Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in Physicochemical, Microbiological and Sensory Properties of Candy Incorporated with Basella Alba upon Storage

ABSTRACT Basella alba (B. alba) is known for health benefits owing to its beneficial phytochemicals. This study aimed to process B. alba leaves into candies which has prolonged shelf life. B. alba candy was analyzed for the physicochemical characteristics like color, pH, titratable acidity, total soluble solid, moisture content, water activity, texture profile analysis. During storage, significant increase in TSS and TA, while a nonsignificant increase in water activity was observed. pH and moisture contents were found to decrease significantly during storage at both ambient and accelerated temperature. Hardness and adhesion of candy showed significant decrease upon storage at both ambient and accelerated temperature. Yeast and mold count of 100 cfu/g suggests that the candies were safe for human consumption during storage at both ambient and accelerated temperature and the desired sensory characteristic of the candy was retained for 28 days at ambient temperature and less than 14 days at accelerated temperature.
Reliability assessment of HV substations equipped with fault current limiter considering changes of failure rate of components

Owing to the increasing electricity load demand on one hand and in order to have a reliable electric network on the other hand, constructions of new transmission lines and generation sources are extremely essential. Such expansions, however, raise the fault current level which can even exceed the maximum short-circuit level rating of the circuit breakers (CBs). High fault current, in addition to the adverse effect on the CB function, can damage other substation equipment suddenly or over the time. To this end, severity of the fault current is a key factor in the substation reliability assessment. In this context, fault current limiters (FCLs) which are employed to decline the fault current level need to be precisely modelled and their impact should be extensively studied. In this study, the effect of FCL on the failure rate of substation equipment is first investigated. A computational technique for assessing the substation reliability is accordingly devised. As the location of FCL installation in a given substation or in substations with different arrangements would not be the same, various credible configurations are incorporated here. The results show that from the perspective of reliability indices, double-bus double-breaker and one breaker and a half configurations are more suitable for FCL installation.
Early Diagnosis of Alzheimer’s Disease in the Primary Care Setting

The burden of Alzheimer’s disease (AD) affects not just the individual but also families, providers, and society. Early recognition and diagnosis of AD may reduce cost by reducing interaction with the health care system, earlier initiation of treatment, and prolonging time to longterm care. Primary care providers, the first contact for diagnosis of patients with AD, are not fulfilling the potential of early diagnosis for a variety of reasons. Biomarkers of AD emerge on average 15 to 20 years before clinical diagnosis, yet currently established biomarkers are not easily available in the primary care setting. A growing body of literature is focused on identifying additional non-invasive early signs of AD. The aims of this program of research were to understand factors contributing to the AD diagnosis variability in primary care settings and methods to improve early diagnosis by primary care providers. Four studies were undertaken to achieve these aims. The first study reported the results of an integrated review estimating the prevalence of missed diagnosis in primary care when compared to trained raters’ diagnoses. The findings call to attention the difficulty primary care providers face to detect and diagnose AD at all levels of the healthcare system. This led to the second study. Chronic pain is a common comorbid ailment seen in those with AD and often is a driving factor of patients seeking medical care. In order to understand the pain experience in those with worsening cognition, the second study was a secondary analysis of a crosssectional ageand sex-matched two group cohort study and found that the experience of pain differs between males and females as a measure of cognition worsened suggesting a possible role of pain as a tool to distinguish those at risk for AD. This finding led to the third study, which was a narrative review conducted to describe how alterations in senses have been associated with the diagnosis of AD. The results suggested differences in smell, taste, vision, hearing, and proprioception were associated with different levels of the AD continuum but points out an obvious gap in the literature concerning other senses. This led to the fourth study examining evidence that the ε4 allele of Apolipoprotein E modifies the experience of pain in those individuals carrying the allele such that greater temperatures are required to elicit pain and the experience of that pain is more unpleasant. Additional studies should expand on the results of this pilot study.
The complete chloroplast genome sequence of Spiraea japonica var. acuminata Franch. (Rosaceae)

