Miracle-dz/UniFAR-FTUs huggingface dataset:
Microvascular reperfusion during endovascular therapy: the balance of supply and demand

Background Endovascular therapy (EVT) has revolutionized the treatment of acute stroke, but large vessel recanalization does not always result in tissue-level reperfusion. Cerebral blood flow (CBF) is not routinely monitored during EVT. We aimed to leverage diffuse correlation spectroscopy (DCS), a novel transcranial optical imaging technique, to assess the relationship between microvascular CBF and post-EVT outcomes. Methods Frontal lobe CBF was monitored by DCS in 40 patients undergoing EVT. Baseline CBF deficit was calculated as the percentage of CBF impairment on pre-EVT CT perfusion. Microvascular reperfusion was calculated as the percentage increase in DCS-derived CBF that occurred with recanalization. The adequacy of reperfusion was defined by persistent CBF deficit, calculated as: baseline CBF deficit − microvascular reperfusion. A good functional outcome was defined as 90-day modified Rankin Scale score ≤2. Results Thirty-six of 40 patients achieved successful recanalization, in whom microvascular reperfusion in itself was not associated with infarct volume or functional outcome. However, patients with good functional outcomes had a smaller persistent CBF deficit (median 1% (IQR −11%–16%)) than patients with poor outcomes (median 28% (IQR 2–50%)) (p=0.02). Smaller persistent CBF deficit was also associated with smaller infarct volume (p=0.004). Multivariate models confirmed that persistent CBF deficit was independently associated with infarct volume and functional outcome. Conclusions CBF augmentation alone does not predict post-EVT outcomes, but when microvascular reperfusion closely matches the baseline CBF deficit, patients experience favorable clinical and radiographic outcomes. By recognizing inadequate reperfusion, bedside CBF monitoring may provide opportunities to personalize post-EVT care aimed at CBF optimization.
Inference and design of antibody specificity: from experiments to models and back

Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Current methods for generating specific binders rely on in vitro selection experiments, but these have limitations in terms of library size and control over specificity profiles. We present a multi-stage approach that overcomes these limitations by combining high-throughput sequencing of phage display experiments with machine learning and biophysical modeling. Our models predict the binding profiles of antibodies against multiple ligands and generate antibody sequences with desired specificity profiles. The approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. We demonstrate that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies.
Exploring the Landscape of Recommender Systems Evaluation: Practices and Perspectives

Recommender systems research and practice are fast-developing topics with growing adoption in a wide variety of information access scenarios. In this paper, we present an overview of research specifically focused on the emphevaluation of recommender systems. We perform a systematic literature review, in which we analyze 57 papers spanning six years (2017–2022). Focusing on the processes surrounding evaluation, we dial in on the methods applied, the datasets utilized, and the metrics used. Our study shows that the predominant experiment type in research on the evaluation of recommender systems is offline experimentation and that online evaluations are primarily used in combination with other experimentation methods, e.g., an offline experiment. Furthermore, we find that only a few datasets (MovieLens, Amazon review dataset) are widely used, while many datasets are used in only a few papers each. We observe a similar scenario when analyzing the employed performance metrics—a few metrics are widely used (precision, nDCG, and Recall), while many others are used in only a few papers. Overall, our review indicates that beyond-accuracy qualities are rarely assessed. Our analysis shows that the research community working on evaluation has focused on the development of evaluation in a rather narrow scope, with the majority of experiments focusing on a few metrics, datasets, and methods.
Evaluation of IEEE 802.11 Ad hoc-Based Wireless Seismic Data Acquisition Networks

This paper investigates the performance of a proposed IEEE 802.11 ad hoc-based wireless seismic data acquisition (WSDA) network architecture. The study centers on examining the network performance in the 2.4 and 5 \SI \giga\hertz bands with focus on addressing WSDA challenges such as scalability, reliability, self-configuration and organization, interference effects, and latency. Routing Protocol for Low-Power and Lossy Networks (RPL) is used as the enabling routing protocol employing the multiple Destination Oriented Directed Acyclic Graph (multi-DODAG) architecture. OMNeT++ discrete event simulator is used to evaluate the network performance, using metrics such as packet delivery ratio (PDR), end-to-end delay (E2ED), packet error rate (PER), retransmission ratio, packet dropped, etc. Results show that the proposed network architecture is scalable and reliable with large number of geophones in the network. In addition, Routing Protocol for Low-Power and Lossy Networks (RPL) proves to be a suitable candidate to enable self-configuration and organization in multi-hop ad hoc WSDA networks.
GNB-RPL: Gaussian Naïve Bayes for RPL Routing Protocol in Smart Grid Communications

