Search Results (235)

Traditional acupoint localization methods rely heavily on manual operation, resulting in high subjectivity and limited accuracy. To improve the precision and stability of acupoint detection, this study integrates machine vision technology with in situ projection to achieve automated recognition and real-time visualization of human acupoints. First, an automatic calibration method based on image processing is proposed for back acupoints. Spinal features are extracted from the blue channel, enhanced using adaptive histogram equalization, and processed through region of interest extraction, minimum-threshold binarization, and morphological operations. Key spinal curve points are then fitted using Bézier functions. Canny edge detection is used to extract the human silhouette, locate the acromion, and derive the pixel scale of the “cun” measurement, enabling coordinate computation for 141 back acupoints. In the deep learning component, an improved YOLOv8-Pose model is developed for acupoint localization. Unlike existing methods that use local attention or the original Object Keypoint Similarity (OKS) loss, we introduce two innovations: a non-local attention module for global dependency modeling, and a novel Efficient Object Keypoint Similarity (EOKS) loss function that incorporates geometric constraints—namely, width, height, and center distance—in addition to Euclidean distance. A non-local attention mechanism is incorporated into the backbone to enhance global feature extraction, and the EOKS loss function is designed to improve spatiogeometric regression accuracy. An inference mechanism is further introduced to derive the remaining acupoints from 49 detected keypoints; experiments demonstrate that the improved model achieves 95.0% detection accuracy, outperforming the baseline by 2.62%, with an inference time of 14.5 ms. Finally, an in situ projection platform is constructed, combining camera calibration, four-point proportional scaling, and an OpenCV 4.5.4-based interactive interface. The system supports real-time translation, rotation, and scaling, enabling accurate projection of detected acupoints onto the human body. Full article

Laser powder bed fusion (L-PBF) has emerged as a core metal additive manufacturing technology for high-end sectors, including aerospace and medical device manufacturing. However, melting anomalies that occur during fabrication accumulate layer by layer, leading to degraded surface quality and impaired mechanical performance of as-built components—a critical bottleneck limiting their large-scale industrial adoption. Accurate and robust layer-wise melting quality recognition remains a challenge due to the complex surface morphologies induced by such melting anomalies. This study presents a machine learning-enabled in situ monitoring approach for layer-wise melting quality identification in L-PBF. By systematically varying laser power and scanning speed, 24 parameter combinations were designed to fabricate specimens with three distinct melting states: over-melting (OM), lack of fusion (LOF), and normal melting. A high-resolution complementary meta–oxide–semiconductor (CMOS) camera was used to capture layer-wise surface images of the specimens, and following abnormal layer filtering and manual validation, a high-quality dataset comprising 5110 layer-wise images was constructed. Two mainstream machine learning approaches were systematically evaluated and optimized for melting quality classification: a support vector machine (SVM) model leveraging handcrafted gray-level co-occurrence matrix (GLCM) texture features achieved a classification accuracy of 96.77%, while a convolutional neural network (CNN) model with end-to-end feature learning directly from raw images attained a superior accuracy of 98.14%. In terms of computational efficiency, the CNN model exhibited a faster inference speed with a per-layer inference time of just 0.036 s, nearly half that of the SVM model (0.068 s per layer). Most critically, the CNN model completely eliminated fatal cross-class misclassification between OM and LOF—an error mode common in the SVM model that would trigger erroneous process corrective actions in practical industrial applications. The findings demonstrate that image-based machine learning provides a reliable technical foundation for intelligent in situ monitoring of the L-PBF process. With its high accuracy, strong robustness, and superior computational efficiency, the CNN model can effectively support on-site operational decision-making, reduce material and time losses, and enhance process stability in industrial settings, thus exhibiting significant potential for practical engineering deployment. Full article

