Search Results (638)

The application of advanced imaging techniques, particularly computed tomography (CT), photogrammetric scanning, and three-dimensional reconstructions of body surfaces and skeletal remains, is becoming a crucial component of Forensic Anthropology. These tools enable a non-invasive and highly standardized analysis of both intact cadavers and human remains recovered from terrestrial or aquatic environments, providing reliable support in identification processes, traumatological reconstruction, and the assessment of taphonomic processes. In the context of estimating the Post-Mortem Interval (PMI) and the Post-Mortem Submersion Interval (PMSI), digital imaging allows for the objective and reproducible documentation of morphological changes associated with decomposition, saponification, skeletonization, and taphonomic patterns specific to the recovery environment. Specifically, CT enables the precise assessment of gas accumulation, transformations in residual soft tissues, and structural bone modifications, while photogrammetry and 3D reconstructions facilitate the longitudinal monitoring of transformative processes in both terrestrial and underwater contexts. These observations enhance the reliability of PMI/PMSI estimates through integrated models that combine morphometric, taphonomic, and environmental data. Beyond PMI/PMSI estimation, imaging techniques play a central role in anthropological bioprofiling, facilitating the estimation of age, sex, and stature, the analysis of dental characteristics, and the evaluation of antemortem or perimortem trauma, including damage caused by terrestrial or fauna. Three-dimensional documentation also provides a permanent, shareable archive suitable for comparative analyses, ensuring transparency and reproducibility in investigations. Although not a complete substitute for traditional autopsy or anthropological examination, imaging serves as an essential complement, particularly in cases where the integrity of remains must be preserved or where environmental conditions hinder the direct handling of osteological material. Future directions include the development of AI-based predictive models for PMI/PMSI estimation using automated analysis of post-mortem changes, greater standardization of imaging protocols for aquatic remains, and the use of digital sensors and multimodal techniques to characterize microstructural alterations not detectable by the naked eye. The integration of high-resolution imaging and advanced analytical algorithms promises to further enhance the reconstructive accuracy and interpretative capacity of Forensic Anthropology. Full article

Advanced oxidation processes using porphyrin-based heterogeneous catalysts hold promise for removing hazardous pollutants from wastewater. Their high visible-light absorption coefficients enable absorption of light from the solar spectrum. Moreover, their conjugated aromatic skeletons and intrinsic electronic properties facilitate the delocalization of photogenerated electrons during photodegradation. Delaying the recombination of photogenerated electron–hole pairs by introducing specific materials increases efficiency, as separated charges have more time to participate in redox reactions, boosting photocatalytic activities. However, applying these photocatalysts for wastewater treatment is challenging owing to facile agglomeration, deactivation, and recovery of the photocatalyst for reuse, which can significantly increase the overall cost. Therefore, new photocatalytic systems comprising porphyrin molecules must be developed. For this purpose, porphyrins can be conjugated to nanomaterials to create hybrid materials with photocatalytic efficiencies superior to those of free-standing starting porphyrins. Various transition metal oxides (TiO₂, ZnO, and Fe₃O₄) nanoparticles, main-group-element oxides (Al₂O₃ and SiO₂) nanoparticles, metal plasmons (silver nanoparticles), carbon-based platforms (graphene, graphene oxide, and g-C₃N₄), and polymer matrices have been used as nanostructured solid supports for the successful fabrication of porphyrin-conjugated hybrid materials. The conjugation of porphyrin molecules to solid supports improves the photocatalytic degradation activity in terms of visible-light conversion ability, recyclability, active porous sites, substrate mobility, separation of photogenerated charge species, recovery for reuse, and chemical stability, along with preventing the generation of secondary pollution. This review discusses the ongoing development of porphyrin-conjugated hybrid nanomaterials for the heterogeneous photocatalytic degradation of organic dyes, pharmaceutical pollutants, heavy metals, pesticides, and human care in water. Several important results and advancements in the field allow for a more efficient wastewater remediation process. Full article

We present a skeleton-based approach to baseball pitch type classification from broadcast video. We leverage Human Pose Estimation and an ST-GCN architecture, improved with a projection-gated temporal downsampler, to learn kinematic signatures of the pitcher’s body, adaptively selecting the most informative frames, enabling pitch type classification without the need for ball tracking. On the MLB-YouTube dataset, our proposed method reaches ~62% six-class accuracy, highlighting body mechanics as a practical biometric cue. Full article

