Miracle-dz/UniFAR-FTUs huggingface dataset:
CNN Model with Parameter Optimisation for Fine-Grained Banana Ripening Stage Classification

Fruit grading is a significant problem in the fruit industry because each maturity stage of the fruit represents a distinct economic worth. Banana is one of the most mass-produced fruits that must be visually classified. However, because human eye perception varies, precise classification using a machine is necessary to standardise the grading system. This research develops a four-layered CNN deep-learning model to classify bananas into seven ripening stages. To train the model, we employed Mazen and Nashat dataset and expanded it using data augmentation techniques. As a baseline, we trained a basic four-layer CNN model and achieved 88.2% of accuracy in fine-grained categorisation due to the similarity of the adjacent ripening class. To enhance the accuracy of our basic model, we applied a parameter optimisation approach to get the best hyper-parameters for the profound banana ripeness indicator. As a result, the time-constrained parameter optimisation method that we utilised successfully increased the model accuracy up to 91.2% and the F1 score at 90.5%, which is satisfactory for fine-grained banana classification compared to the previous research.
Geographic Information System Continuance Adoption and Use to Determine Bidikmisi Scholarship Recipients Distribution

The objective of this study is to find out the continuance adoption and use of a Geographic Information System (GIS) for scholarship recipient distribution in Central Sulawesi. For the data gathering instrument, we employed an online structured questionnaire. One hundred fifty scholarship administrators were selected from fifteen state and private higher education institutions in Palu Central Sulawesi. They were assigned a five-scale survey. All completed questionnaires were analyzed using AMOS. The findings show that the factors of perceived usefulness, perceived ease of use, information quality, system quality, and change management have significantly influenced the continuance adoption and use of the geographic information system by university scholarship administrators. The results highlighted that when a system was developed based on those criteria, the sustainable adoption and use of the geographic information system can be consistently maintained to improve universities' scholarship management and distribution. Our study contributes to the body of knowledge in geographic information system continuance adoption and use within education institutions and to practices that support universities for better scholarship management and distribution.
Effects Of Land Cover, Topography, And Wind On Fire Spread In Kalimantan Using Cellular Automata

Forest and land fires cause various negative impacts on human life and the environment. Understanding the fire spread over an area is important for developing a mitigation strategy. This paper aims to model the fire spread in Kalimantan using Cellular Automata. Simulations of the effects of land cover, terrain, and wind on fire spread during September 2015 are presented. Our results reveal that the proposed model can describe fire spread reliably well, showing that the most impacted area occurs in the southern part of Kalimantan. For the wind effect, its direction to the north is seemingly the dominant direction for the fire spread at most locations and affects fire spreads much faster in lowlands than in other locations. The present study also summarizes that the type of land cover gives more influence to the fire spread rate on lowlands, while the elevation contributes more to accelerating the fire spread over complex terrain.
Urdu Short Paraphrase Detection at Sentence Level

Paraphrase detection systems uncover the relationship between two text fragments and classify them as paraphrased when they convey the same idea; otherwise non-paraphrased. Previously, the researchers have mainly focused on developing resources for the English language for paraphrase detection. There have been very few efforts for paraphrase detection in South Asian languages. However, no research has been conducted on sentence-level paraphrase detection in Urdu, a low-resourced language. It is mainly due to the unavailability of the corpora that focus on the sentence level. The available related studies on the Urdu language only focus on text reuse detection tasks at the passage and document levels. Therefore, this study aims to develop a large-scale manually annotated benchmark Urdu paraphrase detection corpus at the sentence level, based on real cases from journalism. The proposed Urdu Sentential Paraphrases (USP) corpus contains 4,900 sentences (2,941 paraphrased and 1,959 non-paraphrased), manually collected from the Urdu newspapers. Moreover, several techniques were proposed, developed, and compared as a secondary contribution, including Word Embedding (WE), Sentence Transformers (ST), and feature-fusion techniques. N-gram is treated as the baseline technique for our research. The experimental results indicate that our proposed feature-fusion technique is the most suitable for the Urdu paraphrase detection task. Furthermore, the performance increases when features of the proposed (ST) and baseline (N-gram) are combined for the classification task. In addition, The proposed techniques have also been applied to the UPPC corpus to check their performance at the document level. The best result we obtained using the feature fusion technique (F1 = 0.855). Our corpus is available and free to download for research purposes.
Modelling the climate factors affecting forest fire in Sumatra using Random Forest and Artificial Neural Network

