Miracle-dz/UniFAR-FTUs huggingface dataset:
Corruption, Growth, and FDI Spillovers: Evidence from Emerging Market Economies

Countries with substantial increases in economic growth and similar characteristics to developed countries are usually referred to as emerging markets. The problem faced by emerging market countries is the need for more capital investment into the country so that domestic savings are added through efforts to find buy and imports. This study looks at the effect of economic growth, trade openness, control of corruption, and regulatory quality on FDI, specifically in developing countries. The analysis method used the Generalised Method of Moments panel. The data used from 2014-2021 with the analysis areas of Indonesia, Malaysia, the Philippines and Thailand. The results showed that the economic growth variable had a positive and significant effect on FDI, the trade openness variable had a negative and significant effect on FDI, the control of cor-ruption variable had a negative and significant effect on FDI, and the regulatory quality variable had a positive and significant effect on FDI in ASEAN emerging market countries. Keywords: Growth, Trade openness, Control of corruption, and Regulatory quality
Construction of an aerolysin-based multi-epitope vaccine against Aeromonas hydrophila: an in silico machine learning and artificial intelligence-supported approach

Aeromonas hydrophila, a gram-negative coccobacillus bacterium, can cause various infections in humans, including septic arthritis, diarrhea (traveler’s diarrhea), gastroenteritis, skin and wound infections, meningitis, fulminating septicemia, enterocolitis, peritonitis, and endocarditis. It frequently occurs in aquatic environments and readily contacts humans, leading to high infection rates. This bacterium has exhibited resistance to numerous commercial antibiotics, and no vaccine has yet been developed. Aiming to combat the alarmingly high infection rate, this study utilizes in silico techniques to design a multi-epitope vaccine (MEV) candidate against this bacterium based on its aerolysin toxin, which is the most toxic and highly conserved virulence factor among the Aeromonas species. After retrieval, aerolysin was processed for B-cell and T-cell epitope mapping. Once filtered for toxicity, antigenicity, allergenicity, and solubility, the chosen epitopes were combined with an adjuvant and specific linkers to create a vaccine construct. These linkers and the adjuvant enhance the MEV’s ability to elicit robust immune responses. Analyses of the predicted and improved vaccine structure revealed that 75.5%, 19.8%, and 1.3% of its amino acids occupy the most favored, additional allowed, and generously allowed regions, respectively, while its ERRAT score reached nearly 70%. Docking simulations showed the MEV exhibiting the highest interaction and binding energies (−1,023.4 kcal/mol, −923.2 kcal/mol, and −988.3 kcal/mol) with TLR-4, MHC-I, and MHC-II receptors. Further molecular dynamics simulations demonstrated the docked complexes’ remarkable stability and maximum interactions, i.e., uniform RMSD, fluctuated RMSF, and lowest binding net energy. In silico models also predict the vaccine will stimulate a variety of immunological pathways following administration. These analyses suggest the vaccine’s efficacy in inducing robust immune responses against A. hydrophila. With high solubility and no predicted allergic responses or toxicity, it appears safe for administration in both healthy and A. hydrophila-infected individuals.
Exploring the Mediating Role of Sleep Deficit-Related Functional Status in Subacute Stroke Survivors

Background: Understanding post-stroke fatigue (PSF) and its associated factors is crucial for effective therapy and rehabilitation. This study aimed to assess the mediating role of the excessive daytime sleepiness-related functional status (SFS) on the relationship between sleep and the severity of fatigue in subacute stroke survivors. Methods: Subacute stroke survivors (n = 50; male = 38; female = 12), completed a cross-sectional study involving the Pittsburgh sleep quality index (PSQI), the Epworth sleepiness scale (ESS), the insomnia severity index (ISI), the functional outcome of the sleep questionnaire (FOSQ), and the fatigue severity scale (FSS). Results: The SFS mediated the association between the severity of fatigue and sleep problems. The PSQI and FOSQ (b = −0.37, p < 0.001), and the FOSQ and FSS were correlated (b = −0.18, p < 0.05), with a significant indirect effect of the PSQI on the FSS. The ISI correlated with the FOSQ (b = −0.20, p < 0.001), with significant direct (b = 0.15, p < 0.001), as well as indirect, effects of the ISI on the FSS. The ESS correlated with the FOSQ (b = −0.23, p < 0.001), with a significant indirect effect of the ESS on the FSS. Conclusions: In subacute stroke survivors, fatigue and sleep are linked. Increased understanding of sleep-PSF may help in exploring new targets for supplement therapy.
Ecological biogeography of West Usambara Mountains: a study on the influence of abiotic factors to spatial distribution of plant and animal species

