Traditional Methods Research Articles

A Transfer Hydrogenation Approach to Activity-Based Sensing of Formate in Living Cells.

Formate is a major reactive carbon species in one-carbon metabolism, where it serves as an endogenous precursor for amino acid and nucleic acid biosynthesis and a cellular source of NAD(P)H. On the other hand, aberrant elevations in cellular formate are connected to progression of serious diseases, including cancer and Alzheimer's disease. Traditional methods for formate detection in biological environments often rely on sample destruction or extensive processing, resulting in a loss of spatiotemporal information. To help address these limitations, here we present the design, synthesis, and biological evaluation of a first-generation activity-based sensing system for live-cell formate imaging that relies on iridium-mediated transfer hydrogenation chemistry. Formate facilitates an aldehyde-to-alcohol conversion on various fluorophore scaffolds to enable fluorescence detection of this one-carbon unit, including through a two-color ratiometric response with internal calibration. The resulting two-component probe system can detect changes in formate levels in living cells with a high selectivity over potentially competing biological analytes. Moreover, this activity-based sensing system can visualize changes in endogenous formate fluxes through alterations of one-carbon pathways in cell-based models of human colon cancer, presaging the potential utility of this chemical approach to probe the continuum between one-carbon metabolism and signaling in cancer and other diseases.

L1-norm optimisation of rank deficient GNSS networks by an improved Grey Wolf method

An improved grey wolf optimization (GWO) is proposed for direct L1-norm optimization in rank deficient GNSS networks to detect outliers since the standard GWO and the global optimization (GO) algorithms cannot deal with such problems. In contrast to the traditional method for L1-norm minimization which is solved by a cumbersome search of linear programming (LP) problem, in this algorithm, one only requires to input the target function. Furthermore, it has a greater ability to detect gross errors of observables with low reliability compared to traditional methods such as LP. Moreover, it does not consider the correct observables as blunders in contrast to some algorithms such as the iteratively reweighted least-squares (IRLS) method, which may consider them as outliers. The numerical results of real and simulated GNSS networks approve the efficiency of the new algorithm.

Peculiarities of aristocratic education at the beginning of the 19th century

AbstractThis paper examines the education of the Hungarian aristocracy in the period from 1790 to 1848. Since the progressive-minded members of this social class, which was highly qualified by European standards, played a significant role in promoting culture and education, especially since the Reform Era, I wanted to find out what educational characteristics underpinned their education. I explored the question of what trends prevailed in the different directions of home education and how the political, social and intellectual challenges and changes in the first half of the 19th century affected private education. In the course of the research, I applied the traditional method of educational history, source and document analysis and the analytical (deductive) research strategy. After studying the Hungarian, German and English literature on the subject, I examined and evaluated the primary sources available in Hungary - diaries, memoirs, journal articles, autobiographical writings, records of foreign travellers, archival and manuscript sources. In addition to asserting their primary power interests and their role as patrons, a small but crucial group of aristocrats took an active role in raising the level of national culture and education. They championed the cause of education, founded educational institutions and thus became mediators of culture for other strata of society. Their education, enlightened thinking and European erudition undoubtedly contributed to their recognition of this role and task and their commitment to it.

Adaptive Threshold Learning in Frequency Domain for Classification of Breast Cancer Histopathological Images

Breast cancer has become the most common cancer in the world, and biopsy is the most reliable and widely used technique for detecting breast cancer. However, observation of histopathological images is time-consuming and labor-intensive. Currently, CNN has become the mainstream method for breast cancer histopathological image classification research. However, some studies have found that the optical microscope-generated histopathological images have noise, and the output of a well-trained convolutional neural network in image classification tasks can change drastically due to small variations in the input. Therefore, the quality of the image significantly affects the accuracy of the classification. Wavelet transform is a commonly used denoising method, but the selection of the threshold is a difficult problem, and traditional methods are difficult to find the appropriate threshold quickly and accurately. This paper proposes an adaptive threshold selection method that combines threshold selection steps with deep learning methods by using the threshold as a parameter in the CNN model to train. In this way, we associate the threshold with the classification result of the model and find the appropriate value for that image and task by back-propagation in training. The method was experimented on publicly available datasets BreaKHis and BACH. The results in BreaKHis (40x: 94.37%, 100x: 93.85%, 200x: 91.63%, 400x: 93.31%), and BACH (91.25%) demonstrate that our adaptive threshold selection method can improve classification accuracy and is significantly superior to traditional threshold selection methods.

