bluuebunny/crossref_metadata_2025 huggingface dataset:
Interplay between Structural, Electronic and Magnetic Properties in the p0-D Semi-heusler Compounds; the Case of the Li-Based Compounds
Authors: Özdoğan Kemal , Galanakis Iosif
Published: July 2024

Half-metallic semi-Heusler compounds are currently at the forefront of scientific research due to their potential applications in spintronic devices. Unlike other semi-Heuslers, the p0(d0)-d compounds do not appear to crystallize in the typical variant of the C1b structure. We investigate this phenomenon in the p0-d Heusler compounds LiYGa and LiYGe, where Y varies between Ca and Zn, using first-principles ab-initio electronic band structure calculations. We examine the electronic and magnetic properties of these compounds in relation to the three possible C1b structures. Notably, LiVGa, LiVGe, LiMnGa, and LiCrGe are half-metallic ferromagnets across all three variations of the C1b lattice structure. Our findings will serve as a foundation for future experimental studies on these compounds.
New Insights into the Effect of Temperature Variation on the Force Released by Superelastic Orthodontic Wires. Part II: Phase Transition Temperatures
Authors: Santis Roberto De , D'Antò Vincenzo , Santoro Martina , Gloria Antonio
Published: July 2024

(1) Background: The aim of this investigation is to determine the effect of archwires’ deflection on the temperature transitions driving the austenite to martensite transformation (and vice versa) and the force released by NiTi and CuNiTi orthodontic wires. (2) Methods: Round cross-section archwires have been investigated. The calorimetric approach has been used for measuring transition temperatures occurring in the unstressed (e.g. undeflected) state of the wires. Static Thermo-mechanical analysis in a temperature controlled wet environment has been performed in the range of temperature between 5°C and 55°C for measuring temperature transitions occurring in highly deflected archwires in conjunction with the force released by these wires. (3) Results: Plateau forces investigated in Part I and those reported in the literature describes the wire’s behaviour only at deflection levels close to the end point of the force plateau extension detected at 37°C. Upon heating or cooling, the austenite finish (Af) transition temperature and the martensite start (Ms) temperature largely depends on the deflection level for both NiTi and CuNiTi. (4) Conclusions: The plateau force levels at 37°C, broadly reported in the literature, only represent the lower boundary force that a superelastic wire releases at 37°C above its elastic limit. For both NiTi and CuNiTi, plateau force levels at 37°C can be predicted by the static TMA profile matching the end point of the force plateau extension. Transition temperatures in largely deflected wires are significantly higher than those determined through the calorimetric approach.
Production Optimization of an Integrated Asset Model of the Farnsworth Field Unit
Authors: Eastwood-Anaba Angus , Ampomah William , Ameyaw Anthony
Published: July 2024

This study provides a methodology for optimizing oil production and CO2 sequestration using an integrated asset model of the Farnsworth Unit field, incorporating extensive geological, geophysical, and engineering data. Sensitivity analysis identified critical parameters impacting oil recovery and CO2 storage, leading to the construction of a proxy model using polynomial regression and radial basis function neural networks, with the latter proving more effective for "What-if" analysis. The sensitivity analysis indicated that the Corey parameters impact cumulative oil produced the most. Comprehensive history matching validated the model against historical production data, ensuring reliability for forecasting and optimization. Two scenarios, "Do-Nothing" and development strategy, were forecasted over 15 years (2020-2035). The "Do-Nothing" scenario resulted in 9.57 MMSTB of oil recovery and 2,822.70 MMlbs of CO2 storage. The development strategy case improved outcomes with 13.95 MMSTB of oil recovery and 5,061.68 MMlbs of CO2 stored, and was selected for optimization using particle swarm optimization. The optimized strategy achieved 14,043,372 STB of cumulative oil and 4,832.18 MMlbs of CO2 stored, and increased the field NPV by 25.84% to $114,871,730. This study underscores the significance of integrated asset modeling in enhancing oil recovery and optimizing CO2-EOR processes, providing valuable insights into operational conditions and constraints.
A Peak Finding Siamese Convolutional Neural Network (PF-SCNN) for Aero-engine Hot Jet FT-IR Spectrum Classification
Authors: Du Shuhan , Han Wei , Kang Zhenping , Luo Fengkun , Liao Yurong , Li Zhaoming
Published: July 2024

