Search Results (11,407)

Background: Fresh frozen tissues are considered the gold standard for proteomic analyses due to their superior preservation of protein integrity; however, their use is limited by the logistical and financial requirements of long-term cold storage. Formaldehyde-fixed paraffin-embedded (FFPE) tissues provide a practical alternative, owing to their stability and widespread availability in clinical settings. A critical step in FFPE proteomics is deparaffinization, which traditionally relies on organic solvents such as xylene, along with the efficient reversal of formaldehyde-induced crosslinks. Methods: In this study, we evaluated multiple FFPE protein extraction and digestion workflows including chaotropic, surfactant-based, and detergent-free approaches in combination with xylene-free deparaffinization strategies, using label-free data-independent acquisition (DIA) LC-MS/MS. Results: Among the tested methods, a chaotropic, reductant, and surfactant-free in-solution digestion workflow demonstrated robust protein and peptide recovery. A modified version of this protocol further improved peptide coverage while maintaining comparable protein depth. The applicability of the optimized workflow was assessed using FFPE needle biopsy samples from control, hepatic steatosis, and liver fibrosis groups. Exploratory proteomic patterns were observed across conditions, with hepatic steatosis associated with early activation of stress-response pathways, while fibrosis showed evidence suggesting altered lipid metabolism. Conclusions: Overall, this study presents a simple, xylene-free, and MS-compatible workflow for FFPE proteomics that is suitable for low-input clinical samples and may support broader application of archival tissues in proteomic research. Full article

The rapid growth of online shopping has increased demand for home deliveries, leading to sustainability issues and logistical challenges such as labour shortages and congestion. Autonomous delivery vehicles, including drones, street robots, autonomous vans, and mobile vending machines, are emerging as potential solutions. Understanding consumers’ perceptions of these technologies is critical for sustainable implementation. This exploratory study aimed to examine consumer reactions to emerging autonomous delivery services, providing insights into how consumers may respond to autonomous delivery systems encompassing multiple vehicle modes and the resulting policy implications. Eight online focus groups (n = 55) were conducted with a diverse range of participants to examine community attitudes to autonomous delivery services. Participants were shown videos depicting various autonomous delivery methods to foster informed responses. Thematic analysis of the transcripts identified recurring themes relating to participants’ preferences, concerns, and expectations. While participants had some concerns, they were largely receptive to using autonomous delivery services. Positive reactions centred around: (i) convenience, (ii) cost reductions, and (iii) novelty. Identified concerns included: (i) job losses, (ii) practical limitations of the delivery devices, (iii) degradation of urban environments, and (iv) facilitation of unhealthy lifestyles. Overall, the results suggest autonomous delivery systems have the potential to be popular, and proactive government policies are thus likely to be needed to ensure they are implemented in a manner that aligns with community expectations and minimises any negative sustainability outcomes. Full article

Phycobiliproteins (PBPs) are a family of pigment-proteins renowned for their exceptional light-harvesting, fluorescent, and antioxidant properties. Among cyanobacteria, Spirulina stands out as one of the richest natural sources of PBPs, particularly phycocyanin (PC) and allophycocyanin (APC), yet the large-scale production of analytical-grade PBPs remains hampered by an inherently complex downstream process that relies on multiple purification steps, compromising both yield and scalability. This work presents a streamlined strategy to obtain analytical-grade PC, combining ultrasound-assisted extraction (UAE) with an aqueous ionic liquid (IL) solution and a single hydrophobic interaction chromatography (HIC) step, integrated within a biorefinery framework. The proposed approach yielded analytical-grade PC with a recovery of up to 50.44% and enhanced APC purity up to 10.57-fold. Furthermore, the IL was successfully reused in both extraction and purification steps without compromising yield or purity. The environmental performance of the proposed process was assessed through a cradle-to-gate life cycle assessment (LCA), with system boundaries encompassing the following biorefinery stages: cultivation, harvesting and drying, PC extraction and purification, post-processing, and spent biomass valorization via anaerobic digestion. The LCA identified the main environmental hotspots and guided the proposal of targeted process improvements—particularly HIC salt substitution and increased IL recovery—which reduced environmental impacts by 65.9–89.8% across most categories. The proposed strategy was further benchmarked against two model scenarios for analytical-grade PC production, one conventional and one innovative, revealing its relative advantages and limitations. Overall, this work demonstrates a viable pathway for producing high-purity PC that balances process efficiency with environmental sustainability, supporting the development of greener microalgae-based bioprocesses. Full article

