bluuebunny/crossref_metadata_2025 huggingface dataset:
Oil Price Uncertainty Shocks and Global Equity Markets: Evidence from a GVAR Model
Authors: Salisu Afees A. , Gupta Rangan , Demirer Riza
Published: August 2022

This paper examines the propagation of oil price uncertainty shocks to real equity prices using a large-scale Global Vector Autoregressive (GVAR) model of 26 advanced and emerging stock markets. The GVAR framework allows us to capture the transmission of local and global shocks, while simultaneously accounting for individual-country peculiarities. Utilising a recently developed model-free, robust estimate of oil price uncertainty, we document a statistically significant and negative effect of uncertainty shocks emanating from oil prices on the large majority of global stock markets, with the adverse effect of oil price uncertainty shocks found to be stronger for emerging economies as well as net oil-exporting nations. Interestingly, however, global stock markets exhibit a great deal of heterogeneity in their recovery following oil uncertainty shocks as some experience rapid corrections in stock valuations while others suffer from extended slumps. While the results are sensitive to the oil uncertainty measure utilised, they suggest that country diversification in the face of rising oil market uncertainty can still be beneficial for global investors as global stock markets exhibit a rather heterogeneous pattern in their recovery rates against oil market shocks.
Re-Imagining Wild Rivers in Aotearoa New Zealand
Authors: Brierley Gary , Fuller Ian , Williams Gary , Hikuroa Dan , Tilley Alice
Published: August 2022

If wilderness is dead, do wild rivers exist and if so, in what form and in whose construction? This reflective article reviews perspectives on rivers in Aotearoa New Zealand as wild or tamed entities. A historical overview of the socio-cultural and institutional relationships with rivers examines the meanings of rivers in Aotearoa New Zealand through multiple lenses. This includes indigenous Māori knowledge, command-and-control mentalities of a settler society that assert human authority over rivers, the emergence of the environmental movement and associated legislation with a sustainability focus (the Resource Management Act), and recent movement towards co-governance arrangements that incorporate the original intent of Te Tiriti o Waitangi (1840). It is contended that management practices have disconnected society from rivers, and vice versa, creating a sense of environmental loss (solastalgia), especially for Māori. Using rivers in the Greater Wellington Region as examples, prospects to accommodate wild river behaviour in Aotearoa New Zealand are explored. Recognising that re-wilding is no longer a feasible option in most instances, further attempts to tame rivers are also considered to be unrealistic, especially in light of climate change and accentuated flood risk. Reconnecting with indigenous knowledge offers prospects to re-imagine wild rivers in Aotearoa, living generatively with rivers as dynamic and emergent entities.
Clinical and Molecular Aspects of Iron Metabolism in Failing Myocytes
Authors: Kozłowska Bogna , Sochanowicz Barbara , Kraj Leszek , Palusińska Małgorzata , Kołsut Piotr , Szymański Łukasz , Lewicki Sławomir , Kruszewski Marcin , Załęska-Kocięcka Marta , Leszek Przemysław
Published: August 2022

Heart failure (HF) is a common disease that causes significant limitations on the organism’s capacity and, in extreme cases, leads to death. Clinically, iron deficiency (ID) plays an essential role in heart failure by deteriorating the patient’s condition and is a prognostic marker indicating poor clinical outcomes. Therefore, in HF patients, supplementation of iron is recommended. However, iron treatment may cause adverse effects by increasing iron-related apoptosis and the production of oxygen radicals, which may cause additional heart damage. Furthermore, many knowledge gaps exist regarding the complex interplay between iron deficiency and heart failure. Here, we describe the current, comprehensive knowledge about the role of the proteins involved in iron metabolism. We will focus on the molecular and clinical aspects of iron deficiency in HF. We believe that summarizing the new advances in the translational and clinical research regarding iron deficiency in heart failure should broaden clinicians’ awareness of this comorbidity.
Mobile Access to Medical Records in Heart Transplantation Aftercare: Mixed-Methods Study Assessing Usability, Feasibility and Effects of a Mobile Application
Authors: Müller Julia , Weinert Lina , Svensson Laura , Rivinius Rasmus , Kreusser Michael , Heinze Oliver
Published: August 2022

Background: Patient access to medical records can improve quality of care. The phellow application (app) was developed to provide patients access to selected content of their medical record. It was tested at a heart transplantation (HTx) outpatient clinic. The aims of this study were (1) to assess usability of phellow, (2) to determine feasibility of implementation in routine care, and (3) to study the effects app use had on patients’ self-management. Methods: Usability was measured quantitatively through the System Usability Scale (SUS). Furthermore, usability, feasibility, and effects on self-management were qualitatively assessed through interviews with users, non-users, and health care providers. Results: The SUS rating (n = 31) was 79.9, indicating good usability. Twenty-three interviews were conducted. Although appreciation and willingness-to-use were high, usability problems such as incompleteness of record, technical issues, and complex registration procedures were reported. Improved technical support infrastructure, clearly defined responsibilities, and app-specific trainings were suggested for further implementation. Patients described positive effects on their self-management. Conclusions: To be feasible for implementation in routine care, usability problems should be addressed. Feedback on the effect of app use was encouraging. Accompanying research is crucial to monitor usability improvements and to further assess effects of app use on patients.
Numerical Investigation of the U-Steel Guard Rail Used in Train-to-Train Collision Tests
Authors: Xiao Xianliang , Xu Ping , Yao Shuguang , Li Benhuai , Guo Wei , Zhao Hui
Published: August 2022

