Miracle-dz/UniFAR-FTUs huggingface dataset:
Aging effects on dual‐route speech processing networks during speech perception in noise

Healthy aging leads to complex changes in the functional network of speech processing in a noisy environment. The dual‐route neural architecture has been applied to the study of speech processing. Although evidence suggests that senescent increases activity in the brain regions across the dorsal and ventral stream regions to offset reduced periphery, the regulatory mechanism of dual‐route functional networks underlying such compensation remains largely unknown. Here, by utilizing functional near‐infrared spectroscopy (fNIRS), we investigated the compensatory mechanism of the dual‐route functional connectivity, and its relationship with healthy aging by using a speech perception task at varying signal‐to‐noise ratios (SNR) in healthy individuals (young adults, middle‐aged adults, and older adults). Results showed that the speech perception scores showed a significant age‐related decrease with the reduction of the SNR. The analysis results of dual‐route speech processing networks showed that the functional connection of Wernicke's area and homolog Wernicke's area were age‐related increases. Further to clarify the age‐related characteristics of the dual‐route speech processing networks, graph‐theoretical network analysis revealed an age‐related increase in the efficiency of the networks, and the age‐related differences in nodal characteristics were found both in Wernicke's area and homolog Wernicke's area under noise environment. Thus, Wernicke's area might be a key network hub to maintain efficient information transfer across the speech process network with healthy aging. Moreover, older adults would recruit more resources from the homologous Wernicke's area in a noisy environment. The recruitment of the homolog of Wernicke's area might provide a means of compensation for older adults for decoding speech in an adverse listening environment. Together, our results characterized dual‐route speech processing networks at varying noise environments and provided new insight for the compensatory theories of how aging modulates the dual‐route speech processing functional networks.
Methods for Analyzing Temporally Entangled Multimodal Data

: While the challenges of collecting multimodal data are becoming surmountable with the help of the rapid development of sensor technologies, the challenges of analyzing multimodality remain substantial. It is imperative that researchers explore ways to successfully integrate theoretical and methodological frameworks for analyzing multimodal interactions in CSCL contexts. We identified two main approaches to analyzing multimodal data in CSCL settings—triangulating and interleaving—and highlighted the remaining challenges to unfolding the dynamic interplay between different modes with the consideration of temporality. To tackle these challenges, we presented an empirical example of multimodal learning analysis that practically employed the multimodal matrix and ENA for operationalizing and visualizing temporally entangled multimodal interactions. This paper (1) extends the theoretical underpinnings of temporality in studies of learning processes in CSCL settings, and (2) provides empirical evidence that indicates the potential of the interleaving approach to capture the core of complex meaning-making processes.
Aggregating inconsistent information: ranking and clustering

We address optimization problems in which we are given contradictory pieces of input information and the goal is to find a globally consistent solution that minimizes the number of disagreements with the respective inputs. Specifically, the problems we address are rank aggregation, the feedback arc set problem on tournaments, and correlation and consensus clustering. We show that for all these problems (and various weighted versions of them), we can obtain improved approximation factors using essentially the same remarkably simple algorithm. Additionally, we almost settle a long-standing conjecture of Bang-Jensen and Thomassen and show that unless NP⊆BPP, there is no polynomial time algorithm for the problem of minimum feedback arc set in tournaments.
A first parameterization of the pore-structure dependent kinetic adsorption model for O 2 adsorption in biomass conversion modeling

Mass transport properties of the oxidation and gasification agents O 2 , CO 2 , and H 2 O are highly relevant for the modeling of the conversion process of biomasses. Therefore, this study presents experimental investigations on the adsorption kinetics of O 2 on a biomass char using a modified gravimetric sorption device. Based on this comprehensive set of adsorption kinetic data, a first parameterization of the pore-structure dependent kinetic adsorption (PSK) model for O 2 adsorption is presented. This model intends to account for mass transport during biomass conversion in a more meaningful way as it is considered in conventional conversion models. With this parameterization, the model is capable of describing accurately the adsorption kinetics of O 2 as a function of time, temperature, and pressure.
Tackling the Backlog: Support for Completing and Validating Forms

