bluuebunny/crossref_metadata_2025 huggingface dataset:
Anthropometric, Body Composition, and Nutritional Indicators with and without Nutritional Intervention during Nitisinone Therapy in Alkaptonuria
Authors: Ranganath L. R. , Khedr M. , Milan A. M. , Davison A. S. , Hughes A. T. , Norman B. P. , Bygott H. , Luangrath E. , Judd S. , Soulsby C. , Olsson B. , Imrich R.
Published: August 2024

Introduction: Protein nutrition disorder in alkaptonuria (AKU), resulting in increased homogentisic acid (HGA) before nitisinone therapy and increased tyrosine (TYR) during nitisinone therapy, may benefit from dietetic intervention. The aim of this study was to characterise the diet and their effects prospectively in those who received formal dietetic intervention in the nitisinone-receiving National Alkaptonuria Centre (NAC) patients with those who did not in no-nitisinone Suitability of Nitisinone in Alkaptonuria 2 (SN2 N−) and nitisinone-treated SN2 (SN2 N+) randomised study groups. Patients and methods: A total of 63, 69, and 69 AKU patients from the NAC, SN2 N−, and SN2 N+ were studied for anthropometric (weight, BMI), body composition (including muscle mass, %body fat, hand grip strength), chemical characteristics (serum TYR, serum phenylalanine, urine urea or uUREA, and urine creatinine or uCREAT), and corneal keratopathy. Nitisinone 2 mg and 10 mg were employed in the NAC and SN2 N+ groups, respectively. Dieticians managed protein intake in the NAC, while the SN2 N− and SN2 N+ groups only received advice on self-directed protein restriction during four years of study duration. Results: uUREA decreased in the NAC, SN2 N−, and SN2 N+ groups, showing that protein restriction was achieved in these groups. Body weight and BMI increased in the NAC and SN2 N+ groups. uCREAT decreased significantly in SN2 N− and SN2 N+ compared with the NAC over four years of study. Corneal keratopathy was less frequent in the NAC than in the SN2 N+ group. Active dietetic intervention in NAC stabilised lean body mass (muscle mass, hand grip strength) despite a decrease in uUREA and uCREAT, as well as sTYR. Conclusion: Ongoing dietetic intervention prevented loss of lean body mass despite protein restriction and moderated serum tyrosine increase, leading to less prevalent corneal keratopathy. Protein restriction risks fat mass gain.
Fractionated Leaf Extracts of Ocimum gratissimum Inhibit the Proliferation and Induce Apoptosis of A549 Lung Adenocarcinoma Cells
Authors: Curtis Rachael M. , Wang Heng-Shan , Luo Xuan , Dugo Erika B. , Stevens Jacqueline J. , Tchounwou Paul B.
Published: August 2024

Previous in vitro studies in our laboratory demonstrated that ethyl acetate (P2) and water- soluble (PS/PT1) fractionated leaf extracts of Ocimum gratissimum inhibit the proliferation of prostate cancer cells. It has been reported that the crude aqueous extract induces apoptosis in lung adenocarcinoma cells; however, the efficacy of the fractionated extracts against these cells remains unclear. In the present study, we hypothesized that the ability of the fractionated extracts to inhibit proliferation and induce apoptosis is associated with the activation of pro-apoptotic proteins and induction of DNA condensation in A549 cells. Ocimum gratissimum was cultivated and its leaves were harvested, extracted, and fractionated to produce fractions P2 and PS/PT1. Anti-proliferative activity was assessed by direct cell count. For morphological characterization of apoptosis, 4′,6-diamidino-2-phenylindole staining was employed. Western blot analysis was performed to evaluate the apoptotic activity of the fractionated extracts. In data generated from anti-proliferation studies, P2 significantly inhibited cell proliferation in a concentration-dependent manner; PS/PT1 elicited a decrease in the viability of cells, occurring at 500 µg/mL. 4′,6-diamidino-2-phenylindole staining revealed the induction of apoptosis, as evidenced by the formation of apoptotic bodies. Increased levels of pro-apoptotic proteins were observed as the concentrations of the fractionated extracts increased. These results suggest that fractionated leaf extracts of Ocimum gratissimum inhibit the proliferation and induce apoptosis of A549 cells.
Socio-Demographic Factors, Behaviors, Motivations, and Attitudes in Food Waste Management of Romanian Households
Authors: Moroșan Elena , Dărăban Adriana , Popovici Violeta , Rusu Andreea , Ilie Elena Iuliana , Licu Monica , Karampelas Oana , Lupuliasa Dumitru , Ozon Emma Adriana , Maravela Vanessa Maria , Popescu Ioana Andreea
Published: August 2024