Abstract Spirea japonica var. acuminata Franch. (Rosaceae) is a Chinese herbal medicine distributed in southwest and east China. The first complete chloroplast genome of Spirea japonica var. acuminata Franch. was assembled and reported in this study. The genome is 153,822 bp in length and contained 125 encoded genes in total, including 80 protein-coding genes, eight ribosomal RNA genes, and 37 transfer RNA genes. The phylogenomic analysis showed that Spirea japonica var. acuminata Franch. was closely related to Spirea blumei, Spirea trilobata, Spirea mongolica and Spirea insularis according to the current sampling extent.
Distribution and Risk Factors of Multidrug-Resistant Bacteria Infection in Orthopedic Patients

Objective To study the distribution and risk factors of multidrug-resistant bacteria (MDROS) infection in orthopedic patients and to provide reference for clinical prevention and control measures. Methods The data of 239 inpatients with orthopedic trauma from June 2019 to December 2020 were selected as the research objects, and the distribution characteristics of MDROS infection were analyzed through the real-time monitoring system of nosocomial infection. Logistic regression analysis was used to screen out the risk factors causing MDROS infection, and the preventive measures were put forward. Results 178 strains of pathogens were isolated from 239 patients, including 53 strains of MDROS, and the detection rate was 29.78%. The main pathogenic bacteria were ESBLs, MRSA, CRAB, CRE, and MDR/PDRPA. The main infection sites of MDROS in orthopedic patients were the respiratory tract and wound. No CRE1 was detected, and 64.39%, 17.42%, and 14.39% of ESBL-producing bacteria, MRSA, and MDR/PDRPA, respectively, were detected. Logistic multivariate analysis showed that the length of hospital stay, antibiotic use time, open injury, and serum albumin level were independent risk factors of MDROS infection in orthopedic trauma patients. Conclusion To prevent MDROS infection in orthopedic patients, we should start from many aspects, focusing on reducing unnecessary hospitalization days, rationally preventing the use of antibacterial drugs, effectively treating basic diseases, etc., timely and effective thorough debridement, strengthening functional training, reducing bed rest, and strengthening targeted monitoring of related infections which are the keys to reduce MDROS infection in orthopedic patients.
Phytochemical study and antioxidant activity of the most used medicinal and aromatic plants in Morocco

ABSTRACT Genus Mentha is represented in Morocco by 10 species including hybrids. The species of this genus are used as herbal teas and condiments, due to their distinct aroma. In this study, infusions of the fresh aerial parts of three Mentha spp. , commonly cultivated and used in Morocco, were evaluated for phenolic contents and antioxidant activity. Antioxidant capacity was assessed by three tests: scavenger activity by DPPH, FRAP (Ferric Reducing Antioxidant Power), and β-carotene bleaching methods. Infusions of M. piperita possess the best ability to reduce DPPH and chelate iron (IC50 = 7.50 ± 0.19 and 5.84 ± 0.36 μg/mL, respectively), while M. spicata shows great efficiency on inhibiting β-carotene (IC50 = 62.67 ± 0.01 μg/mL). The antioxidant capacity was significantly correlated with the total phenolic content and HPLC chromatographic analyses showed the predominance of phenolic acids, especially rosmarinic acid. Furthermore, by GC-MS analysis of Mentha spp. essential oils, eucalyptol, limonene, carvone, and pulegone were identified as predominant components.
Practical Optimization and Game Theory for 6G Ultra-Dense Networks: Overview and Research Challenges

