Search Results (1,208)

Achieving reliable and real-time inverse kinematics (IK) for 6-degree-of-freedom (6-DoF) spherical-wrist manipulators remains a significant challenge. Analytical formulations often struggle with complex geometries and modeling errors, and standard numerical solvers (e.g., Levenberg–Marquardt) can stall near singularities or converge slowly. Purely data-driven approaches may require large networks and struggle with extrapolation. In this paper, we propose a low-latency, polynomial-based IK solution for spherical-wrist robots. The method leverages spherical coordinates and low-degree polynomial fits for the first three (positional) joints, coupled with a closed-form analytical solver for the final three (wrist) joints. An iterative partial-derivative refinement adjusts the polynomial-based angle estimates using spherical-coordinate errors, ensuring near-zero final error without requiring a full Jacobian matrix. The method systematically enumerates up to eight valid IK solutions per target pose. Our experiments against Levenberg–Marquardt, damped least-squares, and an fmincon baseline show an approximate 8.1× speedup over fmincon while retaining higher accuracy and multi-branch coverage. Future extensions include enhancing robustness through uncertainty propagation, adapting the approach to non-spherical wrists, and developing criteria-based automatic solution-branch selection. Full article

Sea fog is a major meteorological hazard that severely disrupts maritime transportation and economic activities in the South China Sea. As China’s next-generation geostationary meteorological satellite, Fengyun-4B (FY-4B) supplies continuous observations that are well suited for sea fog monitoring, yet a satellite-specific recognition method has been lacking. A key obstacle is the radiometric inconsistency between the Advanced Geostationary Radiation Imager (AGRI) sensors on FY-4A and FY-4B, compounded by the cessation of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) observations, which prevents direct transfer of fog labels. To address these challenges and fill this research gap, we propose a machine learning framework that integrates cross-satellite radiometric recalibration and physical mechanism constraints for robust daytime sea fog detection. First, we innovatively apply a radiation recalibration transfer technique based on the radiative transfer model to normalize FY-4A/B radiances and, together with Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud/fog classification products and ERA5 reanalysis, construct a highly consistent joint training set of FY-4A/B for the winter-spring seasons since 2019. Secondly, to enhance the model’s physical performance, we incorporate key physical parameters related to the sea fog formation process (such as temperature inversion, near-surface humidity, and wind field characteristics) as physical constraints, and combine them with multispectral channel sensitivity and the brightness temperature (BT) standard deviation that characterizes texture smoothness, resulting in an optimized 13-dimensional feature matrix. Using this, we optimize the sea fog recognition model parameters of decision tree (DT), random forest (RF), and support vector machine (SVM) with grid search and particle swarm optimization (PSO) algorithms. The validation results show that the RF model outperforms others with the highest overall classification accuracy (0.91) and probability of detection (POD, 0.81) that surpasses prior FY-4A-based work for the South China Sea (POD 0.71–0.76). More importantly, this study demonstrates that the proposed FY-4B framework provides reliable technical support for operational, continuous sea fog monitoring over the South China Sea. Full article

Objective: We analyzed nanoparticle regulation research to examine the evolution of regulatory frameworks, identify major thematic structures, and evaluate current challenges in the governance of rapidly advancing nanotechnologies. By drawing parallels with the historical development of radiation regulation, the study aimed to contextualize emerging regulatory strategies and derive lessons for future governance. Methods: A total of 9095 PubMed-indexed articles published between January 2015 and October 2025 were analyzed using text mining, keyword frequency analysis, and topic modeling. Preprocessed titles and abstracts were transformed into a TF-IDF (Term Frequency–Inverse Document Frequency) document–term matrix, and NMF (Non-negative Matrix Factorization) was applied to extract semantically coherent topics. Candidate topic numbers (K = 1–12) were evaluated using UMass coherence scores and qualitative interpretability criteria to determine the optimal topic structure. Results: Six major research topics were identified, spanning energy and sensor applications, metal oxide toxicity, antibacterial silver nanoparticles, cancer nano-therapy, and nanoparticle-enabled drug and mRNA delivery. Publication output increased markedly after 2019 with interdisciplinary journals driving much of the growth. Regulatory considerations were increasingly embedded within experimental and biomedical research, particularly in safety assessment and environmental impact analyses. Conclusions: Nanoparticle regulation matured into a dynamic multidisciplinary field. Regulatory efforts should prioritize adaptive, data-informed, and internationally harmonized frameworks that support innovation while ensuring human and environmental safety. These findings provide a data-driven overview of how regulatory thinking was evolved alongside scientific development and highlight areas where future governance efforts were most urgently needed. Full article