(1) Background: Food waste (FW) in Romania is 70 Kg/capita/year, while 70% of food waste comes from public catering, retail services, and households (over 50%–47 million tons). The present study investigates the socio-demographic factors, behaviors, motivations, and attitudes related to food waste management in Romanian households. (2) Methods: A cross-sectional observational study was conducted using an online questionnaire via the Google Forms platform from 15 April 2023 to 15 May 2023. The questionnaire was designed to assess various aspects, such as some socio-demographic information (age, sex, occupation, area of residence, study level, household members number, children <18 years of age); the personal involvement and frequency of food purchases and homemade food cooking; the main sources that generate food waste; the motivation and frequency with which food waste occurs; the level of awareness regarding the impact of food waste; the respondents’ intentions regarding sustainable behaviors and practices for food management; the level of information and familiarity of the respondents with the notions of validity and how these may influence their food consumption decisions. (3) Results: The results show that FW incidence is occasionally (42%), very rarely (43.33%), frequently (15%), and no food waste was reported by 2.66% of respondents. The 35–44 age category records the highest FW frequency, followed by 18–24. The most wasted are homemade food (29.67%), bread and bakery products (27.00%), and fruits and vegetables (14.33%). High involvement in purchasing and buying food following a previously established list reduces FW frequency. The same is valid for high daily involvement in food and homemade cooking. High interest in the FW problem and its perception as a waste of money leads to diminishing it, while guilty feelings increase the FW level (37.50% to 73.33%). (4) Conclusions: The present study shows that household food waste management is a multifactorial process that involves numerous socio-demographic, behavioral, and emotional aspects. Extensive data analysis supports our results, revealing deep self-reported information details and confirming its complex approach.
Sepia pharaonis Ink Mitigates Dehydroepiandrosterone-Induced Insulin Resistance in Mouse Model of Polycystic Ovarian Syndrome
Authors: Yamarthi Prathyusha , Kotipalli Rama Satyasri , Patnaik Samatasai , Veena Kv , Kathirvel Muralidharan , Vutukuri Rajkumar , Bhanoori Manjula
Published: August 2024

The present study aims to evaluate the effect of Sepia pharaonis ink on insulin resistance in PCOS-induced mice. Treatment with sepia ink in dehydroepiandrosterone (DHEA)-induced PCOS mice at various doses of 50 mg/kg, 100 mg/kg, and 200 mg/kg body weight mitigated the insulin resistance in the study groups with decreased concentration of testosterone and increased concentrations of estrogen and progesterone compared to the PCOS group tested by ELISA. The histopathological analysis and restoration of glucose analysis showed a significant reduction in treatment groups. Reduced expression of insulin resistance genes like androgen receptor (AR), insulin receptor substrate 1 (IRS-1), and insulin-like growth factor1 (IGF-1) by qRT-PCR indicate a positive impact of sepia ink in alleviating the symptoms associated with PCOS. Taken together, the results of this study indicate sepia ink as a promising therapeutic intervention and a possible drug target for insulin resistance in diabetes and gynecological disorders like PCOS.
Structural Characterization and Biological Properties Analysis of Exopolysaccharides Produced by Weisella cibaria HDL-4
Authors: Zhou Bosen , Wang Changli , Yang Yi , Yu Wenna , Bin Xiaoyun , Song Gang , Du Renpeng
Published: August 2024

An exopolysaccharide (EPS)-producing strain, identified as Weissella cibaria HDL-4, was isolated from litchi. After separation and purification, the structure and properties of HDL-4 EPS were characterized. The molecular weight of HDL-4 EPS was determined to be 1.9 × 10⁶ Da, with glucose as its monosaccharide component. Fourier transform infrared spectroscopy (FT-IR) and nuclear magnetic resonance (NMR) analyses indicated that HDL-4 EPS was a D-glucan with α-(1→6) and α-(1→4) glycosidic bonds. X-ray diffraction (XRD) analysis revealed that HDL-4 EPS was amorphous. Scanning electron microscope (SEM) and atomic force microscope (AFM) observations showed that HDL-4 EPS possesses pores, irregular protrusions, and a smooth layered structure. Additionally, HDL-4 EPS demonstrated significant thermal stability, remaining stable below 288 °C. It exhibited a strong metal ion adsorption activity, emulsification activity, antioxidant activity, and water-retaining property. Therefore, HDL-4 EPS can be extensively utilized in the food and pharmaceutical industries as an additive and prebiotic.
Halochromic Bacterial Cellulose/Anthocyanins Hybrid Polymer Film with Wound-Healing Potential
Authors: Zubova Ganna , Melnyk Hanna , Zaets Iryna , Sergeyeva Tetyana , Havryliuk Olesia , Rogalsky Sergiy , Khirunenko Lyudmila , Zaika Leonid , Ruban Tetiana , Antonenko Svitlana , Kozyrovska Natalia
Published: August 2024

Polymer-based dressings deriving from natural biomaterials have advantages such as nontoxicity, biocompatibility, and mechanical stability, which are essential for efficient wound healing and microbial infection diagnostics. Here, we designed a prototype of an intelligent hydrogel dressing on the base of bacterial cellulose (BC) for monitoring wound microbial infection due to the uploaded natural pH dye-sensor, anthocyanins (ANC) of elderberry fruit (Sambucus nigra L.). The highest sensor responses to bacterial metabolites for ANC immobilized to BC were observed at pH 5.0 and 6.0. The detection limit of the sensor signals was 3.45 A.U., as it was evaluated with a smartphone-installed application. The FTIR spectral analysis of the hybrid BC/ANC hydrogel films has proved the presence of anthocyanins within the BC matrix. Hybrid films differed from the control ones by thicker microfibrils and larger pores, as detected with scanning electron microscopy. Halochromic BC/ANC films exhibited antimicrobial activities mainly against gram-positive bacteria and yeast. They showed no cytotoxicity for the in vitro human cell lines and mouse fibroblasts within a selected range of anthocyanin concentrations released from the BC/ANC film/dressing prototype. Compared to the control, the in vitro healing test showed overgrowth of primary mouse fibroblasts after applying 0.024–2.4 µg/mL ANC.
Capability Indices for Digitized Industries: A Review and Outlook of Machine Learning Applications for Predictive Process Control
Authors: Mayer Jan , Jochem Roland
Published: August 2024