Traditional flexible polyurethane (PU) foams frequently exhibit limited mechanical support and suboptimal moisture–heat regulation, which can compromise the microenvironmental comfort required for high-quality sleep. In this study, natural luffa fibers (LF) were incorporated as a microstructural modifier to simultaneously enhance the mechanical and moisture–heat regulation performance of PU foams. PU/LF composite foams with varying LF loadings were prepared via in situ polymerization, and their foaming kinetics, cellular morphology evolution, and physicochemical characteristics were systematically investigated. The results indicate that LF functions both as a reinforcing skeleton and as a heterogeneous nucleation site, thereby promoting more uniform bubble formation and controlled open-cell development. At an optimal loading of 4 wt%, the composite foam developed a highly interconnected porous architecture, leading to a 7.9% increase in tensile strength and improvements of 19.4% and 22.6% in moisture absorption and moisture dissipation rates, respectively, effectively alleviating the heat–moisture accumulation typically observed in unmodified PU foams. Ergonomic pillow prototypes fabricated from the optimized composite further exhibited enhanced pressure-relief performance, as evidenced by reduced peak cervical pressure and improved uniformity of contact-area distribution in human–pillow pressure mapping, together with an increased SAG factor, indicating improved load-bearing adaptability under physiological sleep postures. Collectively, these findings elucidate the microstructural regulatory role of biomass-derived luffa fibers within porous polymer matrices and provide a robust material basis for developing high-performance, sustainable, and ergonomically optimized sleep products. Full article

Forensic anthropology, a specialized branch of biological anthropology, plays a crucial role in the identification of human remains, particularly when conventional methods such as fingerprinting are not applicable. In Ecuador, its relevance has increased in response to challenges such as intentional deaths, forced disappearances, violence, mass fatalities, and migration-related deaths. Despite its growing importance, the field faces significant limitations, including restricted access to advanced technologies, limited training opportunities for local forensic anthropologists, and insufficient resources for research and the application of advanced methodologies for victim identification. This article examines the development and current state of forensic anthropology in Ecuador, emphasizing the urgent need for population-specific standards, the establishment of a national osteological collection, and stronger institutional support. It also highlights the contributions of bioarchaeological research and its potential to enhance forensic practices. By analyzing the challenges of identifying skeletonized human remains and other instances of human rights violations, the study underscores the necessity of advancing forensic anthropology in the country. The article further discusses how interdisciplinary efforts have contributed to forensic knowledge in Ecuador and concludes by emphasizing the importance of ethical guidelines, technological integration, and improved infrastructure to strengthen forensic anthropology as both a scientific discipline and a humanitarian tool. Full article

The oily wastewater produced by transformer oil leakage contains pollutants such as mineral oil, metal particles, aged oil and additives, which can disrupt the dissolved oxygen balance in water bodies, pollute soil and endanger human health through the food chain, causing serious environmental pollution. Effective oil–water separation technology is the key to ecological protection and resource recovery. This paper reviews the principles, influencing factors and research progress of traditional (gravity sedimentation, air flotation, adsorption, demulsification) and new (nanocomposite adsorption, metal–organic skeleton materials, superhydrophobic/superlipophilic modified films) transformer oil–water separation technologies. Traditional technologies are mostly applicable to large-particle-free oil and are difficult to adapt to complex matrix wastewater. However, the new technology has significant advantages in separation efficiency (up to over 99.5%), selectivity and cycling stability (with a performance retention rate of over 85% after 20–60 cycles), breaking through the bottlenecks of traditional methods. In the future, it is necessary to develop low-cost and efficient separation technologies, promote the research and development of intelligent responsive materials, upgrade low-carbon preparation processes and their engineering applications, support environmental protection treatment in the power industry and encourage the coupling of material innovation and processes. Full article

Gait recognition methods based on silhouette templates, such as the Gait Energy Image (GEI), achieve high accuracy under controlled conditions but often degrade when appearance varies due to viewpoint, clothing, or carried objects. In contrast, skeleton-based approaches provide interpretable motion cues but remain sensitive to pose-estimation noise. This work proposes two compact 2D skeletal descriptors—Gait Skeleton Images (GSIs)—that encode 3D joint trajectories into line-based and joint-based static templates compatible with standard 2D CNN architectures. A unified processing pipeline is introduced, including skeletal topology normalization, rigid view alignment, orthographic projection, and pixel-level rendering. Core design factors are analyzed on the GRIDDS dataset, where depth-based 3D coordinates provide stable ground truth for evaluating structural choices and rendering parameters. An extensive evaluation is then conducted on the widely used CASIA-B dataset, using 3D coordinates estimated via human pose estimation, to assess robustness under viewpoint, clothing, and carrying covariates. Results show that although GEIs achieve the highest same-view accuracy, GSI variants exhibit reduced degradation under appearance changes and demonstrate greater stability under severe cross-view conditions. These findings indicate that compact skeletal templates can complement appearance-based descriptors and may benefit further from continued advances in 3D human pose estimation. Full article

