Miracle-dz/UniFAR-FTUs huggingface dataset:
Future preferred mode of learning of business undergraduates and its implications

e-Learning was abruptly adopted in many countries to mitigate the adverse consequences of the sudden closure of institutions of higher learning caused by the COVID-19 pandemic. Against this background, this study investigated how business undergraduates want to learn in the future and predictors of their future preferred mode of learning. 251 business undergraduates from a private university in Malaysia participated in an online survey conducted in July 2020, during the sudden closure of institutions of higher learning. Data collected were analysed using the multiple discriminant analysis to develop a characteristics profile of the three groups of business undergraduates (i.e., preferred fully conventional classroom learning, blended learning and fully e-learning) in terms of important predictors. Results revealed that the significant predictors of future preferred mode of learning of business undergraduates, in descending order, were disadvantages of e-learning, advantages of e-learning, self-regulated learning, learning outcomes, information and communications technology infrastructure and training, support and resources. This study concludes with some reflective thoughts about important lessons learned from this unprecedented pandemic pertaining to e-learning readiness to deal with future unexpected crises.
High-resolution assessment of riverbank erosion and stabilisation techniques with associated water quality implications

Agriculture is a key contributor to poor water quality, but the sources of sediment and nutrient losses from agricultural catchments – including from riverbank erosion – are highly variable. Riverbank erosion is particularly difficult to quantify and control. Here, we developed a quick assessment approach to quantify riverbank erosion rates and associated sediment and nutrient loading rates into waterways using airborne LiDAR combined with field-collected data. We applied this approach and explored its relationships to water quality at four sites within the Blackwater catchment in Northern Ireland for two analysis periods. GIS LiDAR image differencing revealed that volume changes in riverbank elevation equated to average erosion rates which indicated spatial and temporal variability in erosion rates. Combining the erosion rates with in-situ riverbank bulk density and total extractable phosphorus content provided sediment and phosphorus loading rates. The relative differences between estimated erosion at the different sites corresponded well with in-stream suspended sediment variations, but patterns for total phosphorus concentrations were more complex. We conclude that the use of LiDAR combined with field data is an innovative means for riverbank erosion quantification. Furthermore, by using LiDAR-to-LiDAR analyses, the reductions in erosion, sediment, and phosphorus loading rates following riverbank stabilization techniques can be determined.
Graph Structured Data Viewed Through a Fourier Lens

Graph-structured data appears in many modern applications like social networks, sensor networks, transportation networks and computer graphics. These applications are defined by an underlying graph (e.g. a social graph) with associated nodal attributes (e.g. number of ad-clicks by an individual). A simple model for such data is that of a graph signal--a function mapping every node to a scalar real value. Our aim is to develop signal processing tools for analysis of such signals de- fined over irregular graph-structured domains, analogous to classical Fourier and Wavelet analysis defined for regular structures like discrete-time sequences and two-dimensional grids.In this work, we start by reviewing the notion of a Graph Fourier Transform (GFT), which has been defined in the literature for graph signals. We examine the spatial and spectral features of circulant graphs, which accommodate linear shift-invariant operations. We describe fundamental operations such as shifting, sampling, graph-reconnection and linear filtering for signals on circulant graphs and derive associated sampling and graph-reconnection theorems. We also develop wavelet filter bank structures for multi resolution analysis of large-scale graphs.We present a method to decompose an arbitrary graph into a linear combination of circulant graphs. This helps extend fundamental operations such as sampling, filtering and multi resolution filter banks to general graphs. We present an application in the area of graph semi-supervised learning where some of the existing algorithms can be viewed as suitably designed filters defined in the GFT domain. We propose a wavelet regularized learning algorithm and evaluate the performance on some real-world datasets.
2-Hydroxypropyl-β-cyclodextrin (HPβCD) as a Potential Therapeutic Agent for Breast Cancer