Carbon emissions produced by forest fires contribute to the global emission increase. The amount of carbon emission may indicate the severity of the fires. In a dry climate condition, forest fires become an unexpected serious problem. This paper investigates the effect of climate variables on forest fires in Sumatra from 1998 to 2018. We employ two methods, Random Forest (RF) and Artificial Neural Network (ANN) to predict the carbon emission in 2019-2021. The total emission over the domain and the fire distribution map are compared in both models. As a result, the RF model is more accurate in predicting the location and intensity in 2019 but overestimates in 2020-2021. This indicates that the RF model gives a slightly better prediction when the carbon emission is high. This result is consistent with the evaluation metrics showing that ANN mostly gives smaller errors. Also, we found that the climate variables are still relevant to describe the carbon emissions through both models with importance scores of more than .
Realtime Video Latency Reduction for Autonomous Vehicle Teleoperation Using RTMP Over UDP Protocols

Real-time video streaming with low latency is essential for autonomous vehicles’ teleoperation. Studies show that the latency for self-driving teleoperation should not exceed 50 milliseconds. Latency could be caused by hardware, software, or network factors. Here, we focus on latency reduction due to network factors. We applied two data protocols: user datagram protocols (UDP) and real-time messaging protocol (RTMP), and afterward tuned the encoder along with data compression. The glass-to-glass method was applied to measure the network performance and video latency. The measurement of video latency achieves 300 milliseconds, which implements a direct connection (without a broadcaster) with the UDP data protocol. While this may still exceed the requirements, the study could be seen as a preliminary effort to deal with data latency for teleoperation driving.
Validation of the “Darryl” PTSD Cartoon Test with Abused Children

Abstract Background There is a lack of reliable and valid PTSD tests for young children that cannot read or are weak readers. The semi-projective cartoon test, “Darryl”, which is read aloud, is a measure that appeals to this age group. The test has been applied in both clinical and epidemiological studies. Objective to validate a cartoon test, “Darryl”, for children aged 6 or older in a population of children suspectedly sexually and/or physically abused. Methods In the Danish Child Centres, 327 children were screened with Darryl as part of an assessment for further intervention. The Bech Youth Inventory was filled out by 113 children, and 63 caregivers filled out the Strengths & Difficulties Questionnaire. Correlations were used to study the convergent validity between the scales and subscales and effect sizes were estimated. Reliability of the scales was investigated using Cronbach’s alpha. Results Following the DSM-IV, 55.7% of the children (n = 182) had a possible PTSD diagnosis. More girls (n = 110, 62.9 %) than boys (n = 72, 47.4%) had PTSD. A total of 21.7 % (n = 71) had subclinical PTSD, lacking only one symptom of the full diagnosis. There was no significant difference in PTSD regarding physical or sexual abuse. Clinical significance The test allows clinicians in the pediatric field to screen for possible PTSD in a population where systematic self-report data have paramount importance. Conclusions Darryl appears to be a valid and reliable test for screening young children who have been physically or sexually abused. The test is helpful for clinician working with young children to identify those have developed trauma symptoms to secure them early treatment.
“We’ve Opened Pandora’s Box, Haven’t We?” Clinical Geneticists’ Views on Ethical Aspects of Genomic Testing in Neonatal Intensive Care