Aiming at solving difficulties related to aero-engine classification and identification, the infrared spectra of aero-engine hot jets are measured by telemetry Fourier transform infrared spectrometer, and a six types aero-engine hot jet spectrum data set is generated. The spectrum data set were randomly sampled at a ratio of 8:1:1 to generate training set, validation set and prediction set. In this paper, a Peak Finding Siamese Convolutional Neural Network (PF-SCNN) is designed to match and classify spectrum data. In the first place, the matching pairs of training set and validation set are made independently with the labels of positive and negative samples creating. The spectrum feature extraction and distance similarity calculation are carried out by the Siamese convolutional neural network (SCNN). When entering the prediction phase, the prediction set and training set are integrated to predict in the trained model. The prediction set labels are ultimately determined by matching them with the training set labels having the highest similarities to ensure accurate predictions. To improve the operation efficiency of the SCNN, a peak finding method is introduced to extract the spectrum peaks. Peak positions of high frequency are counted, while the peak data are extracted. The peak data are used to take the place of the original data for training and prediction. The performance measures are defined as Accuracy, Precision, Recall, Confusion matrix and F1-score, and the prediction accuracy was up to 99%. The experimental results indicate that the proposed PF-SCNN is suitable for our spectral dataset and can complete the task of infrared spectrum classification of aero-engine hot jets.
Knowledge Regarding Ergonomics and Posture, Postural Behaviour as Well as Incidence of Musculoskeletal Pain in University Students in the Northeast of England: A Web-Based Survey
Authors: SALMAN MONA , Bettany Saltikov Josette , Kandasamy Gokulakannan , Aristegui Racero Garikoitz
Published: July 2024

Keywords: knowledge; behaviour; ergonomics; posture; musculoskeletal; pain; university; students; survey.
Study on the Impact of Microscopic Pore Structure Characteristics in Tight Sandstone on Microscopic Remaining Oil After Polymer Flooding
Authors: Xianda Sun , Ling Zhao , Zhang Huili , Xu Chengwu , Qin Xudong , Zeng Maokun
Published: July 2024

The characteristics of tight sandstone, such as small pores, complex pore structures, low permeability, and low porosity, make it difficult to accurately characterize its pore structure. This represents a major challenge in the exploration and development of tight sandstone oil and gas reservoirs. This article conducts a simulation study on the impact of characteristic parameters such as pore size, coordination number, pore-throat ratio, and wettability on microscopic remaining oil after polymer flooding, thereby quantifying the types of remaining oil.
Virtual Agglomeration：An Driver for Total Factor Productivity
Authors: Meng Xiulan
Published: July 2024

(1) Background: The rapid development of information technology has changed the traditional concept of space, gradually shifting the "container space" to the "relational space" . The constraints of geographical spatial elements are diminishing, and the theory of virtual agglomeration is gaining attention. (2) Methods: In this study, we constructed an evaluation system for virtual agglomeration, comprising four primary indicators: virtual infrastructure level, virtual output level, virtual input level, and virtual service level. The TOPSIS method was used to calculate the virtual agglomeration level for each province, and the DEA-Malmquist method was employed to measure provincial total factor productivity (TFP) in China. Using panel data from 30 provincial regions in China from 2013 to 2021, we examined the mechanism and impact path of virtual agglomeration on TFP. (3) Result: There is an inverted U-shaped relationship between virtual agglomeration and TFP in China. The overall development level of virtual agglomeration is relatively low, within the range where the marginal effect of virtual agglomeration on TFP is positive but diminishing, indicating significant potential for further development. The impact of virtual agglomeration on TFP is stronger in the Yangtze River Economic Belt compared to non-Yangtze River areas, and the highest stable point of TFP is higher in central provinces than in eastern and western provinces. (4) Conclusions: This study provides a theoretical basis and practical reference for improving the level of virtual agglomeration .
RPLUW/M: Enhanced RPL on the Internet of Underwater Things
Authors: Homaei MohammadHossein
Published: July 2024

With the widespread use of the Internet of Things, underwater control and monitoring systems for purposes such as ocean data sampling, natural disaster prevention, underwater surveillance, submarine exploration, and the like have become a popular and challenging topic in computers. So far, various topology control and routing solutions have been proposed for these networks. However, as technology expands and applications grow, so does the need for a stable underwater communication platform. On the other hand, underwater communication is associated with challenges such as node mobility, long propagation delays, low bandwidth, limited resources, and high error rates. In this research, for the first time, a topology control platform based on the RPL tree is modelled by applying its structural changes underwater. The proposed RPLUW methods in the case of RPLUWM fixed nodes are introduced to support the mobility of nodes underwater. Flexible objective functions, utilisation of decision-making systems, and application of control schedules in these methods have increased network life, reduced overhead, and increased node efficiency. The simulation results of the proposed method, in comparison with recent methods in this field, show an increase in network efficiency.
A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms
Authors: Gulbay Burak , Demirci Mehmet
Published: July 2024

Advanced persistent threat (APT) attacks are sophisticated and organized attacks commonly motivated by political, financial, and strategic objectives. In order to comprehend their tactics, techniques, and procedures (TTP) and indicators, APT reports are valuable sources. While blue teams typically rely on server logs, firewall rules and user authorizations managed in database tables, attackers have a graph-based mindset. In this work, we propose a framework for discovering and evaluating APTs using graph-based algorithms. Cyber threat intelligence (CTI) was extracted from 40,358 pages of APT reports and transformed into a graph. Centrality, community, and similarity analyses were executed on the graph. As a result, critical and influential APT groups and indicators of compromise (IoC) were discovered. Similar attacks and APT groups were revealed. Analysis results were interpreted to create new strategic CTI that can be utilized in future security operations.
Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study
Authors: Badahman Fatima , Alsobhi Mashael , Alzahrani Almaha , Chevidikunnan Mohamed Faisal , Neamatallah Ziyad , Alqarni Abdullah , Alabasi Umar , Abduljabbar Ahmed , Basuodan Reem , Khan Fayaz
Published: July 2024

