Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein Concentrations in Stored Pooled Platelet Concentrates Treated with Pathogen Inactivation by Amotosalen Plus Ultraviolet a Illumination

Platelet granules contain a diverse group of proteins. Upon activation and during storage, platelets release a number of proteins into the circulation or supernatant of stored platelet concentrate (PC). The aim of this work was to investigate the effect of pathogen inactivation (PI) on a selection of proteins released in stored platelets. Materials and Methods: PCs in platelet additive solution (PAS) were produced from whole blood donations using the buffy coat (BC) method. PCs in the treatment arm were pathogen inactivated with amotosalen and UVA, while PCs in the second arm were used as an untreated platelet control. Concentrations of 36 proteins were monitored in the PCs during storage. Results: The majority of proteins increased in concentration over the storage period. In addition, 10 of the 29 proteins that showed change had significantly different concentrations between the PI treatment and the control at one or more timepoints. A subset of six proteins displayed a PI-related drop in concentration. Conclusions: PI has limited effect on protein concentration stored PC supernatant. The protein’s changes related to PI treatment with elevated concentration implicate accelerated Platelet storage lesion (PSL); in contrast, there are potential novel benefits to PI related decrease in protein concentration that need further investigation.
QUATERNARY CMOS CIRCUITS BASED ON TRANSMISSION FUNCTION THEORY

The transmission function theory is proposed, the quaternary and logic variables are distinguished, and three kinds of new operations (i.e. the threshold comparison operation, transmission operation and union operation) are introduced. Since these operations describe the action principle of metal-oxide semiconductor (MOS) transistors, the transmission functions expressed by using them and logic operations can direct the realization of quaternary complementary metal-oxide semiconductor (CMOS) circuits. In this paper the design of CMOS circuits for some basic transmission functions is proposed. The computer simulation has confirmed that these circuits have correct logic and desirable DC transfer characteristics. Furthermore, the synthesis technique for a quaternary CMOS transmission network is also proposed here.
Klasifikasi Sinyal EEG Menggunakan Model K-Nearest Neighbor Untuk Pengenalan Kata Yang Dibayangkan

Locked in syndrome (LIS) is a condition of complete paralysis in which people with LIS are conscious but unable to move or communicate verbally except to move their eyes or blink. One way that can help LIS sufferers to communicate and interact is through recording brain signals called Electroencephalogram (EEG). In this study, the data from the recording of the EEG signal has gone through the extraction stage. The extracted data is preprocessed and classified using the K-Nearest Neighbor (K-NN) algorithm to be visualized using a web-based application. The results of the classification using the K-Nearest Neighbor algorithm with a value of K = 1 resulted in 82% accuracy, 82% precision and 82% recall. Keywords: LIS, EEG, K-Nearest Neighbor.
Stress Classification using Deep Learning with 1D Convolutional Neural Networks

Stress has been a major problem impacting people in various ways, and it gets serious every day. Identifying whether someone is suffering from stress is crucial before it becomes a severe illness. Artificial Intelligence (AI) interprets external data, learns from such data, and uses the learning to achieve specific goals and tasks. Deep Learning (DL) has created an impact in the field of Artificial Intelligence as it can perform tasks with high accuracy. Therefore, the primary purpose of this paper is to evaluate the performance of 1D Convolutional Neural Networks (1D CNNs) for stress classification. A Psychophysiological stress (PS) dataset is utilized in this paper. The PS dataset consists of twelve features obtained from the expert. The 1D CNNs are trained and tested using 10-fold cross-validation using the PS dataset. The algorithm performance is evaluated based on accuracy and loss matrices. The 1D CNNs outputs 99.7% in stress classification, which outperforms the Backpropagation (BP), only 65.57% in stress classification. Therefore, the findings yield a promising outcome that the 1D CNNs effectively classify stress compared to BP. Further explanation is provided in this paper to prove the efficiency of 1D CNN for the classification of stress.
Prevalence of Aflatoxins in Selected Dry Fruits, Impact of Storage Conditions on Contamination Levels and Associated Health Risks on Pakistani Consumers