To reduce the occurrence of accidents during the train-to-train collision test, it is necessary to carry out research on derailment protection. According to the characteristics of the train-to-train collision test, a method of using a U-steel as a guard rail is proposed. The protection range of the guard rail is obtained through the geometric relationship. The protection ability and damage risks of the guard rail in different impact conditions, impact velocities, and installation positions are studied. Additionally, the section shape is optimized for multiple objectives. The results show that the guard rail can effectively reduce the lateral displacement and the rotation angle of the bogie within the protective range and protect the vehicle, but for some conditions, the damage risk of the guard rail itself is greater. The maximum stress of the optimized structure is reduced by 28.19%, which reduces the risk of damage, and the optimized guard rail still has a good protection ability.
Elaboration of an Algorithm for Solving Hierarchical Inverse Problems in Applied Economics
Authors: Gribanova Ekaterina
Published: August 2022

One of the key tools in an organization’s performance management is the goal tree, which is used for solving both direct and inverse problems. This research deals with goal setting based on a model of the future by presenting the goal and subgoal in the form of concrete quantitative and qualitative characteristics and stepwise formation of factors. A stepwise solution to a factor generation problem is considered on the basis of mathematical symmetry. This paper displays an algorithm for solving hierarchical inverse problems with constraints, which is based on recursively traversing the vertices that constitute the separate characteristics. Iterative methods, modified for the case of nonlinear models and the calculation of constraints, were used to generate solutions to the subproblems. To realize the algorithm, the object-oriented architecture, which simplifies the creation and modification of software, was elaborated. Computational experiments with five types of models were conducted, and the solution to a problem related to fast-food restaurant profit generation was reviewed. The metrics of remoteness from set values and t-statistics were calculated for the purpose of testing the received results, and solutions to the subproblems, with the help of a mathematical package using optimization models and a method of inverse calculations, were also provided. The results of computational experiments speak to the compliance of the received results with set constraints and the solution of separate subproblems with the usage of the mathematical package. The cases with the highest solution accuracy reached are specified.
A Game Theory Proof of Optimal Colorings Resilience to Strong Deviations
Authors: Madeo Dario , Mocenni Chiara , Palma Giulia , Rinaldi Simone
Published: August 2022

This paper provides a formal proof of the conjecture stating that optimal colorings in max k-cut games over unweighted and undirected graphs do not allow the formation of any strongly divergent coalition, i.e., a subset of nodes able to increase their own payoffs simultaneously. The result is obtained by means of a new method grounded on game theory, which consists in splitting the nodes of the graph into three subsets: the coalition itself, the coalition boundary and the nodes without relationship with the coalition. Moreover, we find additional results concerning the properties of optimal colorings.
Detection of Abnormal Cardiac Response Patterns in Cardiac Tissue Using Deep Learning
Authors: Marimon Xavier , Traserra Sara , Jiménez Marcel , Ospina Andrés , Benítez Raúl
Published: August 2022

This study reports a method for the detection of mechanical signaling anomalies in cardiac tissue through the use of deep learning and the design of two anomaly detectors. In contrast to anomaly classifiers, anomaly detectors allow accurate identification of the time position of the anomaly. The first detector used a recurrent neural network (RNN) of long short-term memory (LSTM) type, while the second used an autoencoder. Mechanical contraction data present several challanges, including high presence of noise due to the biological variability in the contraction response, noise introduced by the data acquisition chain and a wide variety of anomalies. Therefore, we present a robust deep-learning-based anomaly detection framework that addresses these main issues, which are difficult to address with standard unsupervised learning techniques. For the time series recording, an experimental model was designed in which signals of cardiac mechanical contraction (right and left atria) of a CD-1 mouse could be acquired in an automatic organ bath, reproducing the physiological conditions. In order to train the anomaly detection models and validate their performance, a database of synthetic signals was designed (n = 800 signals), including a wide range of anomalous events observed in the experimental recordings. The detector based on the LSTM neural network was the most accurate. The performance of this detector was assessed by means of experimental mechanical recordings of cardiac tissue of the right and left atria.
Acyclic Chromatic Index of 1-Planar Graphs
Authors: Yang Wanshun , Wang Yiqiao , Wang Weifan , Liu Juan , Finbow Stephen , Wang Ping
Published: August 2022

The acyclic chromatic index χa′(G) of a graph G is the smallest k for which G is a proper edge colorable using k colors. A 1-planar graph is a graph that can be drawn in plane such that every edge is crossed by at most one other edge. In this paper, we prove that every 1-planar graph G has χa′(G)≤Δ+36, where Δ denotes the maximum degree of G. This strengthens a result that if G is a triangle-free 1-planar graph, then χa′(G)≤Δ+16.
Fuzzy Quality Evaluation and Analysis Model for Improving the Quality of Unleaded Gasoline to Reduce Air Pollution
Authors: Yang Chun-Ming , Huang Tsun-Hung , Chen Kuen-Suan , Chen Chi-Han , Li Shiyao
Published: August 2022

