Search Results (319)

Timely feedback on whole-body vibration (WBV) exposure requires standardised metrics during measurement, yet many workflows record raw acceleration for offline processing, delaying feedback and increasing the data burden for bandwidth-constrained deployment. This paper presents a sensing node computing ISO 2631-1/AS 2670.1 exposure metrics on-device and transmitting metric records, not waveforms. The node combines an LIS2DH microelectromechanical systems (MEMS) accelerometer with an RP2040 microcontroller to calculate weighted root-mean-square (RMS) acceleration, daily exposure A(8), vibration dose value, maximum transient vibration value and crest factor. Algorithm-level verification showed that the implemented W_k filter reproduced the tabulated ISO 2631-1 response within 1.1% across 0.5–80 Hz. The node was compared under laboratory conditions with a CEM DT-178A datalogger whose 20 Hz recordings were reprocessed through a method-matched causal pipeline. Across ten trials per configuration under vertical excitation in the 6.3 Hz one-third-octave band, mean Z-axis A(8) differences were −4.5% (wired) and −7.9% (wireless). The datalogger’s 20 Hz sampling was included within the comparison chain, and the lower realised W_k gain was consistent with the direction and approximate magnitude of the observed offset. Metric-level telemetry reduced the sustained payload rate by up to four orders of magnitude at the summary cadence, supporting bandwidth-constrained uplinks. Full article

To reduce high 1× vibration during staged speed-up of a Turbine Power Simulator (TPS) rotor, a staged incremental on-site balancing method based on a Genetic Algorithm–Salp Swarm Algorithm (GA–SSA) is proposed. SSA is a swarm-intelligence optimizer inspired by salps, gelatinous marine organisms that move collectively in chains. A one-dimensional Timoshenko-beam rotor model with lumped disks and equivalent bearing supports is established and validated using a three-dimensional ANSYS model. From meshes M3 to M4, the equivalent speed associated with the first lateral natural frequency changes by 0.23%. The first three critical-speed errors are 6.75–8.80%, while baseline 1× vibration-amplitude errors remain below 10% and phase errors below 7.1%. Speed-specific influence coefficients are then extracted to formulate a staged incremental balancing model based on the current measured vibration and cumulative correction state. In GA–SSA, the final GA population initializes SSA, and the historical GA best is used as the initial Food. Under equal function-evaluation budgets and 30 paired runs, GA–SSA shows search performance comparable to GA and improves the stability of standalone SSA. On-site tests at 10,358, 25,558, and 38,333 rpm reduce 1× vibration at both rotor ends by 79.0–86.4%, confirming the method’s engineering applicability. Full article

Ensuring food authenticity, traceability, and quality has become a critical challenge in increasingly complex and globalized food supply chains. Conventional post-harvest analytical approaches, while powerful, often operate in isolation and fail to fully capture the influence of pre-harvest conditions on food composition. In parallel, the emergence of smart farming technologies has enabled the collection of high-resolution environmental and agronomic data, offering new opportunities to establish baseline signatures linked to geographical origin and production practices. This review explores the integration of pre-harvest data from precision agriculture with advanced post-harvest analytical techniques, focusing on spectroscopic and isotopic methods for food authentication. Recent advances in vibrational spectroscopy, including near- and mid-infrared, Fourier-transform infrared, and Raman techniques, alongside complementary methods such as nuclear magnetic resonance and fluorescence spectroscopy, have enabled rapid and non-destructive food fingerprinting. In parallel, isotope ratio mass spectrometry and compound-specific isotope analysis provide robust markers of origin, climate conditions, and agricultural inputs through the analysis of stable isotopes of carbon, hydrogen, oxygen, nitrogen, and sulfur. The combination of these analytical approaches with chemometric and machine learning tools facilitates the extraction of meaningful patterns from complex datasets. A central focus of this review is the development of integrated farm-to-fork frameworks that use multi-source data, including field sensor technologies, spectral fingerprints, and isotopic signatures, to enhance traceability and authentication. Applications across a wide range of food systems, including edible oils, beverages, plant-based products, and animal-derived foods, are critically evaluated to highlight the strengths and limitations of current methodologies. Key challenges related to data standardization, system interoperability, cost, portability, miniaturization and regulatory acceptance are discussed, alongside emerging solutions such as artificial intelligence-driven models, digital twins, and blockchain-enabled traceability systems. The review underscores a paradigm shift from reactive testing toward predictive and real-time food authentication systems, driven by the convergence of smart agriculture and advanced analytical chemistry. This integrated approach has the potential to significantly enhance transparency, trust, and sustainability in the global food system. Full article

