Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning profiles and their relation to the experiences of learning generic skills at the end of the first year of university study

University students’ approaches to learning and generic skills have important consequences for studying and success in higher education. However, person-oriented research, which explores how students with different learning profiles evaluate their learning of individual generic skills, has not been the core interest of previous studies. This study investigates the learning profiles of university students (N = 901) in two disciplines and their relations to the experiences of learning critical thinking, collaboration and communication at the end of the first study year. The interrelations of the variables were analyzed with correlations and Latent Profile Analysis (LPA) estimating auxiliary variable relation. LPA yielded three learning profile groups that differed from each other in their approaches to learning: unreflective, dissonant and deep-organized, which, respectively, comprised 7, 58 and 35% of the sample. The experiences of learning generic skills of the three learning profiles varied. The comparison of the profiles showed that students representing the deep-organized profile scored highest on all measured generic skills compared to the other two learning profiles.
Material Discrimination Using X-Ray and Neutron

Background: A nondestructive test is commonly used to inspect the surface defects and internal structure of an object without any physical damage. X-rays generated from an electron accelerator or a tube are one of the methods used for nondestructive testing. The high penetration of X-rays through materials with low atomic numbers makes it difficult to discriminate between these materials using X-ray imaging. The interaction characteristics of neutrons with materials can supplement the limitations of X-ray imaging in material discrimination.Materials and Methods: The radiation image acquisition process for air-cargo security inspection equipment using X-rays and neutrons was simulated using a GEometry ANd Tracking (Geant4) simulation toolkit. Radiation images of phantoms composed of 13 materials were obtained, and the R-value, representing the attenuation ratio of neutrons and gamma rays in a material, was calculated from these images.Results and Discussion: The R-values were calculated from the simulated X-ray and neutron images for each phantom and compared with those obtained in the experiments. The R-values obtained from the experiments were higher than those obtained from the simulations. The difference can be due to the following two causes. The first reason is that there are various facilities or equipment in the experimental environment that scatter neutrons, unlike the simulation. The other is the difference in the neutron signal processing. In the simulation, the neutron signal is the sum of the number of neutrons entering the detector. However, in the experiment, the neutron signal was obtained by superimposing the intensities of the neutron signals. Neutron detectors also detect gamma rays, and the neutron signal cannot be clearly distinguished in the process of separating the two types of radiation. Despite these differences, the two results showed similar trends and the viability of using simulation-based radiation images, particularly in the field of security screening. With further research, the simulation-based radiation images can replace ones from experiments and be used in the related fields.Conclusion: The Korea Atomic Energy Research Institute has developed air-cargo security inspection equipment using neutrons and X-rays. Using this equipment, radiation images and R-values for various materials were obtained. The equipment was reconstructed, and the R-values were obtained for 13 materials using the Geant4 simulation toolkit. The R-values calculated by experiment and simulation show similar trends. Therefore, we confirmed the feasibility of using the simulation-based radiation image.
Do daily news abstracts help nowcasting Swiss GDP growth? *

This paper evaluates whether daily news abstracts help nowcasting Swiss GDP growth. I collect publicly available titles and lead texts from three Swiss newspapers and calculate text-based indicators covering various aspects of the economy. A composite indicator calculated from these measures is highly correlated with macroeconomic data and survey indicators of Swiss economic activity. In a pseudo out-of-sample forecasting exercise, the indicator outperforms a well-known Swiss business cycle indicator for nowcasting Swiss GDP growth if one month of information is available. The results show that short news texts are particularly useful for nowcasting during non-crisis periods. Furthermore, the daily availability of the data makes it possible to identify turning points at an early stage.
Effect of Temperature, pH, and aw on Cereulide Synthesis and Regulator Genes Transcription with Respect to Bacillus cereus Growth and Cereulide Production

Bacillus cereus is a food-borne pathogen that can produce cereulide in the growth period, which causes food poisoning symptoms. Due to its resistance to heat, extreme pH, and proteolytic enzymes, cereulide poses a serious threat to food safety. Temperature, pH, and aw can influence cereulide production, but there is still a lack of research with multi-environmental impacts. In this study, the effects of temperature (15~45 °C), pH (5~8), and aw (0.945~0.996) on the emetic reference strain B. cereus F4810/72 growth, cereulide production, relevant ces genes (cesA, cesB, cesP), and transcription regulators genes (codY and abrB) expression at transcription level were studied. B. cereus survived for 4~53 h or grew to 6.85~8.15 log10 CFU/mL in environmental combinations. Cereulide accumulation was higher in mid-temperature, acidic, or high aw environments. Increased temperature resulted in a lower cereulide concentration at pH 8 or aw of 0.970. The lowest cereulide concentration was found at pH 6.5 with an increased aw from 0.970 to 0.996. Water activity had a strong effect on transcriptional regulator genes as well as the cesB gene, and temperature was the main effect factor of cesP gene expression. Moreover, environmental factors also impact cereulide synthesis at transcriptional levels thereby altering the cereulide concentrations. The interaction of environmental factors may result in the survival of B. cereus without growth for a period. Gene expression is affected by environmental factors, and temperature and pH may be the main factors influencing the correlation between B. cereus growth and cereulide formation. This study contributed to an initial understanding of the intrinsic link between the impact of environmental factors and cereulide formation and provided valuable information for clarifying the mechanism of cereulide synthesis in combined environmental conditions.
Application of NDVI Sentinel-2Ac Imagery for Agricultural Land Development in Food Buffer Area of New Capital City Indonesia

