Processing Methods Research Articles

Recent progress in soft robots: principles, designs, and applications

Abstract With advancements in the manufacturing industry persisting, soft robots have experienced rapid development, progressively emerging as a pivotal focus in the future trajectory of robotic technology. As a new type of robot technology, soft robots have significant differences from traditional robots in terms of principles, driving methods, design control, and other aspects. Here, we sort out and summarize the latest developments in soft robotics. Firstly, typical principles and driving methods were introduced, including rope drive, variable stiffness drive (gas negative pressure, intelligent fluids, etc), electromagnetic drive, and so on. Secondly, the main materials and characteristics of soft robots are analyzed, including hydrogels, shape memory alloys, photosensitive materials, electromagnetic rheological elastomer, biodegradable materials, etc. Then, typical soft robot structures and processing methods were introduced, including fluid static skeleton structures, muscle fluid static skeleton structures, and others. Finally, the problems of soft robots are analyzed, and the future development direction and importance are summarized. This paper highlights the recent progress in smart functional materials, typical biomimetic structures, and assembly methods applicable to soft robots, which is expected to assist the development and advancement of the next generation of soft robots.

Transforming Locust Bean Processing: A Path to Profit for Elderly Women's Microbusiness in Sokoto, Nigeria

This study investigates the value addition potential of African locust beans (Parkia biglobosa) in Sokoto Metropolis, Nigeria, emphasizing the socio-economic characteristics of processors, cost-benefit analysis, and strategies for enhancing the value chain. Despite the nutritional and economic significance of locust beans, their market utilization remains constrained by traditional processing methods, inadequate packaging, and a lack of modern branding. Utilizing a multi-stage sampling technique, data was collected from 60 respondents, revealing that the locust bean processing industry is predominantly elderly female-driven, with limited modern educational and high family sizes. The total variable costs for processing were computed at ₦1,6888.49 revenue of ₦2,473 thus realizing a gross margin of ₦873.2. However, upon value addition of grinding and branded packaging, the variable cost rose to ₦7,220, with revenue from packaged Daddawa reaching ₦12,000, resulting in a Gross margin of ₦4,780. This underscores the financial viability of value addition through improved packaging and branding. The study further identifies key constraints including market disorganization and the need for better processing technologies. The findings advocate for strategic interventions to enhance the economic value of locust beans, promoting broader consumer acceptance and contributing to food security and economic development in the region.

Calendula Flowers (Calendulae officinalis flos): Methods to Obtain Fractions of Biologically Active Substances

INTRODUCTION. The pharmaceutical industry aims at developing sustainable resource-saving technologies for manufacturing medicines. Approaches to the effective use of herbal drugs include applying technologies yielding compounds with various pharmacological effects in one production cycle. To date, no standardised herbal drug processing technology has been designed to produce calendula flower-based medicinal products with various pharmacological effects. The Member States of the Eurasian Economic Union (EAEU) process calendula flowers by extraction methods yielding one fraction of biologically active substances per cycle (either only flavonoids or only carotenoids).AIM. This article aimed to investigate the possibility of stepwise processing of calendula flowers to obtain biologically active substances with different polarities (carotenoids, flavonoids, and polysaccharides) per cycle.MATERIALS AND METHODS. The authors extracted carotenoids using hexane. The study included additional thermal pretreatment of the herbal drug. Carotenoids and flavonoids were quantified by spectrophotometry, and polysaccharides were quantified by gravimetric analysis. After hexane removal, the dry oily residue was dissolved in oil with mechanical stirring to obtain the oil extract. The study included a three-step processing method that comprised hexane extraction (extraction of carotenoids with a low-polarity organic solvent), extraction with a water–organic solvent system (extraction of flavonoids), and aqueous extraction (precipitation of polysaccharides).RESULTS. The lipophilic extracts obtained by single hexane extraction or by single hexane extraction and heat treatment had the maximum carotenoid content (~4%), which was 10 times the carotenoid content in the extracts obtained using a water– organic solvent system. Oil extracts were prepared from the dry residue left after removing hexane by distillation at its boiling point. The content of biologically active substances in the oil extracts was comparable to that in the intact herbal drug. Water–organic solvent extraction yielded 68.5% more flavonoids from the herbal drug extracted with hexane than from the intact herbal drug. In contrast, aqueous extraction applied to the intact herbal drug yielded 1.7 times the amount of flavonoids obtained by aqueous extraction preceded by hexane extraction. The water-soluble polysaccharide fraction extracted from the intact herbal drug had 10 times the flavonoid content observed in the fraction extracted from the processed herbal drug. Each subsequent processing step decreased the total polysaccharide content and the content of individual polysaccharide fractions and increased their purity. The ratio of polysaccharide fractions remained unchanged across all processing methods tested.CONCLUSIONS. Stepwise processing of calendula flowers ensured that one processing cycle provided three fractions containing increased amounts of carotenoids, flavonoids, and polysaccharides. Relative to the intact herbal drug, there was a 3.3-fold increase in the yield of carotenoids, and the yields of flavonoids and polysaccharides increased by 44.8 and 41.3%, respectively. The resulting product had a higher degree of purity than the products obtained by pretreatment and double hexane extraction or by using the intact herbal drug. The stepwise processing method is advisable for the production of medicines that are enriched with a specific group of biologically active substances or contain smaller amounts of impurities.

