bluuebunny/crossref_metadata_2025 huggingface dataset:
Early Diagnosis of Chemotherapy-Linked Cardiotoxicity in Breast Cancer Patients Using Conventional Biomarker Panel: A Prospective Study Protocol
Authors: Balmagambetova Saule , Tlegenova Zhenisgul , Zholdin Bekbolat , Kurmanalina Gulnara , Talipova Iliada , Koyshybaev Arip , Nurmanova Dinara , Sultanbekova Gulmira , Baspayeva Mira , Madinova Saule , Kubenova Kulparshan , Urazova Ainel
Published: November 2022

The prognosis of cancer treatment depends on, among other aspects, the cardiotoxicity of chemotherapy. This research aims to create a feasible algorithm for the early diagnosis of antitumor therapy cardiotoxicity in breast cancer patients. The paper represents a protocol for a prospective cohort study with N 120 eligible participants admitted for treatment with anthracyclines and/or trastuzumab. These patients will be allocated into four risk groups regarding potential cardiotoxic complications. Patients will be examined five times every three months for six biomarkers: cardiac troponin I (cTnI), brain natriuretic peptide (BNP), C-reactive protein (CRP), myeloperoxidase (MPO), galectin-3 (Gal-3), and D-dimer, simultaneously with echocardiographic methods, including speckle tracking. The adjusted relative risk (aOR) of interrupting an entire course of chemotherapy due to cardiotoxic events will be assessed using multiple analyses of proportional Cox risks. The Cox model will also assess associations between baseline biomarker values and time to cardiotoxic events. Moreover, partly conditional survival models will be applied to determine associations between repeated assessments of changes in biomarkers from baseline and time to cancer therapy-related cardiac dysfunction. All models will be adjusted for cancer therapy regimen, baseline LVEF, groups at risk, baseline biomarker values, and age. The decision-tree and principal component analysis (PCA) methods will also be applied. Thus, feasible patterns will be detected.
Decision-Making Approach for an IoRT-Aware Business Process Outsourcing
Authors: Fattouch Najla , Ben Lahmar Imen , Rekik Mouna , Boukadi Khouloud
Published: November 2022

In the context of Industry 4.0, IoRT-aware BPs represent an attractive paradigm that aims to automate the classic business process (BP) using the internet of robotics things (IoRT). Nonetheless, the execution of these processes within the enterprises may be costly due to the consumed resources, recruitment cost, etc. To bridge these gaps, the business process outsourcing (BPO) strategy can be applied to outsource partially or totally a process to external service suppliers. Despite the various advantages of BPO, it is not a trivial task for enterprises to determine which part of the process should be outsourced and which environment would be selected to deploy it. This paper deals with the decision-making outsourcing of an IoRT-aware BP to the fog and/or cloud environments. The fog environment includes devices at the edge of the network which will ensure the latency requirements of some latency-sensitive applications. However, relying on cloud, the availability and computational requirements of applications can be met. Toward these objectives, we realized an in-depth analysis of the enterprise requirements, where we identified a set of relevant criteria that may impact the outsourcing decision. Then, we applied the method based on the removal effects of criteria (MEREC) to automatically generate the weights of the identified criteria. Using these weights, we performed the selection of the suitable execution environment by using the ELECTRE IS method. As an approach evaluation, we sought help from an expert to estimate the precision, recall, and F-score of our approach. The obtained results show that our approach is the most similar to the expert result, and it has acceptable values.
Physiotherapists and Osteopaths’ Attitudes: Training in Management of Temporomandibular Disorders
Authors: Saran Stefano , Saccomanno Sabina , Petricca Maria Teresa , Carganico Andrea , Bocchieri Salvatore , Mastrapasqua Rodolfo Francesco , Caramaschi Elena , Levrini Luca
Published: November 2022

Temporomandibular disorders (TMDs) are a condition which has multifactorial etiology. The most acknowledged method to classify TMDs is the diagnostic criteria (DC) introduced firstly by Dworkin. This protocol considers different aspects that are not only biological, but even psychosocial. Diagnosis is often based on anamnesis, physical examination and instrumental diagnosis. TMDs are classified as intra-articular and/or extra-articular disorders. Common signs and symptoms include jaw pain and dysfunction, earache, headache, facial pain, limitation to opening the mouth, ear pain and temporomandibular joint (TMJ) noises. This study regards two kind of clinicians that started in the last years to be more involved in the treatment of TMDs: osteopaths (OOs) and physiotherapists (PTs). The purpose is to analyze their attitude and clinical approach on patients affected by TMDs. Four hundred therapists answered an anonymous questionnaire regarding TMJ and TMDs. OOs showed greater knowledges on TMDs and TMJ and, the therapists with both qualifications seemed to be most confident in treating patients with TMDs. In conclusion this study highlights OOs and all the clinicians with this qualification, have a higher confidence in treating patients with TMD than the others. Dentists and orthodontists, according to this study, should co-work with OOs and PTs, because they are the specialists more requested by them than other kinds of specialists.
Experimental Investigation of Icing Effects on a Hovering Drone Rotor Performance
Authors: Villeneuve Eric , Samad Abdallah , Volat Christophe , Béland Mathieu , Lapalme Maxime
Published: November 2022