In this study, we aimed to develop a high-performance, anti-fouling ultrafiltration membrane by integrating photocatalytic and Fenton-like functions into a polymer matrix, in order to address the critical challenge of membrane fouling and achieve simultaneous separation and degradation of organic pollutants. To this end, a novel Fe-V_O-TiO₂-embedded polyethersulfone (PES) composite membrane was designed and fabricated using a facile phase inversion method. The key innovation lies in the incorporation of Fe-V_O-TiO₂ nanoparticles containing abundant bulk-phase single-electron-trapped oxygen vacancies, which not only modulate membrane morphology and hydrophilicity but also enable sustained generation of reactive oxygen species for the pollutant degradation under light irradiation and H₂O₂. The optimized Fe-V_O-TiO₂-PES-0.04 membrane exhibited a significantly enhanced pure water flux of 222.6 L·m⁻²·h⁻¹ (2.2 times higher than the pure PES membrane) while maintaining a high bovine serum albumin (BSA) retention of 93% and an improved hydrophilic surface. More importantly, the membrane demonstrated efficient and stable synergistic Photocatalytic-Fenton activity, achieving 82% degradation of norfloxacin (NOR) and retaining 75% efficiency after eight consecutive cycles. A key finding is the membrane’s Photocatalytic-Fenton-assisted self-cleaning capability, with an 80% flux recovery after methylene blue (MB) fouling, which was attributed to in situ reactive oxygen species (·OH) generation (verified by ESR). This work provides a feasible strategy for designing multifunctional membranes with enhanced antifouling performance and extended service life through built-in catalytic self-cleaning. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

This study systematically investigates the kinematic characteristics and static stability of a spatial under-constrained four-cable-driven parallel mechanism, specifically designed for supporting aircraft models in wind tunnel tests. Addressing the inherent strong coupling between kinematics and statics in such systems, an integrated solution framework is proposed. Firstly, a hybrid intelligent algorithm integrating genetic algorithm, chaos optimization, and particle swarm optimization is introduced to efficiently solve the direct and inverse geometric-statics problems, ensuring the identification of physically feasible equilibrium configurations under constraints such as cable tension limits and mechanical interference. Subsequently, a stability evaluation method based on the eigenvalue analysis of the system’s total stiffness matrix is employed, establishing a criterion (minimum eigenvalue λ_min > 0) to identify statically stable equilibrium points. Finally, the static feasible workspace and the static stable workspace are systematically analyzed and quantified, providing practical operational limits for mechanism design and trajectory planning. The effectiveness of the proposed solution framework is validated through numerical computations, simulations, and experimental tests, demonstrating its superiority over benchmark methods. This study provides theoretical support for the design, analysis, and control of under-constrained four-cable-driven parallel mechanisms. Full article

