Miracle-dz/UniFAR-FTUs huggingface dataset:
Extent of Use of Continuous Improvement Process in Engineering Asset Management Practices in Oil and Gas Service Industry in Nigeria

Abstract A systematic approach to realising the extent of problems associated with assets and providing mechanisms for improvement is offered by asset management. Continuous improvement process is an approach to improving organisational performance with small incremental steps, over time. The concept of continuous improvement has been adopted in advanced nations. In the Nigerian oil industry, there are limited information on application and utilisation of continuous improvement process as a tool in engineering asset management. The study examined the extent of the use of continuous improvement process in engineering assets management in the oil and gas industry in Nigeria. It covered 60 oil and gas companies in Nigeria where data were collected through questionnaire and analysed through descriptive and inferential statistics. Continuous improvement process was recently embraced and majority of its initiatives had come from within the people in engineering and maintenance department though other sections within organisation also embraced the approach. The concept has also been used in some stages of facility lifecycle like operation and maintenance, productions or construction of engineering assets.
Quercetin Improves Pulmonary Function and Prevents Emphysema Caused by Exposure to Cigarette Smoke in Male Mice

Chronic obstructive pulmonary disease (COPD) is the major cause of morbidity and mortality worldwide, and cigarette smoke is a key factor in the development of COPD. Thus, the development of effective therapies to prevent the advancement of COPD has become increasingly essential. We hypothesized that quercetin protects lungs in mice exposed to long-term cigarette smoke. Thirty-five C57BL/6 mice were exposed to cigarette smoke (12 cigarettes per day) for 60 days and pretreated with 10 mg/kg/day of quercetin via orogastric gavage. After the experimental protocol, the animals were euthanized and samples were collected for histopathological, antioxidant defense, oxidative stress and inflammatory analysis. The animals exposed to cigarette smoke showed an increase in respiratory rate and hematological parameters, cell influx into the airways, oxidative damage and inflammatory mediators, besides presenting with alterations in the pulmonary histoarchitecture. The animals receiving 10 mg/kg/day of quercetin that were exposed to cigarette smoke presented a reduction in cellular influx, less oxidative damage, reduction in cytokine levels, improvement in the histological pattern and improvement in pulmonary emphysema compared to the group that was only exposed to cigarette smoke. These results suggest that quercetin may be an agent in preventing pulmonary emphysema induced by cigarette smoke.
Epigenetic Therapies and Biomarkers in Breast Cancer

Simple Summary Epigenetic therapies are promising agents for overcoming clinical resistance to conventional treatments in breast cancer. In the assessed trials, the use of epigenetic therapies for the management of breast cancer has not translated from the pre-clinical to clinical setting. However, novel epigenetic treatments remain promising, especially in the era of personalized medicine and improved genomic evaluation. The aim of our review was to assess the published evidence for the clinical utility of epigenetic therapies and their biomarkers in breast cancer and the potential value of epigenetic biomarkers to direct clinical management. Abstract Epigenetic therapies remain a promising, but still not widely used, approach in the management of patients with cancer. To date, the efficacy and use of epigenetic therapies has been demonstrated primarily in the management of haematological malignancies, with limited supportive data in solid malignancies. The most studied epigenetic therapies in breast cancer are those that target DNA methylation and histone modification; however, none have been approved for routine clinical use. The majority of pre-clinical and clinical studies have focused on triple negative breast cancer (TNBC) and hormone-receptor positive breast cancer. Even though the use of epigenetic therapies alone in the treatment of breast cancer has not shown significant clinical benefit, these therapies show most promise in use in combinations with other treatments. With improving technologies available to study the epigenetic landscape in cancer, novel epigenetic alterations are increasingly being identified as potential biomarkers of response to conventional and epigenetic therapies. In this review, we describe epigenetic targets and potential epigenetic biomarkers in breast cancer, with a focus on clinical trials of epigenetic therapies. We describe alterations to the epigenetic landscape in breast cancer and in treatment resistance, highlighting mechanisms and potential targets for epigenetic therapies. We provide an updated review on epigenetic therapies in the pre-clinical and clinical setting in breast cancer, with a focus on potential real-world applications. Finally, we report on the potential value of epigenetic biomarkers in diagnosis, prognosis and prediction of response to therapy, to guide and inform the clinical management of breast cancer patients.
Enhancing the implementation of the Making Every Contact Count brief behavioural intervention programme in Ireland: protocol for the Making MECC Work research programme