Background: Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians. Objective: This study aimed to assess the accuracy level of arti-ficial intelligence as a Clinical Decision Support System (CDSS) called Therapa compared to MRI in predicting lumbar disc herniated patients. Methods: One hundred low back pain patients aged ≥18 years old were included in the study. The study was conducted in 3 stages, at first, a case se-ries was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Lastly, to determine the ac-curacy of the newly developed software, a cross-sectional study was undertaken, involving 100 patients. Results: The software showed a significant diagnostic accuracy with the area under the curve in the ROC analysis determined as 0.84 with a sensitivity of 88% and a specificity of 80%. Conclusion: The study’s findings revealed that CDSS using Therapha has a reasonable level of ef-ficacy, and this can be utilized clinically to acquire faster and more accurate screening of patients with lumbar disc herniation.
Adherence to Continuous Positive Airway Pressure Therapy (CPAP) in Patients With Obstructive Sleep Apnea (OSA): A Prospective Single-Centre Study Assessing Impact on Quality of Life and the Cues to CPAP Use
Authors: Milinovic Karla , Pavlinac Dodig Ivana , Lusic Kalcina Linda , Pecotic Renata , Ivkovic Natalija , Valic Maja , Dogas Zoran
Published: July 2024

Obstructive sleep apnea (OSA) often goes unrecognized despite common symptoms such as excessive daytime sleepiness, fatigue, and impaired quality of life (QoL). CPAP is the gold standard treatment for OSA, but optimal daily usage and time needed for observable effects remain unclear. This study aimed to investigate the short-term effects of CPAP on daytime sleepiness and QoL in patients with severe OSA. Medical records were collected from 87 patients with severe OSA who initiated CPAP therapy. Also, validated questionnaires were used before and after one month of CPAP to analyze QoL - the Calgary Sleep Apnea Quality of Life Index (SAQLI), the Cues to CPAP Use Questionnaire (CCUQ), and daytime sleepiness - the Epworth Sleepiness Scale (ESS). Multiple regression analysis was conducted to identify predictors of CPAP usage. Of the total participants aged 55.6±12.5, 77% were males, and 62% were CPAP adherent. Reductions in daytime sleepiness (ESS) were noted, as well as improvements in both overall QoL (SAQLI) and specifically in the domains of daily functioning, social interactions, emotional well-being, and symptom perception. Important cues for CPAP usage recognized by patients were physicians’ instructions and physicians’ worry regarding patients' condition. Furthermore, multiple regression revealed higher SAQLI scores and lower ESS scores as positive predictors of CPAP usage, along with lower AHI after one month of CPAP being associated with sufficient adherence.
Inactivation Validation of Ebola, Marburg and Lassa Viruses in AVL-Ethanol Treated Viral Cultures
Authors: Cutts Todd , Leung Anders , Banadyga Logan , Krishnan Jay
Published: July 2024

High consequence pathogens such as Ebola, Marburg and Lassa viruses are handled in maximum containment Biosafety Level 4 (BSL-4) laboratories. Genetic material is often isolated from such viruses and subsequently removed from BSL-4 for a multitude of downstream analyses using readily accessible technologies and equipment available at lower biosafety level laboratories. However, it is essential to ensure that these materials are free of viable viruses before removal from BSL-4 to guarantee sample safety. This study details the in-house procedure used for validating the inactivation of Ebola, Marburg and Lassa virus cultures after incubation with AVL lysis buffer (Qiagen) and ethanol. This study’s findings showed no viable virus was detectable when high-titer cultures of Ebola, Marburg, and Lassa viruses were incubated with AVL lysis buffer for 10 minutes, followed by an equal volume of 95% ethanol for 3 minutes, using a method with a sensitivity of ≤0.8 log10 TCID50 as the limit of detection.
Genetic Diversity, Virulence, and Antibiotic Resistance Determinants of Campylobacter jejuni Isolates in Romania
Authors: Baltoiu Madalina , Gradisteanu Pircalabioru Gratiela , Cristea Dana , Sorokin Marinela , Stoica Ileana
Published: July 2024

The emergence of antibiotic-resistant Campylobacter jejuni, a leading cause of gastroenteritis worldwide, presents a significant public health challenge requiring vigilant surveillance and disease control. This study aimed to characterize C. jejuni strains isolated in Romania from 2017-2020, focusing on genetic diversity, virulence, and antibiotic resistance determinants. The isolates underwent phenotypical testing, PCR, and antibiotic resistance assessment using Disc Diffusion Kirby–Bauer for ciprofloxacin, nalidixic acid, erythromycin, and tetracycline. Genetic analysis identified resistance and virulence genes, point mutations, and performed sequence typing (7-gene MLST) to determine genetic relatedness. Results indicated substitutions at position 86 in the amino acid sequence or position 257 in the nucleotide sequence of the gyrA gene in 47 fluoroquinolone-resistant isolates. Additionally, mutations in the rRNA23S gene at positions 2074 and 2075, associated with macrolide resistance, were found in 12 of the 66 isolates. Allelic profiles generated 38 sequence types (STs), including three new STs not present in the reference database. The sequence data analysis revealed a genetically diverse C. jejuni population with a weak clonal structure. This study provides crucial insights into the genetic diversity and antibiotic resistance of C. jejuni strains in Romania, highlighting the need for ongoing surveillance and control measures.
Machine Learning Insights Into Digital Payment Behaviors and Fraud Prediction
Authors: Zhang Xu
Published: July 2024

