Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Benefits of Video-making Projects in Developing English Communication Skills

Project-based learning (PBL) is a learning method that is tested in many stages of education. Notably, video-making projects are frequently used to help English as Second Language (ESL) students actively apply the target language learned in the classroom to real-world situations. However, only a few studies have discussed the use of video-making projects by undergraduate students as active learning assignments and their benefits in developing communication skills. Therefore, the objective of this study is to investigate students’ perceptions of the effects of video-making projects, a form of PBL on their English writing and speaking skills. A qualitative research design was conducted by interviewing students about their experiences with video-making projects. The participants of this study were 7 undergraduate students from the school of computer engineering at a Malaysian public university taking the required course titled English Communication Skills. The students were asked by their teacher to produce one video project as a requirement of the course. The data from the interviews were analyzed qualitatively. The findings of the study revealed that the students' attitudes towards the benefits and the use of video-making projects were overwhelmingly positive. They perceived that there were improvements in their English communication skills by gaining many new words, practicing better pronunciations, and using correct grammar. Consequently, it is strongly recommended to apply the video-making project in writing and speaking classes as an alternative way to enhance students’ writing and speaking abilities.
Speech Intelligibility and Hearing Protector Selection

Abstract : This report provides a summary of speech intelligibility research and offers guidelines for selecting hearing protection based on speech intelligibility. MIL-STD-1472G (11Jan2012) Department of Defense Design Criteria Standard Human Engineering provides the basis for the guidelines using the Modified Rhyme Test and the Articulation Index.
Oral and Intragastric: New Routes of Infection by Leishmania braziliensis and Leishmania infantum?

Although Leishmania transmission in nature is associated with the bite of an infected sandfly vector, other possible transmission routes are speculated to occur, such as the oral route. We evaluated the possibility of infection by this route in golden hamsters (Mesocricetus auratus) using Leishmania braziliensis (Lb) and Leishmania infantum (Li). Hamsters were exposed to experimental oral or intragastrical infection with axenic promastigotes, besides oral ingestion of a suspension of cultivated macrophages infected with amastigotes, lesion-fed Lutzomyia longipalpis, skin lesion or infective spleen fragment. The parasite’s isolation, besides a positive PCR and IFAT, confirmed the intragastric infection by promastigote parasites. The oral ingestion of macrophages infected with L. braziliensis amastigotes was also infective. These results confirmed that Leishmania parasites could infect mammals by the intragastric route through the ingestion of promastigote forms (what can happen after a sandfly ingestion) and by the oral ingestion of infected macrophages (what can happen in nature in a predator–prey interaction). The better understanding of these alternative routes is essential to understand their transmission dynamics in nature. As far as we know, this is the first time that oral and intragastric Leishmania transmission has been experimentally demonstrated, constituting new infection routes, at least for L. infantum and L. braziliensis.
The Moderating Effect of COVID-19 Risk Perception on the Relationship Between Empathy and COVID-19 Volunteer Behavior: A Cross-Sectional Study in Jiangsu, China

The health system has encountered great challenges since the COVID-19 outbreak, volunteers are urgently needed in every situation during this crisis. The current study aimed to explore the relationship between empathy and COVID-19 volunteer behavior, along with the moderating role of COVID-19 risk perception in the above relationship. The cross-sectional survey was conducted online using Wenjuanxing from February 12th to March 16th, 2021, in Jiangsu, China. A total of 1,486 participants completed the Toronto Empathy COVID-19 volunteer behavior and COVID-19 risk perception questionnaires. The SPSS PROCESS macro was yielded to examine the moderating effect. Simple slopes analysis was conducted to detect the associations between empathy and COVID-19 volunteer behavior at three levels of the COVID-19 risk perception. The Johnson-Neyman (J-N) technique was used to calculate where the moderating effect is significance. Results showed that empathy was positively related with COVID-19 volunteer behavior (β= 0.080, p < 0.001). COVID-19 risk perception played a moderation effect on association between empathy and COVID-19 volunteer behavior (β = −0.005, p < 0.001), the greater the levels of COVID-19 risk perception, the weaker the associations between empathy and COVID-19 volunteer behavior. The J-N test showed the association between empathy and COVID-19 volunteer behavior was no longer significant when values of COVID-19 risk perception was >10.71. Current findings could enlighten researchers and policy makers, that fostering volunteerism among public during crisis situation through arousing more empathy and reducing unnecessary risk perception of the public.
Disparities in self-reported extreme weather impacts by race, ethnicity, and income in the United States