Paser Regency is one of the food buffer areas for the New Capital City (IKN) of Indonesia in East Kalimantan Province. The relocation of the capital to IKN increases the number of populations and food demand as well. To meet food needs in IKN in the future, the development of agricultural land is needed. This study aimed to identify the type and direction of land use in Paser Regency to ensure food self-sufficiency. Sentinel-2 satellite Imagery with the Maximum Likelihood Classification (MLC) method was used in this study. The sentinel data from 2018 to 2022 was analyzed to identify the existing land use types over the past five years. The land use direction was determined based on the existing land use type, soil type, and land slope class. The results with an accuracy value for the overall test of 91.68% and kappa accuracy of 0.86 showed a change in land use from 2018 to 2022. Eight types of main land used were identified such as non-swamp rice fields, swamp rice fields, oil palm plantations, moors, shrubs, forests, settlements, and water bodies. The application of NDVI Sentinel-2Ac Imagery can identify a potential area of 13,249.7 hectares for rice field development and can significantly increase rice production to ensure food self-sufficiency in IKN.
Tropical Fruit Waste Management: Developing Pectin-based Biopolymer from Durian Rind (Durio zibethinus)

Durian (Durio zibethinus) is a popular native tropical fruit and is largely produced in Southeast Asia. In Indonesia, 1.71 million metric tons of durian were produced in 2022. However, this high production may lead to organic waste issues due to the large amount of durian rind generation. About 60 – 75% of the durian is the rind, which is mostly discarded, hard to be decomposed, and not utilized optimally. Meanwhile, the durian rind poses as a potential reservoir containing high concentrations of cellulose and pectin. Therefore, this research aimed to (1) extract pectin content from durian rind and (2) synthesize and characterize pectin-based biopolymer films. First, the pectin was extracted from dried durian rind powder using acid extraction and alcoholic precipitation methods. Then, biopolymer synthesis was done by mixing formulations of pectin, chitosan, ethylene glycol, and glycerol solutions to form thin films. About 0.13 – 0.71 g of pectin (0.46% of the pectin yield) was extracted per 100 g of dried durian rind powder. The FTIR spectroscopy results of the extracted pectin confirmed the presence of the O - H groups at 3327.83 cm-1, which indicated the pyranose ring, the main characteristic of pectin. Pectin-based biopolymer films were produced with thickness, transparency, and tensile strength of 0.01 – 0.07 mm, 7.40 - 40.50, and 1.26 – 2.69 MPa, respectively. The findings of the soil burial degradation test for biodegradability showed that the biopolymer experienced weight loss of up to 62% in 14 days. Considering the potential utilization of the durian rind for developing biopolymers as value-added bioproducts from agricultural biomass, tropical fruit waste management should be done properly in the future. This also can be offered as one of the alternative solutions for plastic substitution with proper development in the future.
Technology needs assessment for the development of smart coffee production in Aceh, Indonesia

This research aimed to analyze the need for smart farming implementation in coffee production. A survey was conducted on 100 farmers in Aceh Tengah and Bener Meriah, the main coffee producers in Aceh Province. Data were collected by questionnaire that identified farmers’ needs and performance of four types of smart farming technologies: drip irrigation, fertilizer detectors, fruit ripeness detectors, and dry houses. The needs of technology were assessed by farmers’ perspectives on smart farming regarding its use to reduce product contamination, farm waste, minimize risk to farmers’ health, strengthen social bonds, and increase product quality and quantity. Technology needs assessment was performed by analyzing the determinant factors of farmers’ needs and computing the gap between technology needs and performance to set the priority of smart farming technology for future development. The results showed that fertilizer detectors were the first priority technology needed and considered important by farmers, followed by drip irrigation, dry houses and fruit ripeness detectors. Farmers’ needs for smart farming were significantly affected by the land size. There is a significant difference between farmers’ needs and performance of smart farming technologies. To fill this gap, an effective public-private partnership is needed for technology interventions based on farmers’ needs.
Evaluation of morpho-physiological characters of gamma rays-irradiated soybean mutant lines under shade stress environment