Simple Summary Breast cancer is a global burden with the most severe subtype being triple negative breast cancer (TNBC). Despite advances in conventional therapies, treatment for TNBC is currently lacking. Interestingly, cholesterol has gained interest as a potential therapeutic target due to cancer cells’ increased reliance on this macromolecule. In this study we aimed to assess the effects of cholesterol depletion as a therapeutic target in TNBC. We show that treatment with the cholesterol-depletory agent 2-hydroxypropyl-β-cyclodextrin (HPβCD) impeded the growth of cancer cells and further led to cancer cell death, which could be attributed to an altered cellular cholesterol profile following treatment. Furthermore, mice xenograft studies indicated complete eradication of early-stage tumours with no relapse, followed by a remarkable reduction in intermediate- and late-stage tumours, respectively. We have also identified SFRP1 as a possible molecular target facilitating the therapeutic action of HPβCD. These findings consequently potentiate cholesterol depletion as a novel anticancer strategy to be pursued. Abstract Cholesterol accumulation is documented in various malignancies including breast cancer. Consequently, depleting cholesterol in cancer cells can serve as a viable treatment strategy. We identified the potency of 2-hydroxypropyl-β-cyclodextrin (HPβCD), a cholesterol-depletor in vitro against two breast cancer cell lines: MCF-7 (Oestrogen-receptor positive, ER+) and MDA-MB-231 (Triple negative breast cancer (TNBC)). The results were then compared against two non-cancerous cell lines using cytotoxic-, apoptosis-, and cholesterol-based assays. Treatment with HPβCD showed preferential and significant cytotoxic potential in cancer cells, inducing apoptosis in both cancer cell lines (p < 0.001). This was mediated due to significant depletion of cholesterol (p < 0.001). We further tested HPβCD in a MF-1 mice (n = 14) xenograft model and obtained 73.9%, 94% and 100% reduction in tumour size for late-, intermediate-, and early-stage TNBC, respectively. We also detected molecular-level perturbations in the expression patterns of several genes linked to breast cancer and cholesterol signalling pathways using RT2-PCR arrays and have identified SFRP1 as a direct binding partner to HPβCD through SPR drug interaction analysis. This work unravels mechanistic insights into HPβCD-induced cholesterol depletion, which leads to intrinsic apoptosis induction. Results from this study potentiate employing cholesterol depletion as a promising unconventional anticancer therapeutic strategy, which warrants future clinical investigations.
Microbial Induced Calcite Precipitation (MICP) Potential of Ureolytic Bacillus sp. Isolated from the Soil of Eroded Ecosystems for Stabilizing and Improving the Fertility of Eroded Soils

Abstract The loss of soil from lands due to erosion has a negative effect on ecosystems and food security. Bacillus due to high catabolic capability is an appropriate candidate for application in biocementation process. The aim of this study is isolate and characterize Bacillus sp. with biocementation capability from various ecosystems. The isolates were separated from 400 samples, and characterized by biochemical and molecular methods include the amplification and sequencing analysis of gyrA and 16S rRNA genes. Growth in presence of urea, in different salinity, pH, and temperature, also scanning electron microscope (SEM), X-ray diffraction (XRD), and wind tunnel analysis were applied to determine biocementation ability. A total number of 195 isolates were recovered from environmental samples, of which 25 isolates (12.82%) were identified as urease-positive Bacillus which belonged to 10 species consisting of Bacillus subtilis five strains (20%), Bacillus vallismortis and Bacillus seohaeanensis four strains (16%) each, Bacillus mobilis, Bacillus pseudofirmus, Bacillus cohnii, Bacillus cereus, Bacillus alkalinitrilicus two strains (8%) each, and Bacillus sphaericus and Bacillus megaterium one strain (4%) each. Moreover, 15 urease-positive isolates (7.7%) belonging to Ralostenia, Actinomycete, and Halomonas genera were identified. Optimum conditions for microbial induced calcite precipitation (MICP) by isolates are 30 °C, pH 9, and 6% salinity. The highest rate of calcium carbonate formation and urease activity recorded in B. subtilis with 24.15 mg/mL of calcium carbonate and 4.40 × 103 unit/L of urease, followed by B. mobilis and B. alkalinitrilicus with 22.85 mg/mL of calcium carbonate and 3.93 × 103 unit/L of urease. After MICP the lowest soil loss ratio at a flow rate of 90 km/h, was observed in B. subtilis 100-fold reduction, followed by B. seohaeanensis, B. cereus, B. vallismortis, with 90, 85, 80-folds reduction, respectively. Results indicate that the diversity of Bacillus sp. offers the potential ability for adaptation to harsh and untapped environments, also showed that the use of MICP on the soil surface can have a very significant role in reducing soil losses due to wind erosion.
Reconciling the impact of knowledge management processes on knowledge worker productivity