Resource recovery and regeneration strategies for spent lithium-ion batteries: Toward sustainable high-value cathode materials

Traditional cathode recycling methods have become outdated amid growing concerns for high-value output and environmental friendliness in spent Li-ion battery (LIB) recycling. Our study presents a closed-loop approach that involves selective sulfurization roasting, water leaching, and regeneration, efficiently transforming spent ternary Li batteries (i.e., NCM) into high-performance cathode materials. By combining experimental investigations with density functional theory (DFT) calculations, we elucidate the mechanisms within the NCM-C-S roasting system, providing a theoretical foundation for selective sulfidation. Utilizing in situ X-ray diffraction techniques and a series of consecutive experiments, the study meticulously tracks the evolution of regenerating cathode materials that use transition metal sulfides as their primary raw materials. The Li-rich regenerated NCM exhibits exceptional electrochemical performance, including long-term cycling, high-rate capabilities, reversibility, and stability. The closed-loop approach highlights the sustainability and environmental friendliness of this recycling process, with potential applications in other cathode materials, such as LiCoO2 and LiMn2O4. Compared with traditional methods, this short process approach avoids the complexity of leaching, solvent extraction, and reverse extraction, significantly increasing metal utilization and Li recovery rates while reducing pollution and resource waste.

Optimization of a lipid nanoparticle-based protocol for RNA transfection into primary mononuclear phagocytes.

The effective delivery of synthetic RNA into mononuclear phagocytes is a prerequisite for experimental research and therapeutic development. However, traditional methods are highly ineffective and toxic for these cells. Here, we aimed to optimize a transfection protocol for primary bone marrow-derived phagocytes, specifically dendritic cells and macrophages, using lipid nanoparticles generated by microfluidics. Our results show that a lipid mixture similar to that used in Moderna's COVID-19 messenger RNA vaccine outperforms the others tested. Improved messenger RNA transfection can be achieved by replacing uridine with methylpseudouridine but not methoxyuridine, which interferes with transfection. The addition of diphenyleneiodonium or apocynin can enhance transfection in a cell type-dependent manner without adverse effects, while apolipoprotein E provides no added value. These optimized transfection conditions can also be used for microRNA agonists and antagonists. In sum, this study offers a straightforward, highly efficient, reproducible, and nontoxic protocol to deliver RNA into different primary mononuclear phagocytes in culture.

The Engine Casing Machining Holes Repairing Based on Vibration Wire Feeding

The engine casing components operate in high-temperature and high-pressure environments. Process holes are drilled when defects occur. Welding is employed in the repair of process holes as a process for permanently joining materials. The traditional welding method relies on padding, which results in poor back formation of process holes. Additionally, the shape of the process holes imposes high requirements on the size of the droplet transition. The conventional approach of adjusting a welding current makes it difficult to achieve stable droplet transition and precise formation of small holes. It poses a challenge for the robotic welding process. To deal with this problem, the influence of the high-frequency vibration GTAW process on the directional transition of molten droplets is studied. The molten droplet directional transition process is developed. The impact of vibration energy on the molten pool is reduced. Welding repair experiments for process holes are successfully conducted. When the frequency is 3 Hz, the transition of droplets changes from a continuous one-droplet transition to a discontinuous liquid bridge transition. The residual height and mechanical properties of the repaired area are tested. The experimental results indicated that the residual height after dual-side repair is ≤0.7 mm. The X-ray and fluorescent penetration tests have a 100% first-pass qualification rate. The repaired area demonstrates a hardness of 480 HV and a room-temperature tensile strength of 1069 MPa. The repair process requirements for the casing are met.

