bluuebunny/crossref_metadata_2025 huggingface dataset:
The Combined ICP-MS, ESEM-EDX, and HAADF-STEM-EDX Approach for the Assessment of Metal Sub-Micro- and Nanoparticles in Wheat Grain
Authors: Piergiovanni Maurizio , Mattarozzi Monica , Verleysen Eveline , Siciliani Lisa , Suman Michele , Bianchi Federica , Mast Jan , Careri Maria
Published: July 2024

Metal sub-microparticles (SMPs) and nanoparticles (NPs) presence in food is attributable to increasing pollution from the environment in raw materials and finished products. In the present study, a multifaceted analytical strategy based on Environmental Scanning Electron Microscopy and High-Angle Annular Dark-Field—Scanning Transmission Electron Microscopy coupled with Energy-Dispersive X-ray Spectroscopy (ESEM-EDX, HAADF-STEM-EDX) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was proposed for the detection and characterization of metal and metal-containing SMPs and NPs in durum wheat samples, covering a size measurement range from 1 nm to multiple µm. ESEM-EDX and ICP-MS techniques were applied for the assessment of SMP and NP contamination on the surface of wheat grains collected from seven geographical areas characterized by different natural and anthropic conditions, namely Italy, the USA, Australia, Slovakia, Mexico, Austria, and Russia. ICP-MS showed significant differences among the mean concentration levels of metals, with the USA and Italy having the highest level. ESEM-EDX analysis confirmed ICP-MS concentration measurements and measured the highest presence of particles < 0.8 µm in size in samples from Italy, followed by the USA. Less marked differences were observed when particles < 0.15 µm were considered. HAADF-STEM-EDX was applied to a selected number of samples for a preliminary assessment of internal contamination by metal SMPs and NPs, and to expand the measurable particle size range. The multifaceted approach provided similar results for Fe-containing SMPs and NPs. ICP-MS and ESEM-EDX also highlighted the presence of a significant abundance of Ti- and Al-containing particles, while for STEM-EDX, sample preparation artifacts complicated the interpretation. Finally, HAADF-STEM-EDX results provided relevant information about particles in the low nm range, since, by applying this technique, no particles smaller than 50 nm were observed in accordance with ESEM-EDX.
Synthesis and Investigation of Biological Activity of New Betulonic Acid Derivatives Containing 1,2,3-Triazole Fragments
Authors: Jalmakhanbetova Roza I. , Mukusheva Gulim K. , Abdugalimov Alisher Sh. , Zhumagalieva Zharkyn Zh. , Dehaen Wim , Anthonissen Stijn , Suleimen Yerlan M. , Seidakhmetova Roza B.
Published: July 2024

The results of this study showed that the compounds synthesized by the authors have significant potential due to their antibacterial and cytotoxic properties. The apparent antibacterial activity demonstrated by the compounds suggests that they are active antimicrobial agents against common microbial pathogens that cause various socially significant infectious diseases. Compound 6 showed pronounced antimicrobial activity against the Gram-positive test strain Staphylococcus aureus ATCC 6538, and compound 7 demonstrated pronounced antimicrobial activity against the Gram-negative test strain Escherichia coli ATCC 25922 (MIC = 6.3 µg/mL). This allowed us to consider these compounds to have great potential.
Recent Progress on Multi-Component Reactions Involving Nucleophile, Arynes and CO2
Authors: Gong Shaoxuan , Xie Xiumei , Sun Hongxia , Liu Yuting , Li Junjie , Zhang Zhen
Published: July 2024

Carbon dioxide (CO2) is a non-toxic, abundant and recoverable source of carbon monoxide. Despite its thermodynamically stable and kinetically inert nature, research on CO2 utilisation is ongoing. CO2-based aryne reactions, crucial for synthesising ortho-substituted benzoic acids and their cyclisation products, have garnered significant attention, and multi-component reactions (MCRs) involving CO2, aryne and nucleophilic reagents have been extensively studied. This review highlights recent advancements in CO2 capture reactions utilising phenylalkyne reactive intermediates. Mechanistic insights into these reactions are provided together with prospects for further development in this field.
Versatile Activated Carbon Fibers Derived from the Cotton Fibers Used as CO2 Solid-State Adsorbents and Electrode Materials
Authors: Wang Peiyu , Liu Hang , Zhu Wenting , Chen Wanjun , Wang Xiangli , Yang Le , Yang Bao , Chen Qiong , Limao Cairang , Cairang Zhuoma
Published: July 2024

