bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel Three-Dimensional and Non-Invasive Diagnostic Approach for Distinction between Odontogenic Keratocysts and Ameloblastomas
Authors: Taxis Juergen , Platz Batista da Silva Natascha , Grau Elisabeth , Spanier Gerrit , Nieberle Felix , Maurer Michael , Spoerl Steffen , Meier Johannes K. , Ettl Tobias , Reichert Torsten E. , Ludwig Nils
Published: August 2023

Aim of this study was to demonstrate the diagnostic ability to differentiate odontogenic keratocysts (OKCs) from ameloblastomas (AMs) based on computed tomography (CT) or cone beam computed tomography (CBCT) scans. Preoperative CT and CBCT scans from 2004 to 2019 of OKCs and AMs were analyzed in 51 participants. Lesions were three-dimensionally (3D) assessed and Hounsfield units (HU) as well as gray scale values (GSV) were quantified. Calculated HU spectra were compared within the same imaging modalities using unpaired t-tests and correlated with participants characteristics by calculating Pearsons correlation coefficients. Within the CT scans, AMs had highly significantly higher HU values compared to OKCs (43.52 HU and 19.79 HU, respectively; p < 0.0001). Analogous, within the CBCT scans, AMs had significantly higher GSV compared to OKCs (−413.76 HU and −564.76 HU, respectively; p = 0.0376). These findings were independent from participants’ gender and age, anatomical site, and lesion size, indicating that the HU- and GSV-based difference reflects an individual configuration of the lesion. HU and GSV spectra calculated from CT and CBCT scans can be used to discriminate between OKCs and AMs. This diagnostic approach represents a faster and non-invasive option for preoperative diagnosis of such entities and has potential to facilitate therapeutic decision making.
Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis
Authors: Chu Yan , Ali Syed Muhammad , Lu Mingfeng , Zhang Yanan
Published: August 2023

In bearing fault diagnosis, machine learning methods have been proven effective on the basis of the heterogeneous features extracted from multiple domains, including deep representation features. However, comparatively little research has been performed on fusing these multi-domain heterogeneous features while dealing with the interrelation and redundant problems to precisely discover the bearing faults. Thus, in the current study, a novel diagnostic method, namely the method of incorporating heterogeneous representative features into the random subspace, or IHF-RS, is proposed for accurate bearing fault diagnosis. Primarily, via signal processing methods, statistical features are extracted, and via the deep stack autoencoder (DSAE), deep representation features are acquired. Next, considering the different levels of predictive power of features, a modified lasso method incorporating the random subspace method is introduced to measure the features and produce better base classifiers. Finally, the majority voting strategy is applied to aggregate the outputs of these various base classifiers to enhance the diagnostic performance of the bearing fault. For the proposed method’s validity, two bearing datasets provided by the Case Western Reserve University Bearing Data Center and Paderborn University were utilized for the experiments. The results of the experiment revealed that in bearing fault diagnosis, the proposed method of IHF-RS can be successfully utilized.
On the Entropy and the Maximum Entropy Principle of Uncertain Variables
Authors: Liu Yujun , Ma Guanzhong
Published: August 2023

A new variance formula is developed using the generalized inverse of an increasing function. Based on the variance formula, a new entropy formula for any uncertain variable is provided. Most of the entropy formulas in the literature are special cases of the new entropy formula. Using the new entropy formula, the maximum entropy distribution for unimodel entropy of uncertain variables is provided without using the Euler–Lagrange equation.
A Lightweight Trust Mechanism with Attack Detection for IoT
Authors: Zhou Xujie , Tang Jinchuan , Dang Shuping , Chen Gaojie
Published: August 2023

In this paper, we propose a lightweight and adaptable trust mechanism for the issue of trust evaluation among Internet of Things devices, considering challenges such as limited device resources and trust attacks. Firstly, we propose a trust evaluation approach based on Bayesian statistics and Jøsang’s belief model to quantify a device’s trustworthiness, where evaluators can freely initialize and update trust data with feedback from multiple sources, avoiding the bias of a single message source. It balances the accuracy of estimations and algorithm complexity. Secondly, considering that a trust estimation should reflect a device’s latest status, we propose a forgetting algorithm to ensure that trust estimations can sensitively perceive changes in device status. Compared with conventional methods, it can automatically set its parameters to gain good performance. Finally, to prevent trust attacks from misleading evaluators, we propose a tango algorithm to curb trust attacks and a hypothesis testing-based trust attack detection mechanism. We corroborate the proposed trust mechanism’s performance with simulation, whose results indicate that even if challenged by many colluding attackers that can exploit different trust attacks in combination, it can produce relatively accurate trust estimations, gradually exclude attackers, and quickly restore trust estimations for normal devices.
A Universal Random Coding Ensemble for Sample-Wise Lossy Compression
Authors: Merhav Neri
Published: August 2023

