Subsequent Extraction Research Articles

Template subtraction based methods for non-invasive fetal electrocardiography extraction

Assessment of fetal heart rate (fHR) through non-invasive fetal electrocardiogram (fECG) is challenging task. This study compares the performance of five template subtraction (TS) methods on Labor (12 5-min recordings) and Pregnancy datasets (10 20-min recordings). The methods include TS without adaptation, TS using singular value decomposition (TS_{textrm{SVD}}), TS using linear prediction (TS_{textrm{LP}}), TS using scaling factor (TS_{textrm{SF}}), and sequential analysis (SA). The influence of the chosen maternal wavelet for the continuous wavelet transform (CWT) detector is also compared. The F1 score was used to measure performance. Each recording in both datasets consisted of four signals, resulting in a total comparison of 88 signals for the TS-based methods. The study reported the following results: F1 = 95.71% with TS, F1 = 95.93% with TS_{textrm{SVD}}, F1 = 95.30% with TS_{textrm{LP}}, F1 = 95.82% with TS_{textrm{SF}}, and F1 = 95.99% with SA. The study identified gaus3 as the suitable maternal wavelet for fetal R-peak detection using the CWT detector. Furthermore, the study classified signals from the tested datasets into categories of high, medium, and low quality, providing valuable insights for subsequent fECG signal extraction. This research contributes to advancing the understanding of non-invasive fECG signal processing and lays the groundwork for improving fetal monitoring in clinical settings.

Subsequence Join in Streaming Time Series under Dynamic Time Warping

Subsequence join in time series is to search for couples of similar subsequences from multiple time series. The task is useful in data mining on time series; nevertheless, it is extremely difficult because of its enormous computational cost. The task should use normalized time series during the search execution and be performed under an efficient distance measure to obtain accurate resulting couples. The task is more challenging when it works in a streaming environment in which time-series data might be collected very quickly. To address this problem, we propose an efficient method of subsequence join in streaming time series under Dynamic Time Warping (DTW), supporting z-score normalization. The proposed method utilizes a technique of subsequence extraction based on major extrema of streaming time series to search for couples of similar subsequences from coevolving time series. This method can identify couples of similar subsequences of the same length or different lengths. The experimental results show that the proposed method has high performance and can bring out interesting couples of similar subsequences. In addition, this method acts as an approximation algorithm suitable for a streaming scenario where users often expect fast responses from the task of subsequence join over time-series streams of high rates.

SSE4Rec: Sequential recommendation with subsequence extraction

Sequential recommendation mines the sequential patterns in user behavior data to recommend items to users. Recent studies have mainly followed the language modeling paradigm, on the premise that the next item depends on the sequence of previous items. Notably, differences exist between user behavior and textual data. One key difference is that behavioral sequences can encompass multiple intentions, unlike sentences that typically express a single intention. Furthermore, behavioral sequences emerge freely from users, whereas sentences conform to grammatical rules. This study highlights the risk of treating behavior sequences as a unified sequence, and the resultant potential for overfitting the observed transitions. We mitigated this risk by using subsequence extraction for recommendation (SSE4Rec). This model employs a subsequence extraction module that disperses items into distinct subsequences and groups of related items. Each subsequence is then processed by an independent downstream sequence model, which discourages the memorization of inconsequential transitions. Both the training and inference strategies are inherently integrated into the model. The proposed method was evaluated on four public datasets, whereby it was demonstrated to outperform publicly available alternatives or deliver competitive results. The properties of the model were also explored, further visualizing the output of the subsequence extraction module.

Hot alkaline lysis gDNA extraction from formalin-fixed archival tissues.

