bluuebunny/crossref_metadata_2025 huggingface dataset:
First Principles Study of the Structure–Performance Relation of Pristine Wn+1Cn and Oxygen-Functionalized Wn+1CnO2 MXenes as Cathode Catalysts for Li-O2 Batteries
Authors: Zhu Liwei , Wang Jiajun , Liu Jie , Wang Ruxin , Lin Meixin , Wang Tao , Zhen Yuchao , Xu Jing , Zhao Lianming
Published: April 2024

Li-O2 batteries are considered a highly promising energy storage solution. However, their practical implementation is hindered by the sluggish kinetics of the oxygen reduction (ORR) and oxygen evolution (OER) reactions at cathodes during discharging and charging, respectively. In this work, we investigated the catalytic performance of Wn+1Cn and Wn+1CnO2 MXenes (n = 1, 2, and 3) as cathodes for Li-O2 batteries using first principles calculations. Both Wn+1Cn and Wn+1CnO2 MXenes show high conductivity, and their conductivity is further enhanced with increasing atomic layers, as reflected by the elevated density of states at the Fermi level. The oxygen functionalization can change the electronic properties of WC MXenes from the electrophilic W surface of Wn+1Cn to the nucleophilic O surface of Wn+1CnO2, which is beneficial for the activation of the Li-O bond, and thus promotes the Li+ deintercalation during the charge–discharge process. On both Wn+1Cn and Wn+1CnO2, the rate-determining step (RDS) of ORR is the formation of the (Li2O)2* product, while the RDS of OER is the LiO2* decomposition. The overpotentials of ORR and OER are positively linearly correlated with the adsorption energy of the RDS LixO2* intermediates. By lowering the energy band center, the oxygen functionalization and increasing atomic layers can effectively reduce the adsorption strength of the LixO2* intermediates, thereby reducing the ORR and OER overpotentials. The W4C3O2 MXene shows immense potential as a cathode catalyst for Li-O2 batteries due to its outstanding conductivity and super-low ORR, OER, and total overpotentials (0.25, 0.38, and 0.63 V).
Aluminum Micropillar Surfaces with Hierarchical Micro- and Nanoscale Features for Enhancement of Boiling Heat Transfer Coefficient and Critical Heat Flux
Authors: Hadžić Armin , Može Matic , Zupančič Matevž , Golobič Iztok
Published: April 2024

The rapid progress of electronic devices has necessitated efficient heat dissipation within boiling cooling systems, underscoring the need for improvements in boiling heat transfer coefficient (HTC) and critical heat flux (CHF). While different approaches for micropillar fabrication on copper or silicon substrates have been developed and have shown significant boiling performance improvements, such enhancement approaches on aluminum surfaces are not broadly investigated, despite their industrial applicability. This study introduces a scalable approach to engineering hierarchical micro-nano structures on aluminum surfaces, aiming to simultaneously increase HTC and CHF. One set of samples was produced using a combination of nanosecond laser texturing and chemical etching in hydrochloric acid, while another set underwent an additional laser texturing step. Three distinct micropillar patterns were tested under saturated pool boiling conditions using water at atmospheric pressure. Our findings reveal that microcavities created atop pillars successfully facilitate nucleation and micropillars representing nucleation site areas on a microscale, leading to an enhanced HTC up to 242 kW m−2 K−1. At the same time, the combination of the surrounding hydrophilic porous area enables increased wicking and pillar patterning, defining the vapor–liquid pathways on a macroscale, which leads to an increase in CHF of up to 2609 kW m−2.
Modeling of Self-Aligned Selector Based on Ultra-Thin Metal Oxide for Resistive Random-Access Memory (RRAM) Crossbar Arrays
Authors: Fedotov Mikhail , Korotitsky Viktor , Koveshnikov Sergei
Published: April 2024

Resistive random-access memory (RRAM) is a crucial element for next-generation large-scale memory arrays, analogue neuromorphic computing and energy-efficient System-on-Chip applications. For these applications, RRAM elements are arranged into Crossbar arrays, where rectifying selector devices are required for correct read operation of the memory cells. One of the key advantages of RRAM is its high scalability due to the filamentary mechanism of resistive switching, as the cell conductivity is not dependent on the cell area. Thus, a selector device becomes a limiting factor in Crossbar arrays in terms of scalability, as its area exceeds the minimal possible area of an RRAM cell. We propose a tunnel diode selector, which is self-aligned with an RRAM cell and, thus, occupies the same area. In this study, we address the theoretical and modeling aspects of creating a self-aligned selector with optimal parameters to avoid any deterioration of RRAM cell performance. We investigate the possibilities of using a tunnel diode based on single- and double-layer dielectrics and determine their optimal physical properties to be used in an HfOx-based RRAM Crossbar array.
Recent Advances in Nanotechnology for the Treatment of Dry Eye Disease
Authors: Coco Giulia , Buffon Giacinta , Taloni Andrea , Giannaccare Giuseppe
Published: April 2024

