Search Results (2,693)

Achieving selective, direct conversion of methane into value-added chemicals requires catalysts that can navigate the intrinsic trade-off between C–H bond activation and over-dehydrogenation. Transition metal phosphides (TMPs) have emerged as promising catalysts that can tune this selectivity. This work utilizes density functional theory (DFT) to systematically assess how the transition metal’s identity (M = Fe, Co, Ni) in isostructural M₂P phosphides governs this balance. The findings reveal that the high reactivity of Fe₂P and Co₂P, which facilitates initial methane activation, also promotes facile deep dehydrogenation pathways to coke precursors like CH*. In stark contrast, Ni₂P exhibits a moderated reactivity that kinetically hinders CH* formation while simultaneously exhibiting the lowest activation barrier for the C–C coupling of CH₂* intermediates to form ethylene. This revealed trade-off between the high reactivity of Fe/Co phosphides and the high selectivity of Ni₂P offers a guiding principle for the rational design of advanced bimetallic phosphides for efficient methane upgrading. Full article

Pubertal molting represents a pivotal transition in the life cycle of crustaceans, marking the shift from somatic growth to reproductive development. In mud crabs, mating is known to facilitate this process, yet the molecular mechanisms remain poorly understood. Here, we applied full-length transcriptome sequencing to characterize changes in gene expression and alternative splicing (AS) across post-mating ovarian development. AS analysis revealed extensive transcript diversity, predominantly alternative first exon (AF) and alternative 5′ splice site (A5) events, enriched in genes linked to chromatin remodeling, protein regulation, and metabolism, underscoring AS as a fine-tuning mechanism in ovarian development. Comparative analyses revealed profound molecular reprogramming after mating. In the UM vs. M1 comparison, pathways related to serotonin and catecholamine signaling were enriched, suggesting early neuroendocrine regulation. Serotonin likely promoted, while dopamine inhibited, oocyte maturation, indicating a potential “inhibition–activation” switch. In the UM vs. M3 comparison, pathways associated with oxidative phosphorylation, ATP biosynthesis, and lipid metabolism were upregulated, reflecting heightened energy demands during vitellogenesis. ECM-receptor interaction, HIF-1, and IL-17 signaling pathways further pointed to structural remodeling and tissue regulation. Enhanced antioxidant defenses, including upregulation of SOD2, CAT, GPX4, and GSTO1, highlighted the importance of redox homeostasis. Together, these findings provide the first comprehensive view of transcriptional and splicing dynamics underlying post-mating ovarian maturation in Scylla paramamosain, offering novel insights into the molecular basis of crustacean reproduction. Full article

The recent growing adversarial activity against critical systems, such as the power grid, has raised attention on the necessity of appropriate measures to manage the related risks. In this setting, our research focuses on developing tools for early detection of adversarial activities, taking into account the specificities of the energy sector. We developed a framework to design and deploy AI-based detection models, and since one cannot risk disrupting regular operation with on-site tests, we also included a testbed for evaluation and fine-tuning. In the test environment, adversarial activity that produces realistic artifacts can be injected and monitored, and evidence analyzed by the detection models. In this paper we concentrate on the emulation of attacks inside our framework: A tool called SecuriDN is used to define, through a graphical interface, the network in terms of devices, applications, and protection mechanisms. Using this information, SecuriDN produces sequences of attack steps (based on the MITRE ATT&CK project) that are interpreted and executed by software called Netsploit. A case study related to Distributed Energy Resources is presented in order to show the process stages, highlight the possibilities given by our framework, and discuss possible limitations and future improvements. Full article

