Search Results (72)

Accurate estimation of lithium-ion battery (LIB) state of health (SOH) is critical for prolonging battery life and ensuring safe operation. To address the limitations of existing data-driven models in robustness and feature coupling, this paper presents a new Bagging-PiFormer framework for SOH estimation. The framework integrates ensemble learning with an improved Transformer architecture to achieve accurate and stable performance across various degradation conditions. Specifically, multiple PiFormer base models are trained independently under the Bagging strategy to enhance generalization. Each PiFormer consists of a stack of PiFormer layers, which combines a cross-channel attention mechanism to model voltage–current interactions and a local convolutional feed-forward network (LocalConvFFN) to extract local degradation patterns from charging curves. Residual connections and layer normalization stabilize gradient propagation in deep layers, while a purely linear output head enables precise regression of the continuous SOH values. Experimental results on three datasets demonstrate that the proposed method achieves the lowest MAE, RMSE, and MAXE values among all compared models, reducing overall error by 10–33% relative to mainstream deep-learning methods such as Transformer, CNN-LSTM, and GCN-BiLSTM. These results confirm that the Bagging-PiFormer framework significantly improves both the accuracy and robustness of battery SOH estimation. Full article

This study explores the potential of two marine-derived bacteria, Cytobacillus kochii and Marinobacter, through in silico analysis of their catechol 2,3-dioxygenase (C23O) enzymes. Molecular docking simulations were conducted using AutoDock Vina to assess the binding interactions between C23O enzymes and ten hydrocarbon pollutants, including monocyclic and polycyclic aromatic hydrocarbons (PAHs). Binding affinities ranged from −4 to −8.7 kcal/mol for Cytobacillus kochii, with the highest affinity observed for fluoranthene (−8.7 kcal/mol), followed by pyrene (−8.5 kcal/mol) and phenanthrene (−8.2 kcal/mol). In comparison, Marinobacter’s C23O showed binding affinities between −4.1 and −8 kcal/mol, with fluoranthene (−8 kcal/mol) and phenanthrene (−7.9 kcal/mol) being top performers. Despite slightly lower affinity, Marinobacter exhibits superior environmental resilience under high salinity and temperature, making it valuable for application in fluctuating marine conditions. Structural interaction analysis revealed consistent pi-pi stacking and hydrogen bonding within the active sites, further supporting enzyme–substrate compatibility. These computational findings underscore Cytobacillus kochii ’s superior catalytic potential and Marinobacter’s ecological robustness. The integration of both strains into a microbial consortium offers a promising synergistic approach, combining enzymatic efficiency and environmental adaptability for effective hydrocarbon degradation. While these computational assessments offer valuable predictive insights, further validation through in vitro and in vivo experiments would be beneficial to determine the actual hydrocarbon degradation efficiencies. Full article

This research work presents the design and the development of a cost-effective autonomous mobile robot for locating misplaced objects within unstructured indoor environments. The tools integrated into the proposed system for perception and localization are a hardware architecture equipped with LiDAR, an inertial measurement unit (IMU), and wheel encoders. The system also includes an ROS2-based software stack enabling autonomous navigation via the NAV2 framework and Adaptive Monte Carlo Localization (AMCL). For real-time object detection, a lightweight YOLO11n model is developed and implemented on a Raspberry Pi 4 to enable the robot to identify common household items. The robot’s motion control is achieved by a fuzzy logic-enhanced PID controller that dynamically modifies gain values based on navigation conditions. Remote supervision, task management, and real-time status monitoring are provided by a user-friendly Flutter-based mobile application. Simulations and real-world experiments demonstrate the robustness, modularity, and responsiveness of the robot in dynamic environments. This robot achieves a 3 cm localization error and a 95% task execution success rate. Full article