We propose a universal ensemble for the random selection of rate–distortion codes which is asymptotically optimal in a sample-wise sense. According to this ensemble, each reproduction vector, x^, is selected independently at random under the probability distribution that is proportional to 2−LZ(x^), where LZ(x^) is the code length of x^ pertaining to the 1978 version of the Lempel–Ziv (LZ) algorithm. We show that, with high probability, the resulting codebook gives rise to an asymptotically optimal variable-rate lossy compression scheme under an arbitrary distortion measure, in the sense that a matching converse theorem also holds. According to the converse theorem, even if the decoder knew the ℓ-th order type of source vector in advance (ℓ being a large but fixed positive integer), the performance of the above-mentioned code could not have been improved essentially for the vast majority of codewords pertaining to source vectors in the same type. Finally, we present a discussion of our results, which includes among other things, a clear indication that our coding scheme outperforms the one that selects the reproduction vector with the shortest LZ code length among all vectors that are within the allowed distortion from the source vector.
The Role of Positive and Negative Emotions in Shaping Willingness to Communicate in a Second Language: Self-Perceived Communication Competence as a Moderator
Authors: Nadeem Muhammad Umar , Adnan Muhammad , Zabrodskaja Anastassia
Published: August 2023

Willingness to communicate (WTC) in a second language (L2) is one of the key concepts in L2 communication and learning. Previously, research practice in this area has been more concerned with investigating the relationship between individual negative emotions and WTC. However, insufficient attention has been given to a wide range of positive and negative emotions contributing to WTC. This study aims to address the impact of emotions (positive and negative) and self-perceived communication competence (SPCC) on WTC in an L2 in the context of Pakistan. The broaden-and-build theory of positive emotions has driven theoretical reasoning. Higher education students (N = 401) were considered the participants of this study and were approached conveniently. Data were processed and analyzed in SPSS. The findings revealed that positive emotions and SPCC positively influence WTC in an L2. On the contrary, negative emotions have a negative impact on WTC in an L2. Additionally, SPCC appears to be a significant moderator in the relationship between emotions (positive and negative) and WTC in an L2. The study’s findings have important practical implications for Pakistan’s Higher Education Commission (HEC) in drafting and implementing policies to enhance students’ WTC within higher education classrooms.
Digital Face Manipulation Creation and Detection: A Systematic Review
Authors: Dang Minh , Nguyen Tan N.
Published: August 2023

The introduction of publicly available large-scale datasets and advances in generative adversarial networks (GANs) have revolutionized the generation of hyper-realistic facial images, which are difficult to detect and can rapidly reach millions of people, with adverse impacts on the community. Research on manipulated facial image detection and generation remains scattered and in development. This survey aimed to address this gap by providing a comprehensive analysis of the methods used to produce manipulated face images, with a focus on deepfake technology and emerging techniques for detecting fake images. The review examined four key groups of manipulated face generation techniques: (1) attributes manipulation, (2) facial re-enactment, (3) face swapping, and (4) face synthesis. Through an in-depth investigation, this study sheds light on commonly used datasets, standard manipulated face generation/detection approaches, and benchmarking methods for each manipulation group. Particular emphasis is placed on the advancements and detection techniques related to deepfake technology. Furthermore, the paper explores the benefits of analyzing deepfake while also highlighting the potential threats posed by this technology. Existing challenges in the field are discussed, and several directions for future research are proposed to tackle these challenges effectively. By offering insights into the state of the art for manipulated face image detection and generation, this survey contributes to the advancement of understanding and combating the misuse of deepfake technology.
Cross-Mesh Clock Network Synthesis
Authors: Cheng Wei-Kai , Yeh Zih-Ming , Kao Hsu-Yu , Huang Shih-Hsu
Published: August 2023

In the clock network design, the trade-off between power consumption and timing closure is an important and difficult issue. The clock tree architecture has a shorter wire length and better power consumption, but it is more difficult to achieve timing closure with it. On the other hand, clock mesh architecture is easier to satisfy the clock skew constraint, but it usually has much more power consumption. Therefore, a hybrid clock network architecture that combines both the clock tree and clock mesh seems to be a promising solution. In a normal hybrid mesh/tree structure, a driving buffer is placed in the intersection of mesh lines. In this paper, we propose a novel cross-mesh architecture, and we distribute the buffers to balance the overall switching capacitance, reducing the number of registers connected to a subtree, and the load capacitance of a buffer. With the average dispersion of the overall driving force, our methodology creates small non-zero skew clock trees. In addition, we integrate clock gating, register clustering, and load balancing techniques to optimize clock skew and load capacitance simultaneously. The proposed methodology has four stages: cross-mesh planning, register clustering, mesh line connecting, and load balancing. Experimental results show that our cross-mesh architecture has high tolerance for process variation, and is robust in all the operation modes. Comparing it to the uniform mesh architecture, our methodology and algorithms reduce 28.9% of load capacitance and 80.4% of clock skew on average. Compared to the non-uniform mesh architecture, we also reduce capacitance by 22.4% and skew by 76.7% on average. This illustrates that we can obtain a feasible solution effectively and improve both power consumption and clock skew simultaneously.
Efficiency and Reliability Assessment-Based Selection of the Optimal Common Bus in Hub-Stations
Authors: Tahir Hira , Gelani Hasan Erteza , Saleem Muhammad , Hussain Asif
Published: August 2023

