bluuebunny/crossref_metadata_2025 huggingface dataset:
CGA-MGAN: Metric GAN based on Convolution-augmented Gated Attention for Speech Enhancement
Authors: Chen Haozhe , Zhang Xiaojuan
Published: February 2023

In recent years, neural networks based on attention mechanisms have been increasingly widely used in speech recognition, separation, enhancement, and other fields. In particular, the convolution-augmented transformer has achieved good performance as it can combine the advantages of convolution and self-attention. Recently, the gated attention unit (GAU) has been proposed. Compared with the traditional multi-head self-attention, approaches with GAU are effective and computationally efficient. In this article, we propose a network for speech enhancement called CGA-MGAN, a kind of Metric GAN based on convolution-augmented gated attention. CGA-MGAN captures local and global correlations in speech signals at the same time through the fusion of convolution and gated attention units. Experiments on Voice Bank + DEMAND show that the CGA-MGAN we propose achieves an excellent performance (3.47 PESQ, 0.96 STOI, and 11.09dB SSNR) at a relatively small model size (1.14M).
Fractional Modified Bessel Function of the First Kind of Integer Order
Authors: Martin Andres , Estrada Ernesto
Published: February 2023

Abstract The modified Bessel function (MBF) of the first kind is a fundamental special function in mathematics with applications in a large number of areas. When the order of this function is integer, it has an integral representation which includes the exponential of the cosine function. Here we generalize this MBF to include a fractional parameter, such that the exponential in the previously mentioned integral is replaced by a Mittag-Leffler function. The necessity for this generalization arises from a problem of communication in networks. We find the power series representation of the fractional MBF of the first kind, as well as some differential properties. We give some examples of its utility in graph/networks analysis and mention some fundamental open problems for further investigation.
Evaluation of the Haematological Patterns from up to 985 Days of Long COVID: A Cross-Sectional Study
Authors: Galúcio Vanessa Costa Alves , Menezes Daniel Carvalho de , Lima Patrícia Danielle Lima de , Palácios Vera Regina da Cunha Menezes , Vasconcelos Pedro Fernando da Costa , Quaresma Juarez Antônio Simões , Falcão Luiz Fábio Magno
Published: February 2023

Long COVID affects a significant number of people after acute coronavirus disease 2019 (COVID-19), and haematological changes can persist in the COVID-19 phase. This study aimed to evaluate these haematological laboratory markers, linking them to clinical findings and long-term outcomes in patients with long COVID. This cross-sectional study selected participants from a ‘long COVID’ clinical care programme in the Amazon region. Clinical data and baseline demographics were obtained, and blood samples were collected for quantification of erythrogram-, leukogram-, and plateletgram-related markers. Long COVID was reported for up to 985 days. Patients hospitalised in the acute phase had higher mean red/white cell, platelet, and plateletcrit levels and red cell distribution width. In addition, haematimetric parameters were higher in shorter periods of long COVID. Patients presenting with more than six concomitant long COVID symptoms had a higher white blood cell count, shorter prothrombin time (PT), and increased PT activity. Within up to 985 days of long COVID, our results suggest a probable benign compensation for erythrogram-related markers. Increased levels of leukogram-related markers and increased coagulation activity were observed in the worse long COVID groups, also indicating an exacerbated response after the acute disturbance, which is uncertain and requires further investigation.
A Systematic Review on the Clinical Diagnosis of Transient Hypogammaglobulinemia of Infants
Authors: Justiz-Vaillant Angel , Davis Nikao , Deonarinesingh Candice , De Silva Amir , Dhanpaul Dylan , Dookhoo Chloe , Doorpat Justin , Dopson Alexei , Durgapersad Joash , Palmer Clovis , Asin-Milan Odalis , Hoyte Trudee
Published: February 2023

Background: Transient Hypogammaglobulinemia of Infancy (THI) is a primary immunodeficiency caused by a temporary decline of serum levels of immunoglobulin G (IgG) greater than 2 standard deviations below the mean age-specific reference values in an infant between 5 and 24 months of age. Preterm infants are particularly susceptible to THI, as, in the third trimester of pregnancy, IgG is transferred across the placenta from mother to infant.Objective: To systematically review the diagnostic criteria of Transient Hypogammaglobulinemia of infants.Design & Methods: Systematic Review. Manual searching of 3 electronic databases (PubMed, Medline, & Google Scholar) from September 2021 – April 2022. Abstracts were screened to assess fit to the inclusion criteria. Data was extracted from the selected studies by using an adapted extraction tool from Cochrane.org. Studies were then assessed for bias by using an assessment tool also adapted from Cochrane.org.Results: Of the 215 articles identified, 16 were eligible for examining the diagnostic criteria of THI. These studies were also assessed for bias in 6 domains. 5 studies (31%) had a low risk of bias, while 4 studies (25%) had a high risk of bias, & 7 studies (44%) were unclear for bias.Conclusion: We can conclude that THI is only definitively diagnosed after the abnormal IgG levels have normalized, hence THI is mostly a benign condition, but must be monitored for subsequent recurrent infections. The diagnostic criterion also includes vaccine & isohaemagglutinin responses to differentiate against other immunological disorders in infants.
Combining Digital Image Correlation and Acoustic Emission to Characterize the Flexural Behavior of Flax Biocomposites
Authors: Habibi Mohamed , Laperrière Luc
Published: February 2023