Extreme weather events are expected to increase in frequency and severity due to climate change. However, we lack an understanding of how recent extreme weather events have impacted the U.S. population. We surveyed a representative sample of the U.S. public (n = 1071) in September 2021 about self-reported impacts they experienced from six types of extreme weather events within the past three years. We find that an overwhelming majority (86%) of the U.S. public reported being at least slightly impacted by an extreme weather event, and one-third (34%) reported being either very or extremely impacted by one or more types of extreme weather events. We clustered respondents into four impact groups, representing a composite of self-reported impacts from multiple types of extreme weather events. Respondents in the highest extreme weather impact group are more than 2.5 times as likely to identify as Black or Hispanic and 1.89 times more likely to live in a household with income levels below the Federal poverty level. We also observe reports of higher extreme weather impacts from respondents who are female, do not have a bachelor’s degree and live in a rural area. Our results indicate that extreme weather impacts are being felt by a broad cross-section of the U.S. public, with the highest impacts being disproportionately reported by populations that have previously been found to be more vulnerable to natural disasters and other extreme events.
Predictive Scaling of Elastic Pod Instances for Modern Applications on Public Cloud through Long Short-Term Memory.

In Cloud Computing (CC) environment, container virtualization through Docker engine is bringing the new digital transformation in the enterprise multi-tier application architecture in this modern era. Elastic pod containers attracts and helps the application developers in developing and executing the cloud-native modern applications with key benefits such as light weight, agility to launch, easy deployment through images, consuming less power, minimum cost, less carbon footprints with increased resource utilization and provisioning on public cloud data centers. In existing, reactive auto-scaling mechanism of pod resources is used to add or remove resources manually or rule based for handling static workloads from users and most of the times it may lead to over provision or under provisioning of instances that violates QOS and SLA. In this paper, predictive horizontal scaling of pods utilizing custom metrics with orchestration through Kuberenetes is proposed with LSTM (Long Short-Term Memory) based on Deep Learning (DL) technique. DL is data hungry for right prediction of replicas of a cluster that is needed in advance to run cyclic workloads and to handle the sudden spike of demand by using the cloud large dataset real time traces. Moreover, LSTM model is compared with GRU (Gated Recurrent Unit) and experimental results shows that the results of LSTM prediction has less absolute error rate on comparing with GRU to keep the resource provisioning accuracy better for running the modern workloads seamlessly on public cloud.
A Review of the Lunar 182Hf-182W Isotope System Research

In recent years, the extinct nuclide 182Hf-182W system has been developed as an essential tool to date and trace the lunar origin and evolution. Despite a series of achievements, controversies and problems exist. As a review, this paper details the application principles of the 182Hf-182W isotope system and summarizes the research development on W isotopes of the Moon. A significant radiogenic ε182W excess of 0.24 ± 0.01 was found in the lunar mantle, leading to heated debates. There are three main explanations for the origin of the excess, including (1) radioactive origin; (2) the mantle of the Moon-forming impactor; and (3) disproportional late accretion to the Earth and the Moon. Debates on these explanations have revealed different views on lunar age. The reported ages of the Moon are mainly divided into two views: an early Moon (30–70 Ma after the solar system formation); and a late Moon (>70 Ma after the solar system formation). This paper discusses the possible effects on lunar 182W composition, including the Moon-forming impactor, late veneer, and Oceanus Procellarum-forming projectile. Finally, the unexpected isotopic similarities between the Earth and Moon are discussed.
Signal Processing Approaches for Cardio-Respiratory Biosignals with an Emphasis on Mobile Health Applications

