Experimental Stage Research Articles

Revolutionizing Healthcare: The Role of Machine Learning in the Health Sector

Traditional healthcare systems have grappled with meeting the diverse needs of millions of patients, resulting in inefficiencies and suboptimal outcomes. However, the emergence of machine learning (ML) has ushered in a transformative paradigm shift towards value-based treatment, empowering healthcare providers to deliver personalized and highly effective care. Modern healthcare equipment and devices now integrate internal applications that collect and store comprehensive patient data, providing a rich resource for ML-driven predictive models. In this research article, we explore the profound impact of ML on contemporary healthcare, emphasizing its potential to significantly enhance patient care and optimize resource allocation. Our study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, leveraging extensive datasets encompassing diverse patient populations. We compared several ML algorithms, including Logistic Regression (accuracy: 0.796875), K-Nearest Neighbors (accuracy: 0.7864583333333334), XG Boost (accuracy: 0.78125), and PyTorch (accuracy: 0.7337662337662337), to identify the best-performing model. The achieved accuracies underscore the effectiveness of these ML techniques in disease prediction and underscore the potential for improving patient outcomes. Beyond the technical aspects, we explore the broader implications of value-based treatment and the integration of ML for various healthcare stakeholders. By emphasizing the numerous benefits of personalized and proactive medical care, our findings illustrate the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems. The adoption of ML in healthcare lays the foundation for a more efficient, effective, and patient-centered medical ecosystem, supporting the sustainability and adaptability of healthcare systems in the face of expanding patient populations and complex medical needs. This article significantly contributes to the field by providing comprehensive insights into the experimental stages, showcasing the achieved results, and highlighting the key conclusions derived from our study. By addressing the limitations of the previous abstract, we ensure a more informative and substantial overview of our research, offering valuable knowledge for researchers, practitioners, and decision-makers striving to leverage the power of ML in healthcare innovation.

Revolutionizing Healthcare: The Role of Machine Learning in the Health Sector

Traditional healthcare systems have grappled with meeting the diverse needs of millions of patients, resulting in inefficiencies and suboptimal outcomes. However, the emergence of machine learning (ML) has ushered in a transformative paradigm shift towards value-based treatment, empowering healthcare providers to deliver personalized and highly effective care. Modern healthcare equipment and devices now integrate internal applications that collect and store comprehensive patient data, providing a rich resource for ML-driven predictive models. In this research article, we explore the profound impact of ML on contemporary healthcare, emphasizing its potential to significantly enhance patient care and optimize resource allocation. Our study presents a robust predictive model capable of accurately forecasting patient diseases based on input information and various parameters, leveraging extensive datasets encompassing diverse patient populations. We compared several ML algorithms, including Logistic Regression (accuracy: 0.796875), K-Nearest Neighbors (accuracy: 0.7864583333333334), XG Boost (accuracy: 0.78125), and PyTorch (accuracy: 0.7337662337662337), to identify the best-performing model. The achieved accuracies underscore the effectiveness of these ML techniques in disease prediction and underscore the potential for improving patient outcomes. Beyond the technical aspects, we explore the broader implications of value-based treatment and the integration of ML for various healthcare stakeholders. By emphasizing the numerous benefits of personalized and proactive medical care, our findings illustrate the substantial potential of ML-driven predictive healthcare models to revolutionize traditional healthcare systems. The adoption of ML in healthcare lays the foundation for a more efficient, effective, and patient-centered medical ecosystem, supporting the sustainability and adaptability of healthcare systems in the face of expanding patient populations and complex medical needs. This article significantly contributes to the field by providing comprehensive insights into the experimental stages, showcasing the achieved results, and highlighting the key conclusions derived from our study. By addressing the limitations of the previous abstract, we ensure a more informative and substantial overview of our research, offering valuable knowledge for researchers, practitioners, and decision-makers striving to leverage the power of ML in healthcare innovation.