Activated carbon has an excellent porous structure and is considered a promising adsorbent and electrode material. In this study, activated carbon fibers (ACFs) with abundant microporous structures, derived from natural cotton fibers, were successfully synthesized at a certain temperature in an Ar atmosphere and then activated with KOH. The obtained ACFs were characterized by field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), elemental analysis, nitrogen and carbon dioxide adsorption–desorption analysis, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and N2 adsorption–desorption measurement. The obtained ACFs showed high porous qualities and had a surface area from 673 to 1597 m2/g and a pore volume from 0.33 to 0.79 cm3/g. The CO2 capture capacities of prepared ACFs were measured and the maximum capture capacity for CO2 up to 6.9 mmol/g or 4.6 mmol/g could be achieved at 0 °C or 25 °C and 1 standard atmospheric pressure (1 atm). Furthermore, the electrochemical capacitive properties of as-prepared ACFs in KOH aqueous electrolyte were also studied. It is important to note that the pore volume of the pores below 0.90 nm plays key roles to determine both the CO2 capture ability and the electrochemical capacitance. This study provides guidance for designing porous carbon materials with high CO2 capture capacity or excellent capacitance performance.
Synthesis, Docking, and Machine Learning Studies of Some Novel Quinolinesulfonamides–Triazole Hybrids with Anticancer Activity
Authors: Marciniec Krzysztof , Nowakowska Justyna , Chrobak Elwira , Bębenek Ewa , Latocha Małgorzata
Published: July 2024

In the presented work, a series of 22 hybrids of 8-quinolinesulfonamide and 1,4-disubstituted triazole with antiproliferative activity were designed and synthesised. The title compounds were designed using molecular modelling techniques. For this purpose, machine-learning, molecular docking, and molecular dynamics methods were used. Calculations of the pharmacokinetic parameters (connected with absorption, distribution, metabolism, excretion, and toxicity) of the hybrids were also performed. The new compounds were synthesised via a copper-catalysed azide–alkyne cycloaddition reaction (CuAAC). 8-N-Methyl-N-{[1-(7-chloroquinolin-4-yl)-1H-1,2,3-triazol-4-yl]methyl}quinolinesulfonamide was identified in in silico studies as a potential strong inhibitor of Rho-associated protein kinase and as a compound that has an appropriate pharmacokinetic profile. The results obtained from in vitro experiments confirm the cytotoxicity of derivative 9b in four selected cancer cell lines and the lack of cytotoxicity of this derivative towards normal cells. The results obtained from silico and in vitro experiments indicate that the introduction of another quinolinyl fragment into the inhibitor molecule may have a significant impact on increasing the level of cytotoxicity toward cancer cells and indicate a further direction for future research in order to find new substances suitable for clinical applications in cancer treatment.
Application Prospect of Ion-Imprinted Polymers in Harmless Treatment of Heavy Metal Wastewater
Authors: Du Mengzhen , Xu Zihao , Xue Yingru , Li Fei , Bi Jingtao , Liu Jie , Wang Shizhao , Guo Xiaofu , Zhang Panpan , Yuan Junsheng
Published: July 2024

With the rapid development of industry, the discharge of heavy metal-containing wastewater poses a significant threat to aquatic and terrestrial environments as well as human health. This paper provides a brief introduction to the basic principles of ion-imprinted polymer preparation and focuses on the interaction between template ions and functional monomers. We summarized the current research status on typical heavy metal ions, such as Cu(II), Ni(II), Cd(II), Hg(II), Pb(II), and Cr(VI), as well as metalloid metal ions of the As and Sb classes. Furthermore, it discusses recent advances in multi-ion-imprinted polymers. Finally, the paper addresses the challenges faced by ion-imprinted technology and explores its prospects for application.
Methods to Investigate the Secretome of Senescent Cells
Authors: Samiminemati Afshin , Aprile Domenico , Siniscalco Dario , Di Bernardo Giovanni
Published: July 2024