We evaluate tactile-first robotic traversal on the Department of Homeland Security (DHS) figure-8 mobility test using a two-way repeated-measures design across various algorithms (three tactile policies—M1 reactive, M2 terrain-weighted, M3 memory-augmented; a monocular camera baseline, CB-V; a tactile histogram baseline, T-VFH; and an optional tactile-informed replanner, T-D* Lite) and lighting conditions (Indoor, Outdoor, and Dark). The platform is the custom-built Eleven robot—a quadruped integrating a joint-mounted tactile tentacle with a tip force-sensitive resistor (FSR; Walfront 9snmyvxw25, China; 0–10 kg range, ≈0.1 N resolution @ 83 Hz) and a woven Galvorn carbon-nanotube (CNT) yarn for proprioceptive bend sensing. Control and sensing are fully wireless via an ESP32-S3, Arduino Nano 33 BLE, Raspberry Pi 400, and a mini VESC controller. Across 660 trials, the tactile stack maintained ∼21 ms (p50) policy latency and mid-80% success across all lighting conditions, including total darkness. The memory-augmented tactile policy (M3) exhibited consistent robustness relative to the camera baseline (CB-V), trailing by only ≈3–4% in Indoor and ≈13–16% in Outdoor and Dark conditions. Pre-specified, two one-sided tests (TOSTs) confirmed no speed equivalence in any M3↔CB-V comparison. Unlike vision-based approaches, tactile-first traversal is invariant to illumination and texture—an essential capability for navigation in darkness, smoke, or texture-poor, confined environments. Overall, these results show that a tactile-first, memory-augmented control stack achieves lighting-independent traversal on DHS benchmarks while maintaining competitive latency and success, trading modest speed for robustness and sensing independence. Full article

Xiangwanxian 13 (XWX13), an elite fragrant indica rice, is highly susceptible to rice blast and accumulates cadmium (Cd) in grain above the food safety limit in Cd-contaminated paddies, severely constraining its commercial use. Despite these shortcomings, the variety is widely grown for its high yield and superior grain quality. To overcome these limitations, we conducted marker-assisted backcrossing (MABC) complemented by genome-wide background selection. Four major genes, namely Pi1, Pi2, OsHMA3, and OsNramp5, were precisely introduced into XWX13. Two preferable BC₃MF₅ improved lines iXWX13-1 (stacking Pi1 + Pi2 + OsHMA3) and iXWX13-2 (stacking Pi1 + Pi2 + OsNramp5) were obtained with genomic background recovery rates of 94.44% and 94.63%, evaluated by using the RICE 1K SNP array, respectively. Seedling resistance spectrum assays demonstrated more than a 97% blast resistance rate against 39 Magnaporthe oryzae isolates, and both lines showed enhanced leaf and panicle neck blast resistance in natural nurseries. Multi-site field trials revealed grain Cd concentrations of 0.009–0.077 mg kg⁻¹ in iXWX13-2, 90.98–98.87% lower than those in XWX13. Importantly, yield, major agronomic traits, and grain quality remained indistinguishable from the original variety. This study provides the first demonstration that MABC coupled with SNP array background selection can simultaneously enhance blast resistance and reduce grain Cd in XWX13 without yield or quality penalties, offering a robust strategy for pyramiding multiple desirable genes into elite cultivars. Full article

This work investigates the effect of interface traps on the impedance spectra of dopant-free silicon solar cells. The studied device consists of a crystalline silicon absorber with an a-Si:H/MoOx/ITO stack as the front passivating hole-collecting contact and an a-Si:H/LiF/Al stack as the rear passivating electron-collecting contact. Experimental measurements, including illuminated current–voltage (I–V) characteristics and impedance spectroscopy, were performed on the fabricated devices and after a soft annealing treatment. The annealed cells exhibit an increased open-circuit voltage and a larger Nyquist plot radius. To interpret these results, a numerical model was developed in a TCAD environment. Simulations reveal that traps located at the p/i interface (MoOx/i-a-Si:H) significantly affect the impedance spectra, with higher trap concentrations leading to smaller Nyquist plot circumferences. The numerical impedance curves were aligned to the experimental data, enabling extraction of the interfacial traps concentration. The results highlight the sensitivity of impedance spectroscopy to interfacial quality and confirm that the performance improvement after soft annealing is primarily due to reduced defect density at the MoOx/i-a-Si:H interface. Full article