The global transition to a circular bioeconomy is accelerating the demand for sustainable, high-performance materials. Filamentous fungi represent a promising solution, as they function as living foundries that transform low-value biomass into advanced, self-assembling materials. While mycelium-based composites have proven potential, progress has been predominantly driven by empirical screening of fungal species and substrates. To unlock their full potential, a paradigm shift from empirical screening to rational design is required. This review introduces a conceptual framework centered on the biochemical programming of the fungal cell wall. Viewed through a materials science lens, the cell wall is a dynamic, hierarchical nanocomposite whose properties can be deliberately tuned. We analyze the contributions of its principal components—the chitin–glucan structural scaffold, the glycoprotein functional matrix, and surface-active hydrophobins—to the bulk characteristics of mycelium-derived materials. We then identify biochemical levers for controlling these properties. External factors such as substrate composition and environmental cues (e.g., pH) modulate cell wall architecture through conserved signaling pathways. Complementing these, an internal synthetic biology toolkit enables direct genetic and chemical intervention. Strategies include targeted engineering of biosynthetic and regulatory genes (e.g., CHS, AGS, GCN5), chemical genetics to dynamically adjust synthesis during growth, and modification of surface chemistry for specialized applications like tissue engineering. By integrating fungal cell wall biochemistry, materials science, and synthetic biology, this framework moves the field from incidental discovery toward the intentional creation of smart, functional, and sustainable mycelium-based materials—aligning material innovation with the imperatives of the circular bioeconomy. Full article

As a critical component of rail travel, the train communication network (TCN) is an integrated central platform that is used to realize the train control, condition monitoring, and data transmission, whose failure will disrupt the symmetry of TCN topology and endanger the security of rail trains. To enhance the reliability of TCN, an intelligent fault diagnosis method is proposed based on active learning (AL) and a stacked consistent autoencoder (SCAE), which is capable of building a competitive classifier with a limited amount of labeled training samples. SCAE can learn better feature presentations from electrical multifunction vehicle bus (MVB) signals by reconstructing the same raw input data layer by layer in the unsupervised feature learning phase. In the supervised fine-tuning phase, a deep AL-based fault diagnosis framework is proposed, and a dynamic fusion AL method is presented. The most valuable unlabeled samples are selected for labeling and training by considering uncertainty and similarity simultaneously, and the fusion weight is dynamically adjusted at the different training stages. A TCN experimental platform is constructed, and experimental results show that the proposed method achieves better performance under three different metrics with fewer labeled samples compared to the state-of-the-art methods; it is also symmetrically valid in class-imbalanced data. Full article

TGR5 has emerged as a promising therapeutic target for obesity and metabolic disorders due to its regulatory roles in energy expenditure, glucose homeostasis, thermogenesis, and gut hormone secretion. This review summarizes the structural mechanisms of TGR5 activation, focusing on orthosteric and allosteric ligand interactions, toggle switch dynamics, and G protein coupling based on cryo-EM and docking-based models. A wide range of bioactive natural compounds including oleanolic acid, curcumin, betulinic acid, ursolic acid, quinovic acid, obacunone, nomilin, and 5β-scymnol are examined for their ability to modulate TGR5 signaling and elicit favorable metabolic effects. Molecular docking simulations using CB-Dock2 and PDB ID 7BW0 revealed key interactions within the orthosteric pocket, supporting their mechanistic potential as TGR5 agonists. Emerging strategies in TGR5-directed drug development are also discussed, including gut-restricted agonism to minimize gallbladder-related side effects, biased and allosteric modulation to fine-tune signaling specificity, and AI-guided optimization of natural product scaffolds. These integrated insights provide a structural and pharmacological framework for the rational design of safe and effective TGR5-targeted therapeutics. Full article