In a wide variety of jurisdictions and courts, an accumulation is building up of legal cases waiting to be processed. New technologies offer promising solutions for tackling the backlogs and reducing the build up of cases, but the technologies must be targeted at specific bottlenecks. In this paper we describe work completed to meet these aims in two ways: 1) a tool aimed at reducing the number of incorrectly completed forms submitted; 2) a decision support tool for the identification of in-admissible claims submitted to the court. Both tools were developed for the setting of cases submitted to the European Court of Human Rights. We provide an overview of both these tools and their evaluation in initial trials, with the aim of enabling claimants to get improved access to justice by providing a level of automation for key decision points in the application process.
The role of NK cells and CD39 in the immunological control of tumor metastases

ABSTRACT Tumor metastases are responsible for death in the majority of cancer patients. Here we have explored the role of the ectonucleotidase CD39 in select models of tumor metastases and further tested the therapeutic anticancer activity of the NTPDase inhibitor sodium polyoxotungstate (POM-1). CD39 was expressed on tumor-infiltrating regulatory T cells (Treg), myeloid cells and some NK cells, and it was upregulated on these cells within tumors early after inoculation in vivo. NK cell numbers and effector functions were increased in globally CD39-deficient mice and also in WT mice treated with POM-1. Dosing with POM-1 suppressed experimental and spontaneous metastases in four different tumor models and was well tolerated. This anti-metastatic activity was completely abrogated in mice, that were depleted of NK cells, had IFNγ neutralized or were deficient in CD39 expression in bone marrow-derived cells. POM-1 was highly effective in suppressing metastases when used in combination with BRAFi/MEKi or anti-PD-1/anti-CTLA-4 or IL-2. These data highlight the importance of the CD39 pathway in suppressing NK cell-mediated anti-tumor immunity and validate further the development of CD39-based therapies in the clinic.
Human Performance on the AI Legal Case Verdict Classification Task

We report a study undertaken to analyse human performance on the verdict classification task. Several approaches have addressed this task with outcomes compared against the outcomes from actual legal cases. Results vary and we investigate how classification is done by humans. A key finding is that fact descriptions alone are insufficient for accurate classification, independent of legal background.
Frontotemporal activation diﬀers between perception of simulated cochlear implant speech and speech in background noise: An image-based fNIRS study

In this study we used functional near-infrared spectroscopy (fNIRS) to investigate neural responses in normal-hearing adults as a function of speech recognition accuracy, intelligibility of the speech stimulus, and the manner in which speech is distorted. Participants listened to sentences and reported aloud what they heard. Speech quality was distorted artiﬁcially by vocoding (simulated cochlear implant speech) or naturally by adding background noise. Each type of distortion included high and low-intelligibility conditions. Sentences in quiet were used as baseline comparison. fNIRS data were analyzed using a newly developed image reconstruction approach. First, elevated cortical responses in the middle temporal gyrus (MTG) and middle frontal gyrus (MFG) were associated with speech recognition during the low-intelligibility conditions. Second, activation in the MTG was associated with recognition of vocoded speech with low intelligibility, whereas MFG activity was largely driven by recognition of speech in background noise, suggesting that the cortical response varies as a function of distortion type. Lastly, an accuracy eﬀect in the MFG demonstrated signiﬁcantly higher activation during correct perception relative to incorrect perception of speech. These results suggest that normal-hearing adults (i
Explainable Artificial Intelligence (XAI)

Complex machine learning models perform better. However, we consider these models as black boxes. That’s where Explainable AI (XAI) comes into play. Understanding why a model makes a specific prediction can be as crucial as its accuracy for many applications, researchers, and decision-makers. In many real-world applications, the explainability and transparency of AI systems are indispensable. The research community and industry are giving growing attention to explainability and explainable AI. Compared to traditional machine learning methods, deep neural networks (DNNs) have been very successful. DNNs are comparably weak in explaining their inference processes and results because the data input passes through many layers, and a single prediction can involve millions of mathematical operations. It is difficult for humans to follow the exact mapping from data input to the predicted result. We would have to consider millions of weights that interact in a complex way to understand a prediction by a neural network. To interpret the behavior and predictions of neural networks, we need specific interpretation methods. Thus, a new frontier is opening for researchers.
The Undergraduate Research Journal The Undergraduate Research Journal