This paper is an attempt to present a simple yet innovative planning method for determining the type of common bus in a hub station (HS), devised on efficiency and reliability grounds. The efficiency is evaluated by modeling the efficiency curves of the converters under part-load and full-load conditions, hence enabling a realistic estimate of the efficiency. Reliability evaluation is executed by modeling the failure and repair rate distributions of the HS components. The optimal common bus type selection is based on both the efficiency and reliability metrics of the HS. The deterministic factor in the type of common bus selection is proved to be the number of components in the HS. The results indicate that DC and AC systems have comparable efficiencies with a meagre difference of 1.26%. The failure rate of AC common bus architecture is 22% greater than DC common bus architecture resulting in a 1.788 times reliability advantage of the DC system. Moreover, the greater number of components in the case of a AC common bus adds to the economic advantage of DC with a lower number of components leading DC to be an optimal design.
Diagnosis and Treatment Knowledge Graph Modeling Application Based on Chinese Medical Records
Authors: Wang Jianghan , Qu Zhu , Hu Yihan , Ling Qiyun , Yu Jingyi , Jiang Yushan
Published: August 2023

In this study, a knowledge graph of Chinese medical record data was constructed based on graph database technology. An entity extraction method based on natural language processing, disambiguation, and reorganization for Chinese medical records is proposed, and dictionaries of drugs and treatment plans are constructed. Examples of applications of the knowledge graph in diagnosis and treatment prediction are given. Experimentally, it is found that the knowledge graph based on the graph database is 116.7% faster than the traditional database in complex relational queries.
A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding
Authors: Hong Jingping , Dong Zhihong , Zhang Xue , Song Nannan , Cao Peng
Published: August 2023

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04.
Few-Shot Hyperspectral Image Classification Based on Convolutional Residuals and SAM Siamese Networks
Authors: Xia Mengen , Yuan Guowu , Yang Lingyu , Xia Kunming , Ren Ying , Shi Zhiliang , Zhou Hao
Published: August 2023

With the development of few-shot learning, significant progress has been achieved in hyperspectral image classification using related networks, leading to improved classification outcomes. However, practical few-shot hyperspectral image classification encounters challenges such as network overfitting and insufficient feature extraction during the model training process. To address these issues, we propose a model called CRSSNet (Convolutional Residuals and SAM Siamese Networks) for few-shot hyperspectral image classification. In this model, we deepen the network depth and employ the convolutional residual technique to enhance the feature extraction capabilities and alleviate the problem of network gradient degradation. Additionally, we introduce the Spatial Attention Mechanism (SAM) to effectively leverage spatial information features in hyperspectral images. Lastly, metric learning is employed by comparing the distance between two output feature vectors to determine the label category. Experimental results demonstrate that our method achieves superior classification performance compared to other methods.
Research on Cache Coherence Protocol Verification Method Based on Model Checking
Authors: Zhao Yiqiang , Shi Boning , Zhang Qizhi , Yuan Yidong , He Jiaji
Published: August 2023

This paper analyzes the underlying logic of the processor’s behavior level code. It proposes an automatic model construction and formal verification method for the cache consistency protocol with the aim of ensuring data consistency in the processor and the correctness of the cache function. The main idea of this method is to analyze the register transfer level (RTL) code directly at the module level and variable level, and extract the key modules and key variables according to the code information. Then, based on key variables, conditional behavior statements are retrieved from the code, and unnecessary statements are deleted. The model construction and simplification of related core states are completed automatically, while also simultaneously generating the attribute library to be verified, using “white list” as the construction strategy. Finally, complete cache consistency protocol verification is implemented in the model detector UPPAAL. Ultimately, this mechanism reduces the 142 state-transition path-guided global states of the cache module to be verified into 4 core functional states driven by consistency protocol implementation, effectively reducing the complexity of the formal model, and extracting 32 verification attributes into 6 verification attributes, reducing the verification time cost by 76.19%.
A New Natural Gas Accumulation Model in the Triassic Xujiahe Formation: A Case Study in the Tongjiang-Malubei Area of the Sichuan Basin
Authors: Du Hongquan , Shi Zhiqiang , Chai Haobo , Zeng Tao , Li Bisong , Pan Lei , Tian Yu
Published: August 2023