Understanding the effect of staking sequences and identifying the damage occurring within a structure using a structural health monitoring system are the keys to an efficient design of composite-based parts. In this research, a combination of digital image correlation (DIC) and acoustic emission (AE) is used to locate and classify the type of damage depending on the stacking sequence of the laminate during flexural loading. A comparison of the strain field results for unidirectional, cross-ply, and quasi-isotropic laminates was made in a first attempt to discuss their global behavior and to correlate the different damage patterns with the possible failure mechanisms. The damage was then addressed using a comprehensive interpretation of the acoustic emission signatures using the K-means classification of the acoustic events. The development of each damage mechanism was correlated to the applied load and expressed as a function of the loading rate to highlight the effect of the stacking sequence. Finally, the results of DIC and AE were combined to improve the reliability of the damage investigation without limiting the failure mechanism to matrix cracking, interfacial failure, and fiber breakage, as expected by the unsupervised event clustering.
Liver Fibrosis in Individuals With Hepatitis C Virus Genotype 1B From Brazilian Eastern Amazon
Authors: Da Silva Tatiane Lobato , Dias Apio Ricardo Nazareth , Quaresma Juarez Antônio Simões , Falcão Luiz Fábio Magno
Published: February 2023

INTRODUCTION Hepatitis C virus (HCV) has a high genetic diversity, with seven genotypes with 86 subtypes. This genetic variability confers persistence in the infection and escape of the immune system with evolution to cirrhosis and cancer. Environmental factors can contribute to different disease progression, being essential to assess the viral genotype in the infection and discuss the environmental particularities of Eastern Amazon, and the frequencies of Liver fibrosis between different HCV genotypes in patients living in a region of the Brazilian Eastern Brazilian Amazon. METHODS Consists in an observational cross-sectional study. Sociodemographic and clinical data of 76 individuals diagnosed with Hepatitis C between 2019 and 2020 in public health services were selected. Data collected was tabulated in Microsoft Excel 2010TM spreadsheets and analysed in GraphPad Prism 5.0TM. Liver fibrosis was associated with genetic subtypes. RESULTS Subtype 1b was predominant (42.1%), followed by 1a (13%) and 3a (1.3%). 69.7% of participants had chronic hepatitis, with mild fibrosis (F1/F2) being the most prevalent (38.1%). Severe fibrosis was detected in 75% of individuals infected with the subtype 1b, that is associated with more severe disease. CONCLUSIONS We suggest further studies, to assess other communities in the region, as well as the monitoring of these patients with Liver Elastography to determine the disease evolution and its better management.
Fruit By-Products and Their Industrial Applications for Nutritional Benefits and Health Promotion: A Comprehensive Review
Authors: Teshome Ejigayehu , Teka Tilahun Abera , Nandasiri Ruchira , Rout Jyoti Ranjan , Harouna Difo Voukang , Astatkie Tessema , Urugo Markos Makiso
Published: February 2023

Fruits are commonly used, fresh or processed, to prepare different industrial products with superior nutritional and health-promoting properties. Currently, the demand for processed fruit products has motivated the rapid growth of the fruit processing industries, persuading them to produce an enormous amount of by-products with less utilization. Furthermore, people's shifting dietary habits and lack of awareness of nutritional properties result in an avoidable load of fruit by-products. The knowledge of the value of by-products urges exploration with proper documentation, emphasizing the health benefits of some such products. Hence, this review is prepared by carefully analyzing the recent literature on industrial applications of fruit by-products and their nutritional and health-promoting properties. The use of fruit by-products in food industries for various purposes has been reported in the past and has been reviewed and described here. Fruit by-products are a good source of nutrients and bioactive components, including polyphenols, dietary fibers, and vitamins, implying that they could have an important role for novel, value-added functional food properties. Furthermore, fruit by-products are used as the substrate for the production of organic acids, essential oils, enzymes, fuel, biodegradable packaging materials, and preservatives.
Passive dynamics of the head and forelimb in equine foetuses- an observational study.
Authors: Lusi Carla M , Davies Helen M.S.
Published: February 2023

Passive dynamics is an aspect of locomotion which is entirely dependent on the mechanical configuration and linkages of adjacent body segments. Tension distribution along mechanical linkages enables execution of movement patterns with reduced need for complex neurological pathways and may play a role in reestablishing postural stability following external disturbances. Here we demonstrate a uni-directional mechanical relationship between the equine forelimb and head, which may have implications for balance and forelimb loading in the horse. These observations suggest that forelimb and head movement coordination (observed in the horse during unrestrained locomotion) is largely controlled by the mechanical linkages between body segments, rather than being entirely dependent on neurological input as previously thought. This highlights the potential significance of research directed at investigating passively induced movements in understanding common locomotory patterns. Additionally, it suggests a mode of postural control which may provide instantaneous adjustments to postural disturbances, thus promoting rapid and efficient locomotion.
Enhanced Efficiency at Maximum Power in a Fock-Darwin Model Quantum Dot Engine
Authors: Peña Francisco J. , Myers Nathan , Órdenes Daniel , Albarrán-Arriagada Francisco , Vargas Patricio
Published: February 2023