A scaled version of the APT70 drone rotor, typical of small to medium UAV rotors, was tested in a 9-meter-high cold chamber for a wide range of icing parameters. The drone rotor used has four blades with varying chord and twist settings. The objective of this study was to investigate icing effects on the rotor aerodynamic performance, based on experimental data, for varying rotor speeds, precipitation rates, droplet sizes and air temperatures. Aerodynamic loads were measured using the built-in load cell, and data were compared to photographs taken during testing as well as ice thickness measurements at the end of tests. The impact of each test parameter and their variations on the degradation of the rotor’s performances was evaluated. The results show that larger droplets and lower RPMs and pitch angles generate a more rapid degradation of the performances due to the airflow around the blades and tip-vortex affecting the collection efficiency of the blades. With the smaller droplets, the air temperature did not affect the performance degradation, only the type of ice accumulation. However, with the larger droplets, degradation of the performances was less severe at warmer temperatures since almost no ice accumulated at the tip and droplets were expelled before freezing.
Paleobiogeography of Crown Deer
Authors: Croitor Roman
Published: November 2022

The article describes the paleobiogeographic history of the modern subfamilies so-called “crown deer” of the family Cervidae (Artiodactyla, Mammalia) in the world from the late Miocene to the late Pleistocene. The study overviews the taxonomic diversity and evolutionary radiation of Cervidae from all zoogeographic realms where this systematic group is present in the paleontological record. The evolutionary diversification of the fossil Cervidae is based on the estimations of species body masses that are regarded here as a proxy of occupied ecological niches. The study reveals two important evolutionary radiations of Cervidae during the late Miocene of Eurasia that gave the origin of the modern subfamilies Cervinae and Capreolinae. The evolutionary radiation of Capreolinae during the Pleistocene in South America shows a range of diversity comparable to the late Miocene radiations of Old World deer and provides multiple examples of evolutionary convergences with Eurasian Pleistocene cervids. The article discusses factors that shaped the modern biogeographic distribution of representatives of the subfamilies Cervinae and Capreolinae.
Susceptibility of Tidal Pool Fish Assemblages to Climate Change
Authors: Davis Tom R. , Nimbs Matt
Published: November 2022

There is a need for improved understanding of how climate-change driven rises in sea level and increased ocean temperatures will affect fish assemblages in rocky shore tidal pools. Rising sea levels are predicted to alter habitat availability and increasing ocean temperatures will drive tropicalisation, both of which are likely to alter tidal pool fish assemblages. Consequently, we examined changes in fish assemblages in tidal pools at four sites in Coffs Harbour, Australia, using baited video sampling. Data were collected seasonally at each site, in pools at differing tidal elevations. We identified significant differences in tidal pool fish assemblages among elevations and found that assemblages exhibited cyclic seasonal patterns. Modelling identified that ocean temperature was the most powerful factor for explaining variations in assemblages, followed by pool area and pool elevation. Results highlight that distinct fish assemblages occur in tidal pools at different elevations and indicate that assemblages at higher elevations could be squeezed out by rising sea levels and by increased competition from tropical fish species. Future conservation assessments are needed to determine whether latitudinal and vertical range shifts for tidal pool fishes are possible, with facilitation of these range shifts needed to address displacement of tidal pool fishes by rising sea levels and ocean temperatures.
A Re-Envisioned Multicultural STEM Education for All
Authors: Casto Amanda R.
Published: November 2022

This conceptual article calls for the transformation of the antiquated educational system into an innovative PreK-12 model that embraces the goals of multicultural education and the pedagogical strategies commonly associated with a holistically integrated STEM curriculum. Teaching STEM through a multicultural lens creates rich opportunities where all students, especially those who have been historically marginalized in STEM fields, can develop identities such as scientists, mathematicians, creative artists, and valued citizens as part of a global team effort. Recommendations are made to educational policymakers, curriculum developers, school administrators, teacher educators, and PreK-12 teachers to collectively create a STEM educational model that is equitable, pluralistic, and a path to social justice.
A High Performance and Low Power Triple-Node-Upset Self-Recoverable Latch Design
Authors: Dai Yanyun , Yang Yanfei , Jiang Nan , Qi Pengjia , Chen Qi , Tong Jijun
Published: November 2022

The charge sharing effect is becoming increasingly severe due to the continuous reduction of semiconductor process feature size. In the nanoscale digital circuit, the probability of triple-node upset (TNU) is increasing, which seriously affects the reliability of the circuit. To improve the reliability of the digital circuit, this paper presents an optimized TNU self-recoverable latch (HLTNURL). This latch consists of three dual-node-self-recoverable dual interlocked storage cells (DNSR-DICE) and one clock-gating C-element. Whenever any three nodes invert, the latch is able to self-recover to its correct logical values. The HSPICE simulation results indicate that this latch enables full self-recovery of TNU in all cases. In comparison with existing TNU self-recoverable latches, the proposed HLTNURL latch is able to reduce the power dissipation, delay, area overhead, and area-power-delay product (APDP) by 32.41%, 79.73%, 1.32%, and 88% on average. In addition, the HLTNURL latch proposed in this paper has high reliability and low sensitivity to process, voltage, and temperature (i.e., PVT) variations.
A New Design Method for Class-E Power Amplifiers Using Artificial Intelligence Modeling for Wireless Power Transfer Applications
Authors: Yahya Salah I. , Alameri Ban M. , Jamshidi Mohammad (Behdad) , Roshani Saeed , Chaudhary Muhammad Akmal , Ijemaru Gerald K. , Mezaal Yaqeen Sabah , Roshani Sobhan
Published: November 2022