West Usambara Mountains, Tanzania are famous for rich biodiversity and endemic species of plants and animals. Although there have been extensive studies on plant and animals species, little attention has been given on abiotic factors influencing their spatial distribution. Given rampant degradation of vegetation and associated consequence on biodiversity, knowledge on abiotic factors influencing distribution of species along the landscape become pertinent for conservation. A study was carried out to explore abiotic factors impacting plant and animals species distribution. Soil, landform and land use/ cover were studied using grids of 20 m × 20 m using FAO Guidelines for Soil Profile and Habitat Descriptions. Soils were described, sampled for laboratory analysis. Spatial distribution of plant species were determined in the grids, and along the transects, every time estimating the percent cover and describing the habitat. Distribution of animal spe- cies was studied using both small mammals and rodent burrows as proxies. Trapping was done using different traps sizes, checking daily for animal caught, counting and re-trapping. Rodent burrows were estimated in same grids by examining a width of 0.5 m from end to end of grid and total number of burrows recorded. Determination of species distribution was done using GLM regression. Results show that species are influenced by elevation, which was common to both plants and animals. Topsoil soil depth was positive to plant species whereas hillshade, surface stones, cultivation and atmospheric tempera- ture were negatively influencing plant species. Rock outcrops, surface stones and cultiva tion were positively influencing small mammals distribution. It is concluded that factors influencing distribution of small mammals are elevation, surface stones, rock outcrop and cultivation. Factors influencing plant species are elevation soil depth whereas cul -tivation, hillshade, surface stone and rock out crops negatively impact distribution. For conservation, it is recommended that the best steps are to stop human activities leading to depletion of plant species and accelerating soil erosion and allow for self-regeneration. Control of soil erosion strongly recommended as way of plant species re-establishment.
Preclinical and Early Clinical Development of Tenofovir Alafenamide/Elvitegravir Topical Inserts for Effective On-Demand Vaginal and Rectal HIV Prevention

HIV/AIDS remains a global public health issue, and products available for the prevention of HIV infections are limited, especially those for short-acting, on-demand, user-controlled applications. Topical inserts are products that can be applied vaginally or rectally and have been explored as drug delivery systems. To fill the gap in the HIV prevention product pipeline, CONRAD has developed a topical insert containing tenofovir alafenamide fumarate (TAF) and elvitegravir (EVG), two potent and synergistic antiretrovirals, as a simple, low-cost, and discreet option that can be self-administered vaginally and/or rectally, before and after coitus. In this review, we have described the development path of the TAF/EVG insert up to its current point in clinical testing, highlighting findings from acceptability, preclinical safety, pharmacokinetics, and efficacy evaluations and early clinical studies. In summary, the TAF/EVG inserts are stable, easy to manufacture, low-cost, acceptable, and show highly promising preclinical and clinical results for on-demand topical pre- or post-exposure HIV prevention.
SMFNet: One-Shot Recognition of Chinese Character Font Based on Siamese Metric Model

