Miracle-dz/UniFAR-FTUs huggingface dataset:
Globalization and Investment Opportunities : a Co-integration Study of Arab , U . S . and Emerging Stock Markets

1 This study builds on the MBA project work of one of the authors, Hanin Smahta, and the supervisory work of the first two authors. The authors acknowledge the financial support of the American University of Beirut Research Board (URB) for funding data collection. All errors are the authors'. Abstract There is a debate in the investment circles on whether international diversification is still possible despite growing globalization and the consequent integration between various stock markets all over the world. This study explores whether Arab markets can offer international investors unique risk and return characteristics to diversify international and regional portfolios. A test of co-integration (measuring long-run relationship) is conducted among Arab stock markets, US and emerging markets general indices for a 65-month period (1997-2002). Our results show that Jordan and Kuwait, Jordan and Tunisia, Kuwait and Tunisia, and Kuwait and Saudi Arabia are co-integrated among each other, while the others are not. On the other hand, only Jordan, Kuwait and Morocco are co-integrated with the US general market index. The emerging markets did not co-integrate with any other country index. Periods of boom and recession, before and after March 2000, have different effects on the co-integration results of this paper. Evidence suggests that some Arab markets that co-integrated with the US might offer a probable substitute for people who invest in the US markets, whereas the markets that did not co-integrate with the US might still offer good diversification opportunities.
An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method

Background and Objective: Detection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms. Methods: The algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm with an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the find_peaks function from the scipy PYTHON package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms. Results: The correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R2(87343) =. 99, p<. 001) and PPG (R2(86764) =. 98, p<. 001) waveforms. The algorithm had a lower mean error of dicrotic notch detection (s): 0.0047 (0.0029) for ABP waveforms and 0.0046 (0.0029) for PPG waveforms, compared to 0.0693 (0.0770) for ABP and 0.0968 (0.0909) for PPG waveforms for the established 2nd derivative method. The algorithm has high accuracy of DN detection for SNR of >= -9 dB for ABP waveforms and >= -12 dB for PPG waveforms indicating robust performance in detecting the DN when it is less visibly distinct. Conclusion: Our proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform (DN-less signals). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role.
Electromagnetic signal penetration in a planetary soil simulant: Estimated attenuation rates using GPR and TDR in volcanic deposits on Mount Etna

Ground‐penetrating radar (GPR) is a well‐established geophysical terrestrial exploration method and has recently become one of the most promising for planetary subsurface exploration. Several future landing vehicles like EXOMARS, 2020 NASA ROVER, and Chang'e‐4, to mention a few, will host GPR. A GPR survey has been conducted on volcanic deposits on Mount Etna (Italy), considered a good analogue for Martian and Lunar volcanic terrains, to test a novel methodology for subsoil dielectric properties estimation. The stratigraphy of the volcanic deposits was investigated using 500 MHz and 1 GHz antennas in two different configurations: transverse electric and transverse magnetic. Sloping discontinuities have been used to estimate the loss tangents of the upper layer of such deposits by applying the amplitude‐decay and frequency shift methods and approximating the GPR transmitted signal by Gaussian and Ricker wavelets. The loss tangent values, estimated using these two methodologies, were compared and validated with those retrieved from time domain reflectometry measurements acquired along the radar profiles. The results show that the proposed analysis, together with typical GPR methods for the estimation of the real part of permittivity, can be successfully used to characterize the electrical properties of planetary subsurface and to define some constraints on its lithology of the subsurface.
Short-term outcomes of asphyxiated neonates depending on outborn versus inborn status