This paper presents a new approach to simplify the design of class-E power amplifier (PA) using hybrid artificial neural-optimization network modeling. The class-E PA is designed for wireless power transfer (WPT) applications to be used in biomedical or internet of things (IoT) devices. Artificial neural network (ANN) models are combined with optimization algorithms to support the design of the class-E PA. In several amplifier circuits, the closed form equations cannot be extracted. Hence, the complicated numerical calculations are needed to find the circuit elements values and then to design the amplifier. Therefore, for the first time, ANN modeling is proposed in this paper to predict the values of the circuit elements without using the complex equations. In comparison with the other similar models, high accuracy has been obtained for the proposed model with mean absolute errors (MAEs) of 0.0110 and 0.0099, for train and test results. Moreover, root mean square errors (RMSEs) of 0.0163 and 0.0124 have been achieved for train and test results for the proposed model. Moreover, the best and the worst-case related errors of 0.001 and 0.168 have been obtained, respectively, for the both design examples at different frequencies, which shows high accuracy of the proposed ANN design method. Finally, a design of class-E PA is presented using the circuit elements values that, first, extracted by the analyses, and second, predicted by ANN. The calculated drain efficiencies for the designed class-E amplifiers have been obtained equal to 95.5% and 91.2% by using analyses data and predicted data by proposed ANN, respectively. The comparison between the real and predicted values shows a good agreement.
Maximizing the Electricity Cost-Savings for Local Distribution System Using a New Peak-Shaving Approach Based on Mixed Integer Linear Programming
Authors: Mosbah Hossam , Guerra Eduardo Castillo , Barrera Julian L. Cardenas
Published: November 2022

The objective of this study is to perform peak load shaving at a virtual power plant (VPP) to maximize the electricity cost-saving for local distribution companies (LDCs) while satisfying the necessary operational constraints. It can be achieved by implementing an efficient algorithm to control the conservation voltage reduction technique (CVR) with embedded energy resources (EERs) to optimize electricity costs during peak hours. EERs consist of distributed energy resources (DERs) such as solar and diesel generators and energy storage systems (ESSs) such as utility-scale and residential batteries. An objective function of mixed integer linear programming is formulated as the electricity cost function. Different operational constraints of EERs are formulated to solve the peak shaving optimization problem. The proposed algorithm is tested using data from a real Australian power distribution network. This paper discusses four cases to demonstrate the performance and economic benefits of the control algorithm. Each of these cases illustrates how EERs contribute differently each year, month, and day. Results showed that the proposed algorithm offers significant cost savings and can shave up to three daily peaks.
Enhanced Metaheuristic Algorithm-Based Load Balancing in a 5G Cloud Radio Access Network
Authors: Suresh Krishnamoorthy , Alqahtani Ali , Rajasekaran Thangaraj , Kumar Murugan Suresh , Ranjith Venugopal , Kannadasan Raju , Alqahtani Nayef , Khan Arfat Ahmad
Published: November 2022

Mobile operators must increase investments in network infrastructures due to the emergent growth of the internet and technological advancements. Mobile operators consider cloud-RAN and software defined networking to be developing technologies that can reduce costs and increase scalability for fifth-generation mobile communication networks (5G). A base station consists of two important components, namely baseband (BBU) and remote radio head (RRH) units. Unbalanced data traffic can arise, leading to call dropping and call blocking. When network traffic conditions start to vary, the performance of the system becomes suboptimal. Self-optimization of the network is necessary to reduce the load of overloaded eNode’s with more call blocking, that increase the load of underloaded eNode’s with less utilization of resources. The main objective of a self-organizing network is to reduce call blocking and optimize an unbalanced network. The proposed algorithm is an enhanced version of the cat swarm optimization algorithm performed by the host manager entity to select the best BBU-RRH combination after analyzing the quality-of-service (QoS) information from the remaining BBU-RRH configurations. Optimization is carried out on each user after a QoS analysis for every new BBU-RRH combination. The proposed algorithm is implemented in Matlab R2020a and evaluation is conducted in terms of blocking probability, response time, and throughput. The simulation results show that the proposed ECSO optimization algorithm reduces blocking probability by 10%, throughput is increased by 8%, and response time is reduced by 7% as compared with the existing PSO and CSO algorithms.
Review of Obstacle Detection Systems for Collision Avoidance of Autonomous Underwater Vehicles Tested in a Real Environment
Authors: Kot Rafał
Published: November 2022

