Processing Methods Research Articles

The effectiveness of the project method in teaching humanitarian disciplines

The purpose of this study was to identify the effectiveness of the project method as an innovative tool for the teaching of humanitarian subjects. The research used such methods as follows: questionnaire, observation, experiment, as well as statistical methods for data processing. In the course of the research, it was determined that the students who were engaged in the project activity, during the project, autonomously researched scientific, reference, educational, and methodological literature. A group of experts found that students’ participation in project activities influenced the development of creative activity, and creative systemic thinking contributed to their self-organization. During the survey, it was determined that students' independent work on creating projects ensures the development of personal and functional components of selforganization. The research's scientific novelty lies in its exclusive focus on students studying humanitarian disciplines. The restricted scope of the experiment facilitated the identification of the project approach as such that it can be integrated into the academic system to enhance students' personal and practical proficiencies, thereby promoting their overall growth. Further research can be aimed at determining the effectiveness of using game methods in the course of teaching humanitarian disciplines.

Automated text classification of opinion vs. news French press articles. A comparison of transformer and feature-based approaches

This study explores Natural Language Processing (NLP) methods for distinguishing between press articles belonging to the journalistic genres of ‘objective’ news and ‘subjective’ opinion. Two classification models are compared: CamemBERT, a French transformer model fine-tuned for the task, and a machine learning model using 32 linguistic features. Trained on 8000 Belgian French articles, both models are evaluated on 1000 Canadian French articles. Results show CamemBERT’s superiority but highlight potential for hybrid approaches and emphasizes the need for robust and transparent methods in NLP. The research contributes to understanding NLP’s role in journalism by addressing challenges of point of view detection in press discourse.

A Multi-aperture Coaxial Projector Balancing Shadow Suppression and Deblurring.

This paper proposes a projection system that optically removes the cast shadow in projection mapping. Specifically, we realize the large-aperture (LA) projection using a large-format Fresnel lens to suppress cast shadows by condensing the projection light from a wide viewing angle. However, the resolution and contrast of the projected results are significantly degraded by defocus blur, veiling glare, and stray light caused by the aberration of an LA Fresnel lens. To solve the technical problems, we employ two different approaches: optical and digital image processing methods. First, we introduce a residual projector with a typical aperture lens on the same optical axis as the LA projector, projecting the residual (i.e., high-frequency) components attenuated in the LA projection. These projectors play different roles in shadow suppression and blur compensation, both achieved by projecting simultaneously. Secondly, we optimize the pair of projection images that can balance the shadow suppression and deblurring performance of our projection system. We implemented a proof-of-concept prototype and validated the above-mentioned techniques through projection experiments and a user study.

DocBot AI as Your Personal Health Assistant: Bridging the Gap in Consultations Revolutionizing Healthcare Using Natural Language Processing and Artificial Intelligence

The DocBot Chat Bot/AI system is a novel solution to the challenge of acquiring accurate and personalized health information. By utilizing cutting-edge artificial intelligence (AI) algorithms and natural language processing (NLP) methods, the DocBot system creates an engaging and intuitive interface that lets people convey their health concerns and get personalized advice. This study provides an in-depth assessment of the DocBot system, stressing its key features, AI algorithms, advantages, difficulties, and potential applications. To improve healthcare communication and encourage well-informed decision-making, it is important to highlight DocBot's transformative potential in providing users with accurate and trustworthy health knowledge.

Quality assessment of artisanal palm oil from smallholders in the department of Man, western region of Côte d'Ivoire

In the western region of Côte d'Ivoire, more particularly from the department of Man, artisanal crude palm oil enjoys a reputation for quality among consumers and is the subject of a developed trade. To guarantee consumers quality palm oil, it is crucial to register this product as a Geographical Indication artisanal palm oil. This study aims to evaluate the physicochemical properties of this artisanal palm oil to better understand his quality. A descriptive survey of palm oil processing was conducted among 200 smallholders in 10 localities in the department of Man, Western region of Côte d'Ivoire. Then, samples were collected from the producers and were taken to the laboratory for analysis. The physicochemical properties of CPO samples were determined using standard analytical method. The results showed that manual processing and screw hand press methods were used for palm oil extraction. About 58% of the processors have 11-20 years’ experience in oil palm processing and have no formal education (48.2%). Concerning the physicochemical quality of artisanal palm oil producing, the lipid oxidation, carotenoids contents, DOBI, slip melting point, iodine and saponification indexes were relatively conformed to the standard while FFA, moisture and impurities content were different. These results demonstrate a need for monitoring of artisanal palm oil production by smallholders in the sector who should adopt good agricultural practices.