It is important to estimate the sample data when inspecting the quality of products. Therefore, sampling error and uncertainty in the measurement are inevitable, which may lead to misjudgment in product performance evaluation. Since the important quality characteristics of gasoline belong to one-sided specifications, a one-sided specification capability index was proposed to evaluate whether the process capabilities of various quality characteristics of gasoline reach the required quality levels. The 100(1−α)% upper confidence limits of the index were obtained to ensure low producer’s risk and reduce sampling errors. To deal with fuzzy data and limited sample sizes, a fuzzy testing model based on the 100(1−α)% upper confidence limits of the index was developed. A practice example of 95 unleaded gasoline was used to illustrate the effectiveness and usefulness of the proposed method. The result shows that two quality characteristics—Reid vapor pressure and oxygen content—of the nine quality characteristics of the 95 unleaded gasoline should be considered for improvements. This study provided an evaluation procedure to facilitate quality managers to take the opportunity to improve product quality, promoting the improvement of air quality, and the sustainability of industrial processes or products.
SMoCo: A Powerful and Efficient Method Based on Self-Supervised Learning for Fault Diagnosis of Aero-Engine Bearing under Limited Data
Authors: Yan Zitong , Liu Hongmei
Published: August 2022

Vibration signals collected in real industrial environments are usually limited and unlabeled. In this case, fault diagnosis methods based on deep learning tend to perform poorly. Previous work mainly used the unlabeled data of the same diagnostic object to improve the diagnostic accuracy, but it did not make full use of the easily available unlabeled signals from different sources. In this study, a signal momentum contrast for unsupervised representation learning (SMoCo) based on the contrastive learning algorithm—momentum contrast for unsupervised visual representation Learning (MoCo)—is proposed. It can learn how to automatically extract fault features from unlabeled data collected from different diagnostic objects and then transfer this ability to target diagnostic tasks. On the structure, SMoCo increases the stability by adding batch normalization to the multilayer perceptron (MLP) layer of MoCo and increases the flexibility by adding a predictor to the query network. Using the data augmentation method, SMoCo performs feature extraction on vibration signals from both time and frequency domains, which is called signal multimodal learning (SML). It has been proved by experiments that after pre-training with artificially injected fault bearing data, SMoCo can learn a powerful and robust feature extractor, which can greatly improve the accuracy no matter the target diagnostic data with different working conditions, different failure modes, or even different types of equipment from the pre-training dataset. When faced with the target diagnosis task, SMoCo can achieve accuracy far better than other representative methods in only a very short time, and its excellent robustness regarding the amount of data in both the unlabeled pre-training dataset and the target diagnosis dataset as well as the strong noise demonstrates its great potential and superiority in fault diagnosis.
Tail Asymptotics for a Retrial Queue with Bernoulli Schedule
Authors: Liu Bin , Zhao Yiqiang Q.
Published: August 2022

In this paper, we study the asymptotic behaviour of the tail probability of the number of customers in the steady-state M/G/1 retrial queue with Bernoulli schedule, under the assumption that the service time distribution has a regularly varying tail. Detailed tail asymptotic properties are obtained for the conditional probability of the number of customers in the (priority) queue and orbit, respectively, in terms of the recently proposed exhaustive stochastic decomposition approach. Numerical examples are presented to show the impacts of system parameters on the tail asymptotic probabilities.
Chaotification of One-Dimensional Maps Based on Remainder Operator Addition
Authors: Moysis Lazaros , Kafetzis Ioannis , Baptista Murilo S. , Volos Christos
Published: August 2022

In this work, a chaotification technique is proposed that can be used to enhance the complexity of any one-dimensional map by adding the remainder operator to it. It is shown that by an appropriate parameter choice, the resulting map can achieve a higher Lyapunov exponent compared to its seed map, and all periodic orbits of any period will be unstable, leading to robust chaos. The technique is tested on several maps from the literature, yielding increased chaotic behavior in all cases, as indicated by comparison of the bifurcation and Lyapunov exponent diagrams of the original and resulting maps. Moreover, the effect of the proposed technique in the problem of pseudo-random bit generation is studied. Using a standard bit generation technique, it is shown that the proposed maps demonstrate increased statistical randomness compared to their seed ones, when used as a source for the bit generator. This study illustrates that the proposed method is an efficient chaotification technique for maps that can be used in chaos-based encryption and other relevant applications.
Lump Collision Phenomena to a Nonlinear Physical Model in Coastal Engineering
Authors: Sulaiman Tukur Abdulkadir , Yusuf Abdullahi , Alshomrani Ali Saleh , Baleanu Dumitru
Published: August 2022

In this study, a dimensionally nonlinear evolution equation, which is the integrable shallow water wave-like equation, is investigated utilizing the Hirota bilinear approach. Lump solutions are achieved by its bilinear form and are essential solutions to various kind of nonlinear equations. It has not yet been explored due to its vital physical significant in various field of nonlinear science. In order to establish some more interaction solutions with some novel physical features, we establish collision aspects between lumps and other solutions by using trigonometric, hyperbolic, and exponential functions. The obtained novel types of results for the governing equation includes lump-periodic, two wave, and breather wave solutions. Meanwhile, the figures for these results are graphed. The propagation features of the derived results are depicted. The results reveal that the appropriate physical quantities and attributes of nonlinear waves are related to the parameter values.
Statistical Tables in Spanish Primary School Textbooks
Authors: Gea María M. , Pallauta Jocelyn D. , Batanero Carmen , Valenzuela-Ruiz Silvia M.
Published: August 2022