This paper proposes an enhanced Model Predictive Direct Speed Control (MPDSC) framework for Permanent Magnet Synchronous Motor (PMSM) drives, integrating duty ratio optimization and load torque disturbance compensation to significantly improve both transient and steady-state performance. Traditional finite-control-set MPC strategies, which apply a single voltage vector per sampling interval, often suffer from steady-state ripples, elevated total harmonic distortion (THD), and high computational complexity due to exhaustive switching evaluations. The proposed approach addresses these limitations through a novel dual-stage cost function structure: the first cost function optimizes dynamic response via predictive control of speed error, while the second adaptively minimizes torque ripple and harmonic distortion by adjusting the active–zero voltage vector duty ratio without the need for manual weight tuning. Robustness against time-varying disturbances is further enhanced by integrating a real-time load torque observer into the control loop. The scheme is validated through both MATLAB/Simulink R2020a simulations and real-time experimental testing on a dSPACE 1202 rapid control prototyping platform across small- and large-scale PMSM configurations. Experimental results confirm that the proposed controller achieves a transient speed deviation of just 0.004%, a steady-state ripple of 0.01 rpm, and torque ripple as low as 0.0124 Nm, with THD reduced to approximately 5.5%. The duty ratio-based predictive modulation ensures faster settling time, improved current quality, and greater immunity to load torque disturbances compared to recent duty-ratio MPC implementations. These findings highlight the proposed DR-MPDSC as a computationally efficient and experimentally validated solution for next-generation PMSM drive systems in automotive and industrial domains. Full article

Effective waste management is important for reducing environmental harm, improving recycling operations, and building urban sustainability. However, accurate waste classification remains a critical challenge, as many deep learning models struggle with diverse waste types. In this study, classification accuracy is enhanced using transfer learning, ensemble techniques, and custom architectures. Eleven pre-trained convolutional neural networks, including ResNet-50, EfficientNet variants, and DenseNet-201, were fine-tuned to extract meaningful patterns from waste images. To further improve model performance, ensemble strategies such as weighted averaging, soft voting, and stacking were implemented, resulting in a hybrid model combining ResNet-50, EfficientNetV2-M, and DenseNet-201, which outperformed individual models. In the proposed system, two specialized architectures were developed: EcoMobileNet, an optimized MobileNetV3 Large-based model incorporating Squeeze-and-Excitation blocks for efficient mobile deployment, and EcoDenseNet, a DenseNet-201 variant enhanced with Mish activation for improved feature extraction. The evaluation was conducted on a dataset comprising 4691 images across 10 waste categories, sourced from publicly available repositories. The implementation of EcoMobileNet achieved a test accuracy of 98.08%, while EcoDenseNet reached an accuracy of 97.86%. The hybrid model also attained 98.08% accuracy. Furthermore, the ensemble stacking approach yielded the highest test accuracy of 98.29%, demonstrating its effectiveness in classifying heterogeneous waste types. By leveraging deep learning, the proposed system contributes to the development of scalable, sustainable, and automated waste-sorting solutions, thereby optimizing recycling processes and minimizing environmental impact. Full article

This work aims to study the effect of the bulk rheology of a complex system on the apparent interfacial viscoelastic response of a rising oil droplet of a paraffinic oil (Indopol) undergoing sinusoidal volume dilatations insidean aqueous phase containing a hydrogel. The modulation of the interfacial viscoelasticity is obtained using Span 80 surfactant or fumed silica nanoparticles. The rheology of the continuous phase is tuned by adding 3 to 5 g/L of κ-carrageenan (KC) to switch the continuous aqueous phase from a liquid to a gel state at 15 °C. When KC is liquid, the presence of Span 80 or nanoparticles at the liquid/liquid interface increases the apparent interfacial elastic modulus. However, when KC becomes a weak gel, the apparent interfacial elastic modulus depends on the nature of the surface-active agents. Indeed, if the presence of silica hard nanoparticles enhances the apparent elasticity of the interface, adding Span 80 weakens the apparent elasticity of the interface. These trends are discussed in terms of the localization of the deformation and slippage at the interfaces. Full article