The high efficiency of obstacle detection system (ODS) is essential to obtain the high performance of autonomous underwater vehicles (AUVs) carrying out a mission in a complex underwater environment. Based on the previous literature analysis, that include path planning and collision avoidance algorithms, the solutions which operation was confirmed by tests in a real-world environment were selected for this paper consideration. These studies were subjected to a deeper analysis assessing the effectiveness of the obstacle detection algorithms. The analysis shows that over the years, ODSs being improved and provide greater detection accuracy that results in better AUV response time. Almost all analysed methods are based on the conventional approach to obstacle detection. In the future, even better ODSs parameters could be achieved by using artificial intelligence (AI) methods.
Comparative Performance Analysis of Vibration Prediction Using RNN Techniques
Authors: Lee Ju-Hyung , Hong Jun-Ki
Published: November 2022

Drones are increasingly used in several industries, including rescue, firefighting, and agriculture. If the motor connected to a drone’s propeller is damaged, there is a risk of a drone crash. Therefore, to prevent such incidents, an accurate and quick prediction tool of the motor vibrations in drones is required. In this study, normal and abnormal vibration data were collected from the motor connected to the propeller of a drone. The period and amplitude of the vibrations are consistent in normal vibrations, whereas they are irregular in abnormal vibrations. The collected vibration data were used to train six recurrent neural network (RNN) techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gated recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional GRU (Bi-GRU). Then, the simulation runtime it took for each RNN technique to predict the vibrations and the accuracy of the predicted vibrations were analyzed to compare the performances of the RNN model. Based on the simulation results, the Attn.-LSTM and Attn.-GRU techniques, incorporating the attention mechanism, had the best efficiency compared to the conventional LSTM and GRU techniques, respectively. The attention mechanism calculates the similarity between the input value and the to-be-predicted value in advance and reflects the similarity in the prediction.
A C/X/Ku/K-Band Precision Compact 6-Bit Digital Attenuator with Logic Control Circuits
Authors: Zeng Jialong , Li Jiaxuan , Yuan Yang , Tan Cheng , Yu Zhongjun
Published: November 2022

This paper proposes a C/X/Ku/K band 6-bit digital step attenuator (DSA) which employs a variety of improved attenuation cells to achieve a wide bandwidth, stable amplitude variation, stable phase variation, and small area. In this paper, the improved T-type, π-type, and switched-path type topologies are analyzed theoretically and applied to different attenuation values to achieve the optimal attenuator performance. In addition, in order to reduce the complexity and to improve the stability of the overall radar system, the logic control circuit is integrated in the DSA chip in this paper. Finally, the proposed attenuator is implemented in 0.15μm GaAs technology, which has a maximum attenuation range of 31.5 dB with 0.5 dB steps. The proposed DSA exhibits a root-mean-square (RMS) attenuation error of less than 0.15 dB and an RMS phase error of less than 3°, at 4–24 GHz. The insertion loss (IL) and the area of the DSA are 4.3–4.5 dB and 1.5 mm × 0.4 mm, respectively. Benefiting from the improvements of the attenuation cells and the characteristic of GaAs technology with strong resistance to radiation and power processing capability, the proposed DSA is suitable for spaceborne radar systems.
Classifying Conditions of Speckle and Wrinkle on the Human Face: A Deep Learning Approach
Authors: Chang Tsai-Rong , Tsai Ming-Yen
Published: November 2022

Speckles and wrinkles are common skin conditions on the face, with occurrence ranging from mild to severe, affecting an individual in various ways. In this study, we aim to detect these conditions using an intelligent deep learning approach. First, we applied a face detection model and identified the face image using face positioning techniques. We then split the face into three polygonal areas (forehead, eyes, and cheeks) based on 81 position points. Skin conditions in the images were firstly judged by skin experts and subjectively classified into different categories, from good to bad. Wrinkles were classified into five categories, and speckles were classified into four categories. Next, data augmentation was performed using the following manipulations: changing the HSV hue, image rotation, and horizontal flipping of the original image, in order to facilitate deep learning using the Resnet models. We tested the training using these models each with a different number of layers: ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152. Finally, the K-fold (K = 10) cross-validation process was applied to obtain more rigorous results. Results of the classification are, in general, satisfactory. When compared across models and across skin features, we found that Resnet performance is generally better in terms of average classification accuracy when its architecture has more layers.
Enhancing Sentiment Analysis via Random Majority Under-Sampling with Reduced Time Complexity for Classifying Tweet Reviews
Authors: Almuayqil Saleh Naif , Humayun Mamoona , Jhanjhi N. Z. , Almufareh Maram Fahaad , Khan Navid Ali
Published: November 2022

Twitter has become a unique platform for social interaction from people all around the world, leading to an extensive amount of knowledge that can be used for various reasons. People share and spread their own ideologies and point of views on unique topics leading to the production of a lot of content. Sentiment analysis is of extreme importance to various businesses as it can directly impact their important decisions. Several challenges related to the research subject of sentiment analysis includes issues such as imbalanced dataset, lexical uniqueness, and processing time complexity. Most machine learning models are sequential: they need a considerable amount of time to complete execution. Therefore, we propose a model sentiment analysis specifically designed for imbalanced datasets that can reduce the time complexity of the task by using various text sequenced preprocessing techniques combined with random majority under-sampling. Our proposed model provides competitive results to other models while simultaneously reducing the time complexity for sentiment analysis. The results obtained after the experimentation corroborate that our model provides great results producing the accuracy of 86.5% and F1 score of 0.874 through XGB.
Research on Bidirectional Isolated Charging System Based on Resonant Converter
Authors: Zhou Kai , Sun Yue
Published: November 2022

