Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalized Activity Recognition Using Partially Available Target Data

Recent years have witnessed a growing body of research on autonomous activity recognition models for use in deployment of mobile systems in new settings such as when a wearable system is adopted by a new user. Current research, however, lacks comprehensive frameworks for transfer learning. Specifically, it lacks the ability to deal with partially available data in new settings. To address these limitations, we propose OptiMapper, a novel uninformed cross-subject transfer learning framework for activity recognition. OptiMapper is a combinatorial optimization framework that extracts abstract knowledge across subjects and utilizes this knowledge for developing a personalized and accurate activity recognition model in new subjects. To this end, a novel community-detection-based clustering of unlabeled data is proposed that uses the target user data to construct a network of unannotated sensor observations. The clusters of these target observations are then mapped onto the source clusters using a complete bipartite graph model. In the next step, the mapped labels are conditionally fused with the prediction of a base learner to create a personalized and labeled training dataset for the target user. We present two instantiations of OptiMapper. The first instantiation, which is applicable for transfer learning across domains with identical activity labels, performs a one-to-one bipartite mapping between clusters of the source and target users. The second instantiation performs optimal many-to-one mapping between the source clusters and those of the target. The many-to-one mapping allows us to find an optimal mapping even when the target dataset does not contain sufficient instances of all activity classes. We show that this type of cross-domain mapping can be formulated as a transportation problem and solved optimally. We evaluate our transfer learning techniques on several activity recognition datasets. Our results show that the proposed community detection approach can achieve, on average, 69 percent utilization of the datasets for clustering with an overall clustering accuracy of 87.5 percent. Our results also suggest that the proposed transfer learning algorithms can achieve up to 22.5 percent improvement in the activity recognition accuracy, compared to the state-of-the-art techniques. The experimental results also demonstrate high and sustained performance even in presence of partial data.
Production of Cellulases and Hemicellulases from Cellulytic Fungal Cultures in Submerged Fermentation Using Agricultural Wastes

Untreated, Chemically treated and enzyme treated (Saccharified) sugarcane bagasse, wheat straw and rice straw were evaluated in submerged fermentation for the production of cellulases and hemicellulase by monocultures and mixed cultures of Aspergillus niger and Trichoderma viride in submerged fermentation. Maximum CMCase activity of 6.8 ± 0.5 U/mg and xylanase activity of 105± 2.8 U/mg was obtained in enzyme treated sugarcane bagasse and pretreated wheat straw with mixed culture respectively. However, monocultures of Aspergillus niger and Trichoderma viride showed less enzyme production as compared to mixed cultures in rest of the experimentation.
First TROPOMI Retrieval of Aerosol Effective Height Using O₄ Absorption Band at 477 nm and Aerosol Classification

We retrieved aerosol effective height (AEH) values from TROPOspheric monitoring instrument (TROPOMI) measurements based on the O4 absorption at 477 nm. As the first step in the AEH retrieval algorithm, TROPOMI carbon monoxide and aerosol index products were used for the first time to determine aerosol type. An online radiative transfer calculation method was introduced to the AEH retrieval process. The AEHs were retrieved over northeast Asia (various aerosol types), South Africa (biomass-burning smoke), and the Sahara Desert (dust). The retrieved AEHs were compared with AEHs derived using aerosol extinction profiles from ground and satellite-based lidar measurements. A preliminary evaluation based on comparison with lidar suggests reasonable performance, but shows that cloud masking is the main source of uncertainty. The errors associated with the online-based AEH retrieval algorithm were calculated using synthetic radiance. The total AEH retrieval errors were 0.91 km (0.4 ≤ AOD < 1) and 0.30 km (2 ≤ AOD < 3), highlighting the increased sensitivity of the AEH retrieval algorithm under high AOD conditions. This study demonstrates that the O4 absorption band at 477 nm is applicable to aerosol height retrieval from TROPOMI with a high spatial resolution.
Data Quality Matters: A Case Study on Data Label Correctness for Security Bug Report Prediction