Leveraging machine learning applications for predictive process control signifies a decisive advancement in manufacturing quality management, transitioning from traditional descriptive to predictive capability indices. This review highlights the growing importance of predictive process control, essential for quality assurance and the dynamic adaptability of production lines, which is paramount in satisfying stringent quality standards and evolving consumer demands. The investigation into the integration of comprehensive sensor networks and sophisticated algorithmic analytics enriches continuous improvement strategies, markedly enhancing the accuracy and efficiency of production quality monitoring and control mechanisms. By moving beyond the limits of statistical process control to predictive methods enabled by machine learning algorithms, the study presents a transformative leap in manufacturing processes. The presented findings illustrate the critical role of predictive algorithms in navigating the complexities of process variability, thereby ensuring consistent adherence to established quality specifications. This approach not only facilitates immediate and accurate product quality categorization, increasing overall operational efficiency, but also equips manufacturers to swiftly respond to the variable nature of manufacturing requirements. Furthermore, this research delves into the multifaceted impacts of predictive process control on the manufacturing ecosystem. The ability to predict process quality decrease before it occurs, the optimization of resource allocation, and the anticipation of production bottlenecks before they impact output are among the notable benefits of this technological evolution. These developments to predictive process control is instrumental in propelling the manufacturing industry toward a more agile, sustainable, and customer-centric future. This shift not only complements the industry’s drive toward comprehensive digitization but also promises significant strides in achieving superior process improvements and maintaining a competitive edge on the global market.
Synthesis of 2-Azetidinones via Cycloaddition Approaches: An Update
Authors: Cordero Franca Maria , Giomi Donatella , Machetti Fabrizio
Published: August 2024

The present review is a comprehensive update of the synthesis of monocyclic β-lactams via cycloaddition reactions. According to the IUPAC definition of cycloaddition, both elementary and stepwise processes (formal cycloadditions) have been considered. The years 2019–2022 are covered by the cited literature. The focus of the review is on synthetic aspects with emphasis on the structural scope, reaction conditions, mechanistic aspects, and selectivity results. Selected significant data related to biological activities and synthetic applications are also highlighted.
Scotus, Aquinas, & Radical Orthodoxy: Using the Law of Non-Contradiction to Reframe the Univocalist Debate
Authors: Lyonhart Jonathan David
Published: August 2024

In this paper, I shall argue that the law of non-contradiction can be used to constructively reframe the univocalist debate. Duns Scotus argued famously that a term is univocal in two statements if its unity is sufficient for a contradiction. This logical definition was woven into his arguments against Henry of Ghent’s (and indirectly Thomas Aquinas’) view of analogy, arguing that all successful analogies must be built upon a univocal core. As early as the 1960s, this Scotist univocity had been singled out by French scholars and, by the turn of the century, had become the cherished whipping boy of Radical Orthodoxy, which claims that Scotus was the progenitor of modern onto-theology, nihilism, and secular immanence. While the genealogical critique in its fullness is beyond this paper’s scope, it illustrates the gravity of the question. If the doctrine of analogy is coherent—i.e., if Scotus turned to univocity without cause—then perhaps his condemnation is justified. However—in line with the principle quod est necessarium est licitum (that which is necessary is permissible)—if univocity is necessary for successful theological reference, then perhaps the doctrine of univocity can be defended regardless of its historical usage. This paper will argue that univocity is latent in all successful analogies, commencing with a fairly standard analysis of Scotus’ Ordinatio, then moving beyond Scotus to more constructively suggest that an expanded version of the argument from non-contradiction can help reframe the univocalist debate for today.
Estimation of Maize Biomass at Multi-Growing Stage Using Stem and Leaf Separation Strategies with 3D Radiative Transfer Model and CNN Transfer Learning
Authors: Zhao Dan , Yang Hao , Yang Guijun , Yu Fenghua , Zhang Chengjian , Chen Riqiang , Tang Aohua , Zhang Wenjie , Yang Chen , Xu Tongyu
Published: August 2024

