Search Results (15,974)

A novel general approach to cyclic hydrazide–hydrazone compounds with a dihydropyridine-2-one core has been developed, involving annulation of symmetrical aldazines with 3-arylglutaconic anhydrides. This approach provides the benefits of straightforward and catalyst-free procedures, diastereoselectivity, and the ability to switch between two isomeric dihydropyridine-2-one cores based on the reaction temperature. Several post-modifications were performed on the side functional groups and the core to demonstrate the synthetic potential of the resulting products. This approach significantly expands the chemical diversity of medicinally relevant N-functionalized δ-lactams. Full article

The Digital Product Passport (DPP) is an emergent strategic tool poised to significantly enhance traceability, circularity, and sustainability within industrial supply chains, aligning with evolving European Union regulatory frameworks. This paper introduces a conceptual sensor-based DPP architecture specifically designed for the construction industry, exemplified by a real case study for a bio-based manufacturing company. This framework facilitates a transparent and accessible data management approach, crucial for fostering circular practices and guiding stakeholders in decision-making along the value chain. The proposed architecture addresses critical challenges in product-related traceability and information accessibility across the entire product life cycle, spanning from raw material supply to the construction and installation process (A1–A5 stages). Data collected from the low-tech sensor network and digital tools can generate relevant environmental indicators for Life Cycle Assessment (LCA) and DPP creation, thereby offering a comprehensive, detailed, and certified overview of product attributes and their environmental impacts. The study clarifies the benefits and current barriers to implementing a sensor-based DPP architecture in low-tech construction manufacturing, underscoring the potential of lightweight, interoperable sensing solutions to advance compliance, transparency, and digitalization in traditionally under-digitized sectors like construction materials manufacturing. Full article

Methane (CH₄) is known as the most potent greenhouse gas in the short term. With the growing urgency of mitigating climate change and monitoring CH₄ emissions, many emerging satellite systems have been launched in the past decade to observe CH₄ and other greenhouse gases from space. These satellites are either capable of pinpointing and quantifying super emitters or deriving regional emissions with a more frequent revisit time. This study aims to reconcile emissions estimated from point source satellites and those from regional mapping satellites, and to investigate the potential of integrating point-based quantification and regional-based quantification techniques. To do that, we quantified CH₄ emissions from the Permian Basin separately by applying the divergence method to the TROPOMI Level-2 data product, as well as an event-based approach using CH₄ plumes quantified by Carbon Mapper systems. The resulting annual CH₄ emissions estimates from the Permian Basin in 2024 are 1.83 ± 0.96 Tg and 1.26 [0.78, 2.02] Tg for divergence and event-based methods, respectively. The divergence-based emissions estimate shows a more comprehensive spatial distribution of emissions across the Permian Basin, whereas the event-based approach highlights the grid cells with the short-duration super-emitters. The emissions from grids with detectable emissions under both methods show strong agreement (R² ≈ 0.642). After substituting the overlap cells’ values from divergence-based emissions estimation with those from event-based estimation, the combined emissions estimate is 2.68 [1.88, 3.54] Tg, which is reconciled with Permian Basin emissions estimates from previous studies. We found that CH₄ emissions from the Permian Basin gradually reduced over the past five years. Furthermore, this case study indicates the potential for integrating estimations from both methods to generate a more comprehensive regional emissions estimate. Full article

Determining sow (Sus scrofa domesticus Erxleben) estrus status requires considerable labor investment, and continuous real-time monitoring is impractical. Workers typically identify estrus at scheduled intervals and determine artificial insemination timing based on experience. However, experience-based methods are subjective, vary with operator expertise, and impede standardized management in large-scale farms. This study employs cameras and deep learning to detect sows and analyze postural changes, enabling estrus detection and optimal insemination timing prediction. Experimental results indicate that the proposed method achieved an accuracy of 70% (42/60), where the recommended insemination timing differed by less than 24 h from human decisions. This approach facilitates data-driven estrus detection and insemination scheduling, potentially reducing labor intensity and improving reproductive outcomes, particularly beneficial for labor-intensive and large-scale swine production systems. Full article

