Mycobacterium avium Complex
by Ruxana T. Sadikot, Prabagaran Narayanasamy, Zhihong Yuan, Deandra Smith and Daren L. Knoell
Int. J. Mol. Sci. 2025, 26(19), 9270; https://doi.org/10.3390/ijms26199270 - 23 Sep 2025
Viewed by 293
Abstract
Mitochondria are increasingly recognized as important contributors to immune function, in addition to energy production. They exert this influence through modulation of various signaling pathways that regulate cellular metabolism and immune function in response to pathogens. Peroxisome proliferator-activated receptor (PPAR) coactivator 1 alpha [...] Read more.
Mitochondria are increasingly recognized as important contributors to immune function, in addition to energy production. They exert this influence through modulation of various signaling pathways that regulate cellular metabolism and immune function in response to pathogens. Peroxisome proliferator-activated receptor (PPAR) coactivator 1 alpha (PGC-1α) is the primary transcription factor and regulator involved in mitochondrial biogenesis. Long known to be involved in immune function, zinc (Zn) is also required for proper mitochondrial function. It is increasingly recognized that many cellular immunometabolic activities are also Zn-dependent. Taken together, we investigated the role of Zn deficiency, both dietary and genetically induced, and Zn supplementation in PGC-1α-mediated macrophage mitochondrial biogenesis and immune function following infection with Mycobacterium avium complex (MAC). Our novel findings show that Zn is an important regulator of PGC-1α, TFAM and mitochondrial biogenesis, leading to enhanced bacterial phagocytosis and bacterial killing in macrophages. Mechanistically, we show that the Zn importer ZIP8 (Zrt/Irt-like protein) orchestrates Zn-mediated effects on PGC-1α and mitochondrial function. Taken together, defective Zn biodistribution may increase susceptibility to infection, whereas Zn supplementation may provide a tractable host-directed therapy to enhance the innate immune response in patients vulnerable to MAC infection. Full article
(This article belongs to the Special Issue Molecular and Immune Mechanisms in Pathogenic Mycobacteria Infections)
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23 pages, 1557 KB  
Article
Defect Networks and Waste Reduction in Additive Manufacturing
by Flavia-Petruța-Georgiana Stochioiu, Roxana-Mariana Nechita, Oliver Ulerich and Constantin Stochioiu
Sustainability 2025, 17(18), 8498; https://doi.org/10.3390/su17188498 - 22 Sep 2025
Viewed by 281
Abstract
This study addresses a key challenge in Additive Manufacturing (AM): while it promises sustainable production, manufacturing defects often lead to significant material and energy waste. The purpose of this research is to apply the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to identify [...] Read more.
This study addresses a key challenge in Additive Manufacturing (AM): while it promises sustainable production, manufacturing defects often lead to significant material and energy waste. The purpose of this research is to apply the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to identify and map the cause-and-effect relationships among common AM defects. By doing this, the goal is to pinpoint the most influential ‘root’ causes, allowing for more targeted and effective quality improvements. The methodology is based on a qualitative approach using the expert judgment of a panel of six professionals. The DEMATEL analysis successfully sorted the defects into two categories: those that are primary causes and those that are symptoms or effects. The main findings show that contamination is the most significant causal factor, meaning that it strongly influences other defects. In contrast, dimensional inaccuracy is the most affected factor, acting as a symptom of other underlying issues. In conclusion, the study finds that focusing on mitigating root causes like contamination, warping, and porosity is crucial for achieving improvements across the process chain. This framework allows engineers to prioritize quality control efforts on the fundamental problems, rather than on superficial defects, thereby maximizing efficiency and waste reduction. Ultimately, this research provides a clear, actionable framework for improving quality control and promoting more sustainable manufacturing practices. Full article
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24 pages, 11825 KB  
Article
Explainable AI-Driven Integration of Water–Energy–Food Nexus into Supply–Demand Networks
by Lei Cao, Haonan Zhang, Xueliang Yang, Chaoyu Zhang, Chengbin Xi, Yunlu Zhang and Zhaowu Yu
Land 2025, 14(9), 1920; https://doi.org/10.3390/land14091920 - 20 Sep 2025
Viewed by 346
Abstract
The supply–demand network facilitates regional sustainable development by optimizing resource flows and allocation within the Water–Energy–Food system. However, few studies have constructed such networks from a Water–Energy–Food Nexus (WEF Nexus) supply–demand perspective, and the key driving factors influencing network formation, along with their [...] Read more.
