Browse Articles

CASCADENCE: a layered cascade defense mechanism for federated learning

Abstract This paper aims to enhance the security and robustness of Federated Learning (FL) systems through a multi-layered defense. We address the critical challenge of protecting distributed learning environments from adversarial attacks while maintaining high model performance during both the training and operational phases. The proposed framework is based on integrated approaches that utilize a Gaussian filter with Discrete Fourier Transform (DFT), adversarial training with differential privacy, JPEG compression, randomized smoothing, and adversarial logit pairing. It integrates multiple defense mechanisms based on system requirements, focusing on preserving model performance while ensuring robust protection during both training and testing phases. Our approach extends beyond existing solutions by introducing various staged defense implementations and analyzing their synergistic effects. Experimental results demonstrate that the proposed ensemble defense mechanism achieves the highest performance, maintaining 98.21% accuracy and an F1 score of 0.98 under attack conditions, compared to a baseline accuracy of 90.87%.

Performance evaluation of grid-forming battery energy storage systems for stability enhancement in solar PV plants

Benchmarking hybrid CNN and transformer backbones with graph convolution networks (GCN) for flower growth-stage classification

Investigating bioactive potential of the Serratia sp. CS01 and its pigment for anti-inflammatory, antioxidant and antibacterial action

Ischemic injury and liver graft congestion increase post-transplant interleukin-6 and tumor necrosis factor-alpha in hepatocellular carcinoma

Hydrodynamic evaluation of a spider-inspired underwater robot using distributed flapping fin propulsion

Abstract This paper presents the design and hydrodynamic evaluation of a spider-inspired underwater hexapod robot employing distributed flapping-fin propulsion. Unlike conventional bio-inspired underwater robots with centralized fin actuation, the proposed system integrates flexible bilateral side fins between leg triads, enabling decentralized lift generation and inherent body stabilization. A three-tier validation framework is adopted, comprising (i) quasi-steady analytical estimation of lift, drag, Reynolds number, and lift-to-power scaling, (ii) transient Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent resolving pressure distribution, vortex shedding, and wake evolution, and (iii) preliminary experimental validation using a laboratory-scale prototype in controlled water-tank conditions. Theoretical, numerical, and experimental results show close agreement, with lift predictions within ± 10–15% across operating regimes. CFD results indicate stable hydrodynamic performance over fin-tip velocities U = 0.6–0.8 m/s, yielding a nearly constant lift-to-drag ratio of ≈ 1.3. Non-dimensional analysis confirms operation within an efficient Strouhal number range. The study establishes the hydrodynamic feasibility and energetic advantages of distributed flapping-fin propulsion integrated with a multi-legged body architecture. The results provide validated design insights for the development of manoeuvrable and energy-efficient underwater robotic platforms for inspection, monitoring, and exploration tasks.

An intelligent ethereum blockchain technology for pest detection and smart irrigation in IoT using hybrid deep learning model

Abstract This research discusses the incorporation of IoT with blockchain technique to enhance the efficiency of smart farming systems, particularly focusing on plant disease classification, pest detection, and smart irrigation. The study aims to develop a secure and effective IoT-based smart farming framework using the Ethereum blockchain to store and transmit data, and a Hybrid Convolution Adaptive Recurrent MobileNet (HC-ARMNet) model for predictive analytics, optimized by the Improved Secretary Bird Optimization (ISBO) algorithm. The research employs IoT sensors to acquire real-time data, which is then stored in the Ethereum blockchain to ensure security. The HC-ARMNet model, combining 1D/2D convolutions with recurrent connections, processes this data for pest detection and irrigation management. The ISBO algorithm is leveraged to fine-tune the technique’s parameters. Datasets used: The proposed system utilizes three standard datasets for evaluation. The PlantifyDr Dataset is used for classifying plant disease, and the Pest Detection Dataset is used for recognizing pests. Also, for the smart irrigation process, the significant field images are collected manually. The accuracy, precision, and FNR rates of the ISBO-HC-ARMNet-aided plant disease classification are 94.16%, 94.2% and 5.87%. At the same time, the ISBO-HC-ARMNet-based pest detection process’s accuracy, sensitivity, and specificity are 93.78%, 93.79% and 93.76%, respectively. In addition, the ISBO-HC-ARMNet-based smart irrigation task’s MSE is 3.21, SMAPE is 0.03, and MASE is 30.23. Thus, the designed system showcases promising performance over classical approaches in terms of accuracy and error rates for plant disease classification, pest detection, and smart irrigation. The research concludes that the IoT-aided smart farming framework with blockchain and the HC-ARMNet model provides a robust solution for secure and efficient agricultural management. The system’s predictive capabilities provide accurate and timely data analysis, facilitating to the improvement of precision agriculture. Future work will focus on improving the system with advanced feature extraction strategies to reduce processing time.

