bluuebunny/crossref_metadata_2025 huggingface dataset:
Degradable Nanogels Based on Poly[Oligo(Ethylene Glycol) Methacrylate] (POEGMA) Derivatives through Thermo-Induced Aggregation of Polymer Chain and Subsequent Chemical Crosslinking
Authors: Filipek Katarzyna , Otulakowski Łukasz , Jelonek Katarzyna , Utrata-Wesołek Alicja
Published: April 2024

Polymer nanogels—considered as nanoscale hydrogel particles—are attractive for biological and biomedical applications due to their unique physicochemical flexibility. However, the aggregation or accumulation of nanoparticles in the body or the occurrence of the body’s defense reactions still pose a research challenge. Here, we demonstrate the fabrication of degradable nanogels using thermoresponsive, cytocompatible poly[oligo(ethylene glycol) methacrylate]s-based copolymers (POEGMA). The combination of POEGMA’s beneficial properties (switchable affinity to water, nontoxicity, non-immunogenicity) along with the possibility of nanogel degradation constitute an important approach from a biological point of view. The copolymers of oligo(ethylene glycol) methacrylates were partially modified with short segments of degradable oligo(lactic acid) (OLA) terminated with the acrylate group. Under the influence of temperature, copolymers formed self-assembled nanoparticles, so-called mesoglobules, with sizes of 140–1000 nm. The thermoresponsive behavior of the obtained copolymers and the nanostructure sizes depended on the heating rate and the presence of salts in the aqueous media. The obtained mesoglobules were stabilized by chemical crosslinking via thiol-acrylate Michael addition, leading to nanogels that degraded over time in water, as indicated by the DLS, cryo-TEM, and AFM measurements. Combining these findings with the lack of toxicity of the obtained systems towards human fibroblasts indicates their application potential.
High-Performance, Easy-to-Fabricate, Nanocomposite Heater for Life Sciences and Biomedical Applications
Authors: Whulanza Yudan , Ammar Husein , Haryadi Deni , Pangesty Azizah Intan , Widoretno Widoretno , Subekti Didik Tulus , Charmet Jérôme
Published: April 2024

Microheaters are used in several applications, including medical diagnostics, synthesis, environmental monitoring, and actuation. Conventional microheaters rely on thin-film electrodes microfabricated in a clean-room environment. However, low-cost alternatives based on conductive paste electrodes fabricated using printing techniques have started to emerge over the years. Here, we report a surprising effect that leads to significant electrode performance improvement as confirmed by the thorough characterization of bulk, processed, and conditioned samples. Mixing silver ink and PVA results in the solubilization of performance-hindering organic compounds. These compounds evaporate during heating cycles. The new electrodes, which reach a temperature of 80 °C within 5 min using a current of 7.0 A, display an overall 42% and 35% improvement in the mechanical (hardness) and electrical (resistivity) properties compared to pristine silver ink electrodes. To validate our results, we use the composite heater to amplify and detect parasite DNA from Trypanosoma brucei, associated with African sleeping sickness. Our LAMP test compares well with commercially available systems, confirming the excellent performance of our nanocomposite heaters. Since their fabrication relies on well-established techniques, we anticipate they will find use in a range of applications.
In Situ Reduction of Silver Nanoparticles/Urushiol-Based Polybenzoxazine Composite Coatings with Enhanced Antimicrobial and Antifouling Performances
Authors: Chen Jipeng , Zheng Xiaoxiao , Jian Rongkun , Bai Weibin , Zheng Guocai , Xie Zhipeng , Lin Qi , Lin Fengcai , Xu Yanlian
Published: April 2024

Marine anti-fouling coatings represent an efficient approach to prevent and control the marine biofouling. However, a significant amount of antifouling agent is added to improve the static antifouling performance of the coatings, which leads to an issue whereby static antifouling performance conflicts with eco-friendly traits. Herein, this work reports an in situ reduction synthesis of silver nanoparticles (AgNPs) within polymers to produce composite coatings, aiming to solve the aforementioned issue. Firstly, urushiol-based benzoxazine monomers were synthesized by the Mannich reaction, using an eco-friendly natural product urushiol and n-octylamine and paraformaldehyde as the reactants. Additionally, AgNPs were obtained through the employment of free radicals formed by phenolic hydroxyl groups in the urushiol-based benzoxazine monomers, achieved by the in situ reduction of silver nitrate in benzoxazine. Then, the urushiol-based benzoxazine/AgNPs composite coatings were prepared by the thermosetting method. AgNPs exhibit broad-spectrum and highly efficient antimicrobial properties, with a low risk to human health and a minimal environmental impact. The composite coating containing a small amount of AgNPs (≤1 wt%) exhibits effective inhibition against various types of bacteria and marine microalgae in static immersion, thereby displaying outstanding antifouling properties. This organic polymer and inorganic nanoparticle composite marine antifouling coating, with its simple preparation method and eco-friendliness, presents an effective solution to the conflict between static antifouling effectiveness and environmental sustainability in marine antifouling coatings.
Ag-MWCNT Composites for Improving the Electrical and Thermal Properties of Electronic Paste
Authors: Wang Yunkai , Jing Danlei , Xiong Zikai , Hu Yongqing , Li Wei , Wu Haotian , Zuo Chuan
Published: April 2024