Dry eye disease (DED) incidence is continuously growing, positioning it to become an emergent health issue over the next few years. Several topical treatments are commonly used to treat DED; however, reports indicate that only a minor proportion of drug bioavailability is achieved by the majority of eye drops available on the market. In this context, enhancing drug ability to overcome ocular barriers and prolonging its residence time on the ocular surface represent a new challenge in the field of ocular carrier systems. Therefore, research has focused on the development of multi-functional nanosystems, such as nanoemulsions, liposomes, dendrimers, hydrogels, and other nanosized carriers. These systems are designed to improve topical drug bioavailability and efficacy and, at the same time, require fewer daily administrations, with potentially reduced side effects. This review summarizes the different nanotechnologies developed, their role in DED, and the nanotechnology-based eyedrops currently approved for DED treatment.
A High-Performance and Durable Direct-Ammonia Symmetrical Solid Oxide Fuel Cell with Nano La0.6Sr0.4Fe0.7Ni0.2Mo0.1O3−δ-Decorated Doped Ceria Electrode
Authors: Jiang Hao , Liang Zhixian , Qiu Hao , Yi Yongning , Jiang Shanshan , Xu Jiahuan , Wang Wei , Su Chao , Yang Tao
Published: April 2024

Solid oxide fuel cells (SOFCs) offer a significant advantage over other fuel cells in terms of flexibility in the choice of fuel. Ammonia stands out as an excellent fuel choice for SOFCs due to its easy transportation and storage, carbon-free nature and mature synthesis technology. For direct-ammonia SOFCs (DA-SOFCs), the development of anode catalysts that have efficient catalytic activity for both NH3 decomposition and H2 oxidation reactions is of great significance. Herein, we develop a Mo-doped La0.6Sr0.4Fe0.8Ni0.2O3−δ (La0.6Sr0.4Fe0.7Ni0.2Mo0.1O3−δ, LSFNM) material, and explore its potential as a symmetrical electrode for DA-SOFCs. After reduction, the main cubic perovskite phase of LSFNM remained unchanged, but some FeNi3 alloy nanoparticles and a small amount of SrLaFeO4 oxide phase were generated. Such reduced LSFNM exhibits excellent catalytic activity for ammonia decomposition due to the presence of FeNi3 alloy nanoparticles, ensuring that it can be used as an anode for DA-SOFCs. In addition, LSFNM shows high oxygen reduction reactivity, indicating that it can also be a cathode for DA-SOFCs. Consequently, a direct-ammonia symmetrical SOFC (DA-SSOFC) with the LSFNM-infiltrated doped ceria (LSFNM-SDCi) electrode delivers a superior peak power density (PPD) of 487 mW cm−2 at 800 °C when NH3 fuel is utilised. More importantly, because Mo doping greatly enhances the reduction stability of the material, the DA-SSOFC with the LSFN-MSDCi electrode exhibits strong operational stability without significant degradation for over 400 h at 700 °C.
Comparative Study of Healthy Older and Younger Adults Shows They Have the Same Skin Concentration of Vitamin D3 Precursor, 7-Dehydrocholesterol, and Similar Response to UVR
Authors: Borecka Oktawia , Dutton John J. , Tang Jonathan C. Y. , Fraser William D. , Webb Ann R. , Rhodes Lesley E.
Published: April 2024

Vitamin D3 synthesis in human skin is initiated by solar ultraviolet radiation (UVR) exposure of precursor 7-dehydrocholesterol (7DHC), but influence of age on the early stage of vitamin D3 metabolism is uncertain. We performed a prospective standardised study in healthy ambulant adults aged ≥65 and ≤40 years examining (1) if baseline skin 7DHC concentration differs between younger and older adults and (2) the impact of older age on serum vitamin D3 response to solar simulated UVR. Eleven younger (18–40 years) and 10 older (65–89 years) adults, phototype I–III, received low-dose UVR (95% UVA, 5% UVB, 1.3 SED) to ~35% of the body surface area. Biopsies were taken for 7DHC assay from unexposed skin, skin immediately and 24 h post-UVR, and blood sampled at baseline, 24 h and 7 d post-UVR for vitamin D3 assay. Samples were analysed by HPLC-MS/MS. Baseline skin 7DHC (mean ± SD) was 0.22 ± 0.07 and 0.25 ± 0.08 µg/mg in younger versus older adults (no significant difference). Baseline serum vitamin D3 concentration was 1.5 ± 1.5 and 1.5 ± 1.7 nmol/L in younger versus older adults, respectively, and showed a significant increase in both groups post-UVR (no significant differences between age groups). Thus, skin 7DHC concentration was not a limiting factor for vitamin D3 production in older relative to younger adults. This information assists public health guidance on sun exposure/vitamin D nutrition, with particular relevance to the growing populations of healthy ambulant adults ≥65 years.
The Effectiveness of a Lactobacilli-Based Probiotic Food Supplement on Bone Mineral Density and Bone Metabolism in Australian Early Postmenopausal Women: Protocol for a Double-Blind Randomized Placebo-Controlled Trial
Authors: Resciniti Stephanie M. , Biesiekierski Jessica R. , Ghasem-Zadeh Ali , Moschonis George
Published: April 2024