The word “secretome” was first used to describe the proteins that cells secrete under different circumstances; however, recent studies have proven the existence of other molecules such as RNA and chemical compounds in the secretome. The study of secretome has significance for the diagnosis and treatment of disease as it provides insight into cellular functions, including immune responses, development, and homeostasis. By halting cell division, cellular senescence plays a role in both cancer defense and aging by secreting substances known as senescence-associated secretory phenotypes (SASP). A variety of techniques could be used to analyze the secretome: protein-based approaches like mass spectrometry and protein microarrays, nucleic acid-based methods like RNA sequencing, microarrays, and in silico prediction. Each method offers unique advantages and limitations in characterizing secreted molecules. Top-down and bottom-up strategies for thorough secretome analysis are became possible by mass spectrometry. Understanding cellular function, disease causes, and proper treatment targets is aided by these methodologies. Their approaches, benefits, and drawbacks will all be discussed in this review.
Harnessing AI and NLP Tools for Innovating Brand Name Generation and Evaluation: A Comprehensive Review
Authors: Lemos Marco , Cardoso Pedro J. S. , Rodrigues João M. F.
Published: July 2024

The traditional approach of single-word brand names faces constraints due to trademarks, prompting a shift towards fusing two or more words to craft unique and memorable brands, exemplified by brands such as SalesForce© or SnapChat©. Furthermore, brands such as Kodak©, Xerox©, Google©, Häagen-Dazs©, and Twitter© have become everyday names although they are not real words, underscoring the importance of brandability in the naming process. However, manual evaluation of the vast number of possible combinations poses challenges. Artificial intelligence (AI), particularly natural language processing (NLP), is emerging as a promising solution to address this complexity. Existing online brand name generators often lack the sophistication to comprehensively analyze meaning, sentiment, and semantics, creating an opportunity for AI-driven models to fill this void. In this context, the present document reviews AI, NLP, and text-to-speech tools that might be useful in innovating the brand name generation and evaluation process. A systematic search on Google Scholar, IEEE Xplore, and ScienceDirect was conducted to identify works that could assist in generating and evaluating brand names. This review explores techniques and datasets used to train AI models as well as strategies for leveraging objective data to validate the brandability of generated names. Emotional and semantic aspects of brand names, which are often overlooked in traditional approaches, are discussed as well. A list with more than 75 pivotal datasets is presented. As a result, this review provides an understanding of the potential applications of AI, NLP, and affective computing in brand name generation and evaluation, offering valuable insights for entrepreneurs and researchers alike.
One-Pot Synthesis of Cellulose-Based Carbon Aerogel Loaded with TiO2 and g-C3N4 and Its Photocatalytic Degradation of Rhodamine B
Authors: Liu Fangqin , Fan Mingjie , Liu Xia , Chen Jinyang
Published: July 2024

A cellulose-based carbon aerogel (CTN) loaded with titanium dioxide (TiO2) and graphitic carbon nitride (g-C3N4) was prepared using sol–gel, freeze-drying, and high-temperature carbonization methods. The formation of the sol–gel was carried out through a one-pot method using refining papermaking pulp, tetrabutyl titanate, and urea as raw materials and hectorite as a cross-linking and reinforcing agent. Due to the cross-linking ability of hectorite, the carbonized aerogel maintained a porous structure and had a large specific surface area with low density (0.0209 g/cm3). The analysis of XRD, XPS, and Raman spectra revealed that the titanium dioxide (TiO2) and graphitic carbon nitride (g-C3N4) were uniformly distributed in the CTN, while TEM and SEM observations demonstrated the uniformly distributed three-dimensional porous structure of CTN. The photocatalytic activity of the CTN was determined according to its ability to degrade rhodamine B. The removal rate reached 89% under visible light after 120 min. In addition, the CTN was still stable after five reuse cycles. The proposed catalyst exhibits excellent photocatalytic performance under visible light conditions.
Preoperative Crohn’s Disease Exclusion Diet and Exclusive Enteral Nutrition in Adults with Crohn’s Disease: A Feasibility Randomised Controlled Trial
Authors: Wall Catherine L. , Bensley Rachael , Glyn Tamara , Haines Melissa , Rowbotham David , Bissett Ian , Eglinton Timothy , Gearry Richard B.
Published: July 2024

