Miracle-dz/UniFAR-FTUs huggingface dataset:
Management accounting system as mediator on sustainability performance

This paper aims to investigate the factors of competitive success and examine the relationship between Strategic Management Accounting, management accounting information systems and sustainability performance. This research was conducted with a quantitative approach. The survey involved 125 managers of 4- and 5-star hotels in Bali, Indonesia, a research sample of 154 managers was determined to be given a research questionnaire. Analysis of research data was carried out through Structural Equation Modeling. The results showed that strategic management accounting and Information Management Accounting System had a positive effect on sustainability performance. MAS information also mediates the relationship between strategic management accounting and sustainability performance which can involve a variety of factors. This study only focuses on competitive strategy and MAS information variables, so further research is needed on the sustainability performance of the hospitality industry. The positive relationship between strategic management accounting and social and environmental performance supports the argument that hotels use socio-environmental aspects for their strategic management accounting. Hotel managers also need government regulations and information related to social and environmental issues such as the Indonesian occupational health and safety management system and the Indonesia Green Hotel Award.
Examining students’ intention to use ChatGPT: Does trust matter?

Little knowledge is available on students’ attitudes and behavioural intentions towards using ChatGPT, a breakthrough innovation in recent times. This study bridges this gap by adding two relevant less-explored constructs (i.e., perceived enjoyment and perceived informativeness) to the technology acceptance model and illustrating the moderating effect of trust on the acceptance of ChatGPT. Data was collected from 344 private and public university students from Bangladesh, with the analysis done through structural equation modelling. The results highlight the significance of perceived usefulness, perceived ease of use and perceived informativeness in understanding students’ attitudes towards using ChatGPT for learning, which subsequently predicts their behavioural intention to use it. Interestingly, students’ level of enjoyment was triggered once the trust issue came into play, meaning perceived enjoyment had no substantial impact on attitude unless trust moderates the relationship between perceived enjoyment and attitude towards using ChatGPT. Implications for practice or policy Scholars can get first-hand insights into perceived enjoyment and perceived informativeness with perceived usefulness and perceived ease of use in the context of ChatGPT and education. Practitioners and educators can comprehensively understand the antecedents affecting students’ attitudes in line with their behavioural intention towards ChatGPT. Policymakers can design viable strategies to promote the ethical and sustainable usage of ChatGPT in education.
Immune response of BV-2 microglial cells is impacted by peroxisomal beta-oxidation

Microglia are crucial for brain homeostasis, and dysfunction of these cells is a key driver in most neurodegenerative diseases, including peroxisomal leukodystrophies. In X-linked adrenoleukodystrophy (X-ALD), a neuroinflammatory disorder, very long-chain fatty acid (VLCFA) accumulation due to impaired degradation within peroxisomes results in microglial defects, but the underlying mechanisms remain unclear. Using CRISPR/Cas9 gene editing of key genes in peroxisomal VLCFA breakdown (Abcd1, Abcd2, and Acox1), we recently established easily accessible microglial BV-2 cell models to study the impact of dysfunctional peroxisomal β-oxidation and revealed a disease-associated microglial-like signature in these cell lines. Transcriptomic analysis suggested consequences on the immune response. To clarify how impaired lipid degradation impacts the immune function of microglia, we here used RNA-sequencing and functional assays related to the immune response to compare wild-type and mutant BV-2 cell lines under basal conditions and upon pro-inflammatory lipopolysaccharide (LPS) activation. A majority of genes encoding proinflammatory cytokines, as well as genes involved in phagocytosis, antigen presentation, and co-stimulation of T lymphocytes, were found differentially overexpressed. The transcriptomic alterations were reflected by altered phagocytic capacity, inflammasome activation, increased release of inflammatory cytokines, including TNF, and upregulated response of T lymphocytes primed by mutant BV-2 cells presenting peptides. Together, the present study shows that peroxisomal β-oxidation defects resulting in lipid alterations, including VLCFA accumulation, directly reprogram the main cellular functions of microglia. The elucidation of this link between lipid metabolism and the immune response of microglia will help to better understand the pathogenesis of peroxisomal leukodystrophies.
DYNAMIC ASSESSMENT OF EFL LEARNERS' ACADEMIC WRITING SKILLS AND WRITING ABILITY

This research was carried out to investigate factors related to writing skills and writing ability and how they influence writing skills and writing ability. Participants were 100 undergraduate students aged 18 to 21 at Indonesian Islamic University, consisting of 85 females and 15 males. Two different data analysis were performed: the Hierarchical Factorial Test to identify factors that can share meaningful correlation and a multiple regression test to examine the effect of an independent variable on the dependent variable. The findings showed that all variables of this research including content, organization, vocabulary, language, mechanics, thesis statement, blueprint, main idea, supporting, link, linker, fluency, logical order, rewordings, and clincher are contributing factors to the dependent variables. Sig value was lesser than 0.05, implying that the seven main factors had simultaneous and significant effects on the logical order variable.
Simultaneous Measurement of Direction-Recognizable Curvature and Temperature Based on Symmetry Exposing Tilt FBG