Osteoporosis affects one in three women over the age of 50 and results in fragility fractures. Oestrogen deficiency during and after menopause exacerbates bone loss, accounting for higher prevalence of fragility fractures in women. The gut microbiota (GM) has been proposed as a key regulator of bone health, as it performs vital functions such as immune regulation and biosynthesis of vitamins. Therefore, GM modulation via probiotic supplementation has been proposed as a target for potential therapeutic intervention to reduce bone loss. While promising results have been observed in mouse model studies, translation into human trials is limited. Here, we present the study protocol for a double-blind randomized controlled trial that aims to examine the effectiveness of three lactobacilli strains on volumetric bone mineral density (vBMD), trabecular, and cortical microstructure, as measured using High Resolution peripheral Quantitative Computed Tomography (HR-pQCT). The trial will randomize 124 healthy early postmenopausal women (up to 8 years from menopause) to receive either probiotic or placebo administered once daily for 12 months. Secondary outcomes will investigate the probiotics’ effects on areal BMD and specific mechanistic biomarkers, including bone metabolism and inflammatory markers. The trial is registered with Australian New Zealand Clinical Trials Registry (ACTRN12621000810819).
Mirk/Dyrk1B Kinase Inhibitors in Targeted Cancer Therapy
Authors: Kokkorakis Nikolaos , Zouridakis Marios , Gaitanou Maria
Published: April 2024

During the last years, there has been an increased effort in the discovery of selective and potent kinase inhibitors for targeted cancer therapy. Kinase inhibitors exhibit less toxicity compared to conventional chemotherapy, and several have entered the market. Mirk/Dyrk1B kinase is a promising pharmacological target in cancer since it is overexpressed in many tumors, and its overexpression is correlated with patients’ poor prognosis. Mirk/Dyrk1B acts as a negative cell cycle regulator, maintaining the survival of quiescent cancer cells and conferring their resistance to chemotherapies. Many studies have demonstrated the valuable therapeutic effect of Mirk/Dyrk1B inhibitors in cancer cell lines, mouse xenografts, and patient-derived 3D-organoids, providing a perspective for entering clinical trials. Since the majority of Mirk/Dyrk1B inhibitors target the highly conserved ATP-binding site, they exhibit off-target effects with other kinases, especially with the highly similar Dyrk1A. In this review, apart from summarizing the data establishing Dyrk1B as a therapeutic target in cancer, we highlight the most potent Mirk/Dyrk1B inhibitors recently reported. We also discuss the limitations and perspectives for the structure-based design of Mirk/Dyrk1B potent and highly selective inhibitors based on the accumulated structural data of Dyrk1A and the recent crystal structure of Dyrk1B with AZ191 inhibitor.
Navigating Democracy’s Fragile Boundary: Lessons from Plato on Political Leadership
Authors: Vergaray Alfonso R.
Published: April 2024

This article presents a case that former President of the United States Donald Trump was a tyrant-like leader in the mold of the tyrant in Plato’s Republic. While he does not perfectly embody the tyrant as presented in the Republic, he captures its core feature. Like the tyrant, Trump is driven by unregulated desires that reflect what Plato describes as an extreme freedom that underlies and threatens democratic regimes. Extreme freedom is manifested in Trump’s disregard for social and legal norms, which mirrors the lawlessness of the tyrant. The people, in turn, interpret that posture as a mark of authenticity. Understanding Trump’s appeal in the United States helps alert friends of democracy to the possible rise of tyrant-like figures. In closing, and as a way of remedying the harm done by the tyrannical soul, the article recommends that society help temper tyrant-like passions in the people through a rededication to civic equality.
Ultrasonication Influence on the Morphological Characteristics of Graphene Nanoplatelet Nanocomposites and Their Electrical and Electromagnetic Interference Shielding Behavior
Authors: Collado Ignacio , Jiménez-Suárez Alberto , Vázquez-López Antonio , del Rosario Gilberto , Prolongo Silvia G.
Published: April 2024

Graphene nanoplatelets (GNPs)/epoxy composites have been fabricated via gravity molding. The electrical and thermal properties of the composites have been studied with variable GNP type (C300, C500, and C750, whose surface areas are ~300, 500, and 750 m2/g, respectively), GNP loading (5, 10, 12, and 15 wt.%), and dispersion time via ultrasonication (0, 30, 60, and 120 min). By increasing the time of sonication of the GNP into the epoxy matrix, the electrical conductivity decreases, which is an effect of GNP fragmentation. The best results were observed with 10–12% loading and a higher surface area (C750), as they provide higher electrical conductivity, thereby preserving thermal conductivity. The influence of sonication over electrical conductivity was further analyzed via the study of the composite morphology by means of Raman spectroscopy and X-ray diffraction (XRD), providing information about the aspect ratio of GNPs. Moreover, electromagnetic shielding (EMI) has been studied up to 4 GHz. Composites with C750 and 120 min ultrasonication show the best performance in EMI shielding, influenced by their higher electrical conductivity.
Recycling Polyethylene/Polyamide Multilayer Films with Poly(isoprene-g-Maleic Anhydride) Compatibilizer
Authors: Romeiro Andreia , Teixeira Cidália , Costa Henrique , Coelho Jorge F. J. , Serra Arménio C.
Published: April 2024