Potato (Solanum tuberosum L.) is a globally significant staple crop that faces constant threats from Phytophthora infestans, the causative agent of late blight (LB). The battle between Phytophthora infestans and its host is driven by the molecular interplay of RXLR-encoded avirulence (PiAvr) effectors and nucleotide-binding leucine-rich repeat (NLR) immune receptors in potato. This review provides a comprehensive analysis of the structural characteristics, functional diversity, and evolutionary dynamics of RXLR effectors and the mechanisms by which NLR receptors recognize and respond to them. The study elaborates on both direct and indirect modes of effector recognition by NLRs, highlighting the gene-for-gene interactions that underlie resistance. Additionally, we discuss the molecular strategies employed by P. infestans to evade host immunity, including effector polymorphism, truncation, and transcriptional regulation. Advances in structural biology, functional genomics, and computational modeling have provided valuable insights into effector–receptor interactions, paving the way for innovative resistance breeding strategies. We also discuss the latest approaches to engineering durable resistance, including gene stacking, synthetic NLRs, and CRISPR-based modifications. Understanding these molecular mechanisms is critical for developing resistant potato cultivars and mitigating the devastating effects of LB. This review aims to bridge current knowledge gaps and guide future research efforts in plant immunity and disease management. Full article

Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha (ER-α), and HER2. Using a robust 3D-QSAR model (R² = 0.86; Q²_LOO = 0.86) built from 52 steroidal structures, we identified molecular features associated with high anticancer potential, specifically increased polarizability and reduced electronegativity. From a virtual library of 271 DFT-optimized analogs, 31 compounds were selected based on predicted potency (pIC₅₀ > 7.0) and screened via molecular docking against PR (PDB 2W8Y), HER2 (PDB 7JXH), and ER-α (PDB 6VJD). Seven candidates showed strong binding affinities (ΔG ≤ −9 kcal/mol for at least two targets), with Estero-255 emerging as the most promising. This compound demonstrated excellent conformational stability, a robust hydrogen-bonding network, and consistent multitarget engagement. Molecular dynamics simulations over 100 nanoseconds confirmed the structural integrity of the top ligands, with low RMSD values, compact radii of gyration, and stable binding energy profiles. Key interactions included hydrophobic contacts, π–π stacking, halogen–π interactions, and classical hydrogen bonds with conserved residues across all three targets. These findings highlight Estero-255, alongside Estero-261 and Estero-264, as strong multitarget candidates for further development. By potentially disrupting the PI3K/AKT/mTOR signaling pathway, these compounds offer a promising strategy for overcoming resistance in hormone-driven breast cancer. Experimental validation, including cytotoxicity assays and ADME/Tox profiling, is recommended to confirm their therapeutic potential. Full article

There is an obvious correlation between the photovoltaic (PV) output of different physical levels; that is, the overall power change trend of large-scale regional (high-level) stations can provide a reference for the prediction of the output of sub-regional (low-level) stations. The current PV prediction methods have not deeply explored the multi-level PV power generation elements and have not considered the correlation between different levels, resulting in the inability to obtain potential information on PV power generation. Moreover, traditional probabilistic prediction models lack adaptability, which can lead to a decrease in prediction performance under different PV prediction scenarios. Therefore, a probabilistic prediction method for short-term PV power based on multi-level adaptive dynamic integration is proposed in this paper. Firstly, an analysis is conducted on the multi-level PV power stations together with the influence of the trend of high-level PV power generation on the forecast of low-level power generation. Then, the PV data are decomposed into multiple layers using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and analyzed by combining fuzzy entropy (FE) and mutual information (MI). After that, a new multi-level model prediction method, namely, the improved dual dynamic adaptive stacked generalization (I-Stacking) ensemble learning model, is proposed to construct short-term PV power generation prediction models. Finally, an improved dynamic adaptive kernel density estimation (KDE) method for prediction errors is proposed, which optimizes the performance of the prediction intervals (PIs) through variable bandwidth. Through comparative experiments and analysis using traditional methods, the effectiveness of the proposed method is verified. Full article