Preoperative exclusive enteral nutrition (EEN) improves nutritional status, reduces intestinal inflammation, and likely improves surgical outcomes. Crohn’s disease exclusion diet with partial enteral nutrition (CDED) also reduces intestinal inflammation but its safety preoperatively is unknown. This single-blinded, multicentre, randomised controlled trial of three preoperative nutritional therapies aimed to assess the feasibility of recruiting and retaining patients and collecting primary and secondary effectiveness outcomes. Adults undergoing elective Crohn’s disease surgery with a body mass index (BMI) > 18.5 kg/m2 and without significant weight loss were eligible to participate. Patients were randomly assigned to six weeks of preoperative EEN, CDED, or standard care. Feasibility, nutritional, radiological, and surgical outcomes were recorded. Over 18 months, 48 patients were screened, 17 (35%) were randomised, and 13/17 (76%) patients were retained in the intervention phase. It was feasible to collect primary and secondary effectiveness data; at day 30, three patients had Clavien Dindo Grade 2 complications, and 10 had no complications. Nutritional therapy adherence of patients retained in the study was high. Recruitment and retention of patients who need elective Crohn’s disease surgery for preoperative nutritional therapy is possible, although a shorter duration may improve EEN completion. The impact on surgical outcomes should be assessed in a larger study.
Association between Serum Vitamin D Status and Circadian Syndrome: A Cross-Sectional Study
Authors: Arabi Ahmed , Nasrallah Dima , Mohsen Sara , Abugharbieh Lana , Al-Hashimi Dana , AlMass Shaikha , Albasti Shahd , Al-Ajmi Saeed A. , Khan Muhammad Naseem , Zughaier Susu M.
Published: July 2024

Background: Circadian Syndrome (CircS) encompasses cardiometabolic risk factors and comorbidities, indicating an elevated susceptibility to cardiovascular disease and type 2 diabetes. Methods: This cross-sectional study aimed to investigate the association between vitamin D levels and each of the following: CircS, metabolic syndrome (MetS), and the individual components of CircS. Data from 14,907 adults who participated in the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2018 were utilized. CircS was defined based on MetS components, alongside depression, short sleep, and non-alcoholic fatty liver disease (NAFLD). Results: Our results indicated that low vitamin D levels exhibited meaningful associations with CircS, with vitamin D deficiency and inadequacy demonstrating 2.21-fold (95% CI 1.78–2.74, p < 0.001) and 1.33-fold (95% CI 1.14–1.54, p < 0.001) increases in CircS odds, respectively. The association between vitamin D deficiency and CircS was stronger than that with MetS. Additionally, a dose-response gradient in odds of CircS components, particularly with short sleep duration, was noted as serum vitamin D levels decreased. Conclusions: our findings highlight a significant association between low serum vitamin D levels and CircS and its components, particularly with short sleep. This suggests a potentially pivotal role of vitamin D in the pathogenesis of Circadian syndrome.
Monitoring the Bioprotective Potential of Lactiplantibacillus pentosus Culture on Pathogen Survival and the Shelf-Life of Fresh Ready-to-Eat Salads Stored under Modified Atmosphere Packaging
Authors: Doukaki Angeliki , Papadopoulou Olga S. , Tzavara Chrysavgi , Mantzara Aikaterini-Malevi , Michopoulou Konstantina , Tassou Chrysoula , Skandamis Panagiotis , Nychas George-John , Chorianopoulos Nikos
Published: July 2024

Globally, fresh vegetables or minimally processed salads have been implicated in several foodborne disease outbreaks. This work studied the effect of Lactiplantibacillus pentosus FMCC-B281 cells (F) and its supernatant (S) on spoilage and on the fate of Listeria monocytogenes and Escherichia coli O157:H7 on fresh-cut ready-to-eat (RTE) salads during storage. Also, Fourier transform infrared (FTIR) and multispectral imaging (MSI) analysis were used as rapid and non-destructive techniques to estimate the microbiological status of the samples. Fresh romaine lettuce, rocket cabbage, and white cabbage were used in the present study and were inoculated with L. pentosus and the two pathogens. The strains were grown at 37 °C for 24 h in MRS and BHI broths, respectively, and then were centrifuged to collect the supernatant and the pellet (cells). Cells (F, ~5 log CFU/g), the supernatant (S), and a control (C, broth) were used to spray the leaves of each fresh vegetable that had been previously contaminated (sprayed) with the pathogen (3 log CFU/g). Subsequently, the salads were packed under modified atmosphere packaging (10%CO2/10%O2/80%N2) and stored at 4 and 10 °C until spoilage. During storage, microbiological counts and pH were monitored in parallel with FTIR and MSI analyses. The results showed that during storage, the population of the pathogens increased for lettuce and rocket independent of the treatment. For cabbage, pathogen populations remained stable throughout storage. Regarding the spoilage microbiota, the Pseudomonas population was lower in the F samples, while no differences in the populations of Enterobacteriaceae and yeasts/molds were observed for the C, F, and S samples stored at 4 °C. According to sensory evaluation, the shelf-life was shorter for the control samples in contrast to the S and F samples, where their shelf-life was elongated by 1–2 days. Initial pH values were ca. 6.0 for the three leafy vegetables. An increase in the pH of ca. 0.5 values was recorded until the end of storage at both temperatures for all cases of leafy vegetables. FTIR and MSI analyses did not satisfactorily lead to the estimation of the microbiological quality of salads. In conclusion, the applied bioprotective strain (L. pentosus) can elongate the shelf-life of the RTE salads without an effect on pathogen growth.
Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey
Authors: Shao Chen , Giacoumidis Elias , Billah Syed Moktacim , Li Shi , Li Jialei , Sahu Prashasti , Richter André , Faerber Michael , Kaefer Tobias
Published: July 2024

