Search Results (65)

The CaO-SiO₂-P₂O₅ system is one of the main systems studied aiming for the synthesis of new bioactive materials for bone regeneration. The interest in materials containing calcium-phosphate-silicate phases is determined by their biocompatibility, biodegradability, bioactivity, and osseointegration. The object of the present study is the synthesis by the sol-gel method of biocompatible glass-ceramics in the Ca₂SiO₄-Ca₃(PO₄)₂ subsystem with the composition 6Ca₂SiO₄·Ca₃(PO₄)₂ = Ca₁₅(PO₄)₂(SiO₄)₆. The phase-structural evolution of the samples was monitored using X-ray diffraction analysis (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and surface area analysis. A powder (20–30 µm) glass-ceramic material containing fine crystalline aggregates of dicalcium silicate and plates of silicon-substituted hydroxyapatite was obtained after heat treatment at 700 °C. After heat treatment at 1200 °C, Ca₁₅(PO₄)₂(SiO₄)₆, silicocarnotite Ca₅(PO₄)₂(SiO₄), and pseudowollastonite CaSiO₃ were identified by XRD, and the particle size varied between 20 and 70 µm. The compact glass-ceramic obtained at 1400 °C contained Ca₂SiO₄-Ca₃(PO₄)₂ solid solutions with an α-Ca₂SiO₄ structure as a main crystalline phase. SEM showed the specific morphology of the crystalline phases and illustrated the trend of increasing particle size depending on the synthesis temperature. Effects of the glass-ceramic materials on cell viability of HL-60-derived osteoclast-like cells and on the expression of apoptotic and osteoclast-driven marker suggested that all materials at low concentrations, above 1 µg mL⁻¹, are biocompatible, and S-1400 might have a potential application as a scaffold material for bone regeneration. Full article

Research on young trees’ adaptation to shade has predominantly focused on leaf-level responses, overlooking critical structural and functional adaptations in branch systems. In this study, we address this gap by investigating hierarchical branch morphology–physiology integration in 20-year-old Pinus koraiensis specimens across four distinct light conditions classified by photosynthetic photon flux density (PPFD): three in the understory (low light, LL: 0–25 μmol/m²/s; moderate light, ML: 25–50 μmol/m²/s; and high levels of light, HL: 50–100 μmol/m²/s) and one under full light as a control (FL: 1300–1700 μmol/m²/s). We measured branch base diameter, length, and angle as well as chlorophyll and NSCs content in branches and needles. Branch base diameter and length were more than 1.5-fold higher in the FL Korean pine trees compared to the understory-grown ones, while the branching angle and ratio in the LL Korean pine trees were more than two times greater than those in the FL trees. As light levels increased, Chlorophyll a and b and total chlorophyll (Chla, Chlb, and Chl) concentrations in the needles all significantly decreased. Starch, glucose, and NSC (Starch + Soluble Sugars) concentrations in both needles and branches were the highest in the trees under FL and lowest under ML (except for soluble sugars in branches). Understory young P. koraiensis trees morphologically and physiologically adapt to limited light conditions, growing to be more horizontal, synthesizing more chlorophyll in needles, and attempting to increase their light-foraging ability. We recommend gradually expanding growing spaces to increase light availability for 20-year-old Korean pine trees grown under canopy level. Full article

Light is a source of energy for photosynthesis and hence promotes the regulation of multiple physiological and metabolic processes in photoautotrophic organisms. Understanding how brown macrophytes adjust the physical and biochemical properties of photosynthetic membranes in response to high-irradiance environments has received little attention so far. Particularly, it concerns the lipid flexibility of thylakoid membranes. We examined the lipid classes, fatty acid (FA) profiles, chloroplast ultrastructure, and photosynthetic performance of the brown macroalga Undaria pinnatifida after long-term exposure to high light (HL) and moderate light (ML) intensities, at 400 and 270 µmol photons m⁻² s⁻¹, respectively. U. pinnatifida responded to HL with a reduction in the level of thylakoid membrane lipids, monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerol (DGDG), sulfoquinovosyldiacylglycerol (SQDG), and phosphatidylglycerol (PG), while the character of lipid modulations was specific. The content of storage lipids, triacylglycerols enriched in n-3 polyunsaturated fatty acids (PUFAs), increased under HL. The general response to long-term HL for the studied thylakoid membrane lipids, but not for SQDG, was the remodeling of FA composition towards increasing the percentages of saturated and monounsaturated acyl groups over PUFAs, suggesting a photoprotective strategy against the intensification of lipid peroxidation. In all, we showed that remodeling in photosynthetic membrane lipids accompanied by structural changes in chloroplasts and modulations in photosynthetic performance augmented the ability of U. pinnatifida to counteract high-intensity light, thereby contributing to its survival potential under suboptimal irradiance conditions. Full article