This study introduces an efficient, all-aqueous emulsion-templating strategy for fabricating highly tunable yeast immobilization carriers with superior biocatalytic performance. Utilizing cellulose nanocrystals (CNCs) to stabilize dextran/polyethylene glycol (Dex/PEG) water-in-water emulsions, an architecture-controlled void is obtained by crosslinking the PEG-rich phase with variable concentrations of polyethylene glycol diacrylate (PEGDA) (10–25 wt%). This approach successfully yielded macroporous networks, enabling precise tuning of void diameters from 10.4 to 6.6 μm and interconnected pores from 2.2 to 1.4 μm. The optimally designed carrier, synthesized with 15 wt% PEGDA, featured 9.6 μm voids and robust mechanical strength (0.82 MPa), and facilitated highly efficient yeast encapsulation (~100%). The immobilized yeast demonstrated exceptional fermentation activity, remarkable storage stability (maintaining > 95% productivity after 4 weeks), and high reusability (85% activity retention after seven cycles). These enhancements are attributed to the material’s excellent water retention capacity and the provision of a stable microenvironment. This green and straightforward method represents a significant advance in industrial cell immobilization, offering unparalleled operational stability, protection, and design flexibility. Full article

The vibration properties of materials play a role in their conduction of electric charges. Ionic conductors such as electrodes and solid electrolytes are also relevant in this respect. The vibration properties are typically assessed with infrared and Raman spectroscopy, and inelastic neutron scattering, which all allow for the derivation of the phonon density of states (PDOS) in part of a full portion of the Brioullin zone. Nuclear resonant vibration spectroscopy (NRVS) is a novel method that produces the element-specific PDOS from Mössbauer-active isotopes in a compound. We employed NRVS operando on a pouch cell battery containing a Li⁵⁷FePO₄ electrode, and thus could derive the PDOS of the ⁵⁷Fe in the electrode during charging and discharging. The spectra reveal reversible vibrational changes associated with the two-phase conversion between LiFePO₄ and FePO₄, as well as signatures of metastable intermediate states. We demonstrate how the NRVS data can be used to tune the atomistic simulations to accurately reconstruct the full vibration structures of the battery materials in operando conditions. Unlike optical techniques, NRVS provides bulk-sensitive, element-specific access to the full phonon spectrum under realistic operando conditions. These results establish NRVS as a powerful method to probe lattice dynamics in working batteries and to advance the understanding of ion transport and phase transformation mechanisms in electrode materials. Full article

This study tackles the challenges associated with permanent magnet synchronous motor (PMSM) position control under complex operating conditions—characterized by strong coupling, nonlinearity, and time-varying parameters—which often lead to slow response, low control accuracy, and weak disturbance rejection capability. A high-performance control system is developed based on an active disturbance rejection controller (ADRC), with three key improvements proposed. Firstly, a modified nonlinear function is designed to suppress chattering. Secondly, a delay compensation module is integrated to synchronize the input signals of the extended state observer (ESO). Finally, an automated parameter tuning method is introduced using the Newton-Raphson optimization algorithm. Comparative simulations are conducted to validate the effectiveness of the proposed system, demonstrating its advantages of rapid response, minimal overshoot, and enhanced disturbance rejection capability. For the proposed strategy, the maximum position tracking error is 0.1 rad, the adjustment time is 0.15 s, the dynamic speed drop is 0.025 rad, and the recovery time is 0.15 s—all comprehensive performance indicators outperform those of other control strategies. Additionally, automated parameter tuning eliminates the need for manual adjustments, reduces operational complexity, and improves tuning accuracy, thereby significantly advancing the position control performance of PMSMs. Full article

