Search Results (3,069)

Stingless bees, or meliponines, are essential pollinators in Brazil, with over 300 described species. These bees produce propolis or geopropolis (characterized by the incorporation of mineral material and clay) used to protect their nests. This product has aroused increasing scientific interest due to its therapeutic potential, including antimicrobial and anti-inflammatory activities. However, there is still limited knowledge about its volatile composition, which can vary according to the bee species and the botanical origin of the resins, influencing their biological and aromatic properties. The purpose of this study was to characterize the volatile composition of (geo)propolis produced by different species of native stingless bees from the Southeastern region of Brazil (São Paulo and Minas Gerais) and to detect if this composition is influenced by the species or by the region. The samples were analyzed by headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography–mass spectrometry (GC-MS). The results indicated that, despite some variations, the chemical profile for each species was mostly constant between regions. In São Paulo, about 25% of the features varied between species, whereas in Minas Gerais, only 5% showed significant differences, although one species (Melipona quadrifasciata) presented a very constant composition. Although the local vegetation determines the supply of resins for these bees, differences in the chemical composition of propolis are a result of a species’ choice of plant species. Full article

Mobile robots used in edge-assisted Industrial Internet-of-Things (IIoT) settings generate coupled motion, LiDAR, edge-compute, and network telemetry. Public datasets that place these streams in one tabular format, with scenario labels suitable for machine-learning experiments, are still limited. This data descriptor presents a pilot-real-calibrated indoor robotic IoT benchmark dataset with 120,000 records sampled at 2 Hz across nominal navigation and nine anomaly scenarios. The benchmark rows are generated from physically constrained simulation rules and are explicitly labeled as synthetic benchmark data. Real pilot evidence is included separately: ROS Noetic runs on a TurtleBot3 Burger, successful LD08 LiDAR bringup after resolving a driver mismatch, and NVIDIA Jetson Nano tegrastats logs under normal-navigation workloads. The calibrated file aligns normal-navigation LiDAR and edge-compute distributions with these pilot measurements while keeping the multi-scenario structure needed for controlled anomaly-detection experiments. The package includes CSV files, metadata, a data dictionary, validation reports, baseline scripts, ROS collection utilities, and a plan for future fully physical data collection. The complete dataset is openly available on Zenodo. Full article

Fine-grained, authenticated, traceable, and efficient encrypted access control is indispensable for secure data sharing in mobile edge cloud networks, especially for resource-constrained data requesters. Despite the prevalence of outsourced ciphertext-policy attribute-based encryption (CP-ABE) solutions, existing schemes still suffer from critical practical limitations. First, requester-side transformation keys are typically unverified prior to computationally expensive outsourced decryption operations. Second, commitment-based verification mechanisms fail to validate the identity of data publishers. Third, the online computational overhead scales linearly with either the requester attribute set or the policy-matching set, severely degrading practical efficiency. To address these issues, this paper proposes ePoFSC, a novel policy-oriented functional signcryption scheme for authenticated encrypted data sharing in mobile edge cloud scenarios. The proposed ePoFSC scheme integrates pre-auditing and caching mechanisms for requester trapdoors before online access requests, enabling constant-time operations for request generation, request verification, and request header construction independent of requester attribute scale. In the outsourced decryption phase, ePoFSC offloads all costly pairing and exponentiation operations with constant computational complexity, leaving only lightweight policy-dependent group multiplications for terminal requesters. Furthermore, ePoFSC tightly couples decryption verification with publisher authentication and requester traceability to realize comprehensive access accountability. Rigorous security analysis formally validates the confidentiality, publisher-side unforgeability, and requester traceability of the proposed scheme. Extensive experimental evaluations on the BLS12-381 curve verify that ePoFSC achieves prominent performance superiority over existing state-of-the-art schemes in both the encryption and data recovery phases. Full article