Background: Brief behavioural interventions offered by healthcare professionals to target health behavioural risk factors (e.g. physical activity, diet, smoking and drug and alcohol use) can positively impact patient health outcomes. The Irish Health Service Executive (HSE) Making Every Contact Count (MECC) Programme supports healthcare professionals to offer patients brief opportunistic behavioural interventions during routine consultations. The potential for MECC to impact public health depends on its uptake and implementation. Aim: This protocol outlines the ‘Making MECC Work’ research programme, a HSE/Health Behaviour Change Research Group collaboration to develop an implementation strategy to optimise uptake of MECC in Ireland. The programme will answer three research questions: (1) What determines delivery of MECC brief interventions by healthcare professionals at individual and organisational levels? (2) What are patient attitudes towards, and experiences of, receiving MECC interventions from healthcare professionals? (3) What evidence-informed implementation strategy options can be consensually developed with key stakeholders to optimise MECC implementation? Methods: In Work Package 1, we will examine determinants of MECC delivery by healthcare professionals using a multi-methods approach, including: (WP1.1) a national survey of healthcare professionals who have participated in MECC eLearning training and (WP1.2) a qualitative interview study with relevant healthcare professionals and HSE staff. In Work Package 2, we will examine patient attitudes towards, and experiences of, MECC using qualitative interviews. Work Package 3 will combine findings from Work Packages 1 and 2 using the Behaviour Change Wheel to identify and develop testable implementation strategy options (WP 3.1). Strategies will be refined and prioritised using a key stakeholder consensus process to develop a collaborative implementation blueprint to optimise and scale-up MECC (WP3.2). Discussion: Research programme outputs are expected to positively support the integration of MECC brief behaviour change interventions into the Irish healthcare system and inform the scale-up of behaviour change interventions internationally.
Attentive Multi-Task Prediction of Atmospheric Particulate Matter: Effect of the COVID-19 Pandemic

Air pollution, especially the continual increase in atmospheric particulate matter (PM), is a global environmental challenge. To reduce the PM concentration, a remarkable amount of machine learning-based research has been proposed. However, increasing the accuracy of the predictions and providing clear interpretations of the predictions are challenging. In particular, no studies have addressed models that predict and interpret PM before and after the COVID-19 pandemic. In this paper, we present a two-step predictive and explainable model to obtain insights into reducing PM. We first use attentive multi-task learning to predict the air quality of cities. To accurately predict the concentration of particles with sizes of ~10 μm or ≤2.5 μm (PM10 and PM2.5, respectively), we demonstrate a performance difference between single-task and multi-task learning, as well as among the state-of-the art methods. The proposed attentive model with multi-task learning outperformed the others in terms of accuracy performance. We then used Shapley additive explanations, a representative explainable artificial intelligence framework, to interpret and determine the significance of features for predicting PM10 and PM2.5. We demonstrated the superiority of the proposed approach in predicting and explaining both PM10 and PM2.5 concentrations, and observed a statistically significant difference in air pollution before and after the COVID-19 pandemic.
Effect of Different Modalities of Purgative Preparation on the Diagnostic Yield of Small Bowel Capsule for the Exploration of Suspected Small Bowel Bleeding: A Multicenter Randomized Controlled Trial

INTRODUCTION: The aim of our study was to compare clear liquid diet with 2 different polyethylene glycol (PEG)-based bowel preparation methods regarding diagnostic yield of small bowel capsule endoscopy (SBCE) in patients with suspected small bowel bleeding (SBB). METHODS: In this prospective multicenter randomized controlled trial, consecutive patients undergoing SBCE for suspected SBB between September 2010 and February 2016 were considered. Patients were randomly assigned to standard regimen, that is, clear fluids only (prep 1), standard regimen plus 500 mL PEG after SBCE ingestion (prep 2), or standard regimen plus 2 L PEG plus 500 mL PEG after SBCE ingestion (prep 3). The primary outcome was the detection of at least one clinically significant lesion in the small bowel. The quality of small bowel cleansing was assessed. A questionnaire on the clinical tolerance was filled by the patients. RESULTS: We analyzed 834 patients. No significant difference was observed for detection of P1 or P2 small bowel lesions between prep1 group (40.5%), prep 2 group (40.2%), and prep 3 group (38.5%). Small bowel cleansing was improved in prep 2 and 3 groups compared with that in prep 1 group. Compliance to the preparation and tolerance was better in prep 2 group than in prep 3 group. DISCUSSION: Small bowel purgative before SBCE allowed better quality of cleansing. However, it did not improve diagnostic yield of SBCE for suspected SBB.
Multifunctional Eco-Friendly Synthesis of ZnO Nanoparticles in Biomedical Applications