Importance: In neonates with birth asphyxia (BA) and hypoxic ischemic encephalopathy, therapeutic hypothermia (TH), initiated within six hours, is the only safe and established neuroprotective measure to prevent secondary brain injury. Infants born outside of TH centers have delayed access to cooling. Objective: To compare in-hospital lethality, occurrence of seizures, and functional status at discharge in newborns with BA depending on postnatal transfer to another hospital within 24 hours of admission (outborn versus inborn). Design: Nationwide retrospective cohort study from a comprehensive hospital dataset using codes of the International Classification of Diseases, 10th modification (ICD-10). Clinical and outcome information was retrieved from diagnostic and procedural codes. Hierarchical multilevel logistic regression modelling was performed to quantify the effect of being outborn on target outcomes. Setting: All admissions to German hospitals 2016 - 2021. Participants: Full term neonates with birth asphyxia (ICD-10 code: P21) admitted to a pediatric department on their first day of life. Exposures: Transfer to a pediatric department within 24 hours of admission to an external hospital (=outborn). Main outcomes: In-hospital death; secondary outcomes: seizures and pediatric complex chronic conditions category (PCCC) >= 2. Results: Of 11,703,800 pediatric cases, 25,914 fulfilled the inclusion criteria. Outborns had higher proportions of organ dysfunction, TH, organ replacement therapies, and neurological sequelae in spite of slightly lower proportions of maternal risk factors. The adjusted odds ratios (OR) for death, seizures, and PCCC >= 2 were 4.08 ((95 % confidence interval 3.41 - 4.89), 2.99 (2.65 - 3.38), and 1.76 (1.52 - 2.05), respectively, if infants were outborn compared to inborn. A subgroup analysis among infants receiving TH (n = 3,283) found less pronounced adjusted ORs for death (1.67 (1.29 - 2.17)) and seizures (1.26 (1.07 - 1.48)) and inversed effects for PCCC >= 2 (0.81 (0.64 - 1.02)). Conclusion and relevance: This comprehensive nationwide study found increased odds for adverse outcomes in neonates with BA who were transferred to another facility within 24 hours of hospital admission. Obstetrical units should be linked to a pediatric department to minimize risks of postnatal emergency transfer. Collaboration and coordination between centers should be improved to balance geographical coverage of different level care facilities.
Quantifying the global film festival circuit: Networks, diversity, and public value creation

Film festivals are a key component in the global film industry in terms of trendsetting, publicity, trade, and collaboration. We present an unprecedented analysis of the international film festival circuit, which has so far remained relatively understudied quantitatively, partly due to the limited availability of suitable data sets. We use large-scale data from the Cinando platform of the Cannes Film Market, widely used by industry professionals. We explicitly model festival events as a global network connected by shared films and quantify festivals as aggregates of the metadata of their showcased films. Importantly, we argue against using simple count distributions for discrete labels such as language or production country, as such categories are typically not equidistant. Rather, we propose embedding them in continuous latent vector spaces. We demonstrate how these “festival embeddings” provide insight into changes in programmed content over time, predict festival connections, and can be used to measure diversity in film festival programming across various cultural, social, and geographical variables—which all constitute an aspect of public value creation by film festivals. Our results provide a novel mapping of the film festival circuit between 2009–2021 (616 festivals, 31,989 unique films), highlighting festival types that occupy specific niches, diverse series, and those that evolve over time. We also discuss how these quantitative findings fit into media studies and research on public value creation by cultural industries. With festivals occupying a central position in the film industry, investigations into the data they generate hold opportunities for researchers to better understand industry dynamics and cultural impact, and for organizers, policymakers, and industry actors to make more informed, data-driven decisions. We hope our proposed methodological approach to festival data paves way for more comprehensive film festival studies and large-scale quantitative cultural event analytics in general.
Ordered planar plating/stripping enables deep cycling zinc metal batteries

Metal anodes are emerging as culminating solutions for the development of energy-dense batteries in either aprotic, aqueous, or solid battery configurations. However, unlike traditional intercalation electrodes, the low utilization of “hostless” metal anodes due to the intrinsically disordered plating/stripping impedes their practical applications. Herein, we report ordered planar plating/stripping in a bulk zinc (Zn) anode to achieve an extremely high depth of discharge exceeding 90% with negligible thickness fluctuation and long-term stable cycling. The Zn can be plated/stripped with (0001)Zn preferential orientation throughout the consecutive charge/discharge process, assisted by a self-assembled supramolecular bilayer at the Zn anode-electrolyte interface. Through real-time tracking of the Zn atoms migration, we reveal that the ordered planar plating/stripping is driven by the construction of in-plane Zn─N bindings and the gradient energy landscape at the reaction fronts. The breakthrough results provide alternative insights into the ordered plating/stripping of metal anodes toward rechargeable energy-dense batteries.
Language-based EMA assessments help understand problematic alcohol consumption