Steroid administration, timing, and dose in patients with septic shock in the emergency department: retrospective analysis of a multicenter prospective cohort study

Plasma derived ceramic-metal hybrid PEO coatings with embedded bismuth phases for multifunctional magnesium surfaces

Comparative mitogenomic study of the brown accentor (Prunella fulvescens) and a sympatric relative across an altitudinal gradient

Decoupling Electronic Effects in Oxygen Reduction Catalysts via a Model Nanowire Platform

ABSTRACT Understanding the intrinsic role of electronic structure in governing oxygen reduction reaction (ORR) activity on Pt‐based catalysts remains a long‐standing challenge due to the intrinsic coupling of electronic, strain, and ensemble effects in conventional alloy systems. Here, we establish a well‐defined Pt‐based nanowire (NW) model platform that enables the rigorous decoupling of electronic effects from structural contributions. By selectively incorporating electron‐donating Re (PtRe) or electron‐withdrawing Au (PtAu) into Pt NWs while maintaining identical morphology, surface structure, and coordination environment, the electronic contribution to ORR is isolated with minimal interference of strain and ensemble effects. A consistent activity trend (PtRe > Pt > PtAu) is observed from intrinsic ORR activity to device‐level membrane electrode assembly performance. Crucially, a correlation is established between the electronic structure, intermediate adsorption behavior, and intrinsic activity. Meanwhile, the high‐activity PtRe NW catalyst also delivers a robust durability with mass activity decline of 11.8% and voltage loss of 12 mV after 30,000‐cycle tests. In situ spectroscopy and theoretical calculations results collectively confirm that Re dopants donate electrons to Pt, generating an electron‐rich Pt surface that lowers the adsorption energy of oxygen intermediates and enhances ORR activity, while the Au dopant generates an opposite effect.

Destination image, satisfaction, and perceived value drive tourist loyalty in traditional village tourism

State-variable analysis of pulsed EM-fields in temporally layered media

Protonation‐Triggered Unlocking of Interlayer Carbon Nitride for Rapid Nanosheet Preparation

ABSTRACT The intrinsic topological and electronic merits of graphitic carbon nitride (g‑C 3 N 4 ) are fundamentally obscured by strong π‐π stacking interactions and hydrogen‐bonding interactions. Overcoming these noncovalent barriers without compromising the structural integrity remains a formidable chemical challenge. Herein, we report a targeted electrostatic decoupling strategy via protonation that rapidly unlocks the intralayer framework of g‑C 3 N 4 into discrete, highly crystalline two‐dimensional (2D) nanosheets under ambient conditions. By utilizing trifluoromethanesulfonic acid, selective protonation at the heterocyclic nitrogen sites induces pronounced interlayer electrostatic repulsion and simultaneous intralayer electronic reconstruction, achieving an unprecedented production efficiency of 200 mg·mL −1 ·h −1 . Crucially, the high aspect ratio and structural fidelity of the as‐exfoliated nanosheets enable unambiguous direct observation of the intrinsic lyotropic liquid‐crystalline phase transition in pure g‑C 3 N 4 , resolving long‐standing ambiguities regarding its mesoscopic assembly behavior. Furthermore, the 2D nanosheets display over 50‐fold enhancement in photocatalytic hydrogen peroxide production activity compared to their bulk counterpart, attributed to the reduced thickness and significantly increased exposure of active sites. This work not only provides an efficient route for the exfoliation of layered polymers but also opens new opportunities for their solution‐phase processing.

Intensive care physicians’ experiences of decision fatigue and characteristics of vulnerable clinical decisions

Abstract Decision fatigue (DF) has been proposed to describe changes in decision-making over the course of repeated decisions, but its mechanisms and relevance in clinical practice remain debated. While quantitative studies have reported time-related patterns in medical decisions, qualitative evidence on how DF is experienced and managed in everyday clinical settings is limited. Intensive care units (ICUs), characterised by high decision density, time pressure, and uncertainty, provide a particularly relevant context to explore these processes. This study explored intensive care physicians’ views and experiences on DF and its potential impact on medical decision making in the ICU. 19 semi-structured interviews were conducted in person with ICU physicians from October 2024 to March 2025. All interviews were audio-recorded, transcribed verbatim, and analysed using an inductive thematic analysis approach. Codes and categories were iteratively developed and grouped into higher-level themes and decision characteristics. 19 physicians from three different ICUs participated, including 13 residents and board-certified specialists in executing roles and 6 consultants with supervisory and treatment-planning responsibilities. Three major themes were identified and developed: (1) DF and mental exhaustion occur in the ICU; (2) Perceived effects of DF on decision-making processes and behaviours; and (3) physicians indicate various characteristics of decisions in which the effects of DF are more likely to occur. This study provides new insights into ICU physicians’ experiences of DF and presents a typology of clinical decisions according to their perceived susceptibility to DF as a hypothesis-generating framework. The findings suggest practical implications for workflow design, decision prioritisation, and team-based approaches to support clinical decision-making in the ICU.