Recently, extensive research has been conducted to explore the utilization of machine learning (ML) algorithms in various direct-detected and (self)-coherent short-reach communication applications. These applications encompass a wide range of tasks, including bandwidth request prediction, signal quality monitoring, fault detection, traffic prediction, and digital signal processing (DSP)-based equalization. As a versatile approach, ML demonstrates the ability to address stochastic phenomena in optical systems networks where deterministic methods may fall short. However, when it comes to DSP equalization algorithms such as feed-forward/decision-feedback equalizers (FFEs/DFEs) and Volterra-based nonlinear equalizers, their performance improvements are often marginal, and their complexity is prohibitively high, especially in cost-sensitive short-reach communications scenarios such as passive optical networks (PONs). Time-series ML models offer distinct advantages over frequency-domain models in specific contexts. They excel in capturing temporal dependencies, handling irregular or nonlinear patterns effectively, and accommodating variable time intervals. Within this survey, we outline the application of ML techniques in short-reach communications, specifically emphasizing their utilization in high-bandwidth demanding PONs. We introduce a novel taxonomy for time-series methods employed in ML signal processing, providing a structured classification framework. Our taxonomy categorizes current time-series methods into four distinct groups: traditional methods, Fourier convolution-based methods, transformer-based models, and time-series convolutional networks. Finally, we highlight prospective research directions within this rapidly evolving field and outline specific solutions to mitigate the complexity associated with hardware implementations. We aim to pave the way for more practical and efficient deployment of ML approaches in short-reach optical communication systems by addressing complexity concerns.
A Practicable Optoelectronic Oscillator with Ultra-Low Phase Noise
Authors: Zheng Ziyue , Yu Jinlong , Wang Ju , Ma Chuang , Luo Hao , Su Xuemin , Gao Ye
Published: July 2024

In this paper, an optoelectronic oscillator (OEO) with ultra-low phase noise and high stability based on the injection-locked and phase-locked loop is proposed. In theory, the injection-locked frequency range of the injection signal is studied based on the phase dynamics equation, and the phase noise performance of the injection-locked OEO is analyzed. The role of the phase-locked loop on the frequency stability of the OEO is analyzed based on the phase-locked loop transfer function. In addition, this paper builds an injection-locked OEO based on a phase-locked loop. The injection-locked signal is the high-frequency output of the multiplication crystal oscillator (MCO). At the same time, this MCO synchronously outputs a low-frequency signal, which is used as the reference signal of the phase-locked loop. The experimental results show that the proposed OEO output frequency is 10 GHz, and the phase noise is −89.25 dBc/Hz@100 Hz, −121.71 dBc/Hz@1 kHz, and −145.39 dBc/Hz@10 kHz; the side-mode suppression ratio is 80 dB; the frequency stability is 2.06 × 10−11@1 s, 9.03 × 10−11@10 s, 1.03 × 10−10@100 s, and 3.03 × 10−10@1000 s. Consistent with the theoretical analysis results, the solution takes into account the frequency stability, side-mode suppression ratio, and phase noise performance. The simple structure is more advantageous in practical applications.
Temperature-Dependent Localized Surface Plasmon Resonances of Noble Nanoparticles Covered with Polymers
Authors: Ntemogiannis Dimitrios , Tsarmpopoulou Maria , Moularas Constantinos , Deligiannakis Yiannis , Stamatelatos Alkeos , Maratos Dionysios M. , Ploumis Nikolaos G. , Karoutsos Vagelis , Grammatikopoulos Spyridon , Sigalas Mihail , Poulopoulos Panagiotis
Published: July 2024