Ultra-dense networks (UDNs) have been employed to solve the pressing problems in relation to the increasing demand for higher coverage and capacity of the fifth generation (5G) wireless networks. The deployment of UDNs in a very large scale has been envisioned to break the fundamental deadlocks of beyond 5G or the sixth generation (6G) networks and deliver many more orders of magnitude gains that today’s technologies achieve. However, the mathematical tool to optimize the system performance under the stringent radio resource constraints is widely recognized to be a formidable challenge. System-level performance optimization of current UDNs are usually conducted by relying on numerical simulations, which are often time-consuming and have become extremely difficult in the context of 6G with extremely high density. As such, there is an urgent need for developing a realistic mathematical model for optimizing the 6G UDNs. In this paper, we introduce challenges as well as issues that have to be thoroughly considered while deploying UDNs in realistic environment. We revisit efficient mathematical techniques including game theory and real-time optimization in the context of optimizing UDNs performance. In addition, emerging technologies which are suitable to apply in UDNs are also discussed. Some of them have already been used in UDNs with high efficiency while the others which are still under investigation are expected to boost the performance of UDNs to achieve the requirements of 6G. Importantly, for the first time, we introduce the joint optimal approach between realtime optimization and game theory (ROG) which is an effective tool to solve the optimization problems of large-scale UDNs with low complexity. Then, we describe two approaches for using ROG in UDNs. Finally, some case study of ROG are given to illustrate how to apply ROG for solving the problems of different applications in UDNs.
Association of high cortisol levels in pregnancy and poor postpartum mental health and altered fetal growth. Analysis in the MAASTHI cohort study, Bengaluru

Background: The role of maternal stress levels on mothers’ mental health and fetal growth has been previously studied. We aim to investigate the association of serum cortisol level in pregnancy with birth outcomes and postpartum depressive symptoms in a public health facility, India. Methods: The current study is a part of the MAASTHI prospective cohort that assessed the relationship between maternal psychosocial environment and adverse neonatal outcomes and maternal mental health after birth. We assessed serum cortisol levels in stored blood samples in 230 pregnant women as a biomarker for stress during pregnancy. Pregnant women between 18 and 45 years of age, presenting at ≥ 14 weeks of gestation and providing voluntary written informed consent were recruited in the study. The Edinburgh Postnatal Depression Scale was used to assess postpartum depressive symptoms and detailed infant anthropometric measurements were carried out at birth. Results: A serum cortisol threshold value of >17.60 IU/L seemed to be associated with postpartum depressive symptoms, low birth weight, and lower weight for length/height z-score. No significant association was found between serum cortisol and infants reduced head circumference, the sum of skinfold thickness, and crown-rump length. Conclusion: Our results support the hypothesis that higher maternal cortisol levels may adversely affect a mother's mental health and birth outcomes. These findings have implications for understanding maternal mental health and early developmental psychopathology, both of which are well-known but understudied. Future studies should explore this association at various gestational ages and use epigenetic markers to determine hypothalamic-pituitary axis (HPA) activity during pregnancy and its effect on birth outcomes.
Does Artificial Intelligence Influence the Operational Performance of Companies? A Study

Business in today's world of Technological era is obviously influenced by the Artificial Intelligence (AI). AI needs to be considered as a significant element in the business development and growth. The study allows to consider AI as the digital technology that must be brought together to facilitate enhanced performance of business organizations. The study aims at a pilot study to assess the influence of AI on the operating performance of the companies and thereby fills the gap in the existing literature. This paper aims at establishing the relationship and exploring the influence of artificial Intelligence on operational performance of companies in different sectors in India. An attempt has been made to assess the difference in operational performance in pre and post AI era. In the study, the artificial Intelligence is measured by the variables such as computer hardware and intangibles (computer software etc.). The operating profit and operating cost have been taken as the proxy variables for operational performance of companies. The study was based on secondary data which has been collected from annual reports of sample companies. The sample companies under study comprise of manufacturing, telecommunication, and IT companies for the time period from the year 2004 to 2018. The statistical software i.e., Ms. Excel and EViews 10 have been used to run the t-test and panel regression model for statistical inferences. The study found that artificial Intelligence has a significant influence on companies operating cost as well as operating profit.
Channel Attention GAN Trained With Enhanced Dataset for Single-Image Shadow Removal

