Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Volatile Components on Mixing State and Size Distribution of Individual Black Carbon Aerosols

In this study, the effects of different volatile components on the mixing state and size distribution of ambient individual black carbon (BC) aerosols were measured using thermo-denudation with single-particle soot photometry and single-particle aerosol mass spectrometry from September 9 to September 27, 2018, in Shanghai, China. The volatility, chemical composition, size distribution, and core-shell structure of BC aerosol were also analysed. To understand the influence of heating temperature on the BC core-shell structure, the mixing state of ageing particles at room temperature (25 ° C) and the mixing state of residual material heated to 150 ° C and 300 ° C were analysed. BC was internally mixed with secondary components, such as ammonium nitrate, ammonium sulphate, a small amount of sodium nitrate, potassium nitrate, sodium sulphate and potassium sulphate, and other nonvolatile components. At 300 ° C, the secondary components with low volatility escaped, resulting in a thinner BC coating. However, several low-volatile non-BC substances remained, namely organic species, potassium chloride, potassium nitrate, and other nonvolatile substances. The extremely strong photochemical reaction may have produced large amounts of secondary organic carbon on the BC surface in the afternoon, with the high temperature volatilising the whole particle. However, fresh BC particles emitted from traffic during morning rush hour had ultralow volatility, with relatively fewer low-volatile components on the surface.
Nutrition Risk, Resilience and Effects of a Brief Education Intervention among Community-Dwelling Older Adults during the COVID-19 Pandemic in Alberta, Canada

Up to two-thirds of older Canadian adults have high nutrition risk, which predisposes them to frailty, hospitalization and death. The aim of this study was to examine the effect of a brief education intervention on nutrition risk and use of adaptive strategies to promote dietary resilience among community-dwelling older adults living in Alberta, Canada, during the COVID-19 pandemic. The study design was a single-arm intervention trial with pre–post evaluation. Participants (N = 28, age 65+ years) in the study completed a survey online or via telephone. Questions included the Brief Resilience Scale (BRS), SCREEN-14, a brief poverty screen, and a World Health Organization-guided questionnaire regarding awareness and use of nutrition-related services and resources (S and R). A brief educational intervention involved raising participant awareness of available nutrition S and R. Education was offered via email or postal mail with follow-up surveys administered 3 months later. Baseline and follow-up nutrition risk scores, S and R awareness and use were compared using paired t-test. Three-quarters of participants had a high nutrition risk, but very few reported experiencing financial strain or food insecurity. Those at high nutrition risk were more likely to report eating alone, compared to those who scored as low risk. There was a significant increase in awareness of 20 S and R as a result of the educational intervention, but no change in use. The study shows increasing individual knowledge about services and resources in the community is not sufficient to change use of these services or improve nutrition risk.
Efficient Histogram-Based Gradient Boosting Approach for Accident Severity Prediction With Multisource Data

Many people lose their lives in road accidents because they do not receive timely treatment after the accident from emergency medical services; providing timely emergency services can decrease the fatality rate as well as the severity of accidents. In this study, we predicted the severity of car accidents for use by trauma centers and hospitals for emergency response management. The predictions of our model could be used to decide whether an ambulance unit should be dispatched to the crash site or not. This study used histogram-based gradient boosting (HistGBDT), a modification of the gradient boosting (GBDT) classifier that accelerates the learning process and increases a model’s prediction power. The HistGBDT model was compared with seven state-of-the-art machine learning models: logistic regression, multilayer perceptron, random forest, extremely randomized trees, bagging, AdaBoost, and GBDT. The experiments were conducted on French accident data from 2005 to 2018. The HistGBDT model, with an overall accuracy of 82.5%, recall of 76.7%, and precision of 81.9%, outperformed other models. An analysis of feature importance indicated that safety equipment was the most important feature and vehicle category, department, localization, and region were other significant features. The Fβ measure (i.e., the weighted harmonic mean of recall and precision) was optimized with different weights on recall for the four best performing models to compare the tradeoff between the two crucial performance measures.
A 60‐GHz low‐noise amplifier with +7.258‐dBm third‐order input intercept point using current reuse feedforward distortion cancellation for 5G emerging communication

In the ever‐growing 5G technology, the millimetre‐wave (mmWave) frequency spectrum is the inevitable mainstream for high‐speed wireless communication. The wide‐ranging spectrum bandwidth of the mmWave frequency attracts interest in radio frequency (RF) front‐end design which will be very much beneficial for security and cellular‐based applications such as intersatellite communication and automotive vehicle‐to‐vehicle communication. The RF front end requires low‐power and low‐noise receiver architecture for effective satellite communication, and it needs high throughput with maximum data transfer for cellular‐based applications. To address these challenges, this paper presents a novel low‐noise amplifier (LNA) operating from 57.12‐ to 66.25‐GHz frequency. The design is implemented using UMC 65‐nm CMOS process technology, and the post‐layout simulations are carried out using the Cadence Spectre RF tool. At 60 GHz, the LNA exhibits a power gain of 15.46 dB, minimum noise figure (NF) of 1.32 dB while consuming 6.59 mW from a 1.2‐V supply. The LNA employs the current reuse feedforward distortion cancelation technique to improve the third‐order input intercept point (IIP3) to +7.258 dBm. The proposed LNA provides a relatively better figure of merit compared to some of the recently proposed mmWave LNA architectures.
Functional Load and Pronunciation Instruction in the Iraqi EFL Context