Self-assembled gold and silver nanoparticles were fabricated in medium vacuum conditions on Corning glass substrates by means of DC magnetron sputtering. The samples were deposited either at 420 °C or 440 °C, or they were initially deposited at room temperature followed by post annealing. Subsequently, they were covered with three different polymers, namely Polystyrene-block-polybutadiene-blockpolystyrene (PS-b-PBD-b-PS), Polystyrene-co-methyl methacrylate (PS-co-PMMA) and Polystyreneblock-polyisoprene-block-polystyrene (PS-b-PI-b-PS), using spin coating. Localized surface plasmon resonances were recorded in the temperature range of −25 °C–100 °C. We show that the resonance position changes systematically as a function of temperature. Theoretical calculations carried out via the Rigorous Coupled Wave Analysis support the experimental results. Based on these findings, the investigated materials demonstrate potential as components for the development of temperature sensors.
High–Speed Laser Modulation for Low–Noise Micro–Cantilever Array Deflection Measurement
Authors: Xue Weiwei , Su Yong , Zhang Qingchuan
Published: July 2024

In this paper, an innovative approach is introduced to address the noise issues associated with micro–cantilever array deflection measurement systems employing multiple lasers. Conventional systems are affected by laser mode hopping during switching, resulting in wavelength instability and beam spot fluctuations that take several hundred milliseconds to stabilize. To mitigate these limitations, a high–speed laser modulation technique is utilized, leveraging the averaging effect over multiple modulation cycles within the sampling window. By driving the lasers with a high–frequency carrier signal, a low–noise and stable output suitable for micro–cantilever beam deflection measurement is achieved. The effectiveness of this approach is demonstrated by simultaneously modulating the lasers and rapidly observing the spectral and centroid variations during high–speed switching using a custom–built high–speed spectrometer. The centroid fluctuations are also analyzed under different modulation frequencies. The experimental results confirm that the high–speed modulation method can reduce the standard deviation of beam spot fluctuations by more than 90%, leading to significant improvements in noise reduction compared to traditional laser switching methods. The proposed high–speed laser modulation approach offers a promising solution for enhancing the precision and stability of multi–laser micro–cantilever array deflection measurement systems.
Lights off the Image: Highlight Suppression for Single Texture-Rich Images in Optical Inspection Based on Wavelet Transform and Fusion Strategy
Authors: Sun Xiang , Kong Lingbao , Wang Xiaoqing , Peng Xing , Dong Guangxi
Published: July 2024

A wavelet-transform-based highlight suppression method is presented, aiming at suppressing the highlights of single image with complex texture. The strategy involves the rough extraction of specular information, followed by extracting the high-frequency information in specular information based on multi-level wavelet transform to enhance the texture information in the original images by fusion strategy, and fusing with the same-level specular information to achieve the highlight suppression image. The experimental results demonstrate that the proposed method effectively removed large-area highlights while preserving texture details, and demonstrated the authenticity of the highlight estimation and the ‘lights off’ effect in the highlight-suppressed images. Overall, the method offers a feasibility for addressing the challenges of highlight suppression for visual detection image with rich texture and large-area highlights.
Low-Cost 3D Indoor Visible Light Positioning: Algorithms and Experimental Validation
Authors: Khan Sanjha , Paradells Josep , Catalan Marisa
Published: July 2024

Visible light technology presents significant advancement for indoor IoT applications. These systems offer enhanced bit rate transmission, enabling faster and reliable data transfer. Moreover, optical-based visible light systems facilitate improved location services within indoor environments. However, many of these systems still exhibit limited accuracy within several centimeters, even when relying on costly high-resolution cameras. This paper introduces a novel low-cost visible light system for 3D positioning, designed to enhance indoor positioning accuracy using low-resolution images. Initially, we propose a non-integer pixel (NI-P) algorithm to enhance precision without the need for higher-resolution images. This algorithm allows the system to identify the precise light spot coordinates on the low-resolution images, enabling accurate positioning. Subsequently, we present an algorithm leveraging the precise coordinate data from the previous step to determine the 3D position of objects even in front of errors in the measures. Benefiting from high accuracy, reduced cost, and low complexity, the proposed system is suitable for implementation on low-end hardware platforms, thereby increasing the versatility and feasibility of visible light technologies in indoor settings. Experimental results show an average 2D positioning error of 1.08 cm and 3D error within 1.4 cm at 2.3 m separation between the object and camera, achieved with an average positioning time of 20 ms on a low-end embedded device. Consequently, the proposed system offers fast and highly accurate indoor positioning and tracking capabilities, making it suitable for applications like mobile robots, automated guided vehicles, and indoor parking management. Furthermore, it is easy to deploy and does not require re-calibration.
Recent Advances in Environment-Friendly Polyurethanes from Polyols Recovered from the Recycling and Renewable Resources: A Review
Authors: Pu Mengyuan , Fang Changqing , Zhou Xing , Wang Dong , Lin Yangyang , Lei Wanqing , Li Lu
Published: July 2024