This paper focuses on Chinese character font recognition and introduces a novel one-shot learning model SMFNet for Chinese character font recognition, by thoroughly analyzing the fundamental characteristics of Chinese characters. Due to the considerable quantity and intricate structures of Chinese characters, conventional methods for font recognition face challenges in meeting the necessary requirements. To address this issue, a Siamese metric structure is incorporated as the model framework. This is complemented by coordinate attention modules and multiple loss functions, enabling one-shot font recognition across various categories such as standard printed fonts, soft brush calligraphy fonts, and hard pen calligraphy fonts. The model is trained and tested using the XIKE-CFS Chinese character font style dataset. Experimental results indicate that SMFNet achieves a recognition accuracy of 97.50% on the XIKE-CFS dataset and supports one-shot recognition with an accuracy rate of 92.84%. Furthermore, compared to other prevalent CNN classification models, SMFNet exhibits outstanding performance in terms of parameter quantity and demonstrates excellent capabilities in recognizing new fonts. In summary, this work provides innovative perspectives and methodologies for addressing challenges in the field of Chinese character font recognition.
Waveform retracking of satellite radar altimeter based on singular spectrum analysis

Satellite altimetric waveform retracking plays an important role in improving the accuracy of altimetry data, and three retracking methods had been analyzed. A waveform series was constructed, and the SSA (singular spectrum analysis) was introduced to process waveform series to denoise and reconstruct waveform series. Three retracking methods, which were OCOG, threshold and function fitting method, were used to retrack reconstructed waveforms to correct GDR data. The waveform data of Jason-1 satellite in South China sea were selected to constructe waveform series to denoise and reconstruct by SSA. The reconstructed waveforms became more regular, and the leading edge of single waveform was more easily recognized. The result showed that combining SSA and retracking methods to process waveform data could improve the value of IMP(improvement percentage) compared to using retracking methods merely, which proved that this new method could improve the altimetric accuracy and could be further studied and applied.
CADcompare TM : A Web-based Application that Compares PDF CAD Drawings

This work in progress describes the development of a web application titled CADcompareTM, which automatically compares, displays, and highlights differences in Portable Document Format (PDF) files of computer-aided design (CAD) drawings and is specifically designed to compare multiple student files to an instructor's grading key. CADcompare augments the grading process of technical and engineering CAD drawings by highlighting differences that can be easily missed by a human grader, such as incorrect line type(s), color(s), or double lines (i.e., lines on top of each other). Some CAD software has built-in comparison tools, however, none of the comparison tools accept PDF files to compare, are web-based applications, or can compare multiple student files at once like CADcompare can. Grading engineering CAD drawings with accuracy and fairness can take a lot of time, the intended use of CADcompare is to act as a grading tool to help instructors grade faster, more accurately, and without unintended bias. Spring 2017, a Windows® based proof of concept version of CADcompare installed on a personal computer showcased the strengths of the software. CADcompare was able to compare multiple student drawings to the grading key much faster than previously used methods. Outcomes from user testing prompted the current development of a web-based version. This paper offers general details on how CADcompare compares PDF files, market analysis, the work in progress, and a planned research study comparing grading times with and without CADcompare in an introductory engineering graphics course.
A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids

This paper provides a review of various machine learning approaches that have appeared in the literature aimed at individualizing or personalizing the amplification settings of hearing aids. After stating the limitations associated with the current one-size-fits-all settings of hearing aid prescriptions, a spectrum of studies in engineering and hearing science are discussed. These studies involve making adjustments to prescriptive values in order to enable preferred and individualized settings for a hearing aid user in an audio environment of interest to that user. This review gathers, in one place, a comprehensive collection of works that have been conducted thus far with respect to achieving the personalization or individualization of the amplification function of hearing aids. Furthermore, it underscores the impact that machine learning can have on enabling an improved and personalized hearing experience for hearing aid users. This paper concludes by stating the challenges and future research directions in this area.
The Reflective Mind of the Anxious in Action: Metacognitive Beliefs and Maladaptive Emotional Regulation Strategies Constrain Working Memory Efficiency