The precise estimation of above-ground biomass (AGB) is imperative for the advancement of breeding programs. Optical variables, such as vegetation indices (VI), have been extensively employed in monitoring AGB. However, the limited robustness of inversion models remains a significant impediment to the widespread application of UAV-based multispectral remote sensing in AGB inversion. In this study, a novel stem–leaf separation strategy for AGB estimation is delineated. Convolutional neural network (CNN) and transfer learning (TL) methodologies are integrated to estimate leaf biomass (LGB) across multiple growth stages, followed by the development of an allometric growth model for estimating stem biomass (SGB). To enhance the precision of LGB inversion, the large-scale remote sensing data and image simulation framework over heterogeneous scenes (LESS) model, which is a three-dimensional (3D) radiative transfer model (RTM), was utilized to simulate a more extensive canopy spectral dataset, characterized by a broad distribution of canopy spectra. The CNN model was pre-trained in order to gain prior knowledge, and this knowledge was transferred to a re-trained model with a subset of field-observed samples. Finally, the allometric growth model was utilized to estimate SGB across various growth stages. To further validate the generalizability, transferability, and predictive capability of the proposed method, field samples from 2022 and 2023 were employed as target tasks. The results demonstrated that the 3D RTM + CNN + TL method outperformed best in LGB estimation, achieving an R² of 0.73 and an RMSE of 72.5 g/m² for the 2022 dataset, and an R² of 0.84 and an RMSE of 56.4 g/m² for the 2023 dataset. In contrast, the PROSAIL method yielded an R² of 0.45 and an RMSE of 134.55 g/m² for the 2022 dataset, and an R² of 0.74 and an RMSE of 61.84 g/m² for the 2023 dataset. The accuracy of LGB inversion was poor when using only field-measured samples to train a CNN model without simulated data, with R² values of 0.30 and 0.74. Overall, learning prior knowledge from the simulated dataset and transferring it to a new model significantly enhanced LGB estimation accuracy and model generalization. Additionally, the allometric growth model’s estimation of SGB resulted in an accuracy of 0.87 and 120.87 g/m² for the 2022 dataset, and 0.74 and 86.87 g/m² for the 2023 dataset, exhibiting satisfactory results. Separate estimation of both LGB and SGB based on stem and leaf separation strategies yielded promising results. This method can be extended to the monitor and inversion of other critical variables.
Optical-to-SAR Translation Based on CDA-GAN for High-Quality Training Sample Generation for Ship Detection in SAR Amplitude Images
Authors: Wu Baolong , Wang Haonan , Zhang Cunle , Chen Jianlai
Published: August 2024

Abundant datasets are critical to train models based on deep learning technologies for ship detection applications. Compared with optical images, ship detection based on synthetic aperture radar (SAR) (especially the high-Earth-orbit spaceborne SAR launched recently) lacks enough training samples. A novel cross-domain attention GAN (CDA-GAN) model is proposed for optical-to-SAR translation, which can generate high-quality SAR amplitude training samples of a target by optical image conversion. This high quality includes high geometry structure similarity of the target compared with the corresponding optical image and low background noise around the target. In the proposed model, the cross-domain attention mechanism and cross-domain multi-scale feature fusion are designed to improve the quality of samples for detection based on the generative adversarial network (GAN). Specifically, a cross-domain attention mechanism is designed to simultaneously emphasize discriminative features from optical images and SAR images at the same time. Moreover, a designed cross-domain multi-scale feature fusion module further emphasizes the geometric information and semantic information of the target in a feature graph from the perspective of global features. Finally, a reference loss is introduced in CDA-GAN to completely retain the extra features generated by the cross-domain attention mechanism and cross-domain multi-scale feature fusion module. Experimental results demonstrate that the training samples generated by the proposed CDA-GAN can obtain higher ship detection accuracy using real SAR data than the other state-of-the-art methods. The proposed method is generally available for different orbit SARs and can be extended to the high-Earth-orbit spaceborne SAR case.
Field Measurements of Spatial Air Emissions from Dairy Pastures Using an Unmanned Aircraft System
Authors: Yang Doee , Wang Yuchuan , Akdeniz Neslihan
Published: August 2024

Unmanned aircraft systems (UASs) are emerging as useful tools in environmental studies due to their mobility and ability to cover large areas. In this study, we used an air analyzer attached to a UAS to measure gas and particulate matter (PM) emissions from rotationally grazed dairy pastures in northern Wisconsin. UAS-based sampling enabled wireless data transmission using the LoRa protocol to a ground station, synchronizing with a cloud server. During the measurements, latitude, longitude, and altitude were recorded using a high-precision global positioning system (GPS). Over 1200 measurements per parameter were made during each site visit. The spatial distribution of the emission rates was estimated using the Lagrangian mass balance approach and Kriging interpolation. A horizontal sampling probe effectively minimized the impact of propeller downwash on the measurements. The average concentrations of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) were 800.1 ± 39.7 mg m−3, 1.38 ± 0.063 mg m−3, and 0.71 ± 0.03 mg m−3, respectively. No significant difference was found between CO2 concentrations measured by the UAS sensor and gas chromatography (p = 0.061). Emission maps highlighted variability across the pasture, with an average CO2 emission rate of 1.52 ± 0.80 g day−1 m−2, which was within the range reported in the literature. Future studies could explore the impact of pasture management on air emissions.
Production of Annual Nighttime Light Based on De-Difference Smoothing Algorithm
Authors: Zhang Shuyan , Ma Yong , Shang Erping , Yao Wutao , Qiao Ke , Peng Jian , Yang Jin , Feng Chun
Published: August 2024