Simulation Study on 3D Heterogeneous Photonic Integration with Vertical Microring Coupler

We present a simulation-based study on a 3D heterogeneous photonic integration scheme based on a vertical microring coupler (VμRC). Our research introduces a more compact and efficient layout of photonic devices in the vertical direction, surpassing the limitations of traditional planar integration methods. This investigation focuses on optimizing the performance of the VμRC by analyzing critical parameters such as the dimensions of the microring and the waveguide and the refractive indices of surrounding materials, which serve as the guideline for future manufacturing of the device. The simulation results demonstrate that the careful selection and optimization of these parameters significantly impact the transmittance and coupling characteristics of the VμRC. To demonstrate the validity of this simulation model, we applied it to a few practical cases and achieved comparable results with our previous experiments.

A search tool based on language modelling developed for The Index of Middle English Prose

Non-standardised early vernaculars present a problem for search tools due to the high degree of variation. The challenge lies in the variation found in orthography, syntax, and lexicon between titles, incipits, and explicits in manuscript copies of the same work. Traditional search methods relying on exact string matching or regular expressions fail to address these variations comprehensively. This project presents a web-based search tool specifically designed to handle linguistic and textual variation. The software is made available as a part of the Index of Middle English Prose (IMEP). The search tool addresses the issue of variation by utilizing a database of incipits and explicits, character-based n-gram language models (LMs) built with the Stanford Research Institute Language Modelling (SRILM) toolkit, and a fuzzy search script (IMEP: FSS) written in Python. The tool optimizes for recall, retrieving multiple potential matches for a search string, without attempting to identify the ‘correct’ one. The search process involves looking up exact matches in the database while simultaneously using the fuzzy search script to evaluate the incipits and explicits against a model of the search string, followed by a match of the search string against models of the incipits and explicits. This two-step process shortens the processing time, which would otherwise be unreasonably long, because while using SRILM to match the search string against each incipit or explicit in the IMEP for precision could be time-consuming, running a first step where all texts are matched against a single LM built from the search string allows for faster processing. A web application, built using Django and Docker, combines the results of the direct database lookup and the fuzzy search script, presenting them as a list with exact matches followed by fuzzy matches ordered by increasing model perplexity. The tool is made available Open Access and can be adapted to other datasets.

Synthetic Seed Production and Slow Growth Storage of In Vitro Cultured Plants of Iris pallida Lam.

Iris pallida Lam. is traditionally cultivated in Italy to sell its rhizomes to perfume-producing industries and is particularly sought-after because of its high content of irones, ketone compounds responsible for the violet smell of the orris essence. One of the critical aspects of its cultivation is the propagation method, performed by subdividing and replanting sections of the rhizome, which leads to the sacrifice of salable material. A solution is provided via in vitro propagation using the somatic embryogenesis technique, an effective method that allows the production of plants without the use of the rhizome. To facilitate the scale up of the activities of micropropagation companies, the method of slow growth storage (SGS) for orris plantlets and a somatic embryo encapsulation technique were developed for the first time. Orris plantlets were placed at 4 °C in the dark for 30, 60, 90 and 120 days and monitored 7 and 30 days after treatment. Synthetic seeds were obtained by encapsulating somatic orris embryos in sodium alginate beads, which were stored for 14 and 28 days at 4 °C and 24 °C. The results showed that it is possible to cold-preserve orris plantlets for up to 90 days without significant damages and that orris synthetic seeds can be produced and stored for a short-to-mid-term period. These conservation techniques can be useful for germplasm conservation and can also be integrated in the micropropagation cycle of orris, helping to solve issues related to the traditional propagation method.