To assist resource-based regions in overcoming the bottlenecks of industrial transformation and advancing high-quality development, this paper conducts an in-depth analysis of the internal mechanisms through which new quality productive forces contribute to high-quality development. Based on the construction of a measurement index system, a comprehensive measurement model is established, which includes three components: a coupling coordination degree model integrating the entropy method and grey relational analysis, an impact factor analysis model based on random effects Tobit regression, and a trend prediction model using the GM(1,1) approach. Taking Hebei Province as an example, an empirical analysis was conducted and relevant policy suggestions were proposed. The research findings are summarized as follows: (1) New quality productive forces promote high-quality development through driving, guiding, and synergistic mechanisms; (2) From 2013 to 2022, the coupling coordination degree across various cities in Hebei Province evolved from moderate imbalance to primary coordination, with the spatial pattern transitioning from “higher in the south and lower in the north” to a “central rise” phase, and finally to a stage of “all-round coordination”; (3) Forecast results indicate that inter-city coordination will continue to improve over the next five years; (4) Urbanization, scientific and technological innovation, and government intervention are identified as the core driving factors for promoting coordinated development. This study provides both theoretical methodological support and regional empirical evidence for the role of new quality productive forces in enabling high-quality development in resource-based regions. Full article

Maintaining the freshness of shrimp is a critical issue in quality and safety control within the food processing industry. Traditional methods often rely on destructive techniques, which are difficult to apply in online real-time monitoring. To address this challenge, this study aims to propose a non-destructive approach for shrimp freshness assessment based on imaging and deep learning, enabling efficient and reliable freshness classification. The core innovation of the method lies in constructing an improved GoogLeNet architecture. By incorporating the ELU activation function, L2 regularization, and the RMSProp optimizer, combined with a transfer learning strategy, the model effectively enhances generalization capability and stability under limited sample conditions. Evaluated on a shrimp image dataset rigorously annotated based on TVB-N reference values, the proposed model achieved an accuracy of 93% with a test loss of only 0.2. Ablation studies further confirmed the contribution of architectural and training strategy modifications to performance improvement. The results demonstrate that the method enables rapid, non-contact freshness discrimination, making it suitable for real-time sorting and quality monitoring in shrimp processing lines, and providing a feasible pathway for deployment on edge computing devices. This study offers a practical solution for intelligent non-destructive detection in aquatic products, with strong potential for engineering applications. Full article

Natural product-derived drugs represent a cornerstone of modern pharmacotherapy, with many serving as essential therapeutic agents across diverse medical conditions. Recent advances in structural biology have provided unprecedented insights into the molecular mechanisms underlying their biological activities. This review presents a comprehensive structural analysis of five representative natural product-derived drugs: digoxin, simvastatin, morphine, paclitaxel, and penicillin. Through an examination of high-resolution crystal structures and cryo-electron microscopy (cryo-EM) data, we elucidate how these compounds interact with their respective protein targets and modulate biological functions. The structural data reveal diverse binding mechanisms—ranging from competitive inhibition and covalent modification to allosteric modulation via conformational selection and induced fit—demonstrating how natural products achieve their therapeutic effects through precise molecular recognition. These structural insights provide a molecular foundation for understanding natural product pharmacology and offer valuable guidance for structure-based drug design approaches in developing next-generation therapeutics. Full article

