Miracle-dz/UniFAR-FTUs huggingface dataset:
A Photoelectric Instrument for Light Scattering Measurements and a Differential Refractometer

The method of determining particle sizes and molecular weights by light scattering measurements on solutions is finding increasing fields of application. In order to obtain the necessary data quickly and conveniently, an instrument has been developed which allows the required light scattering measurements to be taken on a routine basis. This apparatus is described. It measures transmission and the scattering of light at 90°; two different schemes can be employed to obtain the angular intensity distribution of the scattered light. A description is also given of a compensating refractometer which is used to determine the difference in refractive index of solvent and solution. The instrument, which is simple in design, gives a direct reading of the refractive index difference and has an accuracy of better than 10−5. The light scattering instrument has been tested using polystyrene of known molecular weight, and has been applied to study the change of apparent molecular weight (or particle size) of a series of polyvinyl ethers as a function of concentration in some cases, also a number of solvents. For a series of polyvinyl n‐butyl ether samples which was studied, the relation between the intrinsic viscosity [ηi] of the benzene solutions and the weight average molecular weight M determined by light scattering is ηi = . 365×10−7M1.45. The method of determining particle sizes and molecular weights by light scattering measurements on solutions is finding increasing fields of application. In order to obtain the necessary data quickly and conveniently, an instrument has been developed which allows the required light scattering measurements to be taken on a routine basis. This apparatus is described. It measures transmission and the scattering of light at 90°; two different schemes can be employed to obtain the angular intensity distribution of the scattered light. A description is also given of a compensating refractometer which is used to determine the difference in refractive index of solvent and solution. The instrument, which is simple in design, gives a direct reading of the refractive index difference and has an accuracy of better than 10−5. The light scattering instrument has been tested using polystyrene of known molecular weight, and has been applied to study the change of apparent molecular weight (or particle size) of a series o.
A multiscale component mode synthesis approach for dynamic analysis of nanostructures

A component mode synthesis‐based multiscale approach is developed for dynamic analysis of nanostructures. The multiscale approach decomposes a nanostructure into atomistic and continuum regions and employs vibrational modes to connect the regions of different scales, enabling a reflectionless atomistic‐to‐continuum coupling. Dynamic response of the coupled atomistic and continuum regions is computed concurrently using a common time scale. Numerical results indicate that the multiscale approach has significant condensation and scaling advantages, and it is well suited for modeling and simulation of large and complex systems. Copyright © 2012 John Wiley & Sons, Ltd.
Electron Irradiation of Titanium-Doped Chromium Nanostructured Thin Films for Higher Conductive Electrodes

The low-energy electron irradiation improves the electrical properties of the Ti-doped chromium thin films obtained by Laser-assisted Thermionic Vacuum Arc (LTVA). The irradiation doses, between 0.6 C/m2 and 326.4 C/m2, operate as a nanozonal melting effect, decreasing the metallic alloy's roughness, and increasing the electrical conductivity at a depth up to 8 nm. Therefore, the grain sizes of the surface nanostructures are increased after irradiation, but the surface roughness is substantially improved. By tailoring the surface parameters like crystallinity and roughness of the metallic thin films used as electrodes in the OLED technologies, a reduction of the Schottky contacts between the metal and semiconductor contact is expected. This fact minimizes the contact resistance between the metallic and semiconductor thin films and increases the charge injection across the OLED sandwich structures.
Channel Contribution in Deep Learning Based Automatic Sleep Scoring—How Many Channels Do We Need?

Machine learning based sleep scoring methods aim to automate the process of annotating polysomnograms with sleep stages. Although sleep signals of multiple modalities and channels should contain more information according to sleep guidelines, most multi-channel multi-modal models in the literature showed only a little performance improvement compared to single-channel EEG models and sometimes even failed to outperform them. In this paper, we investigate whether the high performance of single-channel EEG models can be attributed to specific model features in their deep learning architectures and to which extent multi-channel multi-modal models take the information from different channels of modalities into account. First, we transfer the model features from single-channel EEG models, such as combinations of small and large filters in CNNs, to multi-channel multi-modal models and measure their impacts. Second, we employ two explainability methods, the layer-wise relevance propagation as post-hoc and the embedded channel attention network as intrinsic interpretability methods, to measure the contribution of different channels on predictive performance. We find that i) single-channel model features can improve the performance of multi-channel multi-modal models and ii) multi-channel multi-modal models focus on one important channel per modality and use the remaining channels to complement the information of the focused channels. Our results suggest that more advanced methods for aggregating channel information using complementary information from other channels may improve sleep scoring performance for multi-channel multi-modal models.
Maximum likelihood estimation of spatial lag models in the presence of the error-prone variables