Polymers generally form incompatible mixtures that make the process of recycling difficult, especially the mechanical recycling of mixed plastic waste. One of the most commonly used films in the packaging industry is multilayer films, mainly composed of polyethylene (PE) and polyamide (PA). Recycling these materials with such different molecular structures requires the use of compatibilizers to minimize phase separation and obtain more useful recycled materials. In this work, commercial polyisoprene–graft–maleic anhydride (PI-g-MA) was tested as a compatibilizer for a blend of PE and PA derived from the mechanical recycling of PE/PA multilayer films. Different amounts of PI-g-MA were tested, and the films made with 1.5% PI-g-MA showed the best results in terms of mechanical properties and dart impact. The films were also characterized thermally via thermogravimetric analysis (TG) and differential scanning calorimetry (DSC), using Fourier-transform infrared spectroscopy (FTIR), and morphologically using a scanning electron microscope (SEM). Other parameters, such as tearing and perforation, were analyzed.
A Study on the Adsorption of Methylene Blue by Acid-Modified Coal Measures Kaolin
Authors: Ren Xiaoling , Shu Xinqian , Geng Weiguo , Li Peng , Xu Yane
Published: April 2024

In this paper, coal measure kaolin after flotation decarburization was made into an adsorbent by ball milling and acid modification to absorb methylene blue in water, achieving the treatment of waste with waste. The objective of this paper is to expand the application of coal measure kaolin, reduce its stock, and lower the raw material cost of adsorbents while treating wastewater containing methylene blue. The optimum milling time, acid boiling conditions, and adsorption conditions were investigated. Furthermore, the adsorption mechanism was investigated by kinetic calculation. The results show that the optimum milling time is 7 h. Relatively good acid modification conditions include a boiling temperature of 100 °C, a stirring time of 135 min, a stirring speed of 1000 r·min−1, and a concentration of hydrochloric acid of 8 mol·L−1. When 0.05 g of flotation kaolin adsorbent was used to adsorb the solution with pH 12 and a methylene blue concentration of 100 mg·L−1, the optimal adsorption conditions were a 38.05 °C adsorption temperature, a 160 r·min−1 stirring speed, and a 31.02 min stirring time. Under these optimal conditions, the adsorption quantity reached 39.92 mg·g−1. The adsorption process involves physical adsorption and spontaneous adsorption. The adsorption type is known as the quasi-second-order adsorption kinetic model. The adsorption form is heterogeneous adsorption in which a monolayer and a multi-molecular layer coexist.
Metastasis-Directed Stereotactic Body Radiotherapy in Prostate Cancer Patients Treated with Systemic Therapy and Undergoing Oligoprogression: Report on 11 Consecutive Cases
Authors: Chioccola Emanuele , Caroprese Mara , Goodyear Christina A. , Barillaro Angela , Oliviero Caterina , Clemente Stefania , Feoli Chiara , Formisano Luigi , Farella Antonio , Cella Laura , Conson Manuel , Pacelli Roberto
Published: April 2024

Background: Stereotactic body radiotherapy (SBRT) targeted at metastatic sites of disease progression is emerging as a potential therapeutic approach for managing oligoprogressive prostate cancer. However, a definitive benefit has yet to be demonstrated. Herein, we present our institution’s experience with this treatment approach. Methods: From April 2018 to March 2023, 11 patients affected by oligoprogressive prostate cancer were treated with SBRT targeting the nodal or bone sites of progression while maintaining the ongoing systemic therapy. Three patients were undergoing single-agent ADT (Androgen Deprivation Therapy), while the remaining eight were receiving a subsequent line of systemic therapy. All patients were evaluated with a pre-treatment 68Ga-PSMA-11 or 18F-fluorocholine PET/CT, which demonstrated between one and five localizations of disease. All the active sites were treated with SBRT in one (15–24 Gy) or three (21–27 Gy) fractions, except for one patient, who was treated in five fractions (35 Gy). PSA serum levels were tested at baseline, one month after RT and at least every three months; all patients underwent a post-treatment 68Ga-PSMA-11 or 18F-fluorocholine PET/CT. The evaluated endpoints were PSA response, defined as a post-treatment decrease >50% from baseline measured within 6 months, time to next-line systemic treatment (NEST), local control (LC), biochemical progression-free survival (bPFS), radiological progression-free survival (rPFS) and freedom from polymetastatic progression (FPP). Results: Nineteen lesions were treated (seven nodal and twelve bone). At a median follow-up of 19 months (7–63), 9 of the 11 patients had a PSA response; all patients had local control of the treated metastases. A total of six patients switched to a next-line systemic treatment, with a median NEST of 13 months. Six patients had polymetastatic progression with an FPP median time of 19 months. No patients died during the follow-up period. The SBRT-related toxicity was negligible. Conclusions: Our data support the use of SBRT targeting the sites of oligoprogressive disease before moving to a subsequent line of systemic treatment in patients with metastatic prostate cancer. Prospective studies to evaluate the potential impact of this approach on overall survival are warranted.
The Intersection of Spirituality, Religiosity, and Lifestyle Practices in Religious Communities to Successful Aging: A Review Article
Authors: McManus Deborah Tessitore
Published: April 2024