Soybean, being one of the primary food sources for the Indonesian population, currently faces insufficient production from domestic harvests. Intercropping cultivation technique offers a potential solution to increase soybean production by expanding planting area, such as the area under plantation stands. However, low productivity persists due to shade stress. This study aims to evaluate the morpho-physiological characters of soybean genotypes under shading stress. The genetic materials used were 13 genotypes consisting of 10 promising mutant lines, resulted from mutant induction and selection from soybean variety Mutiara 1, and 3 check varieties, namely Dena 1, Dena 2, and Mutiara 1. This study employed a split plot arrangement, with shading treatment as the whole plot and soybean genotypes as subplots. ANOVA tests and stress tolerance index analyses revealed a significant increase in plant height and stomatal conductivity among soybean genotypes under shading environments. Conversely, shading treatments exhibited a significant impact on reducing plant branching and leaf chlorophyll content.
Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data

Digital health applications using Artificial Intelligence (AI) are a promising opportunity to address the widening gap between available resources and mental health needs globally. Increasingly, passively acquired data from wearables are augmented with carefully selected active data from depressed individuals to develop Machine Learning (ML) models of depression based on mood scores. However, most ML models are black box in nature, and hence the outputs are not explainable. Depression is also multimodal, and the reasons for depression may vary significantly between individuals. Explainable and personalised models will thus be beneficial to clinicians to determine the main features that lead to a decline in the mood state of a depressed individual, thus enabling suitable personalised therapy. This is currently lacking. Therefore, this study presents a methodology for developing personalised and accurate Deep Learning (DL)-based predictive mood models for depression, along with novel methods for identifying the key facets that lead to the exacerbation of depressive symptoms. We illustrate our approach by using an existing multimodal dataset containing longitudinal Ecological Momentary Assessments of depression, lifestyle data from wearables and neurocognitive assessments for 14 mild to moderately depressed participants over one month. We develop classification- and regression-based DL models to predict participants’ current mood scores—a discrete score given to a participant based on the severity of their depressive symptoms. The models are trained inside eight different evolutionary-algorithm-based optimisation schemes that optimise the model parameters for a maximum predictive performance. A five-fold cross-validation scheme is used to verify the DL model’s predictive performance against 10 classical ML-based models, with a model error as low as 6% for some participants. We use the best model from the optimisation process to extract indicators, using SHAP, ALE and Anchors from explainable AI literature to explain why certain predictions are made and how they affect mood. These feature insights can assist health professionals in incorporating personalised interventions into a depressed individual’s treatment regimen.
Group Belonging and Social Identities in the Transition of Asylum-Seekers in Greece: Longitudinal Pathways to Adjustment

Millions of forced migrants settling in host countries often struggle to adjust to their new life. As their inclusion and adjustment within receiving societies has become a global social challenge, studying the factors that support their successful transition is an important topic of research inquiry. The present three-wave longitudinal study examined the role of group belonging and social identification in facilitating the transition of 60 sub-Saharan African asylum-seekers to Greece. Drawing upon the Social Identity Model of Identity Change (SIMIC), we investigated how multiple group memberships before migration, social identity continuity, and social identity gain related to their adjustment over 8 months. On the between-person level, multiple group belonging before migration indirectly contributed to better person-average levels of sociocultural adjustment, physical health functioning and satisfaction, psychological distress, and life satisfaction, by way of higher person-average levels of social identity continuity and/or social identity gain. However, multiple groups before migration also had a direct negative effect on the overall levels of psychological distress. On the within-person level, positive changes in social identity continuity and gain were related to positive changes in different adjustment-related outcomes over time. Our findings are consistent with SIMIC and highlight the importance of group belonging and associated social identities in forced migrants’ transition, in ways that may pave the way for the development of social identity interventions to promote their health, well-being, and successful integration. Future longitudinal and experimental evidence with larger and more diverse samples of forced migrants is needed to establish the generalizability and causality of the observed associations.
FMCW Radar Sensors with Improved Range Precision by Reusing the Neural Network

This paper addresses the challenge of enhancing range precision in radar sensors through supervised learning. However, when the range precision surpasses the range resolution, it leads to a rapid increase in the number of labels, resulting in elevated learning costs. The removal of background noise in indoor environments is also crucial. In response, this study proposes a methodology aiming to increase range precision while mitigating the issue of a growing number of labels in supervised learning. Neural networks learned for a specific section are reused to minimize learning costs and maximize computational efficiency. Formulas and experiments confirmed that identical fractional multiple patterns in the frequency domain can be applied to analyze patterns in other FFT bin positions (representing different target positions). In conclusion, the results suggest that neural networks trained with the same data can be repurposed, enabling efficient hardware implementation.
An ingredient co-occurrence network gives insight into e-liquid flavor complexity