ABSTRACT– The literature has recently devoted close attention to error-prone variables. Nevertheless, only a small number of research have considered measurement error in spatial econometric models. The presence of measurement error in the spatial econometric models needs to be considered as a result of the rise in spatial data analysis, as the relationship between the spatial correlation and measurement error influences parameter estimation. Therefore, in this study, the impacts of classical measurement error on the parameter estimation of the spatial lag model are theoretically examined for both response and explanatory variables. Then, using simulation studies, finite sample properties are investigated for various situations. The major findings indicate that although error-prone response variable has an opposing bias effect on parameter estimations, error-prone explanatory variables have a significant influence effect on the bias of parameter estimations. As a result, it is occasionally possible to obtain unbiased estimates only in certain circumstances.
Sex Lethal Gene Manipulates Gonadal Development of Medaka, Oryzias latipes, through Estrogenic Interventions

Germ cells are pivotal for gonadal sexuality maintenance and reproduction. Sex lethal (sxl), the somatic sex determining gene of Drosophila, is the known regulator and initiator of germ cell femininity in invertebrates. However, the role of the Sxl homologue has rarely been investigated in vertebrates. So, we used medaka to clarify the role of sxl in vertebrate gonadogenesis and sexuality and identified two Sxl homologues, i.e., Sxl1a and Sxl1b. We found that sxl1a specifically expresses in the primordial germ cells (PGC), ovary, (early gonia and oocytes), while sxl1b distributions are ubiquitous. An mRNA overexpression of sxl1a accelerated germ cell numbers in 10 DAH XY fish, and sxl1a knockdown (KD), on the other hand, induced PGC mis-migration, aberrant PGC structuring and ultimately caused significant germ cell reduction in XX fish. Using an in vitro promoter analysis and in vivo steroid treatment, we found a strong link between sxl1a and estrogenic germ cell-population maintenance. Further, using sxl1a-KD and erβ2-knockout fish, we determined that sxl1 acts through erβ2 and controls PGC sexuality. Cumulatively, our study highlights the novel role of sxl1a in germ cell maintenance and sexual identity assignment and thus might become a steppingstone to understanding the commonalities of animal sexual development.
Advanced Nanostructured Materials for Electrocatalysis in Lithium–Sulfur Batteries

Lithium–sulfur (Li-S) batteries are considered as among the most promising electrochemical energy storage devices due to their high theoretical energy density and low cost. However, the inherently complex electrochemical mechanism in Li-S batteries leads to problems such as slow internal reaction kinetics and a severe shuttle effect, which seriously affect the practical application of batteries. Therefore, accelerating the internal electrochemical reactions of Li-S batteries is the key to realize their large-scale applications. This article reviews significant efforts to address the above problems, mainly the catalysis of electrochemical reactions by specific nanostructured materials. Through the rational design of homogeneous and heterogeneous catalysts (including but not limited to strategies such as single atoms, heterostructures, metal compounds, and small-molecule solvents), the chemical reactivity of Li-S batteries has been effectively improved. Here, the application of nanomaterials in the field of electrocatalysis for Li-S batteries is introduced in detail, and the advancement of nanostructures in Li-S batteries is emphasized.
Identification of an Amino Acid Metabolism-Related Gene Signature for Predicting Prognosis in Lung Adenocarcinoma

Abstract: Background Dysregulation of amino acid metabolism (AAM) is an important factor in cancer progression. This study intended to study the prognostic value of AAM-related genes in lung adenocarcinoma (LUAD). Methods: The mRNA expression profiles of LUAD datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were applied as the training and validation sets. After identifying the differentially expressed AAM-related genes, an AAM-related gene signature (AAMRGS) was constructed and validated. Additionally, we systematically analyzed the differences in immune cell infiltration, biological pathways, immunotherapy response, and drug sensitivity between the two AAMRGS subgroups. Results: The prognosis-related signature was constructed on the grounds of key AAM-related genes. LUAD patients were divided into AAMRGS-high and -low groups. Patients in the two subgroups differed in prognosis, tumor microenvironment (TME), biological pathways, and sensitivity to chemotherapy and immunotherapy. The area under the receiver operating characteristics (ROC) and calibration curves showed good predictive ability for the nomogram. Analysis of immune cell infiltration revealed that the TME of the AAMRGS-low group was in a state of immune activation. Conclusion: We constructed an AAMRGS that could effectively predict prognosis and guide treatment strategies for patients with LUAD.
INO80 Is Required for the Cell Cycle Control, Survival, and Differentiation of Mouse ESCs by Transcriptional Regulation