The natural gas in the Triassic Xujiahe Formation (T3x) is reported to be mainly derived from the T3x source rock itself. Here, we report a new natural gas accumulation model, which demonstrates that the T3x gas in the Tongjiang-Malubei (TM) area is derived from both T3x and underline marine source rocks. The T3x gas in the TM area is characterized by CH4 with a gas dryness coefficient above 0.99, indicating a high thermal maturity. The δ13C values of the methane, ethane, and propane in the T3x gas in the TM area are −33.7~−29.2‰, −32.7~−28.3‰, and −32.8~−29.5‰, respectively. Compared with the T3x gas in the Yuanba area, which was sourced from the T3x source rock, the T3x gas in the TM area contains heavier δ13C in methane and lighter δ13C in ethane, showing a partial reversal carbon isotope distribution (δ13C1 > δ13C2). According to their chemical and isotopic compositions, the T3x gas in the TM area was a mixture of coal-type and oil-type gases. The coal-type gas was mainly derived from the type III kerogen of the T3x source rock, and the oil-type gas was derived from the type-I kerogen of marine source rock in the Permian Wujiaping Formation (P3w). The oil-type gas migrated upward along the deep-seated faults that connect the P3w source rock and T3x sandstone reservoirs, and then mixed with coal-type gas in the T3x reservoirs, resulting in large-scale gas accumulation. This new gas accumulation model is controlled by a dual gas source supply and a high efficiency migration via the fault system. The findings of this study can help us to better understand the gas accumulation mechanism with the development of late-stage penetrating faults, which not only have implications for future petroleum exploration and development in the TM area, but also affect other analogous areas in the Sichuan Basin.
Physical and Numerical Simulation of Tight Gas Flow at the Microscale
Authors: Zhang Jianzhong , Gao Shusheng , Xiong Wei , Ye Liyou , Liu Huaxun , Zhu Wenqing , Mu Ying , Niu Wente
Published: August 2023

The porous media in tight reservoirs are mainly composed of micro- and nanopores, gas seepage through which is complex, making it difficult to study. Physical simulation using micron tubes is an intuitive and effective method to study the seepage mechanism of tight gas. The lattice Boltzmann method (LBM) is the most effective method for the tight gas seepage simulation, and it has been widely used. Microscale gas seepage simulation experiments and LBM simulations of micron tubes with different inner diameters were performed. The results showed that in micron tubes, the gas flow increases nonlinearly with an increasing pressure gradient. Influenced by compression and rarefaction effects, the degree of the nonlinearity of pressure distribution in series micron tubes increases with inlet pressure. The existence of a connecting channel between parallel micron tubes breaks the linear distribution of pressure in the original micron tubes, and the gas forms a raised relative high-pressure area at the connection of the two micron tubes; the wider the channel, the greater the bulge. The average gas flow rate in the whole micron tube increases with the channel width, and the seepage capacity increases instead of decreases. The diameter change of one micron tube has no effect on the gas flow in the other micron tube. Although the two micron tubes are connected, they are still relatively independent individuals. These research results lay a foundation for the correct understanding of the characteristics and laws of tight gas seepage in the pores of reservoirs at the micro- and nanoscales, and they have important theoretical significance for the study of seepage mechanisms in tight gas reservoirs.
Large-Eddy Simulation of Utility-Scale Wind Farm Sited over Complex Terrain
Authors: Singh Jagdeep , Alam Jahrul M
Published: August 2023

The realm of wind energy is a rapidly expanding renewable energy technology. Wind farm developers need to understand the interaction between wind farms and the atmospheric flow over complex terrain. Large-eddy simulations provide valuable data for gaining further insight into the impact of rough topography on wind farm performance. In this article, we report the influence of spatial heterogeneity on wind turbine performance. We conducted numerical simulations of a 12×5 wind turbine array over various rough topographies. First, we evaluated our large-eddy simulation method through a mesh convergence analysis, using mean vertical profiles, vertical friction velocity, and resolved and subgrid-scale kinetic energy. Next, we analyzed the effects of surface roughness and dispersive stresses on the performance of fully developed large wind farms. Our results show that the ground roughness element’s flow resistance boosts the power production of large wind farms by almost 68% over an aerodynamically rough surface compared with flat terrain. The dispersive stress analysis revealed that the primary degree of spatial heterogeneity in wind farms is in the streamwise direction, which is the “wake-occupied” region, and the relative contribution of dispersive shear stress to the overall drag may be about 45%. Our observation reveals that the power performance of the wind farm in complex terrain surpasses the drag effect. Our study has implications for improving the design of wind turbines and wind farms in complex terrain to increase their efficiency and energy output.
Bibliometric Evaluation of Energy Efficiency in Agriculture
Authors: Benedek Andrea , Rokicki Tomasz , Szeberényi András
Published: August 2023

In recent years, the importance of energy efficiency in the agricultural sector has become increasingly apparent. As the world faces challenges such as climate change, resource scarcity, and population growth, the need for sustainable and efficient agricultural practices has intensified. Energy plays a crucial role in agricultural production, from powering machinery and irrigation systems to processing and transportation. Therefore, understanding the scientific advancements and collaborative efforts in the field of energy efficiency in agriculture is essential for devising effective strategies, promoting innovation, and achieving global sustainability goals. The aim of this study is to provide insight into and a comprehensive overview of global publications on energy efficiency in agriculture and examine its scientific productivity. The paper explores the research areas and trending topics within the field, as well as the extent of collaboration among authors, institutions, and countries involved in this scientific domain. This investigation is crucial in order to address the current energy shocks and the numerous problems they generate, highlighting the importance of a holistic approach and the need for multiple regions to work together. Only by offering rapid and viable solutions can we effectively overcome this situation.
A Theoretical Study of NH2 Radical Reactions with Propane and Its Kinetic Implications in NH3-Propane Blends’ Oxidation
Authors: Giri Binod Raj , Shrestha Krishna Prasad , Mai Tam V.-T. , Giri Sushant , Adil Mohammad , Naik R. Thirumaleswara , Mauss Fabian , Huynh Lam Kim
Published: August 2023