Formalin fixation of natural history specimens and histopathological material has historically been viewed as an impediment to successful genomic analysis. However, the development of extraction methods specifically tailored to contend with heavily crosslinked archival tissues, re-contextualises millions of previously overlooked specimens as viable molecular assets. Here, we present an easy-to-follow protocol for screening archival wet specimens for molecular viability and subsequent genomic DNA extraction suitable for sequencing. The protocol begins with non-destructive assessment of specimen degradation and preservation media conditions to allow both museum curators and researchers to select specimens most likely to yield an acceptable proportion (20-60%) of mappable endogenous DNA during short-read DNA sequencing. The extraction protocol uses hot alkaline lysis in buffer (0.1M NaOH, 1% SDS, pH 13) to simultaneously lyse and de-crosslink the tissue. To maximise DNA recovery, phenol:chloroform extraction is coupled with a small-fragment optimised SPRI bead clean up. Applied to well-preserved archival tissues, the protocol can yield 1-2 μg DNA per 50 mg of tissue with mean fragment sizes typically ranging from 50-150 bp, which is suitable to recover genomic DNA sufficient to reconstruct complete mitochondrial genomes and achieve up to 25X nuclear genome coverage. We provide guidance for read mapping to a reference genome and discuss the limitations of relying on small fragments for SNP genotyping and de novo genome assembly. This protocol opens the door to broader-scale genetic and phylogenetic analysis of historical specimens, contributing to a deeper understanding of evolutionary trends and adaptation in response to changing environments.

Study on dechlorination salt characteristics of pickling sludge by a water-washing process.

Steel hydrochloric acid pickling sludge (SHPS), containing the heavy metals Fe, Zn, and Ni and a high chloride salt content, is considered a hazardous solid waste. With the gradual reduction of high-grade metal mineral resources such as Fe, Zn and Ni, it is particularly urgent to recycle valuable metals such as Fe, Zn and Ni in solid waste SHPS in order to realize the resource utilization of SHPS and reduce the environmental harm caused by SHPS. In addition, SHPS usually contains different amounts of alkali chloride, which will have a serious adverse impact on the subsequent extraction and smelting process of Fe, Zn and other metals. Therefore, the removal of chloride plays an important role in the resource utilization of valuable metals in SHPS. Thus, in this study, the effects of water washing dechlorination process parameters such as liquid-solid (L/S) ratio, SHPS particle size, washing time and washing frequency on the chloride removal rate were investigated. The best experimental parameters of SHPS washing were obtained. At the same time, the microscopic morphology and crystal phase composition of SHPS before and after washing were explored. The results showed that the optimized conditions were as follows: room temperature, a L/S ratio of 3 : 1, an SHPS particle size of 100 mesh, and 10 min of water washing, repeated two or three times; under these conditions, the removal rate of Cl, Na, Ca, K, Mg, and S reached 96.64-99.68%, 97.38-99.89%, 36.40-60.37%, 49.11-54.82%, 39.18-40.22%, and 36.98-42.13% respectively. The contents of Cl, K, and Na in filter residue (FR) meets the requirements in GB/T 36144-2018 and GB/T 32545-2016. Conversely, the contents of Fe, Zn, Mn and Ni in the FR are enriched, which is more conducive to the subsequent resource utilization of SHPS. The scanning electron microscope (SEM) image shows the particle size of the FR particles is reduced after washing. The X-ray diffractometer (XRD) results proved that the chlorine salt content in the FR after washing was significantly reduced, the diffraction peaks of Al2O3 appeared in the FR, and the diffraction peak intensity of CaCO3, Fe2O3 and SiO2 increased.

Implementação de tecnologias em saúde no Brasil: análise de orientações federais para o sistema público de saúde

Abstract This study aimed to identify the regulatory framework and federal guidelines that support the process of implementing health technologies in the Unified Health System (SUS) through analysis of documents and legislation related to the National Health Technology Management Policy, published between 2009 and 2021. The search and selection of documents and subsequent data extraction were carried out. The documents were grouped into three categories: structural regulatory documents, recommendations on evaluation of technologies, and recommendations on clinical guidelines. In 38.8% of the regulatory documents, citations to implementation related mainly to SUS clinical guidelines were identified; however, no document dedicated to guiding implementation actions was identified. Recommendations related to implementations were identified in 27.1% of the reports and 66.1% of the guidelines, although without standardization and, in general, in little detail, focusing on resources and actions needed for making technology available rather than on methods and interventions for its implementation. The results evidence a gap in formal guidelines to guide the implementation process in Brazil, representing an opportunity for the development of models aligned with the reality of the SUS.