With the development of microelectronics products with high density and high power, it is urgent to improve the electrical and thermal conductivity of electronic paste to achieve the new requirements of packaging materials. In this work, a new synthesis method of Ag-MWCNTs was designed: Firstly, carboxylated MWCNTs and stannous chloride were used as raw materials to prepare high-loading-rate Sn-MWCNT composite material to ensure the high loading rate of metal on the MWCNT surface. Then, Ag-MWCNT composite material was prepared by the chemical displacement method to solve the problem of the low loading rate of silver nanoparticles on the MWCNT surface. On the basis of this innovation, we analyzed and compared the electrical, thermal, and mechanical properties of Ag-MWCNT composite electronic paste. Compared with the electronic paste without adding Ag-MWCNTs, the resistivity was reduced by 77%, the thermal conductivity was increased by 66%, and the shear strength was increased by 15%. Therefore, the addition of Ag-MWCNTs effectively improves the electrical, thermal, and mechanical properties of the paste, making it a promising and competitive choice for new packaging materials in the future.
Mechanical Behavior of Lithium-Ion Battery Separators under Uniaxial and Biaxial Loading Conditions
Authors: Shamchi Sahand , Farahani Behzad V. , Bulla Marian , Kolling Stefan
Published: April 2024

The mechanical integrity of two commercially available lithium-ion battery separators was investigated under uniaxial and biaxial loading conditions. Two dry-processed microporous films with polypropylene (PP)/polyethylene (PE)/polypropylene (PP) compositions were studied: Celgard H2010 Trilayer and Celgard Q20S1HX Ceramic-Coated Trilayer. The uniaxial tests were carried out along the machine direction (MD), transverse direction (TD), and diagonal direction (DD). In order to generate a state of in-plane biaxial tension, a pneumatic bulge test setup was prioritized over the commonly performed punch test in an attempt to eliminate the effects of contact friction. The biaxial flow stress–strain behavior of the membranes was deduced via the Panknin–Kruglov method coupled with a 3D Digital Image Correlation (DIC) technique. The findings demonstrate a high degree of in-plane anisotropy in both membranes. The ceramic coating was found to negatively affect the mechanical performance of the trilayer microporous separator, compromising its strength and stretchability, while preserving its failure mode. Derived from experimentally calibrated constitutive models, a finite element model was developed using the explicit solver OpenRadioss. The numerical model was capable of predicting the biaxial deformation of the semicrystalline membranes up until failure, showing a fairly good correlation with the experimental observations.
Contextualizing Transnational Chinese Christianity: A Relational Approach
Authors: Cao Nanlai , Lin Lijun
Published: April 2024

In recent years, the number of Chinese Christian organizations in Europe has grown considerably compared to other overseas Chinese community organizations. They can mobilize transnational networks and resources to expand religious space in host societies and form a highly visible social force. Although the rise of early Christianity in the Western world has been considered an outcome of inherent religious strength, especially in terms of its central doctrines and religious ethics, this article suggests that in the diasporic Chinese world where Christianity constitutes a non-indigenous religious tradition, social relatedness based on native place, family, and kinship ties provides a more useful context for understanding its dynamic expansion and cross-regional transmission. Drawing on anthropological fieldwork conducted in Europe among overseas Chinese Christian traders and entrepreneurs, this research seeks an alternative framework for understanding the religious-cultural dynamics of Chinese Christianity in the context of transnational migration.
MRFA-Net: Multi-Scale Receptive Feature Aggregation Network for Cloud and Shadow Detection
Authors: Wang Jianxiang , Li Yuanlu , Fan Xiaoting , Zhou Xin , Wu Mingxuan
Published: April 2024

The effective segmentation of clouds and cloud shadows is crucial for surface feature extraction, climate monitoring, and atmospheric correction, but it remains a critical challenge in remote sensing image processing. Cloud features are intricate, with varied distributions and unclear boundaries, making accurate extraction difficult, with only a few networks addressing this challenge. To tackle these issues, we introduce a multi-scale receptive field aggregation network (MRFA-Net). The MRFA-Net comprises an MRFA-Encoder and MRFA-Decoder. Within the encoder, the net includes the asymmetric feature extractor module (AFEM) and multi-scale attention, which capture diverse local features and enhance contextual semantic understanding, respectively. The MRFA-Decoder includes the multi-path decoder module (MDM) for blending features and the global feature refinement module (GFRM) for optimizing information via learnable matrix decomposition. Experimental results demonstrate that our model excelled in generalization and segmentation performance when addressing various complex backgrounds and different category detections, exhibiting advantages in terms of parameter efficiency and computational complexity, with the MRFA-Net achieving a mean intersection over union (MIoU) of 94.12% on our custom Cloud and Shadow dataset, and 87.54% on the open-source HRC_WHU dataset, outperforming other models by at least 0.53% and 0.62%. The proposed model demonstrates applicability in practical scenarios where features are difficult to distinguish.
Estimation of Small-Stream Water Surface Elevation Using UAV Photogrammetry and Deep Learning
Authors: Szostak Radosław , Pietroń Marcin , Wachniew Przemysław , Zimnoch Mirosław , Ćwiąkała Paweł
Published: April 2024