This paper proposes a two-stage bidirectional isolated charging system, which can realize the bidirectional flow of electric energy, not only making the electric energy flow from the grid side to the battery side but also converting the battery’s energy into single-phase alternating current to supply other electric equipment. The charging system also has the function of power factor correction and wide-range voltage output. The front stage of the charging system is a bidirectional totem pole structure power factor correction converter with a voltage and current double closed-loop control strategy to ensure the stability of the DC bus voltage, and the rear stage is a bidirectional CLLLC resonant converter, which adopts a high-frequency soft start strategy to reduce the inrush current during start-up and ensure the safe operation of the converter. Moreover, the frequency control strategy is used to make it have a wide range of DC output characteristics. In this paper, the principle analysis and parameter calculation of the charging system is carried out, the simulation platform and hardware circuit design are built, and a test prototype is piloted to verify the bidirectional operating characteristics of the charging system. The simulation and experimental results demonstrate the correctness of the theoretical analysis and topology design.
The Dynamic Evolution of Global Energy Security and Geopolitical Games: 1995~2019
Authors: Hu Getao , Yang Jun , Li Jun
Published: November 2022

Under the influence of economic globalization, the internationalization trend of energy security issues has become increasingly prominent. This paper adopts the natural discontinuity grading method to classify the energy security status of 102 countries into five categories: Best, Better, Good, Poor and Worse types; reveals the dynamic evolution characteristics and main formation mechanisms of world energy security; and puts forward the game focus of future energy geopolitics. The results show that: (1) during 1995–2019, global energy security presents local turbulence and an overall “J” shaped trend; (2) the global energy security pattern coincides with the international geopolitical order. The countries with the “Best” energy security are found in Western Europe and North America while the countries with “Poor” or “Worse” energy security are located in Asia and the less developed regions of Africa; (3) the main reason why developed economies have better energy security is due to their high energy use efficiency, while developing countries lag behind mainly because of their lower innovation capacity, lower productivity and lower disposable income; and (4) the global energy security landscape is expected to be affected by the changing US–China relationship, coercive energy transition and the uncertainty of the political environment.
SCR Suppressor Mutants: Role in Hypocotyl Gravitropism and Root Growth in Arabidopsis thaliana
Authors: Sharma Archana , Pervaiz Zahida Hassan , Wysocka-Diller Joanna
Published: November 2022

The SCARECROW (SCR) transcription factor plays a key role in plant growth and development. However, we know very little about the role of SCR regulated pathways in plant development. Here, we used the homozygous scr1 mutant Arabidopsis thaliana (Wassilewskija ecotype), which had a T-DNA insertion in the SCR coding region and lacks a detectable SCR transcript. This scr1 mutant has a determinate mode of root growth, shoot agravitropism and abnormal internal architecture in all organs examined. To screen for mutants that suppress the scr1 abnormal phenotypes, we exposed homozygous scr1 seeds to ethyl methane sulphonate (EMS) mutagen. Upon growth out of these mutagenized seeds, thirteen suppressor mutant-harboring strains were identified. All thirteen suppressor-harboring strains were homozygous for scr1 and lacked the SCR transcript. Ten scr hypocotyl gravitropic suppressor lines showed improved hypocotyl gravitropic response. These ten suppressors fall into six complementation groups suggesting six different gene loci. Similarly, three independent scr root length suppressor lines rescued only the root growth phenotype and fell into three complementation groups, suggesting the involvement of three different gene loci. These suppressors might identify novel functions of the SCR gene in plant development.
Mesenchymal Stem Cell Transplantation Increases Antioxidant Protein Expression and Ameliorates GP91/ROS/Inflammasome Signals in Diabetic Cardiomyopathy
Authors: Hu Wei-Syun , Chen Tung-Sheng , Cheang Ka-Hung , Liao Wei-Yu , Chang Chin-Hsien
Published: November 2022

Background: Cardiomyopathy is one of the complications associated with diabetes. Due to its high prevalence, diabetic cardiomyopathy has become an urgent issue for diabetic patients. Various pathological signals are related to diabetic cardiomyopathy progress, including inflammasome. Mesenchymal stem cell transplantation is full of potential for the treatment of diabetic cardiomyopathy because of stem cell cardiac regenerative capability. This study investigates whether mesenchymal stem cell transplantation shows therapeutic effects on diabetic cardiomyopathy through inflammasome signaling regulation. Methods: Wistar male rats were divided into three groups including Sham, T1DM (rats with type 1 diabetes) and T1DM + WJSC (T1DM rats receiving 1 × 106 stem cells per rat). Results: Compared to the Sham, experimental results indicated that several pathological conditions can be observed in heart tissues with T1DM, including structural change, fibrosis, oxidative stress elevation and inflammasome related protein expression. All of these pathological conditions were significantly improved in T1DM rats receiving mesenchymal stem cell transplantation (T1DM + WJSC). Furthermore, the experimental findings suggest that mesenchymal stem cell transplantation exerted antioxidant protein expression in diabetic heart tissues, resulting in a decrease in oxidative stress and inflammasome signaling blockage. Conclusion: These findings imply that mesenchymal stem cell transplantation shows therapeutic effects on diabetic cardiomyopathy through inflammasome regulation induced by oxidative stress.
Biomarkers for Osteoarthritis Diseases
Authors: Braaten Jacob A. , Banovetz Mark T. , DePhillipo Nicholas N. , Familiari Filippo , Russo Raffaella , Kennedy Nicholas I. , LaPrade Robert F.
Published: November 2022

