Miracle-dz/UniFAR-FTUs huggingface dataset:
A Biomimetic MEMS Direction Finding Acoustic Sensor based on MetalMUMPs Process

This paper presents the design and finite element method (FEM) modeling of MEMS acoustic sensor optimized using the design rules of the commercially available Metal-Multi User MEMS Process (MetalMUMPs). The acoustic sensor is modeled with a structural layer of Nickel with 20 μm thickness. The acoustic sensor is designed for the detection of sound direction where the displacement amplitude of the sensor plates vary with the applied pressure and angle at specific sound frequency. Moreover, the stress induced in the sensor, corresponding to the input sound waves, is analyzed using FEM simulations and geometric modifications are made to have the stress in the sensor microstructure less than the yield stress of the Nickel. This allows to minimize the effect of mechanical fatigue on the proposed MEMS acoustic sensor performance. Keywords— Acoustic Sensor, MEMS, PTM, MetalMUMPs, FEM, Yield Strength.
Single-photon detectors integrated in quantum photonic circuits

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ClusterNet: Instance Segmentation in RGB-D Images

We propose a method for instance-level segmentation that uses RGB-D data as input and provides detailed information about the location, geometry and number of individual objects in the scene. This level of understanding is fundamental for autonomous robots. It enables safe and robust decision-making under the large uncertainty of the real-world. In our model, we propose to use the first and second order moments of the object occupancy function to represent an object instance. We train an hourglass Deep Neural Network (DNN) where each pixel in the output votes for the 3D position of the corresponding object center and for the object's size and pose. The final instance segmentation is achieved through clustering in the space of moments. The object-centric training loss is defined on the output of the clustering. Our method outperforms the state-of-the-art instance segmentation method on our synthesized dataset. We show that our method generalizes well on real-world data achieving visually better segmentation results.
Phytochemical composition and bio-functional properties of Apis mellifera propolis from Kenya

There is an increased demand for natural products like propolis, yet little information is available about the chemical composition of African propolis and its bio-functional properties. Therefore, in this study, we aimed to quantify the phytochemicals and determine the antioxidant and antimicrobial properties of Apis mellifera propolis (n = 59) sourced from various regions in Kenya. Principal component analysis (PCA) showed that the sampling region had a remarkable impact on the propolis's composition and bio-functional properties. Generally, the propolis contained high amounts of phytochemicals, particularly alkaloids (5.76 g CE/100 g) and phenols (2.24 g GAE/100 g). Furthermore, analysis of propolis by gas chromatography–mass spectrometry (GC-MS) revealed various compounds with varying bio-functional activities. These compounds included triterpenoids alpha- and beta-amyrin, oleanen-3-yl-acetate, urs-12-en-24-oic acid, lanosta-8,24-dien-3-one, and hydrocarbons tricosane and nondecane, which have been reported to have either antimicrobial or antioxidant activities. The propolis samples collected from hotter climatic conditions contained a higher composition of phytochemicals, and additionally, they displayed higher antioxidant and antimicrobial activities than those obtained from cooler climatic conditions. Key findings of this study demonstrate the occurrence of relatively high phytochemical content in Kenya's propolis, which has antioxidant and antimicrobial properties; hence this potential could be harnessed for disease control.
Individual risk-taking behaviour in black-capped chickadees (Poecile atricapillus) does not predict annual survival

Within species, individuals often show repeatable differences in behaviours, called ‘animal personality’. One behaviour that has been widely studied is how quickly an individual resumes feeding after a disturbance, referred to as boldness or risk-taking. Depending on the mechanism(s) shaping risk-taking behaviour, risk-taking could be positively, negatively, or not associated with differences in overall survival. We studied risk-taking and survival in a population of free-living black-capped chickadees (Poecile atricapillus) in which we previously showed repeatable among-individual differences in risk-taking over the course of several months. We found no evidence that variation in risk-taking is associated with differences in annual survival. We suggest that variation in risk-taking is likely shaped by multiple mechanisms simultaneously, such that the net effect on survival is small or null. For example, among-individual differences in energy demand may favour greater risk-taking without imposing an overall mortality cost if higher energy demand covaries with escape flight performance. We propose directions for future work, including using a multi-trait, multi-year approach to study risk-taking, to allow for stronger inferences regarding the mechanisms shaping these behavioural decisions.
Exceptional diversity and selection pressure on coronavirus host receptors in bats compared to other mammals