Enhancing Veracity: Empirical Evaluation of Fake News Detection Techniques

The relentless surge in the dissemination of fabricated news articles and misleading information poses a substantial threat to the veracity of information, trust in media sources, and the very foundations of democratic processes. In the contemporary landscape of information dissemination, the rampant propagation of false and misleading information has emerged as a paramount concern for both individuals and society at large. Consequently, the development of robust and efficient detection mechanisms has taken on a pivotal role in contemporary information ecosystems. The methodology employed encompasses three pivotal stages: initial data preprocessing, subsequent feature extraction involving the utilization of TF-IDF and Count Vectorization, and eventual classification that draws upon a diverse array of machine learning classifiers, in addition to deep learning models like Recurrent Neural Networks (RNN) and BERT networks. On a carefully selected dataset of instances of fake news, these models underwent rigorous testing and training. This research greatly aids in the continuous battle against disinformation by offering a methodical framework for identifying fake news. It also helps people and organisations make better decisions by shedding light on the relative merits of various deep learning and machine learning methods in this intricate and crucial context.

Implementation of health technologies in Brazil: analysis of federal guidelines for the public health system.

This study aimed to identify the regulatory framework and federal guidelines that support the process of implementing health technologies in the Unified Health System (SUS) through analysis of documents and legislation related to the National Health Technology Management Policy, published between 2009 and 2021. The search and selection of documents and subsequent data extraction were carried out. The documents were grouped into three categories: structural regulatory documents, recommendations on evaluation of technologies, and recommendations on clinical guidelines. In 38.8% of the regulatory documents, citations to implementation related mainly to SUS clinical guidelines were identified; however, no document dedicated to guiding implementation actions was identified. Recommendations related to implementations were identified in 27.1% of the reports and 66.1% of the guidelines, although without standardization and, in general, in little detail, focusing on resources and actions needed for making technology available rather than on methods and interventions for its implementation. The results evidence a gap in formal guidelines to guide the implementation process in Brazil, representing an opportunity for the development of models aligned with the reality of the SUS.

BILSTM-SimAM: An improved algorithm for short-term electric load forecasting based on multi-feature.

With the growing number of user-side resources connected to the distribution system, an occasional imbalance between the distribution side and the user side arises, making short-term power load forecasting technology crucial for addressing this issue. To strengthen the capability of load multi-feature extraction and improve the accuracy of electric load forecasting, we have constructed a novel BILSTM-SimAM network model. First, the entirely non-recursive Variational Mode Decomposition (VMD) signal processing technique is applied to decompose the raw data into Intrinsic Mode Functions (IMF) with significant regularity. This effectively reduces noise in the load sequence and preserves high-frequency data features, making the data more suitable for subsequent feature extraction. Second, a convolutional neural network (CNN) mode incorporates Dropout function to prevent model overfitting, this improves recognition accuracy and accelerates convergence. Finally, the model combines a Bidirectional Long Short-Term Memory (BILSTM) network with a simple parameter-free attention mechanism (SimAM). This combination allows for the extraction of multi-feature from the load data while emphasizing the feature information of key historical time points, further enhancing the model's prediction accuracy. The results indicate that the R2 of the BILSTM-SimAM algorithm model reaches 97.8%, surpassing mainstream models such as Transformer, MLP, and Prophet by 2.0%, 2.7%, and 3.6%, respectively. Additionally, the remaining error metrics also show a reduction, confirming the validity and feasibility of the method proposed.

Cataract Outcomes Following Scleral Buckle Surgery for Retinal Detachment.

