Miracle-dz/UniFAR-FTUs huggingface dataset:
Anatomical variability, multi-modal coordinate systems, and precision targeting in the marmoset brain

Localising accurate brain regions needs careful evaluation in each experimental species due to their individual variability. However, the function and connectivity of brain areas is commonly studied using a single-subject cranial landmark-based stereotactic atlas in animal neuroscience. Here, we address this issue in a small primate, the common marmoset, which is increasingly widely used in systems neuroscience. We developed a non-invasive multi-modal neuroimaging-based targeting pipeline, which accounts for intersubject anatomical variability in cranial and cortical landmarks in marmosets. This methodology allowed creation of multi-modal templates (MarmosetRIKEN20) including head CT and brain MR images, embedded in coordinate systems of anterior and posterior commissures (AC-PC) and CIFTI grayordinates. We found that the horizontal plane of the stereotactic coordinate was significantly rotated in pitch relative to the AC-PC coordinate system (10 degrees, frontal downwards), and had a significant bias and uncertainty due to positioning procedures. We also found that many common cranial and brain landmarks (e.g., bregma, intraparietal sulcus) vary in location across subjects and are substantial relative to average marmoset cortical area dimensions. Combining the neuroimaging-based targeting pipeline with robot-guided surgery enabled proof-of-concept targeting of deep brain structures with an accuracy of 0.2 mm. Altogether, our findings demonstrate substantial intersubject variability in marmoset brain and cranial landmarks, implying that subject-specific neuroimaging-based localization is needed for precision targeting in marmosets. The population-based templates and atlases in grayordinates, created for the first time in marmoset monkeys, should help bridging between macroscale and microscale analyses. Highlights Achieved sub-millimeter localization accuracy of subject-wise brain region Propose a dedicated non-invasive multi-modal subject-specific registration pipeline Construct brain coordinate system in AC-PC and grayordinate spaces Establish multi-modal MRI and CT brain and cortical templates, MarmosetRIKEN20 Quantify intersubject variabilities in marmoset brain Significant bias and uncertainty exist in marmoset stereotactic positioning
Customer Satisfaction with Digital Wallet Services: An Analysis of Security Factors

—This study aimed to determine an efficient framework that caters to the security and consumer satisfaction for digital wallet systems. A quantitative online survey was carried out to test whether the six factors (i.e., transaction speed, authentication, encryption mechanisms, software performance, privacy details, and information provided) positively or negatively impact customer satisfaction. This questionnaire was divided into two sections: the respondents’ demographic data and a survey on security factors that influence customer satisfaction. The questionnaires were distributed to the National University of Malaysia’s professors and students. A sample of 300 respondents undertook the survey. The survey results suggested that many respondents agreed that the stated security factors influenced their satisfaction when using digital wallets. Previous studies indicated that financial security, privacy, system security, cybercrime, and trust impact online purchase intention. The proposed framework in this research explicitly covers the security factors of the digital wallet. This study may help digital wallet providers understand the customer's perspective on digital wallet security aspects, therefore motivating providers to implement appropriately designed regulations that will attract customers to utilize digital wallet services. Formulating appropriate security regulations will generate long-term value, leading to greater digital wallet adoption rates.
Teaching Strategies and Thought Processes

The proper business of the schools, it has long been urged, is that of teaching students "to think. " Only recently, however, have we acquired both the conceptual and the methodological tools necessary to extend our knowledge of the thinking process and to permit our investigation of how teaching procedures may affect its development. A leader in this new enterprise of inquiry, Dr. Taba, together with her colleague, Mr. Elzey, reports here some of the ideas and the method that promise a genuine clarification of how education can meet its time-honored responsibility of elevating cognitive effectiveness.
Effective Malware Detection using Shapely Boosting Algorithm