Unmanned aerial vehicle (UAV) photogrammetry allows the generation of orthophoto and digital surface model (DSM) rasters of terrain. However, DSMs of water bodies mapped using this technique often reveal distortions in the water surface, thereby impeding the accurate sampling of water surface elevation (WSE) from DSMs. This study investigates the capability of deep neural networks to accommodate the aforementioned perturbations and effectively estimate WSE from photogrammetric rasters. Convolutional neural networks (CNNs) were employed for this purpose. Two regression approaches utilizing CNNs were explored: direct regression employing an encoder and a solution based on prediction of the weight mask by an autoencoder architecture, subsequently used to sample values from the photogrammetric DSM. The dataset employed in this study comprises data collected from five case studies of small lowland streams in Poland and Denmark, consisting of 322 DSM and orthophoto raster samples. A grid search was employed to identify the optimal combination of encoder, mask generation architecture, and batch size among multiple candidates. Solutions were evaluated using two cross-validation methods: stratified k-fold cross-validation, where validation subsets maintained the same proportion of samples from all case studies, and leave-one-case-out cross-validation, where the validation dataset originates entirely from a single case study, and the training set consists of samples from other case studies. Depending on the case study and the level of validation strictness, the proposed solution achieved a root mean square error (RMSE) ranging between 2 cm and 16 cm. The proposed method outperforms methods based on the straightforward sampling of photogrammetric DSM, achieving, on average, an 84% lower RMSE for stratified cross-validation and a 62% lower RMSE for all-in-case-out cross-validation. By utilizing data from other research, the proposed solution was compared on the same case study with other UAV-based methods. For that benchmark case study, the proposed solution achieved an RMSE score of 5.9 cm for all-in-case-out cross-validation and 3.5 cm for stratified cross-validation, which is close to the result achieved by the radar-based method (RMSE of 3 cm), which is considered the most accurate method available. The proposed solution is characterized by a high degree of explainability and generalization.
Integrating Optical and SAR Time Series Images for Unsupervised Domain Adaptive Crop Mapping
Authors: Feng Luwei , Gui Dawei , Han Shanshan , Qiu Tianqi , Wang Yumiao
Published: April 2024

Accurate crop mapping is crucial for ensuring food security. Recently, many studies have developed diverse crop mapping models based on deep learning. However, these models generally rely on a large amount of labeled crop samples to investigate the intricate relationship between the crop types of the samples and the corresponding remote sensing features. Moreover, their efficacy is often compromised when applied to other areas owing to the disparities between source and target data. To address this issue, a new multi-modal deep adaptation crop classification network (MDACCN) was proposed in this study. Specifically, MDACCN synergistically exploits time series optical and SAR images using a middle fusion strategy to achieve good classification capacity. Additionally, local maximum mean discrepancy (LMMD) is embedded into the model to measure and decrease domain discrepancies between source and target domains. As a result, a well-trained model in a source domain can still maintain satisfactory accuracy when applied to a target domain. In the training process, MDACCN incorporates the labeled samples from a source domain and unlabeled samples from a target domain. When it comes to the inference process, only unlabeled samples of the target domain are required. To assess the validity of the proposed model, Arkansas State in the United States was chosen as the source domain, and Heilongjiang Province in China was selected as the target domain. Supervised deep learning and traditional machine learning models were chosen as comparison models. The results indicated that the MDACCN achieved inspiring performance in the target domain, surpassing other models with overall accuracy, Kappa, and a macro-averaged F1 score of 0.878, 0.810, and 0.746, respectively. In addition, the crop-type maps produced by the MDACCN exhibited greater consistency with the reference maps. Moreover, the integration of optical and SAR features exhibited a substantial improvement of the model in the target domain compared with using single-modal features. This study indicated the considerable potential of combining multi-modal remote sensing data and an unsupervised domain adaptive approach to provide reliable crop distribution information in areas where labeled samples are missing.
Diapiric Structures in the Tinto River Estuary (SW Spain) Caused by Artificial Load of an Industrial Stockpile
Authors: Morales Juan A. , Carro Berta M. , Borrego José , Diosdado Antonio J. , Aguilar María Eugenia , González Miguel A.
Published: April 2024