In the research of mining software repositories, we need to label a large amount of data to construct a predictive model. The correctness of the labels will affect the performance of a model substantially. However, limited studies have been performed to investigate the impact of mislabeled instances on a predictive model. To bridge the gap, in this article, we perform a case study on the security bug report (SBR) prediction. We found five publicly available datasets for SBR prediction contains many mislabeled instances, which lead to the poor performance of SBR prediction models of recent studies (e.g., the work of Peters et al. and Shu et al.). Furthermore, it might mislead the research direction of SBR prediction. In this article, we first improve the label correctness of these five datasets by manually analyzing each bug report, and we find 749 SBRs, which are originally mislabeled as Non-SBRs (NSBRs). We then evaluate the impacts of datasets label correctness by comparing the performance of the classification models on both the noisy (i.e., before our correction) and the clean (i.e., after our correction) datasets. The results show that the cleaned datasets result in improvement in the performance of classification models. The performance of the approaches proposed by Peters et al. and Shu et al. on the clean datasets is much better than on the noisy datasets. Furthermore, with the clean datasets, the simple text classification models could significantly outperform the security keywords-matrix-based approaches applied by Peters et al. and Shu et al.
The impact of an N1 lymph node examination in patients with early-stage non-small cell lung cancer: a retrospective cohort study

Background The examination of lymph nodes (LNs) is critical for accurate node staging in patients with non-small cell lung cancer (NSCLC), but a consensus on the examinations of hilar and intrapulmonary (N1 station) LNs has not been reached. This study aimed to evaluate the role of LN dissection and pathological examination of N1 LN stations and their effects on survival in patients with stage IA-IIA NSCLC. Methods Data from patients pathologically staged as IA-IIA who underwent radical surgery and confirmed as lacking LN metastases from January 2008 to March 2018 were retrospectively reviewed. The Kaplan-Meier method was used to determine the overall survival (OS) and disease-free survival (DFS). After propensity score matching (PSM), a Cox model was used to determine the prognostic factors. Results Of the 1,935 patients investigated, the median number of N1 stations examined was 3. Patients with at least 2 N1 stations examined had apparently better OS (P=0.002) and DFS (P=0.001). All patients were divided into patients with 0–1 N1 station examined and patients with 2–5 N1 stations examined. After PSM, the number of N1 stations examined was an independent prognostic factor for DFS (P=0.004). Patients with 2–5 N1 stations examined experienced prolonged DFS (P=0.010). Patients in group 12 experienced prolonged OS (P=0.021) and DFS (P=0.026). Patients in group 13 or 14 experienced prolonged OS (P=0.028). Conclusions A larger extent of N1 station examination was associated with prolonged DFS in patients with stage IA-IIA NSCLC after lobectomy. The dissection and examination of at least 2 N1 stations included LNs from the lobar and segmental drainage fields.
Association Between Tumor Mutation Burden and Prognosis in Lower-Grade Glioma: An Exploratory Study 