With the continuous advancement of digital transformation, digital payments are playing an increasingly important role in the financial industry. This study aims to utilize machine learning models to predict and analyze digital payment behavior. Initially, the background and significance of digital payments in the financial sector are introduced. Subsequently, the current status and trends of traditional digital payment distribution are reviewed, alongside related work on digital payment behavior prediction. Methodologically, principles and applications of machine learning models such as logistic regression, decision trees, and random forests are elaborated, along with experimental design and data preprocessing methods. The experimental results and discussion section illustrates the performance of each model in digital payment prediction and explores their impact on credit decisions. This exploration equips financial institutions with more effective user behavior analysis and risk management tools, thereby fostering future development and application of digital payment technologies.
Human Robot Interaction and Human-Robot Intimacy: Acceptance of Robots As Intimate Companions
Authors: Bertoni Sophia , Klaes Christian , Pilacinski Artur
Published: July 2024

Depictions of robots as romantic partners for humans are frequent in popular culture. As robots are becoming part of human society, they gradually assume the role of partners for humans wherever necessary: as assistants, collaborators, or companions. Companion robots are sup-posed to provide social contact to those humans who could not have it otherwise. These com-panion robots are usually not designed to fulfill one of the most important human needs: the one for romantic and intimate contact. Human-robot intimacy remains a vastly unexplored ter-ritory. In this article, we review the state-of-the-art research in intimate robotics. We discuss major issues limiting acceptance of robots as intimate partners, public perception of robots in in-timate roles, and the possible influence of cross-cultural differences in these domains. We also discuss possible negative effects human-robot intimacy may have on human-human contact. Most importantly, we propose a new term “Intimate companion robots” to reduce the negative connotations of other terms used so far and improve the social perception of research in the domain. With this article, we provide an outlook on prospects for the development of intimate companion robots, considering the specific context of their use.
Efficacy and Safety of Poly-L-Lactic Acid in Facial Aesthetics: A Systematic Review
Authors: Signori Roberta , Barbosa Antony de Paula , Cezar-dos-Santos Fernando , Carbone Ana Claudia , Ventura Silvio , Nobre Bryanne Brissian de Souza , Boechat Maria Luiza , Câmara-Souza Mariana Barbosa , Poluha Rodrigo Lorenzi , De la Torre Canales Giancarlo
Published: July 2024

The primary objective of this systematic review study was to investigate the effectiveness, durability, and adverse events of PLLA treatment for aesthetic indications. The search strategy was performed in MEDLINE (Ovid). The electronic literature search of five databases was performed, from the inception of the databases until 12th of February 2024. This was to identify randomized clinical trials that assessed PLLA treatment in adult individuals exhibiting facial aging and/or facial lipoatrophy. Risk of bias was assessed using the Cochrane Risk-of-Bias Tool for Randomized Trials (RoB 2). Eleven RCTs out of 1467 identified citations were included. Four studies showed increased dermal thickness, significant improvement in facial lipoatrophy severity and aesthetic clinical scores, after PLLA treatment with its effects sustained for at least 25 months. Two studies demonstrated the superiority of PLLA over injectable human collagen. Also, three studies showed positive results favoring PLLA when compared with PH gel in lipoatrophy severity, trans epidermal water loss, skin quality, elasticity, and patient satisfaction. All adverse events were mild-to-moderate in intensity, and the main ones worth noting were bruising, hematoma, tenderness, nodules, and edema. Five out of 11 studies were considered as high risk of bias. The evidence on the effectiveness and safety of PLLA for facial rejuvenation is of low quality; thus, the reported high effectiveness, safety, and long-lasting effects for this purpose, should be further investigated.
Enhancing Decarbonation in Solid Waste Management, Wastewater and Contaminated Soil Treatments: The Role of Data Automation and Decision Support Systems
Authors: Mohamed Abdel Mohsen O. , Mohamed Dina , Fayad Adham , Al Nahyan Moza T.
Published: July 2024