The mouth of the Tinto River is located on the southwest coast of the Iberian Peninsula in the northwest of the Gulf of Cadiz. The river flows into an estuarine system shared with the Odiel River, commonly known as the “Ría de Huelva”. In the 1960s, a wide area of ancient salt marshes was transformed by a stockpile of industrial wastes of phosphogypsum, reaching a height of 35 m above the level of the salt marsh at its highest point. Two surveys using high-resolution seismic reflection in conjunction with a parametric profiler were carried out in 2016 and 2018. The purpose of these geophysical studies was the realization of a 3D model of the sedimentary units constituting the most recent filling of the estuary. The records present abundant extrusion structures located on the margins of the waste stockpiles, which break the visible stratification of the surficial units of the estuary. In some sectors, these structures have reached the estuarine surface and have, therefore, a morphological expression on the estuarine floor. The origin of these structures is interpreted as a vertical escape of fluidized sediments from lower units caused by overpressure from stacking.
A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction
Authors: Zhu Rongjie , Song Biao , Qiu Zhongfeng , Tian Yuan
Published: April 2024

Predicting the mesoscale eddies in the ocean is crucial for advancing our understanding of the ocean and climate systems. Establishing spatio-temporal correlation among input data is a significant challenge in mesoscale eddy prediction tasks, especially for deep learning techniques. In this paper, we first present a deep learning solution based on a video prediction model to capture the spatio-temporal correlation and predict future sea surface height data accurately. To enhance the performance of the model, we introduced a novel metadata embedding module that utilizes neural networks to fuse remote sensing metadata with input data, resulting in increased accuracy. To the best of our knowledge, our model outperforms the state-of-the-art method for predicting sea level anomalies. Consequently, a mesoscale eddy detection algorithm will be applied to the predicted sea surface height data to generate mesoscale eddies in future. The proposed solution achieves competitive results, indicating that the prediction error for the eddy center position is 5.6 km for a 3-day prediction and 13.6 km for a 7-day prediction.
Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
Authors: Zhao Paihang , Jiang Linqiang , Tang Tao , Wu Zhidong , Wang Ding
Published: April 2024

Combining multiple devices for localization has important applications in the military field. This paper exploits the land-based short-wave platforms and satellites for fusion localization. The ionospheric reflection height error and satellite position errors have a great impact on the short-wave localization and satellite localization accuracy, respectively. In this paper, an iterative constrained weighted least squares (ICWLS) algorithm is proposed for these two kinds of errors. The algorithm converts the nonconvex equation constraints to linear constraints using the results of the previous iteration, thus ensuring convergence to the globally optimal solution. Simulation results show that the localization accuracy of the algorithm can reach the corresponding Constrained Cramér–Rao Lower Bound (CCRLB). Finally, the localization results of the two methods are fused using Kalman filtering. Simulations show that the fused localization accuracy is improved compared to the single-means localization.
Automatic Detection of the Running Surface of Railway Tracks Based on Laser Profilometer Data and Supervised Machine Learning
Authors: Mauz Florian , Wigger Remo , Gota Alexandru-Elisiu , Kuffa Michal
Published: April 2024

The measurement of the longitudinal rail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head. The detection of the running surface is crucial for vehicle-based rail profile measurements, as well as for defect detection. This paper presents a methodology for the automatic detection of the running surface based on a laser profilometer. The detection of the running surface is performed based on the light reflected from the rail surface. Three rail surfaces with different surface conditions are considered. Supervised machine learning is applied to classify individual surface elements as part of the running surface. Detection by a linear support vector machine is performed with accuracy of >90%. The lateral position of the running surface and its width are calculated. The average deviation from the labeled widths varies between −1.2mm and 5.6mm. The proposed measurement approach could be installed on a train for the future onboard monitoring of the rail network.
A High-Quality Sample Generation Method for Improving Steel Surface Defect Inspection
Authors: He Yu , Li Shuai , Wen Xin , Xu Jing
Published: April 2024

Defect inspection is a critical task in ensuring the surface quality of steel plates. Deep neural networks have the potential to achieve excellent inspection accuracy if defect samples are sufficient. Nevertheless, it is very different to collect enough samples using cameras alone. To a certain extent, generative models can alleviate this problem but poor sample quality can greatly affect the final inspection performance. A sample generation method, which employs a generative adversarial network (GAN), is proposed to generate high-quality defect samples for training accurate inspection models. To improve generation quality, we propose a production-and-elimination, two-stage sample generation process by simulating the formation of defects on the surface of steel plates. The production stage learns to generate defects on defect-free background samples, and the elimination stage learns to erase defects on defective samples. By minimizing the differences between the samples at both stages, the proposed model can make generated background samples close to real ones while guiding the generated defect samples to be more realistic. Experimental results show that the proposed method has the ability to generate high-quality samples that can help train powerful inspection models and thereby improve inspection performance.
Exploring Human–Exoskeleton Interaction Dynamics: An In-Depth Analysis of Knee Flexion–Extension Performance across Varied Robot Assistance–Resistance Configurations
Authors: Mosconi Denis , Moreno Yecid , Siqueira Adriano
Published: April 2024