Statistics is introduced in primary education in Spain, and its teaching is largely supported by textbooks, which are freely provided to the children. In this research, we analyse the activities based on statistical tables included in two complete collections of primary education mathematics textbooks. These activities are classified according to the type of table, the data presented in them, the activity requested from the student in relation to the table, and the data context, according to those suggested in the PISA studies. Using content analysis, we found that tables of distribution of a variable predominate, mainly with frequencies. The most frequent activity is the reading of information from the table, while the personal context appears in the majority of the activities. This analysis serves to highlight how the teaching of statistical tables is developed throughout primary education and inform the teachers about relevant variables that should be considered in their teaching.
Dynamic Connectedness among Vaccine Companies’ Stock Prices: Before and after Vaccines Released
Authors: Sohag Kazi , Gainetdinova Anna , Hammoudeh Shawkat , Shams Riad
Published: August 2022

This study investigates the interconnectedness among the stocks of the publicly listed vaccine-producing companies before and after vaccine releases in 2020/21. In doing so, the study utilizes the daily frequency equity returns of the major vaccine producers, including Moderna, Pfizer, Johnson & Johnson, Sinopharm and AstraZeneca. First, the investigation applies the TVP-VAR Dynamic Connectedness approach to explore the time–frequency connectedness between the stocks of those vaccine producers. The empirical findings demonstrate that Moderna performs as the most prominent net volatility contributor, whereas Sinopharm is the highest net volatility receiver. Interestingly, the vaccine release significantly increases the stock market connectedness among our sampled vaccine companies. Second, the cross-quantile dependency framework allows for the observation of the interconnectedness under the bearish and bullish stock market conditions by splitting any paired variables into 19 quantiles when considering short-, medium- and long-memories. The results also show that a high level of connectivity among the vaccine producers exists under bullish stock market conditions. Notably, Moderna transmits significant volatility spillovers to Sinopharm, Johnson & Johnson and AstraZeneca under both the bearish and bullish conditions, though the volatility transmission from Moderna to Pfizer is less pronounced. The policy implication proposes that the vaccine release allows companies to increase their stock returns and induce substantial volatility spillovers from company to company.
New Criterias of Synchronization for Discrete-Time Recurrent Neural Networks with Time-Varying Delay via Event-Triggered Control
Authors: Yu Lei , Chen Guici , Jiang Feng , Wang Zhi
Published: August 2022

This paper mainly researches the synchronization issue of discrete-time recurrent neural networks (DTRNNs) with time-varying delay based on event-triggered control (ETC). ETC can effectively decrease the quantity of controller updates performed and the utilization of communication resources. By using Lyapunov–Krasovskii functional (LKF), Schur complement lemma, discrete time free weight matrix method, linear matrix inequalities (LMIs) and other analytical methods, the stability conditions of the error system are deduced. Accordingly, a class of event-triggered state feedback controllers is designed. Finally, through two numerical examples with simulations, the effectiveness of the controller is verified.
Explainable Machine Learning Methods for Classification of Brain States during Visual Perception
Authors: Islam Robiul , Andreev Andrey V. , Shusharina Natalia N. , Hramov Alexander E.
Published: August 2022

The aim of this work is to find a good mathematical model for the classification of brain states during visual perception with a focus on the interpretability of the results. To achieve it, we use the deep learning models with different activation functions and optimization methods for their comparison and find the best model for the considered dataset of 31 EEG channels trials. To estimate the influence of different features on the classification process and make the method more interpretable, we use the SHAP library technique. We find that the best optimization method is Adagrad and the worst one is FTRL. In addition, we find that only Adagrad works well for both linear and tangent models. The results could be useful for EEG-based brain–computer interfaces (BCIs) in part for choosing the appropriate machine learning methods and features for the correct training of the BCI intelligent system.
Inference and Local Influence Assessment in a Multifactor Skew-Normal Linear Mixed Model
Authors: Najafi Zeinolabedin , Zare Karim , Mahmoudi Mohammad Reza , Shokri Soheil , Mosavi Amir
Published: August 2022

This work considers a multifactor linear mixed model under heteroscedasticity in random-effect factors and the skew-normal errors for modeling the correlated datasets. We implement an expectation–maximization (EM) algorithm to achieve the maximum likelihood estimates using conditional distributions of the skew-normal distribution. The EM algorithm is also implemented to extend the local influence approach under three model perturbation schemes in this model. Furthermore, a Monte Carlo simulation is conducted to evaluate the efficiency of the estimators. Finally, a real data set is used to make an illustrative comparison among the following four scenarios: normal/skew-normal errors and heteroscedasticity/homoscedasticity in random-effect factors. The empirical studies show our methodology can improve the estimates when the model errors follow from a skew-normal distribution. In addition, the local influence analysis indicates that our model can decrease the effects of anomalous observations in comparison to normal ones.
Negative Feedback Punishment Approach Helps Sanctioning Institutions Achieve Stable, Time-Saving and Low-Cost Performances
Authors: Qian Jun , Sun Xiao , Wang Ziyang , Chai Yueting
Published: August 2022