Nighttime light (NTL) remote sensing has emerged as a powerful tool in various fields such as urban expansion, socio-economic estimation, light pollution, and energy domains. However, current annual NTL products suffer from several critical limitations, including poor consistency, severe background noise, and limited comparability. These issues have significantly interfered with the research of long-term NTL trends and diminished the accuracy of related findings. Therefore, this study developed a de-difference smoothing algorithm for producing high-quality annual NTL products based on monthly National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data. It enabled the construction of a continuous global high-quality NTL dataset, named the De-Difference Smoothed Nighttime Light (DDSNL), covering the period from 2012 to 2023. Comparative analyses were conducted to validate the accuracy and availability of the DDSNL product against the benchmark EOG NPP-VIIRS and NPP-VIIRS-like NTL datasets. The results showed that DDSNL products had strong correlation with the NTL distribution of EOG NPP-VIIRS, but little correlation with NPP-VIIRS-like. Notably, DDSNL demonstrated better background noise reduction and higher separability between NTL and non-NTL areas compared to EOG NPP-VIIRS NTL. In contrast to the complete exclusion of background in NPP-VIIRS-Like, the retention of background values in DDSNL leads to more reasonable representation in the urban fringes. In the analysis of NTL changes matching impervious surface changes, the DDSNL product demonstrated the least interference from noise, resulting in the smallest segmentation threshold and the highest matching accuracy. This indirectly demonstrates the spatial and temporal consistency of the annual DDSNL product, ensuring its reliability in change detection-related studies. The annual DDSNL product developed in this research exhibits high fidelity, strong consistency, and improved comparability, and can provide reliable data reference for applications in electrification and urban studies.
An Unsupervised CNN-Based Pansharpening Framework with Spectral-Spatial Fidelity Balance
Authors: Ciotola Matteo , Guarino Giuseppe , Scarpa Giuseppe
Published: August 2024

In recent years, deep learning techniques for pansharpening multiresolution images have gained increasing interest. Due to the lack of ground truth data, most deep learning solutions rely on synthetic reduced-resolution data for supervised training. This approach has limitations due to the statistical mismatch between real full-resolution and synthetic reduced-resolution data, which affects the models’ generalization capacity. Consequently, there has been a shift towards unsupervised learning frameworks for pansharpening deep learning-based techniques. Unsupervised schemes require defining sophisticated loss functions with at least two components: one for spectral quality, ensuring consistency between the pansharpened image and the input multispectral component, and another for spatial quality, ensuring consistency between the output and the panchromatic input. Despite promising results, there has been limited investigation into the interaction and balance of these loss terms to ensure stability and accuracy. This work explores how unsupervised spatial and spectral consistency losses can be reliably combined preserving the outocome quality. By examining these interactions, we propose a general rule for balancing the two loss components to enhance the stability and performance of unsupervised pansharpening models. Experiments on three state-of-the-art algorithms using WorldView-3 images demonstrate that methods trained with the proposed framework achieve good performance in terms of visual quality and numerical indexes.
Kinematics-Based Predictions of External Loads during Handcycling
Authors: Sipes Griffin C. , Lee Matthew , Halloran Kellie M. , Rice Ian , Kersh Mariana E.
Published: August 2024

The increased risk of cardiovascular disease in people with spinal cord injuries motivates work to identify exercise options that improve health outcomes without causing risk of musculoskeletal injury. Handcycling is an exercise mode that may be beneficial for wheelchair users, but further work is needed to establish appropriate guidelines and requires assessment of the external loads. The goal of this research was to predict the six-degree-of-freedom external loads during handcycling from data similar to those which can be measured from inertial measurement units (segment accelerations and velocities) using machine learning. Five neural network models and two ensemble models were compared against a statistical model. A temporal convolutional network (TCN) yielded the best predictions. Predictions of forces and moments in-plane with the crank were the most accurate (r = 0.95–0.97). The TCN model could predict external loads during activities of different intensities, making it viable for different exercise protocols. The ability to predict the loads associated with forward propulsion using wearable-type data enables the development of informed exercise guidelines.
Motion Clutter Suppression for Non-Cooperative Target Identification Based on Frequency Correlation Dual-SVD Reconstruction
Authors: He Weikun , Luo Yichuan , Shang Xiaoxiao
Published: August 2024

Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between birds and UAVs in environments with strong motion clutter is crucial for improving target monitoring performance and ensuring flight safety. To address the impact of strong motion clutter on discriminating between UAVs and birds, we propose a frequency correlation dual-SVD (singular value decomposition) reconstruction method. This method exploits the strong power and spectral correlation characteristics of motion clutter, contrasted with the weak scattering characteristics of bird and UAV targets, to effectively suppress clutter. Unlike traditional clutter suppression methods based on SVD, our method avoids residual clutter or target loss while preserving the micro-motion characteristics of the targets. Based on the distinct micro-motion characteristics of birds and UAVs, we extract two key features: the sum of normalized large eigenvalues of the target’s micro-motion component and the energy entropy of the time–frequency spectrum of the radar echoes. Subsequently, the kernel fuzzy c-means algorithm is applied to classify bird and UAV targets. The effectiveness of our proposed method is validated through results using both simulation and experimental data.
Compact Silicon-Arrayed Waveguide Gratings with Low Nonuniformity
Authors: Yang Chengkun , Zhou Zhonghao , Gao Xudong , Xu Zhengzhu , Han Shoubao , Chong Yuhua , Min Rui , Yue Yang , Duan Zongming
Published: August 2024