Assessment of supply chain efficiency through a sustainable supply chain management strategy in the textile sector

The textile industry processes fiber into yarn or fabric. Problems often faced by textile companies are delays in raw materials, which result in the production and distribution of fabrics not reaching the target, so consumers are unsatisfied and switch to competing companies. In addition, solid waste from the production process is still disposed of, potentially polluting the environment. This study uses the Green Supply Chain Operation Reference (Green SCOR) approach to evaluate the performance of green supply chain management (GSCM) in textile companies located in Yogyakarta. The findings of this study could significantly impact the textile industry as they provide a comprehensive evaluation of GSCM performance. Twenty-two performance indicators have been validated using the Item-Content Validity Index (I-CVI) method and weighted using the Analytical Hierarchy Process (AHP) method. Based on the calculation results, the GSCM performance value is 60.208. Improvement recommendations are given for three indicators that need to be prioritized based on Importance Performance Analysis (IPA) matrix mapping and the actual condition of the company. Improvements can be made by using the Vacuum Flash Evaporation method so that recycled PVA can be reused, using natural materials or alternative auxiliary materials that are more environmentally friendly, increasing cooperation with companies engaged in processing weaving waste or textile waste appropriately and effectively, checking the concentration of starch solution not only when changing construction but every beam change, and forming a checklist form that is filled in for monitoring after machine settings and preparation of the production process.

Leveraging openCV and image processing for crime identification through facial recognition

To maintain the safety and security of communities, law enforcement agencies provide vital functions such as crime prevention and identification. Identifying people with a criminal record efficiently and accurately is one of the biggest hurdles in this field. This study delves into a fresh strategy for taking on this problem by making use of state-of-the-art image processing methods and machine learning algorithms. The project's main goal is to identify convicted felons by utilizing OpenCV, a robust open-source computer vision toolkit, and different facial recognition algorithms. By giving them a tool that guarantees a faster reaction rate and improved accuracy in suspect identification, the system is aimed to boost the capacities of law enforcement organizations. This project is based on the tenet of criminal psychology, which states that criminals are prone to committing new crimes. The system's goal is to detect and track repeat criminals both before and after they commit crimes by making use of this information. The method works by taking pictures of people who are known to be criminals and putting them in a database. From there, they can be compared to either live video streams or static photographs taken by surveillance cameras. In order to analyze and identify facial characteristics effectively, algorithms like LBPH and the Haar cascade classifier are also used. The system's robustness and efficiency are guaranteed by integrating these approaches into the OpenCV framework. This research makes a valuable contribution to the field of crime prevention by offering law enforcement authorities a dependable and scalable alternative. This system provides a streamlined interface for storing, retrieving, and matching facial photos. It was built using Python 3.5 and uses SQLite to manage the database of offenders' details. Proactively monitoring persons with a criminal record is made easier and faster with the suggested method. Suspect identification is also made easier. This research showcases the possibility of integrating machine learning and image processing to enhance public safety and crime prevention initiatives. It is an early step towards more complete video surveillance systems.

Impact of Tuber Section, Steeping Solution, and Concentration on Physicochemical Properties of Yam (Dioscorea rotundata) Flour

This study examined various factors such as Tuber Cutting (TC), Tuber Processing Method and Duration (TPM and TPD), Soaking Solution Type and Concentration (SST and SSC), as well as Parameter Analytical Temperature (PAT) on flour samples prepared from different parts of yam tubers.