Background Unhealthy alcohol consumption is a severe public health problem. But low to moderate alcohol consumption is associated with high subjective well-being, possibly because alcohol is commonly consumed socially together with friends, who often are important for subjective well-being. Disentangling the health and social complexities of alcohol behavior has been difficult using traditional rating scales with cross-section designs. We aim to better understand these complexities by examining individuals’ everyday affective subjective well-being language, in addition to rating scales, and via both between- and within-person designs across multiple weeks. Method We used daily language and ecological momentary assessment on 908 US restaurant workers (12692 days) over two-week intervals. Participants were asked up to three times a day to “describe your current feelings”, rate their emotions, and report their alcohol behavior in the past 24 hours, including if they were drinking alone or with others. Results Both between and within individuals, language-based subjective well-being predicted alcohol behavior more accurately than corresponding rating scales. Individuals self-reported being happier on days when drinking more, with language characteristic of these days predominantly describing socializing with friends. Between individuals (over several weeks), subjective well-being correlated much more negatively with drinking alone (r = -.29) than it did with total drinking (r = -.10). Aligned with this, people who drank more alone generally described their feelings as sad, stressed and anxious and drinking alone days related to nervous and annoyed language as well as a lower reported subjective well-being. Conclusions Individuals’ daily subjective well-being, as measured via language, in part, explained the social aspects of alcohol drinking. Further, being alone explained this relationship, such that drinking alone was associated with lower subjective well-being.
Australia’s Tinderbox Drought: An extreme natural event likely worsened by human-caused climate change

We examine the characteristics and causes of southeast Australia’s Tinderbox Drought (2017 to 2019) that preceded the Black Summer fire disaster. The Tinderbox Drought was characterized by cool season rainfall deficits of around −50% in three consecutive years, which was exceptionally unlikely in the context of natural variability alone. The precipitation deficits were initiated and sustained by an anomalous atmospheric circulation that diverted oceanic moisture away from the region, despite traditional indicators of drought risk in southeast Australia generally being in neutral states. Moisture deficits were intensified by unusually high temperatures, high vapor pressure deficits, and sustained reductions in terrestrial water availability. Anthropogenic forcing intensified the rainfall deficits of the Tinderbox Drought by around 18% with an interquartile range of 34.9 to −13.3% highlighting the considerable uncertainty in attributing droughts of this kind to human activity. Skillful predictability of this drought was possible by incorporating multiple remote and local predictors through machine learning, providing prospects for improving forecasting of droughts.
Knowledge, Attitudes and Intention to Donate Organs among the Public, Health Sciences Students and Health Personnel: A Scoping Review with a Systematic Review of Malaysian Studies

Background Various barriers lead to a shortage of organs for transplantation in Malaysia. One drive to improve the organ donation rate operates through future healthcare practitioners and practitioner advocacy. This scoping review was carried out to establish and summarise findings about organ donation-related articles among the public, health sciences students and health personnel. A further aim was to synthesise the latest data on knowledge and attitudes towards organ donation in the Malaysian population. Methods PubMed, Scopus, Google Scholar and the Malaysian Medical Repository (MyMedR) were used for a search conducted up to May 2022. Relevant search terms included ‘Organ donation’ and ‘Malaysia’. Journal articles related to knowledge, attitudes and intention were grouped under the general public and health science. Students and health personnel were included. Eligible studies were reviewed by two independent reviewers. Any disagreements were resolved by consensus with a third reviewer. Results The 31 included articles revealed an increased level of awareness among the public regarding organ donation. The analysis identified that nonrecognition of brainstem death (38.5%), no knowledge of how to contact the Organ Transplant Coordinator (82.3%) and never approaching the families of a potential donor (63.9%) led to a lack of confidence among healthcare practitioners to promote organ donation. Conclusion The shortage of organ donors is the result of the failure to identify the expected donor, obtain consent and procure the organs due to the passivity of Malaysian health professionals in promoting the organ donation process.
A Dempster Shafer-based fuzzy multisensor fusion system using airborne lidar and hyperspectral imagery

ABSTRACT Nowadays, accurate spectral reflectance information is provided by hyperspectral (HS) data while light detection and ranging (lidar) data provides precise information about the height and geometrical properties of the surfaces. In the most research papers, data fusion of disparate sensors significantly improves object classification performance compared to that of just an individual sensor. Previous researches on fusion of these two sensors had problems such as crisp classifiers or simple fuzzy decision-making systems. This article tries to overcome these weaknesses by accurate support vector machine (SVM) and Fuzzy SVM as classifiers in crisp and fuzzy decision fusion system and fusion of two sensors by two different methods based on precise theories of Bayesian and Shafer. Also, the proposed method tries to compare the results of fusion of both data using decision fusion system with stacked features strategy. This study focuses on HS and lidar fusion through three main phases. The first phase is based on the using of Noise Weighted Harsanyi-Farrand-Chang method and principal component analysis to overcome the high dimensionality problem of HS data. The second phase is based on the feature extraction and selection strategy on lidar data. Finally, fuzzy SVM and Dempster Shafer methods are applied as fuzzy classification and fuzzy decision fusion strategies on the feature spaces. A co-registered HS and lidar data set from Houston of U.S.A. by 15 classes was available to examine the effectiveness of the proposed method. The results of this study highlight that the combination of HS and lidar data enable reliable mapping of land cover.
The Effect of Internal Variability on Ocean Temperature Adjustment in a Low‐Resolution CESM Initial Condition Ensemble