A symmetry tilt fiber Bragg grating (STFBG) sensor is demonstrated for simultaneous measurement of directional recognizable curvature and temperature utilizing an upgraded tilt fiber Bragg grating (TFBG) writing system. Two TFBG structures are written on the opposite side of a single-mode fiber (SMF) core. More light is restricted in the fiber core due to the secondary reflection of the grating region. When the grating region is curved, the intensity of cladding modes can be enhanced drastically. Thus, compared with the traditional TFBG, the intensity variation of STFBG coupled modes is strengthened, which results in high sensitivity. A high curvature sensitivity of up to 6.172 dB/m−1 is achieved with intensity demodulation. Benefiting from the symmetry characteristics, the STFBG can implement a direction-recognizable curvature sensing in the measurement region of 0°–90°. Meanwhile, based on wavelength demodulation, the sensor can also realize temperature sensing with a sensitivity of 0.010 nm/°C, which does not influence curvature measurement.
Superpixel-Oriented Thick Cloud Removal Method for Multitemporal Remote Sensing Images

Since the information across all bands of the cloud-contaminated region is missing, thick cloud removal for remote sensing images (RSIs) is still a challenging problem. Recently, the availability of rich spatial–spectral–temporal information for multitemporal RSIs provides the possibility for addressing the thick cloud removal problem. However, existing methods explore the holistic redundancy of multitemporal RSIs and neglect the important semantic clue of multitemporal images. In this letter, we propose a superpixel-oriented thick cloud removal (STORM) model for multitemporal images, where the multitemporal superpixel as the generic unit allows us to exploit redundancy with semantic clue in a low-rank optimization problem. To harness the resultant irregular fourth-order tensor (i.e., multitemporal superpixels) in the optimization problem, we cleverly introduce the weighted tensor to transform the irregular tensor into the regular tensor, which naturally leads to a standard low-rank tensor optimization problem. To tackle the tensor optimization problem, we develop a proximal alternating minimization (PAM)-based algorithm. Extensive simulated and real experiments on multitemporal RSIs acquired by Sentinel-2 and Landsat-8 satellites demonstrate the superior performance of the proposed method over the comparison methods.
The Cluster Head Preferred Hierarchical Clustering Routing Protocol Based on G-Means in Wireless Sensor Networks

In hierarchical structures determined by traditional routing protocols of wireless sensor networks, clustering is not structured and the networks prone to generate scatters, leading to some nodes die quickly. So this paper presents a cluster head preferred hierarchical clustering routing protocol based on G-Means (GHPHC). It uses G-Means algorithm to detect each clustering structure, so that each cluster is Gaussian distribution and it avoids the generation of scatters. At the same time, the cluster head preferred algorithm proposed in this paper gives the corresponding weight for each node of all clusters, thus select a suitable cluster head node for every cluster. Experimental results show that compared to traditional routing protocols, the death rate of nodes in a wireless sensor network which uses GHPHC protocol is more slow, and has a longer life cycle.
Abnormal Image Classification Using Vision Transformer for Smart Agriculture

Smart agriculture is an emerging innovative sector that harnesses technological advancements like sensor monitoring, image processing, soil quality evaluation, and automated water systems, all of which contribute towards enhanced crop yields and superior quality of produce. With the progression towards automated systems in smart agriculture, the need for a robust data infrastructure that assures data integrity is critical. This demands an integrated approach with anomaly detection algorithms and a robust classification technique to ensure an error-free data pipeline. This study employs two distinct datasets to achieve abnormal image classification using a vision transformer (ViT) and a convolutional neural network (CNN). The primary dataset, obtained through direct collection from cotton fields in Karachi and Balochistan, Pakistan, comprises high-resolution agricultural images captured using specialised cameras and Unmanned Aerial Vehicles (UAVs). The secondary dataset was sourced from a publicly available database. The two classification models, ViT and CNN, were applied to both datasets. These supervised models are primarily used to classify images as normal or abnormal. The results of the two classification models were also compared, with the ViT model surpassing the CNN model in performance and achieving the highest accuracy on both datasets. The Vision Transformer model proved to be robust for supervised data, showing remarkable accuracy of 96.00% on the public dataset, 99.18% on both camera based images and 97.56% on UAV images of collected dataset as compared to the CNN model's accuracy that are 82.00% on the public dataset and 98.36% on camera and 95.12% on UAV images of collected dataset.
Profiles of biliary microbiota in biliary obstruction patients with Clonorchis sinensis infection