Array waveguide gratings (AWGs) have been widely used in multi-purpose and multi-functional integrated photonic devices for Microwave photonics (MWP) systems. In this paper, we compare the effect of output waveguide configurations on the performance of AWGs. The AWG with an output waveguide converging on the grating circle had larger crosstalk and lower nonuniformity. We also fabricated a 1 × 8 AWG with an output waveguide converging onto the SOI’s grating circle, whose central operation wavelength was around 1550 nm. The fabricated AWG has a chip size of 500 μm × 450 μm. Experimental results show that the adjacent channel crosstalk is −12.68 dB. The center channel insertion loss, as well as 3 dB bandwidth, are 4.18 dB and 1.22 nm at 1550 nm, respectively. The nonuniformity is about 0.494 dB, and the free spectral range is 19.4 nm. The proposed AWG is expected to play an important role in future MWP systems given its good nonuniformity and insertion loss level.
Open-Source Data Analysis Tool for Spectral Small-Angle X-ray Scattering Using Spectroscopic Photon-Counting Detector
Authors: Amer Sabri , Xu Andrew , Badano Aldo , Dahal Eshan
Published: August 2024

Spectral small-angle X-ray scattering (sSAXS) is a powerful technique for material characterization from thicker samples by capturing elastic X-ray scattering data in angle- and energy-dispersive modes at small angles. This approach is enabled by the use of a 2D spectroscopic photon-counting detector that provides energy and position information of scattered photons when a sample is irradiated by a polychromatic X-ray beam. Here, we describe an open-source tool with a graphical interface for analyzing sSAXS data obtained from a 2D spectroscopic photon-counting detector with a large number of energy bins. The tool takes system geometry parameters and raw detector data to output 1D scattering patterns and a 2D spatially-resolved scattering map in the energy range of interest. We validated these features using data from samples of caffeine powder with well-known scattering peaks. This open-source tool will facilitate sSAXS data analysis for various material characterization applications.
Low-Cost Efficient Wireless Intelligent Sensor (LEWIS) for Research and Education
Authors: Sanei Mahsa , Atcitty Solomon , Moreu Fernando
Published: August 2024

Sensors have recently become valuable tools in engineering, providing real-time data for monitoring structures and the environment. They are also emerging as new tools in education and training, offering learners real-time information to reinforce their understanding of engineering concepts. However, sensing technology’s complexity, costs, fabrication and implementation challenges often hinder engineers’ exploration. Simplifying these aspects could make sensors more accessible to engineering students. In this study, the researcher developed, fabricated, and tested an efficient low-cost wireless intelligent sensor aimed at education and research, named LEWIS1. This paper describes the hardware and software architecture of the first prototype and their use, as well as the proposed new versions, LEWIS1-β and LEWIS1-γ, which simplify both hardware and software. The capabilities of the proposed sensor are compared with those of an accurate commercial PCB sensor. This paper also demonstrates examples of outreach efforts and suggests the adoption of the newer versions of LEWIS1 as tools for education and research. The authors also investigated the number of activities and sensor-building workshops that have been conducted since 2015 using the LEWIS sensor, showing an increasing trend in the excitement of people from various professions to participate and learn sensor fabrication.
Enhanced NO2 Gas Sensing Properties Based on Rb-Doped ZnO/In2O3 Heterojunctions at Room Temperature: A Combined DFT and Experimental Study
Authors: Yang Yaning , Cui Jiawen , Luo Zhihua , Luo Zhixin , Sun Yanhui
Published: August 2024

In this work, alkali metal Rb-loaded ZnO/In2O3 heterojunctions were synthesized using a combination of hydrothermal and impregnation methods. The morphology and structure of the synthesized samples were characterized by X-ray diffraction, field emission scanning electron microscopy, and transmission electron microscopy. The enhancement mechanism of the nitrogen dioxide gas sensing performance of the Rb-loaded ZnO/In2O3 heterojunctions was systematically investigated at room temperature using density-functional theory calculations and experimental validation. The experimental tests showed that the Rb-loaded ZnO/In2O3 sensor achieved an excellent response value of 24.2 for 1 ppm NO2, with response and recovery times of 55 and 21 s, respectively. This result is 20 times higher than that of pure ZnO sensors and two times higher than that of ZnO/In2O3 sensors, indicating that the Rb-loaded ZnO/In2O3 sensor has a more pronounced enhancement in performance for NO2. This study not only revealed the mechanism by which Rb loading affects the electronic structure and gas molecule adsorption behavior on the surface of ZnO/In2O3 heterojunctions but also provides theoretical guidance and technical support for the development of high-performance room-temperature NO2 sensors.
Comparison of the Accuracy of Ground Reaction Force Component Estimation between Supervised Machine Learning and Deep Learning Methods Using Pressure Insoles
Authors: Kammoun Amal , Ravier Philippe , Buttelli Olivier
Published: August 2024