An advanced multimodal driver-assistance prototype for emergency-vehicle detection

In the automotive industry, intelligent monitoring systems for advanced human-vehicle interaction aimed at enhancing the safety of drivers and passengers represent a rapidly growing area of research. Safe driving behavior relies on the driver’s awareness of the road context, enabling them to make appropriate decisions and act consistently in anomalous circumstances. A potentially dangerous situation can arise when an emergency vehicle rapidly approaches with sirens blaring. In such cases, it is crucial for the driver to perform the correct maneuvers to prioritize the emergency vehicle. For this purpose, an Advanced Driver Assistance System (ADAS) can provide timely alerts to the driver about an approaching emergency vehicle. In this work, we present a driver-assistance prototype that leverages multimodal information from an integrated audio and video monitoring system. In the initial stage, sound analysis technologies based on computational audio processing are employed to recognize the proximity of an emergency vehicle based on the sound of its siren. When such an event occurs, an in-vehicle monitoring system is activated, analyzing the driver’s facial patterns using deep-learning-based algorithms to assess their awareness. This work illustrates the design of such a prototype, presenting the hardware technologies, the software architecture, and the deep-learning algorithms for audio and video data analysis that make the driver-assistance prototype operational in a commercial car. At this initial experimental stage, the algorithms for analyzing the audio and video data have yielded promising results. The area under the precision-recall curve for siren identification stands at 0.92, while the accuracy in evaluating driver gaze orientation reaches 0.97. In conclusion, engaging in research within this field has the potential to significantly improve road safety by increasing driver awareness and facilitating timely and well-informed reactions to crucial situations. This could substantially reduce risks and ultimately protect lives on the road.

Geometrical Stabilities and Electronic Structures of Ru3 Clusters on Rutile TiO2 for Green Hydrogen Production.

In response to the vital requirement for renewable energy alternatives, this research delves into the complex interactions between ruthenium (Ru3) clusters and rutile titanium dioxide (TiO2) (110) interfaces, with the aim of enhancing photocatalytic water splitting processes to produce environmentally friendly hydrogen. As the world shifts away from traditional fossil fuels, this study utilizes the density functional theory (DFT) and the HSE06 hybrid functional to thoroughly assess the geometric and electronic properties of Ru3 clusters on rutile TiO2 (110) surfaces. Given TiO2's renown role as a photocatalyst and its limitations in visible light absorption, this research investigates the potential of metals like Ru to serve as additional catalysts. The results indicate that the triangular Ru3 cluster exhibits exceptional stability and charge transfer effectiveness when loaded on rutile TiO2 (110). Under ideal adsorption scenarios, the cluster undergoes oxidation, leading to subsequent changes in the electronic configuration of TiO2. Further exploration into TiO2 surfaces with defects shows that Ru3 clusters influence the creation of oxygen vacancies, resulting in a greater stabilization of TiO2 and an increase in the energy required for creating oxygen vacancies. Moreover, the attachment of the Ru3 cluster and the creation of oxygen vacancies lead to the emergence of polaronic and hybrid states centered on specific titanium atoms. These states are vital for enhancing the photocatalytic performance of the material within the visible light spectrum. This DFT study provides essential insights into the role of Ru3 clusters as potential supplementary catalysts in TiO2-based photocatalytic systems, setting the stage for practical experiments and the development of highly efficient photocatalysts for sustainable hydrogen generation. The observed effects on electronic structures and oxygen vacancy generation underscore the intricate relationship between Ru3 clusters and TiO2 interfaces, offering a valuable direction for future research in the pursuit of clean and sustainable energy solutions.

Controlled-environment agriculture for an urbanised world? A comparative analysis of the innovation systems in London, Nairobi and Singapore

Multiple crises and challenges in the food sector are driving a rising need for innovative food production methods that could provide a growing urban population with high-quality, sustainable and healthy food while strengthening the resilience of food systems. Controlled-environment agriculture (CEA) in urban areas has been proposed as one possible pathway to address these demands. Despite its various potential benefits, CEA is still in a conceptual or experimental stage and there has been less research that focuses on the specificities of urban areas where it could be implemented. Using the Urban Food Production Innovation System (UFoPrInS) concept, this paper analyses the urban contexts and enabling and impeding factors for implementing CEA in three contrasting locations: London, Nairobi and Singapore. Based on document analysis and semi-structured expert interviews, our findings show that Singapore is a favourable location because public policies support the implementation of CEA to reduce food import dependency and enhance the resilience of food supply. In London, high food import dependency is increasingly seen as problematic, but the implementation of CEA has been hampered by other policy priorities. In Nairobi, where over half of the population lives in informal settlements without adequate food, water and sanitation, CEA is unlikely to make an economically efficient contribution to food security. We conclude that the implementation of CEA might be suitable in locations with ample capital and knowledge, stable political, social, and infrastructure conditions, and limited space, where value can be linked to hospitality and tourism, supported by positive pricing for resource savings.