Background Clonorchis sinensis (C. sinensis) is a epidemiologically significant food-borne parasite, causing several hepatobiliary diseases. Biliary microbiota community structure might be influenced by infection with pathogens. However, the biliary microbiome of biliary obstruction patients infected with C. sinensis is still an unexplored aspect. Methods A total of 50 biliary obstruction patients were enrolled, including 24 infected with C. sinensis and 26 non-infected subjects. The bile samples were collected by Endoscopic Retrograde Cholangiopancretography. Biliary microbiota alteration was analyzed through high-throughput 16S ribosomal RNA (rRNA) gene sequencing. Results Our findings revealed that there was significant increase in both richness and diversity, as well as changes in the taxonomic composition of the biliary microbiota of C. sinensis infected patients. At the phylum level, C. sinensis infection induced Proteobacteria increased and Firmicutes reduced. At the genus level, the relative abundance of Pseudomonas and Staphylococcus increased significantly, while Enterococcus decreased prominently in infected groups (P < 0.05). The PICRUSt analysis further showed remarkably different metabolic pathways between the two groups. Conclusion C. sinensis infection could modify the biliary microbiota, increasing the abundance and changing the phylogenetic composition of bacterial in biliary obstruction patients. This study may help deepen the understanding of the host-biliary microbiota interplay with C. sinensis infection on the background of biliary obstruction and provide new insights into understanding the pathogenesis of clonorchiasis.
Phase Load Balancing in Active Electric Distribution Networks Using the Phase Swapping Smart Devices on Lateral Branches

An efficient approach using a clustering-based optimal placement methodology of some smart devices in the derivation nodes of the active low voltage electric distribution networks for the real-time phase swapping of lateral branches has been proposed in the paper to solve the phase load balancing problem. The results confirmed the robustness of the proposed approach compared with other solutions, leading to the technical benefits regarding energy saving of 29.24% and decreasing the unbalance degree at the low voltage level of the electric distribution substation (the average value in the analyzed period has been of 1.06 p.u. from 1.24 p.u. recorded in the initial unbalance case).
ScholarWorks@UARK ScholarWorks@UARK

A central goal of conservation biology is to identify and understand the factors that lead to extinction. The Earth is currently undergoing a 6 th mass extinction event, in large part because of human activity. In the last century, rates of extinction have increased anywhere from 8-100 times the background rate of 2 extinctions per 10,000 species every 100 years. However, there remains a debate over whether certain species are predisposed to a higher extinction risk. In particular, it is not known if the macroevolutionary history of a lineage is a major contributor to the probability of extinction, nor is it clear whether the current rise in extinction rates is due strictly to anthropogenic activity. Using life history trait information and the International Union for Conservation of Nature (IUCN) Red List rankings for bats, birds, and odonates in combination with a novel diversification method, MiSSE (Missing State Speciation and Extinction), we test if there is an evolutionary signal of extinction susceptibility independent of the influence that traits can have on rates of diversification. Phylogenetic and non-phylogenetic regressions were run to determine if any specific traits correlated with extinction risk ranking. We find that there is no correlation between diversification rates and IUCN extinction risk. However, larger clutch sizes and range sizes correlated with lower extinction risk and that longer generation lengths for birds did correlate with higher levels of extinction risk. We found no correlation of extinction risk and life history traits in bats and odonates. Our modeling suggests that other factors, such as human-mediated activities, better explain the increased extinction rates.
When Agreement Isn’t Agreement: Deflected Agreement in Najdi Arabic as Aspect

In modern Arabic varieties, subject-verb agreement is complicated by plural nouns which can occur with both plural and singular verbs. These have been called strict and deflected agreement respectively. In some varieties such as Modern Standard Arabic, this deflected agreement is fixed and determined by animacy or other features; however, in other varieties such as Najdi Arabic (central Saudi Arabia), whether strict or deflected agreement occurs is less predictable. Kramer & Winchester (2017) have argued that the selection of verb form corresponds to a difference in meaning, specifically, a herd/clump as opposed to an individuated interpretation, which they locate in the nominal features of the noun. However, based on the results of two experiments, this paper argues for an alternate analysis that this meaning difference originates on the verb and corresponds to a speaker’s mental representation of the event structure. This finding leads to the conclusion that only strict agreement is actually an example of subject-verb agreement, while deflected agreement serves to mark features of the event. This usage of deflected agreement to express event structure, while initially surprising, flows from existing understandings of cognition of number and event semantics.
Adaptive Approaches for Eﬀicient Parallel Algorithms on Cluster-based Systems

: Few years ago, there was a huge development of new parallel and distributed systems. Due to many reasons, such as the inherent heterogeneity, the diversity, and the continuous evolution of such computational supports, it is very hard to solve eﬃciently a target problem by using a single algorithm or to write portable programs that perform well on any architecture. Toward this goal, we propose a generic framework combining communication models and adaptive approaches to deal with the performance modeling problem associated to the design of eﬃcient parallel algorithms on grid computing environments, and we apply this methodology on collective communication operations. Experiments performed on a grid platform prove that the framework provides signiﬁcant performances while determining the best combination model-algorithm depending on the problem and architecture parameters.
Research on detection method of airborne obstacle avoidance lidar