Morphological and Autofluorescence Dataset for ‘Touch’ Epidermal Cell Populations Collected with Imaging Flow Cytometry

Background New methods for processing ‘touch’ or trace biological samples is an ongoing priority for forensic caseworking laboratories. These samples often contain materials from multiple individuals in varying quantities and/or degrees of degradation. Rapid characterization of cellular material before DNA profiling can allow laboratories to screen samples for the presence of multiple contributors or the amount of biological material present. Methods This dataset contains autofluorescence and morphological profiles of epidermal cell populations analyzed using Imaging Flow Cytometry. The epidermal samples were aged for varying amounts of time prior to analysis. Multiple samples from the same individual were also collected to assess profile variations within and across the contributors. Conclusions This data set may be used to investigate variability in epidermal cell populations from different individuals and potential forensic signatures contained within the non-genetic components that comprise touch biological evidence.

SUBSTANTIATION OF METHODOLOGICAL RECOMMENDATIONS FOR THE USE OF INFORMATION AND MEASURING SYSTEM BUILT ON THE LATEST HARDWARE DEVELOPMENTS DURING THE TESTING OF AME

The constant process of development of both hardware and software perfection inevitably leads to the improvement (creation) of existing (new) advanced information and measurement systems. At the same time, there is a need to develop methodological recommendations for the use of advanced information gathering and processing systems built on the basis of the latest hardware systems and complexes and software products for conducting heterogeneous tests. At the same time, there is a need to develop methodological recommendations for the use of a promising IMS for collecting and processing information built on the basis of the latest hardware systems and complexes and software products for conducting heterogeneous tests. Modern systems used for testing AME are measuring complexes that include modules for connecting sensors, modules for converting analogue sensor signals into digital form, modules for storing the received information in digital form and modules for communication with external computerised devices. Depending on the tasks of the test or experiment, the IMS can be supplemented (simplified) by the need to use certain measuring complexes and systems to measure parameters and obtain reliable information. The ideas, principles and algorithms that currently constitute the methodology of information processing have already made it possible to make a significant breakthrough in information processing technology. Processing methods and principles of their implementation for each field have their own specific features, which are primarily determined by the specific type of information carrier, encoding methods and ways of presenting the results of information processing. As a result, information processing arrangements for different areas often appear to be outwardly dissimilar. However, this external dissimilarity hides the same methodology and principles of building processing systems, which is crucial. The authors of the article study the capabilities of the latest hardware systems and complexes which should be included in advanced IMS and substantiate new methodological approaches, in accordance with regulatory and legal documents, to the use of such IMS during testing of AME.