Dry fruits and nuts are nutritious foods with several health-promoting properties. However, they are prone to contamination with aflatoxins at all stages of production and storage. The present study aimed to determine the natural occurrence of aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin G1 (AFG1), aflatoxin G2 (AFG2), and total aflatoxins (AFT) in dates, pistachios, and walnuts collected from four districts of South Punjab (Pakistan), and to assess the associated health risks as estimated by dietary exposure and the Margin of Exposure (MoE) determinations. The contents of AFB1 and AFT in these food products were monitored during storage under three different conditions (open-air, hermetically closed jars, and refrigeration at 4 °C) to determine the most efficient conditions in preventing aflatoxin accumulation. HPLC-fluorescence analysis of 60 samples of these products for aflatoxin contamination showed that 52 (86.7%) samples were contaminated at different levels, with a maximum of 24.2 ng/g. The overall (all samples) mean concentrations of AFB1, AFB2, AFG1, AFG2, and AFT were 3.39 ± 2.96, 1.39 ± 1.68, 1.63 ± 1.48. 1.12 ± 1.23, and 7.54 ± 6.68, respectively. The Estimated Daily Intake (EDI) and MoE of aflatoxins through the consumption of the products ranged from 0.06 ng/kg bw/day to 2.0 ng/kg bw/day and from 84.84 to 2857.13, respectively, indicating that consumers are at high health risk. Significant differences were recorded between aflatoxin levels in the samples stored under different storage conditions, with storage under refrigeration (4 °C) being the most effective in controlling aflatoxin accumulation, although storage in closed jars was also efficient and offers a more flexible alternative to retailers. The findings of the study urge official authorities of Pakistan to implement appropriate regulatory and control measures and surveillance program to alleviate the potential public health risks associated with the consumption of dry fruits and nuts in the scope of their increased consumption.
Differential Strain Analysis: An Investigation Over Its Feasibility For Determining Coal’s Cleat Orientation

Gas production from coal bed methane (CBM) has been rising as supplement to conventional gas reservoirs. Effective CBM production is much determined by well placements with regards to orientation of the coal reservoir’s face cleat. This is true since this cleat type provides the main path of fluid migration from storage to wellbore. It is therefore imperative to understand and determine the general orientation of the cleats of concern. This information is usually obtained from visual description on core samples, and whenever available from analogy to outcrop data. This is considered as insufficient and a means for studying core sample’s interior is required in order to ensure consistency between external and internal appearances. This paper presents an investigative study over the possibility of differential strain analysis (DSA) technique to serve the purpose. The technique is normally used for measuring subsurface in situ stress field, and through the use of a similar basic concept it is proposed to be used for determining orientation of face cleats in coal samples. The study includes utilization of DSA data obtained from measurements on samples taken from great depths. Review and re-working over the data has shown that the technique can well indicate the orientation of face cleats. A general orientation over all tested samples is also indicated with reasonable degree of reliability. This leads into conclusion that the DSA technique can be well used to indicate cleat orientation and therefore help in better characterizing coal.
A Comparative Analysis of Determinants of Family Dynamics of Children with ADHD with and without Comorbid Conduct Disorder

Background: Comorbidity of attention-deficit/hyperactivity disorder (ADHD) is one of the most actively studied topics in the field of child and adolescent psychiatry. Among clinic-referred children, comorbidity is frequently found between ADHD and conduct disorder (CD). However, little is known regarding the probable association between the co-occurrence and external factors. One factor that has recently sparked interest is family dynamics. Objective: To compare the children with attention deficit/ hyperactivity disorder (ADHD) with and without co-morbid conduct disorder (CD) in terms of selected determinants of family dynamics and the influences of catchment area and gender on these differences. Methodology: This case-control study was conducted upon a sample of 114 pre-diagnosed ADHD children from July 2017 to June 2019 at the Liaquat University Hospital & Sir CJIP. After taking written informed consent from parents of children diagnosed with ADHD, the children were re-evaluated using the Diagnostic & Statistical Manual (DSM) - V criteria. Vanderbilt Parent Rating Scale was also used to screen for possible comorbidities with ADHD. Children with intellectual disability were excluded from the study. Family dynamics like family type, family size, socio-economic status, parental educational and occupational status, family relationships, parental discord, and history of psychiatric illness in family were explored via self-structured interview-based questionnaire. The data obtained was analyzed using SPSS v. 21.0. Results: The cumulative mean age of the sample of ADHD children stood at 7.6 years (SD ± 0.52). Most of the sample of the children hailed from urban (64.3%) areas and were raised in joint families (71.4%). Consanguinity was common among the parents (72.8%) and especially prevalent among the ADHD children with co-morbid CD. The key features that were notably high among families of the children with comorbid CD included positive parental psychiatric history, ADHD among siblings and parental discord. Conclusion: After careful consideration, it can be concluded that consanguinity of parents, joint family system. And family history of psychiatric disorder affects the occurrence of comorbidity of conduct disorder among children with ADHD.
MANAJEMEN PENGELOLAAN KELAS SEKOLAH DASAR PADA MASA PANDEMI