We study the performance of an endoreversible magnetic Otto cycle with a working substance composed of a single quantum dot described using the well-known Fock-Darwin model. We find that tuning the intensity of the parabolic trap (geometrical confinement) impacts the proposed cycle’s performance, quantified by the power, work, efficiency, and parameter region where the cycle operates as an engine. We demonstrate that a parameter region exists where the efficiency at maximum output power exceeds the Curzon-Ahlborn efficiency, the efficiency at maximum power achieved by a classical working substance.
Mathematical Modelling and Optimal Control of Malaria Using Awareness-Based Interventions
Authors: Al Basir Fahad , Abraha Teklebirhan
Published: February 2023

Malaria is a critical fevered illness caused by Plasmodium parasites transmitted among people through the bites of infected female Anopheles mosquitoes. Public awareness about the disease is important for the control of disease. This article proposes a mathematical model to study the dynamics of malaria disease transmission with the influence of awareness-based control interventions. We found two equilibria of the model, namely the disease-free and endemic equilibrium. Disease-free equilibrium is stable globally if basic reproduction number (R0) is less than unity (R0<1). Some basic mathematical properties of the proposed model, such as nonnegativity and boundedness of solutions, the feasibility of the equilibrium points and their stability properties, have been studied. Finally, we adopted optimal control to minimize the cost of disease control and solve the problem by formulating Hamiltonian functional. The optimal use of insecticides for controlling the mosquito population is determined. Numerical simulations have been provided for the confirmation of the analytical results. We conclude that media awareness with optimal control approach is best for cost-effective malaria disease management.
ANALISIS KONDISI RANTAI PASOK KOMODITAS KENTANG (SOLANUM TUBEROSUM L.) DI KABUPATEN BREBES
Authors: Khatimah Khusnul
Published: February 2023

The potato is one of the vegetables comodities in Brebes Regency. The last three years, there has been a decline in production. Supply chain that has been implemented is deemed not optimal while the region has potential that can be exploited. The purpose of this study was to determine supply chain of the potato in Brebes Regency.The analytical method uses a Food Supply Chain Network (FSCN). The result showed that conditions based on the aspects of the target, structure, management, resources, and supply chain business processes. Target potatoes are sold to the local Brebes market and outside the region. There are four supply chain structure.It members are farmers, middlemen, wholesalers, small traders, retailers, and consumers. Supply chain management related to partner selection, contractual agreements, transaction systems, government support, and supply chain collaboration. The criteria for selecting partners are bidders with high prices, customers, and middlemen who are bound to provide capital loans to farmers. Transaction system through grace and cash system. The government support also contributes to the sustainability of it. Resources owned by members of the supply chain consist of physical, technology, and capital resources. Business processes can be seen from distribution patterns, support chain members, risk and trust building. Distribution patterns flow products, finance, and information from farmers to consumers, and vice versa. Each member of the potato supply chain requires equipment and input in the form of raw materials in the production process. Each member has different risks in the supply chain business process.
Implementasi Struktur Aljabar Dalam Persilangan Golongan Darah
Authors: Agusfrianto Fakhry Asad , Yanti Gusti Ayu Dwi , Dau Irnawati
Published: February 2023

Dalam kajian struktur aljabar, objek dasar matematika yaitu himpunan dapat diberikan struktur abstrak seperti grup atau ring.  Dalam paper ini akan dikaji struktur aljabar yang terdapat pada persilangan antara dua individu dengan golongan darah dengan sistem ABO, Rhesus, dan MN.
Model Matematika Co-infection Tuberkulosis dan COVID-19 dengan Intervensi Obat Anti Tuberkulosis (OAT)
Authors: Safira Putri Islamiati  , Eti Dwi Wiraningsih  , Devi Eka Wardani Meganingtyas
Published: February 2023

COVID-19 dan tuberkulosis merupakan penyakit menular mematikan dengan gejala yang hampir sama. Apabila kedua infeksi ini menyerang suatu inang, risiko kematian yang diberikan akan sangat tinggi. Penelitian ini membentuk dan menganalisis model penyebaran co-infection tuberkulosis dan COVID-19 dengan intervensi obat OAT. Model ini menghasilkan titik ekuilibrium bebas penyakit model co-infection tuberkulosis dan COVID-19 yang memenuhi keadaan stabil ketika bilangan reproduksi dasar atau jumlah penularan sekunder kurang dari 1. Simulasi numerik dilakukan dengan nilai parameter yang diperoleh dari data kondisi tuberkulosis dan COVID-19 di Indonesia per Maret 2020 hingga Maret 2021, beserta dengan hasilnya dalam bentuk grafik. Berdasarkan nilai parameter model, diperoleh bilangan reproduksi dasar sebesar 0.1819422898. Artinya tidak terdapat penularan co-infection TB dan COVID-19 di dalam populasi.
Analisis Proses Berpikir Siswa dalam Memecahkan Masalah Matematika ditinjau dari Adversity Quotient (AQ) Siswa Kelas VIII MTs Negeri 1 Lombok Barat Tahun Ajaran 2022/2023
Authors: Dahliani Baiq Mery , Turmuzi Muhammad , Wahidaturrahmi Wahidaturrahmi , Azmi Syahrul
Published: February 2023