Describing the on-the-job training experiences of bachelor of science in management accounting students

This study aims to describe the on-the-job training experiences of BSMA students at Saint Columban College, Pagadian City. A single case study was most appropriate for the research’s proper conduct since this study wants to describe and understand the on-the-job training of BSMA students who experience this program. The findings led the researchers to the conclusion that the research participants described their on-the-job training as a journey of learning, growth, and application of their academic knowledge to the real-world context of accounting and management fields. They expressed a sense of practicality and hands-on learning, emphasizing the transition from theoretical concepts to practical applications. Their narratives show the importance of concrete experiences within their training, followed by reflective observations that allow them to derive meaningful insights. The development of abstract conceptualizations and stage of active experimentation highlights the value of these experiences in shaping their professional skills and development.

The expression system influences stability, maturation efficiency, and oligomeric properties of the potassium-chloride co-transporter KCC2

The neuron-specific K+/Cl− co-transporter 2, KCC2, which is critical for brain development, regulates γ-aminobutyric acid-dependent inhibitory neurotransmission. Consistent with its function, mutations in KCC2 are linked to neurodevelopmental disorders, including epilepsy, schizophrenia, and autism. KCC2 possesses 12 transmembrane spans and forms an intertwined dimer. Based on its complex architecture and function, reduced cell surface expression and/or activity have been reported when select disease-associated mutations are present in the gene encoding the protein, SLC12A5. These data suggest that KCC2 might be inherently unstable, as seen for other complex polytopic ion channels, thus making it susceptible to cellular quality control pathways that degrade misfolded proteins. To test these hypotheses, we examined KCC2 stability and/or maturation in five model systems: yeast, HEK293 cells, primary rat neurons, and rat and human brain synaptosomes. Although studies in yeast revealed that KCC2 is selected for endoplasmic reticulum-associated degradation (ERAD), experiments in HEK293 cells supported a more subtle role for ERAD in maintaining steady-state levels of KCC2. Nevertheless, this system allowed for an analysis of KCC2 glycosylation in the ER and Golgi, which serves as a read-out for transport through the secretory pathway. In turn, KCC2 was remarkably stable in primary rat neurons, suggesting that KCC2 folds efficiently in more native systems. Consistent with these data, the mature glycosylated form of KCC2 was abundant in primary rat neurons as well as in rat and human brain. Together, this work details the first insights into the influence that the cellular and membrane environments have on several fundamental KCC2 properties, acknowledges the advantages and disadvantages of each system, and helps set the stage for future experiments to assess KCC2 in a normal or disease setting.

Effectiveness of health education and counseling on stages of change, decisional balance, and smoking cessation self-efficacy: A prospective self-control study

ObjectiveTo examine the effectiveness of health education and counseling on the stages of change, decisional balance, and smoking cessations elf-efficacy in smokers with no intention of quitting. MethodsA prospective self-controlled design was conducted between December 2020 and December 2022. The research period was divided into a control stage (first to fourth weeks) and an experimental stage (fifth to eighth weeks). Patients with coronary artery disease (CAD) and habitually smoked were recruited. Pearson correlation and a one-factor repeated-measurement analysis were performed to assess the effectiveness of the intervention. ResultsIn total, 108 male CAD patients with a mean age of 58.1 years were recruited. After 4 weeks of the intervention, 55 (51%) exhibited behavior change (X 2 = 18.03, p = .001). The decisional balance and smoking cessation self-efficacy scores significantly improved in the experimental stage. No significant differences were observed in the control stage. ConclusionsFour weeks of health education and counseling could effectively improve participants’ stage of change, decisional balance, and smoking cessation self-efficacy. Practice implicationHealthcare professionals can play key roles in helping CAD patients successfully quit smoking through individual education and counseling.