Knee rehabilitation therapy after trauma or neuromotor diseases is fundamental to restore the joint functions as best as possible, exoskeleton robots being an important resource in this context, since they optimize therapy by applying tailored forces to assist or resist movements, contributing to improved patient outcomes and treatment efficiency. One of the points that must be taken into account when using robots in rehabilitation is their interaction with the patient, which must be safe for both and guarantee the effectiveness of the treatment. Therefore, the objective of this study was to assess the interaction between humans and an exoskeleton during the execution of knee flexion–extension movements under various configurations of robot assistance and resistance. The evaluation encompassed considerations of myoelectric activity, muscle recruitment, robot torque, and performed movement. To achieve this, an experimental protocol was implemented, involving an individual wearing the exoskeleton and executing knee flexion–extension motions while seated, with the robot configured in five distinct modes: passive (P), assistance on flexion (FA), assistance on extension (EA), assistance on flexion and extension (CA), and resistance on flexion and extension (CR). Results revealed distinctive patterns of movement and muscle recruitment for each mode, highlighting the complex interplay between human and robot; for example, the largest RMS tracking errors were for the EA mode (13.72 degrees) while the smallest for the CR mode (4.47 degrees), a non-obvious result; in addition, myoelectric activity was demonstrated to be greater for the completely assisted mode than without the robot (the maximum activation levels for the vastus medialis and vastus lateralis muscles were more than double those when the user had assistance from the robot). Tracking errors, muscle activations, and torque values varied across modes, emphasizing the need for careful consideration in configuring exoskeleton assistance and resistance to ensure effective and safe rehabilitation. Understanding these human–robot interactions is essential for developing precise rehabilitation programs, optimizing treatment effectiveness, and enhancing patient safety.
Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System
Authors: Shayne Madina , Molina Leonardo A. , Hu Bin , Chomiak Taylor
Published: April 2024

Smart algorithms for gait kinematic motion prediction in wearable assistive devices including prostheses, bionics, and exoskeletons can ensure safer and more effective device functionality. Although embedded systems can support the use of smart algorithms, there are important limitations associated with computational load. This poses a tangible barrier for models with increased complexity that demand substantial computational resources for superior performance. Forecasting through Recurrent Topology (FReT) represents a computationally lightweight time-series data forecasting algorithm with the ability to update and adapt to the input data structure that can predict complex dynamics. Here, we deployed FReT on an embedded system and evaluated its accuracy, computational time, and precision to forecast gait kinematics from lower-limb motion sensor data from fifteen subjects. FReT was compared to pretrained hyperparameter-optimized NNET and deep-NNET (D-NNET) model architectures, both with static model weight parameters and iteratively updated model weight parameters to enable adaptability to evolving data structures. We found that FReT was not only more accurate than all the network models, reducing the normalized root-mean-square error by almost half on average, but that it also provided the best balance between accuracy, computational time, and precision when considering the combination of these performance variables. The proposed FReT framework on an embedded system, with its improved performance, represents an important step towards the development of new sensor-aided technologies for assistive ambulatory devices.
Tunable High-Sensitivity Four-Frequency Refractive Index Sensor Based on Graphene Metamaterial
Authors: Bao Xu , Yu Shujun , Lu Wenqiang , Hao Zhiqiang , Yi Zao , Cheng Shubo , Tang Bin , Zhang Jianguo , Tang Chaojun , Yi Yougen
Published: April 2024

As graphene-related technology advances, the benefits of graphene metamaterials become more apparent. In this study, a surface-isolated exciton-based absorber is built by running relevant simulations on graphene, which can achieve more than 98% perfect absorption at multiple frequencies in the MWIR (MediumWavelength Infra-Red (MWIR) band as compared to the typical absorber. The absorber consists of three layers: the bottom layer is gold, the middle layer is dielectric, and the top layer is patterned with graphene. Tunability was achieved by electrically altering graphene’s Fermi energy, hence the position of the absorption peak. The influence of graphene’s relaxation time on the sensor is discussed. Due to the symmetry of its structure, different angles of light source incidence have little effect on the absorption rate, leading to polarization insensitivity, especially for TE waves, and this absorber has polarization insensitivity at ultra-wide-angle degrees. The sensor is characterized by its tunability, polarisation insensitivity, and high sensitivity, with a sensitivity of up to 21.60 THz/refractive index unit (RIU). This paper demonstrates the feasibility of the multi-frequency sensor and provides a theoretical basis for the realization of the multi-frequency sensor. This makes it possible to apply it to high-sensitivity sensors.
Bioactives in Cocoa: Novel Findings, Health Benefits, and Extraction Techniques
Authors: Tušek Kristina , Valinger Davor , Jurina Tamara , Sokač Cvetnić Tea , Gajdoš Kljusurić Jasenka , Benković Maja
Published: April 2024