Assessing disaster resilience in mountain villages using an improved DPSIR-A framework and multi-model machine learning

Abstract Amid the dual challenges of global climate change and frequent geological hazards, evaluating the disaster resilience of mountainous villages is crucial for sustainable regional development. This study proposes an integrated resilience assessment framework specifically designed for high-altitude, tourism-dependent ethnic villages. The framework extends the classical DPSIR model by incorporating an Adaptability (A) dimension, which quantifies traditional ecological knowledge and community learning capacity. To address the class imbalance typical of small-sample geological hazard datasets, the study integrates the SMOTE oversampling technique with a multi-model evaluation chain (IVM-SVM-RF). This method overcomes the generalization limitations of machine learning models in small-sample environments. The results show: (1) Model Performance Breakthrough: The SMOTE-enhanced Random Forest (S-RF) model outperforms both SVM and IVM models, with the highest performance (AUC = 0.753, Kappa = 0.754). It is particularly effective in identifying low-resilience areas under small-sample conditions, confirming that SMOTE augmentation corrects class imbalance and improves model accuracy in capturing marginal low-resilience zones. (2) Spatial Differentiation: Zhangzha Town exhibits distinct “topography-constrained economic clustering” patterns of resilience. The high-resilience core zone (13.05%) is concentrated in the Ganhaizi sector, driven by socio-economic adaptability, while the extremely low-resilience zone (9.42%) is scattered along the southern periphery, influenced by steep terrain and delayed responses. (3) Nonlinear Drivers: Feature importance analysis identifies Fractional Vegetation Cover, Rainfall, and Distance from Roads as key resilience determinants. Additionally, “soft resilience” factors, such as Building Disaster Resistance and Villagers’ Disaster Awareness, play significant roles in mitigating physical risks.

4‐Formyl‐N‐Methylpyridinium‐Mediated N‐Terminal Cysteine Modification/Removal Facilitates One‐Pot Multiplex Peptide Ligation

ABSTRACT The chemical synthesis of proteins with site‐specific modifications remains a fundamental challenge in chemical biology. One‐pot peptide ligation strategies have emerged as powerful tools to enhance synthetic efficiency, primarily relying on N‐terminal cysteine (Cys) protection. However, current Cys deprotection conditions require various reagents or pH adjustments during the reaction, rendering downstream processing cumbersome. Here, a visible‐light‐mediated deprotection strategy using 2‐(N‐methylpyridinium‐4‐yl)‐thiazolidine (4‐NMP‐Thz) as a novel N‐terminal Cys‐protecting group is reported. This reaction, catalyzed by [Ru(bpy) 3 ]Cl 2 at physiological pH (6.0–8.0), enables smooth one‐pot multi‐segment peptide assembly. The strategy demonstrates complete orthogonality to native chemical ligation (NCL) and desulfurization conditions, eliminating the requirement for intermediate purification or pH adjustment. This methodology was used to facilitate an efficient one‐pot synthesis of a 400‐amino acid (aa) glycosylated MUC1 glycoprotein bearing 40 O‐glycosyl modifications that is difficult to prepare using previously reported techniques. The 400‐aa MUC1 significantly enhanced antigenic immunogenicity compared with shorter MUC1 glycopeptides. This streamlined approach establishes a robust platform for the construction of complex post‐translationally modified proteins.

Effects of adaptive error-threshold practice on soccer instep kick learning

A multi-pollutant air quality health index for assessing mortality risk of ischemic heart disease in Jinan, China

Uncovering Aggregation‐Induced Emission in Carbon Dots for Color‐Changing Hydrogels and Information Encryption

ABSTRACT Since the discovery of carbon dots (CDs), the typical precursor combination of citric acid (CA)‐urea has been widely used in the scientific community to explore the formation mechanism and luminescence behavior of CDs. However, there have only been a few reports on the synthesis of CDs featuring aggregation‐induced emission (AIE) characteristics. In this study, CA and urea were used to synthesize hydrophilic red‐emissive carbon dots (R‐CDs) that exhibit blue fluorescence in water (dispersed state) and red fluorescence in DMF (aggregated state). The study reveals that the photoluminescence of R‐CDs is governed by π–π stacking interactions between solute molecules as well as solvent effects between solute and solvent molecules, leading to solvent‐responsive emission behavior. By tuning the solvent polarity, the intermolecular distance between R‐CDs can be adjusted, thereby influencing their photoluminescent properties. Taking advantage of the solvent‐responsive color change, anticounterfeiting printing and information encryption applications were designed. Moreover, by combining R‐CDs with poly(vinyl alcohol) (PVA), hydrogel‐based fluorescent information‐encoding materials were successfully fabricated.