We humans are constantly preoccupied with our health and physiological status. From precise measurements such as the 12-lead electrocardiograms recorded in hospitals, we have moved on to mobile acquisition devices, now as versatile as smart-watches and smart-phones. Established signal processing techniques do not cater to the particularities of mobile biomedical health monitoring applications. Moreover, although our capabilities to acquire data are growing, many underlying physiological phenomena remain poorly understood. This thesis focuses on two aspects of biomedical signal processing. First, we investigate the physiological basis of the relationship between cardiac and breathing biosignals. Second, we propose a methodology to understand and use this relationship in health monitoring applications. Part I of this dissertation examines the physiological background of the cardio-respiratory relationship and indexes based on this relationship. We propose a methodology to extract the respiratory sinus arrhythmia (RSA), which is an important aspect of this relationship. Furthermore, we propose novel indexes incorporating dynamics of the cardio-respiratory relationship, using the RSA and the phase lag between RSA and breathing. We then evaluate, systematically, existing and novel indexes under known autonomic stimuli. We demonstrate our indexes to be viable additions to the existing ones, thanks to their performance and physiological merits. Part II focuses on real-time and instantaneous methods for the estimation of the breathing parameters from cardiac activity, which is an important application of the cardio-respiratory relationship. The breathing rate is estimated from electrocardiogram and imaging photoplethysmogram recordings, using two dedicated filtering schemes, one of which is novel. Our algorithm measures this important vital rhythm in a truly real-time manner, with significantly shorter delays than existing methods. Furthermore, we identify situations, in which an important assumption regarding the estimation of breathing parameters from cardiac activity does not hold, and draw a road-map to overcome this problem. In Part III, we use indexes and methodology developed in Parts I and II in two applications for mobile health monitoring, namely, emotion recognition and sleep apnea detection from cardiac and breathing biosignals. Results on challenging datasets show that the cardio-respiratory indexes introduced in the present thesis, especially those related to the phase lag between RSA and breathing, are successful for emotion recognition and sleep apnea detection. The novel indexes reveal to be complementary to previous ones, and bring additional insight into the physiological basis of emotions and apnea episodes. To summarize, the techniques proposed in this thesis help to bypass shortcomings of previous approaches in the understanding and the estimation of cardio-respiratory coupling in real-life mobile health monitoring.
Length, width, shape regularity, and chain structure: time series analysis of phytoplankton morphology from imagery

Functional traits are increasingly used to assess changes in phytoplankton community structure and to link individual characteristics to ecosystem functioning. However, they are usually inferred from taxonomic identification or manually measured for each organism, both time consuming approaches. Instead, we focus on high throughput imaging to describe the main temporal variations of morphological changes of phytoplankton in Narragansett Bay, a coastal time‐series station. We analyzed a 2‐yr dataset of morphological features automatically extracted from continuous imaging of individual phytoplankton images (~ 105 million images collected by an Imaging FlowCytobot). We identified synthetic morphological traits using multivariate analysis and revealed that morphological variations were mainly due to changes in length, width, shape regularity, and chain structure. Morphological changes were especially important in winter with successive peaks of larger cells with increasing complexity and chains more clearly connected. Small nanophytoplankton were present year‐round and constituted the base of the community, especially apparent during the transitions between diatom blooms. High inter‐annual variability was also observed. On a weekly timescale, increases in light were associated with more clearly connected chains while more complex shapes occurred at lower nitrogen concentrations. On an hourly timescale, temperature was the determinant variable constraining cell morphology, with a general negative influence on length and a positive one on width, shape regularity, and chain structure. These first insights into the phytoplankton morphology of Narragansett Bay highlight the possible morphological traits driving the phytoplankton succession in response to light, temperature, and nutrient changes.
A Brief Introduction to Magnetoencephalography (MEG) and Its Clinical Applications

Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal-to-noise ratio (SNRMEG = 2.2 db, SNREEG < 1 db) and spatial resolution (SRMEG = 2–3 mm, SREEG = 7–10 mm) is higher for MEG than EEG, thus MEG potentially facilitates accurate monitoring of cortical activity. We found that the direct electrophysiological MEG signals reflected the physiological status of neurological disorders and play a vital role in disease diagnosis. Single-channel connectivity, as well as brain network analysis, using MEG data acquired during resting state and a given task has been used for the diagnosis of neurological disorders such as epilepsy, Alzheimer’s, Parkinsonism, autism, and schizophrenia. The workflow of MEG and its potential applications in the diagnosis of disease and therapeutic planning are also discussed. We forecast that computer-aided algorithms will play a prominent role in the diagnosis and prediction of neurological diseases in the future. The outcome of this narrative review will aid researchers to utilise MEG in diagnostics.
Resin-Coupled Cyclic Peptides as Proteinase Inhibitors

Many screening methodologies applicable to combinatorial peptide libraries require that the peptide is presented in a biologically active form while remaining covalently-coupled to a synthetic support. Here we describe methodology appropriate to the generation of disulfide cross-linked cyclic peptides that allow high efficiency on­ resin cyclisation. This is applied to the generation of a resin-bound cyclic proteinase inhibitor peptide, which is shown to retain enzyme binding activity.
OTTO: A Python package to simulate, solve and visualize the source-tracking POMDP

The source-tracking problem is a POMDP (partially observable Markov decision process) designed by Vergassola et al. (2007) to mimic the problem of searching for a source of odor in a turbulent flow. Far from being a “toy” POMDP, it incorporates physical models of odor dispersion and detection that reproduce the major features of olfactory searches in turbulence. Solutions to this problem, even approximate, have direct applications to sniffer robots used to track chemicals emitted by explosives, drugs or chemical leaks (Marques & Almeida, 2006; Russell, 1999). They may also shed light on how cognitive animals use olfaction to search for food and mates (Reddy et al., 2022; Vickers, 2000).
Associations Among Depression, Hemoglobin A1c Level, and Prognosis in Patients With Coronary Artery Disease: A Prospective Study

Background Depression is ubiquitous in patients with coronary artery disease (CAD). The relationship between depression and hemoglobin A1c (HbA1c) is controversial. The combined effect of high HbA1c and depression on prognosis is unclear, especially in non-diabetic CAD patients. We sought to explore these associations. Methods 558 CAD patients were included in this prospective study. Patients were grouped by HbA1c levels and the status of clinical depression. The average follow-up period was about 2.2 years, and Cox proportional hazards models were used to compare the differences of prognosis in all the groups. Results Clinical depression had no associations with HbA1c in all CAD patients (P for Pearson correlation = 0.74). In the all four groups, compared to group 1 (patients without clinical depression and low HbA1c), group 3 (without clinical depression and high HbA1c) had a higher risk of MACE (adjusted hazard ratio [aHR], 1.97; 95% confidence interval [CI], 1.2–3.25) and composite events (aHR, 1.67; 95% CI, 1.09–2.053). Group 4 (patients with clinical depression and high HbA1c) had higher HRs for MACE (aHR, 2.9; 95%CI, 1.32–6.38) and composite events (aHR, 2.12; 95% CI, 1.06–4.25). In CAD patients without diabetes, patients with clinical depression and high HbA1c had a higher risk of MACE (HR, 2.71; 95% CI, 1.02–7.19), non-cardiac readmission (HR,3.48; 95% CI, 1.26–9.57) and composite events (HR,2.44; 95% CI, 1.08–5.53) than those with no clinical depression and low HbA1c. In patients with comorbidities of depression and diabetes, patients with depression and high HbA1c more likely to experienced non-cardiac readmissions (HR, 4.49; 95% CI, 1.31–15.38) than patients with no depression and low HbA1c only. In all the above analysis, p-values for interaction between clinical depression and HbA1c were not statistically significant. Conclusions The presence of both depression and high HbA1c lead to a worse prognosis in CAD patients than one risk factor alone, no matter with or without the comorbidity of diabetes in these CAD patients. For patients with CAD and depression, lower HbA1c may be required.
Breeding of Vegetable Cowpea for Nutrition and Climate Resilience in Sub-Saharan Africa: Progress, Opportunities, and Challenges