Sanctioning institutions widely exist in human society. Although these institutions play an important role in the management of social affairs, sanctions are often seen to be costly in terms of both time and money. To enable sanctioning institutions to develop effective sanctions, we propose a negative feedback punishment approach for these institutions that combines the feedback control principle and the negative correlation principle. In the negative feedback punishment approach, the punishment intensity imposed on the group is negatively correlated with the current group cooperation proportion. Through evolutionary simulation and theoretical analysis, we found that the negative feedback punishment approach facilitates more stable, time-saving and low-cost performance by sanctioning institutions than other punishment methods. This work offers a feasible solution for sanctioning institutions to solve social dilemmas and provides a possible theoretical starting point for investigating effective pool punishment measures.
Odd Exponential-Logarithmic Family of Distributions: Features and Modeling
Authors: Chesneau Christophe , Tomy Lishamol , Jose Meenu , Jayamol Kuttappan Vallikkattil
Published: August 2022

This paper introduces a general family of continuous distributions, based on the exponential-logarithmic distribution and the odd transformation. It is called the “odd exponential logarithmic family”. We intend to create novel distributions with desired qualities for practical applications, using the unique properties of the exponential-logarithmic distribution as an initial inspiration. Thus, we present some special members of this family that stand out for the versatile shape properties of their corresponding functions. Then, a comprehensive mathematical treatment of the family is provided, including some asymptotic properties, the determination of the quantile function, a useful sum expression of the probability density function, tractable series expressions for the moments, moment generating function, Rényi entropy and Shannon entropy, as well as results on order statistics and stochastic ordering. We estimate the model parameters quite efficiently by the method of maximum likelihood, with discussions on the observed information matrix and a complete simulation study. As a major interest, the odd exponential logarithmic models reveal how to successfully accommodate various kinds of data. This aspect is demonstrated by using three practical data sets, showing that an odd exponential logarithmic model outperforms two strong competitors in terms of data fitting.
Efficacy and Safety of Acetabular Cup without Screw Fixation in Total Hip Arthroplasty: A Systematic Review and Meta-Analysis
Authors: Miura Takanori , Kijima Hiroaki , Kimura Ryota , Watanabe Jun , Okazaki Yuji , Miyakoshi Naohisa
Published: August 2022

Background and Objectives: Adequate initial fixation of the uncemented acetabular component in total hip arthroplasty is necessary to achieve long-term survival. Although screw fixation contributes to improved cup stability, there is currently no consensus on the use of this method. This study aimed to assess the existing randomized controlled trials (RCTs) on the efficacy and safety of cup fixation in total hip arthroplasty without screws. Materials and Methods: We searched the EMBASE, MEDLINE, Cochrane Central Register of Controlled Trials, World Health Organization International Clinical Trials Registry Platform, and ClinicalTrials.gov databases to identify RCTs published before February 2022. Primary outcomes were reoperation, cup migration, and Harris Hip Score. Secondary outcomes were the presence of a radiolucent line in the acetabular region, translation and rotation movement, and polyethylene wear. We conducted meta-analyses using the random-effects models. The revised Cochrane risk-of-bias tool was used to assess the risk of bias for outcomes of interest; the Grading of Recommendations, Assessment, Development, and Evaluation approach was used to summarize the body of evidence. Results: We included six reports from four studies. Total hip arthroplasty without screw fixation to the acetabular cup had little to no effect on reoperation (pooled relative risk, 0.98; 95% confidence interval, 0.14–6.68; I2 = 0%), cup migration (pooled relative risk, 1.72; 95% confidence interval, 0.29–10.33; I2 = 1%), Harris Hip Score (mean difference, 1.19; 95% confidence interval, −1.31–3.70; I2 = 0%), radiolucent line (pooled relative risk, 5.91; 95% confidence interval, 0.32–109.35), translation and rotation of all axes, and polyethylene wear (mean difference, 0.01; 95% confidence interval, −0.01–0.04; I2 = 0%), with very low certainty of evidence on all measures. Conclusions: The efficacy of acetabular cups without screw fixation in total hip arthroplasty remains uncertain, suggesting the need for prudent clinical application. Further large-scale, well-designed studies with low risk of bias are required.
Acute Posterior Multifocal Placoid Pigment Epitheliopathy (APMPPE): A Comprehensive Approach and Case Series: Systemic Corticosteroid Therapy Is Necessary in a Large Proportion of Cases
Authors: Papasavvas Ioannis , Mantovani Alessandro , Herbort Carl P.
Published: August 2022