Characterization of the complete chloroplast genome of Wolffia arrhiza and comparative genomic analysis with relative Wolffia species

Lemnoideae, commonly referred to as the duckweed, are aquatic plants found worldwide. Wolffia species are known for their extreme reduction in size and complexity, lacking both roots and leaves, and they hold the distinction of being the smallest plants among angiosperms. Interestingly, it belongs to the Araceae family, despite its apparent morphological differences from land plants in the same family. Traditional morphological methods have limitations in classifying these plants, making molecular-level information essential. The chloroplast genome of Wolffia arrhiza is revealed that a total length of 169,602 bp and a total GC content of 35.78%. It follows the typical quadripartite structure, which includes a large single copy (LSC, 92,172 bp) region, a small single copy (SSC, 13,686 bp) region, and a pair of inverted repeat (IR, 31,872 bp each) regions. There are 131 genes characterized, comprising 86 Protein-Coding Genes, 37 Transfer RNA (tRNA) genes, and 8 ribosomal RNA (rRNA) genes. Moreover, 48 simple sequence repeats and 32 long repeat sequences were detected. Comparative analysis between W. arrhiza and six other Lemnoideae species identified 12 hotspots of high nucleotide diversity. In addition, a phylogenetic analysis was performed using 14 species belonging to the Araceae family and one external species as an outgroup. This analysis unveiled W. arrhiza and Wolffia globosa as closely related sister species. Therefore, this research has revealed the complete chloroplast genome data of W. arrhiza, offering a more detailed understanding of its evolutionary position and phylogenetic categorization within the Lemnoideae subfamily.

Energy efficient cluster routing protocol for wireless sensor networks using hybrid metaheuristic approache’s

The development of wireless sensor networks (WSNs) has been particularly notable in the context of smart computing, where various application areas have been identified. These networks consist of self-configured, small sensor nodes with battery power. However, sensors have limited energy and resources at their disposal. When unbalanced nodes exist within the network, it adversely affects the network’s lifetime due to increased power consumption. Designing efficient routing for wireless sensor networks remains a challenging task, particularly in terms of energy efficiency. Optimal solutions to address these challenges involve reducing node energy consumption through the implementation of clustering techniques. The current clustering schemes do not take into account node energy balancing, node density, and scalability when selecting low-energy nodes as cluster heads. The existing system is limited to exploring global opportunities within specified search zones. To enhance the performance of WSNs, a hybrid approach combining artificial bee colony (ABC) and ant colony optimization (ACO) has been developed. This approach helps in selecting an ideal cluster head from a group of terminals. Several factors are considered in the cluster head election, including residual energy at the nodes, distance to neighbors, distance to the base station, node degree, and node centrality. ACO determines the path between the cluster leader and the base station (BS) by selecting the most efficient route in terms of distance, remaining power, and node degrees. The performance of this proposed methodology has been analyzed in terms of energy consumption, network lifetime, and data packets at the base station. A comparison has been made between the outputs of the proposed methods and traditional benchmarking methods. The results reveal a substantial improvement in the average network lifetime, showcasing an increase of 40.50%, 33.17%, 25.00%, and 15.49% when compared to LEACH, Beecluster, iABC, and BeeSensor, respectively. In terms of alive nodes, our solution surpasses LEACH, Beecluster, iABC, and BeeSensor by 28.7%, 22.51%, 20.95%, and 12.47%, respectively. Additionally, the energy consumption of our approach proves to be significantly lower than LEACH, Beecluster, iABC, and BeeSensor, recording reductions of 47.25%, 32.40%, 27.38%, and 22.20%, respectively.

High‐Voltage Recyclable Organic Cathode Enabled by Heteroatomic Substitution for Aqueous Zinc‐Ion Batteries

AbstractN‐type organic compounds present themselves as promising high‐capacity cathodes for aqueous Zn‐ion batteries. However, a common challenge is their working voltages often falling below 1 V versus Zn2+/Zn. To bridge this gap, a high‐voltage organic material is first developed, 5,6,11,12‐tetraazanaphthacene (TANC), using a heteroatomic substitution strategy. TANC feature a large π‐conjugated plane enriched with π−π interactions, which not only enhancing structural stability but also boosting charge transfer kinetics. The TANC cathode is achieved from its dihydro precursor, denoted as 2H‐TANC, via a facile in situ activation process within the battery itself. This electrochemical synthesis method is cost‐effective and environmentally friendly compared to traditional chemical method. The cathode shows a record‐high discharge voltage of 1.15 V (vs Zn2+/Zn) among n‐type organic materials and maintains cycling stability over 47,500 cycles. Furthermore, spent TANC electrodes can be efficiently recycled via a simple extraction process. The work marks a significant step toward the development of high‐voltage, affordable, and recyclable organic electrode materials, steering them to the forefront of future sustainable battery technologies.