Three-dimensional human pose estimation (3D HPE) aims to recover the three-dimensional coordinates of human joints from 2D images or videos to achieve precise quantification of human movement. In 3D HPE tasks based on multi-class complex human action datasets, the performance of existing Graph Convolutional Network (GCN) and Transformer fusion models is constrained by the fixed physical connections of the skeleton, which impedes the modeling of cross-joint long-range semantic dependencies and hinders further performance gains. To address this issue, this study proposes a semantic prior-based non-Euclidean topology enhancement method for multi-class complex human actions, built upon a GCN–Transformer fusion model. The proposed method retains the original physical connections while introducing semantic prior edges; by constructing a hybrid topology structure, it explicitly models long-range semantic dependencies between non-adjacent joints, thereby facilitating the extraction of cross-joint semantic information. Experimental results on the Human3.6M and HumanEva-I datasets surpass those of SOTA baseline models. On the Human3.6M dataset, MPJPE and P-MPJPE are reduced by 1.25% and 0.63%, respectively. For the Walk and Jog actions on the HumanEva-I dataset, MPJPE is reduced by approximately 6.5%. These results demonstrate that the proposed method offers significant advantages for 3D HPE tasks based on multi-class complex human action data. Full article

Background: Estimating the postmortem interval (PMI) is complicated by extrinsic environmental and intrinsic individual factors. Methods: Improved accuracy may be achieved through a better understanding of desiccation. This study examines moisture loss and desiccation in human remains in western North Carolina, validating previous research in central Texas. Ten donated individuals were placed across three seasonal trials at Western Carolina University’s Forensic Osteology Research Station (FOREST). Soft tissue moisture measurements were recorded from 20 locations on the body using a Delmhorst RDM-3^TM meter, and environmental data were recorded on-site. Results: Moisture content declined rapidly until ~500 accumulated degree days (ADD), after which patterns became highly variable. Linear mixed-effects models identified temperature as the strongest predictor of moisture loss, particularly in spring and fall, while precipitation was the most influential in summer, coinciding with rapid skeletonization. Compared to central Texas, western North Carolina exhibited less consistent moisture loss patterns and greater environmental variability. Fixed effects explained 36–63% of moisture variation across body regions, with conditional R² values modestly higher when accounting for individual differences. Conclusions: These findings highlight the importance of region-specific research for PMI estimation. Full article

The graph convolutional network (GCN) has become a mainstream technology in skeleton-based action recognition since it was first applied to this field. However, previous studies often overlooked the pivotal role of heuristic model initialization in the extraction of spatial features, impeding the model from achieving its optimal performance. To address this issue, a lightweight initialization-enhanced adaptive graph convolutional network (LI-AGCN) is proposed, which effectively captures spatiotemporal features while maintaining low computational complexity. LI-AGCN employs three coordinate-based input branches (CIB) to dynamically adjust graph structures, which facilitates the extraction of informative spatial features. In addition, the model incorporates a lightweight and multi-scale temporal module to extract temporal feature, and employs an attention module that considers the temporal, spatial, and channel dimensions simultaneously to enhance key features. Finally, the performance of our proposed model is evaluated on three large-scale public datasets: NTU RGB+D, NTU RGB+D 120, and UAV-Human. The experimental results demonstrate that the LI-AGCN achieves excellent comprehensive performances on these datasets, especially obtaining 90.03% accuracy on the cross-subject benchmark of the NTU RGB+D dataset with only 0.18 million parameters, showcasing the effectiveness of the model. Full article

Presently, with rapid industrialization progressing, massive toxic pollutants have been discharged into nature, causing serious threats to ecosystems and human health. Thus, exploiting advanced functional materials for mitigating environmental challenges is vital. Among them, polyaniline (PANI)-based composites have gained great research attention because of their excellent mass–charge transfer ability, tunable morphology, rich N-containing functional groups, and high structural tunability. Herein, this review systematically summarizes the design principles, composite construction strategies, and adsorption applications of PANI-based composites. Key design principles, including micro-support skeleton construction, conductive skeleton introduction, and selective active site anchoring, are proposed. These principles aim to address defects of single components and realize the improvement of the properties, selectivity, and stability of PANI-based composites. Subsequently, multiple advanced PANI-based composites are further analyzed. Eventually, their applications in electrochemical (e.g., electrosorption) and non-electrochemical adsorption (e.g., typical adsorption) are comprehensively assessed. Overall, this review seeks to deliver valuable insights into the in-depth study of advanced PANI-based composites for effective pollutant remediation. Full article