Knowledge management has been a proven tool to foster organizational performance, innovations, and individual knowledge workers’ productivity. A stream of empirical studies has demonstrated with contradictory results that each single organizational knowledge management process – knowledge creation, knowledge sharing and utilization – can enhance the knowledge workers’ productivity in isolation. In contrast, our study argues with the support of Nonaka’s theory and alignment theory that knowledge utilization is the only frontline and primary knowledge management process which can enhance knowledge workers’ productivity while other knowledge management processes (knowledge creation and knowledge sharing) support and supplement each other as well as improve knowledge utilization. This means that shared and created knowledge will not enhance the productivity of knowledge workers until organizations strive for knowledge utilization. This study used data collected from 336 knowledge workers in the Telecom industry of Pakistan and examined it using partial least squares modelling. The findings indicated that knowledge utilization is the sole frontline and primary knowledge management to enhance the productivity of knowledge workers. Hence, knowledge utilization can only influence productivity indirectly by increasing the utilization of knowledge created and/or shared.
Chemical Characterization and Evaluation of Antimicrobial Properties of the Wild Medicinal Mushroom Ganoderma lucidum Growing in Northern Moroccan Forests

Ganoderma lucidum is an extensively famous medicinal mushroom distributed worldwide. Despite being widely grown in Moroccan forests, there are no studies on its nutritional, nutraceutical and pharmaceutical values. Herein, the objective of this study was to investigate the chemical characterization and antimicrobial properties of G. lucidum methanolic extract. Total phenolic, flavonoid, tannin, ascorbic acid and carotenoid contents were determined by spectrophotometry. The results revealed that the most prevalent bioactive compounds were phenolics and flavonoids, with total values of 154.60 mg GAE/g of dry methanolic extract (dme) and 60.55 mg CE/mg of dme, respectively. A GC–MS analysis identified 80 biologically active molecules, which were mainly divided into the following major groups: sugars (49.49%), organic acids (8.89%), fatty acids (7.75%), amino acids (7.44%), steroids (7.32%), polyphenols (5.92%), and others (13.16%). Additionally, 22 individual phenolic compounds were identified and quantified using HPLC–MS, with emphasis on kaempferol (1714 µg/g of dry weight (dw)), apigenin (1955 µg/g dw) and quercetin (947.2 µg/g dw). The methanolic extract of G. lucidum indicated strong antioxidant capacity by means of the following: DPPH radical-scavenging activity (53.7 µg/mL), β-carotene/linoleate assay (43.75 µg/mL), and reducing power assay (76.62 µg/mL). Furthermore, the extract exhibited potent antimicrobial properties against seven human pathogenic microorganisms, including two bacteria and five fungal strains, at concentrations ranging from 1 to 16 mg/mL. The most sensitive pathogen was Epidermophyton floccosum (MIC = MFC = 1 mg/mL), while Aspergillus fumigatus was the most resistant one (MIC = 16 mg/mL and MFC ≥ 16 mg/mL). Overall, our findings demonstrated valuable nutritional and bioactive compound attributes, and potent antioxidant and antimicrobial properties, of G. lucidum growing in Moroccan forests. Moreover, these findings suggest that the Moroccan mushroom can be extremely useful for the food and medicinal industries to positively affect socioeconomic status.
Acute Effects of Dietary Fiber in Starchy Foods on Glycemic and Insulinemic Responses: A Systematic Review of Randomized Controlled Crossover Trials