The Use of Functional Magnetic Resonance Imaging (fMRI) and Electroencephalogram (EEG) during Self-Reflection: A Scoping Review

The integration of two fields, which are neuroscience and psychology has allowed a deeper understanding of the mental processes in the human mind, through the observation of biological and chemical processes in the brain and nervous system. Even though the gap between neuroscience and the human behavioral sciences is still large, the advancement of technology has helped narrow the gap, especially over the past two decades. This scoping review is aimed to explore the currently published research on self-reflection using the fMRI and the EEG. The present study describes the scope of existing research, and summarizes findings and recommendations for future work. The scoping review methodology guided by the standards of the PRISMA Extension for Scoping Reviews (PRISMA-ScR) was used. Comprehensive searches on the use of the fMRI and the EEG during self-reflection studies were conducted using several electronic databases including Scopus, Science Direct, Web of Science, Emerald Insights, and PubMed. The results obtained showed that all studies did not justify the sample size. The findings also revealed that various terms were used to describe the cognitive aspect of a self-reflection process. The brain functional connectivity data were taken through several experimental stages, which shows the flexibility of the experimental process. The activation of the prefrontal cortex (PFC) is one of the most commonly found regions in most studies, together with other areas associated with the self-reflection process. Based on the results obtained, this scoping review is believed to provide a comprehensive summary of the existing research on self-reflection using neuroimaging techniques.

Risk aversion, impatience, and adoption of conservation agriculture practices among smallholders in Zambia

Sustainable agricultural practices such as conservation agriculture have been promoted in southern Africa for nearly three decades, but their adoption remains low. It is of policy interest to unpack behavioural drivers of adoption to understand why adoption remains lower than anticipated. This paper assesses the effects of risk aversion and impatience on the extent and intensity of the adoption of conservation agriculture using panel data collected from 646 households in 2021 and 2022 in Zambia. We find that 12% and 18% of the smallholders were impatient and risk averse, respectively. There are two main empirical findings based on panel data Probit and Tobit models. First, on the extensive margin, being impatient is correlated with a decreased likelihood of adopting combined minimum-tillage (MT) and rotation by 2.9 percentage points and being risk averse is associated with a decreased propensity of adopting combined minimum tillage (MT) and mulching by 3.2 percentage points. Being risk averse is correlated with a decreased chance of adopting basins by 2.8 percentage points. Second, on the intensive margin, impatience and risk aversion are significantly correlated with reduced adoption intensity of basins, ripping, minimum tillage (MT), and combined MT and rotation by 0.02–0.22 ha. These findings imply a need to embed risk management (e.g., through crop yield insurance) in the scaling of sustainable agricultural practices to incentivise adoption. This can help to nudge initial adoption and to protect farmers from yield penalties that are common in experimentation stages.

Reclamation of iron and copper from BCL slag in Botswana

High-grade copper ores have been depleted over the years, making it a challenge in the mining industry. This investigation focused on a methodology to recover iron (Fe) and copper (Cu) from a copper/nickel slag obtained from the Bamangwato Concession Limited (BCL) mine in Botswana. In this modified flotation approach, the Response Surface Methodology (RSM) was used in conjunction with the Central Composite Design (CCD) and Analysis of Variance (ANOVA) to obtain the best optimal flotation conditions for the recovery of iron and copper. Using the RSM – CCD methodology, the optimal predicted responses were illustrated by a coefficient of determination R2 = 0. 9839 for recovery for Fe and 0.9655 for recovery for Cu. The recovery of copper increased with the increasing dosage of Na2S and collector dosage, while the increase of pH, had a decrease in recovery of copper due to the decline in the stability of the froth, which led to the resistance to form stable bubbles for efficient recovery of copper. Selective flotation of copper and iron was achieved by varying the Na2S dosage to achieve maximum recovery. Under these flotation conditions of PAX (800 g/t), pH (8), −75 μm, sulfurizing agent (Na2S, 1000 g/t), flotation time of 8 min, pH regulator of NaOH and H2SO4 and Methyl Isobutyl Carbinol (MIBC) from the experimental runs merited a grade upgrade of Cu in froth concentrate from 0.581 mass% to 0.884 mass%. An enrichment ratio of 2 was realised, with the recovery of Cu being 62%, whereas Fe in the froth concentrate increased from 69.8 mass% to 71.8 mass%. The main aim was to upgrade the grade and recovery of copper and iron to enhance the recovery for copper and iron in the next experimental stage of leaching.