This study investigates the potential of utilizing the Gaussian Naive Bayes algorithm for enhancing the performance of the Wireless Smart Grid Networks (WSGNs). We have incorporated the Gaussian Naive Bayes algorithm into the widely used Routing Protocol for Low-Power and Lossy Networks (RPL), resulting in an advanced variant named as GNB-RPL. This innovative protocol leverages the Naive Bayes algorithm to optimize routing decisions. Training a Naive Bayes classifier model on a data set of routing metrics enables us to make predictions about the probability of successfully reaching a destination node. Each network node utilizes this classifier to select the route with the highest probability of delivering packets effectively. Our findings demonstrate that GNB-RPL significantly enhances the packet delivery ratio while minimizing end-to-end delay through a comprehensive performance evaluation conducted in a realistic scenario and across different traffic loads. These results show the potential of GNB-RPL as a promising solution for achieving greater efficiency in WSGNs.
Resource-aware Security Configuration for Constrained IoT Devices

The Internet of Things (IoT) is the enabler for new innovations in several domains. It allows the connection of digital services with entities in the physical world. These entities are devices of different sizes ranging from large machinery to tiny sensors. In the latter case, devices are typically characterized by limited resources in terms of computational power, available memory and sometimes limited power supply. As a consequence, the use of security algorithms requires of them to work within the limited resources. This means to find a suitable implementation and configuration for a security algorithm, that performs properly on the device, which may become a challenging task. On the other side, there is the desire to protect valuable assets as strong as possible. Usually, security goals are recorded in security policies, but they do not consider resource availability on the involved device and its power consumption while executing security algorithms. This paper presents an IoT security configuration tool that helps the designer of an IoT environment to experiment with the trade-offs between maximizing security and extending the lifetime of a resource constrained IoT device. The tool is controlled with high-level description of security goals in the form of policies. It allows the designer to validate various (security) configurations for a single IoT device up to a large sensor network
Preliminary study on selecting the high temperature resistance strains and economic traits of Porphyra haitanensis

Owing to selecting the strains of high temperature resistance,F3 generation of new strains coming from hybridization in Porphyra haitanensis(Z-26,Z-61,Z-81,Z-17) was compared with the wild-type control group by inheritable traits.Results indicated that:(1) The four new strains of selective filament strains could normally grow and develop after 40 d cultivated at 29 ℃ high temperature.Such as Z-26 filament grew faster;Z-61 was easily mature and 95% of the conchocelis filament sprouted into sporangial branch.There was only 30% of filament sprouted into sporangial branch in Z-17. (2) Z-26 and Z-61 were cultivated 10 d under the temperature of 30 ℃. Then they turned white but were not dead and could return their intrinsical colour after 3~4 d cultivated at 21 ℃. (3)Blades of Z-81 and Z-17 which were cultivated 7~10 d at 29 ℃ turned white but were not dead and could return normal at 21 ℃. However,when cultivated 10 d under the temperature of 30 ℃,they were rotten and could not be recovered. (4) Wild-type control group was no high temperature resistance and they were rotten even dead after 5 days cultivated under the temperature of 27 ℃. The four stains of slection could resist the high temperature,1~3 ℃ higher,comparing with the wild-type control group. (5) The thallus of the four strains were cultivatied 10 d under the temperature of 26~28 ℃ and their main phycobiliprotein and chlorophyll a were quiet variance comparing with the thallus that were cultivated under the temperature of 21 ℃. It indicated that temporary high temperature did not make effect on their quality.
Towards User-Independent Classiﬁcation of Multimodal Emotional Signals

Coping with differences in the expression of emotions is a challenging task not only for a machine, but also for humans. Since individualism in the expression of emotions may occur at various stages of the emotion generation process, human beings may react quite differently to the same stimulus. Consequently, it comes as no surprise that recognition rates reported for a user-dependent system are signiﬁcantly higher than recognition rates for a user-independent system. Based on empirical data we obtained in our earlier work on the recognition of emotions from biosignals, speech and their combination, we discuss which consequences arise from individual user differences for automated recognition systems and outline how these systems could be adapted to particular user groups.
Multi-Lingual (Urdu And English) Text Detection And Identification In Natural Images Using Attention Based Rnn-Cnn