Abstract The increasing use of genomic testing in neonatal intensive care units (NICU) gives rise to ethical issues. Yet little is known regarding what health professionals implementing the testing think about its ethical aspects. We therefore explored the views of Australian clinical geneticists towards ethical issues in the use of genomic testing in the Neonatal Intensive care Unit (NICU). Semi-structured interviews with 11 clinical geneticists were conducted, transcribed and analysed thematically. Four themes were identified: 1) Consent: the craft is in the conversation, which encapsulated the challenges in the consent process, and with pre-test counseling; 2) Whose autonomy and who decides? This illustrates the balancing of clinical utility and potentially harms the test, and how stakeholder interests are balanced; 3) The winds of change and ethical disruption, recognizing that while professional expertise is vital to clinical decision-making and oversight of mainstreaming, participants also expressed concern over the size of the genetics workforce and 4). Finding Solutions – the resources and mechanisms to prevent and resolve ethical dilemmas when they arise, such as quality genetic counseling, working as a team and drawing on external ethics and legal expertise. The findings highlight the ethical complexities associated with genomic testing in the NICU. They suggest the need for a workforce that has the necessary support and skills to navigate the ethical terrain, drawing on relevant ethical concepts and guidelines to balance the interests of neonates, their careers and health professionals.
Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)

Abstract Satellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to increase the vertical accuracy of most popular satellite DEMs (i.e., the ASTER, Shuttle Radar Topography Mission [SRTM], Forest And Buildings removed Copernicus DEM [FABDEM], and Multi-Error-Removed Improved-Terrain [MERIT]) using the particle swarm optimization (PSO) algorithm. For this purpose, at first, the vertical error of DEMs was estimated via ground truth data. Next, a second-order polynomial was applied to model the vertical error in the study area. To select the polynomial with the highest accuracy, employed for vertical error modeling, the coefficients of the polynomial have been optimized using the PSO algorithm. Finally, the efficiency of the proposed algorithm has been evaluated by other ground truth data and in situ observations. The results show that the mean absolute error (MAE) of SRTM DEM is 4.83 m while this factor for ASTER DEM is 5.35 m, for FABDEM is 4.28, and for MERIT is 3.87. The obtained results indicated that the proposed model could improve the MAE of vertical accuracy of SRTM, ASTER, FABDEM, and MERIT DEMs to 0.83, 0.51, 0.37, and 0.29 m, respectively.
Adaptive robust control of uncertain dynamical systems with multiple time-varying delays

The problem of adaptive robust stabilisation is considered for a class of dynamical systems with multiple time-varying delayed state perturbations, time-varying uncertain parameters, and external disturbances. It is assumed that the upper bounds of the delayed state perturbations, uncertainties and external disturbances are unknown, and that the time-varying delays are any non-negative continuous and bounded functions. In particular, it is not required that the derivatives of time-varying delays have to be less than one. For such a class of uncertain time-delay systems, a new method is presented whereby a class of memoryless continuous adaptive robust state feedback controllers is proposed. By employing a quasi-Lyapunov function, it is shown that the solutions of uncertain time-delay systems can be guaranteed to be uniformly exponentially convergent towards a ball which can be as small as desired. In addition, since the proposed adaptive robust state feedback controllers are completely independent of time delays, the results obtained in the study may be also applicable to a class of dynamical systems with uncertain time delays. Finally, a numerical example is given to demonstrate the validity of the results.
Creative leisure activities, mental health and well-being during 5 months of the COVID-19 pandemic: a fixed effects analysis of data from 3725 US adults

Introduction We investigated whether changes in engagement in home-based creative activities were associated with changes in depressive symptoms, anxiety symptoms and life satisfaction during the COVID-19 pandemic, aiming to replicate findings from the UK in a USA sample. Methods 3725 adults were included from the COVID-19 Social Study in the USA, a panel study collecting data weekly during the COVID-19 pandemic. We measured engagement in eight types of creative leisure activities on the previous weekday between April and September 2020. Data were analysed using fixed effects regression models. Results Increased time spent gardening was associated with reductions in depressive and anxiety symptoms and enhanced life satisfaction. Spending more time doing woodwork/DIY and arts/crafts were also associated with enhanced life satisfaction. However, more time watching television, films or other similar media (not for information on COVID-19) was associated with increased depressive symptoms. Other creative activities were not associated with mental health or well-being. Conclusion Some findings differ from evidence obtained in the UK, demonstrating the importance of replicating research across countries. Our findings should also be considered when formulating guidelines for future stay-at-home directives, enabling individuals to stay well despite the closure of public resources.
A Note on the Stability of Solving a Rank-p Modification of a Linear System by the Sherman–Morrison–Woodbury Formula