Low salinity water injection (LSWI) is one of the leading techniques in terms of enhancing oil recovery in sandstone and carbonate formations. Many studies and lab experiments were performed on the LSWI applications. Some studies have pointed out multiple negative observations, while other studies have shown the positive potential of using LSWI in enhancing oil recovery. However, the technology used in LSWI applications is still considered new and more studies need to be performed to figure out new technologies that are low-cost and more efficient. This review paper examines LSWI by analyzing the benefits and drawbacks of employing LSWI in various formations. Primarily, the paper analyzes previous work on LSWI in order to objectively assess the possibility of improved oil recovery and its mechanism. Following that, the study investigates numerous different implementations of the LSWI approach and the leap that has occurred in the oil business. To study the applicability of LSWI, this critical review discusses the experimental and fieldwork that led to the development of low salinity water (LSW) flooding techniques. Upon completion, the article would represent the applicability of LSW flooding in various reservoir conditions, as mentioned in various research studies.
E valuating P otEntial M EchanisMs for a ltErEd a MPhibian P ErforMancE in t rEatEd W astEWatEr

— Constructed wetlands (CWs) are an attractive solution for wastewater treatment due to their potential capacity to treat wastewater and support native species. These dual purpose CWs, however, can attract wildlife and expose them to aquatic environments high in nutrients and harmful contaminants. Although studies indicate tradeoffs between larval anuran growth and long-term adult survival following development in wastewater, it is unclear what specific mechanism is responsible for these effects. We suggest that behavior could be a mediating process that both responds to the environment and contributes to demography. We evaluated the behavioral responses of Southern Leopard Frog ( Lithobates sphenocephalus ) tadpoles exposed and reared in pond water versus wastewater treatment CWs to identify if behavior was affected by exposure or development in wastewater and to explore two potential causes of behavioral change: increased turbidity and a common contaminant, diphenhydramine. Specifically, we monitored activity, exploration, and predator avoidance. Tadpoles in wastewater were slower to begin moving, traveled shorter distances, and exhibited the shortest burst distances. We did not observe differences between tadpole origin that indicated exposure to wastewater was sufficient to alter behavior independent of developmental processes. Neither of our potential mechanisms for these effects were sufficient to explain the magnitude of behavioral change in treated wastewater. As wastewater can host unique chemical and physical conditions, including contaminant mixtures, learning more about how behavior responds to specific conditions of wastewater may help identify mechanisms to explain why frogs exhibit changes in behavior, growth, and survival following development in wastewater.
Investigation of the Level of Occupational Commitment and Psychological Hardiness in Disaster Workers

Aim: This research aimed to determine the occupational commitment and psychological hardiness levels of disaster workers and to examine the relationship between these two concepts. Method: The sample of this cross-sectionaal study consisted of the personnel of the Provincial Directorate of Disaster and Emergency and the Fire Brigade Workers of the Metropolitan Municipality working in a city that is an important disaster area in Turkey. Introductory information forms including sociodemographic and occupational characteristics, Occupational Commitment Scale, and Psychological Resilience Scale were used to collect data. Data were evaluated using descriptive statistics, dependent groups t-test, one-way analysis of variance (ANOVA), Tukey HSD test, and correlation analysis. Results: The disaster workers scored 29.97 ± 5.74 on the Occupational Commitment Scale and 59.59 ± 16.03 on the Psychological Hardiness Scale. The occupational commitment of the workers, the type of occupation, the state of liking the occupation, and their psychological hardiness was affected by their occupational working time. It was concluded that the occupational commitment of the workers had a moderately statistically significant and positive relationship with hardiness (p < 0.001). Conclusion: In the study, it was determined that the disaster workers have a medium level of occupational commitment and psychological hardiness. It was determined that the psychological hardiness levels of the workers who are committed to their occupation are higher. It was concluded that occupational commitment factors should be taken into account at the point of protecting the mental health of disaster workers and improving their psychological hardiness
Multiparticle dispersion in fully developed turbulence