Subsea tunnels constructed by the drill-and-blast method are increasingly required in modern infrastructure and are often exposed to high groundwater pressure and fractured rock conditions. In such environments, external water pressure acting on initial support strongly affects tunnel stability, durability, and construction safety. Because the initial support is temporary, discontinuous, and prone to cracking, evaluation of its water pressure response remains challenging. Current design practice relies on simplified assumptions and empirical approaches, inadequate for fractured rock masses under high water pressure. This review synthesizes research on external water pressure in tunnels, with emphasis on drill-and-blast subsea tunnels. Empirical reduction coefficient methods, theoretical analytical solutions, numerical techniques, and physical model testing are critically examined in terms of their theoretical basis, applicability, and limitations. Special attention is given to seepage behavior in fractured rock masses, including single-fracture seepage laws, equivalent continuum models, and discrete fracture network approaches, and their ability to represent fracture-controlled flow and water pressure redistribution. The review shows that conventional seepage or seepage–stress coupled methods are insufficient to capture stress redistribution, fracture evolution, and damage-induced permeability changes governing water pressure behavior. By contrast, advanced coupled stress–seepage–damage and stress–seepage–fracturing models provide more physically consistent frameworks for analyzing external water pressure acting on initial support. In addition, hydro-mechanical discrete lattice models are reviewed as a promising meso-scale framework for capturing crack initiation, crack coalescence, and crack-controlled seepage paths that may govern localized external water pressure redistribution behind initial support. However, their application to subsea tunnels remains limited, and current design codes still lack unified calculation methods. Major challenges remain, including the lack of consistent definitions of external water pressure, inadequate consideration of the interaction between tunnel support and surrounding rock, and insufficient validation through laboratory experiments and field observations. Future research should develop mechanism-based methods supported by monitoring and validation to improve subsea tunnel safety. Full article

Artificial intelligence-assisted food detection is increasingly moving from single-task classification toward integrated analytical systems capable of producing multiple quality-related outputs from one sensing workflow. However, most hyperspectral food detection studies still rely on a single spectral range or simple feature concatenation, which limits their ability to exploit complementary physicochemical information from heterogeneous sensors. In this study, an artificial intelligence-enabled visible–near-infrared and short-wave infrared (VNIR-SWIR) hyperspectral fusion framework is proposed for multi-task fruit detection, using origin authentication and bruise localization as representative tasks. The proposed method first constructs an observation-consistent fused representation from high-resolution VNIR images and low-resolution SWIR images. Collaborative spectral unmixing is used to couple cross-modal material distributions, while abundance-consistency and downsampled observation-consistency constraints are introduced to estimate SWIR-informed features on the VNIR spatial grid without assuming measured high-resolution SWIR ground truth. The fused representation is then processed by a shared spectral–spatial deep encoder with two task-specific heads: a fruit-level classification head for origin authentication and a pixel-level segmentation head for bruise detection. Experiments on apple and kiwifruit datasets show that the proposed framework outperforms VNIR-only, SWIR-only, bicubic-fusion, CNMF-style fusion, and TV-regularized fusion baselines under five fruit-level stratified random splits. For origin-category authentication, the proposed method achieved an accuracy of almost 93.85 for apples and almost 94.35 for kiwifruit. For bruise localization, the proposed method achieved higher overall accuracy, average accuracy, and Cohen’s kappa across the evaluated fruit categories. Full article

Phenylglycine (Phg) is a nonproteinogenic α-amino acid found in various bioactive molecules. The C-terminal activation of N-acyl Phg is often accompanied by oxazolone-mediated racemization, arising from the direct attachment of the phenyl ring to the α-carbon. After peptide bond formation with another chiral amino acid, this stereochemical erosion is observed as Phg-site epimerization and diastereomer formation. N-acyl activated esters, particularly N-hydroxysuccinimide (OSu) esters, are widely used for peptide bond formation with proteinogenic α-amino acids. Our previous study on N-trifluoroacetyl phenylglycine (TFA-Phg-OH) revealed that Phg-site epimer formation could still occur when TFA-Phg-OSu was employed as an acyl donor for coupling with amino acid ester hydrochlorides (AA–OMe·HCl) in the presence of a soluble organic base. To address these issues, in this study, we report a base-limited one-pot coupling of TFA-Phg-OH with α-amino acid ester hydrochlorides (AA–OR·HCl; R = Me or tert-Bu) using 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (WSCD·HCl), which effectively suppresses Phg epimerization. The resulting TFA-Phg–AA–OR dipeptides (AA = Ala, Val, Leu, Met, Phg) were all obtained at a >60% yield with a diastereomeric excess (de) ≥ 98.5%. Notably, reducing the amount of triethylamine further minimized epimer formation, while Ba(OH)₂·8H₂O and trifluoroacetic acid enabled stereoretentive deprotection of the N-TFA group and tert-butyl ester, respectively. This workflow provides practical access to both protected and deprotected Phg–AA motifs, thereby facilitating the preparation of unprotected Phg-containing peptide building blocks. Full article