Adaptive vibration control under variable dynamic loading requires damping materials that combine structural stability, efficient energy dissipation, and field-tunable mechanical response. Here, natural-rubber-based magnetorheological elastomers containing coumarone resin (MRE-C), naphthenic oil (MRE-N), or paraffin (MRE-P) were systematically compared through microstructural characterization, dynamic viscoelastic testing, vulcanization kinetics, and equilibrium swelling. MRE-C exhibited pronounced strain-induced softening, whereas MRE-P showed a high relative dynamic sensitivity but limited absolute stiffness. MRE-N achieved the most favorable overall balance, with more continuous anisotropic particle-chain structures and the lowest Payne-effect amplitude (37.90%). At 5 A, its storage modulus increased by 25.8%, close to the 30.9% increase in MRE-P, while maintaining substantially higher absolute G′ and G″ and a nearly unchanged tan δ. MRE-N’s macroscopic advantage was associated with more favorable network formation, with 17.9% and 7.4% lower apparent activation energies and 7.2% and 18.1% higher apparent crosslink densities than MRE-C and MRE-P, respectively. Molecular simulations of representative sulfur bridges further indicated that monosulfidic bridges favored geometric constraint and structural recovery, whereas disulfidic bridges exhibited greater conformational adaptability. These results link plasticizer-dependent vulcanization and crosslinking state with macroscopic magneto-viscoelastic performance and identify naphthenic oil as the most effective of the investigated plasticizers for balancing stiffness, energy dissipation, and magnetic responsiveness. Full article

Food safety and quality analysis is shifting from laboratory-based end-point testing toward faster, lower-volume and matrix-adapted on-site decision-making. Near-infrared (NIR), visible-near-infrared (Vis-NIR), hyperspectral, Raman, surface-enhanced Raman scattering (SERS), fluorescence, colorimetric and terahertz approaches, together with impedance time-series readout, provide complementary information on composition, molecular vibrations, spatial distribution, reaction outputs, or electrical responses. In real foods, however, lipids, proteins, sugars, salts, pigments, particles and native fluorescence can alter spectral baselines, mass transfer and model stability. The value of microfluidics is therefore not limited to miniaturization but lies in organizing filtration, homogenization, splitting, mixing, extraction, enrichment, reaction, and readout positions into a controllable sample-to-signal workflow. This review first distinguishes chemical hazards, biological hazards, authenticity issues, and quality changes according to target and matrix characteristics, and then compares the functional boundaries of continuous-flow, paper-based, droplet, digital-hybrid and enrichment-oriented chips. It further analyses how microfluidics affects detection time, sample and reagent consumption, sensitivity, selectivity, repeatability, portability and cross-matrix applicability through spectral interfaces, signal enhancement, labelled and label-free detection, chemometrics, and machine learning. Representative applications involving pesticides, mycotoxins, pathogens, antibiotics, heavy metals, adulterants, oxidation products, and freshness indicators in real foods are discussed within a unified chain linking chip architecture, spectral signal generation and decision models. Finally, requirements for translation are proposed in terms of standard and real samples, chip-to-chip variation, external model validation, data traceability and scalable manufacturing, providing an operational framework for the joint design of broad-spectrum spectroscopic technologies and microfluidic systems. Full article

Planetary sample return missions can impose extreme mechanical forces on both spacecraft and the returned samples collected from a planetary surface that they contain. The chain of custody phases from entry/descent/landing to curation each present distinct vibration, shock, and dynamic loads that may compromise sample integrity. Interplanetary sample return missions, from the Apollo program to Stardust, Hayabusa, Hayabusa2, and OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security—Regolith Explorer), have refined capsule design to ensure sample integrity. While much attention has understandably been paid to contamination control, thermal control, and mechanical shock at impact, the role of vibrational monitoring on the sample container itself is less frequently explored; yet, it merits attention. Here, we combine a review of vibration sensor aspects relevant to planetary sample return with a Monte Carlo analysis of how capsule-level vibration may propagate into representative lunar sample types. Using Apollo-derived sample container geometries and a representative multi-frequency capsule vibration environment, we show that sample archetypes can exhibit substantially different dynamic responses to the same vibration input. These results illustrate the value of vibration measurements at or near the sample container for documenting the mechanical environment experienced by returned material and interpretation of potential scientific losses during transport and curation. Full article