Even today, where many deep-learning-based methods have been published, single-image shadow removal is a challenging task to achieve high accuracy. This is because the shadow changes depending on various conditions such as the target material or the light source, and it is difficult to estimate all the physical parameters. In this paper, we propose a new single-image shadow removal method (Channel Attention GAN: CANet) using two networks for detecting shadows and removing shadows. Intensity change in shadowed regions has different characteristics depending on the wavelength of light. In addition, the image acquisition system of the camera acquires an image in a state where the RGB values influence each other. Therefore, our method focused on the physical properties of shadows and the camera’s image acquisition system. The proposed network has a structure considering the relationship between color channels. When training this network, we modified the color and added some artifacts to the training images in order to make the training dataset more complex. These image processing are based on the shadow model, considering the camera image acquisition system. With these new proposals, our method can remove shadows in all ISTD, ISTD+, SRD, and SRD+ datasets with higher accuracy than the state-of-the-art methods. The code is available on GitHub: https://github.com/ryo-abiko/CANet.
Pulse coding off-chip learning algorithm for memristive artificial neural network

Memristive neural network has attracted tremendous attention since the memristor array can perform parallel multiply-accumulate calculation (MAC) operations and memory-computation operations as compared with digital CMOS hardware systems. However, owing to the variability of the memristor, the implementation of high-precision neural network in memristive computation units is still difficult. Existing learning algorithms for memristive artificial neural network (ANN) is unable to achieve the performance comparable to high-precision by using CMOS-based system. Here, we propose an algorithm based on off-chip learning for memristive ANN in low precision. Training the ANN in the high-precision in digital CPUs and then quantifying the weight of the network to low precision, the quantified weights are mapped to the memristor arrays based on VTEAM model through using the pulse coding weight-mapping rule. In this work, we execute the inference of trained 5-layers convolution neural network on the memristor arrays and achieve an accuracy close to the inference in the case of high precision (64-bit). Compared with other algorithms-based off-chip learning, the algorithm proposed in the present study can easily implement the mapping process and less influence of the device variability. Our result provides an effective approach to implementing the ANN on the memristive hardware platform.
Éclosion d’Escherichia coli O103 associée à du céleri haché chez des personnes hospitalisées à Victoria, Colombie-Britannique, en 2021

Contexte : En avril 2021, une éclosion d’Escherichia coli (E. coli) O103 producteurs de Shiga-toxines (STEC) a été décelée chez des patients de deux hôpitaux de Victoria, en Colombie-Britannique. L’objectif de cette étude est de décrire l’enquête sur cette éclosion et de déterminer les problèmes de salubrité des aliments pour les produits à haut risque préparés pour les populations vulnérables. Méthodes : Des cas confirmés d’E. coli O103 ont été signalés à l’unité des maladies transmissibles d’Island Health. Le laboratoire provincial de santé publique a procédé au séquençage du génome entier des isolats des cas confirmés, conformément à la pratique courante pour les STEC en Colombie-Britannique. Les renseignements sur l’exposition ont été obtenus par l’entremise d’entretiens avec des cas et de l’examen des menus des hôpitaux. Les autorités sanitaires fédérales et locales ont procédé à une inspection de l’usine de transformation de la source suspecte. Résultats : Six cas confirmés d’E. coli O103 ont été décelés, tous liés par le séquençage du génome entier. La majorité des cas étaient des femmes (67 %) et l’âge médian était de 61 ans (intervalle de 24 à 87 ans). Tous les cas confirmés étaient des patients hospitalisés ou en consultation externe dans deux hôpitaux et ont été exposés à du céleri haché cru dans des sandwichs préparés fournis par les services alimentaires de l’hôpital. Un transformateur local fournissait le céleri haché exclusivement aux deux hôpitaux. L’analyse du produit chez le transformateur était peu fréquente, et le rinçage au chlore avait lieu avant le hachage. Nous croyons que la propagation de la contamination résiduelle par E. coli lors du processus de hachage, en plus des abus de température dans les hôpitaux, ont contribué à cette éclosion. Conclusion : Les légumes crus, comme le céleri, sont une source potentielle de STEC et présentent un risque pour les populations vulnérables. Les recommandations issues de cette éclosion comprennent des tests plus fréquents chez le transformateur, une révision du processus de chloration et de hachage et une révision des pratiques des services alimentaires de l’hôpital pour atténuer les abus de température.
Quantitative Probiotic Analysis of Various Kefir Samples