The power flow problem is one of the most challenging tasks in power systems, affecting both generation cost and energy quality. Optimal power flow (OPF) further complicates this task by requiring the optimal adjustment of system variables and parameters. This paper adapts the Modified Effective Butterfly Optimizer (MEBO) to solve multi-objective optimal power flow (MOOPF) problems with the contribution of optimized weighting using multiple Pareto archives. MEBO is an advanced optimization algorithm that utilizes population reduction and parameter learning to guide subsequent searches for unconstrained problems. The proposed technique has been tested on IEEE 30 and 57 bus test systems, and the results have been compared with existing methods reported in the literature. In the paper, four single-objective functions, namely generator cost, active power loss, fuel emission, and voltage deviation, are used to construct four multi-objective (MO) problems: cost–loss, cost–voltage, cost-emission, and emission–loss. For the cost-emission case, the proposed MEBO achieved compromised solutions of 791.1951 $/h fuel cost with 0.10873 ton/h emission and 801.8172 $/h fuel cost with 0.10044 ton/h emission under different Pareto-based optimization metrics. In the emission–loss case, the algorithm obtained 0.20539 ton/h emission with 3.1403 MW/h power loss, demonstrating the effectiveness of the proposed approach in balancing conflicting objectives. The Pareto curves of MEBO in achieving MO problems are presented, along with the suggested compromised solutions acquired from the literature. In the literature, this is the first application of MEBO for solving MOOPF problems. The results demonstrate that MEBO performs better than most other alternatives; this shows potential for further improvements with respect to the MOOPF problem. Full article

This paper presents a multibeam hybrid beamforming (MHBF) architecture for microwave power transfer (MPT), enabling wireless power delivery to multiple receivers with a reduced number of RF chains. The proposed architecture decouples beam control into the horizontal and vertical dimensions, where horizontal multibeams are generated in the baseband through digital precoding, while the vertical beam direction is controlled by a Butler-matrix-based analog beamformer. In particular, multibeam transmission is achieved using multi-tone signals with distinct phase weights assigned to each tone, enabling beams to be steered toward different directions, while the Butler-matrix-based analog beamformer provides vertical beam-steering capability. Compared with fully digital beamforming (DBF), MHBF enables simultaneous multibeam formation in the horizontal domain with fewer RF chains, thereby reducing hardware overhead and system complexity. To validate the proposed architecture, a 5.8 GHz prototype was designed and fabricated. The experimental results demonstrate three-beam and four-beam operation under a transmit power of 30.57 dBm, while the average received RF power in the single-beam case was 12.11 dBm at a distance of 1 m. In the three-beam and four-beam cases, average received RF power levels of 7.3 dBm and 6.1 dBm per beam were achieved, respectively. RF-to-DC conversion measurements under 430 $\Omega$ and 680 $\Omega$ load conditions further showed average PCE values of up to 38.77% and 35.05% for the three-beam and four-beam cases, respectively. These results confirm the feasibility of simultaneous multibeam wireless power delivery and its potential as an effective solution for multi-receiver operation with reduced RF-chain requirements. Full article

In this paper, a multi-objective optimisation method based on the Non-dominated sorting genetic algorithm II (NSGA-II) is proposed, which proves to be effective in solving the computer-aided molecular design (CAMD) problem in the design of solvents for cooling crystallisation. A multi-objective optimisation model has been developed for the CAMD problem of solvents in the crystallisation process with the toxicity, solubility parameters, and potential recovery of the solvents as objective functions and the feasibility of the molecular structure as constraints. The properties involved are to be calculated by the group contribution method, and the solubility parameters of the solute in the solvent are calculated based on the Universal Quasichemical Functional-group Activity Coefficients (UNIFAC) model. Based on this method, cooling crystallisation solvents for 2-mercaptobenzothiazole (MBT) and sebacic acid were designed. The results indicate that the proposed multi-objective CAMD framework exhibits a certain degree of generality. Even when the optimisation parameters and methods differ from those of other existing frameworks, it does not overlook the optimal solutions under specific design conditions. Furthermore, clustering of the Pareto front for MBT revealed that, since multi-objective optimisation does not aim to obtain a single optimal solution, it can identify multiple candidate solvents that balance potential yield and toxicity. This approach avoids the issue of single-objective optimisation, which tends to overemphasise potential yield at the expense of toxicity. Full article