Defining successful aging continues to be a challenge, given the more recent transition from a bioscientific definition to a more comprehensive and holistic perspective. The holistic perspective may include more subjective aspects of aging. Research has shown that certain factors, such as lifestyle practices of spirituality, religious practice, meditation, communal support, and purposeful living, may be as influential as genetic factors in helping aging adults diminish age-related limitations. Spirituality and religiosity as lifestyle practice resources may assist older adults to mitigate the circumstances of chronic disease and positively impact aging, life quality, and well-being. Religious and/or spiritual communities, such as Women Religious and other clergy and monks, may provide insight into specific practices that promote social exchanges, life meaning, meditative practice, daily prayer, belief in the divine, communal living, and homogeneity of lifestyle that ultimately promote successful aging and resiliency into older age. Research has shown that lifestyle factors may mitigate diseases such as Alzheimer’s and other forms of dementia in older age. The population of older adults has grown consistently on a global level since the turn of the century. This article review seeks to examine aging and emphasize, through research, which lifestyle practices and communities may impact the experience of aging in a more beneficial manner.
Lutheran Theological Education to Christian Education in (South) Africa: A Decolonial Conversion in the African Church
Authors: Mashabela James Kenokeno
Published: April 2024

It can be debated whether a Lutheran identity is still relevant in the midst of ecumenical development in (South) Africa, with special reference to theological education and Christian education. The Lutheran Church is a unique body within the ecumenical family as it contributes to work on the mission of God. Theological education and Christian education are educational centres which aim to promote social justice towards community development. These two educational centres are branches of the Lutheran Church. Taking into account the fact that theological education and Christian education were introduced by European and American missionaries with various church traditions in (South) Africa as part of community development, the purpose of this article is to discuss the impact of Lutheran theological education and Christian education, to demonstrate their contribution in the church, and call for their decolonisation and contextualisation.
Correction: Rose et al. The Effect of In Vitro Maturation (IVM) Protocol Changes on Measures of Oocyte/Embryo Competence. Reprod. Med. 2023, 4, 65–73
Authors: Rose Bruce I. , Nguyen Kevin , Brown Samuel E.
Published: April 2024

**Samuel E [...]
Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II
Authors: Jeon Gwanggil
Published: April 2024

This Special Issue marks the third edition of “Advanced Machine Learning and Deep Learning Approaches for Remote Sensing” [...]
Micro-Doppler Signature Analysis for Space Domain Awareness Using VHF Radar
Authors: Heading Emma , Nguyen Si Tran , Holdsworth David , Reid Iain M.
Published: April 2024

The large quantity of resident space objects orbiting Earth poses a threat to safety and efficient operations in space. Radar sensors are well suited to detecting objects in space including decommissioned satellites and debris, whereas the more commonly used optical sensors are limited by daylight and weather conditions. Observations of three non-operational satellites using a VHF radar system are presented in this paper in the form of micro Doppler signatures associated with rotational motion. Micro Doppler signatures are particularly useful for characterising resident space objects at VHF given the limited bandwidth resulting in poor range resolution. Electromagnetic simulations of the micro Doppler signatures of the defunct satellites are also presented using simple computer-aided design (CAD) models to assist with interpretation of the radar observations. The simulated micro Doppler results are verified using the VHF radar data and provide insight into the attitude and spin axis of the three resident space objects. As future work, this approach will be extended to a larger number of resident space objects which requires a automated processing.
Comprehensive Assessment of NDVI Products Derived from Fengyun Satellites across China
Authors: Wang Lei , Han Xiuzhen , Fang Shibo , Xiao Fengjin
Published: April 2024

NDVI data are crucial for agricultural and environmental research. The Fengyun-3 (FY-3) series satellites are recognized as primary sources for retrieving NDVI products on a global scale. To apply FY-3 NDVI data for long-term studies, such as climate change, this study conducted a thorough evaluation to detect the potentials of the FY-3B and FY-3D satellites for generating a long time series NDVI dataset. For this purpose, the spatiotemporal consistency between the FY-3B and FY-3D satellites was evaluated, and their performances were compared. Then, a grey relational analysis (GRA) method was applied to detect the factors influencing the consistency among the different satellites, and a gradient boosting regression (GBR) model was constructed to create a long-term FY-3 NDVI product. The results indicate an overall high consistency between the FY-3B and FY-3D NDVIs, suggesting that they could be used as complementary datasets for generating a long-term NDVI dataset. The correlations between the FY-3D NDVI and the MODIS NDVI, as well as the leaf area index (LAI) measurements, were both higher than those of FY-3B, which indicates a better performance of FY-3D in retrieving NDVI data. The grey correlation degrees between the NDVI differences and four parameters, which were land cover (LC), DEM, latitude (LAT) and longitude (LON), were calculated, revealing that the LC was the most related to the NDVI differences. Finally, a GBR model with FY-3B NDVI, LC, DEM, LAT and LON as the input variables and FY-3D NDVI as the target variable was established and achieved a robust performance. The R values between the GBR-estimated NDVI and FY-3D NDVI reached 0.947, 0.867 and 0.829 in the training, testing and validation datasets, respectively, indicating the feasibility of the established model for generating long time series NDVI data by combining data from the FY-3B and FY-3D satellites.
Efficiently Refining Beampattern in FDA-MIMO Radar via Alternating Manifold Optimization for Maximizing Signal-to-Interference-Noise Ratio
Authors: Geng Langhuan , Li Yong , Dong Limeng , Tan Yumei , Cheng Wei
Published: April 2024