Background: The therapeutic efficacy of immune checkpoint inhibitor therapy is highly influenced by tumor mutation burden (TMB). The relationship between TMB and prognosis in lower-grade glioma is still unclear. We aimed to explore the relationships and mechanisms between them in lower-grade glioma.Methods: We leveraged somatic mutation data from The Cancer Genome Atlas (TCGA). Clinical cases were divided into high- and low-TMB groups based on the median of TMB. Infiltrating immune cells were analyzed using CIBERSORT and differential expression analysis between the prognostic groups performed. The key genes were identified as intersecting between immune-related genes. Cox regression and survival analysis were performed on the intersecting genes. A total of 7 hub genes were identified. The effect of somatic copy number alterations (SCNA) of the hub genes on immune cell infiltration was analyzed using TIMER, which was used to determine the risk factors and immune infiltration status in LGG. Subsequently, based on hub genes, a TMB Prognosis Index (TMBPI) model was constructed to predict the risk in LGG patients. Besides, this model was validated using data from TCGA and Chinese Glioma Genome Atlas (CGGA). Results: High-TMB favored worse prognosis (P＜0.001) and macrophage infiltration was an independent risk factor (P＜0.001). In the high-TMB group (P=0.033, P=0.009), the proportion of macrophages M0 and M2 increased and monocytes decreased (P=0.006). Besides, the SCNA of the hub genes affected the level of immune cell infiltration by varying degrees among which IGF2BP3, NPNT, and PLA2G2A had a significant impact. The AUC of the ROC curve at 1-, 3- and 5-years were all above 0.74. Conclusions: This study implies that high-TMB correlated with unfavorable prognosis in lower-grade glioma. And high-TMB may have an impact on prognosis by changing tumor microenvironment, caused by the SCNAs of genes. The TMBPI model accurately predicted prognosis in LGG patients.
Synthesis and Raman characterization of single-walled carbon nanotubes growth by injection chemical vapour deposition

In this paper, single-wall carbon nanotubes growth with the injection chemical vapour deposition method is presented. Ferrocene was dissolved in ethanol and such a solution was sprayed into a heated quartz tube reactor under Ar atmosphere. Raman spectroscopic studies using the intensity ratios between the D and G line reveal a very good crystallinity. The spectra display several lines in the low frequency region associated with the radial breathing mode. Based on our results the method presented appears to be more efficient than other more expensive methods like arc-discharge or laser ablation.
Improvement of the ammonium molybdate spectrophotometric method for phosphorus monitoring in freshwater

Ammonium molybdate spectrophotometry(GB 11893 1989) has been widely used to determine phosphorus for fresh waters, with a detection limit of 0.01 mg / L. However, total phosphorus as well as dissolved phosphorus concentration in most fresh waters are lower than that in southern China. Here, we present an improvement of the method for monitoring low phosphorus con⁃ centrations in fresh waters after extensive testing of standard and field samples. The accuracy and sensitivity improved after changing three manipulations: 1) increase the digested sample volume from 25 ml to 50 ml, without secondary dilution after digestion; 2) prepare the chromogenic agent as follows: mixing 50 ml of 14% H2SO4, 5 ml of antimony potassium tartrate solution, 15 ml of am⁃ monium molybdate solution and 30 ml of ascorbic acid solution together just before addition; 3) set the determination wavelength to 880 nm. The improved method is able to accurately determine phosphorus concentrations of 0.003-0.01 mg / L.
Underlying Genes and Molecular Mechanism of Keloids Investigated by Integrated Bioinformatics Analysis

Background: We aimed to identify the overlapping differentially expressed genes (DEGs) of keloids distinguished from normal scar and normal skin and relevant underlying mechanism using integrated bioinformatics methods.Methods: The expression profiles of 18 keloid samples, 7 normal skin and 5 normal scar, were obtained from the GSE7890, GSE44270, GSE92566, and GSE3189 datasets in the Gene Expression Omnibus database. DEGs were identified using the LIMMA package in R. Gene ontology (GO) functional enrichment analysis was performed using the R software. A DEG-associated protein–protein interaction (PPI) network was constructed using STRING and MCODE was used for module analysis of the PPI network. Moreover, the hub genes were verified by qRT-PCR. The predicted DEGs, their regulatory miRNA and TF regulation network was analyzed using miRnet. Results: A total of 978 common DEGs were identified in the keloid samples. Genes with more than 45 interaction degrees, including neuropeptide Y (NPY), opioid receptor mu 1 (OPRM1), cholinergic receptor muscarinic 2 (CHRM2), and proopiomelanocortin (POMC), were found in the PPI network. Hsa-miR-335 and Sp1 as upstream-regulators regulated CHRM2, NPY, and POMC. Functional enrichment analysis revealed that hub genes were commonly enriched in the “G protein-coupled receptor signaling pathway” GO_BP termConclusion: Taken together, CHRM2, NPY, POMC, and OPRM1 potentially have crucial roles in keloid disease. Furthermore, miR-335 and Sp1 are potential targets for preventing keloid formation.
Antagonistic activity of lactic acid bacteria isolated from natural whey starters for water-buffalo Mozzarella cheese manufacture