Dietary fiber (DF) consumption has been associated with improved glycemic control in epidemiological and long-term interventional studies. However, its acute effects are not yet clear. This systematic review aims to elucidate the postprandial effects of DF in starchy products on glycemia and insulinemia. An electronic search of databases was conducted, and forty-one records met the inclusion criteria and underwent a risk-of-bias assessment. It was shown that soluble DF does not clearly affect glycemia in individuals with normal weight, while resistant starch may be more effective in flattening glycemic responses. Concerning insulinemia, both soluble DF and resistant starch have mixed results, with either favorable or no effects. Data on insoluble DF and glucose metabolism are scarce. The same mixed results for glycemia can be seen in healthy volunteers with overweight/obesity, while resistant starch seems to improve insulinemic responses. Finally, more studies need to examine the acute effects of DF in starchy foods on glucose metabolism and insulin secretion in individuals facing glucose abnormalities. Additionally, more studies are needed to prove whether ingesting high-fiber carbohydrate-containing products per se can result in blunted glycemic and insulinemic responses and which DF type and amount are more effective.
Silicon-Lattice-Matched Boron-Doped Gallium Phosphide: A Scalable Acousto-Optic Platform.

The compact size, scalability, and strongly confined fields in integrated photonic devices enable new functionalities in photonic networking and information processing, both classical and quantum. Gallium phosphide (GaP) is a promising material for active integrated photonics due to its high refractive index, wide band gap, strong nonlinear properties, and large acousto-optic figure of merit. In this work we demonstrate that silicon-lattice-matched boron-doped GaP (BGaP), grown at the 12-inch wafer scale, provides similar functionalities as GaP. BGaP optical resonators exhibit intrinsic quality factors exceeding 25,000 and 200,000 at visible and telecom wavelengths respectively. We further demonstrate the electromechanical generation of low-loss acoustic waves and an integrated acousto-optic (AO) modulator. High-resolution spatial and compositional mapping, combined with ab initio calculations indicate two candidates for the excess optical loss in the visible band: the silicon-GaP interface and boron dimers. These results demonstrate the promise of the BGaP material platform for the development of scalable AO technologies at telecom and provide potential pathways toward higher performance at shorter wavelengths. This article is protected by copyright. All rights reserved.
Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields

Text-driven 3D scene generation is widely applicable to video gaming, film industry, and metaverse applications that have a large demand for 3D scenes. However, existing text-to-3D generation methods are limited to producing 3D objects with simple geometries and dreamlike styles that lack realism. In this work, we present Text2NeRF, which is able to generate a wide range of 3D scenes with complicated geometric structures and high-fidelity textures purely from a text prompt. To this end, we adopt NeRF as the 3D representation and leverage a pre-trained text-to-image diffusion model to constrain the 3D reconstruction of the NeRF to reflect the scene description. Specifically, we employ the diffusion model to infer the text-related image as the content prior and use a monocular depth estimation method to offer the geometric prior. Both content and geometric priors are utilized to update the NeRF model. To guarantee textured and geometric consistency between different views, we introduce a progressive scene inpainting and updating strategy for novel view synthesis of the scene. Our method requires no additional training data but only a natural language description of the scene as the input. Extensive experiments demonstrate that our Text2NeRF outperforms existing methods in producing photo-realistic, multi-view consistent, and diverse 3D scenes from a variety of natural language prompts. Our code and model will be available upon acceptance.
Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems

Humans teach others about the world through language and demonstration. When might one of these modalities be more effective than the other? In this work, we study the factors that modulate the effectiveness of language vs. demonstration using multi-agent systems to model human communication. Specifically, we train neural network agents to teach via language or demonstration in a grounded communication task, manipulating 1) the inherent difficulty of the task and 2) the competence of the teacher. We find that teaching by demonstration is more effective in the simplest settings, but language is more effective as task difficulty increases, due to its ability to generalize more effectively to unseen scenarios. Overall, these results provide converging evidence for a tradeoff between language and demonstration as teaching modalities in humans, and make the novel predictions that demonstration may be optimal for easy tasks, while language enables generalization in more challenging settings.
Efficient and Deterministic Search Strategy Based on Residual Projections for Point Cloud Registration