A Methodology for the Design of a Compliant Electrocardiograph: A Case Study

This document presents the methodology for designing an electrocardiograph capable of acquiring IEC 60601-2-25-compliant signals. The objective of developing this methodology is to address a signal incompatibility problem that has existed in academia for years, specifically in physiological processing research. This problem is related to the signal’s sampling rate and/or noise levels, and it becomes evident when one signal processing method is intended to work with another, either as a subsequent or simultaneous process. Even though matching algorithms can be implemented to remedy this incompatibility problem, the ultimate solution is the standardization of signals, which depends exclusively on the standardization of hardware. The signal incompatibility problem is urgent to solve because it makes the integration and scalability of different academic works difficult, preventing academia from reaching the stage of development that commercial equipment displays in automatic interpretation procedures. The design methodology presented in this document addresses the stated problem by creating an open-source hardware device capable of acquiring compliant signals, with careful consideration given to Signal Integrity and EMC concepts—a methodology that can be extended to other physiological acquisition systems. The expedited availability of the device’s design documentation and fabrication files is also an advantage of this work.

Spiral feed polishing uniform removal and compensation strategy of sapphire components

A single crystal sapphire component has been widely used in various high-tech fields because of its significant advantages such as high hardness, high stability, and excellent optical and mechanical properties, and has put forward high requirements for surface accuracy and quality. The existing sapphire polishing technology has problems such as low polishing efficiency, difficult control of polishing accuracy, and difficulty in removing surface defects and subsurface damage introduced by the front grinding process. Therefore, for the polishing and damage removal stage of sapphire optical components, the surface shape accuracy should be strictly controlled, especially for the surface shape accuracy after ultra-precision grinding. It is of great significance to study the uniform removal technology of grinding damage based on not destroying the surface shape. This study focuses on the mechanical polishing of sapphire. Firstly, the characteristics of the sapphire removal function of the elastic polishing tool are analyzed and the stability of the polishing tool is verified. Secondly, by establishing the relationship between the amount of workpiece material removal and the radius of rotation, the feed rate is planned to achieve the effect of uniform spiral removal. By optimizing the center feed speed of the tilting axis small-size polishing tool, the center feature of the polishing surface is controlled to be sharp. Finally, based on the sub-aperture polishing figuring theory, the influence of the center feature on the surface profile is reduced by using the spiral grating processing method. This study provides an efficient and stable strategy for the uniform removal and polishing of rotationally symmetric optical elements (such as aspheric surfaces) of high-hardness material sapphire and is expected to play a role in scenes that are particularly difficult to process, difficult to detect full aperture, difficult to calculate removal function and dwell time, such as higher steep aspheric surfaces and Gaussian aspheric surfaces.

Study curing of epoxy resin by Isophoronediamine/ Triethylenetetramine and reinforced with montmorillonite and effect on compressive strength

Epoxy is a widely used thermosetting resin recognized for its exceptional performance in adhesives, coatings, and various other applications, attributed to its high tensile strength, stiffness, electrical performance, and chemical resistance. Epoxy-clay nanocomposites are extensively employed across diverse industries. The physical and chemical properties of these nanocomposites are influenced by the processing methods, clay modifiers, and curing agents used during their preparation. In this study, epoxy/nanoclay composites based on Diglycidyl Ether Bisphenol-A (DGEBA) will be cross-linked using Isophorone Diamine (IPD), a cycloaliphatic amine, and Triethylenetetramine (TETA), a linear aliphatic amine. The initial phase of the research will assess the impact of different types of cross-linkers, both individually and in combination at various molar ratios (such as Isophorone Diamine: Triethylenetetramine (IPA: TETA) / 25:75 and 75:25), on the compressive strength of the epoxy mortar. In the subsequent phase, the epoxy formulation with an Isophorone Diamine: Triethylenetetramine (IPD: TETA / 75:25), which demonstrates the highest compressive strength, will be selected for further investigation. This formulation will be used to evaluate the effects of different weight percentages (3%, 5%, and 7%) of organically modified montmorillonite (OMMT). The prepared epoxy composites will be characterized using a range of techniques, including Fourier Transform Infrared Spectroscopy (FT-IR), Transmission Electron Microscopy (TEM), and Scanning Electron Microscopy (SEM). The epoxy/nanoclay composite with an IPD: TETA / 75:25 and 3 wt % OMMT is expected to show the highest compressive strength, which is 94 MPa.