Though it is crucial for the development of ESL / EFL learners' speech perception and production, pronunciation is almost neglected in the English textbooks used at Iraqi schools. This negligence is both in terms of content and instruction. In such textbooks, very little pronunciation materials can be found and very limited time for practice is available for the teacher. This article aims to overcome the above shortcoming of English textbooks at Iraqi schools by setting priorities in English pronunciation based on the Functional Load principle. For this aim, an investigation is carried out following a qualitative methodology. The qualitative results have identified a set of phonemic priorities for the teaching of English pronunciation in Iraqi schools. These priorities represent high and low phonemic loads which can enrich the English textbooks in Iraqi schools and guide in some way the teaching of pronunciation.
CP-Link: Exploiting Continuous Spatio-Temporal Check-In Patterns for User Identity Linkage

Driven by the large amount of spatio-temporal data obtained from location-based social networks, the implementation of cross-domain user linkage, also known as the User Identity Linkage (UIL), has attracted increasing research attentions. While most of the existing UIL works discretize the spatio-temporal sparse data when identifying encountering or co-located events for UIL, user’s distinctive behavior patterns implicit in the “check-in” spatio-temporal data with continuous nature pave the way for enhancing UIL performance. In this paper, we propose an approach dubbed CP-Link that exploits user behavior patterns in a continuous way. In CP-Link, the continuous space is divided into irregularly shaped stay regions, and a continuous time-based improved dynamic time warping (IDTW) method is proposed to calculate the similarity. To bridge the gap between the ideal scenario with ample records and the reality with sparse data, we adopt the user-associated location frequent pattern (LFP) model to compensate for the sparse deficiency. Extensive experiments conducted on real-world datasets demonstrate the effectiveness and superiority of CP-Link, which outperforms the state of the arts by more than 20% in terms of the AUC.
Molecular Docking Analysis of Azadirachta Indica Phytocompounds against Androgen Receptor Protein for the Treatment of Prostate Cancer

In affluent countries, prostate cancer (PCa), one of the leading causes of male death, continues to be a serious medical burden. Androgen stimulates growth and advancement primarily through the androgen receptor. PCa is thought to include some molecular pathways in its growth and progression. In this work, several bioactive components of the neem plant (Azadirachta indica) were studied using the molecular docking analysis to identify and create novel medicines or inhibitors that can target androgen receptors for the treatment of prostate cancer. Azadirachta indica has been demonstrated to have therapeutic potential in the treatment of cancer. We investigated the molecular interactions of 30 bioactive chemicals in Azadirachta indica leaves against prostate cancer in this work. Androgen receptor protein is implicated in the genesis and progression of prostate cancer. A screening technique was used to perform molecular docking. PyMOL v 1.1r1 and Discovery studio 2020 were used to execute auto dock vina of the PyRx v 0.8 docking software and visualization, respectively. The SwissADME online software was used to assess the hit compounds’ physicochemical attributes. The selected ligands demonstrated good inhibitory action in docking tests with the target proteins. The binding affinities range from -6.0 to -8.5 (kcal/mol) in the docking results of the selected hits, which is greater than the typical medicines employed. Among the identified ligands, Etofenprox had the greatest binding score with the target protein, whereas 4-hydroxystyrene, 3-Methylbenzaldehyde, and Limonene had the lowest binding energy with the target protein.
PNN for indoor and outdoor scene recognition

A method of indoor and outdoor scene recognition using Probabilistic Neural Network(PNN) is proposed for solve the problems of low accuracy, poor reliability and weak stability in indoor and outdoor scene recognition. The built-in WiFi module, Global Positioning System(GPS) module and BeiDou satellite navigation system(BDS) module of the smartphone was utilized to collect data such as WiFi signal strength and the number of visible satellites. According to the difference of the collected data in indoor and outdoor scenes, input the PNN for training to obtain the indoor and outdoor scene recognition model The model is tested in different environments. The experimental results show that the recognition accuracy of the PNN is higher than the nearest neighbor algorithm (knn), Support Vector Machine (svm), and random forest algorithm. It can reach 92%, and the recognition accuracy is relatively high. It can be used as a reference for constructing indoor and outdoor seamless positioning system.
Novel Design of Neuropeptide-Based Drugs with β-Sheet Breaking Potential in Amyloid-Beta Cascade: Molecular and Structural Deciphers