The statistical geometry of dispersing Lagrangian clusters of four particles (tetrahedra) is studied by means of high-resolution direct numerical simulations of three-dimensional homogeneous isotropic turbulence. We give evidence of a self-similar regime of shape dynamics characterized by almost two-dimensional, strongly elongated geometries. The analysis of four-point velocity-difference statistics and orientation shows that inertial-range eddies typically generate a straining field with a strong extensional component aligned with the elongation direction and weak extensional/compressional components in the orthogonal plane.
Theoretical and Numerical Modelling of the Effect of Viscous and Viscoelastic Fluids on Elastic Properties of Saturated Rocks

XIV INTRODUCTION 1 RESEARCH BACKGROUND 1 THESIS LAYOUT 3 PUBLICATIONS ARISING FROM THE THESIS 5 CHAPTER 1 – FINITE ELEMENT MODELLING OF THE EFFECTIVE ELASTIC PROPERTIES OF PARTIALLY SATURATED ROCKS 7 1.1 BACKGROUND 7 1.2 NUMERICAL METHOD 8 1.3 NUMERICAL TEST SCENARIO 10 1.4 NUMERICAL EXPERIMENTS 13 1.4. 1 FEM SIMULATION OF EFFECTIVE ELASTIC PROPERTIES FOR BOOLEAN RANDOM MODELS 13 1.4. 2 EFFECT OF PORE CHANNEL SIZE 18 1.4. 3 EFFECT OF SPATIAL RESOLUTION 23 1.5 CHAPTER CONCLUSIONS 25 CHAPTER 2 – FINITE ELEMENT MODELLING OF GASSMANN’S FLUID SUBSTITUTION IN HETEROGENEOUS ROCKS 27 2.1 BACKGROUND 27 2.2 METHODOLOGY 30 2.3 DOUBLE SHELL MODEL 32 2.4 MODELS WITH DIFFERENT CLAY DISTRIBUTIONS 35 2.5 CHAPTER CONCLUSIONS 38 CHAPTER 3 – FLUID SUBSTITUTION IN ROCKS SATURATED WITH VISCOELASTIC FLUIDS 40 3.1 BACKGROUND 40 3.2 METHODOLOGY 42 3.2.1 EFFECTIVE-MEDIUM MODEL 42 3.2.2 COMPLEX SHEAR MODULUS OF HEAVY OIL 44
The Effect of Web-Based Education on Teaching the Nursing Process: A Quasi-Experimental Study

Abstract Aim: This study aimed to investigate the effects of web-based education on nursing students’ learning of the nursing process. Method: The study was conducted in a nursing faculty. The population of the study consisted of 90 students. The control and implementation groups were determined by drawing lots. The traditional classroom education method was used in the control group for teaching the nursing process. In the implementation group, web-based education was used in addition to traditional classroom education. “Identification Form”, “Nursing Process Knowledge Form”, “Visual Analog Scale”, “Nursing Process Assessment Form” and the software program developed on the web were used to collect data. Means, standard deviation, number, percentile, independent two samples t-test, Mann-Whitney U test, chi-square test and Brunner-Langer test were used in the analysis of the data. Results: The mean age of students was 20.77±2.25 in the implementation group and 20.70±1.35 in the control group. Of the students, 77.3% in the implementation group and 89.1% in the control group were females. According to 63.3% of the students, the nursing education they received was sufficient. The students in the implementation group were satisfied with the education method used in teaching the nursing process (p<0.005) and their nursing process application skill scores were higher than those of the students in the control group. Conclusion: The web-based education positively contributed to nursing students’ learning of the nursing process and improved the nursing process application skills of the students in the implementation group at higher levels.
The Effect οf Antipsychotic Treatment, During the Perinatal Period, on the Neurodevelopment of Children: A Systematic Review and Meta-Analysis