Theobroma cacao L. seeds, commonly known as cocoa beans, are the foundation for cocoa and chocolate production. Following harvest, these beans undergo a multi-step processing chain including fermentation, drying, roasting, and grinding. This process yields cocoa solids, cocoa butter, and cocoa powder—all fundamental ingredients in the food and beverage industry. Beyond its sensory appeal (flavor, aroma, and texture), cocoa has garnered significant interest for its potential health benefits attributed to a rich profile of bioactive compounds. Cocoa is a well-documented source of polyphenolics, specifically flavanols, alongside methylxanthines, phytosterols, and dietary fibers. These constituents have been associated with a diverse range of bioactivities, including antioxidant, anti-carcinogenic, anti-diabetic, anti-inflammatory, anti-obesity, and anti-allergenic properties, potentially contributing to overall health maintenance. Efficient extraction techniques are crucial for maximizing the recovery of these valuable bioactive components from cocoa plant material. Modern methods are continuously being explored to optimize this process. This review focuses on the established health benefits associated with the bioactive compounds present in cocoa. Additionally, it will explore and discuss contemporary approaches for the extraction of these bioactive compounds from this plant source.
Rethinking Sporting Mystification in the Present Tense: Disneylimpics, Affective Neoliberalism, and the Greatest Transformation
Authors: Yang Junbin
Published: April 2024

While questioning the universalization, naturalization, neutralization, and idealization of sport and physical culture, this paper examines the ultimate mystification process of sport and physical culture by expanding upon two conceptual frameworks: Jules Boykoff’s celebration capitalism and Lawrence Grossberg’s affective landscape. It first analyzes the evolution of the Olympics into a corporatized, commercialized, spectacularized, and celebritized “Disneylimpics” that can consistently evoke an affective reverberation. It then introduces the idea of “affective neoliberalism” to highlight neoliberalism’s affective and ideological aspects. With Grossberg’s concept of affective landscape, this paper explores the internalization and intensification of anxiety and affective isolation within society. Additionally, the paper utilizes Karl Polanyi’s analysis in his influential book, The Great Transformation, to investigate the historical expansion of affective neoliberalism. By highlighting the 11 September 2001, attacks in the United States, it points out provocative militarization and (re)organization of the soul into a fictitious commodity, in addition to labor, land, and money, which triggers the greatest transformation. Lastly, summarizing central arguments, this paper concludes with modest suggestions, mainly focusing on two questions: (1) where are we now? and (2) how can we more effectively respond to the present context?
Bayesian Mediation Analysis with an Application to Explore Racial Disparities in the Diagnostic Age of Breast Cancer
Authors: Cao Wentao , Hagan Joseph , Yu Qingzhao
Published: April 2024

A mediation effect refers to the effect transmitted by a mediator intervening in the relationship between an exposure variable and a response variable. Mediation analysis is widely used to identify significant mediators and to make inferences on their effects. The Bayesian method allows researchers to incorporate prior information from previous knowledge into the analysis, deal with the hierarchical structure of variables, and estimate the quantities of interest from the posterior distributions. This paper proposes three Bayesian mediation analysis methods to make inferences on mediation effects. Our proposed methods are the following: (1) the function of coefficients method; (2) the product of partial difference method; and (3) the re-sampling method. We apply these three methods to explore racial disparities in the diagnostic age of breast cancer patients in Louisiana. We found that African American (AA) patients are diagnosed at an average of 4.37 years younger compared with Caucasian (CA) patients (57.40 versus 61.77, p< 0.0001). We also found that the racial disparity can be explained by patients’ insurance (12.90%), marital status (17.17%), cancer stage (3.27%), and residential environmental factors, including the percent of the population under age 18 (3.07%) and the environmental factor of intersection density (9.02%).
New Goodness-of-Fit Tests for the Kumaraswamy Distribution
Authors: Giles David E.
Published: April 2024

The two-parameter distribution known as the Kumaraswamy distribution is a very flexible alternative to the beta distribution with the same (0,1) support. Originally proposed in the field of hydrology, it has subsequently received a good deal of positive attention in both the theoretical and applied statistics literatures. Interestingly, the problem of testing formally for the appropriateness of the Kumaraswamy distribution appears to have received little or no attention to date. To fill this gap, in this paper, we apply a “biased transformation” methodology to several standard goodness-of-fit tests based on the empirical distribution function. A simulation study reveals that these (modified) tests perform well in the context of the Kumaraswamy distribution, in terms of both their low size distortion and respectable power. In particular, the “biased transformation” Anderson–Darling test dominates the other tests that are considered.
Report on the 11th National Congress AICPE (Associazione Italiana di Chirurgia Plastica Estetica) Held in Rimini, Italy, 12–14 April 2024
Authors: Riggio Egidio
Published: April 2024