The supply–demand network facilitates regional sustainable development by optimizing resource flows and allocation within the Water–Energy–Food system. However, few studies have constructed such networks from a Water–Energy–Food Nexus (WEF Nexus) supply–demand perspective, and the key driving factors influencing network formation, along with their underlying mechanisms, remain poorly understood. To bridge this gap, we propose a new framework for constructing WEF Nexus supply–demand networks via explainable artificial intelligence (EAI). Taking the Bohai Rim urban agglomeration as an example, we identified the key factors affecting the long-term supply and demand of the WEF Nexus and their mechanisms using the XGBoost-SHAP model. By quantifying the magnitude and direction of these factors’ influences, we constructed supply–demand networks and further developed optimization strategies that consider complex factor interactions and distinct thresholds. Key findings include: (1) Identification of 114 stable supply sources and 128 chronic deficit sources, forming 472 high-efficiency and 296 standard supply–demand corridors, with 6 major supply potential zones delineated. (2) Precipitation, vegetation coverage, human activity intensity, cropland distribution, and temperature emerged as primary determinants in descending order of importance. (3) Synergistic analysis revealed significant negative interactions between human activity and precipitation/vegetation, but positive correlation with temperature, with distinct nonlinear thresholds across zones. Based on these findings, we proposed a differentiated optimization strategy. Our study constructs a supply–demand network from the perspective of the WEF Nexus and highlights the importance of threshold effects and interactions among key factors in the construction and optimization of the network. The research results are also applicable to other urban agglomerations facing similar challenges. Full article
(This article belongs to the Section Landscape Ecology)
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51 pages, 1846 KB  
Review
A Review of Methodologies for Photovoltaic Energy Generation Forecasting in the Building Sector
by Omid Pedram, Ana Soares and Pedro Moura
Energies 2025, 18(18), 5007; https://doi.org/10.3390/en18185007 - 20 Sep 2025
Viewed by 453
Abstract
Photovoltaic (PV) systems are swiftly expanding within the building sector, offering significant benefits such as renewable energy integration, yet introducing challenges due to mismatches between local generation and demand. With the increasing availability of data and advanced modeling tools, stakeholders are increasingly motivated [...] Read more.
Photovoltaic (PV) systems are swiftly expanding within the building sector, offering significant benefits such as renewable energy integration, yet introducing challenges due to mismatches between local generation and demand. With the increasing availability of data and advanced modeling tools, stakeholders are increasingly motivated to adopt energy management and optimization techniques, where accurate forecasting of PV generation is essential. While the existing literature provides valuable insights, a comprehensive review of methodologies specifically tailored for the forecast of PV generation in buildings remains scarce. This study aims to address this gap by analyzing the forecasting methods, data requirements, and performance metrics employed, with the primary objective of providing an in-depth review of previous research. The findings highlight the critical role of improving PV energy generation forecasting accuracy in enhancing energy management and optimization for individual buildings. Additionally, the study identifies key challenges and opportunities for future research, such as the limited exploration of localized environmental and operational factors (such as partial shading, dust, and dirt); insufficient data on building-specific PV output patterns; and the need to account for variability in PV generation. By clarifying the current state of PV energy forecasting methodologies, this research lays essential groundwork for future advancements in the field. Full article
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18 pages, 3566 KB  
Article
Solar-Pumped Ce:Nd:YAG Laser Amplifier Design
by Joana Almeida, Bruno D. Tibúrcio, Hugo Costa, Cláudia R. Vistas and Dawei Liang
Energies 2025, 18(18), 5009; https://doi.org/10.3390/en18185009 - 20 Sep 2025
Viewed by 229
Abstract
A solar-pumped Ce:Nd:YAG laser amplifier design is proposed to address the challenge of scaling output power in solar-pumped laser oscillators while maintaining high beam quality. The design employs a 1.33 m2 flat Fresnel lens with a 2 m focal length as a [...] Read more.
A solar-pumped Ce:Nd:YAG laser amplifier design is proposed to address the challenge of scaling output power in solar-pumped laser oscillators while maintaining high beam quality. The design employs a 1.33 m2 flat Fresnel lens with a 2 m focal length as a primary concentrator, which is combined with a secondary homogenizing concentrator, featuring 40 mm × 40 mm input aperture, 200 mm length, and 11.3 mm × 26 mm output aperture, to provide efficient coupling and uniform distribution of solar radiation onto a 2.9 mm thick Ce:Nd:YAG slab with 11.3 mm × 26 mm surface area and two beveled corners. This geometry enables multiple total internal reflections of a 1064 nm TEM00 mode seed laser beam inside the slab, ensuring efficient interaction with the active Ce3+ and Nd3+ ions in the gain medium. Performed numerical analysis shows that the present approach can deliver a uniform solar pump power density of 2.5 W/mm2 to the slab amplifier. This value is 2.05-times higher than the numerically calculated power density incident on the Nd:YAG slab of the previous solar-pumped amplifier that achieved the highest continuous-wave laser gain of 1.64. Furthermore, the optimized slab geometry with 0.44 width-to-height ratio allows the seed laser to undergo 32 internal reflections, extending its optical path length by a factor of 1.45 compared to the earlier design. These numerical achievements, combined with the Ce:Nd:YAG medium’s capacity to deliver nearly 1.57-times more laser power than Nd:YAG, reveal the potential of proposed design to yield a gain enhancement factor of 4.16, making the first solar-pumped Ce:Nd:YAG amplifier a promising solution toward energy-efficient, sustainable solutions for terrestrial and space applications. Full article
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