The shift to sustainable energy has accelerated the development of thermoelectric (TE) material for direct heat-to-electricity conversion without batteries or grid reliance. Cu-Ni alloys show promise for high-power, thermally stable TE applications like waste heat recovery and electronics cooling but require thermal conductivity and microstructure optimization. This study investigates additive manufacturing (AM) of Cu-Ni alloys via laser powder-directed energy deposition (L-DED), enabling precise control over deposition parameters. Track geometries were analyzed using linear mass density (M_L) and linear heat input (H_L), which influence deposition quality and microstructural characteristics. A weighted qualitative process parameter decision matrix was developed to evaluate process conditions systematically. Optimal deposition was achieved with H_L < 70 J/mm for M_L ~0.016–0.021 g/mm and 98 J/mm < H_L < 137 J/mm for M_L = 0.026 g/mm, corresponding to an energy-to-mass ratio of ~4000 ± 500 kJ/g. While this study does not directly assess thermoelectric properties, it provides essential first-layer insights into how processing conditions affect track geometry, defect formation, and microstructure—information that is foundational for optimizing multi-layer builds and, ultimately, improving thermoelectric performance. These findings mark a critical step toward predictive process optimization and the accelerated design of Cu-Ni-based thermoelectric materials using AM techniques. Full article

FPGA-RICH is an FPGA-based online partial particle identification system for the NA62 experiment employing AI techniques. Integrated between the readout of the Ring Imaging Cherenkov detector (RICH) and the low-level trigger processor (L0TP+), FPGA-RICH implements a fast pipeline to process in real-time the RICH raw hit data stream, producing trigger primitives containing elaborate physics information—e.g., the number of charged particles in a physics event—that L0TP+ can use to improve trigger decision efficiency. Deployed on a single FPGA, the system combines classical online processing with a compact Neural Network algorithm to achieve efficient event classification while managing the challenging ∼10 MHz throughput requirement of NA62. The streaming pipeline ensures ∼1 $\mathsf{\mu }$s latency, comparable to that of the NA62 detectors, allowing its seamless integration in the existing TDAQ setup as an additional detector. Development leverages High-Level Synthesis (HLS) and the open-source hls4ml package software–hardware codesign workflow, enabling fast and flexible reprogramming, debugging, and performance optimization. We describe the implementation of the full processing pipeline, the Neural Network classifier, their functional validation, performance metrics and the system’s current status and outlook. Full article

Recently, multiple new technologies have emerged for automating the development of neural network (NN) accelerators for both field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs). This paper explores methodologies for translating NN algorithms into chip layouts, with a focus on end-to-end automation, cost-effectiveness, and open-source software. We present a robust framework for developing NN-to-silicon solutions and demonstrate a seamless plug-and-play automation flow using TensorFlow, Vivado HLS, HLS4ML, and Openlane2. SkyWater Technologies’ 130 nm PDK (Sky130) is employed to successfully generate layouts for two small NN examples under 1000 parameters, incorporating dense, activation, and 2D convolution layers. The results affirm that current open-source tools effectively automate low-complexity neural network architectures and deliver faster performance through FPGA structures. However, this improved performance comes at the cost of increased die area compared to bare-metal designs. While this showcases significant progress in accessible NN automation, achieving manufacturing-ready layouts for more complex NN architectures remains a challenge due to current tool limitations and heightened computational demands, which points to exciting opportunities for future advancements. Full article