Currently, the world population is increasing, and humanity is facing food and nutritional scarcity. Climate change and variability are a major threat to global food and nutritional security, reducing crop productivity in the tropical and subtropical regions of the globe. Cowpea has the potential to make a significant contribution to global food and nutritional security. In addition, it can be part of a sustainable food system, being a genetic resource for future crop improvement, contributing to resilience and improving agricultural sustainability under climate change conditions. In malnutrition prone regions of sub-Saharan Africa (SSA) countries, cowpea has become a strategic dryland legume crop for addressing food insecurity and malnutrition. Therefore, this review aims to assess the contribution of cowpea to SSA countries as a climate-resilient crop and the existing production challenges and perspectives. Cowpea leaves and immature pods are rich in diverse nutrients, with high levels of protein, vitamins, macro and micronutrients, minerals, fiber, and carbohydrates compared to its grain. In addition, cowpea is truly a multifunctional crop for maintaining good health and for reducing non-communicable human diseases. However, as a leafy vegetable, cowpea has not been researched and promoted sufficiently because it has not been promoted as a food security crop due to its low yield potential, susceptibility to biotic and abiotic stresses, quality assurance issues, policy regulation, and cultural beliefs (it is considered a livestock feed). The development of superior cowpea as a leafy vegetable can be approached in different ways, such as conventional breeding and gene stacking, speed breeding, mutation breeding, space breeding, demand-led breeding, a pan-omics approach, and local government policies. The successful breeding of cowpea genotypes that are high-yielding with a good nutritional value as well as having resistance to biotics and tolerant to abiotic stress could also be used to address food security and malnutrition-related challenges in sub-Saharan Africa.
Genomic Survey of PEBP Gene Family in Rice: Identification, Phylogenetic Analysis, and Expression Profiles in Organs and under Abiotic Stresses

Phosphatidylethanolamine-binding-protein (PEBP) domain-containing proteins play important roles in multiple developmental processes of plants; however, functions of few members in the PEBP gene family have been elucidated in rice and other crops. In this study, we found that twenty OsPEBPs genes identified in rice are not evenly distributed on the chromosomes. Four colinear pairs are identified, suggesting the duplication of OsPEBPs during evolution. The OsPEBPs are classified into six subgroups by phylogenetic analysis. The structure of all the OsPEBP genes and encoded proteins are similar. The 262 PEBP domain-containing proteins from crops are divided into six groups. The number of colinear pairs varies between rice and other crops. More than thirty cis-acting elements in the promoter region of OsPEBPs are discovered. Expression profiles of OsPEBP genes are differential. Most of the OsPEBPs expression can be regulated by NaCl, ABA, JA, and light, indicating that OsPEBPs may be involved in the control of the response to the environmental signals. These results lay sound foundation to further explore their functions in development of rice and crops.
Avascular Necrosis of Femoral Head—Overview and Current State of the Art

Avascular necrosis (AVN) of the femoral head is caused by disruption of the blood supply to the proximal femur. The alterations in the blood supply may occur following a traumatic event or result from a non-traumatic cause. Femoral neck fracture and hip dislocation and associated surgical procedures, corticosteroid therapy, and alcohol abuse frequently lead to AVN development. Type of fracture (displaced or undisplaced) and time between injury and surgery are the most critical factors in assessing the risk of developing AVN. Diagnosis of AVN can be established based on patients’ complaints, medical history, and radiographic findings. There is no consensus on the treatment of patients with AVN to date. Non-surgical methods are dedicated to patients in the early pre-collapse stages of the disease and consist of pharmacotherapy and physiotherapy. Surgery is recommended for patients with advanced disease.