Nanomaterials with multiple enzyme-like activities offer significant opportunity for constructing multifunctional sensing methods. In this work, a hydrangea flower-like cerium dioxide nanomaterial (CeO₂ NF) with both peroxidase (POD)- and hydrolase-like activities, which was surface-modified by polyvinylpyrrolidone (PVP) in situ, was prepared through an oil bath method. Based on the POD-like activity of CeO₂ NFs, an “on-off” fluorescence method was established for chiral recognition of arginine (Arg) enantiomers. Meanwhile, utilizing the hydrolase-like activity of CeO₂ NFs and their synergistic interaction with alkaline phosphatase (ALP), an “on-off-on” fluorescence method was developed for the detection of p-nitrophenol (p-NP) and ALP. The sensor demonstrated excellent chiral selectivity for Arg enantiomers, with a high enantiomeric factor (ef) of up to 2.48, allowing for the quantitative detection of L-Arg in the range of 770–940 μM, with a limit of detection (LOD) of 26.00 μM. Furthermore, it exhibited high sensitivity for p-NP and ALP detection, with linear ranges of 10.0–84.3 μM and 300–2000 mU/mL, and LODs of 7.07 μM and 200 mU/mL, respectively. Through an enzyme kinetic analysis, fluorescence lifetime measurement, zeta potential analysis, and density functional theory (DFT) calculations, the underlying catalytic and chiral recognition mechanisms were proposed. Finally, the method was validated through the accurate detection of L-Arg, p-NP, and ALP in real samples (rabbit plasma, food-grade amino acid, and water samples). Full article

Electrochemical sensors have emerged as promising tools for rapid pesticide screening in food and environmental samples because they combine simple instrumentation, fast response, portability, and compatibility with disposable electrodes. This review organizes recent progress through a cross-system framework linking pesticide class, interfacial electrochemical process, and material design. Carbon materials, metal–organic frameworks and their derivatives, metal nanoparticles, metal compounds, conducting polymers, MXene-based composites, and selected emerging materials are compared in terms of enrichment capability, charge-transfer regulation, catalytic amplification, recognition-layer integration, and suitability for real-sample analysis. Emphasis is placed on issues that are often under-discussed in performance-centered surveys, including matrix interference, electrode fouling, batch-to-batch reproducibility, storage stability, scalability, and cost-effectiveness. Representative examples show that the most useful advances arise not simply from lowering the limit of detection but from improving structure–function understanding and translating interfacial design into robust analytical performance. Future work should prioritize standardized fabrication and benchmarking protocols, in situ and operando identification of active sites and interface evolution, matrix-specific antifouling validation, multiresidue and metabolite analysis, and hybrid portable devices coupled with intelligent readout. Full article

The mechanical sensitivity of energetic materials is closely linked to the stability of their microstructures; however, in situ observation of their dynamic response under external mechanical stimuli at the atomic scale remains challenging. Here, we propose a deep-learning-based intelligent analysis method for scanning tunneling microscopy (STM) images of a next-generation insensitive energetic material 3-nitro-1,2,4-triazol-5-one (NTO). We design SpecMol, a lightweight segmentation network with frequency-domain awareness, which achieves high-precision segmentation and orientation recognition of individual NTO molecules in adsorption images. Building upon this, we apply localized external forces to one-dimensional NTO nanochains via in situ STM tip manipulation and quantitatively analyze the geometric evolution of their fundamental building blocks—dimers. Experimental results reveal that, following mechanical perturbation, the relative orientation angle within the dimer (averaging approximately 14.55°) remains highly stable (CCC = 0.834), confirming the remarkable structural rigidity of NTO dimers. This study provides, for the first time, direct microscopic evidence at real-space atomic resolution for the low mechanical sensitivity of NTO, elucidating that its exceptional local structural stability originates from rigid dimeric units stabilized by an extensive hydrogen-bonding network. Our findings not only deepen the fundamental understanding of the safety performance of energetic materials but also demonstrate the powerful potential of integrating artificial intelligence with advanced characterization techniques for molecular-scale functional materials research. Full article

Hapten-based immunotherapies represent a promising strategy to enhance the immunogenicity of tumor antigens and promote antitumor immune responses. Chemical conjugation of small haptens to antigens generates novel antigenic determinants that increase immune recognition. Mechanistic studies indicate that haptenation enhances antigen uptake, dendritic cell maturation, and the activation of both cellular and humoral immunity. In preclinical models, hapten-modified antigens induce robust immune activation, tumor regression, and durable immune memory. Clinically, dinitrophenyl-modified autologous tumor cell vaccines elicit delayed-type hypersensitivity responses and clonal T-cell expansion, with evidence of clinical activity and a favorable safety profile. However, their clinical benefit remains to be confirmed in larger, randomized studies. Emerging strategies include in situ haptenation and bihaptenized or stressed hapten-modified allogeneic platforms, which aim to expand epitope diversity and enhance immune priming. Hapten-based immunotherapies offer a clinically feasible approach to converting poorly immunogenic tumors into effective immune targets. Full article