Thirty-four Streptococcus thermophilus, 72 lactococci, 27 enterococci, 30 lactobacilli and 30 leuconostocs isolated from natural whey starters for water-buffalo Mozzarella cheese manufacture were screened for antagonistic activity. Antagonistic activity due to acidification was widespread among lactobacilli. No strain among these microorganisms as well as among Str. thermophilus or leuconostocs produced bacteriocins. Lactococci (16.7%) and enterococci (40.7%) were found to produce bacteriocin-like inhibitors in solid media. Four and three different types of bacteriocins, respectively, produced by lactococci and enterococci both on solid and liquid media could be differentiated by cluster analysis considering the inhibitory spectrum against 37 indicators, including lactic acid bacteria, spoilage and pathogenic organisms, cross-inhibition patterns, and sensitivity to heat and proteolytic enzymes. Some substances clearly differed from known bacteriocins. The lactococcins found are of great interest as potential food preservatives because of their activity towards Clostridium spp. Some enterocins showed interesting activity towards Listeria monocytogenes. Media for optimal bacteriocin production varied among strains. In the case of lactococcal strain 140, a surprisingly highly active bacteriocin was produced under all the conditions assayed.
An in-depth evaluation of personal barriers to technology adoption in irrigated agriculture in South Africa

Increasing pressure on water resources is driving the development of technology to improve water-use efficiency in irrigation. Uptake of these technological advances are essential to ensure long-term water security in catchments, particularly in water-scarce regions and where agricultural activities and urban centres compete for the same resources. Research suggests that uptake of technology lags far behind the development of new products. The study presented in this paper interviewed 29 commercial farmers from the water-scarce Central Breede River Valley in South Africa to investigate their reasons behind the use or non-use of irrigation technology for scheduling, and in particular the uptake of a free, government-funded remote-sensing service called FruitLook. Evaluating the uptake of a free service eliminates monetary cost as one key barrier to uptake. In-depth interviews revealed a high uptake of technology (83%), but use of only one type – soil water measurement. Among the farmers that use water-use efficiency technology, 78% use the same probe service provider. Perceived accuracy and ease of use, as well as personalised after-sales service are the key reasons for this probe’s popularity. While 86% of the farmers have heard about FruitLook, only one farmer uses it for irrigation purposes. The non-use of the free service can mainly be attributed to the time cost associated with the product’s initial set-up, use, and interpretation of information. The study revealed that the integration of information from various products is essential for farmers – too much information in different formats is too time-costly. Developers of new technology should focus on these latter two findings to improve the likelihood of new product uptake.
Bifunctional Ruthenium Nanoparticle-SILP Catalysts (RuNPs@SILP) for the Hydrodeoxygenation of Eucalyptol under Batch and Continuous Flow Conditions

Ruthenium nanoparticles immobilized on acid-functionalized supported ionic liquid phases (RuNPs@SILPs) were applied in the hydrodeoxygenation of eucalyptol. RuNPs@SILPs were highly effective and selective catalysts toward the synthesis of the deoxygenation product p-menthane, in which the catalytic properties of the bifunctional catalysts was dependent on the acidity of the SILP. Integration of the acid and metal catalytic species onto a single-support allowed for the implementation of RuNPs@SILP into a continuous flow process. The modular synthesis of these bifunctional catalysts also allowed for an investigation into the roles of the individual and combined acid and metal functionalities in the hydrodeoxygenation of eucalyptol.
Financial Performance Appraisal Using Economic Values Added in Emerging Markets: Evidence from Egyptian Listed Firms