Major depressive disorder (MDD) is the most common mood disorder among adults. Despite the wide use of pharmacological agents by those with MDD, the evidence indicates that only a small fraction of patients benefits, and many individuals using antidepressant therapy relapse. Side effects are numerous with antidepressants, which can be a factor in patient medication compliance. Along with psychotherapy and fine-tuning lifestyle components, another emerging option in treating MDD is the use of bioactive natural products known as nutraceuticals. We present the scientific findings specific to select nutraceuticals (e.g., omega-3 fatty acids, S-adenosyl-methionine, folate-based compounds, and vitamin D) either as a monotherapy or as adjunctive therapy to a pharmaceutical antidepressant, for treatment of MDD. Many studies demonstrate that nutraceuticals result in a decrease in depressive symptoms with fewer side effects as traditional medications and have the potential to improve the result of antidepressants, especially in individuals experiencing resistance to medication. From a therapeutic perspective, a holistic approach incorporating psychotherapy, pharmacological therapy, and lifestyle factors (inclusive of nutraceutical use) appears most logical and could provide for enhanced treatment efficacy. Full article

Recent decades have been characterized by increasing numbers of bone tissue injuries and diseases resulting in the formation of bone defects. The number of such bone defects has also grown due to active surgical approaches implemented after surgical interventions for oncological, infectious, and dystrophic bone lesions. To repair such bone defects requires the use of bone tissue substitutes. Nowadays, constructs based on matrices of various compositions and structures, supplemented with the addition of biologically active components (including growth factors and cells), are the most promising approaches used in bone tissue engineering. The properties of the matrices are of the utmost importance in construct formation. This work presents the results of a comprehensive study of matrices of various natures intended for the formation of complex constructs for bone tissue engineering. Using a set of methods for studying the physical, mechanical, and biological characteristics, the total and associated porosity of the studied matrices, the structure, the mechanical parameters, and the level of cytotoxicity and cytocompatibility were determined. It was shown that all the studied materials were not cytotoxic (cytotoxicity rank of all matrices = 0–1). All matrices were porous, but samples of materials of biological origin had large pores ranging in size from 100 to 1000 μm, and pores of the hybrid polymer were sized from 0.1 to 100 μm. Total and open porosity ranged from 89% and 79% for the allogeneic matrix up to 67% and 48% for the hybrid polymer, respectively, while the σ values (compressive stress at break) of samples of all studied materials were close to each other. When human test culture MSCs interact with samples of these materials, it was shown that the cells adhere to the surface and structure of all materials and retain typical morphology, while also demonstrating the ability to proliferate and migrate along the surface and into the matrix structure, i.e., all materials are cytocompatible. Based on the data obtained, it can be assumed that all the studied matrices can be used for model biomedical studies and as a basis for constructs for bone tissue engineering. An adequate choice of research method at the earliest stages of the development of each material will ensure the most effective approaches for further work and subsequent use of this product. Full article

We argue that quantitative analysis of community-based speech data furnishes an indispensable adjunct to theoretical and experimental studies targeting the acquisition of relativization. Drawing on a comparative sociolinguistic approach, we make use of three corpora of natural speech to investigate second-language (L2) speakers’ acquisition of restrictive relative clauses in English. These corpora comprise: (i) spontaneous L2 speech; (ii) a local baseline variety of the target language (TL); and (iii) L2 speakers’ first language (L1), French. These complementary datasets enable us to explore the extent to which L2 speakers reproduce the discursive frequency of relative markers, as well as their fine-grained linguistic conditioning, in the local TL baseline variety. Comparisons with French facilitate exploration of possible L1 transfer effects on L2 speakers’ production of English restrictive relative clauses. Results indicate that evidence of L1 transfer effects on L2 speakers’ restrictive relative clauses is tenuous. A pivotal finding is that L2 speakers, in the aggregate, closely approximate TL constraints on relative marker selection, although they use the subject relativizer who significantly less often than their TL counterparts. We implicate affiliation with, and integration into, the local TL community as key factors facilitating the propagation of TL vernacular norms to L2 speakers. Full article