This study aims to determine students' thinking processes in solving mathematical problems. Data analysis was carried out based on Ilman's thinking process theory in terms of the students' adversity quotient. This type of research is a qualitative descriptive research. The selection of research subjects was based on the recommendation of the mathematics teacher and obtained class VIII A MTs Negeri 1 West Lombok for the 2022/2023 academic year as research subjects. To deepen the research results, 2 students were selected from each type of AQ students. Data collection techniques using adversity response profile questionnaires, students' thinking process tests and interviews. The data analysis technique used is data reduction, data presentation and conclusion. From the research results it can be seen that climber type students tend to use conceptual thinking processes in solving mathematical problems. Where climber students are able to express what is known and asked in questions with their own sentences, answer with concepts that have been learned and are able to explain the steps taken. Meanwhile, camper students tend to use semi-conceptual thinking processes in solving mathematical problems. Where camper students are able to express what is known and asked in questions, in answering they tend to use concepts that have been learned even though they are incomplete and are less able to explain the steps taken.
ENVIRONMENTAL, SOCIAL, GOVERNANCE INVESTING, COVID-19, AND CORPORATE PERFORMANCE IN MUSLIM COUNTRIES
Authors: Tekin Hasan , Güçlü Fatih
Published: February 2023

We examine the impact of Environment, Social, and Governance–ESG investing on corporate performance of non-financial firms in Muslim countries during the pandemic. Employing the random effect panel model with 1,546 firm-year observations, we find that the ESG combined score and its pillars have significant influence on corporate performance during the COVID-19 period. Namely, the performance of firms with higher ESG is relatively less affected as compared to the performance of firms with lower ESG.  We also note that firms in Malaysia and the United Arab Emirates with high ESG have better operational (financial) performance. Finally, from the sectorial perspective, health care and energy (consumer staples) firms with higher ESG have higher operational (financial) performance during the pandemic.
Study Evaluates Surfactant EOR From Tight Carbonates
Authors: Carpenter Chris
Published: February 2023

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 209418, “Surfactant Enhanced Oil Recovery From Tight Carbonates: Core-Scale Experiments to Reservoir-Scale Modeling,” by Yue Shi, SPE, and Kishore Mohanty, SPE, The University of Texas at Austin. The paper has not been peer reviewed.

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Most carbonate reservoirs are oil-wet/mixed-wet and heterogeneous at multiple scales. The majority of the injected water flows through the high-permeability regions and fractures and bypasses the oil in the matrix because of high negative capillary pressure (Pc). To enhance oil recovery from such reservoirs, the sign of the Pc should be changed by wettability alteration (WA) or the Pc should be reduced by lowering interfacial tension (IFT). In the complete paper, surfactants that can either alter wettability or develop ultralow IFT were identified through laboratory measurements for the target carbonate reservoir.

Introduction

Contact-angle, IFT, phase-behavior, and imbibition tests were first performed, and surfactants that can either alter wettability or develop ultralow IFT were identified. Then, a laboratory-scale imbibition model was built with commercial reservoir simulation software. A sensitivity study was performed to evaluate the effects of residual oil saturation (Sor), alteration of Pc and relative permeability (Kr), IFT, and matrix scale on oil recovery by surfactant. Next, the laboratory-scale imbibition model was used to history match the experimental data and parameters were tuned for reservoir-scale simulation. Finally, a 3D layered field-scale model was developed to study the performance of the selected surfactants in injection/soak/production (ISP) tests.

Materials

Eight anionic surfactants (An 1 through An 8) and two quaternary ammonium cationic surfactants (Cat 1 and 2) were used. Silurian dolomite (SD) outcrop cores were used in this study. Fluid samples were obtained from a West Texas dolomite-rich reservoir. The oil has a density of 0.84 g/mL at 25°C and a viscosity of 7.46 cp at reservoir temperature (35°C). It has an acid number of 0.25 mg KOH/g and a base number of 1.0 mg KOH/g. The reservoir has been waterflooded for years, and total dissolved solids of brine has decreased from 112,668 to 40,393 ppm for recent produced water (PW). The authors detail the methodology for aqueous-stability, contact-angle-measurement, IFT-measurement, phase-behavior, and spontaneous-imbibition tests in the complete paper.
Technology Focus: Well Integrity (January 2023)
Authors: Dhawan Sandeep
Published: February 2023

Moving into 2023, perhaps it will be safe to say that the era of the low-carbon energy mix has begun as the primary energy production landscape is changing fast. Until now, fossils fuels have dominated, and may still for the next few years dominate, the energy mix; however, a shift is taking place that will gain momentum, driven by global efforts toward addressing climate-change challenges and the large cost to human health caused by fossil fuels. Several cleaner energy solutions such as renewables are expanding global footprints. Some of these mix options require wells similar to those for oil and gas but with convoluted integrity challenges. This means that in the future, well integrity will become even more important and will continue to be part of the energy-mix solution. It is time to double down on how we, as part of the energy-mix industry, understand the many aspects of well integrity.