Efficient FPGA Implementation of Convolutional Neural Networks and Long Short-Term Memory for Radar Emitter Signal Recognition

In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter signals. However, complex deep learning algorithms and data preprocessing operations have a huge demand for computing power, which cannot meet the requirements of low power consumption and high real-time processing scenarios. Therefore, many research works have remained in the experimental stage and cannot be actually implemented. To tackle this problem, this paper proposes a resource reuse computing acceleration platform based on field programmable gate arrays (FPGA), and implements a one-dimensional (1D) convolutional neural network (CNN) and long short-term memory (LSTM) neural network (NN) model for radar emitter signal recognition, directly targeting the intermediate frequency (IF) data of radar emitter signal for classification and recognition. The implementation of the 1D-CNN-LSTM neural network on FPGA is realized by multiplexing the same systolic array to accomplish the parallel acceleration of 1D convolution and matrix vector multiplication operations. We implemented our network on Xilinx XCKU040 to evaluate the effectiveness of our proposed solution. Our experiments show that the system can achieve 7.34 giga operations per second (GOPS) data throughput with only 5.022 W power consumption when the radar emitter signal recognition rate is 96.53%, which greatly improves the energy efficiency ratio and real-time performance of the radar emitter recognition system.

Toward improved image‐based root phenotyping: Handling temporal and cross‐site domain shifts in crop root segmentation models

AbstractCrop root segmentation models developed through deep learning have increased the throughput of in situ crop phenotyping studies. However, models trained to identify roots in one image dataset may not accurately identify roots in another dataset, especially when the new dataset contains known differences, called domain shifts. The objective of this study was to quantify how model performance changes when models are used to segment image datasets that contain domain shifts and evaluate approaches to reduce error associated with domain shifts. We collected maize root images at two growth stages (V7 and R2) in a field experiment and manually segmented images to measure total root length (TRL). We developed five segmentation models and evaluated each model's ability to handle a temporal (growth‐stage) domain shift. For the V7 growth stage, a growth‐stage‐specific model trained only on images captured at the V7 growth stage was best suited for measuring TRL. At the R2 growth stage, combining images from both growth stages into a single dataset to train a model resulted in the most accurate TRL measurements. We applied two of the field models to images from a greenhouse experiment to evaluate how model performance changed when exposed to a cross‐site domain shift. Field models were less accurate than models trained only on the greenhouse images even when crop growth stage was identical. Although models may perform well for one experiment, model error increases when applied to images from different experiments even when crop species, growth stage, and soil type are similar.

Administration of blue light in the morning and no blue-ray light in the evening improves the circadian functions of non-24-hour shift workers

ABSTRACT In modern 24-hour society, various round-the-clock services have entailed shift work, resulting in non-24-hour schedules. However, the extent of behavioral and physiological alterations by non-24-hour schedules remains unclear, and particularly, effective interventions to restore the circadian functions of non-24-hour shift workers are rarely explored. In this study, we investigate the effects of a simulated non-24-hour military shift work schedule on daily rhythms and sleep, and establish an intervention measure to restore the circadian functions of non-24-hour shift workers. The three stages of experiments were conducted. The stage-one experiment was to establish a comprehensive evaluation index of the circadian rhythms and sleep for all 60 participants by analyzing wristwatch-recorded physiological parameters and sleep. The stage-two experiment evaluated the effects of an intervention strategy on physiological rhythms and sleep. The stage-three experiment was to examine the participants’ physiological and behavioral disturbances under the simulated non-24-hour military shift work schedule and their improvements by the optimal lighting apparatus. We found that wristwatch-recorded physiological parameters display robust rhythmicity, and the phases of systolic blood pressures and heart rates can be used as reliable estimators for the human body time. The simulated non-24-hour military shift work schedule significantly disrupts the daily rhythms of oxygen saturation levels, blood pressures, heart rates, and reduces sleep quality. Administration of blue light in the morning and no blue-ray light in the evening improves the amplitude and synchronization of daily rhythms of the non-24-hour participants. These findings demonstrate the harmful consequences of the non-24-hour shift work schedule and provide a non-invasive strategy to improve the well-being and work efficiency of the non-24-hour shift population.