Joint transceiver beamforming is a fundamental and crucial research task in the field of signal processing. Despite extensive efforts made in recent years, the joint transceiver beamforming of frequency diverse array (FDA)-based multiple-input and multiple-output (MIMO) radar has received relatively less attention and is confronted with some tricky challenges, such as range–angle decoupling and the interaction between multiple performance metrics. In this paper, we initially derive the generalized ambiguity function of the FDA-MIMO radar to explore the intrinsic correlation between its waveform design and resolution. Following that, the joint beamforming optimization is formulated as a nonconvex bivariate quadratic programming problem (NBQP) with the aim of maximizing the Signal-to-Interference-Noise Ratio (SINR) of the FDA-MIMO radar system. Building upon this, we introduce an innovative alternating manifold optimization with nested iteration (AMO-NI) algorithm to address the NBQP. By incorporating manifold optimization into iterative updates of transmit waveform and receiving filter, the AMO-NI algorithm considers the interdependencies among the optimization variables. The algorithm efficiently and expeditiously finds global optimum solutions within a finite number of iterations. Compared with other methods, our approach yields a superior beampattern and higher SINR.
A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
Authors: Ramdani Fatwa
Published: April 2024

Jakarta holds the distinction of being the largest capital city among ASEAN countries and ranks as the second-largest metropolitan area in the world, following Tokyo. Despite numerous studies examining the diverse urban land use and land cover patterns within the city, the recent state of urban green spaces has not been adequately assessed and mapped precisely. Most previous studies have primarily focused on urban built-up areas and manmade structures. In this research, the first-ever detailed map of Jakarta’s urban green spaces as of 2023 was generated, with a resolution of three meters. This study employed a combination of supervised classification and evaluated two machine learning algorithms to achieve the highest accuracy possible. To achieve this, various satellite images were utilized, including VV and VH polarizations from Sentinel-1, multiple bands from Sentinel-2, and eight bands from Planet. The Planet data were subsequently transformed into the Red-Edge Triangulated Vegetation Index and Red-Edge Triangulated Wetness Index. The data training and testing samples for urban green spaces were obtained using the Street View images available on Google Maps. The results revealed that using the Random Forest classifier algorithm and only eight bands of Planet images achieved an accuracy rate of 84.9%, while a combination of multiple images achieved an impressive 95.9% accuracy rate. Jakarta’s urban areas cover approximately 33.2% of green spaces. This study provides unprecedented insights into the type, size, and spatial distribution of Jakarta’s urban green spaces, enabling urban residents and stakeholders to explore and promote healthier living and better manage these green areas. Additionally, a previously unexplored concept, Jakarta’s urban green belt, is introduced.
Geological Hazard Susceptibility Analysis and Developmental Characteristics Based on Slope Unit, Using the Xinxian County, Henan Province as an Example
Authors: Yang Wentao , Niu Ruiqing , Si Rongjun , Li Jun
Published: April 2024

Geological hazards in Xinxian County, Xinyang City, Henan Province, are characterized by their small scale, wide distribution, and significant influence from regional tectonics. This study focuses on collapses and landslide hazards within the area, selecting twelve evaluation factors: aspect, slope shape, normalized difference vegetation index (NDVI), topographic relief, distance from geological structure, slope, distance from roads, land use cover type, area of land change (2012–2022), average annual rainfall (2012–2022), and river network density. Utilizing data from historical disaster sites across the region, the information quantity method and hierarchical analysis method are employed to ascertain the information quantity and weight of each factor. Subsequently, a random forest model is applied to perform susceptibility zoning of geological hazards in Xinxian County and to examine the characteristics of these geological disasters. The results show that in the study area, the primary factors influencing the development of geohazards are the distance from roads, rock groups, and distance from geological structure areas. A comparison of the susceptibility results obtained through two methods, the analytic hierarchy process information quantity method and the random forests model, reveals that the former exhibits a higher accuracy. This model categorizes the geohazard susceptibility in the study area into four levels: low, medium, high, and very high. Notably, the areas of very high and high susceptibility together cover 559.17 km2, constituting 35.99% of the study area’s total area, and encompass 57 disaster sites, which represent 72.15% of all disaster sites. Geological hazards in Xinxian County frequently manifest on steep canyon inclines, along the curved and concave banks of mountain rivers, within watershed regions, on gully inclines, atop steep cliffs, and on artificially created slopes, among other sites. Areas with very high and high vulnerability to these hazards are mainly concentrated near the county’s geological formations. The gneiss formations are widely exposed in Xinxian County, and the gneisses’ strength is significantly changed under weathering, which makes the properties of the different degrees of weathering of the rock and soil bodies play a decisive role in the stability of the slopes. This paper provides a basis for evaluating and preventing geologic hazards in the Dabie mountainous area of the South Henan Province, and the spatial planning of the national territory.
A Self-Temperature Compensation Barometer Based on All-Quartz Resonant Pressure Sensor
Authors: Han Dongxiang , Yuan Shenfang , Feng Congwei , Yang Ting
Published: April 2024