Class management is one of the most important factors in teaching and learning activities that can provide facilities in schools, especially classes to carry out various learning activities. This study aims to describe how teachers in classroom management in elementary schools during the pandemic can improve learning for the better. This study uses a qualitative approach with a descriptive method. Data collection techniques in research using observation, interviews and documentation. The results showed that classroom management carried out by teachers in grade IV at SD Negeri Dukuhwringin 01 during the pandemic was quite effective, because the learning system is carried out boldly and attractively so that it can help students at the level of understanding previous knowledge and arouse students' enthusiasm for learning with complementary learning systems that make learning effectiveness better.
Accuracy of the Logistic EuroSCORE in Predicting Long-Term Survival Following Isolated Aortic Valve Replacement

Objective: To assess the ability of the logistic EuroSCORE to predict long-term mortality of patients undergoing isolated Surgical Aortic Valve Replacement (SAVR). Methods: A retrospective review of all patients undergoing SAVR between September 1999, and March 2018 was done. Results: 2018 patients were eligible for inclusion in the study. Patients were grouped accord-ing to risk: low (n = 506), intermediate (n = 609), and high-risk (n = 903) depending on their logistic EuroSCORE values. The 30-day mortality of the low-risk group was 0.47%. The one-, five-, 10-, 15-, and 20-year mortality was 1.66%, 4.9%, 14.9%, 24.3%, and 43.8%, respectively. Intermediate-risk group 30-day mortality was 0.66%. The one-, five-, 10-, 15-, and 20-year mortality was 3.28%, 11.9%, 32%, 54.8%, and 82.6%, respectively. The 30-day mortality of the high-risk group was 3.99%. The one-, five-, 10-, 15-, and 20-year mortality was 8.2%, 27%, 55.4%, 78.6%, and 87%, respectively. Conclusion: Our results confirm that the lES is accurate in predicting long-term mortality outcomes of SAVR. This real-world data provides evidence of the potential usefulness of the EuroSCORE to help the heart team and patients decide on appropriate interventions for aortic stenosis.
Deep-learning-based quantum imaging using NOON states

The phase sensitivity of photonic NOON states scales O(1/N), which reaches the Heisenberg limit and indicates a great potential in high-quality optical phase sensing. However, the NOON states with large photon number N are experimentally difficult both to prepare and to operate. Such a fact severely limits their practical use. In this article, we soften the requirements for high-quality imaging based on NOON states with large N by introducing deep-learning methods. Specifically, we show that, with the help of deep-learning network, the fluctuation of the images obtained by the NOON states when N = 2 can be reduced to that of the currently infeasible imaging by the NOON states when N = 8. We numerically investigate our results obtained by two types of deep-learning models—deep neural network and convolutional denoising autoencoders, and characterize the imaging quality using the root mean square error. By comparison, we find that small-N NOON state imaging data is sufficient for training the deep-learning models of our schemes, which supports its direct application to the imaging processes.
Analysis of Distributed English Chunk Recognition under Correlation Evaluation Based on Deep Belief Network Model

Most of the traditional methods of English text chunk recognition are solved by setting the corresponding phrase identifier numbers and eventually transforming the chunk recognition problem into a lexical annotation problem. In language recognition, the traditional MFCC features are easily contaminated by noise and have weak noise immunity due to the insufficient amount of information on each frame of the signal. At the same time, SDC feature extraction methods commonly used today require artificial settings in parameter selection, which increases the uncertainty of recognition results. The method of identifying English text chunks by association evaluation of central word extensions identifies English text chunks from a different perspective. It has the following features: (i) each phase is considered as a cluster with the central word as the core, and the internal composition pattern of each phrase is fully considered; (ii) the results are dynamically evaluated using association and confidence. The results show that the proposed method can achieve higher recognition rate than traditional feature extraction methods. The recognition rate is faster, and the F -measure value of English block recognition reaches 94.05%, which is comparable to the best results so far.
Morphological and ecological data confirm Reithrodontomys cherrii as a distinct species from Reithrodontomys mexicanus