The reaction of NH2 radicals with C3H8 is crucial for understanding the combustion behavior of NH3/C3H8 blends. In this study, we investigated the temperature dependence of the rate coefficients for the hydrogen abstraction reactions of C3H8 by NH2 radicals using high-level theoretical approaches. The potential energy surface was constructed at the CCSD(T)/cc-pV(T, Q)//M06-2X/aug-cc-pVTZ level of theory, and the rate coefficients were computed using conventional transition state theory, incorporating the corrections for quantum tunneling and hindered internal rotors (HIR). The computed rate coefficients showed a strong curvature in the Arrhenius behavior, capturing the experimental literature data well at low temperatures. However, at T > 1500 K, the theory severely overpredicted the experimental data. The available theoretical studies did not align with the experiment at high temperatures, and the possible reasons for this discrepancy are discussed. At 300 K, the reaction of NH2 with C3H8 predominantly occurs at the secondary C-H site, which accounts for approximately 95% of the total reaction flux. However, the hydrogen abstraction reaction at the primary C-H site becomes the dominant reaction above 1700 K. A composite kinetic model was built, which incorporated the computed rate coefficients for NH2 + C3H8 reactions. The importance of NH2 + C3H8 reactions in predicting the combustion behavior of NH3/C3H8 blends was demonstrated by kinetic modeling.
Irradiation Analysis of Tensile Membrane Structures for Building-Integrated Photovoltaics
Authors: Marchwiński Janusz , Milošević Vuk , Stefańska Anna , Lucchi Elena
Published: August 2023

A dynamic development in building-integrated photovoltaics (BIPVs) has been observed in recent years. One of the manifestations of this trend is the integration of photovoltaic cells with tensile membrane structures, including canopies. Such solutions bring mutual benefits—the roofs provide a potentially large area for the application of photovoltaic cells while contributing to the improvement of the energy efficiency of the building. However, what is lacking is thorough research on the most favourable photovoltaic cell exposure within these roofs. This paper investigates the optimal position of photovoltaic cells in terms of energy gains related to exposure to solar radiation. Hypar geometries were simulated as the most characteristic of tensile membrane roofs and, simultaneously, the least obvious in the research context. Simulations were performed for 54 roof samples with the following geometric variables: roof height (1.0, 3.0 m) and membrane prestress (1:3, 1:1, 3:1). The research was conducted for three roof orientations defined by azimuth angles of 0, 22.5, and 45 degrees and three geographic locations, Oslo, Vienna, and Lisbon, representing Northern, Central, and Southern Europe, respectively. The Sofistik and Rhino + Ladybug software were used to create models and simulations. The study results show significant differences in the roof irradiation and, consequently, the optimal location of BIPVs depending on the above variables. Generally, it is the curvature that is the most important variable-less curved roofs are more irradiated and thus more suitable for BIPVs. Prestress and the azimuth angle are of lesser significance, but defining the optimal use of a BIPV depends on the adopted scenario regarding the percentage of membrane coverage with PVs—other recommendations concern the strategy of total or partial roof coverage with PV cells. The difference between optimally and incorrectly designed roofs may amount to a 50% electricity gain from PV cells.
Recent Developments in Ceria-Driven Solar Thermochemical Water and Carbon Dioxide Splitting Redox Cycle
Authors: Bhosale Rahul R.
Published: August 2023

Metal oxide (MO) based solar thermochemical H2O (WS) and CO2 splitting (CDS) is one of the most promising and potential-containing processes that can be used to produce H2 and syngas (liquid fuel precursor). Several non-volatile and volatile MOs were considered redox materials for the solar-driven WS and CDS operation. Among all the examined redox materials, based on their high O2 storage capacity, faster oxidation kinetics, and good stability, ceria and doped ceria materials are deemed to be one of the best alternatives for the operation of the thermochemical redox reactions associated with the WS and CDS. Pure ceria was used for solar fuel production for the first time in 2006. A review paper highlighting the work done on the ceria-based solar thermochemical redox WS and CDS cycle from 2006 until 2016 is already published elsewhere by the author. This review paper presents all the significant findings reported in applying pure ceria and doped ceria materials for the WS and CDS by research teams worldwide.
FE-Simulation and Experimental Characterisation of Environmental Effects on the Diffusion and Mechanical Performance of Hyperelastic Adhesive Joints
Authors: Fernandes Pedro H. E. , Wulf Andreas , Nagel Christof , Beber Vinicius C.
Published: August 2023