IoT Based Precision Farming

This project focuses on leveraging drone images of the pests equipped with advanced sensors for pest detection in crops, combined with methods for image processing to identify diseases. The ultimate goal is to enhance crop health and productivity through timely and targeted pesticide application. Image processing techniques are used to detect signs of diseases and pests in the captured images. The use of machine learning CNN algorithm enhances the system’s ability to accurately classify and diagnose crop heath issues. Upon detection of pests, the IOT platform triggers a response mechanism to deploy a precision pesticide spraying system. This ensures targeted and localized treatment, reducing the overall use of pesticides and minimizing environmental impact. This project involves capturing images of pests using a camera, followed by processing these images to extract key features using various image processing techniques. The extracted features are analyzed using algorithms, primarily Convolutional Neural Networks (CNNs), to detect variations in color and other dominant characteristics in the images. By comparing these features across samples, the system can identify pests and plant diseases more efficiently. This approach aims to provide a quicker and more cost-effective solution for pest detection and disease management.

Catalytic functionalization of MCM-41 and MCM-48 with copper by modified template ion-exchange method for low-temperature NH3-SCR process: the role of complexing agents for copper dispersion

AbstractMesoporous silicas of MCM-41 and MCM-48 types were synthesized and modified with copper by template ion-exchange (TIE) technique. A high dispersion of deposited copper species was managed by subsequent treatment of the samples with ammonia, urea, or acetonitrile solution. Copper-modified silicas were analysed in terms of their chemical composition (ICP-OES), ordering of porous structure (XRD), textural parameters (low-temperature N2 sorption), dispersion and form of introduced copper species (UV–vis-DR, XRD, H2-TPR, NH3-TPD). Mesoporous silicas modified with copper exhibited promising catalytic efficiency in selective catalytic reduction of NO with ammonia (NH3-SCR). The samples containing dispersed copper species (predominantly monomeric copper cations) presented enhanced catalytic activity compared to catalysts containing CuO aggregates. On the other hand, CuO aggregates showed greater catalytic activity in the side process of direct ammonia oxidation by oxygen present in the reaction mixture. It was demonstrated that TIE post-treatment of the samples with urea resulted in most effective improving dispersion of deposited copper and is less destructive for ordered porous structures of mesoporous silica comparing to treatment with ammonia solution. Graphical abstract

The collapse of modern exposures in the recording of X-RAY images in medicine

This article reviewed the methods and algorithms for processing and diagnosing X-ray images that are widely used in medicine today. In order to improve the implementation of deep learning in modern medicine and further improve the widespread use of HMLs, the method, algorithm and model of X-ray image processing using the Raspberry Pi device were analyzed.

T. MARTEMIANOVA’S PROPAEDEUTIC COURSE “PRO-PHYSICS”: EXPERIMENTAL EVALUATION OF EFFECTIVENESS

The purpose of this work was to evaluate the effectiveness of the propaedeutic course “PRO-physics” by T. Martemianova. The methods of this research are: analysis of sources on the issues of propaedeutics of physics, empirical methods (analysis of data arrays from electronic systems of educational process management, lists of winners and prize-winners of olympiads, statistics of admission to universities, survey methods), statistical methods of results processing. The study proposes a methodology for assessing the effectiveness of propaedeutic courses. The study revealed that students who took the “PRO-physics” propaedeutic course programme subsequently have higher results in physics olympiads at all levels, more often enter universities from the top 20 of the RAEX rating, better cope with intermediate and final control activities in physics within the framework of school education. The results of the questionnaire survey of teachers and students have shown high efficiency of the propaedeutic course under consideration.

Geographic Information System Using Postgre SQL-QGIS for Slum Squatter Distribnution Mapping in North Jakarta

This research aim to build a geographic information system that displays a mapping of slum building distribution in North Jakarta. One of spatial software was developed using a method is the Rational Unified Process (RUP) method. This research aim to build a geographic information system that displays a mapping of building distribution in North Jakarta. This system was built using Postgre SQL and QGIS to build map. The research finding in the research is that there is a tendency for built-up areas to grow in slum areas due to the absence of rental fees, land and building costs, but the feasibility conditions of these areas are very unsuitable. This research is limited to implementing the use of Postgre SQL and QGIS software. Because the rendering process for the visual display is very difficult. I hope that in the future this research will be developed interesting 3D visualization accompanied by charts. The implication of this research is to make it easier to see the distribution of dense and slum areas and become a step for the government in making policies. The originality of this research is that there is still no one who combines the two software in mapping analysis.