Growing evidence has revealed the pivotal role of inflammatory biomarkers in the pathogenesis of osteoarthritis. There is significant interest in the prognostic value of select biomarkers, given the potential for early identification and treatment of patients at risk of osteoarthritis prior to the development of irreversible clinical disease. Clinical trials of novel therapeutics that disrupt the inflammatory pathways of osteoarthritis are also ongoing. The purpose of this review is to summarize the current literature on key biomarkers within the context of osteoarthritis pathogenesis, clinical symptom development, and treatment capabilities. Multiple recent studies have established biomarkers that signal the existence of osteoarthritis pathology and the development of clinical symptomology. However, prior to implementation in clinical practice, additional research is required to precisely define the prognostic value for numerous biomarkers and standardize their measurement. Biomarker-driven investigations represent a promising avenue for the early diagnosis and treatment of osteoarthritis.
tert-Butyl Carbazate (N-Boc-Hydrazine)
Authors: Aitken R. Alan , Slawin Alexandra M. Z.
Published: November 2022

The X-ray structure of the title compound was determined for the first time. There are four independent molecules that form a repeat unit packed in the crystal in alternating pairs with orthogonal orientation and further joined into infinite chains by hydrogen bonding.
First-Principles Study on Mechanical and Optical Behavior of Plutonium Oxide under Typical Structural Phases and Vacancy Defects
Authors: Cheng Jin-Xing , Yang Fei , Wang Qing-Bo , He Yuan-Yuan , Liu Yi-Nuo , Hu Zi-Yu , Wen Wei-Wei , Wu You-Peng , Zheng Cheng-Yin , Yu Ai , Lu Xin , Zhang Yue
Published: November 2022

The chemical corrosion aging of plutonium is a very important topic. It is easy to be corroded and produces oxidation products of various valence states because of its 5f electron orbit between local and non-local. On the one hand, the phase diagram of plutonium and oxygen is complex, so there is still not enough research on typical structural phases. On the other hand, most of the studies on plutonium oxide focus on PuO2 and Pu2O3 with stoichiometric ratio, while the understanding of non-stoichiometric ratio, especially for Pu2O3-x, is not deep enough. Based on this, using the DFT + U theoretical scheme of density functional theory, we have systematically studied the structural stability, lattice parameters, electronic structure, mechanical and optical properties of six typical high temperature phases of β-Pu2O3, α-Pu2O3,γ-Pu2O3, PuO, α-PuO2,γ-PuO2. Further, the mechanical properties and optical behavior of Pu2O3-x under different oxygen vacancy concentrations are analyzed and discussed in detail. The result shows that the elasticity modulus of single crystal in mechanical properties is directly related to the oxygen/plutonium ratio and crystal system. As the number of oxygen vacancies increases, the mechanical constants continue to increase. In terms of optical properties, PuO has the best optical properties, and the light absorption rate decreases with the increase of oxygen vacancy concentration.
Elaboration of New Materials Using Hydrothermal Methods
Authors: Ursu Daniel , Casut Cristian , Miclau Marinela
Published: November 2022

“Elaboration of New Materials Using Hydrothermal Methods” is a new and open Special Issue of Materials, which aims to publish original research and review papers on that present state-of-the-art advances in the research on the hydrothermal synthesis of new materials [...]
Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions
Authors: Karolczuk Aleksander , Skibicki Dariusz , Pejkowski Łukasz
Published: November 2022

In this paper, a new method for fatigue life prediction under multiaxial stress-strain conditions is developed. The method applies machine learning with the Gaussian process for regression to build a fatigue model. The fatigue failure mechanisms are reflected in the model by the application of the physics-based stress and strain invariants as input quantities. The application of the machine learning algorithm solved the problem of assigning an adequate parametric fatigue model to given material and loading conditions. The model was verified using the experimental data on the CuZn37 brass subjected to various cyclic loadings, including non-proportional multiaxial strain paths. The performance of the machine learning-based fatigue life prediction model is higher than the performance of the well-known parametric models.
Discrete Element Modeling of the Meso-Mechanical Response of Asphalt Pavement under Vehicle Load
Authors: Zhang Dong , Wu Chunying , Cai Lili , Bian Jiang , Yin Chaoen
Published: November 2022