:- Urdu, an Indo-Aryan language predominantly spoken in South Asia, holds significant importance as the national language and lingua franca of Pakistan. Additionally, it is recognized as an official language alongside English in Pakistan. In India, Urdu is listed as an Eighth Schedule language, acknowledging its cultural heritage and status by the Constitution. Furthermore, several Indian states confer official status to Urdu. In Nepal, Urdu is registered as a regional dialect, and in South Africa, it enjoys protection as a language under the constitution. While being a minority language in Afghanistan and Bangladesh without official recognition. Urdu has witnessed an increase in its usage among internet users in recent times. However, building a robust Recognition system (RS) for cursive nature languages like Urdu presents challenges due to certain complexities. These challenges become more intricate when dealing with variations in text size, fonts, colors, orientation, lighting conditions, and noise within the dataset. To address these issues, deep learning models have shown promising results in data modeling and handling large datasets. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have proven effective in various research areas, including text recognition, voice recognition, and Natural Language Processing (NLP). This paper introduces a CNN-RNN model with an attention mechanism for Urdu image text recognition. The model takes an input image and generates feature sequences using a CNN. These sequences are then processed by a bidirectional RNN to obtain the features in the correct order. However, to improve text segmentation, a bidirectional RNN with an attention mechanism is employed to produce the output. The attention mechanism enables the model to focus on relevant information from the feature sequences. The model undergoes end-to-end training through a
Activities of methionine- γ -lyase in the acidophilic archaeon “ Ferroplasma acidarmanus ” strain fer1

: Biogeochemical processes on exposed pyrite ores result in extremely high levels of sulfuric acid at these locations. Acidophiles that thrive in these conditions must overcome significant challenges, including an environment with proton concentrations at pH 3 or below. The role of sulfur metabolism in the archaeon “ Ferroplasma acidarmanus ” strain fer1’s ability to thrive in this environment was investigated due to its growth-dependent production of methanethiol, a volatile organic sulfur compound. Two putative sequences for methionine-γ -lyase (EC 4.4.1.11), an enzyme known to carry out α , γ -elimination on L-methionine to produce methanethiol, were identified in fer1. Bioinformatic analyses identified a conserved pyridoxal-5 ′ -phosphate (PLP) binding domain and a partially conserved catalytic domain in both putative sequences. Detection of PLP-dependent and L-methionine-dependent production of α -keto compounds and thiol groups in fer1 confirmed the presence of methionine- γ -lyase activity. Further, fer1 lysate was capable of processing related substrates, including D-methionine, L-cysteine, L-cystathionine, and L/D-homocysteine. When the two putative fer1 methionine- γ lyase gene-coded proteins were expressed in Escherichia coli cells, one sequence demonstrated an ability to carry out α , γ -elimination activity, while the other exhibited γ -replacement activity. These fer1 methionine- γ -lyases also exhibited optimum pH, substrate specificity, and catalytic preferences that are different from methionine- γ -lyases from other organisms. These differences are discussed in the context of molecular phylogeny constructed using a maximum likelihood algorithm based on methionine- γ -lyase sequences from a diverse selection of organisms.
Anxiety among student nurses in the Philippines: A gender and year-level analysis and its contributing factors in the COVID-19 context

Background: The shift to online learning during the COVID-19 pandemic has raised concerns and heightened anxiety levels, especially among student nurses adapting to academic and clinical challenges. Objective: This study aimed to assess the anxiety levels of student nurses during the COVID-19 pandemic, examining gender and year-level differences and identifying contributing factors. Methods: A quantitative descriptive research design was employed, involving 305 student nurses from Levels 1 to 4, selected using proportionate stratified and systematic random sampling at a Philippine nursing school. Data were collected in March 2022 using the Beck Anxiety Inventory (BAI) and a self-made questionnaire. Descriptive and inferential statistics, including frequencies, percentages, means, Independent t-tests, and ANOVA, were utilized with a significance level of 0.05. Results: The mean anxiety level among student nurses was 1.61, significantly differing by year level and gender (p <0.05). The most contributing factor of anxiety was ‘the adjustments to make with the sudden change of face-to-face classes to online or distance learning mode’ (90.16%), while the least factor was ‘lack of financial sources' (46.23%). Conclusion: Student nurses had moderate anxiety levels, with gender and year level as significant contributing factors. Female student nurses experienced higher anxiety levels than males, and lower-level students experienced greater anxiety than their higher-level counterparts. Various factors, including academic, social, financial, and perceived health risk-related factors, affected student nurses’ anxiety levels, with academic-related factors having the most significant impact and financial-related factors having the least impact.
The Analysis of Sustainability Development of Eastern and South Eastern Europe in thePost Socialist Period