Optimal Image Characterization for In-Bed Posture Classification by Using SVM Algorithm

Identifying patient posture while they are lying in bed is an important task in medical applications such as monitoring a patient after a surgical intervention, sleep supervision to identify behavioral and physiological markers, or for bedsore prevention. An acceptable strategy to identify the patient’s position is the classification of images created from a grid of pressure sensors located in the bed. These samples can be arranged based on supervised learning methods. Usually, image conditioning is required before images are loaded into a learning method to increase classification accuracy. However, continuous monitoring of a person requires large amounts of time and computational resources if complex pre-processing algorithms are used. So, the problem is to classify the image posture of patients with different weights, heights, and positions by using minimal sample conditioning for a specific supervised learning method. In this work, it is proposed to identify the patient posture from pressure sensor images by using well-known and simple conditioning techniques and selecting the optimal texture descriptors for the Support Vector Machine (SVM) method. This is in order to obtain the best classification and to avoid image over-processing in the conditioning stage for the SVM. The experimental stages are performed with the color models Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV). The results show an increase in accuracy from 86.9% to 92.9% and in kappa value from 0.825 to 0.904 using image conditioning with histogram equalization and a median filter, respectively.

Comparison of Different DNA Isolation Protocols in Old and New Insect Samples

Insects belong to the Arthropoda phylum and are the most numerous animal group in the world, with more than 1.000.000 species. Insects ensure the sustainability of life on earth by pollinating plants, improving soil texture by decomposing organic materials, and providing nutrients to humans. DNA isolation is a necessity for performing analyses such as Sanger sequencing, DNA fingerprinting, and gene-genome analysis. Isolated insect DNA samples can be used to carry out studies such as species identification, taxonomic relations, comparison of insect-plant and –microfauna relations, revealing genomic, population, and genetic diversity, bio-geography determination, combating agricultural pests, etc. In this study, it is aimed to compare three different isolation methods in terms of time and quality for obtaining genetic material from museum samples (old) and new samples of insects. The samples used in this investigation belong to the same insect species of Cerambycidae family (Coleoptera). Samples collected at most 9-12 months ago are considered as “new specimen”, whereas “old specimens” consist samples of 7-28 years old. Three DNA isolation protocols: Phenol-Chloroform, Salting Out and DArT seq, are compared in this study. Considering quality gel images and A260/280 and A260/230 values in newly collected samples, the best results in terms of quality were obtained with the phenol-chloroform method. When A260/280 values were examined alone, DArT seq protocol appears to be the most appropriate protocol in terms of purity in both old and new insect samples. In terms of time, the Phenol-chloroform method was found to be the shortest protocol lasting for about 2 h. Accordingly, the phenol-chloroform method was deduced to be the better choice for both old and new insect samples. After concentration adjustments, the obtained DNAs can be used for the next experimental stages or stored as stock material at -20 ºC. Determining the appropriate protocol to obtain high-quality DNA in a short time is important for the continuation of the studies. Our findings are important in terms of accelerating the studies for identification and determining systematic status and especially in examining the taxonomic relationships of the insect species. Using molecular taxonomy in addition to classical taxonomy allows the identification of insects on a species basis. These types of studies will generate useful information for researchers who study insect molecular biology.

Implementation of Contextual Teaching and Learning Based Physics Module on Newton's Law Material to Improve Critical Thinking Skills of Class X Students

This research consisted of two stages, namely the first stage of developing a CTL-based Physics module using Research and Development, through the ADDIE model approach, and the second stage of experimentation at MA Al-Ihsan Boarding School Riau. The experimental stage is to see the effect of using the developed module on improving students' critical thinking skills. The assessment of students' critical thinking skills used pretest and posttest critical thinking skills questions. Data analysis used SPSS Statistics 24 Software. The results of the module development research showed that the feasibility value by experts was 0.879 in the valid category. This shows that the CTL-based modules produced in this study are considered feasible to be used in learning Newton's Law material. The results of the experimental phase research show that there is a significant effect of the use of CTL-based modules on students' critical thinking skills. In the experimental class, critical thinking before treatment was 43.35, the low category increased to 84 good categories after treatment. Whereas in the control class critical thinking before treatment 39.4 low categories increased to 74 good categories after treatment. This means that the experimental class has further increased its critical thinking compared to the control class. The conclusion of this study is that the CTL-based Physics module on Newton's Law material is valid and the use of the developed module can improve students' critical thinking.