In operational applications, hyperelastic adhesive joints are exposed to environmental conditions (moisture and temperature) that affect their mechanical performance. The understanding of how the environment can influence the joint durability through both static and cyclic loading is a key aspect to ensure safety and avoid over-dimensioning. The current work presents an investigation of the effect of environment conditions on the diffusion and mechanical performance of two different hyperelastic adhesive joints (a polyurethane and a silicon-modified polymer). To assess the process of moisture mass diffusion, pure adhesive samples were weighted for 387 days when subjected to outdoor weathering conditions. An FEA-diffusion procedure method was demonstrated by (i) predicting the saturation concentration at steady conditions of 40 °C/15% r.h. (40/15) and 40 °C/60% r.h. (40/60), and (ii) predicting the experienced mass change due to outdoor weathering. The reversibility of the effect of conditioning at 40 °C/60% r.h. on the mechanical properties of the adhesives was assessed via quasi-static and fatigue tensile shear testing. The results support the conclusion that conditioning with the surrogate climate of 40 °C/60% r.h. does not cause irreversible damage, as any potential decrease in shear modulus, tensile shear strength and fatigue life due to 40/60 conditioning can be reversed by re-drying at 40/15.
Machine Grading of High-Density Hardwoods (Southern Blue Gum) from Tensile Testing
Authors: Martins Carlos , Moltini Gonzalo , Dias Alfredo M. P. G. , Baño Vanesa
Published: August 2023

Hardwoods commonly have high mechanical properties, which makes them interesting for structural use, but softwoods dominate the structural timber market in Europe. Tensile strength classes are recommended for engineered wood products. However, current European standards do not provide tensile strength classes for hardwoods and the declaration of tensile properties from machine grading in the industry is not yet possible. The present paper aims to contribute to the revision of European standards through the technical group CEN/TC124/WG2/TG2: Tensile strength classes for hardwoods, of the European Standardisation Committee. An experimental campaign which involved machine grading and tensile testing of over 569 boards of Southern blue gum (Eucalyptus globulus Labill.) from Spain and Portugal was made. Six new tensile strength classes were defined, from ET24 (ft,0,k = 24 N/mm2, Et,0,m = 18 kN/mm2 and ρk = 590 kg/m3) to ET42 (ft,0,k = 42 N/mm2, Et,0,m = 23 kN/mm2 and ρk = 640 kg/m3). Machine grading made possible the definition of six strength class combinations. Four combinations resulted in 40% of the sample being assigned to the higher strength class, with low percentages of rejection (varying between 1% and 14%). This demonstrates the high mechanical properties of the species and the performance improvement of machine grading with respect to current visual grading.
Classifying Mountain Vegetation Types Using Object-Oriented Machine Learning Methods Based on Different Feature Combinations
Authors: Fu Xiaoli , Zhou Wenzuo , Zhou Xinyao , Li Feng , Hu Yichen
Published: August 2023

Mountainous vegetation type classification plays a fundamental role in resource investigation in forested areas, making it necessary to accurately identify mountain vegetation types. However, Mountainous vegetation growth is readily affected by terrain and climate, which often makes interpretation difficult. This study utilizes Sentinel-2A images and object-oriented machine learning methods to map vegetation types in the complex mountainous region of Jiuzhaigou County, China, incorporating multiple auxiliary features. The results showed that the inclusion of different features improved the accuracy of mountain vegetation type classification, with terrain features, vegetation indices, and spectral features providing significant benefits. After feature selection, the accuracy of mountain vegetation type classification was further improved. The random forest recursive feature elimination (RF_RFE) algorithm outperformed the RliefF algorithm in recognizing mountain vegetation types. Extreme learning machine (ELM), random forest (RF), rotation forest (ROF), and ROF_ELM algorithms all achieved good classification performance, with an overall accuracy greater than 84.62%. Comparing the mountain vegetation type distribution maps obtained using different classifiers, we found that classification algorithms with the same base classifier ensemble exhibited similar performance. Overall, the ROF algorithm performed the best, achieving an overall accuracy of 89.68%, an average accuracy of 88.48%, and a Kappa coefficient of 0.879.
Landscape Features Impact the Spatial Heterogeneity of Visitation Density within a Comprehensive Park: What Are the Seasonal and Diurnal Variations?
Authors: Peng Yulin , Gan Dexin , Cai Zhengwu , Xiao Mingxi , Shu Di , Wu Can , Yu Xiaoying , Li Xiaoma
Published: August 2023