Since the collapse of socialism, Eastern Europe and South Eastern (Balkans) Europe countries have been expeditiously changing as social, economic and politic structure. Some former socialist countries (as Bulgaria, Slovenia, Slovakia, Latvia, Lithuania, Poland and Romania) and Greece became full member of European Union. Some Balkan countries (Serbia, Montenegro, Croatia, Bosnia-Herzegovina, and Macedonia) lived difficult war years. After the wars, they have started to struggle for economic, social and political reconstruction process. Some CIS (Commonwealth of Independent States) countries (as Moldova, Ukraine and Belarus) tried to adapt market economics. Each country in this region wants bigger real per capita income, better welfare level, and generally become a developed country. But these countries have some political, economic and social problems in development and sustainability process. The aim of this paper is to analysis the countries in terms of development indicators such as per capita GDP growth rates, Human Development Index values and Sustainable Human Development Index values in the period of 2000-2010. It will be used the tools of spatial statistics (ESDA -Exploratory Spatial Data Analysis). Keywords: Sustainable Development, per capita GDP growth, Human Development Index, Sustainable Human Development Index, ESDA (Exploratory Spatial Data Analysis)
Recovering Discrete Prosody Inputs via Invert-Classify

Modeling prosody in Text-to-Speech (TTS) is challenging due to ambiguous orthography and the high cost of annotating prosodic events. This study focuses on the modeling of contrastive focus, the emphasis of a word to contrast it to presup-positions held by an interlocutor. Modeling of contrastive focus can be done in TTS by using binary, symbolic inputs at the word level in a supervised setting. To address the absence of annotated data, we propose the Invert-Classify method, which leverages a frozen TTS model and unlabeled parallel text-speech data to recover missing contrastive focus inputs. Our approach achieves a binary F-score of 0.71 for contrastive focus annotation recovery, utilizing only 10% of annotated training data. These findings establish fundamental insights and techniques that can be extended and refined for other prosody modeling methods in TTS.
The Role Of Social Media As An Information Tool In The Students’ Choice Of University: The Moderating Role Of Emotional Attachment

Higher education has observed incremental increase in enrollment over the past four decades. This growing rate of students’ enrolment on one side has increased the number of universities and on the other end made it difficult for HEIs to compete for students. Students’ induction is a costly process. To overcome financial pressure on HEIs of many countries of the world, institutes/universities are practicing various tactics to enroll larger number of students. HEIs are practicing new modes of communication to approach their students. HEIs’ admission offices have rapidly embraced the blessing of social media in their efforts to meet enrollment goals. This study examined role of social media as an information tool among students in their university choice and the moderating role of emotional attachment. A quantitative research was initiated. Business students of three purposively selected HEI’s participated in the data collection. A sample of 260 respondents were statistically tested to investigate the relationship between Social Media Interaction (SMI) and electronic Word-Of-Mouth (eWOM). Results revealed strong evidence that SMI has a significant positive effect on eWOM. Furthermore, Emotional Attachment (EA) of the students to HEI brand significantly moderate the relationship between SMI and eWOM.
A Movie Success Prediction Based On Public Reviews Using Supervised Learning

Movies is the art form that provide entertainment, express emotions, giving social message and teach people something. There are different types of Films Genres available like sci-fi films, family drama films, romantic films, action films, horror films, musical films, adventures films etc. Movies have the power to learn about new culture, experience a different perspective, or open our eyes to a world we know nothing about. By watching movies, people can boost their moods, feeling relax by listening musics and can reduce stress. Some movies are good or bad depending upon the story, music, star-cast, public review etc. There are some common factors which describe movie hit or flop that include, a well written script, music, songs, actors and actresses, photography, and good story. There are some other factors also which include film revenue collection, best story, public reviews etc. By using machine learning approach we can predict the movies success. The movie success totally depends on public reviews and comments. If story is too good then automatically audience will watch the movie and they will do the mouth publicity about the film.
Object Oriented Analysis And Design Of El Gamal Digital Signature Algorithm For Document Authentication In ELearning

Now-a-days E-Learning System is very popular. But in this COVID-19 pandemic situation, it has become necessity. Classes, examinations, even result generation have all been done through online. But it involves several types of threats and risk factors as it is based on electronic communication media. To implement authenticity, integrity and non-repudiation of the information, we must use suitable encryption technique. In this paper we have proposed an object oriented analysis and design of El Gamaldigital signature algorithm to implement authenticity, integrity and non-repudiation of mark sheet which would be generated by the Admin for the student at the time of publishing the result of an examination. We also have analysed the performance using a standard object oriented metric such as Metric for Object Oriented Design (MOOD metric) to ensure the software quality as well as justifying the good software design of our proposed model.
for Virtual Agent Dialogs

Integrating culture into the behavioral model of virtual agents has come into focus lately. When investigating verbal aspects of behavior, nonverbal behaviors are desirably added automatically, driven by the speech-act. In this paper, we present a corpus driven approach of generating gestures in a culture-speciﬁc way that accompany agent dialogs. The frequency of gestures and gesture-types, the correlation of gesture-types and speech-acts as well as the expressivity of gestures have been analyzed in the two cultures of Germany and Japan and integrated into a demonstrator.