Seasonal human coronaviruses (HCoVs) continue to undergo adaptive evolution under structural and immune-mediated constraints. We investigated the molecular epidemiology and spike (S) protein structural variation of circulating coronaviruses in Riyadh, Saudi Arabia, during the 2025–2026 winter season, with particular emphasis on genotype persistence and glycosylation architecture in HCoV-OC43. Among 293 nasopharyngeal aspirates (NPAs) collected from hospitalized patients with acute respiratory illness, HCoV-OC43 was detected in 26 cases (8.87%), whereas other seasonal coronaviruses were not identified. Partial sequencing of the S gene revealed 97.84–98.23% nucleotide identity relative to the prototype strain VR-759, with amino acid substitutions distributed at discrete positions rather than within extended variable domains, indicating structural conservation. Phylogenetic reconstruction demonstrated that all Riyadh isolates clustered within genotype C, together with previously circulating local strains, supporting sustained endemic persistence and in situ evolution. In silico analysis of the S protein glycosylation landscape identified four invariant N-linked glycosylation motifs (N-X-S/T) at residues 46, 121, 134, and 190, reflecting strong structural constraints on glycan-dependent folding and antigenic configuration. A genotype-associated K68N substitution generated an additional N-glycosylation motif (68NGTD) in multiple Riyadh isolates, potentially modifying local glycan shielding without disrupting the overall glycosylation framework. The preservation of core glycosylation sites alongside selective motif acquisition suggests evolutionary fine-tuning of S surface topology rather than large-scale structural remodeling. Collectively, these findings indicate that genotype C persistence in Riyadh is accompanied by conserved S architecture and subtle glycosylation adjustments that may modulate immune recognition while maintaining structural integrity. Continued high-resolution molecular surveillance will be critical for defining the functional consequences of S microevolution in endemic HCoVs. Full article

Despite the long-standing recognition of nickel as an effective electrocatalyst for the alkaline hydrogen evolution reaction (HER), the majority of extant studies primarily focus on initial catalytic performance or short-term stability under relatively low current densities. In practical alkaline water electrolysis, however, electrodes operate continuously at elevated current densities for extended periods, where surface chemical states and electrochemical responses may evolve dynamically. A systematic understanding of such time-dependent behaviour remains limited, particularly for electrodeposited nickel under sustained operation. In this study, the long-term HER performance of electrodeposited Ni electrodes at a current density of 100 mA cm⁻² over 120 h is investigated. The objective of this study is to correlate the evolution of electrochemical performance with changes in surface chemical states during prolonged electrolysis. To this end, a combination of methods was employed, including polarization measurements, electrochemical impedance analysis, double-layer capacitance evaluation, and ex situ surface characterization. In contrast to the tendency to prioritize absolute enhancement of activity, this study places greater emphasis on the transient decline–recovery–stabilization behaviour that is observed during operation. Furthermore, it discusses the potential relationship of this behaviour with surface hydroxylation and restructuring processes. The present study utilizes a time-resolved analysis to elucidate the dynamic surface evolution of nickel electrodes under practical alkaline HER conditions, thereby underscoring the significance of evaluating catalyst durability beyond the confines of short-term measurements. The findings presented herein contribute to a more realistic assessment of nickel-based electrodes for alkaline water electrolysis applications. Full article

Introduction: Myxoid liposarcoma (MLPS) is a rare soft tissue sarcoma comprising 5–10% of adult cases, most often in the thigh. Diagnosis is challenging due to nonspecific imaging findings and resemblance to benign lesions. Case Report: A 42-year-old male presented with a painless, enlarging upper right medial thigh mass. CT and ultrasound suggested a complex solid lesion, possibly benign. Outpatient surgical excision revealed a red, gelatinous, non-encapsulated mass. Frozen section suggested a myxomatous spindle cell tumor. Final pathology confirmed MLPS FNCLCC grade 2 (intermediate grade) with DDIT3 rearrangement on fluorescence in situ hybridization (FISH). Margins were negative but close. Postoperative PET scan and Signatera MRD assay were negative for metastasis. Given the tumor’s size (>10 cm) and known radiosensitivity, adjuvant radiotherapy (60–66 Gy) was initiated. Discussion: MLPS features myxoid stroma, plexiform vasculature, and, in high-grade tumors, a round cell component. The FUS::DDIT3 fusion gene is diagnostic. While MRI offers superior soft tissue characterization, definitive diagnosis requires pathology and molecular testing. Surgical excision with negative margins remains standard, with radiotherapy recommended for large tumors or close margins to reduce recurrence. This case highlights the limitations of preoperative imaging and the value of intraoperative pathology in guiding management. Conclusions: Early recognition, accurate diagnosis, and tailored multimodal treatment are essential for MLPS. Given the potential for recurrence, late extrapulmonary metastases, long-term surveillance with imaging, and molecular assays are critical for optimizing outcomes. Full article