Estimating the rigid transformation between two LiDAR scans through putative 3D correspondences is a typical point cloud registration paradigm. Current 3D feature matching approaches commonly lead to numerous outlier correspondences, making outlier-robust registration techniques indispensable. Many recent studies have adopted the branch and bound (BnB) optimization framework to solve the correspondence-based point cloud registration problem globally and deterministically. Nonetheless, BnB-based methods are time-consuming to search the entire 6-dimensional parameter space, since their computational complexity is exponential to the dimension of the solution domain. In order to enhance algorithm efficiency, existing works attempt to decouple the 6 degrees of freedom (DOF) original problem into two 3-DOF sub-problems, thereby reducing the dimension of the parameter space. In contrast, our proposed approach introduces a novel pose decoupling strategy based on residual projections, effectively decomposing the raw problem into three 2-DOF rotation search sub-problems. Subsequently, we employ a novel BnB-based search method to solve these sub-problems, achieving efficient and deterministic registration. Furthermore, our method can be adapted to address the challenging problem of simultaneous pose and correspondence registration (SPCR). Through extensive experiments conducted on synthetic and real-world datasets, we demonstrate that our proposed method outperforms state-of-the-art methods in terms of efficiency, while simultaneously ensuring robustness.
A Theory of First Order Mean Field Type Control Problems and their Equations

In this article, by using several new crucial {\it a priori} estimates which are still absent in the literature, we provide a comprehensive resolution of the first order generic mean field type control problems and also establish the global-in-time classical solutions of their Bellman and master equations. Rather than developing the analytical approach via tackling the Bellman and master equation directly, we apply the maximum principle approach by considering the induced forward-backward ordinary differential equation (FBODE) system; indeed, we first show the local-in-time unique existence of the solution of the FBODE system for a variety of terminal data by Banach fixed point argument, and then provide crucial a priori estimates of bounding the sensitivity of the terminal data for the backward equation by utilizing a monotonicity condition that can be deduced from the positive definiteness of the Schur complement of the Hessian matrix of the Lagrangian in the lifted version and manipulating first order condition appropriately; this uniform bound over the whole planning horizon $[0,T]$ allows us to partition $[0,T]$ into a number of sub-intervals with a common small length and then glue the consecutive local-in-time solutions together to form the unique global-in-time solution of the FBODE system. The regularity of the global-in-time solution follows from that of the local ones due to the regularity assumptions on the coefficient functions. Moreover, the regularity of the value function will also be shown with the aid of the regularity of the solution couple of the FBODE system and the regularity assumptions on the coefficient functions, with which we can further deduce that this value function and its linear functional derivative satisfy the Bellman and master equations, respectively.
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability

Recent research has observed that in machine learning optimization, gradient descent (GD) often operates at the edge of stability (EoS) [Cohen, et al., 2021], where the stepsizes are set to be large, resulting in non-monotonic losses induced by the GD iterates. This paper studies the convergence and implicit bias of constant-stepsize GD for logistic regression on linearly separable data in the EoS regime. Despite the presence of local oscillations, we prove that the logistic loss can be minimized by GD with \emph{any} constant stepsize over a long time scale. Furthermore, we prove that with \emph{any} constant stepsize, the GD iterates tend to infinity when projected to a max-margin direction (the hard-margin SVM direction) and converge to a fixed vector that minimizes a strongly convex potential when projected to the orthogonal complement of the max-margin direction. In contrast, we also show that in the EoS regime, GD iterates may diverge catastrophically under the exponential loss, highlighting the superiority of the logistic loss. These theoretical findings are in line with numerical simulations and complement existing theories on the convergence and implicit bias of GD for logistic regression, which are only applicable when the stepsizes are sufficiently small.