Many data processing applications involve binary matrices for storing digital information. At present, there are limited results in the literature about algorithms for inverting large binary matrices. This paper contributes the following three results. First, the divide-and-conquer methods for efficiently inverting large matrices over finite fields such as Strassen’s matrix inversion often fail on singular sub-blocks, even if the original matrix is non-singular. It is proposed to combine Strassen’s method with the PLU factorization at each recursive step in order to obtain robust pivoting, which correctly inverts all non-singular matrices over any finite field. The resulting algorithm is shown to maintain the sub-cubic time complexity. Second, although there are theoretical studies on how to systematically enumerate all invertible matrices over finite fields without redundancy, no practical algorithm has been reported in the literature that is easy to understand and also suitable for enumerating large matrices. The use of Bruhat decomposition has been proposed to enumerate all invertible matrices. It leverages the linear group-theoretic structure and defines an ordered sequence of invertible matrices, so that each matrix is generated exactly once. Third, large binary matrices have about 29% probability to be invertible. In some applications, it may be desirable to repair the singular matrices by performing a small number of bit-flips. It is shown that the minimum number of bit-flips is equal to the matrix rank deficiency, i.e., the minimum Hamming distance from the general linear group. The required bit-flips are identified by pivoting during the matrix inversion, so the matrix rank can be restored. The correctness and the time complexity of the proposed algorithms were verified both theoretically and empirically. The reference implementation of these algorithms in C++ is available on Github. Full article

The simultaneous measurement of strain and temperature using Fiber Bragg Grating (FBG) sensors presents a significant challenge due to the intrinsic cross-sensitivity of the Bragg wavelength. While recent studies have increasingly employed “black-box” machine learning algorithms to address this ambiguity, such approaches often overlook the physical limitations of the sensor’s spectral response. This paper challenges the assumption that advanced algorithms alone can compensate for data that is physically ambiguous. We propose a “Sensor-Algorithm Co-Design” methodology, demonstrating that robust discrimination is achievable only when the sensor architecture exhibits a unique, orthogonal physical signature. Using a rigorous Transfer Matrix Method (TMM) and 4 × 4 polarization analysis, we evaluate three distinct architectures. Quantitative analysis reveals that a standard Quadratically Chirped FBG (QC-FBG) functions as an “ill-conditioned baseline” failing to distinguish measurands due to feature space collapse ($K_{cond}>4600$). Conversely, we validate two robust co-designs: (1) An Amplitude-Modulated Superstructure FBG (S-FBG) paired with an Artificial Neural Network (ANN), utilizing thermally induced duty-cycle variations to achieve high accuracy ($~3.4 °\mathrm{C}$ error) under noise; and (2) A Polarization-Diverse Inverse-Gaussian FBG (IG-FBG) paired with a 4 × 4 K-matrix, exploiting strain-induced birefringence ($K_{cond}\approx 64$). Furthermore, we address the data scarcity issue in AI-driven sensing by introducing a Physics-Informed Neural Network (PINN) strategy. By embedding TMM physics directly into the loss function, the PINN improves data efficiency by 2.2× compared to standard models, effectively bridging the gap between physical modeling and data-driven inference, addressing the critical data scarcity bottleneck identified in recent optical sensing roadmaps. Full article

This study introduces a novel generalization: the generalized geometric Frank matrix, which extends the classical Frank matrix and its known variants. We systematically examine its algebraic structure, providing detailed analyses of its factorizations, determinant, inverse, and various norm computations. Furthermore, we investigate the reciprocal form of the reciprocal generalized geometric Frank matrix and reveal a variety of its intriguing algebraic properties. To illustrate the applicability of our theoretical results, we present a compelling example using Fibonacci number entries within the Frank matrix framework. Additionally, we analyze how the spread’s upper bounds are influenced by variations in the parameter r and the matrix dimension. Also, to formally assess the computational implications of these structural choices, we use Big O notation to describe how the computational cost scales with the matrix size $n$ and the iteration count $k(r)$. Our findings demonstrate that selecting $r<1$ and utilizing lower-dimensional generalized geometric Frank matrices can yield tighter bounds and significantly reduce computational complexity. These results highlight the potential of the proposed matrix class for optimization problems where efficiency is critical. Full article