The annual congress of the Italian Association of Plastic Aesthetic Surgery (AICPE) is one of the most relevant conference meetings in Europe concerning aesthetic plastic surgery as there are a number of participants and a parterre of invited speakers chosen for their renowned scientific value [...]
Obtaining Sustainable Electrical Energy from Pepper Waste
Authors: Segundo Rojas-Flores , Magaly De , Luis Cabanillas-Chirinos , Otiniano Nélida , Soto-Deza Nancy , Terrones-Rodriguez Nicole , Mayra De
Published: April 2024

Currently, two significant problems involve the government, population, and environment: the accelerated increase in organic waste and the need to replace conventional energy with environmentally sustainable energy. The sustainable use of organic waste is being intensely investigated to generate energy plants that produce alternative sustainable electrical energy beneficial to the population at a low cost. The novelty of this research is given by the use of pepper waste as fuel in the generation of bioelectricity, giving added value to these types of waste, benefiting farmers and companies dedicated to the export and import of these fruits, because they will be able to generate their own electrical energy using their own waste at a lower cost. For this reason, this research uses pepper waste as fuel in single-chamber microbial fuel cells manufactured at a low cost as its primary objective. The maximum values of the electric current (5.118 ± 0.065 mA) and electric potential (1.018 ± 0.101 V) were shown on the fourteenth day, with an optimal operating pH of 7.141 ± 0.134 and electrical conductivity of 112.846 ± 4.888 mS/cm. Likewise, a reduction in the COD was observed from 1210.15 ± 0.89 mg/L to 190.36 ± 16.58 mg/L in the 35 days of monitoring and with a maximum ORP of 426.995 ± 8.615 mV, whose internal resistance was 33.541 ± 2.471 Ω. The peak power density was 154.142 ± 8.151 mW/cm2 at a current density of 4.834 A/cm2, and the Rossellomorea marisflavi strain was identified with 99.57% identity.
Suburban Sustainability in Budapest Agglomeration—The Case of Törökbálint
Authors: Kocsis János B. , Tomay Kyra
Published: April 2024

The rapid growth of the metropolitan area around Budapest has led to increased traffic, infrastructure problems, and social and economic issues in the suburbs, making the sustainability of these areas a key topic in professional and public discussions. This article aims to explore the intricacies of sustainability in a suburban town by utilizing the Smart Growth framework, which is highly pertinent in Hungary. The focus of scientific and public discussions revolves around urban planning to curb urban sprawl and enhance public transportation usage. Our analysis concentrates on compactness, commuting, and community, utilizing quantitative survey data between 2018 and 2023. Our findings demonstrate that the town under analysis is dense regarding common and regular services, but depends significantly on the central area for more specialized services. The local community is tightly interconnected and shows no evidence of atomization. The commuting patterns show that polycentricity leads to new spatial connections and a growing reliance on cars, even while the quantity of vehicles is still growing. These results indicate that assessing the sustainability of suburbs may not be universally applicable, and examination should be conducted at the town level. Secondly, the sustainability of suburban areas may be distinct from the sustainability of denser, urbanized places.
Exploring Spatial-Temporal Coupling and Its Driving Factors of Green and Low-Carbon Urban Land Use Efficiency and High-Quality Economic Development in China
Authors: Peng Lina , Liang Juan , Wang Kexin , Xiao Wenqian , Zou Jian , Hong Yuxuan , Ding Rui
Published: April 2024

Green and low-carbon use of urban land (GLUUL) and high-quality economic development (HED) are two closely linked and mutually reinforcing systems, and their coordinated development is of great theoretical and practical significance to the realization of green and sustainable urban development. Based on theoretical analysis, this paper used data from 2005 to 2020 to measure GLUUL efficiency and HED level and their coupling coordination degree (CCD) successively of 282 cities in China, and then analyzed in-depth the main factors affecting CCD and its spatial–temporal heterogeneity using the GTWR model. This study found that (1) GLUUL efficiency and HED levels are increasing with different trends, and the development is unbalanced. High-value cities in the two systems show a staggered distribution pattern. (2) The CCD of the two was dominated by primary and intermediate coordination types, and the overall became increasingly coordinated, with the “intermediate coordination—advanced development” type having the highest representation. (3) There is a gradual convergence of CCD spatial differences, showing an overall spatial distribution pattern that is “high in the northwest and southeast, low in the central area”. (4) The influence degree and direction of different factors on CCD are distinguishing. The positive impact of industrial structure upgrading (Isu) is obviously greater than other factors, which has the strongest effect on the industrial corridor along the Yangtze River and the Beijing–Tianjin–Hebei region. The findings can offer insightful recommendations for promoting sustainable development in China and similar developing countries and regions.
Sustainable Development of Chinese Family Businesses: Exploring the Role of Succession Planning in Maintaining Organizational Sustainability from the Perspective of Socioemotional Wealth
Authors: Li Zeyu , Mustapha Mazlina , Hassan Ahmad Fahmi Sheikh , Saidin Saidatunur Fauzi
Published: April 2024