An acylation reaction was performed on the crystalline surface of cellulose in a composite consisting of microfibrillated cellulose (MFC) and hydroxyapatite (HAP) with an inorganic weight fraction of 68%. The composite was acylated using acetic anhydride, propanoic anhydride, and butanoic anhydride in pyridine in the presence of potassium carbonate at 120 °C for 1 h. The formation of ester linkages was confirmed by infrared spectroscopy, and X-ray diffraction analysis showed that the crystalline structure of cellulose was retained after acylation. From the intensity of the carbonyl stretching vibration in the infrared spectra, the degree of substitution of the acetylated sample was estimated to be approximately 0.2. The acylated MFC–HAP composites were uniaxially hot-pressed at 120 °C and 300 MPa, and the resulting molded specimens were subjected to three-point bending tests. A yield point appeared at a bending strain of 1.1–1.4%, followed by plastic deformation and final fracture, indicating that they exhibited ductile fracture. The elastic moduli were 5.9 GPa (acetyl), 7.6 GPa (propanoyl), 7.4 GPa (butanoyl), 3.6 GPa (hexanoyl), and 7.1 GPa (before acylation), indicating that acyl groups with medium chain lengths did not reduce the rigidity of the composites. When the molded specimens were immersed in water at room temperature for 24 h, the water absorption ratios were 29% (acetyl), 24% (propanoyl), 17% (butanoyl), and 18% (hexanoyl), demonstrating that water resistance improved with increasing acyl chain length. In summary, propanoylation and butanoylation improved the water resistance of the composites without compromising their rigidity in the dry state. Full article

Background/Objectives: Rotigotine is a conformationally flexible chiral dopamine agonist, but its solution-phase chiroptical behavior and conformational heterogeneity have not been systematically assessed. This study characterized its electronic circular dichroism (ECD), optical rotatory dispersion (ORD), and vibrational circular dichroism (VCD) responses and evaluated their consistency with its absolute configuration. Methods: ECD and ORD data were acquired for rotigotine ((S)-1) and its simplified analog (S)-2 in acetonitrile and methanol, and VCD spectra were recorded in appropriate deuterated solvents. Conformational searches, Boltzmann weighting, and density functional theory/time-dependent density functional theory calculations were performed. A principal-component-analysis/torsion (PCA-Tor) workflow selected a compact but structurally representative conformer ensemble for (S)-1. Results: In acetonitrile, (S)-1 exhibited a positive Cotton effect (CE) at 208.5 nm and a negative CE at 194.0 nm. The Boltzmann-weighted ECD spectrum reproduced the intense negative band below 200 nm and the positive band near 210 nm. The calculated VCD spectrum agreed with the signs and positions of the main experimental bands in the 1500–1100 cm⁻¹ region. Experimental ORD curves of both compounds were negative in acetonitrile and methanol. The calculations reproduced the sign and overall trend, although their magnitudes were protocol- and population-sensitive. Ring puckering strongly affected the calculated ECD and ORD data, whereas side-chain flexibility expanded the accessible conformational space. Conclusions: The combined results support the known S configuration of rotigotine and demonstrate the value of representative conformational sampling for flexible chiral pharmaceuticals. This study also defines the practical utility and limitations of simplified molecular models in chiroptical analysis. Full article

Magnetic molecular thin films provide a platform for nanoscale control of spin density, morphology and low-dimensional magnetism. We use co-deposition of closely isostructural iron phthalocyanine (FePc) and metal-free phthalocyanine (H₂Pc) onto heated substrates to prepare diluted thin films with systematically varied Fe spin densities. Structural and surface characterization shows that H₂Pc incorporation modifies film growth, producing a monotonic dependence of surface roughness on dilution and a grain size minimum for mixed FePc:H₂Pc films. Vibrating sample magnetometry reveals a nonlinear suppression of the magnetic response with increasing H₂Pc content, exceeding the reduction expected from FePc concentration alone. Below 5 K, the saturation magnetization is markedly reduced in diluted films compared with undiluted FePc, suggesting that molecular packing, Fe chain length and nanoscale morphology influence the magnetic coupling strength. These findings provide insight into FePc:H₂Pc co-deposition as a route to chemically tunable magnetic molecular nanomaterials and highlight the importance of structurally compatible molecular dilution for magnetic sensing applications. Full article