Our work discusses the investigation of 75 peptide-based drugs with the potential ability to break the β-sheet structures of amyloid-beta peptides from senile plaques. Hence, this study offers a unique insight into the design of neuropeptide-based drugs with β-sheet breaker potential in the amyloid-beta cascade for Alzheimer’s disease (AD). We started with five peptides (15QKLVFF20, 16KLVFF20, 17LVFF20, 16KLVF19 and 15QKLV18), to which 14 different organic acids were attached at the N-terminal. It was necessary to evaluate the physiochemical features of these sequences due to the biological correlation with our proposal. Hence, the preliminary analysis of different pharmacological features provided the necessary data to select the peptides with the best biocompatibility for administration purposes. Our approaches demonstrated that the peptides 17LVFF20, NA-17LVFF20, 16KLVF19 and NA-16KLVF19 (NA-nicotinic acid) have the ability to interfere with fibril formation and hence improve the neuro and cognitive functions. Moreover, the peptide conjugate NA-16KLVF19 possesses attractive pharmacological properties, demonstrated by in silico and in vitro studies. Tandem mass spectrometry showed no fragmentation for the spectra of 16KLVF19. Such important results suggest that under the action of protease, the peptide cleavage does not occur at all. Additionally, circular dichroism confirmed docking simulations and showed that NA-16KLVF19 may improve the β-sheet breaker mechanism, and thus the entanglement process of amyloid-beta peptides can be more effective.
miR-184-5p inhibits cell proliferation, invasion and predicts prognosis of clear cell renal cell carcinoma by targeting NUS1 dehydrodolichyl diphosphate synthase subunit: Results from large-scale comprehensive identification and validation

Clear cell renal cell carcinoma (ccRCC) has become a common malignant cancer with increasing incidence rate and high recurrence risk in genitourinary oncology around the world. Recently, miRNAs were identified to affect pathogenesis, development, molecular functions, and prognosis of ccRCC. In this study, microRNA-184-5p (miR-184-5p) was identified from three independent ccRCC cohorts and was determined as a significantly distinct prognostic biomarker. Relative miR-184-5p expression was found in A-498 and 786-O ccRCC cells compared with HK-2 cells. After ccRCC cells were transfected with miR-184-5p mimics or inhibitor, biological abilities of miR-184-5p in tumor cell proliferation, cycle, apoptosis and invasion were determined. Additionally, we confirmed the direct relationship between miR-184-5p and NUS1 dehydrodolichyl diphosphate synthase subunit (NUS1) by using the Luciferase reporter and rescue assays. These results indicated that the expression level of miR-184-5p in human ccRCC cells and tissues was reduced, and the up-regulation of miR-184-5p regulated A-498 and 786-O cell proliferation, invasion and apoptosis by directly targeting NUS1. These findings may provide new theoretical targets for treatment strategies and drug development of ccRCC.
An Overview on Deep Learning Techniques for Video Compressive Sensing

The use of compressive sensing in several applications has allowed to capture impressive results, especially in various applications such as image and video processing and it has become a promising direction of scientific research. It provides extensive application value in optimizing video surveillance networks. In this paper, we introduce recent state-of-the-art video compressive sensing methods based on neural networks and categorize them into different categories. We compare these approaches by analyzing the networks architectures. Then, we present their pros and cons. The general conclusion of the paper identify open research challenges and point out future research directions. The goal of this paper is to overview the current approaches in image and video compressive sensing and demonstrate their powerful impact in computer vision when using well designed compressive sensing algorithms.
Wound-Microenvironment Engineering through Advanced-Dressing Bioprinting

In patients with comorbidities, a large number of wounds become chronic, representing an overwhelming economic burden for healthcare systems. Engineering the microenvironment is a paramount trend to activate cells and burst-healing mechanisms. The extrusion bioprinting of advanced dressings was performed with novel composite bioinks made by blending adipose decellularized extracellular matrix with plasma and human dermal fibroblasts. Rheological and microstructural assessments of the composite hydrogels supported post-printing cell viability and proliferation over time. Embedded fibroblasts expressed steady concentrations of extracellular matrix proteins, including type 1, 3 and 4 collagens and fibronectin. ELISA assessments, multiplex protein arrays and ensuing bioinformatic analyses revealed paracrine activities corresponding to wound-healing activation through the modulation of inflammation and angiogenesis. The two modalities of advanced dressings, differing in platelet number, showed differences in the release of inflammatory and angiogenic cytokines, including interleukin 8 (IL-8), monocyte chemotactic protein 1 (MCP-1), vascular endothelial growth factor (VEGF) and hepatocyte growth factor (HGF). The conditioned media stimulated human-dermal-cell proliferation over time. Our findings open the door to engineering the microenvironment as a strategy to enhance healing.
Improving Efficiency for Retail Warehouse Using Data Envelopment Analysis