Repurposing of existing oil and gas wells for carbon storage driven by cost optimization currently is under discussion in many parts of the world. While this makes sense commercially, it is critical to assess the in-situ state and, more importantly, the suitability of the existing flow-wet well barriers’ metallurgy for repurposing because failure conditions and risk envelopes change. A holistic review of flow-wet material conformance for repurposing is currently a subject of low focus, but due diligence on a case-by-case basis is imperative lest these wells present well-integrity issues with consequences when operational.

As the well-integrity role grows, well surveillance and complete monitoring with artificial intelligence also will play a crucial role in the journey ahead.

Recommended additional reading at OnePetro: www.onepetro.org.

IPTC 21472 Features, Events, and Processes-Based Model Development for Assessing Well-Integrity Risk Related to CO2 Storage in Central Luconia Gas Fields in Sarawak by Parimal A. Patil, Petronas, et al.
Technology Focus: Digital Data Acquisition (January 2023)
Authors: Asarpota Jyotsna
Published: February 2023

With the rise of machine learning and other artificial intelligence methods, the ability to digest data for potential application in the petroleum industry has increased tremendously. Data is seen as the key to reducing uncertainty in decisions for this capital‑intensive industry where the costs of bad or delayed decisions are huge. Thus, the right data delivered at the right time can revolutionize the industry and save precious time and money.

Gaining insights from digital data in the highly technical exploration and production industry requires experience, knowledge, and awareness about the acquisition of the data. The technologies presented here aim to facilitate the decision‑making process while requiring less time and lower costs without sacrificing the accuracy of the data and still decreasing the probability of human errors. They show how data collected from different vantage points can be integrated with conventional data‑acquisition methods to help visualize and reduce the uncertainty of the subsurface.

Developments in other industries in automation and electronics have enabled modernization and miniaturization of oilfield instruments. Our industry seeks ideas and methods that are reliable, convenient, and practical to inform and guide operations, filling the gaps where and when conventional data is not available.

Recommended additional reading at OnePetro: www.onepetro.org.

OTC 31327 Density Measurement of Three-Phase Flows Inside Vertical Piping Using Planar Laser-Induced Fluorescence by Amy Brooke McCleney, Southwest Research Institute, et al.

OTC 31836 Subsea Multiphase Flowmeter Measurement Performance Assurance With an Applied Data Validation and Reconciliation Surveillance Methodology by Emmelyn Graham, BP, et al.

SPE 208712 IADC Code Upgrade: Data Collection and Work Flow Required To Conduct Bit Forensics and Create Effective Changes in Practices or Design by William Watson, Shell, et al.
Real-Time Fiber-Optics Solution Opens Door to the Wellbore Environment
Authors: Carpenter Chris
Published: February 2023

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 209540, “Real-Time Fiber Optics: A Door Opener for the Wellbore Environment,” by Håkon S. Bakka, SPE, Stefan Dümmong, and Kjetil E. Haavik, SPE, Equinor, et al. The paper has not been peer reviewed.

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For the Johan Sverdrup asset, Equinor has developed a data-streaming solution that allows for real-time processing and interpretation of fiber-optic (FO) data. In its current state, it processes approximately 500 TB of data each week, with insights made accessible to the end user through a web-based front-end platform. In the complete paper, the authors illustrate the benefits of this system and how it can provide improved understanding of well conditions and processes.

The FO System

The FO system consists of three main components. The first is the FO-acquisition system, which is delivered by the distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) vendors. These FO interrogators deliver data in a streaming format, allowing for continuous data transfer to the second component, the high-performance processing cluster. This cluster transforms the high-bandwidth FO data into lower-bandwidth FO feature data. The lower-bandwidth data then is transferred to the operator’s cloud solution, which makes data available for analysis. In its current state, the system experiences delays of only 2–3 seconds from data acquisition to FO features being made available for analysis in the cloud platform.

One of the novelties of the system design is its flexibility. Because the components are distinct and only interact through application programming interfaces, they can be interchanged based on different use cases. DAS and DTS interrogators have different qualities, so, for example, these can be swapped based on needs; because processing requirements will be based on incoming data specifications, these also can be adapted. This result is a system that can be tailored to the requirements of specific monitoring applications.

Examples of Real-Time Analysis

The complete paper presents examples that show the varied applicability of real-time FO data to tasks including the following:

- Wellbore cleanup and fluid tracking

- Well-intervention-tool tracking and monitoring

- Gas-lift-valve (GLV) leak detection

- Fluid-phase distinction

- Overburden monitoring

This synopsis will include discussions of the first three of these applications.
Imbalanced Working States Recognition of Sucker Rod Well Dynamometer Cards Based on Data Generation and Diversity Augmentation
Authors: Yin Chengzhe , Zhang Kai , Zhang Liming , Wang Zhenpeng , Liu Piyang , Zhang Huaqing , Yang Yongfei , Yao Jun
Published: February 2023