Background and objectives: Acute posterior multifocal pigment epitheliopathy/acute multifocal ischaemic choriocapillaritis (APMPPE/AMIC) is part of the group of choriocapillaritis entities. The aim of this article was to report a series of patients with emphasis on the clinical presentation and treatment paradigms. Materials and Methods: Retrospective case series study performed in the Centre for Ophthalmic Specialised care (COS), Lausanne, Switzerland, on patients diagnosed from 2000 to 2021 with APMPPE/AMIC. Procedures performed at presentation and upon follow-up (when available) included best corrected visual acuity (BCVA), routine ocular examination, laser flare photometry (LFP) microperimetry (when available) and visual field testing. Imaging investigations included spectral domain optical coherence tomography (SD-OCT)/enhanced depth imaging OCT (EDI-OCT), OCT angiography (OCT-A) as well as fluorescein and indocyanine green angiography (FA, ICGA). The presence or not of prodromal systemic viral-like symptoms was noted. The localisation of lesions whether foveal or extrafoveal, divided the patients into 2 groups (foveal, peri-or parafoveal). Exclusion criteria were patients diagnosed with APMPPE/AMIC and a positive QuantiFERON test and/or VDRL-TPHA tests. Results: Nineteen (35 eyes) of 1664 new patients (1.14%) were diagnosed with APMPPE/AMIC and included in our study. 13 (68%) were male and 6 (32%) were female. The mean age was 33.1 ± 9.2 years. 16 (84%) patients mentioned a viral prodromal episode or other systemic symptoms, and 3 (16%) did not mention any episode before the onset of ocular symptoms. 15 (39%) out of 38 eyes had foveal localisation of the lesions, 20 (52.6%) had peri- or para-foveal localisations and 3 eyes were normal [3 unilateral cases (15%)]. Mean BCVA at presentation was 0.83 ± 0.24 for the whole group. It was 0.58 ± 0.28 for the group with foveal lesions, increasing to 0.97 ± 0.13 at last follow-up (p = 0.0028). For the group with extrafoveal lesions mean BCVA at presentation was 0.94 ± 0.18, improving to 1.18± 0.10 at last follow-up (p = 0.0039). 13 (68%) patients received prednisone treatment, of whom 2 (10%) received additionally at least one immunosuppressive agent, 4 (20%) patients received no treatment and in 2 patients the information was unavailable. All patients in the foveal lesion group received corticosteroid treatment except one who evolved to bilateral macular atrophy. Conclusions: APMPPE/AMIC is a primary choriocapillaritis. Although it is thought that the disease is self-limited, treatment is necessary in most cases, especially when lesions are located in the fovea.
Development of Antifouling Thin-Film Composite/Nanocomposite Membranes for Removal of Phosphate and Malachite Green Dye
Authors: Borpatra Gohain Moucham , Karki Sachin , Yadav Diksha , Yadav Archana , Thakare Neha R. , Hazarika Swapnali , Lee Hyung Keun , Ingole Pravin G.
Published: August 2022

Nowadays polymer-based thin film nanocomposite (TFN) membrane technologies are showing key interest to improve the separation properties. TFN membranes are well known in diverse fields but developing highly improved TFN membranes for the removal of low concentration solutions is the main challenge for the researchers. Application of functional nanomaterials, incorporated in TFN membranes provides better performance as permeance and selectivity. The polymer membrane-based separation process plays an important role in the chemical industry for the isolation of products and recovery of different important types of reactants. Due to the reduction in investment, less operating costs and safety issues membrane methods are mainly used for the separation process. Membranes do good separation of dyes and ions, yet their separation efficiency is challenged when the impurity is in low concentration. Herewith, we have developed, UiO-66-NH2 incorporated TFN membranes through interfacial polymerization between piperazine (PIP) and trimesoyl chloride (TMC) for separating malachite green dye and phosphate from water in their low concentration. A comparative study between thin-film composite (TFC) and TFN has been carried out to comprehend the benefit of loading nanoparticles. To provide mechanical strength to the polyamide layer ultra-porous polysulfone support was made through phase inversion. As a result, outstanding separation values of malachite green (MG) 91.90 ± 3% rejection with 13.32 ± 0.6 Lm−2h−1 flux and phosphate 78.36 ± 3% rejection with 22.22 ± 1.1 Lm−2h−1 flux by TFN membrane were obtained. The antifouling tendency of the membranes was examined by using bovine serum albumin (BSA)-mixed feed and deionized water, the study showed a good ~84% antifouling tendency of TFN membrane with a small ~14% irreversible fouling. Membrane’s antibacterial test against E. coli. and S. aureus. also revealed that the TFN membrane possesses antibacterial activity as well. We believe that the present work is an approach to obtaining good results from the membranes under tricky conditions.
Study of MC:DN-Based Biopolymer Blend Electrolytes with Inserted Zn-Metal Complex for Energy Storage Devices with Improved Electrochemical Performance
Authors: Dannoun Elham M. A. , Aziz Shujahadeen B. , Abdulwahid Rebar T. , Al-Saeedi Sameerah I. , Nofal Muaffaq M. , Sadiq Niyaz M. , Hadi Jihad M.
Published: August 2022