Warehouse has an important role in supply chain management and has many complex activities that require special attention. This study aims to improve warehouse efficiency performance. Data Envelopment Analysis (DEA) method is employed to obtain the level of efficiency and benchmarking on five indicators, namely financial, productivity, utilization, quality, and cycle time along with five business processes in warehousing, i.e. receiving, put away, storage, order picking, and shipping. The decision making unit is a warehouse in four retailers in Yogyakarta province, in Indonesia. The input and output variables are selected based on the highest priority weight using the Analytical Hierarchy Process (AHP). The most important variable for receiving is productivity (receipt per man-hour), variable for put away is cycle time (put away cycle time), variable for storage is utilization (% location and cube occupied), variable for order picking is cycle time (order picking cycle time) and variable for shipping is productivity (order prepared for shipment per man-hour). The research results show that the benchmarking model with DEA can be used to increase warehouse efficiency performance by up to 22% by increasing receiving and shipping productivity, increasing storage utilization and reducing cycle time at put away and order picking.
Protective potential of aqueous extract from Dichrocephala integrifolia (Linn. f.) O. Kuntze (Asteraceae) on blood and biochemical constituents in ethanol-induced hepato-nephrotoxicity in rats

Dichrocephala integrifola is a plant used empirically to manage hepatic affections. This study evaluates the preventive effects of the aqueous leave extract of Dichrocephala integrifolia on the blood and biochemical parameters in ethanol-induced hepato-nephrotoxicity in rats. To achieve this, the animals received different treatments (p.o): a normal control group and a negative group receiving respectively distilled water (10 mL/kg) and ethanol 40° (4 g/kg) with distilled water simultaneously; 3 other groups receiving the two doses of the plant extract or Simepar (100 mg/kg) and ethanol simultaneously. At the end of the experimental period, haematological and biochemical analyses were performed. The ingestion of ethanol provoked a decrease (at least p< 0.01) in the liver relative weight. The concentration of RBC, HGB, HCT; MCV; PLT; WBC; LYMP; NEUT; MONO dropped while the levels of WBC raised. Ethanol administration caused an ( p< 0.001) increase in malondialdehyde even SOD and catalase activities whereas a decreased in nitrites and GSH was noticed. Transaminase levels, ALP, bilirubin and lipid profile were significantly altered associated with a micro-architectural disorganization materialized by hepatic cytolysis, diffuse infiltration of periportal inflammatory cells and dilation of sinusoids capillaries as well as an architectural disorganization of the kidney parenchyma with leukocyte infiltration. The extract protected the alteration of these parameters. It prevented the amplification of alterations induced by ethanol on hepatic and renal micro-architecture tissue whatever the doses. Data suggest the antioxidant and hepatoprotective activities of the extract, attesting the use of this extract in the treatment of liver affection in traditional medicine.
A Two-Population Cooperative Multiobjective Differential Evolution Algorithm for Batching Scheduling Problem

Batch processing machine (BPM) scheduling problem is a NP hard problem for it includes machine allocation, job grouping, and batch scheduling. In this paper, to address the BPM scheduling problem with unrelated parallel machine, a multiobjective algorithm based on multipopulation coevolution is proposed to minimize the total energy consumption and the completion time simultaneously. Firstly, the mixed integer programming model of the problem is established, and four heuristic decoding rules are proposed. Secondly, to improve the diversity and convergence of the algorithm, the population is divided into two populations: each of the populations evolves independently by using different decoding rules, and the two populations will communicate through a common external archive set every certain number of generations. Thirdly, an initialization strategy and a variable neighborhood search algorithm (VNS) are proposed to improve the overall performance of the algorithm. Finally, in order to evaluate the proposed algorithm, a large number of comparative experiments with the state-of-the-art multiobjective algorithms are carried out, and the experimental results proved the effectiveness of the proposed algorithm.
A dynamic sourcing strategy considering supply disruption risks

This study presents a formulation that supports decision-making by determining the optimal number of standby suppliers required to respond to supply failure risks. The problem of supply failure is modelled through a standby approach, in which failure is time-dependent. The probabilities of supply interruption, financial loss caused by supply failure, and operating cost of working with suppliers are modelled to yield the expected total cost, which enables organisations to determine the optimal number of suppliers. Two possible modes of substitution failure are considered in the standby model to enhance the analysis. A set of sensitivity analyses are performed for several input parameters to illustrate the model’s behaviour. The analysis provides an optimal sourcing strategy depending on a combination of supply risk, operational cost vs. loss ratio and length of the supply period. The proposed model indicates the benefits of cost savings, unlike other dynamic models that use multiple suppliers simultaneously.