During sucker rod pump production, there is a commonly seen problem of class imbalance, which refers to the differences in the amount of data accumulated under different working conditions. This problem in rod pump diagnosis can lead to unsatisfactory classification results of surface dynamometer cards under working conditions with fewer samples. Therefore, this study adopts the conditional generative adversarial nets (CGANs) improved by mini-batch method to address the problem of class imbalance. CGAN is an efficient method of multiclass data generation, which learns the properties of dynamometer cards by training the generator and discriminator networks. CGAN is modified by mini-batch strategy to avoid mode collapse and enable the interaction among input samples of discriminator, so that the generated dynamometer cards can be much more diverse. Results show that the shapes of generated dynamometer cards are basically consistent with those of real samples, and the structural similarity (SSIM) among the generated samples decreases, indicating that the generated dynamometer cards have more types of shape. Meanwhile, as the generated dynamometer cards become more diverse, their differences with real samples in data distribution are reduced, according to the calculation of sliced Wasserstein (SW) distance. Based on real and generated dynamometer cards, we developed the classifiers for working condition diagnosis of rod pump through convolutional neural network (CNN). The classification results of the validation set indicate that without the mini-batch method, the recall of generated categories for pump hitting down and leakage has increased by 12 and 5.3%, respectively; in contrast, with the mini-batch method, the recall has increased more obviously by 7, 24, and 2%, respectively, for gas lock, pump hitting down, and leakage. Our research results have demonstrated that the proposed method can effectively solve the problem of insufficient data accumulation in the oil field.
Methicillin Resistant Staphylococcus Aureus Contamination on Physiotherapy Equipment Used in Selected Tertiary and Private Hospitals in Anambra State, Nigeria
Authors: Akobundu Uzoamaka Nwakego , Otuo Banner Ofonime , Akosile Christopher Olusanja , Okonkwo Uchenna Prosper , Ihegihu Ebere Yvonne , Ochiabuto Ogochukwu Barbara , Chukwuemeka Uchechukwu Martha
Published: February 2023

Background: Methicillin-resistant Staphylococcus aureus (MRSA) refers to a group of Gram-positive bacteria that are genetically distinct from other strains of Staphylococcus aureus.
Aims / Objectives: The aim of this study was to determine the prevalence of Methicillin resistant staphylococcus aureus bacterial contaminations on physiotherapy equipment in selected hospitals in Anambra State, Nigeria.
Methods: This study is a cross-sectional study. A total of Sixty-eight (68) swab samples were collected from four physiotherapy departments, two private and two tertiary hospitals. The study population is made up of 14 equipment from private hospital A, 20 from tertiary hospital B, 20 from private hospital C and 14 from tertiary hospital D.
Results: The Methicillin-Resistant staphylococcus auerus contamination of electrotherapy equipment was found to have a high prevalence rate of 5(38.5%) in hospital C and D while hospital A and B shows no prevalence. Exercise therapy equipment also had a higher infection contamination and prevalence rate of 22 (36.8%) in all the hospitals, diagnostic equipment had a prevalence rate of 2(16.7%) in hospital A and hospital D while hospital B and C had no prevalence. Orthotic equipment had a prevalence rate of 1(100%) in hospital C and none in other hospitals. Walking aids showed a low prevalence rate of 1(16.7% in hospital C and none in hospital.
Conclusions: Physiotherapy departments have a high risk of compromising patients’ safety through spreading infections via physiotherapy modalities.
The Prognostic Value of Prognostic Nutritional Index in Patients With Advanced or Metastatic Gastric Cancer Treated With Immunotherapy
Authors: Pan Yuting , Dai Guanghai
Published: February 2023

Background
In recent years, the therapeutic effect of monoclonal antibody against programmed cell death protein-1 (PD-1) monotherapy or combined chemotherapy in patients with locally advanced or metastatic gastric or gastroesophageal junction (G/GEJ) cancer has been confirmed by many studies. A number of clinical studies of anti-PD-1 antibody combined with chemotherapy in the perioperative treatment of locally advanced and resectable G/GEJ cancer are underway. The exploration and discovery of new biomarker combinations based on tumor characteristics and tumor microenvironment, especially tumor immune microenvironment, are helpful to screen superior patients and realize precise immunotherapy.
Methods
We selected 268 consecutive AGC patients who were treated with ICI therapy from December 2014 to May 2021. We measured their pretreatment the prognostic nutritional index (PNI) levels and performed univariate and multivariate Cox regression analyses of progression-free survival (PFS) or overall survival (OS) after ICI therapy.
Results
The low pretreatment PNI level of AGC patients was significantly correlated with shorter PFS (P<0.001) and OS (P<0.001) after ICI treatment. In univariate and multivariate analyses of the associations between neutrophil lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR) or PNI, and OS or PFS, PNI (HR=1.483; 95% CI, 1.129-1.947; P=0.005) and NLR (HR=1.531; 95% CI, 1.164-2.014; P=0.002), but not LMR (HR=1.130; 95% CI, 0.829-1.538;P=0.440) are independent prognostic factors for PFS. Moreover, PNI (HR=1.385; 95% CI, 1.004-1.911; P=0.048) and LMR (HR=1.433; 95% CI, 1.039-1.977; P=0.028), but not NLR (HR=1.277; 95% CI, 0.896-1.820; P=0.176) are independent prognostic factors for OS.
Conclusion
The pretreatment PNI might help to identify AGC patients who will obtain a survival benefit from ICI therapy. The low pretreatment PNI is related to unsatisfactory survival outcomes.
Real world safety and exploratory efficacy of gene therapy for patients with 5q-Spinal Muscular Atrophy in a Brazilian cohort
Authors: Mendonca Rodrigo , Ortega Adriana , Matsui Ciro , Linden Vanessa Van Der , Kerstenetzky Marcelo , Grossklauss Luis Fernando , Lucas Elizabeth , Polido Graziela , Zanoteli Edmar
Published: February 2023