Background: Managing mental disorders in the perinatal period is a challenge. The most common mental disorders encountered in this period are major depression, bipolar disorder, anxiety and psychotic disorders. Purpose: This study aimed to investigate the neurodevelopmental effects in children born to mothers who were on antipsychotic medication, during pregnancy. Methodology: Databases MEDLINE, Embase, Cochrane, PsychInfo, Google Scholar and Scopus searched according to inclusion and exclusion criteria. The studies involved mothers with preconception or relapsed mental illness receiving antipsychotic medication during the perinatal period and their children were screened for any neurodevelopmental disorders. From the initial search of the literature, 277 articles emerged and applying the criteria, according to the methodology, we identified 15 articles. Results: Three studies were identified in which children were exposed and developed ADHD with a pooled odds ratio of 2.33 (95% CI = 1.69 – 3.20, p<0.001) indicating that children exposed to antipsychotics are more likely to develop ADHD than unexposed children. Regarding socio-emotional and mental development, it was found that the pooled odds ratio was 2.33 (95% CI = 1.68 – 3.22, p<0.001) indicating that exposed children are more likely to show delay compared to unexposed children. This relationship was statistically significant. Finally, it was found that children who were exposed and showed a delay in motor development had a pooled odds ratio of 2.93 (95% CI = 1.86 – 4.61, p<0.001). Discussion: Fetal exposure to antipsychotic medication can cause short-term developmental delays in motor, social-emotional, and adaptive behavior. However, further investigation is needed since the number of studies.
Wildfire Susceptibility Mapping in Baikal Natural Territory Using Random Forest

Wildfires are a significant problem in Irkutsk Oblast. They are caused by climate change, thunderstorms, and human factors. In this study, we use the Random Forest machine learning method to map the wildfire susceptibility of Irkutsk Oblast based on data from remote sensing, meteorology, government forestry authorities, and emergency situations. The main contributions of the paper are the following: an improved domain model that describes information about weather conditions, vegetation type, and infrastructure of the region in the context of the possible risk of wildfires; a database of wildfires in Irkutsk Oblast from 2017 to 2020; the results of an analysis of factors that cause wildfires and risk assessment based on Random Forest in the form of fire hazard mapping. In this paper, we collected and visualized data on wildfires and factors influencing their occurrence: meteorological, topographic, characteristics of vegetation, and human activity (social factors). Data sets describing two classes, “fire” and “no fire”, were generated. We introduced a classification according to which the probability of a wildfire in each specific cell of the territory can be determined and a wildfire risk map built. The use of the Random Forest method allowed us to achieve the following risk assessment accuracy indicators: accuracy—0.89, F1-score—0.88, and AUC—0.96. The comparison of the results with earlier ones obtained using case-based reasoning revealed that the application of the case-based approach can be considered the initial stage for deeper investigations with the use of Random Forest for more accurate forecasting.
297-A Brief Survey on Multilevel Inverter Topology

-In this paper, a brief review of different multilevel inverter topologies is discussed. An inverter is a power electronic device that adapts DC power into AC power at desired output voltage and frequency. Multilevel converter nowadays has become an interesting area in the field of industrial applications. Conventional power electronic converters are able to produce an output voltage that switches between two voltage levels only. In the view of Multilevel inverter topology (MLI), several DC voltage levels are added together to generate a continuous output waveform. Multilevel inverter generates the desired output voltage from several DC voltage levels at its input. The input sided voltage levels are usually obtained from renewable energy sources, capacitor voltage sources, fuel cells, etc. The different multilevel inverter topologies are: Cascaded H-bridge converter, Diode clamped inverter and Flying capacitor multilevel inverter. Multilevel inverter nowadays is used for medium voltage and high power applications. The different field of applications includes its use as UPS, High voltage DC transmission, Variable Frequency Drives, in pumps, conveyors, etc. The disadvantage of MLI is the need for isolated power supplies, design complexity and switching control circuits.