The three Ground Reaction Force (GRF) components can be estimated using pressure insole sensors. In this paper, we compare the accuracy of estimating GRF components for both feet using six methods: three Deep Learning (DL) methods (Artificial Neural Network, Long Short-Term Memory, and Convolutional Neural Network) and three Supervised Machine Learning (SML) methods (Least Squares, Support Vector Regression, and Random Forest (RF)). Data were collected from nine subjects across six activities: normal and slow walking, static with and without carrying a load, and two Manual Material Handling activities. This study has two main contributions: first, the estimation of GRF components (Fx, Fy, and Fz) during the six activities, two of which have never been studied; second, the comparison of the accuracy of GRF component estimation between the six methods for each activity. RF provided the most accurate estimation for static situations, with mean RMSE values of RMSE_Fx = 1.65 N, RMSE_Fy = 1.35 N, and RMSE_Fz = 7.97 N for the mean absolute values measured by the force plate (reference) RMSE_Fx = 14.10 N, RMSE_Fy = 3.83 N, and RMSE_Fz = 397.45 N. In our study, we found that RF, an SML method, surpassed the experimented DL methods.
Differential Signal-Amplitude-Modulated Multi-Beam Remote Optical Touch Based on Grating Antenna
Authors: Huang Yanwen , Lin Weiqiang , Wu Peijin , Wang Yongxin , Guo Ziyuan , Huang Pengcheng , Ye Zhicheng
Published: August 2024

As screen sizes are becoming larger and larger, exceeding human physical limitations for direct interaction via touching, remote control is inevitable. However, among the current solutions, inertial gyroscopes are susceptible to positional inaccuracies, and gesture recognition is limited by cameras’ focus depths and viewing angles. Provided that the issue of ghost points can be effectively addressed, grating antenna light-trapping technology is an ideal candidate for multipoint inputs. Therefore, we propose a differential amplitude modulation scheme for grating antenna-based multi-beam optical touch, which can recognize different incidence points. The amplitude of the incident beams was first coded with different pulse widths. Then, following the capture of incident beams by the grating antenna and their conversion into electrical currents by the aligned detector arrays, the incident points of the individual beams were recognized and differentiated. The scheme was successfully verified on an 18-inch screen, where two-point optical touch with a position accuracy error of under 3 mm and a response time of less than 7 ms under a modulation frequency of 10 kHz on both incident beams was achieved. This work demonstrates a practical method to achieve remote multi-point touch, which can make digital mice more accurately represent the users’ pointing directions by obeying the natural three-point one-line aiming rule instantaneously.
Biomechanical and Physiological Variables in Dynamic and Functional Balance Control during Single-Leg Loading in Individuals with Chronic Ankle Instability: A Scoping Review
Authors: Phuaklikhit Chairat , Junsri Thanwarat , Saito Seiji , Muraki Satoshi , Loh Ping Yeap
Published: August 2024

Background: This scoping review summarizes the tasks and outcomes in dynamic and functional balance assessments of individuals with chronic ankle instability, focusing on the physiological and biomechanical characteristics. Method: A comprehensive literature search was conducted in PubMed, Scopus, Web of Science, and MEDLINE databases in September 2023 and revised in April 2024. Studies evaluating dynamic and functional balance in chronic ankle instability using clinical tests, as well as biomechanical and physiological outcomes, were included. Results: Out of 536 publications, 31 met the screening criteria. A history of ankle sprain was the main focus of the inclusion criteria (28 articles, 90%). The star excursion balance test, emphasizing maximum reach distance, was the most common quantitative task (12 articles, 66%). Physiological data mainly came from electromyography studies (7 articles, 23%), while biomechanical variables were often assessed through center of pressure studies using force plates (17 articles, 55%). Conclusions: The preferred quantitative clinical assessment was the star excursion balance test, focusing on normalized reach outcomes. Qualitative functional balance assessments emphasize landing activities and center of pressure displacement. Electromyography is commonly used to analyze the tibialis anterior and peroneus longus muscles. However, there is a lack of qualitative data on dynamic balance control, including morphological characteristics and the center of mass adaptation.
Water Resource Regulation and Evaluation Method Based on Optimization of Drought-Limited Water Level in Reservoir Group
Authors: He Sheng , Wang Dongmei , Sang Xuefeng , Niu Geng
Published: August 2024

Reservoirs, as critical nodes in regional water management, play an increasingly important role in drought mitigation. This study aims to optimize the drought-limited water level in the reservoir group and propose an evaluation method for selecting the optimal regulation scheme during drought periods. The reservoir water supply module within the Water Allocation and Simulation (WAS) model was enhanced to optimize the drought-limited water level of the reservoir group. A comprehensive adaptation index (CAI) was developed to quantitatively evaluate the effectiveness of water resource regulation under various drought scenarios. This methodology was applied to large and medium-sized reservoirs in the central Yunnan Province, China. The results show that the optimized drought-limited water level significantly improved the water supply performance of the reservoir group during drought years. Specifically, the optimized drought-limited water level notably reduced severe water shortage for water users in the long series and typical drought years, effectively mitigating the impacts of drought. Additionally, the most suitable water resource regulation strategies for different drought scenarios were identified. These research findings can provide technical references for reservoir management departments and drought operations authorities to formulate drought-limited level for the reservoir group and implement regional drought early warning and defense decision-making.
Assessing the Influence of Open Innovation among Chinese Cities on Enterprise Carbon Emissions
Authors: Chen Xiaoyan , Wan Liwen , Cheng Qunqun , Shang Yuping
Published: August 2024