Enhancing the Nutritional and Bioactive Properties of Bee Pollen: A Comprehensive Review of Processing Techniques

Bee pollen is recognized as a superfood due to its high content of nutrients and bioactive compounds. However, its bioavailability is restricted by a degradation-resistant outer layer known as exine. Physical and biotechnological techniques have recently been developed to degrade this layer and improve pollen’s nutritional and functional profile. This review examines how processing methods such as fermentation, enzymatic hydrolysis, ultrasound, and drying affect pollen’s chemical profile, nutrient content, and bioactive compounds. The review also considers changes in exine structure and possible synergistic effects between these methods. In addition, the challenges associated with the commercialization of processed bee pollen are examined, including issues such as product standardization, stability during storage, and market acceptance. The objective was to provide a understanding of the efficacy of these techniques, their physicochemical conditions, and their effect on the nutritional value of the pollen. The work also analyzes whether pollen transformation is necessary to maximize its benefits and offers conclusions based on the analysis of available methods, helping to determine whether pollen transformation is a valid strategy for inclusion in functional foods and its impact on consumer health. Although the literature reports that pollen transformation influences its final quality, further studies are needed to demonstrate the need for pollen exine modification, which could lead to greater market availability of pollen-based products with functional properties.

Data quality assessment of aggregated LCI datasets: A case study on fossil‐based and bio‐based plastic food packaging

AbstractEnvironmental impacts resulting from plastic food packaging, made from both fossil‐based and bio‐based polymers, are increasingly analyzed in life cycle assessment (LCA) studies. However, the literature reveals significant variations in results for the same polymer within the same scope. To enhance the reliability of these assessments, data quality assessment (DQA) plays a relevant role. However, despite most of the LCA studies employing aggregated life cycle inventory (LCI) datasets, in the literature, DQA methods for aggregated processes are not available. To fill this gap, in this paper, a DQA for aggregated LCI datasets is proposed and demonstrated through its application to 101 aggregated LCI datasets, extracted from Ecoinvent and GaBi databases. The DQA method has been developed by adapting and integrating the pedigree matrix and the data quality ranking proposed by the recently published EC Plastic LCA method. The three data quality indicators (DQIs) used are technological, geographical, and time‐related representativeness. The application of this method exhibits an overall positive evaluation of the selected datasets with differences among the three DQIs. Moreover, it highlights the role of metadata structure in adequately supporting a robust DQA. Indeed, in the absence of a common framework that defines, assesses, and provides access to data quality information, transparency must be assured by the operator in the metadata interpretation and related assumptions along the DQA process. Finally, although the proposed DQA method was developed for the plastic sector, its application can be extended to LCI aggregated datasets relevant to other sectors, materials, and products.

Significant pre-post effects on a patient-reported outcome measure in inpatient rehabilitation

Abstract Background We conducted a pilot study for the Swiss National Association for Quality Development in Hospitals and Clinics (ANQ) in the inpatient rehabilitation setting. The aim was to assess the suitability of a generic health-related quality of life patient-reported outcome measure (PROM) as an outcome quality indicator for public reporting across all rehabilitation domains on a national level. Methods In a multi-stage expert-based selection process, the PROMIS Global Health 10 was selected for piloting. We collected data from 29 rehabilitation clinics from April to December 2023. Mean T-scores and effect sizes were analysed across different rehabilitation domains. Patients’ need for assistance in completing the questionnaire was also assessed. Results A dataset of 2261 cases yielded a cleaned sample of 2083, with 1217 complete cases. Mean T-scores for physical health (PHS) and mental health (MHS) significantly differed (p < 0.001) at admission and discharge across all rehabilitation domains. The largest effects were observed in PHS for pulmonary (38.9(7.3); 46.0(7.6); dz = 0.95) and cardiologic rehabilitation (41.7(7.4); 47.9(7.2); dz = 0.85), and in MHS for internal medicine (44.1(6.9); 49.1(5.6); dz = 0.80) and psychosomatic rehabilitation (36.0(7.2); 45.2(8.2); dz = 1.19), while the smallest effects were noted in PHS for paraplegia (36.3(7.2); 39.8(7.2); dz = 0.50) and in MHS for the musculoskeletal rehabilitation domain (46.2(7.9); 48.7(7.4); dz = 0.33). Of all complete cases, 40.5% needed assistance in filling in the PROM and support needs were distributed unequally across rehabilitation domains, ranging from 10.0% in psychosomatic to 70.3% in geriatric rehabilitation. Conclusions The study presents a nuanced understanding of HRQOL across various rehabilitation domains, with notable effects observed across all domains, alongside significant assistance needs. Key messages • PROMIS-GH10 shows significant pre-post effects on physical and mental health in Swiss inpatient rehabilitation, with notable effects observed across all domains. • Observable barriers in implementation are evident due to substantial assistance requirements.