The effectiveness of conventional accounting methods used for firm financial performance measurement and analysis has been criticized. Theorists started to put forward modern financial performance measures although their significance is still under examination by the researchers. Therefore, this study aims to investigate the impact of three contemporary economic based measures; economic value added (EVA), refined economic value added (REVA) and economic value added momentum (EVA Momentum) on firm financial performance in addition to finding out the economic value added based measure with the highest explanatory power relevant to firm financial performance via conducting relative and incremental information content analyses. Data were collected for companies listed in the Egyptian Stock Exchange over the period 2010-2019 excluding the financial sector. Generalized Least Squares (GLS) regression is conducted using the statistical package of EViews-version 10. The results showed significant impact of all the economic value added measures on firm financial performance. In addition, it was found that REVA could be considered as the most effective economic measure in improving and explaining the financial performance.
Adoption of the World Health Organization access, watch reserve index to evaluate and monitor the use of antibiotics at a tertiary care hospital in India

Background: The World Health Organization classification of antibiotics classifies key antibiotics into access, watch, and reserve (AWaRe) categories. Categorization provides a novel metrics called “AWaRe index” to study and monitor antibiotic consumption globally and within the same setting over time. Aim: The aim of this study is to compare the use of antibiotics in 2 years using the AWaRe index tool. Materials and Methods: A retrospective study was conducted in SMS Hospital, Jaipur to collect data regarding total antibiotics consumed between January 2017 and December 2018 from drug distribution centers in hospital premises using the AWaRe classification. Data were then compared on yearly basis. Results: In 2017, 53.31% of antibiotics consumed belonged to access, 40.09% to watch, and 3.40% to reserve category, respectively, as compared to 41.21%, 46.94%, and 8.15%, respectively, in 2018. Conclusion: Increased use of watch (17%) and reserve antibiotics (140%) over 1 year with the same infection scenario in a similar setting indicates resistance in evolution.
Isolation and identification of PAH-degradating strains and their degradation capability

Fluorenen, phenathalene, anthracene and pyrene were used as carbon and energey source to isolate and screen PAHs-degrading strains. Fourteen strains capable of degrading PAHs were isolated from the petroleum in seawater contaminated. Analyses was done by HPLC, strain No.10 showed the strongest degrading ability in the mixing system of fluorene、phenathalene、anthracene and pyrene. Its degradation performance and growth curve were tested. The degradation rates of fluorenen, phenathalene, anthracene and pyrene were 95.27%、90.46%、28% and 80% respectively in the mixing system within 30d. Under the condition of single PAH existence,the degradation rates of fluorene、phenathalene and anthracene by strain No.10 increased to 98.91%、94.32%、52.17% respectively while the degradation rate of pyrene decreased to 62.47%. Compared to the mixing system, the logarithmic growth phase of strain No.10 was shortened one third in single PAH system. Based on the results of physio-biochemical identification and phylogenetical analyses of 16S rDNA sequence, strain No.10 was belonged to Pseudomonas sp,named as Pseudomonas sp FAP10.
Mobility-Driven and Energy-Efficient Deployment of Edge Data Centers in Urban Environments

Multi-access edge computing (MEC) brings storage and computational capabilities at the edge of the network into so-called edge data centers (EDCs) to better support low-latency applications. In this paper, we tackle the problem of EDC deployment in urban environments. Previous research on mobile phone data has exposed a strong correlation between the demand for mobile communications and the urban tissue. For example, joint analysis of mobile data and vehicle traffic can be extrapolated to estimate demand for transportation and human activities, thereby inferring the land use of the area where such activities take place. Our work takes into account the mobility of citizens and their spatial patterns to estimate the optimal placement of MEC EDCs in urban environments, in order to minimize outages while guaranteeing energy-efficiency. This is achieved by modeling both the energy consumption attributed to network components (e.g., base stations) and computing components (e.g., servers). We propose and compare three heuristics and show that mobility-aware deployments achieve superior performance. The results are obtained with a custom-designed simulator able to operate over large-scale realistic urban environments.