Polyurethane (PU) is among the most universal polymers and has been extensively applied in many fields, such as construction, machinery, furniture, clothing, textile, packaging and biomedicine. Traditionally, as the main starting materials for PU, polyols deeply depend on petroleum stock. From the perspective of recycling and environmental friendliness, advanced PU synthesis, using diversified resources as feedstocks, aims to develop versatile products with excellent properties to achieve the transformation from a fossil fuel-driven energy economy to renewable and sustainable ones. This review focuses on the recent development in the synthesis and modification of PU by extracting value-added monomers for polyols from waste polymers and natural bio-based polymers, such as the recycled waste polymers: polyethylene terephthalate (PET), PU and polycarbonate (PC); the biomaterials: vegetable oil, lignin, cashew nut shell liquid and plant straw; and biomacromolecules: polysaccharides and protein. To design these advanced polyurethane formulations, it is essential to understand the structure–property relationships of PU from recycling polyols. In a word, this bottom-up path provides a material recycling approach to PU design for printing and packaging, as well as biomedical, building and wearable electronics applications.
Enhanced Antibacterial Activity of Vancomycin Loaded on Functionalized Polyketones
Authors: Rampazzo Rachele , Vavasori Andrea , Ronchin Lucio , Riello Pietro , Marchiori Martina , Saorin Gloria , Beghetto Valentina
Published: July 2024

Today, polymeric drug delivery systems (DDS) appear as an interesting solution against bacterial resistance, having great advantages such as low toxicity, biocompatibility, and biodegradability. In this work, two polyketones (PK) have been post-functionalized with sodium taurinate (PKT) or potassium sulfanilate (PKSK) and employed as carriers for Vancomycin against bacterial infections. Modified PKs were easily prepared by the Paal–Knorr reaction and loaded with Vancomycin at a variable pH. All polymers were characterized by FT-IR, DSC, TGA, SEM, and elemental analysis. Antimicrobial activity was tested against Gram-positive Staphylococcus aureus ATCC 25923 and correlated to the different pHs used for its loading (between 2.3 and 8.8). In particular, the minimum inhibitory concentrations achieved with PKT and PKSK loaded with Vancomycin were similar, at 0.23 μg/mL and 0.24 μg/mL, respectively, i.e., six times lower than that with Vancomycin alone. The use of post-functionalized aliphatic polyketones has thus been demonstrated to be a promising way to obtain very efficient polymeric DDS.
The Effect of Fibrillation, Semi-Dry Pressing, and Surface Treatment on the Barrier Properties of Water Molecules and Oxygen on Food Packaging Paper
Authors: Duan Yuqing , Wang Shumei , Xu Tingting , Bian Huiyang , Dai Hongqi
Published: July 2024

The characteristics of fiber morphology and paper structure are critical to the barrier properties of food packaging paper. Herein, this study aimed to use pulp fibrillation, paper semi-dry pressing and carboxymethyl starch (CMS) coating to flatten the fibers, which were formed on the paper surface with good barrier properties due to the tight bond between fibers. The results showed that the permeability of paper was reduced by 87.56%, from 81.44 μm/Pa·s to 10.13 μm/Pa·s after the pulp fibrillation treatment (60 °SR). Moreover, semi-dry pressing treatment contributed to decreasing the water vapor transmission coefficient (WVP) by 50.98% to 2.74 × 10−10 g/m·s·Pa, and the oxygen permeation coefficient (OP) decreased by 98.04% to 1.93 × 10−14 cm3·cm/cm2·s·Pa. After coating the paper surface with titanium dioxide (TiO2) and CMS, the WVP of the paper was further reduced to 1.55 × 10−10 g/m·s·Pa, and OP was reduced to 0.19 × 10−14 cm3·cm/cm2·s·Pa. These values were 72.27% and 99.8% lower than those of the original paper, respectively. Therefore, through pulp fibrillation, semi-dry pressing of paper, TiO2 filling, and surface coating with CMS, there is no need to use synthetic polymer surface film-forming agents to achieve the high barrier properties that are required for low water and oxygen molecules permeation in food packaging paper.
Inter-Oligomer Interaction Influence on Photoluminescence in Cis-Polyacetylene Semiconductor Materials
Authors: Keya Kamrun N. , Han Yulun , Xia Wenjie , Kilin Dmitri
Published: July 2024