The empowering role of health literacy in combatting fake news, misinformation and infodemics

Abstract Background In times of the rapid digital transformation, people need to acquire specific knowledge, skills and attitudes to deal with data, digital information and technology. Such skills are particularly important in times of crisis, as the COVID-19 pandemic has shown. Alongside the pandemic, a so called “infodemic” has emerged, i.e., an overabundance of information, accompanied by misinformation, which impedes sound decision-making processes, affects health literacy (HL) and impacts public health. However, while misinformation poses a well-known threat to our health and well-being, we still lack viable concepts and approaches to satisfactorily solving this issue. Thus, we aimed at reviewing the concept of HL in view of the infodemic and health-related challenges. Methods In a 12-month participatory process in Switzerland in 2023, we investigated the empowering role of HL in light of the polycrisis. On behalf of the Swiss Federal Office of Public Health, we conducted a literature review on existing HL definitions. Then, we conducted 6 expert interviews and 2 focus group interviews with other 10 experts in HL and associated domains, which guided and informed the review process. Results As a result, HL can be understood as a bundle of competencies to proactively deal with health-related information, services, and challenges and thereby empowers people to manage their and other’s health and well-being. Thus, HL empowers people to better manage digital information and services and promotes critical thinking. This in turn is necessary to assess information quality, uncover misinformation and to adequately manage health data and information. Conclusions HL represents a crucial prerequisite for individuals, professionals, and decision-makers to find trustworthy health information, to reflect upon the quality and credibility of sources and content, and to understand the complex interrelations of the determinants of health and is therefore crucial for public health. Key messages • Health literacy empowers people to adequately deal with fake news, misinformation and infodemics. • Strengthening health literacy offers great potential for public health to promote critical thinking and to rebuild trust.

RESEARCH PROGRESS ON STRENGTHENING PROCESS OF H13 STEEL FOR SHIELD CUTTER RING

As China continues to advance the modernization process, a large number of infrastructure construction projects need to use shield machines to build tunnels. As a key component of the shield machine/TBM direct contact working environment, the hob is extremely complex and extremely harsh due to the extremely complex and extremely harsh working environment, resulting in hobs. It is very easy to damage when in service. The damage of the hob directly leads to many problems such as project delay and capital consumption. Therefore, improving the mechanical properties of the hob and prolonging the service life is an important engineering problem to be solved urgently. Based on the service performance of disc cutter, this paper analyzes its failure form and wear mechanism, and summarizes the strengthening process and improvement method of the H13 steel disc cutter ring. In this paper, the advantages and disadvantages of previous researchers and optimization methods are reviewed for H13 steel material composition, heat treatment process, surface coating strengthening process, and other processes. Finally, the performance strengthening of hob cutter ring is discussed and prospected, in order to provide a reference for hob strengthening technology path and industrialization realization.