The integrative taxonomy approach has recently been widely suggested in systematic studies. Lines of evidence such as the geometric morphometrics and ecological analyses have been useful for discriminating between genetically well-differentiated species. Within the genus Reithrodontomys, R. mexicanus is one of the more taxonomically complex species, being considered a cryptic species complex. R. cherrii was considered a subspecies of R. mexicanus, until molecular evidence raised it to the species-level. Herein, we evaluate these two forms using morphological and ecological data based on the premise that they constitute genetically differentiated species. We carried out geometric morphometric analyses on dorsal and ventral views of the skull. Landmark and semi-landmark configurations for both views of the skull were selected based on previous studies of cricetid rodents. We tested the presence of sexual dimorphism, and the skull shape and size differences between species on both cranial views. Additionally, we characterized the environmental space of each species habitat using bioclimatic variables, elevation, and the Normalized Difference Vegetation Index (NDVI). Females and males of R. mexicanus and R. cherrii did not show sexual dimorphism in shape or size of both skull views. We found significant differences between the two species in both shape and size of the skull. Cranial structures of the ventral view were more useful to differentiate both species. R. mexicanus exhibited a broader environmental space than R. cherrii, with relatively similar values of temperature and elevation, but not of precipitation. The pairwise comparison showed significant differences in the majority of the environmental variables analyzed. Although for each view, we found statistical differences in the skull shape of R. cherrii and R. mexicanus, the ventral side showed major resolutive power differentiating both species. Our findings suggest that R. cherrii tends to have a larger skull than R. mexicanus. However, the morphological and pelage coloration similarity between these species reported in the past, could explain the previous inclusion of R. cherrii as a subspecies of R. mexicanus. R. mexicanus occurs in a variety of vegetation-types coinciding with the broader environmental space that it occupies compared to that of R. cherrii. The natural areas where both species are distributed were associated with high NDVI values. Our results complement the molecular evidence and, under an integrative taxonomy approach, support R. cherrii as a different species from R. mexicanus.
A novel high‐gain bidirectional DC–DC converter for EV onboard charger applications

In electric vehicle (EV) onboard applications, the bidirectional DC‐DC converter is proposed in this paper to interface low terminal voltage battery with DC bus of the motor. The proposed bidirectional DC–DC converter is configured based on a ladder switching network of inductors and a capacitor. Due to this configuration, the proposed converter has a simple structure, and it can archive high voltage gain with fewer components. A detailed comparative analysis is presented to verify the uniqueness and superiority of the proposed topology. Furthermore, the detailed steady‐state analysis and working principles are presented in this paper. Finally, the 300‐W prototype is built, and the experimental results are presented to verify the performance and feasibility of the proposed bidirectional converter.
WATCH: Two-Stage Discrete Cross-Media Hashing

Due to the explosive growth of multimedia data in recent years, cross-media hashing (CMH) approaches have recently received increasing attention. To learn the hash codes, most existing supervised CMH algorithms employ the strict binary label information, which has tiny gaps between the incorrect labels (0) and the true labels (1), increasing the risk of classification error. Besides, most existing CMH approaches are one-stage algorithms, in which the hash functions and binary codes can be learned synchronously, complicating the optimization. To avoid NP-hard optimization, which is the consequence of the discrete constraints of the objective function, many approaches utilize a relaxation strategy. However, this optimisation trick may cause large quantization errors. To address these issues, we present a novel tWo-stAge discreTe Cross-media Hashing method based on a smooth matrix factorization and label relaxation, named WATCH. The proposed WATCH controls the margins adaptively by the novel label relaxation strategy. This innovation reduces the quantization error significantly. Besides, WATCH is a two-stage model. In stage 1, we employ a discrete smooth matrix factorization model. Then, the hash codes can be generated discretely, reducing the large quantization loss greatly. In stage 2, we adopt an effective hash function learning strategy, which produces a more effective hash function. Comprehensive experiments on three popular datasets demonstrate that WATCH achieves significantly better performance than several state-of-the-art approaches.
A Trefoil Knot Polymer Chain Translocates through a Funnel-like Channel: A Multi-Particle Collision Dynamics Study

With combining multi-particle collision dynamics (MPCD) for the solvent and molecular dynamics (MD) for the polymer chains, we have studied the conformation and untying behaviors of a trefoil knot polymer chain translocated through a confined funnel-like channel. For the trefoil knot chain, we found that the untying knot behavior mostly happens during the translocation process, and the translocation behavior of linear chains is also simulated as a comparison. Some characteristics of the trefoil knot chain during translocation process, such as average gyration radius and the average end-to-end distances are discussed, and we statistic the scale relations of the translocation time versus the chain length, and that of the chain rigidity. This study may help to understand translocation behaviors of the knotted linear polymer chain in the capillary flow field.
Implementation of Taxation Data Integration in State-Owned Enterprises to Strengthen Good Corporate Governance

This study aims to analyze the taxation data integration within the State-Owned Enterprises (BUMN) in strengthening Good Corporate Governance (GCG). This study is descriptive with a qualitative method and uses a content analysis approach. The authors conducted the study by doing a review of the literature on GCG and taxation. The literature comes from books, journals, regulations, policies, electronic sources, the internet, previous research results, press releases, and newspapers. The result of this study suggest that the taxation data integration within BUMN is an essential innovation means of improving corporate governance. The taxation data integration program will indirectly make it easier for BUMN to meet their tax rights and obligation. Additional benefits of tax governance include lowering the likelihood of disputes occurring and avoiding long audit processes, which both lower taxpayer compliance costs and lessen the likelihood of potential disputes.