Malware constitutes a prime exploitation tool to attack the vulnerabilities in software that lead to a threat to security. The number of malware gets generated as exploitation tools need effective methods to detect them. Machine learning methods are effective in detecting malware. The effectiveness of machine learning models can be increased by analyzing how the features that build the model contribute to the detection of malware. The model can be made robust by getting insight into how features contribute to each sample that is fed to a trained model. In this paper, the boosting machine learning model based on LightGBM is enhanced with Shapley value to detect the contribution of the top nine features for classification such as true positive or true negative and for misclassification such as false positive or false negative. This insight in the model can be used for effective and robust malware detection and to avoid wrong detections such as false positive and false negative. The comparison of the top features and their contribution in shapely value for each category of the sample gives insight and inductive learning into the model to know the reasons for misclassification. Inductive learning can be transformed into rules. The prediction by the trained model can be re-evaluated with such inductive learning and rules to ensure effective and robust prediction and avoid misclassification. The performance of models gives 98.48 at maximum and 97.45 at a minimum by 10 fold cross-validation. Keywords—Artificial intelligence; machine learning; malware detection; shapely value; decision plot; waterfall plot
Spectroscopic sensing of eight metal ions in aqueous solutions using silver nanoparticles

Anthropogenic releases from different outlets of industry, municipal sewage and the road traffic can give rise to higher concentrations of the heavy metals in the food commodities which imposes a threat to human health and environment. A simple silver nanoparticle (Ag NPs) used for the sensing of heavy metal ions, Cd2+, Cu2+, Fe2+, Hg2+, Mn2+, Ni2+, Pb2+ and Zn2+ in aqueous solution is described by qualitative and quantitatively using spectroscopic tool. FE-SEM and TEM images confirmed that the particles are spherical in shape with an average diameter of 23.4 nm. In presence of heavy metal ions with Ag NPs, a new peak at around 925, 898, 643, 665, 688, and 838 nm of Cd2+, Cu2+, Fe2+, Hg2+, Mn2+ and Zn2+ appeared in addition to the peak found at 410 nm of Ag NPs. Further, the addition of Ni2+ and Pb2+ metal ion solution with Ag NPs increased the SPR band from 410 nm to 436 and 462 nm respectively. Citrate functionalized Ag NPs are aggregated in solution in the presence of divalent metal ions by an ions-template chelating process and easily measurable change in the UV-vis absorption spectrum of the particles. Further, studies also confirmed the interaction of Ag NPs with metal ions using FT-IR spectroscopy. The proposed method was found to be useful for simple UV-vis spectroscopic sensing of metal ions in aqueous solutions and real contaminated samples.
Fake news detection using text similarity approach

: In this era of digitization, most of the peopleget news from internet and often it can be difficult to tell whether stories are credible ornot. Information overload and a general lack of understanding about how the internetworks by people has also contributed to an increase in fake news or hoax stories.Traditionally we got our news from trusted sources, journalists and media outlets that arerequired to follow strict codes of practice. However, the internet has enabled a completelynew way to publish, share and consume information and news with very little regulation oreditorial standards.Our aim is to develop an automatic fake news detection system foranalyzing the credibility of online news. So that the reader become aware about the newsthat is factually incorrect and optimized for sharing. News articles are nothing but a pieceof text. Hence, the proposed work divided into two subtasks; Text Analysis andPerformance Evaluation. Text analysis is for the transformation of text into numericalfeatures. These numerical features used for matching the similarity betweenqueried article and other articles. For articles similarity we have used hybrid of three textsimilarity approaches namely N gram (Character Based Similarity), TF*IDF (Term BasedSimilarity) and Cosine Similarity (Corpus Based Similarity). System tested for 100 news articles and analyzed that if more than three articles found to be similar with ≥0.70 matching value will result to truthiness of the input article.
Evolving neural network with dual roulette wheel selection

According to the viewpoint that mapping function of the neural network from input to output is in fact a generalized series expansion, a new crossover strategy that takes dual roulette wheel selection to choose the crossover segment according to its function is proposed. It can avoid segments with similar functions appearing in the same off-spring, and then the occurrence of the "Competing Conventions Problem" in genetic optimizing neural (networks) is prevented. Simulation on a two-class classification problem shows that such strategy is very efficient, and the network being evolved is well convergent until the best global optimum solution is found.
Digital Manufacturing of Resonance Mems From A Single-Layer Fused Silica Material