As global concerns about climate change intensify, the need for effective strategies to reduce carbon emissions has never been more urgent. This paper explores the crucial role of data automation (DA) and decision support systems (DSS) in enhancing decarbonation efforts within the realms of solid waste management (SWM), wastewater treatment (WWT), and contaminated soil remediation (CSR). Specifically, the paper provides: (i) an overview of the carbon footprint (CFP) in relation to environmental management (EM) and the role of DA and DSS in decarbonization; (ii) DA case studies in areas of SWM, WWT, and CSR; (iii) 3. life cycle assessment (LCA) based DSS case studies in areas of SWM, WWT, and CSR; (iv) multi-criteria decision analysis (MCDA) based DSS case studies in areas of SWM, WWT, and CSR; and (v) optimal contractual delivery methods based DSS case studies in EM practices. The analysis disclosed that adoption of DA and DSS in SWM, WWT, and CSR holds significant potential for decarbonizing these EM processes. By optimizing operations, enhancing resource efficiency, and integrating renewable energy sources, these technologies contribute to the reduction of GHG emissions and promote sustainable environmental practices. As the demand for more effective and eco-friendly solutions grows, the role of DA and DSS will become increasingly pivotal in achieving global decarbonization goals.
Optimising Clinical Epidemiology in Disease Outbreaks: Analysis of ISARIC-WHO COVID-19 Case Report Form Utilisation
Authors: Merson Laura , Duque Sara , Garcia-Gallo Esteban , Yeabah Trokon Omarley , Rylance Jamie , Diaz Janet , Flahault Antoine , ISARIC Clinical Characterisation Group .
Published: July 2024

Standardised forms for capturing clinical data promote consistency in data collection and analysis across study sites, enabling faster, higher-quality evidence generation. ISARIC and the World Health Organization have developed case report forms (CRFs) for the clinical characterisation of several infectious disease outbreaks. To improve the design and quality of future forms, we analysed the inclusion and completion rates of the 243 fields on the ISARIC-WHO COVID-19 CRF. Data from 42 diverse collaborations, covering 1,886 hospitals and 950,064 patients were analysed. A mean of 129.6 fields (53%) were included in the adapted CRFs implemented across the sites. Consistent patterns of field inclusion and completion aligned with globally recognised research priorities in outbreaks of novel infectious diseases. Outcome status was the most highly included (95.2%) and completed (89.8%) field, followed by admission demographics (79.1% and 91.6%), comorbidities (77.9% and 79.0%), signs & symptoms (68.9% and 78.4%) and vitals (70.3% and 69.1%). Mean field completion was higher in severe patients (70.2%) than in all patients (61.6%). The results indicate that clinical characterisation CRFs can be streamlined to reduce data collection time, including the modularisation of CRFs to offer a choice of data volume collection and the separation of critical care interventions. This data-driven approach to designing CRFs enhances the efficiency of data collection to inform patient care and public health response.
Visualization of Bibliometric Data Analysis on AI & Machine Learning Topic from the Scilit Abstract Database for 2021-2023
Authors: Chigarev Boris
Published: July 2024

Objectives. The aim of this study was to demonstrate the ability to visualize the results of the Scilit platform's bibliometric data analysis on the topic "AI & Machine Learning" to identify publications reflecting specific issues of the topic. Data source. Bibliometric records exported from the Scilit platform on the topic "AI & Machine Learning" for the years 2021–2023 were used. For each year, 6,000 records were downloaded in CSV and RIS format. Programs and utilities used. VOSviewer, Scimago Graphica, Inkscape, FP-growth utility, GSDMM algorithm. Used services: Elicit, QuillBot, Litmaps. Results. It has been shown that bibliometric data from the open access abstract database Scilit can serve as a quality alternative to subscription-only databases. Data exported from the Scilit platform require preprocessing to make them available in a format that can be processed by programs such as VOSviewer and Scimago Graphica. The use of GSDMM and FP-growth algorithms is effective for structuring bibliometric data for further visualization. The Scimago Graphica software provides wide possibilities for building compound diagrams, in particular, for representing the network of keywords in such important coordinates for bibliometric analysis as average year of publication and average normalized citation, as well as for building an alluvial diagram of co-occurrence of more than two keywords. The possibility of using such services as elicit.com, quillbot.com and app.litmaps.com to accelerate the selection of publications on the topic under study is shown.
Contribution of echocardiography and 2D strain in the detection of subtle myocardial involvement in group A and B patients with chronic obstructive pulmonary disease
Authors: Kaddoussi Rania , Chamtouri Ikram , Dhouib Wafa , Fatima ezzahra Elassoufi , Daami Monia , Jomaa Walid , Rouetbi Wissal , Ben Hamda Khaldoun
Published: July 2024

Background. Myocardial involvement mediated by chronic obstructive pulmonary disease (COPD) is a common cause of morbidity and mortality. Conventional transthoracic echocardiography (TTE) parameters are poor in the detection of subclinical myocardial dysfunction.
Aim.To investigate the contribution of strain in the early detection of cardiac damage in stable COPD patients
Methods. Group A and B patients with COPD were enrolled in this study. The COPD assessment test, spirometry, 6-minute walk test, and both conventional TTE and strain were performed in these patients.
Results. 80 COPD patients, with a mean age 65.6 ± 8.9 years, were included. The. Left ventricular ejection fraction (LVEF) was 60.7 ± 5.1%. Right atrium and right ventricle (RV) strain were 24.5 ± 6.6% and -19.9 ± 3.7%, respectively. Additionally, left ventricle global longitudinal strain (LV GLS) was -21.1 ± 2. Forty-eight patients had impaired RV strain. Compared to COPD patient with normal RV strain, those with RV reduced starin had a lower 6meter walk distance (6MWD) (p=0.001) and forced expiratory volume in the first second (FEV1) (p=0.012), and a higher CAT score (p=0.012). A reduced RV strain was correlated with a higher risk of hospitalizations for acute exacerbation in the post inclusion year (55% versus 25%; p=0.024). No deaths were recorded during the follow-up period. No significant factors causing neither RA starin alteration nor LV GLS reduction were revealed.