Human African Trypanosomiasis (HAT; sleeping sickness) and Malaria are life-threatening protozoan infections in tropical regions, with limited treatment options. As part of our ongoing efforts to discover new aminosteroid alkaloids from the Buxaceae family with antiprotozoal activity, which might serve as leads to new drugs against these infections, we investigated the dichloromethane extract from the leaves of Buxus obtusifolia (Mildbr.) Hutch. collected in Kenya, a species native to Kenya and Tanzania. To the best of our knowledge, and based on the most recent comprehensive literature review, this study represents the first phytochemical investigation of this plant. The alkaloid-enriched fraction yielded a total of 24 aminosteroid alkaloids, including 18 hitherto undescribed compounds (2, 3, 5–9, 11, 12, 15–19, and 21–24), along with six known compounds, two of which (1 and 4) are described as constituents of a natural source for the first time. Obtusiaminocyclin (24) represents the first Buxus alkaloid with a novel carbocyclic steroid skeleton with a cyclopropane ring comprising C-9, C-19 and C-11 accompanied by an unprecedented amino bridge between C-3 and C-10. The structures of the isolated compounds were determined using UHPLC/+ESI-QqTOF-MS/MS and NMR spectroscopy. The total crude extract, the alkaloid-enriched fraction, CPC subfractions and all isolated compounds were tested for in vitro antiprotozoal activity against Trypanosoma brucei rhodesiense (Tbr, responsible for East African HAT) and Plasmodium falciparum (Pf, responsible for tropical Malaria) as well as cytotoxicity against mammalian cells (L6 cell line). Deoxycyclovirobuxeine-B (12) (IC₅₀ = 0.8 µmol/L, SI = 108) and 29-trimethoxybenzoyloxy-obtusibuxoline (5) (IC₅₀ = 0.5 µmol/L, SI = 11) showed the highest activities with good selectivity indices against Tbr and Pf, respectively. Consequently, our findings provide valuable aminosteroid candidates that can serve as promising leads in our ongoing search for new drugs against HAT and Malaria. Full article

Forensic identification of individuals faces significant challenges, particularly when conventional biometric features such as the face are hidden. This paper examines the historical development and revival of body patterns (anthropometric rig) as biometric comparison feature, from historical Bertillonage to modern, computer-assisted methods such as digital anthropometric rig matching and the connection to 3D human pose estimation (HPE). It highlights both the mathematical and methodological foundations of this revival, as well as the potential and limitations of applying artificial intelligence (AI) in the context of digital anthropometric rig matching. The aim is to highlight the development of potential and challenges for the forensic validity of the person-specific digital skeleton. This clearly shows the time required for manual work, which underlines the need for automation. The time required can be reduced by approaches that use AI. However, these methods are often not yet up to the requirements in a forensic context. Full article

Sitting posture recognition, defined as automatically localizing and categorizing seated human postures, has become essential for large-scale ergonomics assessment and longitudinal health-risk monitoring in classrooms and offices. However, in real-world multi-person scenes, pervasive occlusions and overlaps induce keypoint misalignment, causing global-attention backbones to fail to localize critical local structures. Moreover, annotation scarcity makes small-sample training commonplace, leaving models insufficiently robust to misalignment perturbations and thereby limiting cross-domain generalization. To address these challenges, we propose LAViTSPose, a lightweight cascaded framework for sitting posture recognition. Concretely, a YOLOR-based detector trained with a Range-aware IoU (RaIoU) loss yields tight person crops under partial visibility; ESBody suppresses cross-person leakage and estimates occlusion/head-orientation cues; a compact ViT head (MLiT) with Spatial Displacement Contact (SDC) and a learnable temperature (LT) mechanism performs skeleton-only classification with a local structural-consistency regularizer. From an information-theoretic perspective, our design enhances discriminative feature compactness and reduces structural entropy under occlusion and annotation scarcity. We conducted a systematic evaluation on the USSP dataset, and the results show that LAViTSPose outperforms existing methods on both sitting posture classification and face-orientation recognition while meeting real-time inference requirements. Full article

In intelligent sports education, current action quality assessment (AQA) methods face significant limitations: regression-based methods are heavily dependent on high-quality annotated data, while unsupervised methods lack sufficient accuracy and degrade performance when handling long-duration sequences. To address these challenges, this paper introduces a novel indirect scoring method integrating action anomaly detection with a Quick Action Quality Assessment (QAQA) algorithm. In this method, the proposed anomaly detection module dynamically adjusts action quality scores by identifying and analyzing acceleration outliers between frames, effectively improving the robustness and accuracy of sports AQA. Moreover, the QAQA algorithm utilizes a multi-resolution approach, including coarsening, projection, and refinement, to significantly reduce computational complexity to $O\left(n\right)$, alleviating the computational burden typically associated with long sequence analyses. Experimental results demonstrate that our method outperforms traditional methods in execution efficiency and scoring accuracy. The proposed system improves algorithmic performance and effectively contributes to intelligent sports training and education. Full article