This paper presents a laser-based manufacturing process for fabrication of Micro-Electro-Mechanical (MEM) resonators from Fused Silica (FS) as the structural material. Femtosecond Laser-Induced Chemical Etching (FLICE) has been an enabling technology to micromachine complex 3-dimensional micro-fluidics and micro-optics from FS material. We utilized FLICE in a 3-steps process for fabricating capacitive MEM resonators, for the first time. For demonstration, a Disk Resonator Gyroscope (DRG) design was “digitally written” and released using the introduced process, and structural characterization results are presented. We experimentally demonstrated electrostatic actuation and detection of the 100 kHz FS-DRG and measured a quality factor on the order of 0.6M. By optimizing laser-writing parameters, we demonstrated that channels with an aspect ratio as high as 55:1 could be achieved. Our results demonstrate the potential of FLICE-like processes to become an alternative to conventional plasma etching technology for realizing high quality factor FS MEM resonators with ultra-high capacitive transduction.
Low-Bandwidth Web Searching Using Categorization and Summarization

We describe a system that acts as a proxy between web search engines and low-bandwidth, feature-poor clients such as cell phones. We discuss the implementation design choices we made in creating multi-resolution descriptions of documents found in a web search query. Finally, we analyze the results from a user evaluation that showed a 38% reduction in band-width when using this approach over a text-based Google search.
Innovation Pattern Heterogeneity: Data-Driven Retrieval of Firms’ Approaches to Innovation

According to a strong and diversified theoretical framework, innovation is one of the usual suspects in defining differences in firm performance. Understanding the diversity that exists within the population of innovative firms is essential for developing appropriate innovation policies. Our study explored the diversity of innovation patterns among Norwegian firms included in the 2018 Community Innovation Survey (CIS2018). By applying factor analysis to a wide array of survey variables and a large sample of firms, we identified eleven typical approaches to innovation, which connect innovation inputs and outputs at the firm level. A main outcome of our study is a novel fine-grained view of innovation as a multifaceted concept. Our research path helps us to find commonalities in innovation behavior across industries and, as a consequence, to better isolate those innovation patterns that differentiate industries from one another. We also show the relation between firm size, on one hand, and each of the firm scores associated to the eleven typical approaches to innovation, on the other hand, thus uncovering ways in which small firms may survive in sectors dominated by large firms.
A Survey of Ramp and Stair Use among Older Adults

Forty-three community-dwelling adults aged 57 to 95 participated in survey exploring characteristics of ramp use by older ambulant people. Twenty-three respondents said they ascended ramps instead of stairs most of the time, and 14 said they ascended ramps some of the time. Similar numbers were reported for descent. Overall, respondents felt less fatigued, less likely to trip, and more comfortable when using ramps rather than stairs for ascending one level. When descending one level, balance, tripping, and comfort were the strongest determinants of ramp use. Respondents indicated that descent was more problematic, particularly in regard to balance and tripping. The presence of handrails often determined the choice of route. Results from this survey provided the basis for an experiment evaluating the abilities of older people to traverse ramps of various slopes. The ADA Accessibility Guidelines implicitly assume that a ramp accommodates everyone. This study indicates that entrances should have both ramps and stairs.
RAPD analysis and field screening of bread wheat and barley accessions for resistance to cereal leafminer Syringopais temperatella

(Syringopais temperatella Led.) is a threat to wheat and barley. Resistant varieties are preferable due to environmental and human considerations and for their sustainability. However, no attention has been focused on resistant studies against cereal leafminer worldwide. Concomitantly, this study aimed at screening wheat and barley accessions as sources of S. temperatella resistance under semi-arid conditions of Karak-Jordan. It was also designed to evaluate the RAPD markers potential for identifying the accessions based on their resistance. The rank accessions order resulted from least to most according to infestation percentage and presence of larvae on leaves: Acsad 1245 (wheat), Acsad 1273 (wheat), 1614 (barley) and Umkais (wheat). The rank order of the same accessions from most to least according to grain yield and straw biomass: 1614, Acsad 1245, Umkais and Acsad 1273. Data on DNA (RAPD) markers revealed higher polymorphism level among accessions. Two hundred bands were noticed; and 199 were polymorphic. Total number of amplification products/primer ranged from 16 with primer (OPI-08) to 25 with primer (OPA-10), and the PCR size products ranged between 200-3500 bp.
On the Role of Coronal Shocks for Accelerating Solar Energetic Electrons