Combustion of aviation jet fuel emits a complex mixture of pollutants linked to adverse health outcomes among airport personnel and nearby communities. While epidemiological studies showed the detrimental effects of aviation-derived air pollutants on human health, the molecular mechanisms of the interactions of these pollutants with cellular biomolecules like proteins that drive the adverse health effects remain poorly understood. In this study, we performed molecular docking simulations of 272 pollutant–protein complexes using AutoDock Vina 1.2.7 to characterize the binding strength of the pollutants with the selected proteins. We selected 34 aviation-derived pollutants that constitute three chemical categories of pollutants: volatile organic compounds (VOCs), polyaromatic hydrocarbons (PAHs), and organophosphate esters (OPEs). Each pollutant was docked to eight proteins that play critical roles in endocrine, metabolic, transport, and neurophysiological functions, where functional disruption is implicated in disease. The effect of binding of multiple pollutants was analyzed. Our results indicate that aliphatic and monoaromatic VOCs display low (<6 kcal/mol) binding affinities while PAHs and organophosphate esters exhibit strong (>7 kcal/mol) binding affinities. Furthermore, the binding strength of PAHs exhibits a positive correlation with the increasing number of aromatic rings in the pollutants, ranging from nearly 7 kcal/mol for two aromatic rings to more than 15 kcal/mol for five aromatic rings. Analysis of intermolecular interactions showed that these interactions are predominantly stabilized by hydrophobic, pi-stacking, and hydrogen bonding interactions. Simultaneous docking of multiple pollutants revealed the increased binding strength of the resulting complexes, highlighting the detrimental effect of exposure to pollutant mixtures found in ambient air near airports. We provide a priority list of pollutants that regulatory authorities can use to further develop targeted mitigation strategies to protect the vulnerable personnel and communities near airports. Full article

Chronic diseases such as diabetes and cancer are the leading causes of mortality worldwide. Receptors for Advanced Glycation End products (RAGEs) are ubiquitous factors that catalyse Advanced Glycation End products (AGEs), proteins, and lipids that become glycated from sugar ingestion. RAGEs are cell surface receptor proteins and play a broad role in mediating the effects of AGEs on cells, contributing to modifying biological macromolecules like proteins and lipids, which can cause Reactive Oxygen Species (ROS) generation, inflammation, and cancer. We targeted RAGE inhibition analysis and screening of United States Food and Drug Administration (FDA) libraries through molecular docking studies that identified the four most suitable FDA compounds: Zytiga, Paliperidone, Targretin, and Irinotecan. We compared them with the control substrate, Carboxymethyllysine, which showed good binding interaction through hydrogen bonding, hydrophobic interactions, and π-stacking at active site residues of the target protein. Following a 100 ns simulation run, the docked complex revealed that the Root Mean Square Deviation (RMSD) values of two drugs, Irinotecan (1.3 ± 0.2 nm) and Paliperidone (1.2 ± 0.3 nm), were relatively stable. Subsequently, the Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) determined that the Paliperidone molecule had a high negative energy of −13.49 kcal/mol, and the Absorption, Distribution, Metabolism, and Excretion (ADME) properties were in control for use in the mentioned cases. We extended this with many in vitro studies, including an immunoblotting assay, which revealed that RAGEs with High Mobility Group Box 1 (HMGB1) showed higher expression, while RAGEs with Paliperidone showed lower expressions. Furthermore, cell proliferation assay and Apoptosis assay (Annexin-V/PI staining) results revealed that Paliperidone was an effective anti-glycation and anti-apoptotic drug—however, more extensive in vivo studies are needed before its use. Full article

Accurate prediction of natural gas purchase volumes is crucial for both the economy and the environment. It not only facilitates the rational allocation of resources for companies but also helps to reduce operational costs. Although existing prediction methods have achieved some success in addressing the nonlinear relationships in natural gas purchases, there remains potential for further improvement. To address this issue, a stacking ensemble learning model was developed to enhance the ability to handle complex nonlinear problems. This model integrates diverse algorithms and incorporates weather factors, while regionalizing characteristics of natural gas usage, thereby achieving accurate forecasts of natural gas purchase volumes. We selected three distinctly different base models—Informer, multiple linear regression (MLR), and support vector regression (SVR)—for our research. By conducting four different feature combination experiments for each base model, including weather, time, regional, and usage features, we constructed 12 foundational models. Subsequently, we integrated these base models using a meta-learner to form the final stacking ensemble model. The experimental results indicate that the stacking ensemble model outperforms individual models across key metrics, including $R^2$, MRE, and RMSE. Notably, the $R^2$ values improved by 4–15% compared to the 12 base models. The model was subsequently applied to predict natural gas purchase volumes in Pi County, Chengdu, China. In November 2024, a side-by-side comparison of the predicted and actual data revealed a maximum error of just 5.39%. This exceptional accuracy effectively meets forecasting requirements, underscoring the model’s predictive strength in the energy sector. Full article