Numerical simulation is an effective way to study the mechanical response of asphalt pavement, which is very important for the pavement structural design. In this study, a three-dimensional meso-structure discrete element model of asphalt pavement was generated with the FISH programming language and its meso-mechanical response under vehicle load was analyzed. The contact forces within the asphalt pavement, in asphalt mastic, in coarse aggregates and between asphalt mastic and coarse aggregates were studied. The results of the study show that the contact forces within the asphalt mixture are highly uneven. The number of contact points in coarse aggregates account only for about 10% of all contact points while the sum of the contact forces in coarse aggregates contributes to over 50% of all contact forces. This demonstrates that the coarse aggregates bear most of the vehicle load. The average normal contact force in coarse aggregates is about 5 N and the average tangential contact force in coarse aggregates is about 2 N. The modeling results provide a quantitative understanding of the distribution of loading in asphalt pavement.
Adsorption Behaviour of Reactive Blue 194 on Raw Ramie Yarn in Palm Oil and Water Media
Authors: Lin Lina , Li Le , Xiao Lexin , Zhang Cong , Li Xueqing , Pervez Md. Nahid , Zhang Youqing , Nuruzzaman Md. , Mondal Md. Ibrahim H. , Cai Yingjie , Naddeo Vincenzo
Published: November 2022

As an edible oil, palm oil is also safe and reliable in dyeing, and the residual palm oil after dyeing can be recycled and used continuously, which is green and environmentally friendly and has great research prospects. In this research, raw ramie yarn, used for traditional grass cloth, was dyed in a palm oil medium using Reactive Blue 194. Studying the adsorption and diffusion behaviour in the dyeing process is necessary. Additionally, the kinetics and isotherm model of dyeing raw ramie yarn with Reactive Blue 194 in palm oil is studied, and the adsorption behaviour between them is discussed. For a better understanding, the raw ramie yarn dyeing adsorption behaviour was also carried out in a water medium. It was found that the dyeing rates in palm oil are distinctly faster than in water. Kinetics data suggested that the pseudo-second-order model fitted for both dyeing mediums (palm oil and water) of the adsorption of the Reactive Blue 194 dye onto raw ramie yarn. Afterward, the adsorption isotherms’ results denote that the Langmuir model was suitable for palm oil dyeing medium while the Freundlich model was suited for water medium. Overall, this study has demonstrated that raw ramie yarn dyeing in a palm oil medium could be a sustainable colouration route for textile fibres with a greater dye exhaustion percentage.
Thermal Properties and Flammability Characteristics of a Series of DGEBA-Based Thermosets Loaded with a Novel Bisphenol Containing DOPO and Phenylphosphonate Units
Authors: Hamciuc Corneliu , Vlad-Bubulac Tăchiță , Serbezeanu Diana , Macsim Ana-Maria , Lisa Gabriela , Anghel Ion , Şofran Ioana-Emilia
Published: November 2022

Despite a recent sustained preoccupation for developing biobased epoxies with enhanced applicability, such products have not been widely accepted for industry because of their inferior characteristics compared to classic petroleum-based epoxy thermosets. Therefore, significant effort is being made to improve the flame retardance of the most commonly used epoxies, such as diglycidyl ether-based bisphenol A (DGEBA), bisphenol F (DGEBF), novalac epoxy, and others, while continuously avoiding the use of hazardous halogen-containing flame retardants. Herein, a phosphorus-containing bisphenol, bis(4-(((4-hydroxyphenyl)amino)(6-oxido-6H-dibenzo[c,e][1,2]oxaphosphinin-6-yl)methyl)phenyl) phenylphosphonate (BPH), was synthesized by reacting bis(4-formylphenyl)phenylphosphonate with 4-hydroxybenzaldehyde followed by the addition of 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) to the resulting azomethine groups. Environmentally friendly epoxy-based polymer thermosets were prepared by using epoxy resin as polymer matrix and a mixture of BPH and 4,4′-diaminodiphenylsulfone (DDS) as hardeners. A hyperbranched phthalocyanine polymer (HPc) and BaTiO3 nanoparticles were incorporated into epoxy resin to improve the characteristics of the final products. The structure and morphology of epoxy thermosets were evaluated by infrared spectroscopy and scanning electron microscopy (SEM), while the flammability characteristics were evaluated by microscale combustion calorimetry. Thermal properties were determined by thermogravimetric analysis and differential scanning calorimetry. The surface morphology of the char residues obtained by pyrolysis was studied by SEM analysis.
Mixture Optimization of Cementitious Materials Using Machine Learning and Metaheuristic Algorithms: State of the Art and Future Prospects
Authors: Song Yaxin , Wang Xudong , Li Houchang , He Yanjun , Zhang Zilong , Huang Jiandong
Published: November 2022