Conclusion. Group A and B COPD patients having normal conventional TTE parameters, speckle tracking is a key parameter in the detection of subclinical myocardial dysfunction.
Diagnosis of Chronic Granulomatous Disease: Strengths and Challenges in the Genomic Era
Authors: O’Donovan Conor John , Tan Lay Teng , Abidin Mohd Azri Zainal , Roderick Marion Ruth , Grammatikos Alexandros , Bernatoniene Jolanta
Published: July 2024

Chronic granulomatous disease (CGD) is a group of rare primary inborn errors of immunity characterised by a defect in the phagocyte respiratory burst, which leads to severe and life-threatening infective and inflammatory complications. Despite recent advances in our understanding of the genetic and molecular pathophysiology of X-linked and autosomal recessive CGD, and growth in the availability of functional and genetic testing, there remain significant barriers to early and accurate diagnosis. In the current review, we provide an up-to-date summary of CGD pathophysiology, which underpins methods of diagnostic testing for CGD and closely related disorders. We present an overview of the benefits of early diagnosis and when to suspect and test for CGD. We discuss current and historical methods for functional testing of NADPH oxidase activity, as well as assays for measuring protein expression of NADPH oxidase subunits. Lastly, we focus on genetic and genomic methods employed to diagnose CGD, including gene-targeted panels, comprehensive genomic testing and ancillary methods. Throughout, we highlight general limitations of testing, and caveats specific to interpretation of results in the context of CGD and related disorders, and provide an outlook for newborn screening and the future.
A Life Cycle Analysis Applied to Municipal Effluents in Africa Has the Potential to Make a Significant Contribution to the Field
Authors: Chirinza Nicolau , Muguirrima Paulino , García-Ramírez Tania , Zerpa Federico A. Leon , Mendieta Pino Carlos Alberto
Published: July 2024

The objective of the described activity is to develop technologies or proposals that can be implemented within the cycle to enhance the relationship between climate change, water, energy, and food. The focus is on analyzing natural treatment systems for wastewater (NTSW) within the context of Macaronesia, considering factors such as life cycle assessment (LCA), carbon footprint, impacts, and mitigation capacity. The analysis of real case data from the Canary Islands and Cape Verde will inform the development of appropriate technologies tailored to different areas and scales within Macaronesia. This work includes a comprehensive life cycle analysis of the Santa Catarina (Cape Verde) NTSW. This analysis encompasses: a) Inventory analysis of the construction phase: This involves the assessment of inputs and outputs associated with the construction of the NTSW, including materials, energy consumption, transportation, and waste generation. The maintenance and operation phase is then evaluated, with a focus on the ongoing maintenance and operation activities required for the NTSW, including energy consumption, water usage, chemical inputs (if any), labour, and equipment maintenance. Finally, the impacts of the NTSW are evaluated. The environmental, social, and economic impacts generated by the NTSW are assessed. This includes an analysis of factors such as carbon emissions, water usage, land use, ecosystem impacts, human health effects, and economic costs. By conducting a comprehensive analysis of the Santa Catarina NTSW, the document aims to provide insights into the environmental performance and sustainability of the system. This information can then be used to identify areas for improvement, develop mitigation strategies, and inform decision-making processes regarding wastewater treatment technologies in Macaronesia. Furthermore, lessons learned from real case studies in the Canary Islands and Cape Verde can be applied to similar regions within the Macaronesia archipelago.
Numerical Modeling and Simulation of Vehicular Crashes Into Three-Bar Metal Bridge Rail
Authors: Fang Howie , Jaus Christopher , Wang Qian , Palta Emre , Pachocki Lukasz , Bruski Dawid
Published: July 2024

Advanced Finite Element (FE) modeling and simulations were performed on vehicular crashes into a Three-bar Metal Bridge Rail (TMBR). The FE models of a sedan, a pickup truck, and a TMBR section were adopted in the crash simulations subject to Manual for Assessing Safety Hardware (MASH) Test Level 2 (TL-2) and Test Level 3 (TL-3) requirements. The test vehicle models were first validated using full-scale physical crash tests conducted on a two-bar metal bridge using a sedan and a pickup truck with similar overall physical properties and sizes to their respective vehicles used in the simulations. The validated vehicular models were then used to evaluate the crash performance of the TMBR using MASH Evaluation Criteria for structural adequacy, occupant risk, and post-impact trajectory. The TMBR met all MASH TL-2 requirements but failed to meet the MASH TL-3 Criteria H and N requirements when impacted by the sedan. The TMBR was also evaluated under in-service conditions (behind a 1.52-m wide sidewalk) and impacted by the sedan under MASH TL-3 conditions. The simulation results showed that the TMBR behind a sidewalk met all safety requirements except for the occupant impact velocity in the longitudinal direction, which exceeded the MASH limit by 3.93%.
Lactylation: A Novel Post-Translational Modification with Clinical Implications in CNS Diseases
Authors: Liu Junyan , Zhao Fengyan , Qu Yi
Published: July 2024