This paper reports a self-temperature compensation barometer based on a quartz resonant pressure sensor. A novel sensor chip that contains a double-ended tuning fork (DETF) resonator and a single-ended tuning fork (SETF) resonator is designed and fabricated. The two resonators are designed on the same diaphragm. The DETF resonator works as a pressure sensor. To reduce the influence of the temperature drift, the SETF resonator works as a temperature compensation sensor, which senses the instantaneous temperature of the DETF resonator. The temperature compensation method based on polynomial fitting is studied. The experimental results show that the accuracy is 0.019% F.S. in a pressure range of 200~1200 hPa over a temperature range of −20 °C~+60 °C. The absolute errors of the barometer are within ±23 Pa. To verify its actual performance, a drone flight test was conducted. The test results are consistent with the actual flight trajectory.
Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review
Authors: Al-Hamadani Mokhaled , Fadhel Mohammed , Alzubaidi Laith , Harangi Balazs
Published: April 2024

Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential decision-making problems with simultaneous sampling, evaluation, and feedback. As a result, RL techniques have become suitable candidates for developing powerful solutions in various domains. In this study, we present a comprehensive and systematic review of RL algorithms and applications. This review commences with an exploration of the foundations of RL and proceeds to examine each algorithm in detail, concluding with a comparative analysis of RL algorithms based on several criteria. This review then extends to two key applications of RL: robotics and healthcare. In robotics manipulation, RL enhances precision and adaptability in tasks such as object grasping and autonomous learning. In healthcare, this review turns its focus to the realm of cell growth problems, clarifying how RL has provided a data-driven approach for optimizing the growth of cell cultures and the development of therapeutic solutions. This review offers a comprehensive overview, shedding light on the evolving landscape of RL and its potential in two diverse yet interconnected fields.
Effect of Rule Modifications on Kinematic Parameters Using Maturity Stage as a Moderating Variable in U-10 Football Players
Authors: García-Angulo Francisco Javier , Palao José Manuel , Giménez-Egido José María , Ortega-Toro Enrique
Published: April 2024

The aim of the present study was to analyse the effects of regulatory modifications in competitive situations on cinematic variables, considering maturity stage as a moderating factor, in youth football players. A quasi-experimental study was conducted in which 45 players with a mean age of 9.47 ± 0.54 participated. The independent variable analysed was the modification of rules (playing time, scoring, and specific rules). The dependent variables analysed were cinematic variables. These variables were recorded with WimuTM. The maturity stage was considered a moderating factor in this effect. The main results indicate that the modified competition reduced the total distance covered, maximum acceleration speed, and distance covered in acceleration and deceleration in different speed zones. In addition, the maturity stage was found to moderate the effect of the intervention on the total distance covered, distance covered by accelerating in zone 3, and distance covered by decelerating in zone 3. Thus, the proposed modification appeared to reduce the physical demand for competition. Furthermore, it reduced the differences between players with early maturational development and those with late maturational development.
YOLOv8-C2f-Faster-EMA: An Improved Underwater Trash Detection Model Based on YOLOv8
Authors: Zhu Jin , Hu Tao , Zheng Linhan , Zhou Nan , Ge Huilin , Hong Zhichao
Published: April 2024

Anthropogenic waste deposition in aquatic environments precipitates a decline in water quality, engendering pollution that adversely impacts human health, ecological integrity, and economic endeavors. The evolution of underwater robotic technologies heralds a new era in the timely identification and extraction of submerged litter, offering a proactive measure against the scourge of water pollution. This study introduces a refined YOLOv8-based algorithm tailored for the enhanced detection of small-scale underwater debris, aiming to mitigate the prevalent challenges of high miss and false detection rates in aquatic settings. The research presents the YOLOv8-C2f-Faster-EMA algorithm, which optimizes the backbone, neck layer, and C2f module for underwater characteristics and incorporates an effective attention mechanism. This algorithm improves the accuracy of underwater litter detection while simplifying the computational model. Empirical evidence underscores the superiority of this method over the conventional YOLOv8n framework, manifesting in a significant uplift in detection performance. Notably, the proposed method realized a 6.7% increase in precision (P), a 4.1% surge in recall (R), and a 5% enhancement in mean average precision (mAP). Transcending its foundational utility in marine conservation, this methodology harbors potential for subsequent integration into remote sensing ventures. Such an adaptation could substantially enhance the precision of detection models, particularly in the realm of localized surveillance, thereby broadening the scope of its applicability and impact.
Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets
Authors: Sattar Shoaib , Mumtaz Rafia , Qadir Mamoon , Mumtaz Sadaf , Khan Muhammad Ajmal , De Waele Timo , De Poorter Eli , Moerman Ingrid , Shahid Adnan
Published: April 2024

ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.
Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data
Authors: Lan Xiaoyu , Jiang Lai , Ma Shuang , Tian Ye , Wang Yupeng , Wang Ershen
Published: April 2024

Partially impaired sensor arrays pose a significant challenge in accurately estimating signal parameters. The occurrence of bad data is highly probable, resulting in random loss of source information and substantial performance degradation in parameter estimation. In this paper, a tensor variational sparse Bayesian learning (TVSBL) method is proposed for the estimate of direction of arrival (DOA) and polarization parameters jointly based on a conformal polarization sensitive array (CPSA), taking into account scenarios with the partially impaired sensor array. First, a sparse tensor-based received data model is developed for CPSAs that incorporates bad data. Then, a column vector detection method is proposed to diagnose the positions of the impaired sensors. In scenarios involving partially impaired sensor arrays, a low-rank matrix completion method is employed to recover the random loss of signal information. Finally, variational sparse Bayesian learning (VSBL) and minimum eigenvector methods are utilized sequentially to obtain the DOA and polarization parameters estimation, successively. Furthermore, the Cramér-Rao bound is given for the proposed method. Simulation results validated the effectiveness of the proposed method.
Comparative Study on Simulated Outdoor Navigation for Agricultural Robots
Authors: Khanzada Feeza Khan , Delavari Elahe , Jeong Woojin , Cho Young Seek , Kwon Jaerock
Published: April 2024

This research presents a comprehensive comparative analysis of SLAM algorithms and Deep Neural Network (DNN)-based Behavior Cloning (BC) navigation in outdoor agricultural environments. The study categorizes SLAM algorithms into laser-based and vision-based approaches, addressing the specific challenges posed by uneven terrain and the similarity between aisles in an orchard farm. The DNN-based BC navigation technique proves efficient, exhibiting reduced human intervention and providing a viable alternative for agricultural navigation. Despite the DNN-based BC navigation approach taking more time to reach its target due to a constant throttle limit for steady speed, the overall performance in terms of driving deviation and human intervention is notable compared to conventional SLAM algorithms. We provide comprehensive evaluation criteria for selecting optimal techniques for outdoor agricultural navigations. The algorithms were tested in three different scenarios: Precision, Speed, and Autonomy. Our proposed performance metric, P, is weighted and normalized. The DNN-based BC algorithm showed the best performance among the others, with a performance of 0.92 in the Precision and Autonomy scenarios. When Speed is more important, the RTAB-Map showed the best score with 0.96. In a case where Autonomy has a higher priority, Gmapping also showed a comparable performance of 0.92 with the DNN-based BC.
Robust Offloading for Edge Computing-Assisted Sensing and Communication Systems: A Deep Reinforcement Learning Approach
Authors: Shen Li , Li Bin , Zhu Xiaojie
Published: April 2024

In this paper, we consider an integrated sensing, communication, and computation (ISCC) system to alleviate the spectrum congestion and computation burden problem. Specifically, while serving communication users, a base station (BS) actively engages in sensing targets and collaborates seamlessly with the edge server to concurrently process the acquired sensing data for efficient target recognition. A significant challenge in edge computing systems arises from the inherent uncertainty in computations, mainly stemming from the unpredictable complexity of tasks. With this consideration, we address the computation uncertainty by formulating a robust communication and computing resource allocation problem in ISCC systems. The primary goal of the system is to minimize total energy consumption while adhering to perception and delay constraints. This is achieved through the optimization of transmit beamforming, offloading ratio, and computing resource allocation, effectively managing the trade-offs between local execution and edge computing. To overcome this challenge, we employ a Markov decision process (MDP) in conjunction with the proximal policy optimization (PPO) algorithm, establishing an adaptive learning strategy. The proposed algorithm stands out for its rapid training speed, ensuring compliance with latency requirements for perception and computation in applications. Simulation results highlight its robustness and effectiveness within ISCC systems compared to baseline approaches.
Synthesis of Quaternary-Ammonium-Lignin-Based Ionic Liquids and Comparison of Extraction Behavior of Co(II) and Ni(II) with 2-Ethylhexyl Phosphoric Acid Mono-2-Ethylhexyl Ester
Authors: Li Guijiang , Xu Wenze
Published: April 2024

The escalating demand for cobalt in modern industry necessitates the recycling or extraction of this resource for sustainable development. Despite the abundance of lignin in nature, its utilization remains low, highlighting the need to enhance its value-added potential. This study focuses on the synthesis of quaternary ammonium lignin (QAL) and 2-ethylhexyl phosphoric acid mono-2-ethylhexyl ester (P507) as ionic liquid (QP–IL) compounds for the extraction of metal ions. A comparison of the extraction behavior of Co(II) and Ni(II) from chloride solution between QP–IL and P507 revealed varying extraction ratios under different conditions, with QP–IL demonstrating a higher cobalt extractability than P507. Furthermore, under identical conditions, QP–IL exhibited superior Co/Ni separation performance (βCo/Ni) compared to P507. Ultimately, QP–IL proved to be more effective than P507 in separating cobalt from mixed solutions.