Study on fuel management strategy for accident-tolerant fuel on in-pile irradiation test

Within the context of the long-term development plan of batch loading of accident-tolerant fuel assemblies, an equivalent physical model of lead test assemblies is established based on the requirements of radiation testing and the applicability of analysis software. The quantitative assessment has been conducted on the impacts of parameters including zirconium alloy cladding coating and pellet additives on reactivity of fuel assembly. Concurrently, the fuel management strategy is optimized in alignment with the irradiation test objectives of ATF assemblies, leading to the creation of an evaluation process tailored for ATF assembly loading pattern. Focusing on the irradiation of the lead test assemblies into a nuclear power plant in China, three-cycle nuclear reactor loading pattern are designed, which are taking into account the economic, safety, and irradiation testing goals of the nuclear power plant. Simultaneously, the neutronic characteristics are also evaluated, quantitatively. The results show that the established equivalent physical model can effectively represent the characteristics of the lead test assemblies. The loading patterns designed based on the Optimised fuel management strategy achieve the desired goals in terms of economy, safety and radiation test requirements. The designed loading pattern evaluation process and equivalent model analysis method in this work provide guidance for the subsequent engineering design and application of irradiation testing for lead test assemblies.

Enhancing Ice Nucleation: The Role of Surface Roughness in Electrofreezing Using Laser Shock Processed Al6061 T6 Electrodes

The present study investigates the impact of the electrode surface roughness on the electrofreezing of water. This research focuses on how the electrode microstructure induced by a laser treatment affects the nucleation and growth of ice crystals under controlled electric fields. For this, electrofreezing experiments of deionized water over electrodes with varying surface roughnesses and crystalline textures were conducted. The electrodes of the Al6061 T6 alloy were microstructured via the Laser Shock Processing (LSP) method. For this purpose, the pulse densities during the LSP process were varied (900, 1600, and 2500 pulses/cm2). The increase in pulse density was correlated to the microstructural features and average roughness of the LSP-treated Al6061 alloy. A wave-like microstructure was induced upon the LSP treatment, with roughnesses between 3.5 and 6 µm at the selected pulse densities. The results indicate that electrode roughness significantly influences the electrofreezing process. Rougher electrodes were found to increase the nucleation temperature, suggesting enhanced ice nucleation activity. These findings are attributed to the increased electric field concentration at the asperities of the rough surfaces and the (111) planes of the Al6061 alloy, which may facilitate the alignment of water molecules and the formation of critical ice nuclei.

Determining Potential Groundwater Recharge Zones in the North Gujarat Region of Gujarat State Using Techniques of Geoinformatics and Multi-Criteria Decision Analysis

The rapid increase in population and industrialization has significantly raised the consumption of groundwater resources for domestic, agricultural, and industrial purposes. This study aims to develop groundwater recharge potential zone maps to assist in the construction of artificial recharge structures in the North Gujarat Region of Gujarat, India. An integrated approach of remote sensing (RS), geographical information system (GIS), and multi-criteria decision analysis (MCDA) were used to achieve this goal. Influencing factors of groundwater availability included geology, soil, lineament density, slope, rainfall, land use/land cover, geomorphology, and drainage density, were analyzed to delineate potential groundwater recharge zones. each influencing parameter was determined by using Saaty's Analytic Hierarchy Process (AHP) method, and sub-parameters were ranked according to the AHP scale. A weighted overlay analysis tool in ArcGIS software was employed to map the groundwater recharge potential zones in the North Gujarat region. The classified results of the recharge potential area in the study area consist of excellent (9.93%), good (27.90%), moderate (17.58%), poor (3.92%), and very poor (0.92%) These findings provide valuable insights for effectively planning and managing groundwater resource development in the region.