Lactate, an important metabolic product, provides energy to neural cells during energy depletion or high demand and acts as a signaling molecule in the central nervous system. Recent studies revealed that lactate-mediated protein lactylation regulates gene transcription and influences cell fate, metabolic processes, inflammation, and immune responses. This review comprehensively examines the regulatory roles and mechanisms of lactylation in neurodevelopment, neuropsychiatric disorders, brain tumors, and cerebrovascular diseases. This analysis indicates that lactylation has multifaceted effects on central nervous system function and pathology, particularly in hypoxia-induced brain damage. Highlighting its potential as a novel therapeutic target, lactylation may play a significant role in treating neurological diseases. By summarizing current findings, this review aims to provide insights and guide future research and clinical strategies for central nervous system disorders.
The Effects of Climate on the Death and Growth Decline of Pinus densiflora Siebold & Zucc
Authors: Shin Jae-Soo , Lee Yo-Seop , Kim Eun-Sook , Jang Keun-Chang , Lee Ju-Seok , Seo Jeong-Wook
Published: July 2024

The present study aimed to date the death of Korean red pine (Pinus densiflora Siebold & Zucc.) at Sogwang-ri, where is famous for the Korean red pine forests in Korea, using a dendrochronological method, and to investigate the effect of climatic factors on the death and growth decline of the pine trees. For the study, 51 Korean red pines were selected from three stands (Jungmi-gol A, Jungmi-gol B, and Neonam-gol) in the study area, and for the ring-width measurement the increment cores were extracted from the selected trees. The stand chronologies for the Jungmi-gol and Neonam-gol were established using the mean values of individual time series of the living trees from each site, and the chronologies were further used for cross-dating with individual time series of the dead trees to date their death. The abrupt growth reduction was also investigated based on the consecutive ring-width growth decline trend to know the vitality during the living. Results revealed that the red pines at Jungmi-gol A were dead in 2020, at Jungmi-gol B in 2012 or 2013, and at Neonam-gol in 2019. So, it was verified that the death of the red pines at the study sites occurred from high to low elevations. In the analysis of the role of climate in the annual ring growth, the trees at Jungmi-gol A and B located on the southwest slope showed more ring-width growth when precipitation in April, July, and August were increasing, at Neoman-gol located on the north slope when precipitation only in July was increasing. Although the correlations of the ring-width growths at the sites with temperature and precipitation were somehow different from each other, Drought Stress Index (DSI) from May to September at all sites were all negative. Therefore, water stress caused by increased temperature and decreased precipitation is the most significant climatic parameter for the death or growth decline of the pine trees in the study area.
Color Standardization of Chemical Solution Images Using Template-Based Histogram Matching in Deep Learning Regression
Authors: Kwiek Patrycja , Jakubowska Małgorzata
Published: July 2024

Color distortion in an image poses a challenge for classification and regression when the input data consists of pictures. Because of that, a new algorithm for color standardization of photos is proposed, which serves as a base for a deep neural network regression model. This approach utilizes a custom color template, which was developed based on an initial series of studies and digital imaging. Using the equalized histogram of R, G, B channels from the digital template and its photo, a color mapping strategy is computed. By applying this approach, the histograms are adjusted, and the colors of photos taken with a smartphone are standardized. The proposed algorithm has been developed for a series of photos where the entire surface roughly maintains a uniform color, and the differences in color between the photographs of individual objects are minor. The optimized approach was validated in the colorimetric determination procedure of vitamin C. The dataset for deep neural network in the regression variant was formed from photos of samples under two separate lighting conditions. For the concentration range of vitamin C from 0 to 87.72 µg·mL-1, the RMSE for the test set ranged between 0.75 and 1.95 µg·mL-1, in comparison to the non-standardized variant, where this indicator was at the level of 1.48-2.29 µg·mL-1. The consistency of the predicted concentration results with actual data expressed using R2 was between 0.9956 and 0.9999, for each of the standardized variants. This approach allows for the removal of light reflections on the shiny surface of solutions, which is a common problem in liquid samples. The color matching algorithm has a universal character, and its application scope is not limited.
Could the Spike Protein Derived from mRNA Vaccines Negatively Impact Beneficial Bacteria in the Gut?
Authors: Rubio-Casillas Alberto , Fabrowski Mark , Brogna Carlo , Cowley David , Redwan Elrashdy M. , Uversky Vladimir N.
Published: July 2024

The emergence of mRNA vaccines for SARS-CoV-2 has opened a new page in vaccine development; nevertheless, concerns of the experts have been expressed about unintentional side effects on the gut microbiota (GM). Previous studies showed that this virus acts as a bacteriophage, which infects and destroys specific bacterial strains in the GM. The present manuscript hypothesizes that the synthetic spike protein could create changes in the composition and the functioning of the GM by entering the intestinal cells after vaccination and impairing the symbiotic relationship between intestinal cells and the GM. An experimental protocol to test the hypothesis is suggested.
Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Diseases in a Typical South India Station
Authors: Babu Gurujukota Ramesh , Gokuldhev M. , Brahmanandam P. S.
Published: July 2024