Urban parks are the main place for physical activities, generating numerous benefits to enhance human well-being. Many studies have investigated the impacts of landscape features on park visitation but ignored their seasonal and diurnal variations. Taking the Hunan Martyr Park in China as an example, this study aims to assess the seasonal and diurnal variations in the impacts of landscape features on visitation density. We quantified visitation density for 109 activity zones for both daytime and nighttime in summer and winter and investigated the impacts of landscape features of the activity zone and the surrounding environments on visitation density based on 16 quantitative indicators. The results show that: (1) The impacts of ground condition, connection to water, distance to park entrances, and distance to stores were consistent in different periods. Paved activity zones, zones close to water, zones further away from the park entrances, and zones closer to the stores had significantly higher visitation density. (2) Shading degree, connected pathway, and distance to toilets had contrasting impacts between summer and winter. Zones with a higher shading degree attracted more visitors in summer and deterred visitors in winter. Zones with fewer pathways connected and zones farther away from toilets had higher visitation density in summer but not in winter. (3) Shading degree, number of trees, presence of facilities (e.g., benches, tables, pavilion, and light devices), and connected pathway had contrasting impacts between daytime and nighttime. Zones with a high shading degree, zones with benches and pavilions, and zones without trees had higher visitation density in the daytime. Zones with light devices, zones with more connected pathways, and zones without tables had higher visitation density at nighttime. These findings highlighted the seasonal and diurnal variation of the impacts of landscape features on park visitation and can help to improve urban park design, especially for cities with hot summers and cold winters.
Exposure Assessment of Heavy Metals and Microplastic-like Particles from Consumption of Bivalves
Authors: Tanaviyutpakdee Pharrunrat , Karnpanit Weeraya
Published: August 2023

The aim of this study was to determine the contamination of lead (Pb), cadmium (Cd) and microplastic (MP)-like particles in bivalves and estimate the exposure of the Thai population to these contaminants due to bivalve consumption. Clams, mussels and cockles were purchased from five wholesale seafood markets located on the upper Gulf of Thailand during the period 2017–2019. Determinations of Cd and Pb in the bivalves were conducted using a graphite furnace atomic absorption spectrometer (GFAAS). Visualization was conducted using a stereomicroscope to investigate the morphology and content of MP-like particles in the bivalve samples. The average Pb contents in clams, mussels and cockles were 112, 64 and 151 µg/kg wet wt., respectively. The average Cd contents were 126, 107 and 457 µg/kg wet wt. for clams, mussels and cockles, respectively. The average number of MP-like particles in bivalve samples varied from not detected to 1.2 items/g wet wt. and not detected to 4.3 items/individual. The exposure to Pb, Cd and MP-like particles due to bivalve consumption varied between 0.005 and 0.29 µg/kg bw/day, 0.017 and 28.9 µg/kg bw/month and 0.015 and 27.5 items/person/day, respectively. There was no potential health risk of exposure to Pb and Cd due to bivalve consumption in any age group. However, a high consumption of cockles with high Cd levels (the worst-case scenario) in children may be of concern.
Intrinsic Fluorescence Markers for Food Characteristics, Shelf Life, and Safety Estimation: Advanced Analytical Approach
Authors: Radotić Ksenija , Stanković Mira , Bartolić Dragana , Natić Maja
Published: August 2023

Food is a complex matrix of proteins, fats, minerals, vitamins, and other components. Various analytical methods are currently used for food testing. However, most of the used methods require sample preprocessing and expensive chemicals. New analytical methods are needed for quick and economic measurement of food quality and safety. Fluorescence spectroscopy is a simple and quick method to measure food quality, without sample preprocessing. This technique has been developed for food samples due to the application of a front-face measuring setup. Fluorescent compounds–fluorophores in the food samples are highly sensitive to their environment. Information about molecular structure and changes in food samples is obtained by the measurement of excitation–emission matrices of the endogenous fluorophores and by applying multivariate chemometric tools. Synchronous fluorescence spectroscopy is an advantageous screening mode used in food analysis. The fluorescent markers in food are amino acids tryptophan and tyrosine; the structural proteins collagen and elastin; the enzymes and co-enzymes NADH and FAD; vitamins; lipids; porphyrins; and mycotoxins in certain food types. The review provides information on the principles of the fluorescence measurements of food samples and the advantages of this method over the others. An analysis of the fluorescence spectroscopy applications in screening the various food types is provided.
Stereo-Complex and Click-Chemical Bicrosslinked Amphiphilic Network Gels with Temperature/pH Response
Authors: Yang Wanying , Wang Jiaqi , Jia Lingjiang , Li Jingyi , Liu Shouxin
Published: August 2023

Stimulus-responsive hydrogels have been widely used in the field of drug delivery because of their three-dimensional pore size and the ability to change the drug release rate with the change in external environment. In this paper, the temperature-sensitive monomer 2-methyl-2-acrylate-2-(2-methoxyethoxy-ethyl) ethyl ester (MEO2MA) and oligoethylene glycol methyl ether methacrylate (OEGMA) as well as the pH-sensitive monomer N,N-Diethylaminoethyl methacrylate (DEAEMA) were used to make the gel with temperature and pH response. Four kinds of physicochemical double-crosslinked amphiphilic co-network gels with different polymerization degrees were prepared by the one-pot method using the stereocomplex between polylactic acid as physical crosslinking and click chemistry as chemical crosslinking. By testing morphology, swelling, thermal stability and mechanical properties, the properties of the four hydrogels were compared. Finally, the drug release rate of the four gels was tested by UV–Vis spectrophotometer. It was found that the synthetic hydrogels had a good drug release rate and targeting, and had great application prospect in drug delivery.
Characteristics of a Lipid Hydrogel and Bigel as Matrices for Ascorbic Acid Stabilization
Authors: Loza-Rodríguez Noèlia , Millán-Sánchez Aina , López Olga
Published: August 2023