Material conveying and collection are critical to harvesting efficiency, crop quality, energy consumption, and operational continuity in combine harvesters. However, existing studies mainly focus on individual technologies, while systematic criteria for technology comparison and selection remain insufficient. This review critically analyzes major conveying and collection technologies, mechanism-based simulation methods, and intelligent sensing and control strategies. Screw, clamping-flexible, chain/vibrating, and pneumatic conveying systems are compared in terms of conveying efficiency, crop damage and material loss, energy consumption, reliability, and adaptability. DEM, dynamic/vibro-acoustic analysis, and CFD–DEM are further evaluated according to their applicable mechanisms, physical fidelity, and computational cost. Recent advances in multi-source sensing, data-driven prediction, and feedforward–feedback control are summarized. Based on these comparisons, a system-level optimization framework is proposed, emphasizing efficiency, quality preservation, and energy efficiency while maintaining operational reliability and adaptability. The review indicates that no single technology is universally optimal; technology selection should be matched to crop properties, operating conditions, and dominant performance objectives. Future research should focus on material-property-informed technology selection, mechanism–data hybrid modeling, and adaptive closed-loop control for intelligent harvesting systems. Full article

Cross-border trade security risks are often embedded in inconsistencies among trade documents, logistics trajectories, hardware sensor states, and financial settlement activities. Existing methods primarily rely on structured declaration fields, making it difficult to verify digital declarations against actual physical processes or to generate complete evidence suitable for regulatory review. To address these challenges, TradeSense-EQA is proposed as a cross-border trade security anomaly detection and evidence-grounded English question-answering framework. Multisource sensing information, including trade documents, GPS/AIS trajectories, RFID records, electronic seal events, port weighing data, temperature and humidity measurements, vibration signals, container door states, and visual images, is jointly modeled within the framework. The reliability-aware representation module dynamically adjusts sensing-channel weights according to data missingness, sampling intervals, device health states, and communication quality. The trade-process-constrained module identifies anomalies across declaration, packing, transportation, transshipment, arrival, and customs clearance stages and generates process-consistent evidence chains. The evidence-grounded question-answering module answers English trade risk questions on the basis of verified documentary fields and sensor records, while confidence estimation and abstention mechanisms are incorporated to reduce factual hallucinations. Experimental results demonstrate that TradeSense-EQA achieved an Accuracy of $0.918$, a Precision of $0.909$, a Recall of $0.897$, a Macro-F1 of $0.903$, and a ROC-AUC of $0.958$ on the cross-border trade anomaly detection task, outperforming baseline methods including XGBoost, LightGBM, TCN, Transformer, BERT, CLIP, and VisualBERT. On the English trade risk question-answering task, Exact Match, Token-level F1, BLEU, ROUGE-L, and BERTScore reached $0.782$, $0.851$, $0.668$, $0.801$, and $0.934$, respectively. Ablation results further confirmed the effectiveness of hardware sensing input, reliability-aware weighting, declaration–fact alignment, process-graph reasoning, and evidence-constrained generation. The proposed framework provides a reliable, interpretable, and auditable artificial intelligence-driven sensing solution for customs supervision, port security, international logistics review, and trade-background investigation. Full article

The study of degradation processes is of fundamental and applied interest. To understand the degradation of cellulose nitrate and to develop sensitive diagnostic methods, combined experimental and theoretical investigations are essential. Sensitive, non-destructive, and contactless methods for diagnosing the state of cellulose nitrate include IR absorption and Raman spectroscopy. While significant experimental work exists, the theoretical modeling of degradation processes, including the prediction of potential products, remains underdeveloped. Therefore, in this work, the structures and vibrational properties of molecular clusters representing segments of the cellulose nitrate chain were modeled using density functional theory (DFT). This approach yielded simulated IR and Raman spectra, allowing for the identification of peaks corresponding to the nitrate group. The elimination of this group to form a ketone was shown to alter peak contours across a broad spectral range. The most significant changes in the Raman spectra were observed at 605 and 851 cm⁻¹. Correspondingly, the key changes in the IR absorption spectra occurred at 836, 1034, 1169, 1283, and 1767–1781 cm⁻¹. The frequency trends for these diagnostic peaks across different model structures were analyzed and compared with experimental spectra from the literature. The demonstrated correlation between specific peak-frequency changes and the modeled degradation products constitutes the principal novelty of this work. Full article