The hybrid optimization of modern cementitious materials requires concrete to meet many competing objectives (e.g., mechanical properties, cost, workability, environmental requirements, and durability). This paper reviews the current literature on optimizing mixing ratios using machine learning and metaheuristic optimization algorithms based on past studies on varying methods. In this review, we first discuss the conventional methods for mixing optimization of cementitious materials. Then, the problem expression of hybrid optimization is discussed, including decision variables, constraints, machine learning algorithms for modeling objectives, and metaheuristic optimization algorithms for searching the best mixture ratio. Finally, we explore the development prospects of this field, including, expanding the database by combining field data, considering more influencing variables, and considering more competitive targets in the production of functional cemented materials. In addition, to overcome the limitation of the swarm intelligence-based multi-objective optimization (MOO) algorithm in hybrid optimization, this paper proposes a new MOO algorithm based on individual intelligence (multi-objective beetle antenna search algorithm). The development of computationally efficient robust MOO models will continue to make progress in the field of hybrid optimization. This review is adapted for engineers and researchers who want to optimize the mixture proportions of cementitious materials using machine learning and metaheuristic algorithms.
Factory Crystallized Silicates for Monolithic Metal-Free Restorations: A Flexural Strength and Translucency Comparison Test
Authors: Vichi Alessandro , Zhao Zejiao , Paolone Gaetano , Scotti Nicola , Mutahar Mahdi , Goracci Cecilia , Louca Chris
Published: November 2022

Flexural strength (FS) and translucency (Contrast Ratio-CR) of three different factory crystallized silica-based glass ceramics, Celtra Duo (CD), N!ce (NI) and Li-Si Block, a lithium disilicate, IPS e.max CAD (LD), and a leucite-reinforced feldspathic ceramic, Empress CAD (EM), in two different translucencies (HT and LT) for use in chairside dental restorations have been compared. CAD blocks of the materials were cut into beams and tiles and processed following manufacturers’ instructions. The beams were tested (3-PBT) to determine flexural strength, Weibull characteristic strength, and Weibull modulus; and tiles were tested to determine CR. All data were statistically analyzed. In addition, SEM analysis of the materials was performed. Differences in flexural strength (FS) and translucency (CR) between the materials were found to be statistically significant. FS decreased as follows (MPa): LDHT 350.88 ± 19.77 (a) = LDLT 343.57 ± 18.48 (a) > LSLT 202.15 ± 17.41 (b) = LSHT 196.93 ± 8.87 > NIHT 186.69 ± 13.06 (c) = CDLT 184.73 ± 13.63 (c) = CDHT 174.15 ± 21.76 (c) = NILT 172.12 ± 11.98 (c) > EMHT 131.16 ± 13.33 (e) = EMLT 127.65 ± 11.09. CR decreased as follows (mean ± sd): CDLT 74.1 ± 1.1 (a); LSLT 74.0 ± 1.1 (ab); NILT 73.3 ± 0.8 (ab); EMLT 73.0 ± 1.5 (ab); NIHT 72.4 ± 1.0 (bc); LDLT 71.3 ± 1.1 (bc); LTHT 65.2 ± 0.9 (de); LSHT 63.8 ± 1.1 (def); EMHT 636 ± 1.2 (ef); CDHT 62.2 ± 0.8 (f). Our findings show that factory-crystallized lithium silicate glass ceramics fulfill ISO standards for Classes 1 and 2. Therefore, they can be considered viable alternatives to produce single-unit restorations with a chairside procedure not requiring thermal treatment.
Brain-Inspired Navigation Model Based on the Distribution of Polarized Sky-Light
Authors: Li Jinshan , Chu Jinkui , Zhang Ran , Tong Kun
Published: November 2022

This paper proposes a brain-inspired navigation model based on absolute heading for the autonomous navigation of unmanned platforms. The proposed model combined the sand ant’s strategy of acquiring absolute heading from the sky environment and the brain-inspired navigation system, which is closer to the navigation mechanism of migratory animals. Firstly, a brain-inspired grid cell network model and an absolute heading-based head-direction cell network model were constructed based on the continuous attractor network (CAN). Then, an absolute heading-based environmental vision template was constructed using the line scan intensity distribution curve, and the path integration error was corrected using the environmental vision template. Finally, a topological cognitive node was constructed according to the grid cell, the head direction cell, the environmental visual template, the absolute heading information, and the position information. Numerous topological nodes formed the absolute heading-based topological map. The model is a topological navigation method not limited to strict geometric space scale, and its position and absolute heading are decoupled. The experimental results showed that the proposed model is superior to the other methods in terms of the accuracy of visual template recognition, as well as the accuracy and topology consistency of the constructed environment topology map.
Variable Dimensional Scaling Method: A Novel Method for Path Planning and Inverse Kinematics
Authors: Jia Longfei , Yu Zhiyuan , Zhou Haiping , Pan Zhe , Ou Yangsheng , Guo Yaxing , Huang Yuping
Published: November 2022

Traditional methods for solving the inverse kinematics of a hyper-redundant manipulator (HRM) can only plan the path of the end-effector with a complicated solving process, where obstacle avoidance is also not considered. To solve the above problems, a novel method for solving inverse kinematics of HRM is proposed in this paper: the variable dimension scaling method (VDSM), which can solve complex inverse kinematics while avoiding obstacles. Through this method, the path of the end-effector is scaled under a certain proportion and is adjusted depending on the position of the obstacle, which has good universality. The number of link angles changed is as small as possible in the process of achieving the end-effector moving along the desired path. With the redundancy of HRM, obstacle avoidance can be implemented in any environment by the proposed method. Through simulation and experiments in different environments, the above advantages of VDSM are verified.