Exploring Recent NLP Advances for Tamil: Word Vectors and Hybrid Deep Learning Architectures

The advancements of deep learning methods and the availability of large corpora and data sets have led to an exponential increase in the performance of Natural Language Processing (NLP) methods resulting in successful NLP applications for various day-to-day tasks such as Language translation, Voice to text, Grammar checking, Sentiment analysis, etc. These advancements enabled the well-resourced languages to adapt themselves to the digital era while the gap for the low-resource languages widened. This research work explores the suitability of the recent advancements in NLP for Tamil, a low-resource language spoken mainly in South India, Sri Lanka, and Malaysia. From the literature survey, it has been found that there is a lack of comprehensive study on the effect of the recent advancements of NLP for the Tamil text. To fill this gap, this research work analysed the performance of deep learning based text representation and classification approaches namely word embedding, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) for Tamil text classification tasks. Different dimensional pretrained word2Vec and FastText word vectors were built for Tamil and their effectiveness on Text classification was evaluated. The study found that the pre-trained 300- dimensional FastText word vector showed better performance than other pre-trained word vectors for Tamil text classification. Further, in this study, four simple hybrid CNN and Bi-GRU models were proposed for Tamil text classification and their performances were evaluated. The study found that hybrid CNN and Bi-GRU models perform better compared to the classical machine learning models, individual CNN and RNN models, and the Multilingual BERT model. These results confirm that the jointly learned embeddings with different deep learning architectures like CNN and RNN can achieve remarkable results for Tamil text classification, thus ensuring that the deep learning approaches can be successful for NLP on Tamil text.

Smart Supervision of Public Expenditure: A Review on Data Capture, Storage, Processing, and Interoperability with a Case Study from Colombia

Effective fiscal control and monitoring of public management are critical for preventing and mitigating corruption, which in turn, enhances government performance and benefits citizens. Given the vast amounts of data involved in government operations, applying advanced data analysis methods is essential for strengthening fiscal oversight. This paper explores data management strategies aimed at enhancing fiscal control, beginning with a bibliometric study to underscore the relevance of this research. The study reviews existing data capture techniques that facilitate fiscal oversight, addresses the challenges of data storage in terms of its nature and the potential for contributing to this goal, and discusses data processing methods that yield actionable insights for analysis and decision-making. Additionally, the paper deals with data interoperability, emphasizing the importance of these practices in ensuring accurate and reliable analysis, especially given the diversity and volume of data within government operations. Data visualization is highlighted as a crucial component, enabling the detection of anomalies and promoting informed decision-making through clear and effective visual representations. The research concludes with a case study on the modernization of fiscal control in Colombia, focusing on the identification of user requirements for various data-related processes. This study provides valuable insights for modern audit and fiscal control entities, emphasizing that data capture, storage, processing, interoperability, and visualization are integral to the effective supervision of public expenditure. By ensuring that public funds are managed with transparency, accountability, and efficiency, the research advances the literature by addressing both the technological aspects of data management and the essential process improvements and human factors required for successful implementation.

Up-conversion materials for solid-state lighting

AbstractConventional phosphor-converted white LEDs (WLEDs), using (Stokes) down-conversion light emission, suffer from a red deficiency that increases the Color Temperature towards “cool white light” above 5000 K. The present work describes a phosphor-Up-converted WLED approach that wishes to overcome this issue by achieving white light generation (WLG) through Up-conversion photoluminescence (UCPL), without energy losses due to Stokes shift and excited by a 980 nm LED instead of the more expensive blue LEDs. The UC materials include thin films of lanthanide (Ln)-doped aluminosilicate glass multilayers, alternating (Ln)-doped aluminosilicate and titania films in the form of 1-D photonic crystals and Ln ion-implanted materials. Sol-gel (SG) processing has been used as a low-cost high purity processing method to prepare titania, as well as aluminosilicate films containing Er3+, Tm3+ and Yb3+, for WLG and possible application as WLED materials. The total Ln content varied from a high of 22 mol% to as low as 5.8 mol%. The materials prepared have been characterized by UV-Vis-NIR (using a variable angle specular reflection accessory), plus FTIR and UCPL spectroscopies. The UC light emission was analyzed with the help of CIE chromaticity diagrams. Graphical abstract

Fast autofocusing based on single-pixel moment detection

Traditional image processing-based autofocusing techniques require the acquisition, storage, and processing of large amounts of image sequences, constraining focusing speed and cost. Here we propose an autofocusing technique, which directly and exactly acquires the geometric moments of the target object in real time at different locations by means of a proper image modulation and detection by a single-pixel detector. An autofocusing criterion is then formulated using the central moments, and the fast acquisition of the focal point is achieved by searching for the position that minimizes the criterion. Theoretical analysis and experimental validation of the method are performed and the results show that the method can achieve fast and accurate autofocusing. The proposed method requires only three single-pixel detections for each focusing position of the target object to evaluate the focusing criterion without imaging the target object. The method does not require any active object-to-camera distance measurement. Comparing to local differential methods such as contrast or gradient measurement, our method is more stable to noise and requires very little data compared with the traditional image processing methods. It may find a wide range of potential applications and prospects, particularly in low-light imaging and near-infra imaging, where the level of noise is typically high.