In the context of global climate change and intensified water resource constraints, studying the evolution of the urban–agricultural–ecological spatial structure and the water–heat–vegetation responses driven by large-scale irrigation and drainage projects in arid and semi-arid regions is of great significance. Based on multitemporal remote sensing data from 1985 to 2015, this study takes the Inner Mongolia Hetao Plain as the research area, constructs a “multifunctionality–dynamic evolution” dual-principle classification system for urban–agricultural–ecological space, and adopts the technical process of “separate interpretation of each single land type using the maximum likelihood algorithm followed by merging with conflict pixel resolution” to improve the classification accuracy to 90.82%. Through a land use transfer matrix, a standard deviation ellipse model, surface temperature (LST) inversion, and vegetation fractional coverage (VFC) analysis, this study systematically reveals the spatiotemporal differentiation patterns of spatial structure evolution and surface parameter responses throughout the project’s life cycle. The results show the following: (1) The spatial structure follows the path of “short-term intense disturbance–long-term stable optimization”, with agricultural space stability increasing by 4.8%, the ecological core area retention rate exceeding 90%, and urban space expanding with a shift from external encroachment to internal filling, realizing “stable grain yield with unchanged cultivated land area and improved ecological quality with controlled green space loss”. (2) The overall VFC shows a trend of “central area stable increase (annual growth rate 0.8%), eastern area fluctuating recovery (cyclic amplitude ±12%), and western area local improvement (key patches increased by 18%)”. (3) The LST-VFC relationship presents spatiotemporal misalignment, with a 0.8–1.2 °C anomalous cooling in the central region during the construction period (despite a 15% VFC decrease), driven by irrigation water thermal inertia, and a disrupted linear correlation after completion due to crop phenology changes and plastic film mulching. (4) Irrigation and drainage projects optimize water resource allocation, constructing a hub regulation model integrated with the Water–Energy–Food (WEF) Nexus, providing a replicable paradigm for ecological effect assessment of major water conservancy projects in arid regions. Full article

In this paper, a new polymeric membrane including polymers (cellulose acetate, polyethylene glycol 400), copolymer poly(4-vinylpyridine)-block-polystyrene, and TiO₂ nanoparticles were synthesized by the phase inversion method. In order to investigate the presence and the influence of the TiO₂ nanoparticles on the membrane matrix, a polymeric membrane without TiO₂ nanoparticles was prepared by the same preparation method. The structure of the polymeric membranes was characterized by several techniques, such as Fourier transform infrared spectroscopy and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy, thermogravimetric analysis, and impedance spectroscopy. Also, the water contact angle, water retention, and porosity were determined. The results showed that the TiO₂ nanoparticles were incorporated into the pores and onto the surface of the polymeric membrane, which resulted in a more uniform structure. In addition, these polymeric membranes were tested for the removal of cadmium ions from synthetic waste-water using a laboratory-scale electrochemical separation system with a custom-built setup. The results showed that the polymeric membrane with TiO₂ nanoparticles showed a high cadmium ions removal rate (95.53%), compared to the polymeric membrane without TiO₂ nanoparticles (85.29%), after a 1.5 h electrochemical separation test. The final results indicated that the polymeric membranes prepared with TiO₂ nanoparticles had excellent thermal stability and exhibited the best ionic conductivity. The electrochemical separation system proved that the obtained polymeric membranes effectively remove cadmium from the synthetic waste-water. Full article

Polysulfone (PSU) membranes are widely recognized for their thermal stability, mechanical strength, and chemical resistance, making them suitable for diverse separation applications. This review highlights recent advances in PSU membrane development, focusing on fabrication techniques, structural modifications, and emerging applications. Phase inversion remains the predominant method for membrane synthesis, allowing precise control over morphology and performance. Functional enhancements through blending, chemical grafting, and incorporation of nanomaterials—such as metal–organic frameworks (MOFs), carbon nanotubes, and zwitterionic polymers—have significantly improved gas separation, and water purification., In gas separation, PSU-based mixed matrix membranes demonstrate enhanced CO₂/CH₄ selectivity, particularly when integrated with MOFs like ZIF-7 and ZIF-8. In water treatment, PSU membranes effectively remove algal toxins and heavy metals, with surface modifications improving hydrophilicity and antifouling properties. Despite these advancements, challenges remain in optimizing cross-linking strategies and understanding structure–property relationships. This review provides a comprehensive overview of PSU membrane technologies and outlines future directions for their development in sustainable and high-performance separation systems. Full article