The Attentional Control Theory (ACT) posits that, while trait anxiety may not directly impact performance, it can influence processing efficiency by prompting the use of compensatory mechanisms. The specific nature of these mechanisms, which might be reflective, is not detailed by the ACT. In a study involving 110 students (M = 20.12; SD = 2.10), surveys were administered to assess the students’ metacognitive beliefs, trait anxiety, and emotion regulation strategies (ERSs). The participants engaged in two working memory exercises: the digit span task from the WAIS-IV and an emotional n-back task. The findings indicated that anxiety, metacognitive beliefs, and maladaptive ERSs did not affect task performance but were correlated with increased response times. Several regression analyses demonstrated that a lack of confidence in one’s cognitive abilities and maladaptive ERSs predict higher reaction times (RT) in the n-back task. Additionally, maladaptive ERSs also predict an increased use of strategies in the digit span task. Finally, two mediation analyses revealed that anxiety increases processing efficiency, and this relation is mediated by the use of maladaptive ERSs. These results underscore the importance of the reflective level in mediating the effects of trait anxiety on efficiency. They highlight the necessity of incorporating metacognitive beliefs and maladaptive emotion regulation strategies for a thorough comprehension of the Attentional Control Theory. Recognizing these factors offers valuable perspectives for enhancing cognitive capabilities and fostering academic achievement.
Driving frugal innovation in SMEs: how sustainable leadership, knowledge sources and information credibility make a difference

This study investigates the driving factors behind frugal innovation in Small and Medium-sized Enterprises (SMEs). It specifically examines sustainable leadership as an independent variable, considering its impact on frugal innovation, with sources of knowledge mediating this relationship and information credibility moderating the effects. Employing a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, data were gathered from 325 employees of SMEs in Pakistan. This methodology was chosen for its ability to handle complex relationships between multiple variables simultaneously, offering robust insights into the interplay among sustainable leadership, sources of knowledge, information credibility, and frugal innovation. The results reveal significant associations between sustainable leadership, sources of knowledge, information credibility, and frugal innovation. Sustainable leadership demonstrates a substantial influence on both sources of knowledge and frugal innovation. Furthermore, sources of knowledge play a vital role in mediating the relationship between sustainable leadership and frugal innovation. Information credibility emerges as a significant moderator, affecting the pathways between sustainable leadership, sources of knowledge, and frugal innovation. The findings underscore the importance of sustainable leadership and credible information sources in driving frugal innovation within SMEs. They highlight the intricate interdependencies among these variables and emphasize the pivotal role of information credibility in shaping these dynamics. These results carry significant implications for SMEs in Pakistan, shedding light on the mechanisms through which sustainable leadership and reliable knowledge sources can stimulate frugal innovation in emerging economies.
Prediction of acute kidney injury after cardiac surgery with fibrinogen-to-albumin ratio: a prospective observational study

Background The occurrence of acute kidney injury (AKI) following cardiac surgery is common and linked to unfavorable consequences while identifying it in its early stages remains a challenge. The aim of this research was to examine whether the fibrinogen-to-albumin ratio (FAR), an innovative inflammation-related risk indicator, has the ability to predict the development of AKI in individuals after cardiac surgery. Methods Patients who underwent cardiac surgery from February 2023 to March 2023 and were admitted to the Cardiac Surgery Intensive Care Unit of a tertiary teaching hospital were included in this prospective observational study. AKI was defined according to the KDIGO criteria. To assess the diagnostic value of the FAR in predicting AKI, calculations were performed for the area under the receiver operating characteristic curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results Of the 260 enrolled patients, 85 developed AKI with an incidence of 32.7%. Based on the multivariate logistic analyses, FAR at admission [odds ratio (OR), 1.197; 95% confidence interval (CI), 1.064–1.347, p = 0.003] was an independent risk factor for AKI. The receiver operating characteristic (ROC) curve indicated that FAR on admission was a significant predictor of AKI [AUC, 0.685, 95% CI: 0.616–0.754]. Although the AUC-ROC of the prediction model was not substantially improved by adding FAR, continuous NRI and IDI were significantly improved. Conclusions FAR is independently associated with the occurrence of AKI after cardiac surgery and can significantly improve AKI prediction over the clinical prediction model.
The diabacare cloud: predicting diabetes using machine learning