Accurate stockpile volume estimation is essential for industries that manage bulk materials across various stages of production. Conventional ground-based methods such as walking wheels, total stations, Global Navigation Satellite Systems (GNSSs), and Terrestrial Laser Scanners (TLSs) have been widely used, but often involve significant safety risks, particularly when accessing hard-to-reach or hazardous areas. Unmanned Aerial Systems (UASs) provide a safer and more efficient alternative for surveying irregularly shaped stockpiles. This study evaluates UAS-based methods for estimating the volume of coal stockpiles at a storage facility near Cadiz, Ohio. Two sensor platforms were deployed: a Freefly Alta X quadcopter equipped with a Real-Time Kinematic (RTK) Light Detection and Ranging (LiDAR, active sensor) and a WingtraOne UAS with Post-Processed Kinematic (PPK) multispectral imaging (optical, passive sensor). Three approaches were compared: (1) LiDAR; (2) Structure-from-Motion (SfM) photogrammetry with a Digital Surface Model (DSM) and Digital Terrain Model (DTM) (SfM–DTM); and (3) an SfM-derived DSM combined with a kriging-interpolated DTM (SfM–intDTM). An automated boundary detection workflow was developed, integrating slope thresholding, Near-Infrared (NIR) spectral filtering, and Canny edge detection. Volume estimates from SfM–DTM and SfM–intDTM closely matched LiDAR-based reference estimates, with Root Mean Square Error (RMSE) values of 147.51 m³ and 146.18 m³, respectively. The SfM–intDTM approach achieved a Mean Absolute Percentage Error (MAPE) of ~2%, indicating strong agreement with LiDAR and improved accuracy compared to prior studies. A sensitivity analysis further highlighted the role of spatial resolution in volume estimation. While RMSE values remained consistent (141–162 m³) and the MAPE below 2.5% for resolutions between 0.06 m and 5 m, accuracy declined at coarser resolutions, with the MAPE rising to 11.76% at 10 m. This emphasizes the need to balance the resolution with the study objectives, geographic extent, and computational costs when selecting elevation data for volume estimation. Overall, UAS-based SfM photogrammetry combined with interpolated DTMs and automated boundary extraction offers a scalable, cost-effective, and accurate approach for stockpile volume estimation. The methodology is well-suited for both the high-precision monitoring of individual stockpiles and broader regional-scale assessments and can be readily adapted to other domains such as quarrying, agricultural storage, and forestry operations. Full article

Rehabilitation plans are based on pavement condition assessments, which are crucial to modern pavement management systems. However, some of the disadvantages of conventional approaches for road maintenance and repair include the time consumption, high costs, visual errors, seasonal limitations, and low accuracy. Continuous and efficient pavement monitoring is essential, necessitating reliable equipment that can function in a variety of weather and traffic conditions. UAVs offer a practical and eco-friendly alternative for tasks including road inspections, dam monitoring, and the production of 3D ground models and orthophotos. They are more affordable, accessible, and safe than traditional field surveys, and they reduce the environmental effects of pavement management by using less fuel and producing less greenhouse gas emissions. This study uses UAV technology in conjunction with ground control points (GCPs) to assess the kind and amount of damage in flexible pavements. Vertical photogrammetric mapping was utilized to produce 3D road models, which were then processed and analyzed using Agisoft Photoscan (Metashape Professional (64 bit)) software. The sorts of fractures, patch areas, and rut depths on pavement surfaces may be accurately identified and measured thanks to this technique. When compared to field exams, the findings demonstrated an outstanding accuracy with errors of around 3.54 mm in the rut depth, 4.44 cm² for patch and pothole areas, and a 96% accuracy rate in identifying cracked locations and crack varieties. This study demonstrates how adding GCPs may enhance the UAV image accuracy, particularly in challenging weather and traffic conditions, and promote sustainable pavement management strategies by lowering carbon emissions and resource consumption. Full article