This study develops a hybrid machine learning (ML) algorithm integrated with IoT technology to improve the accuracy and efficiency of soil monitoring and tomato crop disease prediction in Anakapalle, a south Indian station. An IoT device collected one-minute and critical soil parameters—humidity, temperature, pH values, nitrogen (N), phosphorus (P), and potassium (K)—essential for assessing soil health and optimizing crop growth. Kendall's correlations were computed to rank these parameters for utilization in hybrid ML techniques. Various ML algorithms including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Tree (DT), Random Forest (RF), and Logistic Regression (LR) were evaluated. A novel hybrid algorithm, 'Bayesian Optimization with KNN', was introduced to combine multiple ML techniques and enhance predictive performance. The hybrid algorithm demonstrated superior results with 95% accuracy, precision, and recall, and an F1 score of 94%, while individual ML algorithms achieved varying results: KNN (80% accuracy), SVM (82%), DT (77%), RF (80%), and LR (81%) with differing precision, recall, and F1 scores. This hybrid ML approach proved highly effective in predicting tomato crop diseases in natural environments, underscoring the synergistic benefits of IoT and advanced ML techniques in optimizing agricultural practices.
 Evolution, Childhood Development, and Disease: The Role of Redundant and Degenerate Neural Networks in Cognitive Maximization
Authors: Montgomery Richard Murdoch
Published: July 2024

This small article examines the pivotal roles of redundant and degenerate neural networks in the brain across three critical dimensions: evolutionary development, early childhood neural formation, and their implications in neurological diseases. Redundant networks, characterized by parallel neural pathways with similar functions, provide a foundation for cognitive resilience, ensuring continuity of brain functions despite damage or developmental anomalies. Degenerate networks, comprising structurally varied but functionally equivalent pathways, contribute to cognitive flexibility and adaptability, essential for innovative problem-solving and learning. From an evolutionary perspective, these networks have been instrumental in the brain's adaptation to diverse environmental challenges, facilitating cognitive maximization and the development of complex behaviors. The genetic underpinnings and environmental influences that shaped these networks are explored, highlighting their contribution to the evolutionary success of the human species. In the realm of early childhood development, the formation and reinforcement of these networks are crucial. The brain's plasticity during this period leads to the establishment of robust redundant networks and versatile degenerate networks, shaped significantly by environmental stimuli and learning experiences. This developmental phase lays the groundwork for cognitive abilities and resilience to neurological challenges later in life. Furthermore, the paper delves into the implications of these networks in the context of brain diseases. The disruption of the equilibrium between redundancy and degeneracy is linked to various neurological conditions, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke and Huntington's disease. Understanding the roles and balances of these networks offers insights into the pathophysiology of these diseases and potential therapeutic approaches. This review underscores the significance of redundant and degenerate neural networks in the evolutionary context, their critical formation during early childhood, and their profound impact on brain health and disease. It highlights the need for further research into these networks to enhance our understanding of brain function and to develop targeted interventions for neurological disorders.
Beyond the P-Value: The Significance of Inconclusive Results and the Role of the IIRF in Advancing Scientific Inquiry
Authors: Frimpong Victor , Wolfs Bert
Published: July 2024

In scientific research, there is a noticeable lack of specific studies focused on inconclusive results, in contrast to the abundance of studies dedicated to conclusive or positive outcomes. While conclusive results offer definitive answers, inconclusive results, often viewed less favorably, do not provide such clarity. However, they play a vital role in the scientific process by identifying gaps and inspiring future research. This paper introduces the pioneering Integrated Inconclusive Results Framework, a significant development that addresses the challenges associated with inconclusive results. The Framework aims to shift the perception of inconclusive results from setbacks to crucial components of scientific progress, ultimately enhancing scientific inquiry's rigor, transparency, and overall quality. The paper delves into the historical treatment of inconclusive results, causes, and strategies for handling them, offering insights into utilizing them to inspire and guide future research, thereby strengthening the scientific community and instilling confidence and reassurance in the direction of scientific research.
Theoretical Framework for Future Developments: AI-Based Predictions and Risk Assessment for Coastal Ports in the Bulgarian Sector of the Black Sea
Authors: Sotirov Milen Krasimirov , Petrova Valentina Markova , Dimitrov Dimitar Petkov
Published: July 2024

The integration of Artificial Intelligence (AI) in predicting and managing risks for coastal port developments significantly enhances planning and operational efficiency. This paper presents a theoretical framework utilizing AI to predict future scenarios based on geological, hydrodynamic, and oceanographic factors. Focusing on the effective implementation of AI in ports, with a specific emphasis on Bulgarian Black Sea coastal areas such as Nesebur, Sarafovo, Sozopol, St. Anastasia Island, and the region of Karantinata in Varna, the study explores AI applications for site selection, risk assessment, and operational optimization to ensure resilient, efficient, and sustainable port infrastructure.