Ascorbic acid (AA) has many health benefits, including immune and cardiovascular deficiency protection, prenatal problems, and skin diseases. Unfortunately, AA is easily oxidized and has limited bioavailability. Thus, the development of formulations that stabilize and enhance the efficacy of AA is a challenge. In this study, 4% AA was encapsulated in two recently developed gels, a hydrogel and a bigel. The hydrogel was formed exclusively with lipids and water, and the bigel was a combination of the hydrogel with an oleogel formed with olive oil and beeswax. The effect of AA in gel microstructures was determined using X-ray scattering, rheology, and texture analysis. Additionally, the capacity of these materials to protect AA from degradation upon temperature and sunlight was studied. Results showed that the incorporation of AA into both materials did not affect their microstructure. Moreover, hydrogel-protected AA showed only 2% degradation after three months at 8 °C, while in aqueous solution, it degraded by 12%. Regarding sunlight, bigel showed a good shielding effect, exhibiting only 2% AA degradation after 22 h of exposure, whereas in aqueous solution, AA degraded by 10%. These results suggest that both proposed gels could be used in biomedical applications and the field of food.
Multidisciplinary Management of Rett Syndrome: Twenty Years’ Experience
Authors: Vilvarajan Sandra , McDonald Madeleine , Douglas Lyndal , Newham Jessica , Kirkland Robyn , Tzannes Gloria , Tay Diane , Christodoulou John , Thompson Susan , Ellaway Carolyn
Published: August 2023

Over the last 20 years, the understanding and natural history of Rett syndrome has advanced, but to date no cure has emerged, with multidisciplinary management being symptomatic and supportive. This study provides a comprehensive review of the clinical features, comorbidities and multidisciplinary management of a well-characterized cohort of females with classical Rett syndrome. We aim to improve awareness and understanding of Rett syndrome amongst pediatricians, pediatric subspecialists and allied health professionals to enable early diagnosis and a streamlined enrolment approach for future clinical trials. Rett syndrome, a complex X-linked condition, affecting mainly females, is due to pathogenic variants of the MECP2 gene in most affected individuals. The Rett syndrome Multidisciplinary Management clinic at The Children’s Hospital at Westmead, Sydney, Australia, was established in 2000. This retrospective analysis of individuals who attended the clinic from 2000 to 2020 was performed to identify the incidence and predicted age of onset of Rett syndrome related comorbidities, disease progression and to review management principles. Data collected included age of Rett syndrome diagnosis, MECP2 genotype, clinical features and medical comorbidities, such as sleep disturbance, seizures, breathing irregularities, scoliosis, mobility, hand stereotypies, hand function, constipation, feeding ability, use of gastrostomy, communication skills, QTc prolongation, anthropometry, and bruxism. Analysis of 103 girls who fulfilled the clinical diagnostic criteria for classical Rett syndrome with a pathogenic variant of the MECP2 gene showed a median age of diagnosis of 3 years. The most frequent MECP2 variant was c.502 C>T.
Identification of Novel Intronic SNPs in Transporter Genes Associated with Metformin Side Effects
Authors: Schweighofer Natascha , Strasser Moritz , Obermayer Anna , Trummer Olivia , Sourij Harald , Sourij Caren , Obermayer-Pietsch Barbara
Published: August 2023

Metformin is a widely used and effective medication in type 2 diabetes (T2DM) as well as in polycystic ovary syndrome (PCOS). Single nucleotide polymorphisms (SNPs) contribute to the occurrence of metformin side effects. The aim of the present study was to identify intronic genetic variants modifying the occurrence of metformin side effects and to replicate them in individuals with T2DM and in women with PCOS. We performed Next Generation Sequencing (Illumina Next Seq) of 115 SNPs in a discovery cohort of 120 metformin users and conducted a systematic literature review. Selected SNPs were analysed in two independent cohorts of individuals with either T2DM or PCOS, using 5′-3′exonucleaseassay. A total of 14 SNPs in the organic cation transporters (OCTs) showed associations with side effects in an unadjusted binary logistic regression model, with eight SNPs remaining significantly associated after appropriate adjustment in the discovery cohort. Five SNPs were confirmed in a combined analysis of both replication cohorts but showed different association patterns in subgroup analyses. In an unweighted polygenic risk score (PRS), the risk for metformin side effects increased with the number of risk alleles. Intronic SNPs in the OCT cluster contribute to the development of metformin side effects in individuals with T2DM and in women with PCOS and are therefore of interest for personalized therapy options.