Artificial intelligence is rapidly changing education. However, many learning chatbots are still reactive tools, which respond to arbitrary questions without leading learners through a meaningful pedagogical journey. This article presents a deterministic state-machine orchestrator coupled with a local large language model and a knowledge-graph-framed tutoring strategy for personalized education. The proposed virtual tutoring agent is designed to combine the flexibility of conversational AI with the reliability of explicit instructional states, key performance indicator (KPI) tracking, learner profiling, and controlled transitions between explanation, practice, feedback, assessment, and remediation. The system is not meant to replace the teacher, but rather to act as a teaching co-pilot that provides ongoing feedback, personalized learning paths, accessibility, and safer deployment by processing data locally. The study also presents a compact interview-based evaluation framework and statistical analysis of user perceptions across interactivity, individuality, proactivity, security, accessibility, gamification, and global preference for educational agents over classical chatbots. The findings show that learners appreciate personalized and interactive support and that proactivity is the key feature that distinguishes an educational agent from a regular chatbot. With this article we argue that deterministic orchestration can help make AI tutoring more transparent, controllable, and ethically fit for real learning contexts. Finally, it discusses privacy, educational value, limitations and future improvements to be made before the large-scale adoption of such systems. Full article

The atmospheric weighted mean temperature ($T_m$) is critical for converting a zenith wet delay ($ZWD$) to precipitable water vapor ($PWV$). However, the existing $T_m$ models still have certain limitations: Those driven by surface-measured parameters achieve high accuracy but depend heavily on in situ instruments, incurring high costs and lacking forecasting capability. Empirical models avoid measured data but fail to capture short-term $T_m$ variations, leading to lower accuracy. Daily weather forecast data—which are low-cost, readily available, and reflective of short-term changes—offer a promising alternative. This study develops a gridded $T_m$ model named CGTV-Tm, which couples temperature and water vapor pressure, using ERA5 reanalysis data over China (2019–2023). The model can be driven by daily weather forecast data. A dual vertical correction method is also proposed to improve performance. Validation against 2024 ERA5 and radiosonde data shows that CGTV-Tm achieves RMSEs of 2.38 K (vs. ERA5) and 2.64 K (vs. radiosonde), significantly outperforming the Bevis (3.61 K, 3.67 K), PTm (3.19 K, 2.94 K), and CGT-Tm (2.71 K, 3.08 K) models. When driven by daily weather forecast data, CGTV-Tm achieves an RMSE of 2.90 K, improving accuracy by 29.6% and 21.2% over the state-of-the-art empirical models GPT3 and HGPT2, respectively. These results demonstrate that CGTV-Tm not only surpasses traditional linear $T_m$ models that rely solely on surface temperature but also, by using weather forecast data, it removes dependence on in situ instruments, offering a superior low-cost solution for real-time GNSS (Global Navigation Satellite System) $PWV$ retrieval. Full article

Dust concentration during intermittent processing of powder and granular materials is characterized by high-frequency abrupt changes, local accumulation, and complex coupling among multiple sensors. Existing forecasting models still exhibit limitations in modeling global dependencies and characterizing local trends. To address these issues, this paper proposes an iTransformer-based dust concentration forecasting model that integrates a dual-stage feed-forward network and a DLinear branch. With iTransformer as the backbone network, the proposed model captures the coupling relationships among multi-source sensing signals through variate-wise modeling. A progressive dual-stage feed-forward feature refinement mechanism is constructed to enhance the model’s representation capability for transient variations and peak fluctuations in dust concentration. In addition, a collaborative modeling framework consisting of an iTransformer main branch and a DLinear auxiliary branch is designed to jointly learn global nonlinear features and local linear trends. An adaptive gated fusion mechanism is further introduced to dynamically allocate the contribution weights of different branches according to sequential characteristics. Experiments were conducted on a public 1 Hz smoke-sensing dataset, which was used as a proxy benchmark for high-frequency multivariate PM2.5 forecasting rather than direct industrial dust data. Under the setting of a 300-step input length and a 60-step forecasting horizon, the proposed model achieves an MSE of 1.8292 × 10⁻³, an MAE of 0.0334, an RMSE of 0.0428, an MAPE of 0.0177, and an R² of 0.9744, outperforming the compared baseline models in overall performance. The results indicate that the proposed method improves overall forecasting accuracy and provides a methodological reference for sensor-driven particulate concentration forecasting and early warning, while further validation using field data from actual powder and granular material processing workshops is still required before practical deployment. Full article