Spinal muscular atrophy (SMA) is a motor neuron disease associated with progressive muscle weakness, ventilatory failure, and reduced survival. Onasemnogene abeparvoves is the first gene replacement therapy (GT) approved to treat this condition. An observational retrospective study was conducted to assess adverse events and efficacy of GT in SMA patients. Forty-one patients with SMA (24 females, 58.5% and 33 SMA type 1, 80.1%) were included. The mean age at GT dosing was 18 (± 6.4) months. Thirty-six patients (87.8%) were under previous treatment with nusinersen, and 10 (24.4%) continued nusinersen after GT. Mean CHOP-INTEND increased 13 points after 6 months and this finding did not differ between groups according to nusinersen maintenance after GT (p = 0.949). Among SMA type 1 patients, 14 (46.6%) reached the ability to sit alone. Liver transaminases elevation at least two times higher than the upper limit of normal value occurred in 29 (70.7%) patients. Thrombocytopenia occurred in 13 (31.7%) patients, and one presented thrombotic microangiopathy. Older age (> 2 years) was associated with more prolonged use of corticosteroids (p = 0.021). GT is effective in SMA patients, combined nusinersen after GT does not present additional gain in motor function, and older age is associated with prolonged corticosteroid use.
Explainable prediction of catalysing enzymes from reactions using multilayer perceptrons
Authors: Probst Daniel
Published: February 2023

Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways or the challenge of finding more sustainable enzyme-catalysed alternatives to traditional organic reactions are just two examples of tasks that require an association between reaction and enzyme. However, given the lack of large and balanced annotated data sets of enzyme-catalysed reactions, assigning an enzyme to a reaction still relies on expert-curated rules and databases. Here, we present a data-driven explainable human-in-the-loop machine learning approach to support and ultimately automate the association of a catalysing enzyme with a given biochemical reaction. In addition, the proposed method is capable of predicting enzymes as candidate catalysts for arbitrary organic reactions. Finally, the introduced explainability and visualisation methods can easily be generalised to support other machine-learning approaches involving chemical and biochemical reactions.
Iterative Decorrelation Analysis, Unit of Measure Preserving Transformations and Latent Biomarker Discovery
Authors: Tamez-Peña José Gerardo
Published: February 2023

Background Numerous biomarker discovery studies and exploratory clinical studies extract a large set of measurable variables, which often have varying degrees of correlation among them. This data collinearity can impact statistical model interpretation and hinder the discovery of potential associations between measured variables and the observed outcome. Exploratory Factor Analysis (EFA), Principal Component Analysis (PCA), and Machine-Learning (ML) can be used to discover latent variables associated with disease progression or outcome by computing transformation matrices, but the interpretation of unsupervised/supervised latent variables in high-dimensional datasets can be challenging. Results This study describe and reports the performance of the iterative decorrelation analysis algorithm (IDeA). The algorithm iteratively analyzes the correlation matrix of the data, updating the transformation coefficients until it reaches the desired correlation goal. The output of IDeA is a basis-transformation matrix that preserves the data dimensionality and unit of measure of the original observed variables. The main advantages of the IDeA basis transformations are sparsity and interpretability. The transformation does not alter uncorrelated features, thus statistical modeling and biomarker discovery in the new transformed basis can be a combination of novel latent variables and a sizable subset of unaltered variables. The algorithm was tested on five multidimensional/hyperdimensional and multimodal sets, demonstrating the effect of decorrelation parameters, interpretability, and latent biomarker discovery. Conclusions The iterative decorrelation approach provides a simple to use tool for researchers to explore the association between correlated features in hyperdimensional/multimodal settings and to decorrelate significant associations via latent variables that preserve the unit of measurement. An efficient computer implementation of IDeA is available in the FRESA.CAD R package (https://cran.r-project.org/web/packages/FRESA.CAD/index.html).
Measuring cumulus expansion of mammalian oocytes: comparing the reliability of methods and how artificial intelligence could automate the measurement
Authors: Raes Annelies , Azari-Dolatabad Nima , Athanasiou Georgios , Sadeghi Hafez , Andueza Sebastian Gonzalez , Arcos Josep Lluis , Cerquides Jesus , Pavani Krishna Chaitanya , Opsomer Geert , Pascottini Osvaldo Bogado , Smits Katrien , Velez Daniel Angel , Soom Ann Van
Published: February 2023