We study the role of coronal mass ejection (CME) driven shocks in the acceleration of solar energetic electrons. Using observations by the two STEREO spacecraft, we correlate electron peak intensities of solar energetic particle events measured in situ with various parameters of the associated coronal shocks. These shock parameters were derived by combining 3D shock reconstructions with global modeling of the corona. This modeling technique provides also shock properties in the specific shock regions that are magnetically connected to the two STEREO spacecraft. We find significant correlations between the peak intensities and the Mach number of the shock with correlation coefficients of about 0.7, which are similar for electrons at ∼1 MeV and protons at >60 MeV. Lower-energy electrons with <100 keV show a smaller correlation coefficient of 0.47. The causal relationship between electron intensities and the shock properties is supported by the vanishing correlations when peak intensities at STEREO A are related with the Alfvénic Mach number at the magnetic footpoint of STEREO B and vice versa, which yields correlation coefficients of 0.03 and −0.13 for ∼1 MeV and <100 keV electron peak intensities, respectively. We conclude that the high-energy electrons are accelerated mainly by the shock, while the low-energy electrons are likely produced by a mixture of flare and shock-related acceleration processes.
Improving the Alfvén Wave Solar Atmosphere Model Based on Parker Solar Probe Data

In van der Holst et al. (2019), we modeled the solar corona and inner heliosphere of the first encounter of NASA’s Parker Solar Probe (PSP) using the Alfvén Wave Solar atmosphere Model (AWSoM) with Air Force Data Assimilative Photospheric flux Transport–Global Oscillation Network Group magnetograms, and made predictions of the state of the solar wind plasma for the first encounter. AWSoM uses low-frequency Alfvén wave turbulence to address the coronal heating and acceleration. Here, we revise our simulations, by introducing improvements in the energy partitioning of the wave dissipation to the electron and anisotropic proton heating and using a better grid design. We compare the new AWSoM results with the PSP data and find improved agreement with the magnetic field, turbulence level, and parallel proton plasma beta. To deduce the sources of the solar wind observed by PSP, we use the AWSoM model to determine the field line connectivity between PSP locations near the perihelion at 2018 November 6 UT 03:27 and the solar surface. Close to the perihelion, the field lines trace back to a negative-polarity region about the equator.
Discovery of a Short-period and Unusually Helium-deficient Dwarf Nova KSP-OT-201701a by the KMTNet Supernova Program

We present the first ever discovery of a short-period and unusually helium-deficient dwarf nova KSP-OT-201701a by the Korea Microlensing Telescope Network Supernova Program. The source shows three superoutbursts, each led by a precursor outburst, and several normal outbursts in BVI during the span of ∼2.6 yr with supercycle and normal cycle lengths of about 360 and 76 days, respectively. Spectroscopic observations near the end of a superoutburst reveal the presence of strong double-peaked H i emission lines together with weak He i emission lines. The helium-to-hydrogen intensity ratios measured by He i λ5876 and Hα lines are 0.10 ± 0.01 at a quiescent phase and 0.26 ± 0.04 at an outburst phase, similar to the ratios found in long-period dwarf novae, while significantly lower than those in helium cataclysmic variables (He CVs). Its orbital period of 51.91 ± 2.50 minutes, which is estimated based on time-series spectroscopy, is a bit shorter than the superhump period of 56.52 ± 0.19 minutes, as expected from the gravitational interaction between the eccentric disk and the secondary star. We measure its mass ratio to be 0.37−0.21+0.32 using the superhump period excess of 0.089 ± 0.053. The short orbital period, which is under the period minimum, the unusual helium deficiency, and the large mass ratio suggest that KSP-OT-201701a is a transition object evolving to an He CV from a long-period dwarf nova with an evolved secondary star.
Neural networks explanation models to support earthquake mitigation decision-making