High-ammonium poultry digestate from thermophilic dry anaerobic digestion is often recycled, but excessive ammonia accumulation may inhibit anaerobic digestion and reduce process stability. This study developed a gas-driven ammonia stripping process coupled with gypsum-mediated absorption for digestate deammonification, nitrogen recovery, and CO₂-assisted carbonate formation. Laboratory stripping experiments were conducted using simulated biogas to evaluate the effects of pH, temperature, and gas–liquid ratio. Under the selected condition of pH 11, 65 °C, and a gas–liquid ratio of 2, NH₄⁺-N in 10 L digestate decreased from approximately 7980 to 1648 mg L⁻¹ within 12 h, corresponding to about 80% removal. In the absorption step, the slightly soluble CaSO₄ solution showed more stable NH₃ capture than the CaSO₄ suspension, and the corrected NH₃-N recovery reached approximately 90–95%. XRD, SEM-EDS, precipitate mass estimation, and gas-phase CO₂ variation supported the formation of CaCO₃-containing precipitates. Pilot-scale operation using real biogas further reduced NH₄⁺-N from approximately 8000 to 700–800 mg L⁻¹ during 36 h of extended pilot-scale operation. Overall, the coupled process provides a preliminary resource-recovery route integrating ammonia burden reduction, nitrogen recovery, sulfate transfer, and CO₂-assisted carbonate precipitation. However, full-scale sustainability still requires further long-term operation, complete nitrogen–carbon–calcium–sulfur mass balances, complete heat and energy-balance assessment, product-quality evaluation, and techno-economic or life-cycle assessment. Full article

Gallium (Ga)-based liquid metal (LM) composites, particularly those based on eutectic gallium–indium (EGaIn) and related alloys, have emerged as a promising materials platform for soft and deformable energy storage owing to their unique combination of metallic conductivity, fluidic deformability, and adaptive interfaces. Despite rapid advances in LM-enabled devices, binders remain insufficiently understood and are still commonly regarded as passive structural components. Here, we present a comprehensive binder-centered perspective for LM composites, establishing the binder as a key regulator of electro-chemo-mechanical coupling, interfacial stability, transport behavior, and processability in soft energy systems. We show that tailored binder chemistries in Ga-based LM systems—including stretchable batteries, printable conductors, and soft electrochemical devices—govern LM droplet dispersion, suppress coalescence and leakage, and preserve conductive percolation under large deformation, while enabling room-temperature fabrication and printability through rheological regulation and interfacial wetting. Beyond mechanical confinement, emerging binder functionalities—including dynamic bonding, supramolecular interactions, ionically conductive networks, and reversible polymer architectures—enable self-healing interfaces, adaptive transport pathways, and robust adhesion in deformable devices. By integrating recent advances in stretchable batteries, flexible supercapacitors, printable electronics, and multifunctional soft energy systems, we establish a unified multiscale framework linking binder molecular design to device-level electrochemical and mechanical performance. We further discuss sustainability and manufacturing considerations, including recyclable polymer networks, low-temperature fabrication, and scalable processing strategies. Finally, we outline current challenges and future opportunities toward programmable binder systems with tunable viscoelasticity, interfacial reactivity, and adaptive functionality. This Review establishes binder-centered engineering as a key pathway for transforming LM composites from proof-of-concept materials into resilient, manufacturable, and multifunctional soft energy technologies for wearable, stretchable, and biointegrated electronics. Full article

Offshore geothermal energy and the reuse of decommissioned oil and gas platforms are emerging as linked pathways for reducing the carbon intensity of marine energy supply while extending the value of mature offshore assets. This review examines offshore geothermal development from a full-chain perspective that connects resource assessment, platform and wellbore reuse, heat extraction, medium- and low-temperature conversion, multi-energy coupling, techno-economic evaluation and environmental risk management. The paper first clarifies the resource logic of offshore geothermal systems, especially sedimentary-basin resources that spatially overlap with mature petroleum provinces. It then analyzes two principal engineering routes: the reuse of existing offshore platforms as energy hubs and the reutilization of abandoned wells as open-loop or closed-loop heat-extraction systems. The review finds that platform and wellbore reuse can reduce drilling demand, shorten offshore construction cycles and lower life-cycle environmental burdens, but engineering feasibility remains constrained by wellbore integrity, thermal losses, corrosion and scaling, platform life extension, regulatory liability and the limited availability of field-scale demonstration data. Coupling geothermal energy with offshore wind power, hydrogen production, OTEC and desalination can improve system stability and equipment utilization; however, standardized assessment boundaries and comparable cost models are still insufficient. Future research should focus on resource-engineering-economic integrated assessment, standardized reuse packages, long-term offshore reliability databases, corrosion-resistant material systems, auditable TEA/LCA models and risk-based regulatory frameworks. This review provides a technical basis for offshore geothermal pilot projects and for the low-carbon transformation of offshore oil and gas infrastructure. Full article