Externally governed hydrogel swelling is a highly convenient yet inherently challenging process, as it requires both responsive materials and appropriately tuned external stimuli. In this work, for the first time, the influence of simultaneous action of external physical fields—ultrasound (US) and microwave heating (MW), combined with cooling—on the isothermal swelling kinetics of poly(methacrylic acid) (PMAA) hydrogel was investigated and compared with swelling under conventional thermal heating (TH) under isothermal conditions. Swelling kinetics were monitored over a temperature range of 248–318 K, under simultaneous cooling with either US, MW, or TH. The well-established Peppas model was used to determine swelling kinetics parameters, revealing a significant acceleration in the swelling process under MW (up to 48.8 times at 313 K), as well as different water penetrating mechanisms (non-Fickian diffusion) compared to TH and US (Super-case II). Additionally, it was demonstrated that the swelling conversion curves could be mathematically described using a “shrinking boundary surfaces” model. Isothermal swelling constants and the corresponding kinetic parameters (activation energy E_a and pre-exponential factor ln A) were calculated. The results confirmed that external physical fields significantly influence the thermal activation and swelling behavior of PMAA xerogels, offering insight into field-responsive transport processes in hydrogel networks. Full article

Over the past years, with the growing interest in sustainable biomaterials, marine collagen has been emerging as an interesting alternative to bovine collagen. It is more easily absorbed by the body and has higher bioavailability. In this study, collagen was extracted from Dicentrarchus labrax (sea bass) skin, a fishery by-product, thus valorizing waste streams while reducing environmental impact. To overcome the intrinsic weak mechanical of collagen, it was combined with chitosan to produce composite scaffolds for skin tissue engineering. The incorporation of collagen proved crucial for scaffold performance: (i) it promoted the formation of an open-pore architecture, favorable for cell infiltration and proliferation; (ii) it enhanced swelling behavior suitable for exudate absorption and maintenance of a moist wound environment; (iii) by tuning the chitosan/collagen ratio, it enabled us to control the degradation rate; (iv) it conferred antioxidant properties; and (iv) by adjusting collagen/chitosan concentrations, it allowed fine-tuning of mechanical properties, ensuring sufficient strength to resist stresses encountered during wound healing. In vitro assays demonstrated that the scaffolds were non-cytotoxic and effectively supported mouse adipose tissue fibroblasts’ adhesion and proliferation. Finally, all formulations exhibited marked bactericidal activity against the human pathogen Staphylococcus aureus and the methicillin-resistant Staphylococcus aureus, with a Log reduction greater than 3 (a reduction of at least 99.9% in bacterial growth) compared to the control. Collectively, these findings highlight collagen not only as a sustainable resource but also as a functional component that drives the structural, physicochemical, biological, and antimicrobial performance of chitosan/collagen scaffolds for skin tissue engineering. Full article

The electrification of agricultural equipment is a critical pathway to address the dual challenges of increasing global food production and ensuring sustainable agricultural development. As the core power unit, the permanent magnet synchronous motor (PMSM) drive system faces severe challenges in achieving high performance, robustness, and reliable control in complex farmland environments characterized by sudden load changes, extreme operating conditions, and strong interference. This paper provides a comprehensive review of key technological advancements in PMSM drive systems for agricultural electrification. First, it analyzes solutions to enhance the reliability of power converters, including high-frequency silicon carbide (SiC)/gallium nitride (GaN) power device packaging, thermal management, and electromagnetic compatibility (EMC) design. Second, it systematically elaborates on high-performance motor control algorithms such as Direct Torque Control (DTC) and Model Predictive Control (MPC) for improving dynamic response; robust control strategies like Sliding Mode Control (SMC) and Active Disturbance Rejection Control (ADRC) for enhancing resilience; and the latest progress in fault-tolerant control architectures incorporating sensorless technology. Furthermore, the paper identifies core challenges in large-scale applications, including environmental adaptability, real-time multi-machine coordination, and high reliability requirements. Innovatively, this review proposes a closed-loop intelligent control paradigm encompassing environmental disturbance prediction, control parameter self-tuning, and actuator dynamic response. This paradigm provides theoretical support for enhancing the autonomous adaptability and operational quality of agricultural machinery in unstructured environments. Finally, future trends involving deep AI integration, collaborative hardware innovation, and agricultural ecosystem construction are outlined. Full article