To investigate refractive, visual, and safety outcomes of cataract surgery performed after scleral buckling (SB) for retinal detachment (RD). A chart review at an academic medical center identified eyes with history of SB followed by subsequent cataract extraction between 2010 and 2022. Eyes with less than 3 weeks follow-up, silicone oil at time of biometry measurement, previous cornea surgery, or co-existing pathology impacting refractive outcomes were excluded. Predicted postoperative spherical equivalents (SE) were calculated with the Barrett Universal II (BU2), Kane, and SRK/T formulas for the implanted intraocular lens (IOL), and complications occurring within 1 year of surgery were abstracted. Sixty eyes of 60 patients met criteria for inclusion, and 40 (66.7%) had postoperative refraction recorded. Absolute prediction errors were 0.49, 0.45, and 0.52D with BU2, Kane, and SRK/T, respectively. Actual postoperative refraction was within 0.5 and 1.0 D of predicted in 26 (65.0%) and 36 (90.0%) using BU2, 23 (58%) and 37 (93%) using Kane, and 21 (52.5%) and 36 (90.0%) using SRK/T. In eyes with macula-on RD, corrected distance visual acuity (CDVA) of logMAR 0.301 (≈20/40) and logMAR 0.544 (≈20/70) or better was achieved in 12 (75.0%) and 15 (93.8%) of eyes. For macula-off RD eyes, these proportions were 19 (63.3%) and 24 (80.0%), respectively. Posterior capsular opacification requiring Nd: YAG capsulotomy was the most frequent complication in 30 (56.7%) eyes. Refractive outcomes of cataract surgery following SB may be modestly reduced, even when using modern formulas. Nevertheless, cataract surgery in this population results in favorable visual outcomes.

Технология переработки сухих отходов сорбции и растворов хлоридов сурьмы

Reducing the loss of valuable components during the processing of complex gold-antimony ores, increasing the extraction of antimony during the flotation of sulfide minerals, is an urgent scientific problem. The aim of the study is to maximize the extraction of gold and antimony from dry sorption waste after gold cyanidation, and to improve the reagent regime of the antimony mineral flotation process. Research objectives are as follows: evaluation of the technology efficiency for processing dry sorption waste; extraction of gold from the cake of acidic leaching of antimony; production of various antimony-containing products from solutions of antimony chlorides; study of the possibility of replacing lead with a mixture of zinc and copper cations to hydrophobize the surface of antimony sulfide minerals during flotation. The object of the research is man-made and natural mineral raw materials containing gold and antimony. The following research methodology and methods are used: information analysis, evaluation of existing scientific developments, methods of theoretical and experimental research. A new technological solution has been proposed: acidic (a mixture of hydrochloric acid and hydrogen hydroxide) hydrometallurgical technology for processing dry sorption waste in order to extract gold and antimony. Acid treatment makes it possible to completely transfer antimony into solution, significantly improves the quality of the cake for subsequent cyanidation and gold extraction, reduces the volume of material for processing by cyanidation, and simplifies cyanidation technology. Sb2S3 activation and the possibility of lead (Pb(NO3) replacement have been studied 2) a mixture of zinc and copper (ZnSO4 and CuSO4) during the flotation of antimony ores from the Khipkoshinsky deposit in the Transbaikal Region. This has made it possible to create favorable conditions for interaction with xanthogenate and hydrophobize the surface of sulfide minerals. Theoretically, hydrogen sulfide substitutes such as NaCNS, KCNS, CuCNS for use in the flotation process were evaluated.

Hydrometallurgical processing of molybdenum middlings from Shatyrkul-Zhaysan cluster ore

This research investigates the hydrometallurgical processing of molybdenum middlings extracted from copper-molybdenum ore in the Shatyrkul-Zhaysan cluster in Kazakhstan. A molybdenum intermediate obtained after selective flotation of the copper-molybdenum concentrate was used, with a recovery of 0.07%, a molybdenum content of 22.23% and an extraction of 74.91%. The mineralogical analysis shows molybdenite and chalcopyrite as the main minerals. In experiments, atmospheric leaching with nitric acid in single-stage and two-stage countercurrent processes was investigated to optimize molybdenum extraction and reduce acid consumption. The optimum conditions obtained were: 300 g/L nitric acid, 100 g/L sulfuric acid, 90?C temperature and 2 hours leaching time in the single-stage leaching, which extracted 98.8% molybdenum. The two-stage leaching under optimized conditions allows the extraction of 94.3% molybdenum in solutions with lower residual acidity (0.89 g-eq/L) and redox potential (550 mV) without reducing the extraction of valuable component. Molybdenum extraction reaches 94.3% in the subsequent solvent extraction stage from two-stage leaching solutions. The final product, calcium molybdate with a molybdenum content of 46.83%, meets commercial grade specifications. This research demonstrates an effective process for hydrometallurgical production of commercial grade calcium molybdenite from copper-molybdenum ore, with high molybdenum recovery, reduced acid consumption through two-stage leaching, and minimal hazardous discharges.