Friction stir welding (FSW) has emerged as a solid-state joining technique offering notable advantages over traditional welding methods. Gas metal arc welding (GMAW), a fusion-based process, remains widely used due to its high efficiency, productivity, weld quality, and ease of automation. To combine the benefits of both techniques, a hybrid welding approach integrating GMAW and FSW has been developed. This study investigates the impact of this hybrid technique on the joint quality and properties of AA5083-H111 and AA6082-T6 aluminum alloys. Butt joints were produced on 6 mm thick plates, with variations in friction process parameters. Characterization included macro- and microstructural analyses, mechanical testing (hardness and tensile strength), and corrosion resistance evaluation through stress corrosion cracking tests. Results showed that FSW significantly refined and homogenized the microstructure in both alloys. AA5083-H111 welds achieved a joint efficiency of 99%, while AA6082-T6 reached 66.7%, differences attributed to their distinct strengthening mechanisms and the thermal–mechanical effects of FSW. To assess hydrogen-related behavior, slow strain rate tensile (SSRT) tests were conducted in both inert and hydrogen-rich environments. Hydrogen content was measured in arc, friction, and overlap zones, revealing variations depending on the alloy and microstructure. Despite these differences, both alloys exhibited negligible hydrogen embrittlement. In conclusion, the GMAW–FSW hybrid process successfully produced sound joints with good mechanical and corrosion resistance performance in both aluminum alloys. The findings demonstrate the potential of hybrid welding as a viable method for enhancing weld quality and performance in applications involving dissimilar aluminum alloys. Full article

In the new digital era, industrial robots are central to machining and flexible production in intelligent manufacturing. However, the rigidity of the six degrees-of-freedom (DOFs) serial robot is insufficient, which leads to chatter during machining and limits its application in high-precision machining, especially in the field of drilling, reaming and milling. A new method was proposed for modeling the global stiffness of the robot drilling system that incorporated the end-effector. Based on the virtual joint method and linear superposition principle, and considering the flexibility of the robot arm, the global stiffness model of the robot drilling system was established by simplifying the modeling process with dual quaternion. The results of the model validation experiments of deformation show that the maximum relative error of resultant end deformation is 8.80%, and the average relative error of resultant end deformation is 7.21%. This method provides a new method of global stiffness modeling for the robot drilling system, including the end-effector, and a new approach for stiffness improvement to overcoming the problem of insufficient robot stiffness in intelligent manufacturing industry. Full article

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer (BC), comprising approximately 20% of newly diagnosed BC cases. The poor prognosis, high recurrence rates, and inefficacy of hormone-based therapies make TNBC one of the greatest challenges in contemporary oncology. The unique immunological features of TNBC, including relatively high tumor mutational burden, abundance of tumor-infiltrating lymphocytes, and elevated PD-L1 expression, offer a wide range of opportunities for immunotherapeutic approaches, of which the most progressive and promising are neoantigen-driven ones. This review examines the current landscape of neoantigen-based therapeutic approaches in TNBC treatment, spanning from discovery methodologies to clinical applications. We provide a critical analysis of the tumor microenvironment (TME) in TNBC, highlighting the balance between its immunoactivating (CD8+ T-cells, dendritic cells) and immunosuppressive (regulatory T-cells, M2 macrophages) components as the key determinant of therapeutic success, as well as reviewing the emerging approaches to TME reprogramming and recruiting in favor of better outcomes. We also present state-of the-art methods in neoantigen identification and prioritization, covering the landscape of technological platforms and prediction algorithms, addressing the existing accuracy limitations along with emerging computational solutions, and comprehensively discussing the TNBC neoantigen spectrum. Our analysis shows the strong domination of patient-specific (“private”) neoantigens over shared variants in the TNBC, with TP53 as the only gene with recurrent variants. Finally, we extensively cover neoantigen-recruiting therapeutic modalities including adoptive cell therapies, personalized vaccine platforms (peptide-based, mRNA/DNA vaccines, dendritic cell vaccines), and oncolytic viruses-based approaches. Our study of current clinical trials demonstrates the substantial gap between early proof-of-concept experiments and further applicability of neoantigen-driven therapies. The major challenges hampering the success of such methods include neoantigen prediction inaccuracy rates, high manufacturing costs, and time consumption. Promising ways to overcome these difficulties include the development of combinational strategies, TME modeling and modifying, and improvement of the therapy delivery properties, along with the optimization of production workflows and cost-effectiveness of vaccine development. Full article