Cumulus expansion is an important indicator of oocyte maturation, often correlated with greater oocyte developmental capacity. Although multiple methods have been described to assess cumulus expansion, none of them is considered a gold standard. Additionally, these methods are subjective and time-consuming. Here, the reliability of three cumulus expansion measurement methods was evaluated and a deep learning model was created to automatically perform the measurement. Cumulus-oocyte complexes were compared before and after in vitro maturation by three independent observers using three methods: (1) measurement of the cumulus area, (2) measurement of three distances between the zona pellucida and outer cumulus, and (3) scoring cumulus expansion on a 5-point Likert scale. Inter- and intra-observer agreements were calculated using intraclass-correlation coefficients (ICC). The area method resulted in the best overall agreement with an ICC of 0.89 versus 0.54 and 0.30 for the 3-distance and scoring method, respectively. Therefore, the area method served as the base to create the deep learning model, which outperformed two observers while equivalent to the third. Measuring the area is the most reliable method to manually evaluate cumulus expansion, whilst deep learning automatically performs the calculation with human-level accuracy and could therefore be a valuable prospective tool for embryologists.
Direct operando observation and control of charge states in organic light-emitting diodes with a thermally activated delayed fluorescence material
Authors: Yumoto Shintaro , Katsumata Junya , Osawa Fumiya , Wada Yoshimasa , Suzuki Katsuaki , Kaji Hironori , Marumoto Kazuhiro
Published: February 2023

Organic light-emitting diodes (OLEDs) with thermally activated delayed fluorescence (TADF) materials have advantages such as high efficiency with low cost compared to OLEDs with conventional fluorescence and phosphorescence materials. To attain further high device performance, clarifying internal charge states in OLEDs at a microscopic viewpoint is crucial; however, only a few such studies have been performed. Here, we report a microscopic investigation into internal charge states in OLEDs with a TADF material by electron spin resonance (ESR) at a molecular level. We observed operando ESR signals of the OLEDs and identified their origins due to a hole-transport material PEDOT:PSS, gap states at an electron-injection layer, and a host material CBP in the light-emitting layer by performing density functional theory calculation and studying thin films used in the OLED. The ESR intensity varied with increasing applied bias before and after the light emission. We find leakage electrons in the OLED at a molecular level, which is suppressed by a further electron-blocking layer MoO3 between the PEDOT:PSS and light-emitting layer, resulting in the enhancement of luminance with a low-voltage drive. Such microscopic information and applying our method to other OLEDs will further improve the OLED performance from the microscopic viewpoint.
Identification and Classification of Pneumonia using CNN Model with Chest X- ray Image
Authors: Kumar Suraj , Prakash Shiva
Published: February 2023

Pneumonia is a bacterial, fungal, or viral infection that affects one or both lungs. It is a serious disease in which the air sacs in both lungs become clogged with pus and other substances. So, several frameworks and models have been built to properly assess such diseases, but there is still space for improvement. In this study, we used CXR images to train a CNN model to detect and classify Pneumonia disease in the lungs and also present how training accuracy and validation accuracy as well as training loss and validation loss vary when changing the size of the input image. The Kaggle CXR dataset is used, which was already created and pre-processed. The Convolutional Neural Network method is used in the research in close collaboration with some other data augmentation frameworks to enhance classification accuracy, which also will help to enhance training and validation accuracies, as well as characterize the precision of the Convolutional Neural Network model and achieve various results. The training and validation accuracy of our model are 0.9757 and 0.9568, respectively, and the training and validation loss are 0.0857 and 0.1399.
Living beyond loss: A qualitative investigation of caregivers' experiences after the death of their relatives with Amyotrophic Lateral Sclerosis
Authors: Volpato Eleonora , Banfi Paolo , Poletti Valentina , Pagnini Francesco
Published: February 2023

Background
The psychological experiences of a caregiver of an Amyotrophic Lateral Sclerosis (ALS) patient after the death of their loved one can vary greatly depending on the individual. Common experiences include grief, sadness, loneliness, guilt, and a sense of purposelessness.
Objectives
To investigate the prevalent emotional experiences encountered by caregivers and/or individuals who have provided care for ALS patients following their passing.
Methods
The present study used the Interpretative Phenomenological Approach (IPA) to qualitatively explore the accounts of 41 (Mean Age = 59.78; Men: 39.02%) bereaved caregivers of people affected by ALS.
Results
The results highlighted 5 superordinate themes (“Caregiver’s perception of his/her life”, “Caregiver’s feelings”, “Caregiver’s life after patient’s death”, “Caregiver’s disease description”, “Caregiver’s help resources”), 12 themes and 30 subthemes. In particular, it emerged that the transition from life before ALS (described as “a peaceful landscape” to caregiver life (mostly compared to colour “black” was lived as a “shock”, during which caregivers also had to change their needs (“Caregiver’s needs transformation”. On the other side, as literature confirmed, caregiver’s life after the patient’s death was both characterized by a sense of “re-birth” and “emptiness”, which could be filled by “psychological assistance” and different type of "social support".
Conclusions
Providing psychological support to caregivers of ALS patients after the death of their loved one is important for addressing their emotional needs, reducing isolation, and helping them navigate practical challenges and plans.