Carbonic anhydrases (CAs) are a group of zinc-containing enzymes involved in many physiological processes through their role in the maintenance of the equilibrium between bicarbonate and CO₂ levels. Human carbonic anhydrases (hCAs) are recognized as important drug targets due to their major implication in the development of diseases including cancer. Sulfanilamide derivatives have been widely studied and have shown remarkable efficiency in inhibiting carbonic anhydrases, with the presence of SO₂NH₂ in their structure. Therefore, the sulfonamide moiety is considered as the leading scaffold in the search for new hCA inhibitors. Moreover, the introduction of an enaminone to sulfonamide-based CA inhibitors showed an enhancement of inhibitory activity. In this context, we were interested in the in silico investigation of benzenesulfonamide derivatives containing β-enaminone that were synthesized from dicarbonyl compounds and sulfanilamide under microwave irradiation. The in silico assessment includes a molecular docking simulation against hCA II (PDB: 2AW1). The docked ligands showed good docking score values (−8.099 and −7.053 kcal.mol⁻¹), which indicates a good stability of the studied compounds within the active site. Further, significant interactions with the residues of the active site were observed, including metal coordination with Zn 262, an H-bond with Thr 199, and pi–pi stacking with the side chain of His94, which are considered as the key interactions for CA inhibition. A complementary in silico study that involved ADMET prediction was performed to learn more about the pharmacokinetic properties and the toxicity of the products in order to comprehend their ability to become drug-candidates. Full article

Knowledge of reliable geometries and associated intermolecular interaction energy (ΔE) values at key fragments of the potential energy surface (PES) in the gas phase is indispensable for the modeling of various properties of the pyrene dimer (PYD) and other important aggregate systems of a comparatively large size (ca. 50 atoms). The performance of the domain-based local pair natural orbital (DLPNO) variant of the coupled-cluster theory with singles, doubles and perturbative triples in the complete basis set limit [CCSD(T)/CBS] method for highly accurate predictions of the ΔE at a variety of regions of the PES was established for a representative set of pi-stacked dimers, which also includes the PYD. For geometries with the distance between stacked monomers close to a value of such a distance in the ΔE minimum structure, an excellent agreement between the canonical CCSD(T)/CBS results and their DLPNO counterparts was found. This finding enabled us to accurately characterize the lowest-lying configurations of the PYD, and the physical origin of their stabilization was thoroughly analyzed. The proposed DLPNO-CCSD(T)/CBS procedure should be applied with the aim of safely locating a global minimum of the PES and firmly establishing the pertaining ΔE of even larger dimers in studies of packing motifs of organic electronic devices and other novel materials. Full article

Accurate prediction of the compression index (c_c) is essential for geotechnical infrastructure design, especially in clay-rich coastal regions. Traditional methods for determining $c_c$ are often time-consuming and inconsistent due to regional variability. This study presents an explainable ensemble learning framework for predicting the $c_c$ of clays. Using a comprehensive dataset of 1080 global samples, four key geotechnical input variables—liquid limit (LL), plasticity index (PI), initial void ratio (e₀), and natural water content w—were leveraged for accurate $c_c$ prediction. Missing data were addressed with K-Nearest Neighbors (KNN) imputation, effectively filling data gaps while preserving the dataset’s distribution characteristics. Ensemble learning techniques, including Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and a Stacking model, were applied. Among these, the Stacking model demonstrated the highest predictive performance with a Root Mean Squared Error (RMSE) of 0.061, a Mean Absolute Error (MAE) of 0.043, and a Coefficient of Determination ($R^2$) value of 0.848 on the test set. Model interpretability was ensured through SHapley Additive exPlanations (SHAP), with $e_0$ identified as the most influential predictor. The proposed framework significantly improves both prediction accuracy and interpretability, offering a valuable tool to enhance geotechnical design efficiency in coastal and clay-rich environments. Full article