In this study, a chaos theory-based characterization method is proposed to address the nonlinear behavior of acoustic emission (AE) signals during the startup and shutdown phases of dry gas seals. AE signals were collected through a controlled experiment at three distinct phases: startup, normal operation, and shutdown. Analysis of these signals identified a transition speed of 350 r/min between the mixed lubrication (ML) and hydrodynamic lubrication (HL) states. The maximum Lyapunov exponent, correlation dimension, K-entropy, and attractors of the AE signals throughout the operation of the dry gas seal are calculated and analyzed. The findings indicate that the chaotic features of these signals reflect the friction state of the seal system. Specifically, when the maximum Lyapunov exponent is greater than zero, the system exhibits chaotic behavior. The correlation dimension and K-entropy first increase and then decrease in boundary and hybrid lubrication states, while remaining stable in the hydrodynamic lubrication state. Attractors exhibit clustering in boundary lubrication and dispersion in mixed lubrication states. The proposed method achieves an accuracy of 98.6% in recognizing the friction states of dry gas seals. Therefore, the maximum Lyapunov exponent, correlation dimension, and K-entropy are reliable tools for characterizing friction states, while attractors serve as a complementary diagnostic feature. This approach provides a novel framework for utilizing AE signals to evaluate the friction states of dry gas seals. Full article

Tiny machine learning (TinyML) demands the development of edge solutions that are both low-latency and power-efficient. To achieve these on System-on-Chip (SoC) FPGAs, co-design methodologies, such as hls4ml, have emerged aiming to speed up the design process. In this context, fast estimation of FPGA’s utilized resources is needed to rapidly assess the feasibility of a design. In this paper, we propose a resource estimator for fully customized (bespoke) multilayer perceptrons (MLPs) designed through the hls4ml workflow. Through the analysis of bespoke MLPs synthesized using Xilinx High-Level Synthesis (HLS) tools, we developed resource estimation models for the dense layers’ arithmetic modules and registers. These models consider the unique characteristics inherent to the bespoke nature of the MLPs. Our estimator was evaluated on six different architectures for synthetic and real benchmarks, which were designed using Xilinx Vitis HLS 2022.1 targeting the ZYNQ-7000 FPGAs. Our experimental analysis demonstrates that our estimator can accurately predict the required resources in terms of the utilized Look-Up Tables (LUTs), Flip-Flops (FFs), and Digital Signal Processing (DSP) units in less than 147 ms of single-threaded execution. Full article

Sweet cherry (Prunus avium L.) is a highly valued fruit, and optimal nutrient management is crucial for enhancing yield and fruit quality. However, the over-application of chemical fertilizers in cherry cultivation leads to environmental issues such as soil degradation and nutrient runoff. To address this, foliar application, a more targeted and eco-friendly fertilization method, presents a promising alternative. This study evaluates the effects of pre-harvest foliar application of calcium (Ca) (150 and 300 g hL⁻¹) and seaweed extracts (75 and 150 mL hL⁻¹), both individually and in combination, on the physiological and biochemical responses of ‘Burlat’ sweet cherry trees. Key physiological parameters, including plant water status, photosynthetic performance, and leaf metabolites, were analyzed. Results show that trees treated with seaweed extracts or with combined Ca and seaweed application had improved water status, higher sugar, starch, and protein content, as well as enhanced antioxidant activity and phenolic content compared to those treated solely with calcium. However, the combined treatment did not significantly enhance overall tree performance compared to individual applications. This study highlights the potential of seaweed-based biostimulants in sustainable cherry production. Full article

Two new 2p–3d complexes, (Et₃NH)[ML(hfac)₂], have been obtained using the nitronyl-nitroxide radical (HL) derived from 2-hydroxy-3-methoxy-5-nitrobenzaldehyde (M = Mn 1; Co 2). The two compounds are isomorphous and their structures consist of anionic mononuclear species, [M(hfac)₂L]⁻, M = Mn 1; Co 2, and triethylammonium cations, Et₃NH⁺. The metal ions adopt an octahedral geometry, being coordinated by phenoxido and aminoxyl oxygen atoms from the ligand and four oxygen atoms from the hexafluoroacetylacetonato (hfac⁻) ligand. The cryomagnetic behaviors of the two compounds reveal relatively strong antiferromagnetic M(II)-Rad interactions (J_MnRad = −191 cm⁻¹, J_CoRad = −166 cm⁻¹ with H = −JS_MS_Rad). The EPR spectra (X- and Q-band) of compound 1 below 70 K show the characteristical features of a S = 2 spin system with zero field splitting terms of D = 0.26 cm⁻¹ and E = 0.031 cm⁻¹. Full article