The Invisible Tropical Tuber Crop: Edible Aroids (Araceae) Sold as “Tajer” in the Netherlands

AbstractEdible aroids (plants from the family Araceae) are among the top five most cultivated tuber crops globally, but their consumer acceptance is hindered by acridity. Aroids contain sap that severely irritates the throat and lips if not properly processed. However, no in-depth studies exist on acridity in edible aroids and how to diminish it. We used ethnobotanical methods to document the diversity of edible aroids available in the Dutch marketplace and how consumers handle acridity. We grew corms in a greenhouse to obtain additional morphological information and used flow cytometry to assess ploidy. We collected 73 samples and interviewed 71 people. At least three species, Colocasia esculenta, Xanthosoma violaceum, and Caladium bicolor, were sold under the generic name “tajer.” Different plant parts with various forms of processing were imported from diverse geographic origins and sold mainly to immigrant customers. Interviewees mentioned various processing methods to avoid and reduce acridity. We distinguished nine cultivar groups of Colocasia esculenta and four of Xanthosoma violaceum. Both Colocasia esculenta and Xanthosoma violaceum were also cultivated in Dutch greenhouses. We present the first report on edible aroids cultivation in northern Europe and draw attention to the lack of labeling and cooking instructions.

Predicting diabetes prognosis using machine learning techniques in a Hungarian clinical database

Abstract Background Chronic conditions such as type 2 diabetes mellitus have great impact on patients’ quality of life. Although clinical databases provide a perfect base for research aimed at improving diabetes care, analyzing such databases requires extensive pre-processing, mainly due to the large amount of unstructured data. Our study aimed to present the steps of generating a dataset from a large clinical database, and to apply machine learning-based analytical techniques regarding. Methods Data of the Clinical Center of University of Debrecen was used. To structure the unstructured data, regular expressions and natural language processing methods were used. The main machine learning models were as follows: Gradient Boosting Machines to predict the risk of complication development; Long Short-Term Memory Networks to forecast future health outcomes. All analysis and procedures were done using Python. Results The database contains approximately 1600 tables, with a total size of 1.9 terabytes, where the largest table is 21.07 gigabytes with approximately 44.64 million rows. The final dataset consisted of 40,332 patients. Most variables originate from the unstructured data, including complications and comorbidities of diabetes, as well as physical and laboratory parameters. Related to laboratory parameters, the number of measurements and the median value for every half-year were available. The diagnosis time of the complications’ occurrence is also presented. Machine learning methods were more accurate compared with traditional statistical methods in predicting the prognosis (p < 0.05). Discussion Our research highlights the importance of clinical data in chronic disease management. There are challenges in pre-processing and managing datasets, but machine learning-based methods are very efficient not only in extracting useful information from unstructured data but also in predicting the prognosis and identifying potential intervention points for better care. Key messages • Natural language processing can be used to obtain useful information from unstructured clinical data. • Using machine learning techniques on clinical data could improve diabates care.

Bio-Based and Biodegradable Polymeric Materials for a Circular Economy

Nowadays, plastic contamination worldwide is a concerning reality that can be addressed with appropriate society education as well as looking for innovative polymeric alternatives based on the reuse of waste and recycling with a circular economy point of view, thus taking into consideration that a future world without plastic is quite impossible to conceive. In this regard, in this review, we focus on sustainable polymeric materials, biodegradable and bio-based polymers, additives, and micro/nanoparticles to be used to obtain new environmentally friendly polymeric-based materials. Although biodegradable polymers possess poorer overall properties than traditional ones, they have gained a huge interest in many industrial sectors due to their inherent biodegradability in natural environments. Therefore, several strategies have been proposed to improve their properties and extend their industrial applications. Blending strategies, as well as the development of composites and nanocomposites, have shown promising perspectives for improving their performances, emphasizing biopolymeric blend formulations and bio-based micro and nanoparticles to produce fully sustainable polymeric-based materials. The Review also summarizes recent developments in polymeric blends, composites, and nanocomposite plasticization, with a particular focus on naturally derived plasticizers and their chemical modifications to increase their compatibility with the polymeric matrices. The current state of the art of the most important bio-based and biodegradable polymers is also reviewed, mainly focusing on their synthesis and processing methods scalable to the industrial sector, such as melt and solution blending approaches like melt-extrusion, injection molding, film forming as well as solution electrospinning, among others, without neglecting their degradation processes.

Response to “Comment on ‘Fast data processing method for multispectral radiation thermometry based on Euclidean distance optimization’”

Response to “Comment on ‘Fast data processing method for multispectral radiation thermometry based on Euclidean distance optimization’”