Stable and ionic conducting electrolytes are needed to make supercapacitors more feasible, because liquid electrolytes have leakage problems and easily undergo solvent evaporation. Polymer-based electrolytes meet the criteria, yet they lack good efficiency due to limited segmental motion. Since metal complexes have crosslinking centers that can be coordinated with the polymer segments, they are regarded as an adequate method to improve the performance of the polymer-based electrolytes. To prepare plasticized proton conducting polymer composite (PPC), a simple and successful process was used. Using a solution casting process, methylcellulose and dextran were blended and impregnated with ammonium thiocyanate and zinc metal complex. A range of electrochemical techniques were used to analyze the PPC, including transference number measurement (TNM), linear sweep voltammetry (LSV), cyclic voltammetry (CV), galvanostatic charge–discharge (GCD), and electrochemical impedance spectroscopy (EIS). The ionic conductivity of the prepared system was found to be 3.59 × 10−3 S/cm using the EIS method. The use of glycerol plasticizer improves the transport characteristics, according to the findings. The carrier species is found to have ionic mobility of 5.77 × 10−5 cm2 V−1 s−1 and diffusion coefficient of 1.48 × 10−6 cm2 s−1 for the carrier density 3.4 × 1020 cm−3. The TNM revealed that anions and cations were the predominant carriers in electrolyte systems, with an ionic transference value of 0.972. The LSV approach demonstrated that, up to 2.05 V, the film was stable, which is sufficient for energy device applications. The prepared PPC was used to create an electrical double-layer capacitor (EDLC) device. The CV plot exhibited the absence of Faradaic peaks in the CV plot, making it practically have a rectangular form. Using the GCD experiment, the EDLC exhibited low equivalence series resistance of only 65 Ω at the first cycle. The average energy density, power density, and specific capacitance values were determined to be 15 Wh/kg, 350 W/kg, and 128 F/g, respectively.
Development and Application of Biodegradable Metals
Authors: Maier Petra
Published: August 2022

Magnesium-, zinc- and iron-based alloys as biodegradable metals have been a focus of scientific attention for their ability to eliminate the need for a second surgery in order to remove implants made with such materials [...]
Mechanical Properties of AA2024 Aluminum/MWCNTs Nanocomposites Produced Using Different Powder Metallurgy Methods
Authors: Stergioudi Fani , Prospathopoulos Alexandros , Farazas Alexandros , Tsirogiannis Evangelos Ch. , Michailidis Nikolaos
Published: August 2022

Metal matrix composites are a class of materials with high potential for industrial application due to the multifaceted properties that they possess. In the present research, mechanical alloying via ball milling was used to produce AA2024 powder that was subsequently reinforced with multiwalled carbon nanotubes (MWCNTs). Dispersion of the MWCNTs in the AA2024 matrix was achieved also by ball milling. Two different powder metallurgy sequencies, (i) double pressing double sintering and (ii) hot pressing were used for compaction and consolidation of the AA2024 reinforced by MWCNTs. The produced nanocomposites outperform the pristine AA2024 in terms of compressive strength, elongation to failure, and microhardness. The strengthening mechanism was associated with the homogeneous distribution of MWCNTs in the AA2024 matrix and their efficient interfacial bonding, which was attested also by microstructural characterization. Hot pressing resulted in higher mechanical properties of the nanocomposite material. However, when reinforcement content was above 2 wt.% a dramatic decrease of mechanical properties was observed, attributed to clustering and inhomogeneous dispersion of the MWCNTs. The homogeneous dispersion of MWCNTs in the AA2024 matrix and the retaining of their structural integrity are pivotal in increasing the mechanical properties, which can be directly associated with the efficient interfacial load transfer between MWCNTs and AA2024 matrix.
3D Printing of Mg-Based Bulk Metallic Glasses with Proper Laser Power and Scanning Speed
Authors: Zhao Zijian , Yang Guang , Zhao Kun
Published: August 2022

Additive manufacturing allows for the fabrication of large-sized metallic glasses with complex geometries, which overcomes the size limitation due to limited glass-forming ability. To investigate the effect of synthesis parameters on the Mg-based metallic glasses, Mg65Cu20Zn5Y10 was fabricated by laser-based powder bed fusion under different scanning speeds and laser powers. For high energy density, the samples showed severe crystallization and macrocracks, while for low energy density, the samples contained pore defects and unfused powders. Three-dimensionally printed samples were used for the compression test, and the mechanical properties were analyzed by Weibull statistics. Our work identifies proper parameters for 3D printing Mg-based metallic glasses, which provide a necessary, fundamental basis for the fabrication of 3D-printed Mg-based metallic glass materials with improved performance.
Fatigue Failure in Engineered Components and How It Can Be Eliminated: Case Studies on the Influence of Bifilms
Authors: Campbell John , Tiryakioğlu Murat
Published: August 2022

The fatigue of engineered components involves more than the fatigue of metals as studied in laboratories. The miniscule laboratory test pieces cannot represent the pre-existing macroscopic crack defects in real engineering components. This brief study illustrates five examples in which major cracks are pre-existing as a result of the presence of bifilm defects. The pre-existing defects account for up to 90 per cent of the failure of so-called fatigue failure. The presence of pre-existing bifilm defects is of overwhelming importance. It is, with regret, suggested that the attempts at the elimination of so-called fatigue failures by only studying fatigue is misguided. The so-called fatigue failures of engineering components can be understood and addressed by realizing the major contributions of bifilms.
Behaviour of Aluminium EN AW 6082 T6 Columns Exposed to Transient Heating—Experimental and Numerical Analysis
Authors: Goreta Marko , Torić Neno , Boko Ivica , Divić Vladimir
Published: August 2022