The rapid development of Internet of Things technologies has accelerated the deployment of smart home systems. However, perception solutions based on visual sensors remain constrained by illumination sensitivity, occlusion, and privacy concerns. Frequency-modulated continuous-wave (FMCW) millimeter-wave radar provides a promising alternative because it operates independently of lighting conditions, is robust to environmental changes, and preserves user privacy. To address multiple-extended-target tracking in cluttered indoor environments, this paper proposes a high-accuracy tracking algorithm that combines an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, an optimized Nearest-Neighbor Data Association (NNDA) scheme, and an Extended Kalman Filter (EKF). The improved DBSCAN algorithm introduces spatial-extent constraints, velocity-consistency checks, and candidate-cluster validation to cluster raw radar point clouds and convert extended targets into representative point targets with little additional computational cost. The optimized NNDA scheme then integrates clustering information into the association process, improving the matching accuracy between existing tracks and current measurements. Finally, the EKF estimates the state of each target from the associated measurements. Real-world experiments show that the proposed algorithm achieves tracking errors below 0.4 m in typical motion scenarios, maintains continuous tracking in two-person crossing scenarios, and reaches 93.3% counting accuracy in five-person scenarios. These results outperform the tracking system based on the commercial Texas Instruments (TI) IWR6843ISK millimeter-wave radar evaluation board. The proposed method offers a reliable and privacy-preserving sensing solution for smart homes, elderly care, and intelligent building applications. Full article

Complex products continue to face low iterative-design efficiency and poor cross-generation data compatibility, while existing product-gene research is still constrained by the predominance of qualitative approaches, ambiguous representations of hierarchical associations, and insufficient standardization. Based on the principles of decomposition and reconstruction and the systems thinking of genetic engineering, this study develops a generic three-level framework for product genes at the platform, assembly, and component levels. Hierarchical mapping functions and parameter-constraint equations are introduced to enable quantitative representation, and a quantitative product-gene information system is established, including a core-parameter quantification model and inter-/intra-level association-strength models. By integrating multiple international standards, the study further constructs a tripartite standardized description system covering metadata, semantics, and format, and proposes a mathematical mapping method from product information to standardized formats. A case study of Company A’s Platform B and Concept Vehicle C shows that the association-strength model achieves the required adaptation threshold, thereby validating the proposed framework. This study provides quantitative theoretical support for the platform-based and intelligent development of complex products and offers an implementable technical solution for product-gene reuse and data sharing, particularly in the new energy vehicle industry. Full article

Against the backdrop of rapid urbanization, urban land-resource use faces the dual challenge of improving efficiency while maintaining ecological sustainability. Enhancing urban land green use efficiency contributes to the achievement of the United Nations Sustainable Development Goals, particularly SDG 11 and SDG 15. As an emerging governance approach for urban green infrastructure, the National Sponge City Policy (NSCP) aims to address urban waterlogging through nature-based solutions while improving land multifunctionality and ecological carrying capacity. Based on city-level panel data from 2005 to 2022, this study employs a difference-in-differences (DID) approach to identify the policy effect of the NSCP on ULGUE and further examines three transmission channels: innovation effects, infrastructure-support effects, and population-agglomeration effects. The novelty of this study lies in integrating the NSCP into the analytical framework of urban land green use efficiency, extending previous research that mainly focused on waterlogging control, water-resource management, and ecological benefits, and further developing a “policy intervention-factor reallocation-ULGUE improvement” mechanism pathway. The empirical results show that the NSCP significantly improves land green use efficiency in pilot areas, and this conclusion remains valid across multiple robustness checks. The mechanism analysis indicates that strengthened green innovation capacity, improved green infrastructure, and population agglomeration are key channels through which the policy effect is realized. Heterogeneity analysis further reveals that the policy effect varies across regions, dominant industrial structures, and industrial-base types. Overall, the NSCP promotes green spatial governance and efficient resource utilization, providing important institutional experience for coordinating ecological protection and urban development. Full article