The paper presents a statistical model based on the two-weighted exponential distribution (TWED) to examine censored Human Immunodeficiency Virus (HIV) survival information. Identifying HIV as a disability, the study endorses an inclusive and sustainable health policy framework through some predictive findings. The TWED provides an accurate representation of the inherent hazard patterns and also improves the modelling of survival data. The parameter estimation is achieved in both a classical maximum likelihood estimation (MLE) and a Bayesian approach. Bayesian inference can be carried out under general entropy loss conditions and the symmetric squared error loss function through the Markov Chain Monte Carlo (MCMC) method. Based on the symmetric properties of the inverse of the Fisher information matrix, the asymptotic confidence intervals (ACLs) for the MLEs are constructed. Moreover, two-sided symmetric credible intervals (CRIs) of Bayesian estimates are also constructed based on the MCMC results that are based on symmetric normal proposals. The simulation studies are very important for indicating the correctness and probability of a statistical estimator. Implementing the model on actual HIV data illustrates its usefulness. Altogether, the paper supports the idea that statistics play an essential role in promoting disability-friendly and sustainable research in the field of public health in general. Full article

The accelerating industrial demand for high-speed manipulation has necessitated the development of robotic architectures that effectively balance dynamic performance with workspace size. While serial SCARA robots offer large workspaces and parallel Delta robots provide high acceleration, existing architectures fail to combine these benefits effectively for specific four-degree-of-freedom (4-DoF) Schoenflies motion tasks. This study introduces and characterizes a novel 4-DoF parallel topology, having a symmetrical build-up, which is distinguished by its use of modular 2-DoF linear drive units. The research methodology entails the structural synthesis of the kinematic chain followed by kinematic analysis using vector algebra to derive closed-form inverse geometric models. Additionally, the Jacobian matrix is formulated to evaluate velocity transmission and systematically classify singular configurations, while the dexterity index is defined to assess the rotational capabilities of the mechanism. Numerical simulations of pick-and-place trajectory were also conducted, varying trajectory curvature to analyze kinematic behavior. The results demonstrate that the proposed modular architecture yields a highly symmetric and homogeneous workspace that can be scaled simply by adjusting the drive module lengths. Furthermore, the singularity and dexterity analyses reveal a substantial, singularity-free operational workspace, although tighter trajectory curvatures were found to impose higher velocity demands on the joints. In conclusion, the proposed mechanism successfully achieves the targeted Schoenflies motion, offering a solution for automated industrial tasks. Full article

Recent studies have demonstrated that the abundance of brown adipose tissue is inversely associated with obesity in humans. Promoting the browning of white adipocytes therefore represents a promising therapeutic strategy for obesity treatment. LncRNAs are known regulators of adipocyte differentiation and metabolic processes. However, their specific roles in adipocyte browning remain poorly characterized. In this study, we performed transcriptomic analyses using publicly available RNA-seq datasets of mouse white, brown and beige adipose tissues from the EMBL-EBI database. Our analytical workflow included raw data quality control, alignment to the reference genome, transcript assembly, coding potential assessment and differential expression analysis. Functional annotation was conducted through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Key lncRNAs were further validated via Reverse Transcription Quantitative PCR (RT-qPCR). We identified 794 novel lncRNAs and 1499 DEGs, among which 95 were common across all three adipocyte types. Two lncRNAs, MSTRG.12661 and MSTRG.17758, were found to be closely related to critical biological processes, including extracellular matrix organization and fatty acid oxidation. Functional prediction suggests their potential involvement in adipocyte type-specific differentiation. In conclusion, our study reveals novel lncRNAs that may regulate adipocyte differentiation, offering new candidate targets for obesity treatment via induction of white adipose tissue browning. Full article