This work describes an environmental-friendly preparation of ZnO nanoparticles using aqueous oat extract. The advanced electrochemical and optical features of green synthesized ZnONPs displayed excellent antibacterial activity and exhibited an important role in pharmaceutical determinations. The formation of nanoscale ZnO was confirmed using various spectroscopic and microscopic investigations. The formed nanoparticles were found to be around 100 nm. The as-prepared ZnONPs were monitored for their antibacterial potential against different bacterial strains. The inhibition zones for ZnONPs were found as Escherichia coli (16 mm), Pseudomonas aeruginosa (17 mm), Staphylococcus aureus (12 mm) and Bacillus subtilis (11 mm) using a 30-µg mL−1 sample concentration. In addition, ZnONPs exhibited significant antioxidant effects, from 58 to 67%, with an average IC50 value of 0.88 ± 0.03 scavenging activity and from 53 to 71% (IC50 value of 0.73 ± 0.05) versus the scavenging free radicals DPPH and ABTS, respectively. The photocatalytic potential of ZnONPs for Rhodamine B dye degradation under UV irradiation was calculated. The photodegradation process was carried out as a function of time-dependent and complete degradation (nearly 98%), with color removal after 120 min. Conclusively, the synthesized ZnONPs using oat biomass might provide a great promise in the future for biomedical applications.
DIET MONITORING THROUGH BREATHING SIGNAL ANALYSIS USING WEARABLE SENSORS

This dissertation presents a framework of wearable food and drink intake monitoring system that analyzes human breathing signal for identifying swallows during the intake process. The system works based on a key observation that a person’s otherwise continuous breathing cycles are interrupted by brief intra-cycle apneas during the swallows. This dissertation develops wireless wearable electronics for capturing and processing human breathing signal, and algorithms for identifying intake-related swallows via recognizing apneas extracted from breathing signal. A family of apnea detection mechanisms including matched filters and machine learning has been developed. Algorithms are developed for detecting various types of swallowing events including for solid and liquid in the presence of many artifacts presents in free-living conditions. It is demonstrated that using these algorithms and the electronics, run-time intake monitoring and analysis are feasible at acceptable accuracy levels. Further accuracy improvements were explored using a Hidden Markov Model (HMM) based mechanism that leverages known temporal locality in the human swallow sequence. Finally, it was demonstrated
Equal Access to University Education in Chile? An Application Using Spatial Heckman Probit Models

This study contributes to the debate on accessibility of higher education in Chile, with a special focus on the geospatial dimension of access to university studies. This paper addresses the central question of whether geography (physical distance and neighborhood effects) plays a significant role in determining the accessibility of higher education to students in Chile. We use Heckman probit-type (Heckit) models to adjust for selection during application for higher education—that is, pre-selection among applications to study at university and, ultimately, admission (or denial) to a higher educational institution. Of all high school graduates who took the university selection test (PSU), only 37.9% were able to attend higher education. The results show that the geospatial elements—neighborhood characteristics and distance from the city of Santiago—have a significant local effect on the student’s application and access to Chilean universities. Specifically, the most significant local range for each candidate is 300 neighbors. We also find that, when distance to the capital city increases, the probability of applying to university increases to a threshold of 1400 km, at which point probability begins to decrease.
Log-periodic dipole array-based MIMO antenna for the mobile handsets

A novel design of multiple input–multiple output (MIMO) antenna based on the log-periodic dipole array (LPDA) for the mobile handsets is presented. The antenna operates in a wide frequency range of 1.86–3.84 GHz compatible with GSM, LTE, WLAN, and WiMAX. The MIMO design consists of two symmetrical and orthogonal radiating elements and a neutralization line (NL). The radiating element comprises a LPDA having a series of printed dipoles connected together with a microstrip line. Triangular and rectangular cuts on ground plane are carried out in accordance with the LPDAs so as to increase the impedance bandwidth and isolation. The antenna elements are linked with the NL to further enhance the isolation between the radiating elements. The performance of the MIMO antenna is analyzed in terms of S-parameters, radiation pattern, gain, total efficiency, envelope correlation coefficient, mean effective gain, multiplexing efficiency; and then it is verified through the measurements. The results demonstrate that the proposed MIMO antenna has good characteristics of wideband, gain, radiation pattern, isolation, and diversity.
Gold and silver production in imperial China: Technological choices in their social-economic and environmental settings