Precise regulation of the cell cycle of embryonic stem cells (ESCs) is critical for their self-maintenance and differentiation. The cell cycle of ESCs differs from that of somatic cells and is different depending on the cell culture conditions. However, the cell cycle regulation in ESCs via epigenetic mechanisms remains unclear. Here, we showed that the ATP-dependent chromatin remodeler Ino80 regulates the cell cycle genes in ESCs under primed conditions. Ino80 loss led to a significantly extended length of the G1-phase in ESCs grown under primed culture conditions. Ino80 directly bound to the transcription start site and regulated the expression of cell cycle-related genes. Furthermore, Ino80 loss induced cell apoptosis. However, the regulatory mechanism of Ino80 in differentiating ESC cycle slightly differed; an extended S-phase was detected in differentiating inducible Ino80 knockout ESCs. RNA-seq analysis of differentiating ESCs revealed that the expression of genes associated with organ development cell cycle is persistently altered in Ino80 knockout cells, suggesting that cell cycle regulation by Ino80 is not limited to undifferentiated ESCs. Therefore, our study establishes the function of Ino80 in ESC cycle via transcriptional regulation, at least partly. Moreover, this Ino80 function may be universal to other cell types.
Multiple Novel Human Norovirus Recombinants Identified in Wastewater in Pretoria, South Africa by Next-Generation Sequencing

The genogroup II genotype 4 (GII.4) noroviruses are a major cause of viral gastroenteritis. Since the emergence of the Sydney_2012 variant, no novel norovirus GII.4 variants have been reported. The high diversity of noroviruses and periodic emergence of novel strains necessitates continuous global surveillance. The aim of this study was to assess the diversity of noroviruses in selected wastewater samples from Pretoria, South Africa (SA) using amplicon-based next-generation sequencing (NGS). Between June 2018 and August 2020, 200 raw sewage and final effluent samples were collected fortnightly from two wastewater treatment plants in Pretoria. Viruses were recovered using skimmed milk flocculation and glass wool adsorption-elution virus recovery methods and screened for noroviruses using a one-step real-time reverse-transcription PCR (RT-PCR). The norovirus BC genotyping region (570–579 bp) was amplified from detected norovirus strains and subjected to Illumina MiSeq NGS. Noroviruses were detected in 81% (162/200) of samples. The majority (89%, 89/100) of raw sewage samples were positive for at least one norovirus, compared with 73% (73/100) of final effluent samples. Overall, a total of 89 different GI and GII RdRp-capsid combinations were identified, including 51 putative novel recombinants, 34 previously reported RdRp-capsid combinations, one emerging novel recombinant and three Sanger-sequencing confirmed novel recombinants.
A Method of Grasping Detection for Kiwifruit Harvesting Robot Based on Deep Learning

Kiwifruit harvesting with robotics can be troublesome due to the clustering feature. The gripper of the end effector will easily cause unstable fruit grasping, or the bending and separation action will interfere with the neighboring fruit because of an inappropriate grasping angle, which will further affect the success rate. Therefore, predicting the correct grasping angle for each fruit can guide the gripper to safely approach, grasp, bend and separate the fruit. To improve the grasping rate and harvesting success rate, this study proposed a grasping detection method for a kiwifruit harvesting robot based on the GG-CNN2. Based on the vertical downward growth characteristics of kiwifruit, the grasping configuration of the manipulator was defined. The clustered kiwifruit was mainly divided into single fruit, linear cluster, and other cluster, and the grasping dataset included depth images, color images, and grasping labels. The GG-CNN2 was improved based on focal loss to prevent the algorithm from generating the optimal grasping configuration in the background or at the edge of the fruit. The performance test of the grasping detection network and the verification test of robotic picking were carried out in orchards. The results showed that the number of parameters of GG-CNN2 was 66.7 k, the average image calculation speed was 58 ms, and the average grasping detection accuracy was 76.0%, which ensures the grasping detection can run in real time. The verification test results indicated that the manipulator combined with the position information provided by the target detection network YOLO v4 and the grasping angle provided by the grasping detection network GG-CNN2 could achieve a harvesting success rate of 88.7% and a fruit drop rate of 4.8%; the average picking time was 6.5 s. Compared with the method in which the target detection network only provides fruit position information, this method presented the advantages of harvesting rate and fruit drop rate when harvesting linear clusters, especially other cluster, and the picking time was slightly increased. Therefore, the grasping detection method proposed in this study is suitable for near-neighbor multi-kiwifruit picking, and it can improve the success rate of robotic harvesting.
ITER-FEAT - the future international burning plasma experiment - present status