Plunger pumps serve as core power equipment in oilfield water injection systems, where their reliable operation directly affects crude oil recovery efficiency and production safety. Failures such as mechanical wear and seal leakage can cause injection pressure fluctuations, increased energy consumption, and even pipeline burst accidents. This study addresses the challenges in plunger pump fault diagnosis, including the difficulty in capturing multi-scale fault features, interference from redundant information in high-dimensional feature spaces, and high model computational complexity. We propose a lightweight fault diagnosis approach called Multi-scale Attention Neural Network (MSLAN), which combines multi-scale convolution and attention mechanisms. In this model, a Separable Multi-scale Fusion Module (SMSF) employs parallel multi-branch convolutional kernels to acquire fault signatures across multiple scales, while computational overhead is reduced through depthwise separable convolution and shared pointwise convolution. Additionally, a Multi-Branch Parallel Attention Module (MBPA) is introduced to finely model complex inter-channel dependencies through a four-branch parallel structure, enhancing the perception of key features and suppressing redundant information. Experimental results on a self-constructed plunger pump dataset, the Case Western Reserve University bearing dataset, and the Southeast University gearbox dataset demonstrate that MSLAN achieves F1-scores of 88.95%, 98.89%, and 99.90%, respectively. While maintaining high diagnostic accuracy, the model exhibits significantly lower parameter count and computational cost compared to baseline models, effectively balancing diagnostic precision and computational efficiency. Ablation studies and visualization analyses further validate the effectiveness of each module. This study establishes an accurate and efficient intelligent fault diagnosis solution for plunger pumps, which is also readily applicable to a broader range of rotating machinery. Full article

Multi-cloud is becoming more challenging to secure as traditional perimeter-based security models have a hard time protecting workloads running across multiple cloud platforms, identities, and services. To address this challenge, organizations are shifting to Zero-Trust Architecture (ZTA), which focuses on constant verification and stringent access control, coupled with anomaly detection methodologies to gain better visibility and threat detection in the distributed cloud environment. This paper presents a Systematic Literature Review (SLR) of anomaly detection approaches in multi-cloud environments and how these are applied in zero-trust security models. The review is conducted according to the guidelines of the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020), and is based on studies published between 2020 and 2025 selected from the databases of the following journals: Institute of Electrical and Electronics (IEEE) Xplore, Science Direct, MDPI, Google Scholar, and the Saudi Digital Library. Studies found on benchmark datasets such as CICIDS-2017 and UNSW-NB15 are not evaluated, as none addressed real multi-cloud environments. Although zero trust is highlighted in general, very few studies have implemented basics of zero trust such as micro-segmentation, identity federation, and enforcement through policy. Overall, this review identifies gaps around cross-cloud validation, explainability, and compliance-aware security design, including lack of attention to regulations such as the GDPR and HIPAA. These findings provide helpful recommendations for future research and development on practical and security solutions for multi-cloud environments. Full article

Accurate analysis of lesions in colonoscopic images is essential for computer-aided diagnosis. However, most existing methods are designed for single-lesion segmentation and assume a predefined lesion category, limiting their applicability in real-world scenarios where multiple lesion types exhibit similar visual characteristics. To address this issue, we propose a unified framework for the joint classification and segmentation of dual-type lesions in colonoscopic images, enabling simultaneous identification and localization of submucosal lesions and polyps/adenomas. The proposed method integrates joint supervision, context-aware feature enhancement, and ambiguity-aware optimization to improve consistency between semantic recognition and spatial delineation. In particular, a soft-label supervision strategy is introduced to alleviate semantic ambiguity, while an imbalance-aware loss design enhances segmentation accuracy and reduces false negative predictions. Extensive experiments on both private and public datasets demonstrate that the proposed method achieves superior performance compared with representative CNN- and transformer-based approaches. Notably, the method shows clear advantages in segmentation accuracy, localization precision, and robustness under challenging conditions. Ablation studies further confirm the effectiveness of each component in the proposed framework. These results indicate that the proposed approach provides an effective solution for dual-type lesion analysis and has the potential to assist clinical decision-making in gastrointestinal endoscopy. Full article