To address ambiguous maneuver boundaries, background interference, and uneven multi-sensor quality in long-duration flight parameter recordings, this paper proposes an adaptive flight maneuver boundary localization algorithm that integrates spectral entropy-weighted multi-channel spectrogram fusion with attitude-constrained structural correction. Multi-channel Short-Time Fourier Transform (STFT) spectrograms are first constructed from flight parameter time series. Spectral entropy (SE) is introduced to quantify the uncertainty of each channel’s time–frequency energy distribution and is combined with the maneuver activation ratio (MAR) and the linear contrast ratio (LCR) to form objective credibility weights, thereby suppressing channels dominated by aerodynamic turbulence and high frequency structural vibration. Normal overload soft gating and logarithmic noise floor subtraction are then applied to obtain an enhanced fused spectrogram, from which candidate intervals are extracted by low band energy thresholding. Finally, roll and pitch angle steady-state priors refine the event structure through local boundary refinement, cross-segment expansion/chain merging, and semantic post-processing, recovering continuous maneuvers fragmented by instantaneous energy valleys. On the held-out test sorties (SE_018–SE_020; 61 annotated intervals), the proposed algorithm achieves Precision, Recall, and F1-scores of 0.967. On the full primary corpus of 20 sorties (461 intervals), used for ablation and sensitivity analyses, the corresponding figures are Precision 0.934, Recall 0.959, and F1 0.946, with start and end boundary mean absolute errors of 1.484 s and 1.471 s. Under the same IoU protocol, consistent superiority is observed against learning-based baselines, and an independent external set of 10 sorties yields F1 = 0.938. The results indicate that entropy-constrained multi-sensor time–frequency fusion mainly improves maneuver/background separability, whereas attitude-constrained structural correction restores the integrity of long continuous maneuvers. Full article

Deep drilling equipment operates under high-load, strong-vibration, intermittent-impact, and variable environmental conditions, which motivate sensing systems that provide low-noise acquisition, synchronized triaxial measurements, local data integrity, and quantitative measurement-chain characterization. This paper presents a Zynq UltraScale+ MPSoC-based triaxial vibration sensing station for deep drilling equipment applications. The modular station integrates conditioned-voltage triaxial accelerometer interfaces, analog signal conditioning, 24-bit simultaneous analog-to-digital conversion, electrical isolation, local solid-state-drive storage, Ethernet/wireless communication, and GPS-disciplined oven-controlled crystal oscillator (OCXO) timing. The programmable logic performs deterministic acquisition, GPS pulse processing, oscillator calibration, and DMA transfer, while the processing system facilitates storage, network communication, device-state management, and host computer interaction. The sensing electronics are evaluated through zero-input noise, an experiment-specific input-amplitude-to-noise ratio, gain linearity, thermal stability, repeatability, and station-to-station local-PPS timing tests. The characterized electronics achieve a mean equivalent input noise of 0.31 microvolts, a test-derived ratio of 135.08 dB, and a mean station-to-station local-PPS falling-edge difference of 0.34 microseconds. A lightweight post-acquisition interpretation workflow using learnable multichannel weighted fusion, a convolutional autoencoder, a training-distribution-based quantile threshold, and an auxiliary classification branch achieves 0.9705 accuracy and 0.9704 F1-score on a public triaxial bearing dataset under the reported protocol. A crane-based experiment evaluates deployment feasibility and the sensing–analysis workflow using controlled operating events and a removable stationary mass disturbance. The results provide an engineering sensing basis for distributed monitoring studies on deep drilling equipment. Full article

Aircraft control surface systems are prone to resonance when the modal frequency of the transmission chain approaches the excitation frequency under varying load and operating conditions. To address this problem, this paper introduces a PEA-based preload regulation branch into the load bearing path of the transmission chain and develops a schedulable cross-layer mapping model from input voltage to modal frequency. The model follows the path of voltage, additional preload, axial equivalent stiffness, output side equivalent torsional stiffness, and modal frequency, and is used to judge whether the PEA branch has enough frequency shifting authority at a given working point. Based on this map, a resonance margin-constrained active stiffness scheduling method is established. Instead of applying a fixed or maximum voltage, the method calculates the minimum input voltage required to move the target modal frequency outside the dangerous resonance region. The numerical results show that increasing the voltage from 0 V to 150 V shifts the target modal frequency from approximately 56.4 Hz to 66.3 Hz, which indicates the available frequency regulation range of the added branch. For the studied excitation condition, the scheduler selects 64 V as the first feasible input and achieves a resonance margin of 1.003. The corresponding target mode RMS response is reduced by about 58.6%. Although the maximum voltage case gives stronger RMS attenuation, it requires larger preload and higher relative electrical energy demand. The proposed method is therefore not intended to maximize vibration attenuation under all conditions. It provides a resonance margin-based voltage selection rule that converts local PEA-based stiffness regulation into a system-level target modal frequency-scheduling mechanism for the studied transmission chain. Full article