Identifying the factors affecting organizational sustainability is a crucial topic in the field of social science and business research. Especially in family businesses, the most crucial issue is how to maintain corporate sustainability across generations. In this regard, succession planning plays a key role in maintaining the sustainable development of family businesses. From the perspective of socioemotional wealth, this study discusses the motivations and consequences of intrafamily succession by measuring the impact of the internal determining factors of succession planning on family business performance. Based on a sample of 281 Chinese family firms, this study uncovers the relationship between succession planning and organizational performance. The core findings of this study include succession planning is positively related to organizational performance in the matter of the successor’s training; succession planning has a positive effect on organizational performance in terms of the successor’s self-preparation; and succession planning is positively correlated with organizational performance in the aspect of the relationship between the successor and business. By illustrating that the formulation of succession planning is an essential pursuit for family businesses to preserve sustainability and socioemotional wealth, the results reveal ways to facilitate succession planning through internal factors in the family business. This study contributes to organizational sustainable development literature, family business sustainability studies, and succession management research by validating the positive relationship between succession planning and organizational performance, indicating that succession planning is a vital driving force for achieving organizational sustainability.
Methane Emission Estimation Tools as a Basis for Sustainable Underground Mining of Gas-Bearing Coal Seams
Authors: Sidorenko Sergey , Trushnikov Vyacheslav , Sidorenko Andrey
Published: April 2024

Underground coal mining of gas-bearing coal seams is accompanied by the emission of large amounts of methane, which increases with depth. Coal seam methane is not only a major cause of major accidents in coal mines, but is also a greenhouse gas that has a significant negative impact on the Earth’s atmosphere. Analysis of the efficiency of underground coal mining suggests that as the depth of mining increases, the productivity of a longwall decreases by a factor of 3–5 or more, while the specific volume of methane emitted increases manifold and the efficiency of methane management decreases. Effective management of coal seam methane can only be achieved by monitoring its content at key points in a system of workings. Monitoring of methane not only eliminates the risk of explosions, but also lets us assess the effectiveness of using methane management techniques and their parameters to improve efficiency and reduce the cost of methane management (including a methane drainage) for ensuring sustainable underground coal mining. The aim of this article is to develop a software and hardware complex for monitoring methane in a coal mine by creating a simulation model for monitoring methane. The Arduino Uno board and the methane sensor MQ-4 were used for this purpose. In this article, the causes of methane emissions in coal mines, gas control systems, the structure of the mine monitoring system, and the causes of risks and occurrence of accidents in coal mines are considered. As a result of the work, the mathematical model of the methane measurement sensor was developed; the Arduino Uno board developed a simulation system for methane monitoring; and the numerical results of the research are presented in the graphs.
Digital and Green Behaviour: An Exploratory Study on Italian Consumers
Authors: Palmieri Nadia , Boccia Flavio , Covino Daniela
Published: April 2024

This paper aimed to understand consumer behaviour based on different constructs of buying behaviour antecedents, which included attitudes towards social aspects, green values, the value placed on digital channels, and green advertisements. Through an online survey of 650 respondents, we investigated the characteristics that affect consumer behaviour in Italy. A structural equation modelling (SEM) method was then used to explore the effects of the identified constructs on people’s buying behaviour, as well as evaluate their relative importance. On the one hand, the findings showed that among the identified constructs, attitudes towards social aspects have a direct effect on eco-friendly consumption, while green values positively impact both buying behaviour and openness to green publicity. On the other hand, green publicity only shows a weak impact on buying behaviours, while peoples’ attitudes towards digital channels have a positive effect on both eco-friendly consumption and on buying behaviour. In other words, the results suggest that people’s digital propensity, paired with environmental sensibility, may affect the online purchase of sustainable food. In light of the above, these results should be important for makers and experts dealing with green products, particularly sellers. In fact, sellers and marketers should consider our results as insights into their segmentation, targeting, and positioning strategies in markets.
Research on Image Perception of Tourist Destinations Based on the BERT-BiLSTM-CNN-Attention Model
Authors: Wen Tingxin , Xu Xinyu
Published: April 2024

Image perception of tourism destinations plays an important role in destination marketing and management. Considering that long-distance feature information of travel review text is difficult to capture and local key information is ignored, BiLSTM and CNN are improved to propose a travel text classification method based on BERT-BiLSTM-CNN-Attention hybrid neural network model. Taking Sanya City as the research object, we adopt the emotion classification and content analysis methods and construct the tourism destination image perception analysis framework based on the “cognitive-emotional” three-dimensional model, providing a research perspective for the sustainable development of tourism in Sanya City. The results show that the accuracy of the proposed model reaches 93.18%, which is better than other models. Tourists’ perception of destination image includes four aspects: tourism resources, tourism environment perception, tourism infrastructure and supporting services, as well as tourism activities. Positive emotions dominate emotional image, and negative emotions are mainly focused on tourism infrastructure and supporting services. On the overall image perception, tourists have a high evaluation of the tourism image of Sanya City. This research has some implications for tourism destinations, such as improving their management programs, enhancing their marketing strategies, and achieving long-term sustainable development of their destinations.