The focus of effort in the ITER engineering design activities (EDA) since 1998 has been the development of a new design to meet revised technical objectives and a cost reduction target of about 50% of the previously accepted cost estimate. Drawing on the design solutions already developed and qualified during the EDA and using the latest physics results and outputs from technology researched development projects, the Joint Central Team and Home Teams, working together, have been able to converge towards a design which meets, in general, the revised objectives and provides acceptable margins against the unavoidable uncertainties in performance projections. The new design will allow the exploration of a range of burning plasma conditions in which energetic α-particles are the dominant source of plasma heating and the main determinants of plasma behaviour, with the capacity to progress towards possible modes of steady state operation. Such plasma performance both necessitates, and provides a test-bed for, a range of advanced technologies required to establish fusion as a practical energy source. As such the new ITER design, whilst having reduced technical objectives from its predecessor, will nonetheless meet the programmatic objective of providing an integrated demonstration of the scientific and technological feasibility of fusion energy. The main features of the current design and of its projected performance are presented and the outlook for construction and operation is discussed.
Personality and Emotion in Strong-Story Narrative Planning

Believable characters are core elements of a coherent story. Qualities that make story characters more believable include goals, beliefs, personality, and emotion. We propose computational models of emotion and personality by adapting the Ortony, Clore, and Collins (OCC) model of emotion and the Five-Factor personality model. Our models are formulated into multiagent strong-story narrative planning with the promise of being highly reusable and domain independent. We evaluate these models using multiple human subject studies. We show that our model's reasoning about character emotions matches the expectations of human readers, and using our emotion model, we can generate a larger set of stories than precedent narrative planners. We also demonstrate that human readers can perceive and recognize the personalities of story characters through their consistent behavior generated by our model. Our final experiment supports that human readers significantly find the behavior generated by our models of emotion and personality more believable than behavior that lacks either or both.
Readiness of Anonymous Credentials for Real Environment Deployment

— Attribute-based Credentials (ABCs) are a promising technology for protecting users’ privacy and digital identity. We can use ABCs in a multitude of contexts. For instance, we can prove the validity of transportation tickets, demonstrate the legal age, prove the health status, or certify access rights in the company environment. All of this, we can do without disclosing complete personal identity. Nevertheless, ABCs generally require computational power that some wearable devices cannot cope with. In this paper, we present our implementation of a privacy-enhancing authentication system based on ABCs technology. The system is suitable for deployment in real-world scenarios and uses a wide range of differently powerful user devices (e.g., smart cards, smartphones, and wearables). Based on our implementation results, we also discuss the implementations aspects of ABCs, their readiness, and usability in real-world applications.
Enhancing the student learning experience: Co‐teaching biochemistry and clinical sciences within the dental curriculum

Basic knowledge of biochemistry underpins oral and dental care. Undergraduate dental students do not always engage well with basic science teaching due to not appreciating its clinical relevance. Co‐teaching provides one approach to overcome students' disengagement and involves two lecturers, with complementary expertise, presenting the curriculum together. This study investigated student experiences and engagement using co‐teaching to integrate biochemistry with clinical sciences in the students' second‐year dental curriculum. Two successive second year dental student cohorts were co‐taught. Content was delivered by a biochemist and an oral biologist, either online (during the 2020 COVID lockdown) or in‐person (2021). Each cohort was surveyed at the end of the teaching module using an online questionnaire containing both interval scale and free‐text questions. Responses were received from 39 (42%) and 64 (85%) of students in 2020 and 2021, respectively. Students from both cohorts preferred the co‐teaching approach with a mean of 8.74 on a 10‐point interval scale. In 2020 and 2021, 77% and 76% of participants, respectively, preferred a combined biochemistry and clinical dentistry delivery, either in‐person (37%), via Zoom (19%) or via video recording (14%). Thematic analysis of responses revealed students experienced enhanced engagement when co‐taught and they attributed this to integration of the curriculum making the content more relevant and stimulating. Students preferred co‐teaching to individual subjects being taught by a single teacher. Co‐teaching established the relevance of theoretical biochemistry to clinical dental sciences and enhanced the students' learning experience.
Deep and Statistical Features Classification Model for Electroencephalography Signals

People strive to make sense of the complex electroencephalography (EEG) data generated by the brain. This study uses a prepared dataset to examine how easily people with alcohol use disorder (AUD) could be distinguished from healthy people. The signals from each electrode are connected to one another and are first represented as a single signal. The signal is then denoised through variation mode decomposition (VMD) during the preprocessing stage. The statistical and deep feature extraction phases are the two subsequent phases. The crucial step in the suggested strategy is to classify data using a combination of these two unique qualities. Deep and statistical feature performance was evaluated independently. Then, using the eigenvectors created by merging all of the collected features, classification was carried out using our DSFC (Deep - Statistical Features Classification) model. Although the classification accuracy rate using only statistical features was 81.2 percent and the classification accuracy rate using only deep learning was 95.71 percent, the classification accuracy rate utilizing hybrid features created using the suggested DSFC technique was 99.2%. Therefore, it can be proven that combining statistical and deep features can produce beneficial results.