The production of gold and silver was of major importance for almost all ancient societies around the world but has been rarely studied archaeologically. The high flexibility of this production process allows it to be conducted through various technological routes, while the choice among these potential routes was mainly determined by the producers’ social-economic and environmental settings. The comparison of these choices in different settings can therefore reveal information about numerous facets of ancient societies. The subject of this thesis is to study the gold and silver smelting technology in imperial China based on archaeological materials from the sites of Baojia, Mengshan and Yanchuan, all broadly dated to the Tang-Yuan period (7th-14th century AD). The archaeometallurgical approaches were employed to investigate the production remains such as slag, furnace fragment, crucible and fuel-ash slag directly generated from the smelting processes, and to reconstruct the chaine operatoire for these three sites. A range of unique technologies identified at these sites depicted a diversified picture of gold and silver production in imperial China. Iron reduction process (IRP) and iron oxide reduction process (IORP) were identified to be the main mechanisms for smelting the precious metal-bearing lead sulphide ores, while both furnaces and the coal-fired crucibles were used as the reaction containers. The dynamic relationships between the smelting technology and their broader context are examined to reveal a range of factors which constrained the technological choices of the smelters and led to the formation of varied technological traditions in the different regions of China. This research also contributes to the development of the characterisation and interpretation methodology for the precious metal and lead smelting remains within and beyond China, and demonstrates the strength of a combination of high resolution technological reconstructions and contextualised comparative studies in the inquiry of the pre-modern craft production systems.
Evaluation of Efficiency of Transcoding Steganography

Abstract Nowadays cyber criminalists’ interest in incorporation of steganography into armory of rogue hackers is on the rise and information hiding techniques are becoming the new black among Black Hats. In this paper we focus on analyzing the efficiency of the recently proposed IP telephony information hiding method called transcoding steganography (TranSteg) that enables hidden communication with a high steganographic bandwidth while retaining good voice quality. Specifically, we focus on analyzing which voice codecs would be the most favourable for TranSteg to minimize the negative influence on voice quality while maximizing the obtained steganographic bandwidth and limiting the risk of detection.
Reduction of Open-Circuit DC Winding Induced Voltage and Torque Pulsation in the Wound Field Switched Flux Machine by Stator Axial Pairing of Tooth Tips

Stator axial pairing of tooth tips is proposed in this article to suppress the open-circuit dc winding induced voltage and torque pulsation in the wound field switched flux machine. In the proposed method, the stator is divided into two segments axially and these two segments have different arcs of tooth tip. The finite element analysis results show that the proposed technique can reduce the on-load torque ripple from 17.91% to 6.99%, reduce the peak-to-peak value of cogging torque by 90.66% from 5.14 N·m to 0.48 N·m, and reduce the peak-to-peak value of open-circuit dc winding induced voltage at 1500 r/min by 84.39% from 79.58 to 12.42 V. Although the efficiency of the paired machine is slightly reduced from 84.93% to 84.11%, the average electromagnetic torque does not deteriorate by more than 5%. The paired machine is prototyped and tested, and the experimental results validate the effectiveness of the proposed method.
Key Points of Advanced Oxidation Processes (AOPs) for Wastewater, Organic Pollutants and Pharmaceutical Waste Treatment: A Mini Review

Advanced oxidation procedures (AOPs) refer to a variety of technical procedures that produce OH radicals to sufficiently oxidize wastewater, organic pollutant streams, and toxic effluents from industrial, hospital, pharmaceutical and municipal wastes. Through the implementation of such procedures, the (post) treatment of such waste effluents leads to products that are more susceptible to bioremediation, are less toxic and possess less pollutant load. The basic mechanism produces free OH radicals and other reactive species such as superoxide anions, hydrogen peroxide, etc. A basic classification of AOPs is presented in this short review, analyzing the processes of UV/H2O2, Fenton and photo-Fenton, ozone-based (O3) processes, photocatalysis and sonolysis from chemical and equipment points of view to clarify the nature of the reactive species in each AOP and their advantages. Finally, combined AOP implementations are favored through the literature as an efficient solution in addressing the issue of global environmental waste management.
Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm

: Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes. This modified algorithm works on different sets of plant leaves. The proposed algorithm examines several benchmark functions with ade-quate performance. On ten plant leaf images, this classification method was validated. The proposed model calculates precision, recall, F-measurement, and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms. Based on experimental data, it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%.
Flexible Beamforming of Dynamic MIMO Networks Through Fully Convolutional Model

Existing deep learning-based beamforming models’ input sizes are fixed, and they can only be used in multiple-input multiple-output (MIMO) networks containing a fixed number of users and base stations. Therefore, they cannot work and need retraining when the number of users is dynamic. In this letter, a fully convolutional beamforming model named FC-BFNet is proposed, where data of different dimensions can be addressed to adapt to the dynamic environment without retraining the model. Specifically, FC-BFNet has two computational parts which are mainly made up of convolution layers because the convolution operation is not sensitive to the size of the input data. Differently and importantly, the first part extra uses max-pooling to calculate the underlying features of the input and stores max-pooling indices, while the second part extra leverages up-pooling based on stored indices to obtain the expected results from the underlying features and ensure the size consistency of input and output. Extensive experiments demonstrate FC-BFNet has better dynamic environment adaptability and sum-rate performance than other baselines, while maintaining great computing efficiency.