INTRODUCTION Part of the appeal of e-cigarettes lies in their available flavors. To achieve attractive flavors, e-liquids contain many different flavoring agents, which allow many flavoring combinations. To advance our knowledge of e-liquid flavors and compositions and to evaluate the effect of legislation, we determined whether there are ingredient combinations that are frequently used together. METHODS We used e-cigarette ingredient data from the European Common Entry Gate system (EU-CEG) as available on 31 December 2022. RESULTS In e-liquids, we found 214 ingredient pairs with a co-occurrence odds ratio greater than 10. Together, these consisted of 62 unique ingredients. Network analysis revealed that ingredients were grouped together based on their flavor and/or chemical structure. We identified two densely connected regions (clusters) in the network. One consisted of six ingredients with sweet-vanilla-creamy flavors. The second cluster consisted of 13 ingredients. While some of these have fruity flavors, others, such as alkyl carboxylic acids and dimethyl sulfide, are known to have unpleasant flavors. Additional data and literature analyses indicated that alkyl carboxylic acids can contribute to a creamy and sweet-fruity taste, whereas dimethyl sulfide can contribute to a more refined fruity taste. CONCLUSIONS These results exemplify that the flavor of e-liquids is not just the sum of its parts. Big data analyses on product data can be used to detect such patterns, but expert knowledge and additional data are needed for further interpretation. Monitoring of e-liquid flavors as well as ingredients will remain important to regulate e-liquid product attractiveness.
Winter Epibiotic Community of the Red King Crab Paralithodes camtschaticus in Sayda Bay (Barents Sea)

Simple Summary Information on epibionts and symbionts of the introduced Barents Sea red king crab during the winter period is currently lacking. To address this gap, we examined the species composition and infestation indices of epibionts on this host in the northern part of Kola Bay in November. Our findings indicate a higher infestation rate of barnacles and amphipods during the winter season compared to the summer. In contrast, infestation rates of other species remained low during winter, reflecting seasonal fluctuations in both the size distribution of red king crabs and prevailing temperature conditions. Our research provides novel data on the growth patterns of commonly associated organisms, which could prove invaluable in determining the age of red king crabs in the Barents Sea. Abstract The species composition of epibiotic communities on red king crab was investigated in Sayda Bay, Russia, during November of 2015 and 2016. The community consisted of 12 species in total. Among epibionts, the barnacle Balanus crenatus was most prevalent (67.0%), while the amphipod Ischyrocerus commensalis was the most frequent symbiont (77.3%). Infestation levels in May–June 2005 and September 2004 were higher, as a larger proportion of small crabs without fouling species were present during those seasons. The lower infestation intensities recorded for other common associated organisms during winter can be attributed to their increased mortality due to unfavorable temperature conditions. The localization of epibionts and symbionts were related to larval settlement patterns of attached species and feeding behavior of mobile species. Monthly growth increments for I. commensalis and B. crenatus were estimated at 2 mm in body length and 1.35 mm in basal diameter, respectively. Size-at-age data for epibionts can aid in the age determination of large male crabs that may skip an annual molt.
Sodium Starch Glycolate (SSG) from Sago Starch (Metroxylon sago) as a Superdisintegrant: Synthesis and Characterization

The characteristics of sago starch exhibit remarkable resemblances to those of cassava, potato, and maize starches. This review intends to discuss and summarize the synthesis and characterization of sodium starch glycolate (SSG) from sago starch as a superdisintegrant from published journals using keywords in PubMed, Scopus, and ScienceDirect databases by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020). There are many methods for synthesizing sodium starch glycolate (SSG). Other methods may include the aqueous, extrusion, organic solvent slurry, and dry methods. Sago starch is a novel form of high-yield starch with significant development potential. After cross-linking, the phosphorus content of sago starch increases by approximately 0.3 mg/g, corresponding to approximately one phosphate ester group per 500 anhydroglucose units. The degree of substitution (DS) of sodium starch glycolate (SSG) from sago ranges from 0.25 to 0.30; in drug formulations, sodium starch glycolate (SSG) from sago ranges from 2% to 8% w/w. Higher levels of sodium starch glycolate (SSG) (2% and 4% w/w) resulted in shorter disintegration times (within 1 min). Sago starch is more swellable and less enzymatically digestible than pea and corn starch. These investigations demonstrate that sago starch is a novel form of high-yield starch with tremendous potential for novel development as superdisintegrant tablets and capsules.