Adaptive mask-based brain extraction method for head CT images.

Brain extraction is an important prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion identification, localization, and segmentation. To address the problem that the current traditional image segmentation methods are fast in extraction but poor in robustness, while the Full Convolutional Neural Network (FCN) is robust and accurate but relatively slow in extraction, this paper proposes an adaptive mask-based brain extraction method, namely AMBBEM, to achieve brain extraction better. The method first uses threshold segmentation, median filtering, and closed operations for segmentation, generates a mask for the first time, then combines the ResNet50 model, region growing algorithm, and image properties analysis to further segment the mask, and finally complete brain extraction by multiplying the original image and the mask. The algorithm was tested on 22 test sets containing different lesions, and the results showed MPA = 0.9963, MIoU = 0.9924, and MBF = 0.9914, which were equivalent to the extraction effect of the Deeplabv3+ model. However, the method can complete brain extraction of approximately 6.16 head CT images in 1 second, much faster than Deeplabv3+, U-net, and SegNet models. In summary, this method can achieve accurate brain extraction from head CT images more quickly, creating good conditions for subsequent brain volume measurement and feature extraction of intracranial lesions.

Research and development of Lycium barbarum compound beverage and preliminary verification of its efficacy

AbstractLycium barbarum contains a variety of phytochemicals, primarily polysaccharides, and other potent compounds. L. barbarum is a plant that has several uses in contemporary society, including food processing and everyday eating. However, through traditional processing methods the medicinal or edible value of L. barbarum cannot be fully exploited, to further improve the utilisation rate of L. barbarum and enrich the variety of L. barbarum market. The L. barbarum compound beverage, which uses L. barbarum as its primary ingredient, was created through a series of trials, including the creation of tea beverage recipes and research on the most suitable content of stabilisers, flavouring agent formulations and so on. In addition, several validation tests, including those involving hypoglycaemic and lipid-lowering were conducted. The results indicated that 0.1 g mL−1 stabiliser CMC-Na, 0.2 g mL−1 erythritol, 3 g mL−1 citric acid, 8% L. barbarum juice, and 6% chrysanthemum tea cold brew extract were the best combinations for food additive formulations. Additionally, compound beverages have hypoglycemic and lipid-lowering effects. In conclusion, the research and development of L. barbarum compound beverages can not only increase the rate at which L. barbarum is utilised but also offer fresh ideas for the research and development of L. barbarum as they transition from cash crop agricultural products to market-oriented standardised processed products.

A Review on Interactive Air Canvas Application

In today's dynamic landscape of human- computer interaction, there's a growing demand for interfaces that seamlessly merge the digital and physical realms. Traditional input methods like keyboards and mice, though reliable, fall short in meeting the expectations of users seeking more intuitive engagement with technology. The Air Canvas project, leveraging OpenCV and MediaPipe, addresses this need by creating an interactive drawing platform that monitors real- time hand movements. Users can paint in mid-air, translating hand gestures into vibrant strokes on a digital canvas. This work highlights the potential of computer vision and gesture recognition to bridge the gap between technology and art, offering immersive and creative applications. Writing, a fundamental form of communication, is explored through a motion-to-text converter for smart wearables. By tracking finger movements using computer vision, this system generates text for purposes like texting and emails, presenting a valuable communication tool, especially for the deaf community. Keywords— OpenCV, MediaPipe, Interfaces, Real-time hand movements, Gesture Recognition.