Currently, China is the largest carbon emitter and the pressure of carbon reduction in China is very severe. However, the lack of technological innovation momentum is a bottleneck factor that restricts carbon reduction in Chinese cities. In this context, open innovation is gradually replacing closed innovation and playing an increasingly important role in improving the technological innovation performance of enterprises. Analysis shows that a large amount of literature has explored the impacts of industry technological innovation and green technology innovation on carbon emissions, while there is little research on how open innovation affects carbon emissions. This study calculates cities’ open innovation indicators and the carbon emission intensity indicators of listed enterprises. Using a three-fixed-effects model, it examines the effect and mechanism of open innovation on carbon emissions of enterprises and verifies the heterogeneity effect. The research results indicate that open innovation can significantly reduce the carbon emission intensity of enterprises by reducing transaction costs and upgrading the industrial structure. Further heterogeneity analysis shows that open innovation has an obvious carbon emission reduction effect on non-state-owned, polluting, small- and medium-sized enterprises and enterprises in central cities.
Thiocracking of Multi-Materials: High-Strength Composites from Post-Consumer Food Packaging Jars
Authors: Derr Katelyn M. , Smith Rhett C.
Published: August 2024

A significant waste material threatening sustainability efforts are post-consumer food packaging goods. These ubiquitous multi-materials comprise chemically disparate components and are thus challenging targets for recycling. Herein, we undertake a proof-of-principle study in which we use a single-stage method to convert post-consumer multi-material food packaging (post-consumer peanut butter jars) to a high compressive strength composite (PBJS90). This is accomplished by thiocracking the ground jar pulp (10 wt. %) with elemental sulfur (90 wt. %) at 320 °C for 2 h. This is the first application of thiocracking to such mixed-material post-consumer goods. Composite synthesis proceeded with 100% atom economy, a low E factor of 0.02, and negative global warming potential of −0.099 kg CO2e/kg. Furthermore, the compressive strength of PBJS90 (37.7 MPa) is over twice that required for Portland cement building foundations. The simplicity of composite synthesis using a lower temperature/shorter heating time than needed for mineral cements, and exclusive use of waste materials as precursors are ecologically beneficial and represent an important proof-of-principle approach to using thiocracking as a strategy for upcycling multi-materials to useful composites.
Understanding the Relationship between Urban Form and Urban Shrinkage among Medium-Sized Cities in Poland and Its Implications for Sustainability
Authors: Szymczyk Ewa , Bukowski Mateusz , Kenworthy Jeffrey Raymond
Published: August 2024

Understanding the relationship between urban form and urban shrinkage is crucial for developing sustainable urban policies, particularly in medium-sized cities facing demographic and economic challenges. This study investigates the complex relationship between urban form and urban shrinkage in medium-sized Polish cities (population of 20,000 to 100,000), highlighting the implications for sustainability. Utilising a comprehensive multi-factor approach, it analyses the shrinkage and growth trends over 15 years (2006–2021) by establishing a shrinkage/growth score based on social, demographic, and economic factors for each city. It examines spatial aspects, particularly urban form compactness and population density, using Corine Land Cover (CLC) spatial data, making the methodology applicable to urban areas across Europe. The findings reveal no significant overall correlation between urban compactness and shrinkage/growth score across all cities. However, a positive correlation exists within “urban municipalities”, indicating that less compact urban areas tend to experience more shrinkage. Additionally, a temporary negative correlation between population density and shrinkage/growth score was observed from 2006 to 2016, which shifted to a positive trend in “urban municipalities” from 2016 to 2021. These results highlight urban shrinkage’s complex and dynamic nature and its potential ties to urban form. The study concludes with recommendations for urban policymakers and planners regarding compact and dense urban strategies to mitigate the adverse effects of shrinkage and enhance urban resilience and sustainability. While the trends change, the study highlights the need for further analysis of these relationships.
Solar Photovoltaics Value Chain and End-of-Life Management Practices: A Systematic Literature Review
Authors: Amrollahi Biyouki Zahra , Zaman Atiq , Marinova Dora , Minunno Roberto , Askari Shayegan Maryam
Published: August 2024

Many challenges emerge in the life cycle of solar photovoltaic (PV) panels throughout the processes of their deployment and use in residential, commercial, industrial and transportation sectors. There is a growing need for total product recovery by recycling and reusing the solar panel base and other components in a way that is economically efficient and environmentally sound. This study highlights the urgency to develop and implement a suitable system for the collection and management of photovoltaic systems at their end-of-life cycle and the need for professional implementation of circular strategies in the solar PV value chain. To achieve this goal, a systematic literature review of 81 peer-reviewed articles, published in English between 2013 and 2023, was conducted. The main purpose of the analysis is to examine the value chain of the solar panels covering the period of design, construction, use, end of life, recovery or landfill. The two processes that are investigated include the extent of end-of-life management of PV panels and the extent of circular strategies to reach a sustainable and comprehensive business model. It is argued that the current obstacles faced by solar energy businesses create new opportunities and challenges for innovation within a circular PV industry, and appropriate policies and trained professionals are needed for the implementation of the Sustainable Development Goals (SDGs), including SDG12, in the solar PV value chain.