Machine learning (ML) is the buzz all around the technology industry and is illuminating each and every sector of human lives, be it, healthcare, finance, bioinformatics, data science, mechanical engineering, agriculture or even smart cities nowadays. ML consists of supervised and unsupervised techniques. Due to the availability of data in abundance, supervised ML has been the most preferred method in the field of data mining. In this research paper, a publicly available dataset for diabetes detection is tested to understand the efficiency of classification of a number of supervised ML algorithms to find the most accurate model. The dataset consisted of data of 768 persons out of which 500 were control and 268 were patients we found that the Random Forest algorithm outperformed the other 6 classification algorithm. In the first iteration, the Random Forest algorithm reached 78.44% accuracy. The tweaks performed in the paper outclassed the original random forest algorithm with a difference of 1.08% reaching a score of 79.52%. Further, iteration I gave 171 whilst iteration II gave 173 correct predictions out of the total 218 test data.
Implementation and Initial Validation of the Singhal Cavitation Model in OpenFOAM

Cavitation fundamentally affects the seakeeping performance of marine platforms. Both numerical methods and physical experiments are required to efficiently investigate cavitation about complex geometries. Herein, the Kunz, Merkle and Schnerr-Sauer models that are native to the OpenFOAM software toolkit were investigated. Because of the many parameters that have to be tuned, it was concluded that these models were unreliable for use against geometries where validation data for comparison are not available. Instead, the Singhal Full Cavitation Model was implemented in OpenFOAM as this model only has two parameters to tune, although in practice, the parameters actually remain constant. The results of flow over a nominally two-dimensional NACA 66 (MOD) foil were validated against those from the CFDACE+ (ACE+) software tool and physical experiments. The low-Reynolds number turbulence models performed well at all cavitation numbers. In contrast, while the high Reynolds number turbulence models performed well at low cavitation numbers, they showed significant variation from the expected inception point.
Fisheries stock assessments for commercial Alaskan species, accounting for age-size-structured population dynamics

Stock assessment for commercial Alaskan species, accounting for age-size-structured population dynamics Caitlin I. Allen Akselrud Chair of the Supervisory Committee: Professor André E. Punt School of Aquatic and Fishery Sciences This thesis develops a framework for conducting stock assessments that unifies ageand sizestructured assessment models, creating what we term “age-size-structured models. ” Several studies have explored the application of age-size-structured models to fisheries data. However, most are specific applications or were limited in scope. There is a dearth of generalized singlestock age-size-structured assessment models, which led to the creation and testing of such a model. An age-size-structured assessment method is developed, and examined first using a simulation study, and then using data for two species in the Eastern Bering Sea: Tanner crab (Chionoecetes bairdi) and Pacific cod (Gadus macrocephalus). Simulations are used to test under which conditions the assessment method is biased or imprecise. The simulations are summarized using a measure of the relative error between true and estimated spawning stock biomass. The simulation study suggested little bias and relatively high precision, such that the estimation method was considered acceptable for use in management. The results from the example application were compared to those from the official NOAA assessments. The estimates of mature male biomass for Tanner crab were found to be similar to those from the most recent stock assessment, but the estimates of spawning stock biomass for Pacific cod differed between the age-size-structured model and the latest official assessment, particularly after the mid-2000s. Some of the dissimilarity may be because maturity and growth are modelled based on size-dynamics in the age-sizestructured model, while the official assessment considers only age-dynamics. Nevertheless, the age-size-structured model produces relatively robust estimates, and could be used as an alternative model to which current stock assessments could be compared. This generalized age-size-structured fishery stock assessment method could be applied to any data-rich stock globally, particularly where a species exhibits a mix of ageand size-based life history and fishery dynamics.