Real-time measurement of tea bud phenotypes via mobile devices is constrained by model lightweighting challenges, and research on non-contact measurement of tea bud phenotypes based on key points remains largely unexplored. Information on the growth posture of tea buds is an important basis for determining tea maturity grades, quality monitoring, and tea breeding. Therefore, this work develops a deep learning-enabled YOLOv8p-Tea model to estimate key point information of tea bud posture and automatically obtain three-dimensional point cloud information of tea buds by integrating depth information, thereby achieving in situ measurement of tea bud phenotypic parameters. Meanwhile, the model is trained and validated using a tea bud (one-bud-three-leaf) image dataset, and its effectiveness is demonstrated through experiments. Compared to the YOLOv8p-pose model, the model achieves a mAP50 of 98.3%, a P of 97%, and parameters of 0.72 M, with mAP50 and P improved by 1.5% and 1.9%, respectively, and the parameter count is reduced by 25%. To validate the accuracy of phenotypic extraction, the model was deployed on edge devices, and 30 tea buds with one bud and three leaves were randomly selected in a tea garden. The final in situ measurement results showed an MRE of 6.63%. Experimental findings indicate that the developed method is capable of not only effectively estimate tea bud posture but also accurately achieves in situ measurement of tea bud phenotypes, which holds potential applications for meeting the construction needs of smart tea gardens and optimizing tea breeding. Full article

Metal additive manufacturing (AM) offers unprecedented opportunities to fabricate complex, lightweight metallic components, yet its practical deployment remains fundamentally constrained by defects arising from rapid melting and solidification. Cyclic thermal transients generate cracks, pores, residual stresses, and lack-of-fusion regions, undermining mechanical performance and reliability. Ultrasonic field-assisted laser-based additive manufacturing (UF-LBAM) has emerged as a powerful approach to manipulate melt pool dynamics and suppress defect formation. Nevertheless, the governing physical mechanisms remain poorly understood, particularly under highly non-equilibrium ultrasonic excitation, where acoustic pressure oscillations, melt convection, cavitation, and solidification are intricately coupled across multiple temporal and spatial scales. Here, we provide a systematic review of X-ray based fundamental studies in UF-LBAM and the diverse applications of machine learning (ML), detailing the literature selection criteria and methodology. We highlight advances spanning synchrotron X-ray revealed physical phenomena, ML-driven real-time monitoring and defect prediction, and pathways toward industrial implementation. Critical challenges persist, including fundamental physics gaps, transferability of ML models across alloy systems, and real-time control limitations. We further identify promising directions for the field, such as physics-informed models, multimodal diagnostics, and closed-loop control, which together promise to unlock the full potential of UF-LBAM for high-performance metal component fabrication. Full article

Full-penetration laser welding (FPLW) is increasingly adopted in manufacturing pipelines, yet its industrial scalability is constrained by in-process defect formation, particularly incomplete penetration. To address this, we propose a sensor-driven framework for non-destructive monitoring and automated defect detection that uses infrared (IR) thermography as the primary in situ sensing modality and applies deep learning to the acquired thermal signals. High-speed IR camera recordings were processed to track spatter and the weld zone, yielding a time series of physically interpretable spatiotemporal features (mean spatter area, mean spatter temperature, number of spatters, and mean welding zone temperature). Defect recognition is formulated as a multi-label classification problem targeting incomplete penetration, sagging, shrinkage groove, and linear misalignment, and multiple temporal models were evaluated on the same sensor-derived feature sequences. Experimental validation on 09G2S pipeline steel demonstrates that the proposed time series pipeline based on a hybrid CNN–transformer achieves a mean Average Precision (mAP) of 0.85 while preserving near-real-time inference on a CPU. The results indicate that IR thermography-based spatter dynamics provide actionable sensing signatures for automated defect prediction and can serve as a foundation for closed-loop quality control in industrial laser pipeline welding. Full article