Pandemics originating from non-human animals highlight the need to understand how natural hosts have evolved in response to emerging human pathogens and which groups may be susceptible to infection and/or potential reservoirs to mitigate public health and conservation concerns. Multiple zoonotic coronaviruses, such as severe acute respiratory syndrome-associated coronavirus (SARS-CoV), SARS-CoV-2 and Middle Eastern respiratory syndrome-associated coronavirus (MERS-CoV), are hypothesized to have evolved in bats. We investigate angiotensin-converting enzyme 2 (ACE2), the host protein bound by SARS-CoV and SARS-CoV-2, and dipeptidyl-peptidase 4 (DPP4 or CD26), the host protein bound by MERS-CoV, in the largest bat datasets to date. Both the ACE2 and DPP4 genes are under strong selection pressure in bats, more so than in other mammals, and in residues that contact viruses. Additionally, mammalian groups vary in their similarity to humans in residues that contact SARS-CoV, SARS-CoV-2 and MERS-CoV, and increased similarity to humans in binding residues is broadly predictive of susceptibility to SARS-CoV-2. This work augments our understanding of the relationship between coronaviruses and mammals, particularly bats, provides taxonomically diverse data for studies of how host proteins are bound by coronaviruses and can inform surveillance, conservation and public health efforts.
Integrable nonlinear evolution equations in three spatial dimensions

There are integrable nonlinear evolution equations in two spatial variables. The solution of the initial value problem of these equations necessitated the introduction of novel mathematical formalisms. Indeed, the classical Riemann–Hilbert problem used for the solution of integrable equations in one spatial variable was replaced by a non-local Riemann–Hilbert problem or, more importantly, by the so-called d-bar formalism. The construction of integrable nonlinear evolution equations in three spatial dimensions has remained the key open problem in the area of integrability. For example, the two versions of the Kadomtsev–Petviashvili (KP) equation constitute two-dimensional generalizations of the celebrated Korteweg–de Vries equation. Are there three-dimensional generalizations of the KP equations? Here, we present such equations. Furthermore, we introduce a novel non-local d-bar formalism for solving the associated initial value problem.
Peptide mass fingerprinting of preserved collagen in archaeological fish bones for the identification of flatfish in European waters

Bones of Pleuronectiformes (flatfish) are often not identified to species due to the lack of diagnostic features on bones that allow adequate distinction between taxa. This hinders in-depth understanding of archaeological fish assemblages and particularly flatfish fisheries throughout history. This is especially true for the North Sea region, where several commercially significant species have been exploited for centuries, yet their archaeological remains continue to be understudied. In this research, eight peptide biomarkers for 18 different species of Pleuronectiformes from European waters are described using MALDI-TOF MS and liquid chromatography tandem mass spectrometry data obtained from modern reference specimens. Bone samples (n = 202) from three archaeological sites in the UK and France dating to the medieval period (ca seventh–sixteenth century CE) were analysed using zooarchaeology by mass spectrometry (ZooMS). Of the 201 that produced good quality spectra, 196 were identified as flatfish species, revealing a switch in targeted species through time and indicating that ZooMS offers a more reliable and informative approach for species identification than osteological methods alone. We recommend this approach for future studies of archaeological flatfish remains as the precise species uncovered from a site can tell much about the origin of the fish, where people fished and whether they traded between regions.
Conceptualizing Information Systems Development as an Organizational Routine

Research acknowledges that information systems development (ISD) teams experiment with, overlook, or adapt the methods that they purportedly use. Given this, one stream of research adopts a perspective focused on prescriptions based on the idea of a method. Another stream of research adopts a perspective anchored in the view that human agency plays a critical role in the unfolding of ISD projects. We suggest that our understanding of ISD can be enriched by mobilizing both perspectives. Specifically, we propose a conceptualization of ISD as an organizational routine based on the ontology developed by Feldman and Pentland. We build on the elements of this ontology- (1) the idea of a routine (the ostensive aspect) and (2) its enactment (the performative aspect) as the two mutually constitutive aspects of organizational routines; and (3) the role of artifacts as mediators of the relationship between actors and the ostensive and performative aspects of organizational routines - to develop theoretical arguments explaining the benefits of applying this ontology to the ISD phenomenon. Extending the contextual boundaries of Feldman and Pentland's ontology, we propose research avenues that have the potential to contribute to our understanding of this core phenomenon of the IS discipline.
A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning

Computational models that formalize complex human behaviors enable study and understanding of such behaviors. However, collecting behavior data required to estimate the parameters of such models is often tedious and resource intensive. Thus, estimating dataset size as part of data collection planning (also known as Sample Size Determination) is important to reduce the time and effort of behavior data collection while maintaining an accurate estimate of model parameters. In this article, we present a sample size determination method based on Uncertainty Quantification (UQ) for a specific Inverse Reinforcement Learning (IRL) model of human behavior, in two cases: (1) pre-hoc experiment design—conducted in the planning stage before any data is collected, to guide the estimation of how many samples to collect; and (2) post-hoc dataset analysis—performed after data is collected, to decide if the existing dataset has sufficient samples and whether more data is needed. We validate our approach in experiments with a realistic model of behaviors of people with Multiple Sclerosis (MS) and illustrate how to pick a reasonable sample size target. Our work enables model designers to perform a deeper, principled investigation of the effects of dataset size on IRL model parameters.
Directing Technology Addiction Research in Information Systems