In this paper, we address the stability of the Sherman–Morrison–Woodbury formula. Our main result states that if the original matrices, A and B, are well conditioned, then there exists matrices U and V such that the Sherman–Morrison–Woodbury formula is stable when applied to $A = B - UV^T $.
Spatial variability of herbage yield, grazing capacity and plant diversity in a tropical savannah rangeland ecosystem

To improve ruminant livestock production, evaluation of rangelands must be a routine. Rangeland evaluation gives information about the vegetation structure, biomass yield and quality. The Guinea savannah rangelands of Ghana lack research that characterises the spatial variability of herbage yield and quality. It was hypothesised that there is spatial heterogeneity in herbage yield, grazing capacity and plant diversity in the Guinea savannah rangelands of Ghana. The objective was to evaluate the spatial structure of herbage production and grazing capacity in the Guinea savannah rangelands of Ghana for sustainable livestock production. Data were collected from 105 sampling sites and integrated into geo-statistics, using ordinary kriging interpolation to generate herbage yield and grazing capacity estimates. Herbage yield and grazing capacity ranged from 0.63 t ha−1 to 13.43 t ha−1 and 0.18 LU ha−1 to 3.79 LU ha−1 respectively. The root mean square error and the average standard error values were close (2.38 and 2.51 respectively for herbage yield and 0.67 and 0.71 respectively for grazing capacity). Species diversity using the Shannon’s index ranged from 1.13 to 2.40. There was spatial heterogeneity in herbage yield, grazing capacity and species diversity in Ghana’s Guinea savannah rangelands with some parts needing effective site-specific improvement strategies for sustainable livestock production.
How do university lecturers learn to teach with film? Formal and informal academic development

ABSTRACT Multimedia technologies are increasingly used by university lecturers to stay relevant in contemporary media-saturated society. Across disciplines, using multimedia in teaching often means integrating videos, including feature films and/or television series (FF/TV), into learning activities and assessments. Like other media technologies, effective integration of FF/TV into teaching can be achieved through coordinated training and practice. This study explores formal and informal venues of academic development (AD) to understand how lecturers learn to teach with film. A model of four key aspects is proposed as a research outcome, detailing lecturers’ requirements and considerations regarding teaching with FF/TV for future AD options.
Neisseria leonis sp. nov. isolated from rabbits, reclassification of Uruburuella suis, Uruburuella testudinis, Kingella potus, Bergeriella denitrificans and Morococcus cerebrosus into Neisseria genus and reclassification of Neisseria shayeganii into Eikenella genus