Complicated environment and obstacles like power lines are pernicious to low-flying helicopters. A novel obstacle detection algorithm for onboard LiDAR evadible system is proposed, which lowers false alarm rate (FAR) and raises detection speed (DS). Firstly, the pass-through filter and planarization voxel filter are used for denoising and sampling reduction. Then, the point cloud is separated by adaptive threshold elevation filtering. Finally, the power lines are extracted with line feature constraint. Experimental results show the maximum detection distance for power lines is up to 800 m, recall rate, over 95%, false alarm rate, below 5%, and that the detection time is less than 100 ms.
On Binary Sequence Design via PSL Minimization

We introduce an efficient gradient based algorithm to minimize the peak sidelobe level (PSL) for binary sequence designs with or without low correlation zone requirements. The proposed algorithm calculates the effective step sizes by leveraging fast Fourier transform operations and employing low-complexity updates, and its local convergence is proved. Numerical examples are provided to demonstrate that our approach can outperform the state-of-the-art algorithms in generating binary sequences with lower PSL values.
BUILDING RESILIENCE FOR ADAPTATION TO CLIMATE CHANGE IN THE AGRICULTURE SECTOR

BACKGROUND AND OBJECTIVES OF THE WORKSHOP This workshop was a follow-up of the Joint OECD-INEA-FAO Workshop on Agriculture and Adaptation to Climate Change, which was held in June 2010. One of the conclusions of that 2010 Workshop was that, as climate change brings new uncertainties, adds new risks and changes already existing risks, one of the most effective ways for agriculture to adapt to climate change could be to increase its resilience. This is why this workshop started from the various types of risks to which agriculture is prone, considered the impact that climate change is expected to have on them, and discussed various risk management strategies, depending on types of risks, and the country and region in question. This two-day workshop consisted of four sessions including setting the scene, types of risks and risk management, case studies and, finally, tools, policies and institutions.
An Insight into Functional Metagenomics: A High-Throughput Approach to Decipher Food–Microbiota–Host Interactions in the Human Gut

Our understanding of the symbiotic relationship between the microbiota and its host has constantly evolved since our understanding that the “self” was not only defined by our genetic patrimony but also by the genomes of bugs living in us. The first culture-based methods highlighted the important functions of the microbiota. However, these methods had strong limitations and did not allow for a full understanding of the complex relationships that occur at the interface between the microbiota and the host. The recent development of metagenomic approaches has been a groundbreaking step towards this understanding. Its use has provided new insights and perspectives. In the present chapter, we will describe the advances of functional metagenomics to decipher food–microbiota and host–microbiota interactions. This powerful high-throughput approach allows for the assessment of the microbiota as a whole (including non-cultured bacteria) and enabled the discovery of new signaling pathways and functions involved in the crosstalk between food, the gut microbiota and its host. We will present the pipeline and highlight the most important studies that helped to develop the field. To conclude, we will emphasize the most recent developments and hot topics in functional metagenomics.
Analysis of Bulk Transcriptome Sequencing Data and in vitro Experiments Reveal SIN3A as a Potential Target for Diabetic Foot Ulcer

Background Diabetic foot ulcers (DFUs) represent a severe complication of diabetes associated with reduced quality of life, lower limb amputations, hospitalizations, increased incidence, and mortality. Importantly, a significant number of pathogenic genes remain unexplored in DFUs. Methods A series of bioinformatics analyses were performed on publicly available bulk transcriptome sequencing datasets GSE134431 and GSE80178 to explore the transcriptomic changes in DFUs and select core genes for in vitro functional validation. In a focused examination, the differential expression analysis unveiled distinctions in gene expression patterns between DFUs and non-ulcerated diabetic skin tissues. Enriched functional annotations of differentially expressed genes were explored using the DAVID online tool. Protein-protein interaction analysis was conducted to investigate interactions among differentially expressed genes and select core genes. Knockdown or overexpression of core genes in HaCaT keratinocytes was performed to assess their impact on cell proliferation and migration. Results Ten core genes were identified. Cell Counting Kit-8 (CCK-8) and scratch assays demonstrated that downregulation of the core gene SIN3A significantly inhibited the migration and proliferation of HaCaT keratinocytes, while overexpression of SIN3A reversed the high-glucose-induced suppression of HaCaT cell viability and migration. Conclusion SIN3A expression is downregulated in DFUs. In vitro, SIN3A promotes the proliferation and migration of HaCaT keratinocytes, suggesting it may be a potential therapeutic target for DFUs.