In this second part of a series of articles to direct technology addiction research in the information systems discipline, we discuss the history, conceptualization, and measurement of technology addiction. We admit that it is possible to label the phenomenon as overuse or excessive use as long as it is defined and measured by the presence and the magnitude of the six core symptoms of behavioral addictions: salience, mood modification, tolerance, withdrawal, conflict, and relapse. The advantage of this terminology is that it does not attribute one's problems to helplessness and does not pathologize the behavior, implying that it may possibly be corrected. Nevertheless, we posit that the term technology addiction is currently the most reasonable choice that may need to be adjusted as we learn more about this phenomenon and its potential similarities to and differences from established behavioral addictions. Dependence, obsessive/compulsive use, and pathological/problem use terms should not be used as synonyms for technology addiction as a form of mental disorder. Researchers should not include the name of the IT artifact as the subject of addiction (e.g., "Facebook addiction"). Instead, they should focus on the activity that is mediated through the IT artifact (e.g., "addiction to Facebook use").
Improving the quality of online learning: A study on teacher-student interaction based on network multi-modal data analysis

Online learning has become a new trend in fragmented learning, with a large number of users. And, the emotional engagement of learners in their learning is a central topic in online learning research. However, many studies have found that online learners do not perform well in this area. This study used 10 instructional videos with 112,9 and 395,401 available subtitles and pop-ups. Emotional classifications of the corresponding data for teachers and learners were obtained based on the results of machine learning, deep learning and manual classification. A stimulus-organisms-response (S-O-R) model was applied to verify the applicability of teachers' emotional support and emotional engagement in learning to influence learners' retention intentions in online education. The results show that teachers' verbal emotions have a significant impact on students' learning experiences and that students' intention to continue learning increases when teachers' emotions have an interactive effect with students' emotions.
First principles investigation on the hydrogen adsorption on planar aluminene with boron, carbon, and nitrogen as impurities

Materials including two-dimensional systems have been widely investigated for potential hydrogen storage. In this work, hydrogen adsorption on planar hexagonal aluminene with boron, carbon, and nitrogen decorations was studied using density functional theory. Results showed that boron, carbon, and nitrogen atom can be chemisorped without barriers on aluminene sites: top, hollow, and bridge. Density of states (DOS) showed that these systems remained metallic and nonmagnetic except for adatoms on top site of aluminene but its ferromagnetism are passivated once hydrogen atoms are adsorbed. The p orbitals of the adatoms and aluminium atoms formed the chemical bonding as shown by the DOS and supported by the charge density difference. Potential energy barriers were found in hydrogen adsorption on all systems indicating additional energy is needed to adsorb hydrogen on all systems. Hydrogen is chemisorped on surfaces where adatoms are at the top and bridge site through its s orbital and p orbital of the adatom, while H2 is physisorped for adatoms on the hollow site. The presence of boron, carbon, and nitrogen atoms on aluminene can reduce the hydrogen capacity of pristine aluminene due to the potential energy barriers. GRAPHICAL ABSTRACT
Variation of length and sequence of the nuclear ribosomal DNA internal transcribed spacer 1 supports “hermit-to-king” crab hypothesis

Lithodoid and paguroid crabs are morphologically assigned to the superfamilies Lithodoidea and Paguroidea, respectively. Molecular analyses, however, have revealed closer genetic proximity of the lithodoid crabs to the family Paguridae than to other families of Paguroidea, provoking a long debate. We investigated the length and nucleotide sequence variation of the nuclear ribosomal DNA internal transcribed spacer 1 (ITS1) in lithodoid and paguroid species. Uniquely short ITS1s (215–253 bp) were observed in seven lithodoid species belonging to the families Lithodidae and Hapalogastridae. In contrast, ITS1 length varied considerably in 13 paguroid species belonging to the families Coenobitidae, Diogenidae, and Paguridae. Short-to-long ITS1s (238–1090 bp) were observed in five species of the family Paguridae, and medium to long ITS1s (573–1166 bp) were observed in eight species of the families Coenobitidae and Diogenidae. Interestingly, ITS1s of considerably different sizes coexist in individual paguroid species. Nucleotide sequence analysis indicated that the short ITS1s observed in the family Paguridae were descendants of longer ITS1s and were homologous to the short ITS1 of lithodoid species. ITS1 sequences of the families Coenobitidae and Diogenidae shared no nucleotide elements similar to those of lithodoid and pagurid species. These molecular signals indicate that the short ITS1 in pagurid lineage was passed on to lithodoid lineage, strongly supporting the “hermit-to-king” crab hypothesis.
The R&D of HTR-STAC Program Package: Source Term Analysis Codes for Pebble-Bed High-Temperature Gas-Cooled Reactor