Drug resistance is one of the major obstacles to the clinical use of doxorubicin, an extensively used chemotherapeutic drug to treat various cancers, including leukemia. Inhibition of the nuclear factor erythroid 2-related factor 2 (NRF2) seems a promising strategy to reverse chemoresistance in cancer cells. NRF2 is a transcription factor that regulates both antioxidant defense and drug detoxification mechanisms. In this study, we investigated the potential of three inhibitors of NRF2—K67, retinoic acid and ML-385—to overcome doxorubicin resistance in promyelocytic leukemia HL-60 cells. For this purpose, low-dose doxorubicin was used to establish doxorubicin-resistant HL-60/DR cells. The expression of NRF2 and its main repressor, Kelch-like ECH-associated protein 1 (KEAP1), at mRNA and protein levels was examined. HL-60/DR cells overexpressed NRF2 at mRNA and protein levels and down-regulated KEAP1 protein compared to drug-sensitive HL-60 cells. The effects of NRF2 inhibitors on doxorubicin-resistant HL-60/DR cell viability, apoptosis, and intracellular reactive oxygen species (ROS) levels were analyzed. We observed that NRF2 inhibitors significantly sensitized doxorubicin-resistant HL-60/DR cells to doxorubicin, which was associated with increased intracellular ROS levels and the expression of CAS-9, suggesting the participation of the mitochondrial-dependent apoptosis pathway. Furthermore, ML-385 inhibitor was used to study the expression of NRF2–KEAP1 pathway genes. NRF2 gene and protein expression remained unchanged; however, we noted the down-regulation of KEAP1 protein upon ML-385 treatment. Additionally, the expression of NRF2-regulated antioxidant and detoxification genes including SOD2, HMOX2, and GSS was maintained upon ML-385 treatment. In conclusion, our results demonstrated that all the studied inhibitors, namely K67, retinoic acid, and ML-385, increased the efficacy of doxorubicin in doxorubicin-resistant HL-60/DR cells, and suggested a potential strategy of combination therapy using NRF2 inhibitors and doxorubicin in overcoming doxorubicin resistance in leukemia. Full article

Gully erosion poses a significant global concern due to its role in land degradation and soil erosion, particularly pronounced in Northeast China’s diverse agro-geomorphic regions. However, there is a lack of comprehensive studies on gully characteristics, development rates, and the topographic threshold of gully formation in these areas. To address this gap, we selected three different agro-geomorphic watersheds, named HL (Hailun), ML (Muling), and YKS (Yakeshi), with areas of 30.88 km², 31.53 km², and 21.98 km², respectively. Utilizing high-resolution (2.1 m, 2 m) remote sensing imagery (ZY-3, GF-1), we analyzed morphological parameters (length, width, area, perimeter, etc.) and land use changes for all permanent gullies between 2013 and 2023. Approximately 30% of gullies were selected for detailed study of the upstream drainage area and gully head slopes to establish the topographic threshold for gully formation (S = a·A^−b). In HL, ML, and YKS, average gully lengths were 526.22 m, 208.64 m, and 614.20 m, respectively, with corresponding widths of 13.28 m, 8.45 m, and 9.32 m. The gully number densities in the three areas were 3.14, 25.18, and 0.82/km², respectively, with a gully density of 1.65, 5.25, and 0.50 km km⁻², and 3%, 5%, and 1% of the land has disappeared due to gully erosion, respectively. YKS exhibited the highest gully head retreat rate at 17.50 m yr⁻¹, significantly surpassing HL (12.24 m yr⁻¹) and ML (7.11 m yr⁻¹). Areal erosion rates were highest in HL (277.79 m² yr⁻¹) and lowest in YKS (105.22 m² yr⁻¹), with ML intermediate at 243.36 m² yr⁻¹. However, there was no significant difference in gully expansion rate (0.37–0.42 m yr⁻¹) among the three areas (p > 0.05). Differences in gully development dynamics among the three regions were influenced by land use, slope, and topographic factors. The topographic threshold (S = a·A^−b) for gully formation varied: HL emphasized drainage area (a = 0.052, b = 0.52), YKS highlighted soil resistance (a = 0.12, b = 0.36), and the parameters a and b of ML fell within the range between these of HL and YKS (a = 0.044, b = 0.27). This study has enriched the scope and database of global gully erosion research, providing a scientific basis for gully erosion prevention and control planning in Northeast China. Full article