Extração e avaliação de atividades antimicrobianas de óleos essenciais de casca de laranja (Citrus nobilis) cultivada no município de Can Tho, Vietnã

ABSTRACT: This study determined the extraction conditions, chemical composition, and antimicrobial activities of the essential oils of Citrus nobilis. The results illustrated that soaking in the 9% (w/v) NaCl solution for 2 h with a sample and solvent ratio of 1:3 (w/v) and a subsequent extraction time of 45 min yielded the highest extraction efficiency, reaching 3.66% (w/w). The main chemical components of the essential oils were limonene (90.42%), β-myrcene (4.7%), and α-pinene (1.22%). The minimum inhibitory concentration (MIC) at the density of 5x104, 5x105, and 5x106 cells/mL of Escherichia coli were 100, 125, and 125 mg/mL, respectively; for Staphylococcus aureus were 75, 100, and 125 mg/mL, respectively; and for Bacillus cereus were 50, 75, and 100 mg/mL, respectively. The minimum bactericidal concentration (MBC) at densities of 5x104, 5x105, 5x106 cells/mL of E. coli were 175, 225, 225 mg/mL, S. aureus were 150, 200, 225 mg/mL, and B. cereus were 125, 175, 200 mg/mL, respectively. The inhibitory activities of Aspergillus flavus of orange essential oils according to the agar dilution method at concentrations of 0.025-0.1%) on the 5th day were 8.84-30.61%.

Metagenomic 18S rDNA reads revealed zonation of eukaryotic communities in the Yongle blue hole.

The Yongle blue hole (YBH), situated in the South China Sea, represents a compelling subject of study in marine microbiology due to its unique redox-layered microbial ecosystems. However, the diversity and ecology of microbial eukaryotes within the YBH remains underexplored. This study endeavors to bridge this gap through the application of the in situ microbial filtration and fixation (ISMIFF) device to collect 0.22-30 μm microbial samples from 21 water layers of YBH. Subsequent extraction of 18S rRNA metagenomic reads of 21 metagenomes and 10 metatranscriptomes facilitated a comprehensive analysis of community structures. Findings revealed a pronounced superiority in the diversity and richness of eukaryotic microorganisms in the oxic zone compared to its suboxic and anoxic counterparts. Notably, Dinophyceae and Maxillopoda emerged as the predominant taxa based on the analysis of the 18S rRNA reads for the V4 and V9 regions, which showed stratification In their relative abundance and suggested their potential role in the thermo-halocline boundaries and oxic-anoxic interface. Specifically, In these eukaryotic microbial communities, Dinophyceae exhibited significant abundance at 20 m (20.01%) and 105 m (26.13%) water depths, while Maxillopoda was prevalent at 40 m (22.84%), 80 m (23.19%), and 100 m (15.42%) depths. A part of these organisms, identified as larvae and protists, were likely attracted by swarming chemosynthetic bacterial prey prevailing at the thermo-halocline boundaries and oxic-anoxic interface. Furthermore, the phylogenetic relationships of the major 18S operational taxonomic units (OTUs) showed a close adjacency to known species, except for three Dinophyceae OTUs. In conclusion, this study provides critical insights into the vertical distribution and transcriptional activity of <30-μm eukaryotic microbes, shedding light on the taxonomic novelty of eukaryotic microorganisms within the semi-enclosed blue holes.