Fault tolerant micro-programmed control unit for SEU and MBU mitigation in space based digital systems

The Micro-Programmed Control Unit (MPCU) offers an alternative to conventional hardwired control circuits by employing a control store for transmitting control signals through stored control bits. Traditionally, Hamming codes are used for Single-Event Upset (SEU) protection in memory components. However, these Error Control Codes (ECC) encounter challenges, such as inconsistent code rates and limited adaptability for Multiple Bit Upsets (MBU). To address MBU protection, ECC codes with soft decoding logic are commonly utilized, leading to increased hardware complexity. This paper introduces a lightweight encoding and hard-decoding logic capable of detecting and correcting up to n2∗ upsets, achieving a code rate of ≈ 0.37. Through evaluations against both traditional and recent ECC methods, the proposed approach demonstrates enhanced fault correction capabilities compared to Hamming codes. Furthermore, it maintains a consistent code rate, incurs lower area overhead, and exhibits less hardware complexity than MBU codes by employing hard decoding logic. The suggested methodology effectively mitigates both MBU and SEUs in control stores, providing reduced delay and making it well-suited for ensuring reliability in high-speed memory applications like MPCUs.

Underwater Audio and Data Transmission System using Li-Fi Technology

Underwater communication system utilizing Li-Fi (Light Fidelity) technology for transmitting audio and data. The system addresses limitations of traditional methods by leveraging an Arduino Uno microcontroller for control, lasers for data transmission, and solar panels for reception. To enhance received audio signals, an amplifier circuit is employed before feeding them into a speaker for playback. Data is recovered and processed on the receiving end using the Arduino Uno. This research explores the feasibility of Li-Fi for underwater applications. The paper details the design and development of the communication system, including hardware components like lasers, solar panels, amplifier circuits, and speakers, along with the software implemented on the Arduino Uno. The results focus on the successful transmission and reception of both audio (heard through the speaker) and text data processed by the Arduino Uno. This work paves the way for further development of Li-Fi based underwater communication solutions for various applications. Keywords- Underwater Communication, Li-Fi (Light Fidelity), Arduino Uno, Laser Diode, Solar Panel, Audio Transmission, Data Transmission, Amplifier Circuit, Speaker

Similarity Measurement and Retrieval of Three-Dimensional Voxel Model Based on Symbolic Operator

Three-dimensional voxel models are widely applied in various fields such as 3D imaging, industrial design, and medical imaging. The advancement of 3D modeling techniques and measurement devices has made the generation of three-dimensional models more convenient. The exponential increase in the number of 3D models presents a significant challenge for model retrieval. Currently, these models are numerous and typically represented as point clouds or meshes, resulting in sparse data and high feature dimensions within the retrieval database. Traditional methods for 3D model retrieval suffer from high computational complexity and slow retrieval speeds. To address this issue, this paper combines spatial-filling curves with octree structures and proposes a novel approach for representing three-dimensional voxel model sequence data features, along with a similarity measurement method based on symbolic operators. This approach enables efficient similarity calculations and rapid dimensionality reduction for the three-dimensional model database, facilitating efficient similarity calculations and expedited retrieval.

Anti-Windup Pitch Angle Control for Wind Turbines Based on Bounded Uncertainty and Disturbance Estimator

Due to physical limitations and safety requirements, the rate and amplitude of change in wind turbines’ pitch angle are limited, which will bring integral saturation problems to the control system. This leads to the deterioration of the pitch control system’s performance or even an instability problem. This paper designs an anti-windup robust pitch angle control strategy to deal with pitch rate constraint issue to enhance the safety of the control system. First, to facilitate controller design, a filtered tracking-error technique is employed to transform the nonaffine form into an affine one. Subsequently, a feedback robust controller based on an uncertainty and disturbance estimator (UDE) is developed to handle the model’s uncertainty and external disturbances. To address the issue of integral saturation in the pitch system and guarantee its safety, an elliptical bounded constraint is integrated into the designed UDE strategy. This bounded UDE controller can improve the stability of power generation quality, reducing the mechanical loads on components. Finally, the effectiveness of the proposed scheme is verified on the Wind Turbine Blockset platform in Matlab/Simulink. It can achieve better performance than traditional methods.