The paper presents an experimental and numerical analysis of EN AW 6082 T6 aluminium alloy columns exposed to high-temperature creep in transient conditions. Transient tests with columns subjected to a constant heating rate for a persistent external load in the form of the horizontal and transversal forces were carried out. A total of ten columns were examined with varying ratios of horizontal and transversal loads. The test results were compared to numerical results obtained from ANSYS 16.2. The coefficients for an ANSYS built-in Modified Time Hardening creep model were calibrated from the previously conducted tests on coupons and used as a base for the numerical analysis of the column. The study results reveal that creep reduces column load-bearing capacity, starting at temperatures above 150 °C. Furthermore, the level of reduction in the aluminium column capacity, which manifests itself as a runaway failure of the column between the creep and creep-free model, deviates with a difference exceeding 160% in vertical displacement upon failure, while the creep model correlates very well with the results obtained from the tested specimens in terms of failure time and the displacement ratio.
Effect of Pulse Current Density on Microstructure of Ti-6Al-4V Alloy by Laser Powder Bed Fusion
Authors: Fan Shijing , He Bo , Liu Meishuai
Published: August 2022

This work thoroughly investigated the microstructure of laser powder bed fusion (LPBF) Ti64 alloy induced by electropulsing (ECP) with different current densities. The results show that as the electric current density increases, the small-sized (<10 μm) α′ martensites increase and the large-sized (>10 μm) α′ martensites decrease, resulting in a typical grading microstructure treated by the ECP treatment. The thermodynamic barrier was found to decrease as the electric current passed, recrystallization occurred, and the percentage of small-sized α′ martensites increased. At the same time, the unique electrothermal coupling effect of ECP treatment acted on the needle-like α′ martensites, causing an energy concentration on the tip to break the adjacent α′ martensite and the percentage of large-sized α′ martensites to decrease. When the current density of ECP treatment was 48.37 A/mm2, the grading phenomenon of acicular α′ martensites was the most obvious and its size distribution changed significantly, which simultaneously improved the strength and elongation of LPBF-Ti64 alloy. Thus, the typical grading phenomenon of the α′ martensites plays a decisive role in improving the mechanical properties of the LPBF-Ti64 samples induced by the ECP treatment. The present results provide new information on the ECP processing additive manufacturing metallic materials.
Recycling Aluminium AA6061 Chips with Reinforced Boron Carbide (B4C) and Zirconia (ZrO2) Particles via Hot Extrusion
Authors: Al-Alimi Sami , Shamsudin Shazarel , Yusuf Nur Kamilah , Lajis Mohd Amri , Zhou Wenbin , Didane Djamal Hissein , Sadeq Safwan , Saif Yazid , Wahib Ahmed , Harun Zawati
Published: August 2022

Compared to the recycling process by remelting, hot extrusion significantly reduces the energy consumption and CO2 emission and ensures good mechanical and microstructural properties. This study investigates the effects of reinforcing aluminium AA6061 chips with mixed boron carbide (B4C) and zirconia (ZrO2) particles by employing a design of experiment (DOE) under 550 °C processing temperature and three hours preheating time. The findings showed that compressive strength (CS) and hardness increased with up to 5% added particles, and beyond 5%, the yielded values decreased because of materials agglomeration. However, the decreasing density was dependent on the addition of ZrO2 particles. The distribution of particles with different volume fractions of mixed particles was investigated by employing SEM, AFM, and EDS tests. Thus, the process can produce a net shape structure that utilises material-bonding consolidation to provide sufficient support to reuse the recovered materials in engineering applications, such as in the automotive industry.
Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma
Authors: Bruschi Maurizio , Kajana Xhuliana , Petretto Andrea , Bartolucci Martina , Pavanello Marco , Ghiggeri Gian Marco , Panfoli Isabella , Candiano Giovanni
Published: August 2022

Medulloblastoma (MB) is the most common pediatric malignant central nervous system tumor. Overall survival in MB depends on treatment tuning. There is aneed for biomarkers of residual disease and recurrence. We analyzed the proteome of waste cerebrospinal fluid (CSF) from extraventricular drainage (EVD) from six children bearing various subtypes of MB and six controls needing EVD insertion for unrelated causes. Samples included total CSF, microvesicles, exosomes, and proteins captured by combinatorial peptide ligand library (CPLL). Liquid chromatography-coupled tandem mass spectrometry proteomics identified 3560 proteins in CSF from control and MB patients, 2412 (67.7%) of which were overlapping, and 346 (9.7%) and 805 (22.6%) were exclusive. Multidimensional scaling analysis discriminated samples. The weighted gene co-expression network analysis (WGCNA) identified those modules functionally associated with the samples. A ranked core of 192 proteins allowed distinguishing between control and MB samples. Machine learning highlighted long-chain fatty acid transport protein 4 (SLC27A4) and laminin B-type (LMNB1) as proteins that maximized the discrimination between control and MB samples. Machine learning WGCNA and support vector machine learning were able to distinguish between MB versus non-tumor/hemorrhagic controls. The two potential protein biomarkers for the discrimination between control and MB may guide therapy and predict recurrences, improving the MB patients’ quality of life.