Due to its large heat capacity and circulation, the ocean contributes signi ﬁ cantly to global heat uptake, global heat transport, spatial temperature patterns, and variability. Quantifying ocean heat uptake across different temporal and spatial scales is important to quantify Earth's climate response to anthropogenic warming. Here we evaluate ocean adjustment time scales from two different fully coupled climate model ensembles using the Community Earth System Model. Both ensembles use the same model version, anthropogenic and natural forcings, and coupling con ﬁ gurations, but we initialize the ensembles in two different ways: (1) sampling joint internal variability of the ocean – atmosphere system (unique atmosphere and ocean conditions) and (2) sampling the internal variability of the atmosphere only (unique atmosphere, identical ocean conditions). Uncertainty due to internal variability is used as a proxy to quantify the time scales of ocean temperature adjustment at different depths and basins in Community Earth System Model. Time scales of equilibration are longer in the deep ocean than the upper ocean, highlighting the vertical structure of dynamic adjustment. The Atlantic equilibrates on shorter time scales (82 years above 1,000 m, 140 years below 1,000 m) relative to the Paci ﬁ c (106 years above 1,000 m, 444 years below 1,000 m) in Community Earth System Model due to the large North Atlantic Deep Water formation and strong overturning circulation in the Atlantic. These results have broad implications for analyzing internal climate variability, ocean adjustment, and drift in global coupled model experiments and intercomparisons. 1,000 compared to the Paci ﬁ c (106 years above 1,000 444 years below 1,000 in the Community Earth System Model. These results have broad implications for studying climate change and internal climate variability in Earth system models.
Vision transformer with masked autoencoders for referable diabetic retinopathy classification based on large-size retina image

Computer-aided diagnosis systems based on deep learning algorithms have shown potential applications in rapid diagnosis of diabetic retinopathy (DR). Due to the superior performance of Transformer over convolutional neural networks (CNN) on natural images, we attempted to develop a new model to classify referable DR based on a limited number of large-size retinal images by using Transformer. Vision Transformer (ViT) with Masked Autoencoders (MAE) was applied in this study to improve the classification performance of referable DR. We collected over 100,000 publicly fundus retinal images larger than 224×224, and then pre-trained ViT on these retinal images using MAE. The pre-trained ViT was applied to classify referable DR, the performance was also compared with that of ViT pre-trained using ImageNet. The improvement in model classification performance by pre-training with over 100,000 retinal images using MAE is superior to that pre-trained with ImageNet. The accuracy, area under curve (AUC), highest sensitivity and highest specificity of the present model are 93.42%, 0.9853, 0.973 and 0.9539, respectively. This study shows that MAE can provide more flexibility to the input image and substantially reduce the number of images required. Meanwhile, the pretraining dataset scale in this study is much smaller than ImageNet, and the pre-trained weights from ImageNet are not required also.
The impact of digital transformation on enterprise performance: An empirical analysis based on China’s manufacturing export enterprises

Currently, countries worldwide are embracing digital strategies, enabling enterprises to utilize digital technology, digital supply chains, blockchain, and additional digital measures to increase their competitiveness. This paper analyzed the correlation between the digital transformation of manufacturing export enterprises and their business and export performance, focusing on China’s manufacturing export enterprises through empirical analysis. The study investigated the influence of digital transformation on enterprise performance. Using the Resource Based View theory and Trade theory, hypotheses were proposed and regression models were developed to analyze a sample of 1007 enterprises listed on the Shanghai and Shenzhen Stock markets from 2012 to 2019. The study conducted regression analysis, intermediate effect test, robustness test, stage lag, and heterogeneity analysis. The study found that (1) Manufacturing export enterprises listed in the stock market implemented digital transformation, leading to a significant positive impact on their overall performance. (2) Digital transformation led to cost reduction, improved R&D intensity, and enhanced human resources, among other benefits for enterprise performance. (3) According to the fractal analysis, non-state-owned enterprises exhibited more favorable effects on enterprise performance, and the digital transformation of manufacturing export companies in developed regions had a more significant impact on their performance. Finally, the study’s empirical results yielded pertinent proposals for digital transformation.