This study assessed whether industrial kefirs have enough probiotic diversity and number, which is important in health benefits, to examine the difference between fruit kefir (FK) and plain kefir (PK) products in terms of probiotic content, and to reveal how much they preserve the probiotic content until the expiry date. The number of total aerobic mesophilic bacteria (TAMB), Lactobacillus, Lactococcus, Leuconostoc, acetic acid bacteria, and yeast was determined using Standard Plate Count Agar, MRS (De Man, Rogosa and Sharpe) agar, M17 agar, MSE (Mayeux, Sandine & Elliker) agar, Acetobacter agar and Sabouraud dextrose agar with chloramphenicol. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF-MS) and API 20AUX were used to identify the isolates. TAMB in PK was 4.3x105 to 2.9x108; in FK, 1.8x106 to 3.6x108. Lactobacillus paracasei, L.casei, Lactococcus lactis, Leuconostoc mesenteroides, Leuconostoc spp, Streptococcus spp, and Candida kefyr were isolated in different combinations and number. Acetic acid bacteria were not isolated. There was no difference in FK and PK of the same trademarks. The number of lactic acid bacteria (LAB) in FK and PK at two different times was similar. Total LAB, lactococci, lactobacilli, and yeast populations increased during storage (p<0.05). Although the number of microorganisms of samples decreased at expiration dates, they still had enough microorganism numbers indicated in a codex.
Essential fatty acids composition and oxidative stability of frozen minced carp meat

ABSTRACT This study aimed to introduce minced common carp (Cyprinus caprio L.) meat as a nutritional valuable by-product from carp filleting. This research was focused on fatty acid composition and considered its sensitivity to oxidation during frozen storage (−20°C). Additionally, copper chloride was used to magnify possible oxidation reactions. A better understanding of minced carp meat quality and deterioration during frozen storage could help to promote the usage of this underestimated by-product. The utilization of this by-product for human consumption could be a useful way to increase the carp aquaculture economy and sustainability. The fatty acid profile showed a favorable content of essential fatty acids, as well as a beneficial ratio of ω-3/ω-6 polyunsaturated fatty acids. Furthermore, during 4 months of frozen storage (−20°C), no significant changes were detected in fatty acid composition. Negligible changes were observed in the oxidation of lipids and proteins. The nutritional value and storage stability of minced carp meat was shown.
Fast Unsupervised Feature Selection With Bipartite Graph and $\ell _{2,0}$ℓ2,0-Norm Constraint

— Since obtaining data labels is a time-consuming and laborious task, unsupervised feature selection has become a popular feature selection technique. However, the current unsupervised feature selection methods are facing three challenges: (1) they rely on a ﬁxed similarity matrix derived from the original data, which will affect their performance; (2) due to the limitation of sparsity, they can only obtain sub-optimal solutions; (3) they have high computational complexity and cannot handle large-scale data. To solve this dilemma, we propose a fast unsupervised feature selection algorithm with bipartite graph and ‘ 2 ; 0 -norm constraint (BGCFS). We use the original data and the selected anchors to construct an adaptive bipartite graph in the subspace, and apply the ‘ 2 ; 0 -norm constraint to the projection matrix for feature selection. In this way, we can update the adaptive bipartite graph and the projection matrix simultaneously, and we can get the feature subset directly, without sorting the features. In addition, we propose an iterative algorithm that can solve the proposed problem globally to obtain a closed-form solution, and we provide a strict proof of convergence for it. Experiments on eight real data sets with different scales show that our method can select more valuable feature subsets more quickly.