Digital twin in high throughput chromatographic process development for monoclonal antibodies

The monoclonal antibody (mAb) industry is becoming increasingly digitalized. Digital twins are becoming increasingly important to test or validate processes before manufacturing. High-Throughput Process Development (HTPD) has been progressively used as a tool for process development and innovation. The combination of High-Throughput Screening with fast computational methods allows to study processes in-silico in a fast and efficient manner. This paper presents a hybrid approach for HTPD where equal importance is given to experimental, computational and decision-making stages. Equilibrium adsorption isotherms of 13 protein A and 16 Cation-Exchange resins were determined with pure mAb. The influence of other components in the clarified cell culture supernatant (harvest) has been under-investigated. This work contributes with a methodology for the study of equilibrium adsorption of mAb in harvest to different protein A resins and compares the adsorption behavior with the pure sample experiments. Column chromatography was modelled using a Lumped Kinetic Model, with an overall mass transfer coefficient parameter (kov). The screening results showed that the harvest solution had virtually no influence on the adsorption behavior of mAb to the different protein A resins tested. kov was found to have a linear correlation with the sample feed concentration, which is in line with mass transfer theory. The hybrid approach for HTPD presented highlights the roles of the computational, experimental, and decision-making stages in process development, and how it can be implemented to develop a chromatographic process. The proposed white-box digital twin helps to accelerate chromatographic process development.

Dynamics of digital change – measuring the digital transformation and its impacts on the innovation activities of SMEs

PurposeDigital transformation has gained particular interest among academics and policymakers in recent years. However, the empirical quantification of digital transformation stages and their impact on innovation in small and medium-sized enterprises (SMEs) remains understudied. Therefore, this study aims to investigate the impact of digital transformation stages on a differentiated measurement of innovation performance in SMEs.Design/methodology/approachThe authors propose a simplified one-dimensional digital maturity path to estimate the stages of digital transformation in SMEs. The authors validate their approach with a cluster analysis and perform an ordered logistic regression to estimate the impact of digital transformation stages on SMEs' innovation performance.FindingsThe authors' results show that digital transformation in general has a positive impact on SMEs' innovation performance. More precisely, the authors find that the early stage of digital transformation has a detrimental effect on innovation performance, while significant and positive effects can be expected from the experimental stage onward. Furthermore, the advanced stage of digital transformation significantly increases the probability of producing radical innovations.Originality/valueThis study contributes to the ongoing discussion about the relationship between digital transformation and innovation in SMEs by presenting an approach to quantify digital transformation stages in SMEs. Additionally, this study provides new insights into the specific dynamics of the relationship between different stages of digital transformation and their impact on a differentiated measurement of innovation performance, including technological, non-technological and radical innovation.

Perancangan Interactive Puppets Edukasi Diskriminasi pada Anak 10-12 Tahun

Discrimination is a problem that is still often found in society, especially among children. Children still cannot distinguish between right and wrong treatment regarding the actions and words they do and say. This design was created to educate children through interactive learning media so they do not behave discriminatorily. This design uses several research methods, namely existing studies, literature studies, observation, in-depth interviews, and quantitative data collection in the form of questionnaires. The data will be processed at the experimental study stage, pre-test, and user testing stages. This method was carried out to obtain child behaviour, child psychology, and media data, which became a reference in this design. From the results of research and interviews that have been conducted, the output chosen as the primary media is a contemporary puppet. This media is designed to make learning more enjoyable. Children will be invited to participate in the stories being told and be able to interact freely with the stories as well as supporting media in the form of interactive guidebooks as references and instructions from the leading media. Through this media, it is hoped that it can educate children to respect and love themselves and others, respect differences, and prevent them from discriminating against others.