Genome sequence-based identification of two strains (3986T and 51.81) isolated from rabbits in France in 1972 and 1981 and deposited in the Collection of Institut Pasteur (CIP) has led to the description of a novel species in the genus Neisseria. The cells of both strains were non-motile, Gram-stain-negative and diplococcobacilli. Optimal growth on trypticase soy agar was recorded at 37°C and pH 8.5 in aerobic conditions. Phylogeny based on 16S rRNA gene placed the strains close to Neisseria bacilliformis ATCC BAA-1200T (96.38%) nesting with the members of Neisseriaceae family. Furthermore, the phylogenetic analysis based on bac120 gene set from the Genome Taxonomy Database (GTDB) placed both strains within the monophyletic Neisseria clade, which also included type strains of Morococcus cerebrosus, Bergeriella denitrificans, Kingella potus, Uruburuella suis and Uruburuella testudinis. However, Neisseria shayeganii strain 871T was placed outside Neisseria clade and close to the members of Eikenella genus. Strains 3986T and 51.81 were placed in a branch distinct from all species of the genus Neisseria and exhibited the average nucleotide identity (ANI) and digital DNA–DNA hybridization (dDDH) values below the species demarcation values. In contrast, ANI value within the two strains was 96.9% confirming that they represent same species. The genomic DNA G+C content of strain 3986T was 56.92%. Based on the phylogenetic and phenotypic data, the strains 3986T and 51.81 represent a novel species of the genus Neisseria, for which the name Neisseria leonis sp. nov. is proposed (type strain 3986T = CIP 109994T = LMG 32907T). Additionally, based on phylogenetic analysis, DUS dialect and average amino acid identity (AAI) values, we also proposed the reclassification of Morococcus cerebrosus, Bergeriella denitrificans, Kingella potus, Uruburuella suis and Uruburuella testudinis into Neisseria genus and Neisseria shayeganii into Eikenella genus. Author Notes The GenBank accession numbers for the 16S rRNA gene sequence of strains 3986T and 51.81 are respectively OQ121838.1 and OQ428162.1. The draft genome sequences have been deposited in GenBank under the accession numbers JAPQFK000000000 (strain 3986T) and JAPQFL000000000 (strain 51.81). Further explanations mentioned in the article as well as 7 supplementary tables and 7 supplementary figures are available with the online version of this article.
The structural basis of the talin–KANK1 interaction that coordinates the actin and microtubule cytoskeletons at focal adhesions

Adhesion between cells and the extracellular matrix (ECM) is mediated by heterodimeric (αβ) integrin receptors that are intracellularly linked to the contractile actomyosin machinery. One of the proteins that control this link is talin, which organises cytosolic signalling proteins into discrete complexes on β-integrin tails referred to as focal adhesions (FAs). The adapter protein KANK1 binds to talin in the region of FAs known as the adhesion belt. Here, we developed a novel crystallographic method to resolve the talin-KANK1 complex. This structure revealed that the talin binding KN motif of KANK1 has a novel fold, where a β-turn stabilises the α-helical region, explaining its specific interaction with talin R7 and high affinity. Single point mutants in KANK1 identified from the structure abolished the interaction and enabled us to examine KANK1 enrichment in the adhesion belt. Strikingly, in cells expressing a constitutively active form of vinculin that keeps the FA structure intact even in the presence of myosin inhibitors, KANK1 localises throughout the entire FA structure even when actomyosin tension is released. We propose a model whereby actomyosin forces on talin eliminate KANK1 from talin binding in the centre of FAs while retaining it at the adhesion periphery.
FUSDelta14 mutation impairs normal brain development and causes systemic metabolic alterations

FUS (Fused in sarcoma) is a ubiquitously expressed DNA/RNA binding protein. Mutations in FUS cause aggressive juvenile forms of amyotrophic lateral sclerosis (ALS), as in the case with the FUSDelta14 mutation. While most studies have focused on the role of FUS in motor neuron degeneration, little is known about the effect of FUS mutations in the whole body, and the impact of FUS mutations in the correct development of the nervous system. We studied pleiotropic phenotypes in a physiological knock-in mouse model carrying the FUSDelta14 mutation in homozygosity. RNA sequencing was conducting in six different tissues (frontal cortex, spinal cord, tibialis anterior muscle, white and brown adipose tissue and liver) to identify the genes and pathways altered by the FUSDelta14 mutant protein in the systemic transcriptome. Additionally, brain structural magnetic resonance imaging (MRI) and histological characterisation was conducted in young mice to study the role of FUS mutation in the brain development. FUS mutant protein was upregulated and mislocalised in the cytoplasm in most cells of the tissues analysed. We identified few genes commonly altered in all tissues by this mutation, although most genes and pathways affected were generally tissue-specific. Phenotypic assessment of mice revealed systemic metabolic alterations related to the pathway changes identified. MRI brain scans revealed that homozygous FUSDelta14 brains were smaller and displayed significant morphological alterations including a thinner cortex, reduced neuronal number and increased gliosis, which correlated with early cognitive impairment and fatal seizures. We demonstrated that the disease aetiology of FUS mutations can include neurodevelopmental and systemic alterations, which should be taken into consideration in the clinic.