Source term analysis is important in the design and safety analysis of advanced nuclear reactor and also provides a radiation safety analysis basis for Modular High-Temperature Gas-Cooled Reactor (HTR). High-Temperature Gas-Cooled Reactor-Pebble-bed Modules (HTR-PM) design by China is a typical Gen-IV and due to different safety concepts and systems, the implements of source term analysis in light water reactors are not entirely applicable to HTR-PM. To solve this problem, HTR-PM Source Term Analysis Code (HTR-STAC) has been developed and related V&V has been finished. HTR-STAC consists of five units, including LOOP (Primary Circuit Source Term Analysis Code), NORMAL (Normal Condition Airborne Source Term Analysis Code), ARCC (Accident Release Category Calculation code), CARBON (C-14 Source Term Analysis Code), and TRUM (Tritium Source Term Analysis Code). LOOP and NORMAL may be used as calculating primary circuit coolant radioactivity and the release of airborne radioactivity to the environment under normal operating conditions of HTR-PM, respectively. The code ARCC composed of several source term analysis programs in the different typical accidents scenario, including SGTR (Steam Generator Tube Rupture), LOCA (Loss of Coolant Accident), and the Transient Process, is compiled based on the results given by LOOP and NORMAL. CARBON and TRUM are developed to calculate the productions of C-14 and H-3 through a different mechanism. Furthermore, the V&V has been performed and show some positive results.
FunFun: ITS‐based functional annotator of fungal communities

Fungi are inseparable companions of human life, they can be found in both the environment and human organs including skin, respiratory tract and gut. Studies of fungal communities are of great interest to modern biology, partially due to their specific way of life and the presence of unique biochemical pathways they have. Fungi have been shown to be both producers of useful compounds, such as antibiotics and organic acids, and pathogens of various diseases. When considering the selected fungal community, in a number of cases it is rather difficult to evaluate its functional capabilities, which is partially caused by some technical difficulties in the analysis and annotation of whole eukaryotic genomes. In practice, the taxonomic composition of fungal communities is determined using short marker sequences. The most popular fungal taxonomy markers are ITS (internal transcribed spacer) sequences. Here, we present FunFun, the instrument that allows to evaluate the functional content of an individual fungus or mycobiome based on ITS sequencing data.
Novel metabolic role for CD47 in pancreatic β-cell insulin secretion and islet transplant outcomes

Diabetes is a global public health burden and is characterized clinically by a relative or absolute insulin deficiency. Therapeutic agents that stimulate and improve insulin secretion and insulin sensitivity are in high demand as diabetic treatment. CD47 is a cell surface glycoprotein implicated in multiple cellular functions, including recognition of self, angiogenesis, and nitric oxide signaling, however its role in the regulation of insulin secretion remains unknown. For the first time we demonstrate that CD47 receptor signaling inhibits insulin release from β-cells and that it can be pharmacologically exploited to boost insulin secretion. CD47 depletion stimulates insulin granule exocytosis via activation of the Rho GTPase Cdc42. CD47 deficiency improved glucose clearance and insulin sensitivity in mice. CD47 blockade enhanced islet transplantation efficiency and improved outcomes. Further, anti-CD47 antibody treatment delayed the onset of diabetes in non-obese diabetic mice and protected them from overt diabetes. Our findings identify CD47 as a previously unrecognized regulator of insulin secretion and its manipulation in β-cells offers a novel therapeutic opportunity for diabetes and islet transplantation by correcting insulin deficiency. One Sentence Summary CD47 limits insulin secretion and islet transplant outcomes
SMART ATTENDANCE SYSTEM USING REAL TIME FACE RECOGNITION

: Attendance management system is a necessary tool for taking attendance in any environment where it is essential and critical. In this proposed method, an automated attendance system will recognize the face of the student and the features are extracted using History of Oriented Gradients(HOG). Support Vector Machine (SVM) is used for clustering the extracted features by comparing with the student face in the database and marks it as present. An automatic message is sent to the parent in case of the absence of the student. An automatic mail is sent to the faculty reporting the number of students present and absent on daily basis. Thus, this method helps in saving time when attendance is taken manually and also no proxy can be given as well. This method of recording the attendance using face recognition automates the records for further reference.