ENERGY EFFICIENCY OF USING THE ACCUMULATED IN THE SOIL OF SOLAR ENERGY IN THE HEAT PUMP HEATING SYSTEM

A heat pump scheme is proposed to ensure heat supply to the building, which during the heating period of the year uses the energy received in the summer by solar collectors located on this building and stored in the soil battery. A thermodynamic analysis of the efficiency of the heat pump heating system in combination with a vertical soil heat exchanger during the accumulation of solar radiation in the soil in the summer period and with the subsequent extraction of this heat from the soil in the heating period was carried out. The main operating parameters of the heat pump heating system (optimal speed of the heat carrier, minimum specific consumption of external energy, effective coefficient of transformation of the heat pump from the month of the heating period) were determined using balance equations at different values of the depth of the well and the diameter of the pipe. Graphical dependences of energy efficiency indicators on the determining parameters of the system were constructed and analyzed. The obtained ratios between the parameters can be used in calculating the optimal operating conditions of the vertical soil heat exchanger in heat pump systems of low-temperature heating in order to achieve its maximum energy efficiency.

Fundus Image Classification for the Early Detection of Issues in the DR for the Effective Disease Diagnosis

Diabetic Retinopathy and fundus images, “DR” could also refer to Digital Retinography, which involves the digital imaging of the retina. Digital retinography allows for the storage, retrieval, and transmission of retinal images for analysis and diagnosis. It plays a crucial role in screening for diabetic retinopathy and other eye diseases. The use of artificial intelligence and machine learning algorithms in the analysis of fundus images has gained traction in recent years. These technologies can assist healthcare professionals in the early detection and monitoring of diabetic retinopathy by analyzing patterns and abnormalities in the retinal images. This paper introduces a novel approach to image analysis, combining Local Semantic Object Feature Extraction (LsOFE) and Gray Level Co-occurrence Matrix (GLCM) metrics for robust object recognition and classification. The LsOFE process captures intricate local features, enhancing the representation of objects in images. The subsequent GLCM-based feature extraction provides detailed insights into texture characteristics. The final classification step, utilizing LsOFE features, demonstrates high precision, recall, and F1-Score for most images, showcasing the effectiveness of the proposed methodology. However, challenges are identified in the classification of specific images, suggesting areas for improvement and refinement. Overall, this research contributes a valuable framework for image processing, with the potential for broader applications in fields such as computer vision and pattern recognition. The results presented underscore the promising performance of the proposed approach, offering a foundation for further exploration and optimization in the realm of image analysis.

Spectral uncertainty analysis of grassland and its camouflage materials based on land-based hyperspectral images

Abstract Spectral uncertainty is one of the most prominent spectral characteristics of hyperspectral images. Compared to the process of remote sensing hyperspectral imaging, hyperspectral imaging under land-based imaging conditions has the characteristics of variable detection directions, random imaging times, and complex environmental conditions, resulting in increased spectral uncertainty of targets in land-based hyperspectral images. The spectral uncertainty of the target mainly refers to the phenomenon of “Same spectral reflectance but different objects” and “Same object but different spectral reflectance” which poses significant difficulties for subsequent hyperspectral image target detection and recognition. In order to analyze the spectral uncertainty of hyperspectral images in land-based applications and address the issue of spectral uncertainty in similar targets, a spectral uncertainty evaluation index based on standard deviation vector was proposed. For the overall spectral separability between different types of targets, a quantitative index based on Jaccard Distance (JD-SSI) is proposed to measure the spectral separability of different kinds of targets. The experiment focused on grassland and its four typical camouflage materials, analyzing the spectral intra class differences and inter class separability of each target with grassland. It is a fundamental work for studying the spectral characteristics of land-based hyperspectral images, providing a new approach for subsequent spectral band extraction, hyperspectral image classification, and target detection tasks.

Direct Impact of the Air on Mutant Cells for Mutagenicity Assessments in Urban Environments.