Casing collar locator (CCL) measurements are widely used as reliable depth markers for positioning downhole instruments in cased-hole operations, enabling accurate depth control for operations such as perforation. However, autonomous collar recognition in downhole environments remains challenging because CCL signals are often corrupted by toolstring- or casing-induced magnetic interference, while stringent size and power budgets limit the use of computationally intensive algorithms and specific operations require real-time, in situ processing. To address these constraints, we propose Collar Recognition Nets (CRNs), a family of domain-specific lightweight 1-D convolutional neural networks for collar signature recognition from streaming CCL waveforms. With depthwise separable convolutions and input pooling, CRNs optimize efficiency without sacrificing accuracy. Our most compact model achieves an F1-score of 0.972 on field data with only 1985 parameters and 8208 MACs, and deployed on an ARM Cortex-M7-based embedded system using the TensorFlow Lite for Microcontrollers (TFLM) library, the model demonstrates a throughput of 1000 inferences per second and 343.2 μs latency, confirming the feasibility of robust, autonomous, and real-time collar recognition under stringent downhole constraints. Full article

Background and Clinical Significance: Plasmablastic lymphoma (PBL) is a rare and highly aggressive B-cell neoplasm most often associated with immunodeficiency. Transformation of chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) into PBL is exceptionally uncommon, particularly in immunocompetent individuals. This paper describes a rare synchronous SLL-to-PBL transformation and summarizes current knowledge on synchronous and metachronous cases reported in the literature. Case Presentation A midle-aged immunocompetent patent presented with generalized lymphadenopathy and lumbar pain. Concurrent biopsies of an axillary lymph node and a retroperitoneal mass were obtained. Diagnostic evaluation included immunohistochemistry; fluorescent in situ hybridization (FISH); PCR-based assessment of IGH, IGK, and IGL loci; and next-generation sequencing (NGS) of IGHV to assess clonal relatedness. The patient was treated with six cycles of Dara-CHOP, followed by autologous stem cell transplantation and maintenance therapy with daratumumab and ibrutinib. The axillary node showed SLL (CD20+, CD5+, CD23+), while the retroperitoneal mass demonstrated classic features of PBL (CD138+, MUM1+, MYC+, Ki-67 ~100%, CD20−). FISH detected MYC rearrangement in the PBL component. PCR and NGS confirmed identical IGHV1-69 rearrangements, establishing clonal relatedness and Richter transformation. A review of published cases shows that both synchronous and metachronous CLL/SLL-to-PBL transformations are exceedingly rare. The patient achieved partial metabolic remission after treatment and remains in sustained metabolic response 24 months after diagnosis. Conclusions: This case highlights a rare example of synchronous CLL/SLL-to-PBL transformation in an immunocompetent patient. Integration of detailed molecular diagnostics enabled early recognition and guided a personalized treatment approach incorporating CD38-targeted therapy and BTK inhibition, resulting in an excellent long-term clinical outcome. Full article

Graphene-based sensors and biosensors are attractive candidates for chemical and biological threat detection due to their high surface sensitivity, rapid transduction, and low-power operation, yet real-world deployment remains constrained by cross-sensitivity, interface instability in biosensing, and limited validation under operational conditions. This review consolidates key requirements for Chemical, Biological, Radiological, and Nuclear (CBRN) detection and proposes a structured roadmap to guide the transition from laboratory demonstrations to field-relevant sensing systems. The roadmap is explicitly modular and non-linear, integrating (i) qualitative research planning and gap analysis, (ii) computational screening via molecular docking as a hypothesis-generation tool with well-defined limitations, (iii) graphene electrode fabrication and functionalization using pulsed laser deposition (PLD) to enable tunable thickness/defect engineering and strong interface control, (iv) multiscale characterization combining laboratory methods with in situ/portable diagnostics, and (v) field-oriented performance evaluation focused on response time, stability, selectivity against industrial interferents, and false-positive/false-negative behavior. Iterative feedback loops connect all modules, enabling progressive refinement of material processing, recognition chemistry, and device architecture. By framing success in terms of technology-maturity progression and operational metrics, this roadmap provides a practical, defense-relevant framework for developing deployable graphene-based CBRN sensing platforms. Full article