Laser Cutting of Titanium Alloy Plates: A Review of Processing, Microstructure, and Mechanical Properties

The growing use of titanium alloys has led to the gradual replacement of traditional processing methods by laser cutting technology, making it the preferred method for processing titanium alloy plates due to its high efficiency, precision, and adaptability. In this review, the characteristics of laser cutting technology and its application in titanium alloy plate processing are summarized, outlining several aspects of the cutting process, microstructure, and mechanical properties of the material after cutting, along with simulation predictions. Previous research categorized laser-cutting input parameters into beam parameters and process parameters, with the commonly used parameters being the laser power, cutting speed, and gas pressure. Various parameter combinations can achieve different cutting qualities, and seven indices can be used to evaluate the cutting process, with the surface roughness and slit width serving as the most common indices. Different auxiliary gases have shown a significant impact on the laser cutting quality, with commonly used gases consisting of nitrogen, argon, and air. Argon-assisted cutting generally results in better surface quality. Due to the rapid temperature change, the titanium alloy microstructure will undergo a non-diffusive martensitic phase transformation during laser cutting, producing a heat-affected zone. Experimental studies and simulations of the mechanical properties have shown that the occurrence of a martensitic phase transformation increases the hardness and residual tensile stress of the material, which reduces the fatigue strength and static tensile properties. In addition, studies have found that the more streaks appear on the cut surface, the lower the fatigue strength is, with fatigue cracks arising from the stripes. Hence, the established analytical solution model and three-dimensional finite element model can effectively predict the temperature distribution and residual stress during the cutting process. This can provide a better understanding of the high residual stress characteristics of the cutting edge and the stripe formation mechanism, allowing researchers to better explore the mechanism of laser cutting.

A systematic quality-integrated diagnostic method for complex product assembly using multi-task spatial–temporal transfer learning

AbstractThe assembly process is generally considered one of the primary factors influencing the quality of complex products. Currently, most existing quality-integrated diagnostic methods for products tend to deteriorate over different processes and degrade over time. To address this issue, this paper introduces a systematic quality-integrated diagnostic method for complex product assembly processes. First, the influence factors and error sources in the complex assembly process are analyzed using the 5M1E and FAHP methodologies. Next, similarity-based multi-task clustering and dismantling using RGMM is applied to divide the assembly tasks. Finally, MMD-MSE is employed to develop a quality prediction model for the complex assembly process using a spatial–temporal transfer learning approach. Experiments were conducted on an array antenna assembly task, comparing the proposed method with conventional methods. The results show that the accuracy and PrUP of the proposed model are 97.6% and 95.2%, respectively, for the quality-integrated diagnostic of complex assembly processes, with a fluctuation in accuracy of less than 6%. The diagnostic results effectively meet expert evaluations and provide a stable, reliable, and practical solution for addressing quality fluctuations in complex production assembly processes.

The Influence of Pervaporation on Ferulic Acid and Maltol in Dealcoholised Beer

Non-alcoholic beer is becoming more and more popular every year. Due to the high demand for such drinks, numerous breweries decided to produce non-alcoholic beer. There are various methods to create a beer with a reduced alcohol content. Among them are biological methods influencing the biochemistry of the brewing process and physical methods focused on removing ethanol from ready beer. Thus far, the most popular methods are vacuum rectification and reverse osmosis. This work evaluated another method called pervaporation for non-alcoholic beer production. During the study, low-alcohol beer (0.58 vol.%) was achieved from standard beer (3.62 vol.%) using pervaporation. The colour of the product remained unchanged at level 7 EBC. The concentration of ferulic acid decreased from 11.5 to 9.1 mg/dm3, and maltol was concentrated, reaching a concentration of 38 mg/dm3 in the final retentate during a 5 h process.