The rapid digitalization of airport operations has transformed airports into complex data-driven ecosystems, where effective data governance has become a critical challenge. While prior studies have explored big data applications in aviation, limited attention has been given to the interdependent structure of data governance challenges at the airport level. This study proposes a decision-oriented analytical framework integrating the entropy and DEMATEL methods in two sequential stages systematically identify, prioritize, and model the causal interactions among key big data challenges in airport ecosystems. Using Istanbul Airport (IGA) as a case study, an initial, expert-based assessment was conducted to assess nine critical challenges, including data privacy, integration, organizational culture, and regulatory compliance. The results revealed that data privacy and security is not only the most critical factor but also a primary causal driver, influencing multiple downstream challenges such as ethical considerations and regulatory compliance. The findings further demonstrate that technical and organizational barriers are strongly interconnected, requiring sequenced, system-level interventions rather than isolated solutions. By combining objective weighting with causal analysis, this study contributes to the literature by providing a holistic and actionable decision support framework for airport data governance. The proposed approach offers practical insights for airport authorities and policymakers to design more resilient, secure, and data-driven operational environments. Full article

Most UAV missions currently require visiting multiple checkpoints to perform field tasks in environments with varying levels of obstacle complexity. These missions become more challenging because UAVs have limited onboard resources, particularly in terms of energy, making it necessary to determine a safe and efficient path that enables all required visits to be completed while minimizing both travel distance and energy consumption. To address these challenges, this study proposes a hybrid fuzzy metaheuristic approach that integrates Cuckoo Search and Tabu Search for multi-objective UAV path planning. The proposed approach generates collision-free paths in environments with static obstacles and employs fuzzy logic to construct a unified evaluation function, in which distance and energy values are mapped to membership functions and combined into a single fitness score to guide the optimization process. Cuckoo Search drives global exploration of the solution space, while Tabu Search refines solutions locally. Together, they improve path quality and avoid premature convergence. Experimental results across two scenarios with varying obstacle densities and checkpoint counts demonstrate the efficacy of the proposed hybrid approach. Compared with two baseline algorithms, the hybrid approach achieves reductions in path length ranging from 0.01% to 42.11% and in energy consumption ranging from 0.08% to 27.91%, depending on scenario complexity. Moreover, it maintains a high success rate of 96–100% as both checkpoint counts and obstacle density increase, whereas the baseline algorithms drop to 3–13% in more complex environments. These results highlight the effectiveness and scalability of the approach for multi-checkpoint UAV path planning in obstacle-rich environments. Full article

Designing lightweight composite sandwich structures is challenging due to the conflicting objectives of minimizing structural weight and cost while satisfying strength and stiffness requirements. The optimization procedure becomes more complex when multiple discrete design variables and nonlinear material behavior are involved. This study presents a newly developed optimization methodology for a sandwich structure composed of Fiber Reinforced Polymer (FRP) laminated facesheets and an aluminum honeycomb core. To reduce the computational cost associated with repeated high-fidelity Finite Element (FE) analyses, a surrogate modeling strategy based on Artificial Neural Networks (ANNs) is employed to approximate the structural response. The applied dataset is generated using Monte Carlo simulation in which combinations of design variables are used as inputs, and the corresponding structural responses obtained from the analytical formulation are used as outputs for training the ANN surrogate model. The trained ANN model is integrated with a Multi-Objective Niching Memetic Particle Swarm Optimization (MO-NMPSO) algorithm to simultaneously minimize structural weight and material cost while satisfying constraints on facesheet strength, wrinkling, intra-cell buckling, deflection, core shear failure and structural thickness. The resulting Pareto-optimal solutions are validated through detailed FE simulations, demonstrating the reliability of the newly elaborated optimization framework. The results of the newly developed computationally efficient optimization procedure provide a diverse set of optimal design solutions for the investigated sandwich structure. Full article