Urban air pollution is recognized as a critical problem for public health and is classified as a carcinogen for humans. A great number of studies have focused on the monitoring of urban air mutagenicity. One of the best-known and applied methods for assessing mutagenicity is the Ames test, a bacterial reverse mutation test. The classic protocol for assessing air mutagenicity involves the concentration of particulate matter (PM) on filters and subsequent extraction using organic solvents. This work aimed to develop a method for the evaluation of air mutagenicity directly impacted by air on microbial plates already containing an Ames' microbial sensor. A specific six-month sampling campaign was carried out in Turin in a period with high air pollution. Samples were tested for mutagenicity on Salmonella typhimurium strains TA98, TA100, and YG1024 with the traditional method and with the new direct method. The new protocol is able to evaluate the mutagenicity of the sampled air and obtain repeatable results. The final sensitivity is similar to the traditional method (≈10 net revertants/m3); however, the mutagenic response is due to the complete air pollution mixture, including volatile and semivolatile pollutants avoiding the concentration of filters and the following laborious extraction procedures. Despite some critical issues in contamination control, the method is easier, faster, and less expensive than traditional methods.

Application and Analysis of Array Production Logging Technology for Multiphase Flow in Horizontal Wells

Production logging (PL) instruments play a pivotal role in the comprehensive management and monitoring of oil and gas reservoirs. These devices facilitate the resolution of complex flow diagnosis challenges throughout the life cycle of hydrocarbon field exploitation. However, the advent of highly deviated well drilling technology has exposed certain limitations inherent in conventional centralized logging sensing techniques. When fluid flow within horizontal wells becomes segregated or even laminar, these traditional methods struggle to accurately decipher the zonal productions of oil, gas, and water. To address this challenge, multi-array production logging tools were developed in the late 1990s. Historically, these tools were characterized by considerable lengths, reaching up to 30 feet for an entire suite incorporating flow speed and holdup sensors that were not always collocated. Despite the integration of multiple sensors, uncertainties in determining flow profiles persisted. In this paper, we propose a novel integrated multi-parameter evaluation method based on measurements from a recently developed ultracompact flow array sensing tool, aimed at enhancing the accuracy of reservoir evaluation. The validity of the multi-parameter method is substantiated through a comparison of the new tool with an industry benchmark array PL tool on the same well. By combining the monitoring results, an optimization strategy for oil and gas extraction is presented, which is expected to improve the oil and gas recovery rate, thereby providing guidance for subsequent extraction endeavors. Moreover, we demonstrate how this innovative integrated workflow significantly enhances energy savings and efficiency, further underlining its value in modern oil and gas field management.

Modification of pectin with high-pressure processing treatment of fresh orange peel before pectin extraction: Part I. The effects on pectin extraction and structural properties

The possibility of utilizing endogenous plant pectin methyl-esterase (PME) to modify pectin in source material prior to pectin extraction is proposed and explored in this study. This is based on the reports about activation of endogenous PME in plant-based foods by high-pressure processing (HPP) treatment. Fresh orange (Hamlin) peel from a local commercial juice plant was pretreated with HPP at different combinations of pressures and durations, followed by pectin extraction with a commercial method. The effects of HPP pretreatments on pectin yield and structural properties of the extracted pectins were determined. HPP pretreatment increased the yield of subsequent pectin extraction by 41.10 %. Crude extracts from HPP-treated peels showed higher PME activities than the untreated peel. Pectins extracted from HPP pretreated peel (Hp) had a lower degree of methyl-esterification (DM), accompanied by an increase in degree of blockiness (DB) of non-esterified galacturonic acid (GalA) than the control (pectin extracted from un-treated peel). GalA content and linearity of pectin (LP) were higher, while neutral sugar content and degree of branching (DBr) were lower in Hp. The reduction in weight average molecular weight of Hp was minor, and it was positively correlated with a reduction in neutral sugars, indicating an effect of pectin debranching without degradation of the pectin main chain. The effects of HPP on the pectin main chain, degree, and distribution of methyl-esterification in this study were similar to the reported effects of orange PME on extracted pectin. The identified pectin structural features were validated by Fourier Transform Infrared Spectroscopy analysis and a newly developed lateral flow assay. The study indicates that HPP modification of pectin structure in fresh orange peel before pectin extraction may be an efficient and economical way to generate pectin with desirable structural features and to increase pectin yield.