This article presents an ingestion procedure towards an interoperable repository called ALPACS (Anonymized Local Picture Archiving and Communication System). ALPACS provides services to clinical and hospital users, who can access the repository data through an Artificial Intelligence (AI) application called PROXIMITY. This article shows the automated procedure for data ingestion from the medical imaging provider to the ALPACS repository. The data ingestion procedure was successfully applied by the data provider (Hospital Clínico de la Universidad de Chile, HCUCH) using a pseudo-anonymization algorithm at the source, thereby ensuring that the privacy of patients’ sensitive data is respected. Data transfer was carried out using international communication standards for health systems, which allows for replication of the procedure by other institutions that provide medical images. Objectives: This article aims to create a repository of 33,000 medical CT images and 33,000 diagnostic reports with international standards (HL7 HAPI FHIR, DICOM, SNOMED). This goal requires devising a data ingestion procedure that can be replicated by other provider institutions, guaranteeing data privacy by implementing a pseudo-anonymization algorithm at the source, and generating labels from annotations via NLP. Methodology: Our approach involves hybrid on-premise/cloud deployment of PACS and FHIR services, including transfer services for anonymized data to populate the repository through a structured ingestion procedure. We used NLP over the diagnostic reports to generate annotations, which were then used to train ML algorithms for content-based similar exam recovery. Outcomes: We successfully implemented ALPACS and PROXIMITY 2.0, ingesting almost 19,000 thorax CT exams to date along with their corresponding reports. Full article

The Large Hadron Collider (LHC) is being prepared for an extensive upgrade to boost its particle discovery potential. The new phase, High Luminosity LHC, will operate at a factor-of-five-increased luminosity (the number proportional to the rate of collisions). Consequently, such an increase in luminosity will result in enormous quantities of generated data that cannot be transmitted or stored with the currently available resources and time. However, the vast majority of the generated data consist of uninteresting data or pile-up data containing few interesting events or electromagnetic showers. High-Luminosity LHC detectors, including the Compact Muon Solenoid (CMS), will thus have to rely on innovative approaches like the proposed one to select interesting collision data. In charge of data reduction/selection at the early stages of data streaming is a level 1 trigger (L1T), a real-time event selection system. The final step of the L1T is a global trigger, which uses sub-system algorithms to make a final decision about signal acceptance/rejection within a decision time of around 12 microseconds. For one of these sub-system L1T algorithms, we propose using quantized neural network models deployed in targeted L1T devices, namely, field-programmable gate arrays (FPGA), as a classifier between electromagnetic and pile-up/quantum chromodynamics showers. The developed quantized neural network operates in an end-to-end manner using raw detector data to speed up the classification process. The proposed data reduction methods further decrease model size while retaining accuracy. The proposed approach was tested with simulated data (since the detector is still in the production stage) and took less than 1 microsecond, achieving real-time signal–background classification with a classification accuracy of $97.37$% for 2-bit-only quantization and $97.44$% for quantization augmented with the data reduction approach (compared to $98.61$% for the full-precision, standard network). Full article

Nanomaterials play a crucial role in various aspects of modern life. Zirconia nanoparticles, extensively employed in medicine for fortifying and stabilizing implants in reconstructive medicine, exhibit unique electrical, thermal, catalytic, sensory, optical, and mechanical properties. While these nanoparticles have shown antibacterial activity, they also exhibit cytotoxic effects on human cells. Our research focuses on understanding how the cells of the human immune system (both the innate response, namely HL-60 and U-937, and the acquired response, namely HUT-78 and COLO-720L) respond to the presence of zirconium (IV) oxide nanoparticles (ZrO₂-NPs). Viability tests indicate that ZrO₂-NPs exert the highest cytotoxicity on HL-60 > U-937 > HUT-78 > COLO 720L cell lines. Notably, concentrations exceeding 100 μg mL⁻¹ of ZrO₂-NPs result in significant cytotoxicity. These nanoparticles readily penetrate the cell membrane, causing mitochondrial damage, and their cytotoxicity is associated with heightened oxidative stress in cells. The use of ZrO₂-NP-based materials may pose a risk to immune system cells, the first responders to foreign entities in the body. Biofunctionalizing the surface of ZrO₂-NPs could serve as an effective strategy to mitigate cytotoxicity and introduce new properties for biomedical applications. Full article