Rapid urbanization and climate change have made cities more vulnerable to the occurrence of natural disasters. Earthquakes are among the natural disasters than severely impair communities, with the occurrence of the events in Haiti 2021 and Japan 2011 being vivid examples of the ability of these events to disrupt our lives. Motivated by minimizing the risk that earthquakes impose, researchers have developed complex models to accurately determine the impacts of these events, allowing to quantify the effect of mitigation actions. These earthquake models are computationally expensive, challenging the ability of researchers to translate their results into public policy. It is in that regard that neural networks have risen to generate surrogate models that rapidly and accurately can estimate the impacts of earthquakes. However, there is significant skepticism in disaster science to use neural networks due to their lack of interpretability. Considering the previous challenges, this study aims to develop a neural network to rapidly and accurately predict recovery time of buildings that experience damage after an earthquake. Moreover, besides just calibrating a neural network, this project implements explanation models to understand the internal mechanisms of neural network prediction and propose mitigation actions based on these results.
A Frequency Agile Semicircular Slot Antenna For Cognitive Radio System

A frequency agile antenna is proposed with its ground plane having a semicircular shaped slot which is capable of switching the frequency to different bands, one at a time, in the wide frequency range of 5.33 GHz to 9.90 GHz. To achieve frequency agility, switching of six RF PIN diodes which are placed along the slot length is done in various combinations. Frequency tuning ratio of about 1.85 : 1 can be achieved using this design. Results such as return losses, gain, bandwidth, and radiation patterns are presented in this paper.
Cytomegalovirus (CMV) Infection Suppresses The Expression Levels of Serum Cytokines in Follicular Lymphoma Patients

Follicular lymphoma (FL) is an indolent B-cell neoplasm. Increasing evidence suggests that chronic cytomegalovirus infection impacts the immunity of FL patients. This study was designed to unveil the impact of CMV infection on FL biology. Pretreatment serum samples from 42 FL patients recruited into a phase 3 FL clinical trial to compare two alternative frontline chemoimmunotherapy regimens were used. Twenty-one (21) of these patients had tested positive for CMV IgG antibodies, whereas 21 patients were negative. A multiplex assay was employed to measure the expression levels of 27 cytokines in each of these samples. Results were then compared between CMV-positive and CMV- negative groups using the Mann-Whitney U test using IBM SPSS Statistics version 20. Sera from CMV-positive FL patients showed decreased expression levels of most cytokines than those from the CMV-negative population. Data shows a statistically significant down-regulation of IL-6, IL-8, IL-9, IL-17A, FGF-basic, MIP-1α, and MIP-1β expression in CMV-positive FL patients compared to the CMV-negative group. Overall, our results suggest that CMV infection down-regulates the expression levels of serum cytokines in FL. Complementary studies to investigate immune cells in these patients' blood and tissue biopsies could provide a comprehensive perspective on the impact of CMV infection on FL.
Acceleration and deceleration controls of laser processing machine for connecting rod fracture splitting notch in engine

The laser processing machine is used to cut the notch of big-end hole of connecting rod in engine at high speed.In this paper,a preprocessing method was proposed to study the acceleration and deceleration for a laser notching machine.In order to eliminate the overcut as well as the remains because of the importing M function,a look-ahead means was put forward to deal with the acceleration/deceleration of this laser notching machine.Then,a new NC processing program combining the result from the former program was presented.Finally,the connecting rod was splitted after laser cuting notch.By checking over the connecting rob in a microscope,the results show that the splitting notch is almost the same in the depths(the depth tolerances of all points are ±0.02 mm) for all types of connecting rods.Moreover,there are no overcut or remains at all.These conclusions illustrate that this means greatly improves the stability and precision of the laser notching machine.