With the increasing number of stroke patients, there is a growing demand for lower-limb rehabilitation exoskeletons. While current mechanisms are preferred for their light weight and dexterous design in limited environments, the alignment of the structures and motion are still not matched perfectly to human movements. This study develops a novel structure and configuration optimization method for knee part rehabilitation with special passive self-alignment modules. The driving segment is mechanically coupled to the patients’ lower limb. All components are designed with high rigidity and fully constrained to ensure smooth and continuous motion. Then, the kinematics are systematically derived to establish the foundation for the control system. Next, the application of the particle swarm optimization algorithm determines the optimal parameters for each revolute joint during the bending motion, and reduces the non-ideal S-shaped motion deformation curve caused by the offset of the joint rotation center and the load at the end effector successfully. The final results demonstrate that the optimized SRE achieves 97.5% motion accuracy under large-angle knee movement. This work presents simulation-only validation, and clinical testing remains future work. The proposed mechanism provides a promising solution for post-stroke rehabilitation, and is also applicable to geriatric lower-limb weakness and orthopedic postoperative recovery. Full article

Power data terminals deployed in smart-grid environments generate massive amounts of operational data, yet the sensitive nature of these data and the existence of cross-region silos make centralized model training difficult in practice. Federated learning offers a natural alternative by enabling collaborative model optimization without transferring raw data, but its direct use in power terminal scenarios is still limited by four coupled challenges: update leakage, malicious or abnormal client behavior, constrained terminal-side resources, and severe Non-IID data heterogeneity. To address these issues, we develop SFL-PDT, a hierarchical federated learning framework tailored to power data terminals. The proposed method is built on a server–edge–terminal architecture. Within this architecture, edge nodes aggregate terminal updates from relatively homogeneous regional groups and perform local robustness screening, while the central server aggregates edge-level updates across heterogeneous regions and coordinates the privacy budget schedule for protected updates. It combines adaptive privacy-aware update perturbation, robust suppression of suspicious regional updates, terminal-oriented update compression, and similarity-guided aggregation for heterogeneous data distributions. Experiments on two representative power-system tasks, load forecasting and fault diagnosis, demonstrate that SFL-PDT achieves a superior overall balance among privacy protection, robustness, efficiency, and predictive performance. Compared with the evaluated baselines, the proposed method more effectively reduces reconstruction-related leakage under different privacy budgets, lowers leakage similarity under gradient inversion attacks, and maintains robust performance when malicious clients participate. It also converges faster and more stably under heterogeneous data partitions. In addition, SFL-PDT achieves the best overall predictive results, reaching an MAE of 0.021 for load forecasting and an accuracy of 88.2% for fault diagnosis, while reducing average terminal-side local training time from 4.3 s to 2.9 s and per-round upload volume from 4.2 MB to 1.5 MB relative to FedAvg. These results suggest that SFL-PDT is a practical solution for secure, efficient, and heterogeneity-aware collaborative learning in power data terminal environments. Full article

To address the structural complexity, limited detection sensitivity, and environmental susceptibility of the stable operating point in conventional fiber-optic current transformers for low-current detection, this study proposes a fiber-optic current transformer based on the coupling of an extrinsic Fabry–Perot interferometer (EFPI) and a lead zirconate titanate piezoelectric ceramic (PZT). In the proposed sensor, a toroidal magnetic core and an induction winding are used as the current pickup unit to convert the measured alternating current into an induced voltage. This induced voltage directly drives the PZT to generate axial displacement, causing periodic variations in the length of the air Fabry–Perot cavity formed between the fiber end face and the coated quartz diaphragm. As a result, the current signal is converted into an optical interference intensity signal. To prevent the static operating point from deviating from the optimal linear region during EFPI intensity demodulation, a DC-component-feedback-based operating point control method is proposed. By adjusting the driving voltage of the fiber Fabry–Perot tunable filter, the center wavelength of the incident narrowband demodulation light can track the optimal operating point of the interference spectrum, thereby improving the stability of the intensity demodulation process. Experimental results show that the fabricated sensor can generate a stable reflected interference spectrum and exhibits a relatively flat frequency response within the range of 0–7 kHz, indicating its potential for power-frequency current detection under the present laboratory conditions. When the measured current is 0.13 mA, the sensor can still produce a distinguishable sinusoidal output signal. When the measured current increases to 75 mA, obvious nonlinear distortion appears in the output signal, indicating that the sensor is approaching the boundary of its linear detection range. Within the linear operating region, the output peak-to-peak value shows good linearity with the measured current. The results indicate that the proposed EFPI-PZT fiber-optic current transformer has the advantages of a relatively simple structure, clear low-current response, and adjustable structural parameters, providing a reference for the miniaturized design and further development of new fiber-optic current sensors. Full article