Semiconducting conjugated polymers (CPs) are pivotal in advancing organic electronics, offering tunable properties for solar cells and field-effect transistors. Here, we carry out first-principle calculations to study individual cis-polyacetylene (cis-PA) oligomers and their ensembles. The ground electronic structures are obtained using density functional theory (DFT), and excited state dynamics are explored by computing nonadiabatic couplings (NACs) between electronic and nuclear degrees of freedom. We compute the nonradiative relaxation of charge carriers and photoluminescence (PL) using the Redfield theory. Our findings show that electrons relax faster than holes. The ensemble of oligomers shows faster relaxation compared to the single oligomer. The calculated PL spectra show features from both interband and intraband transitions. The ensemble shows broader line widths, redshift of transition energies, and lower intensities compared to the single oligomer. This comparative study suggests that the dispersion forces and orbital hybridizations between chains are the leading contributors to the variation in PL. It provides insights into the fundamental behaviors of CPs and the molecular-level understanding for the design of more efficient optoelectronic devices.
Cellulose Acetate Microparticles Synthesized from Agave sisalana Perrine for Controlled Release of Simvastatin
Authors: Alves Larissa Pereira , Oliveira Kevin da Silva , Santos Ana Cláudia Gonçalves dos , Melo Demis Ferreira de , Moreira Lívia Maria Coelho de Carvalho , Oshiro Junior João Augusto , Silva Dayanne Tomaz Casimiro da , Cavalcanti Airlla Laana de Medeiros , Damasceno Bolívar Ponciano Goulart de Lima
Published: July 2024

Simvastatin (SIM) is widely prescribed to treat hyperlipidemia, despite its limitations, such as a short half-life and low oral bioavailability. To overcome these drawbacks, the development of a controlled-release formulation is desirable. This study aims to develop a microparticulate system based on cellulose acetate (ACT) obtained from Agave sisalana Perrine to promote a controlled SIM release. SIM-loaded microparticles (SMP) were prepared using the solvent emulsification-evaporation method. Several parameters were evaluated, including particle size, surface charge, morphology, encapsulation efficiency, thermochemical characteristics, crystallinity, and in vitro release profile. ACT exhibited favorable flow properties after acetylation, with a degree of substitution values superior to 2.5, as confirmed by both the chemical route and H-NMR, indicating the formation of cellulose triacetate. The obtained SMP were spherical with an average size ranging from 1842 to 1857 nm, a zeta potential of −4.45 mV, and a high SIM incorporation efficiency (98%). Thermal and XRD analyses revealed that SIM was homogeneously dispersed into the polymeric matrix in its amorphous state. In vitro studies using dialysis bags revealed that the controlled SIM release from microparticles was higher under simulated intestinal conditions and followed the Higuchi kinetic model. Our results suggest that ACT-based microparticles are a promising system for SIM delivery, which can improve its bioavailability, and result in better patient compliance.
Editorial of the Topic “Environmental and Health Issues and Solutions for Anticoccidials and Other Emerging Pollutants of Special Concern”
Authors: Núñez-Delgado Avelino , Bontempi Elza , Zhou Yaoyu , Álvarez-Rodríguez Esperanza , López-Ramón María Victoria , Coccia Mario , Zhang Zhien , Santás-Miguel Vanesa , Race Marco
Published: July 2024

The editors of this Topic, entitled “Environmental and Health Issues and Solutions for Anticoccidials and other Emerging Pollutants of Special Concern”, proposed it with the knowledge that emerging pollutants continue to be of crucial importance [...]
CVTNet: A Fusion of Convolutional Neural Networks and Vision Transformer for Wetland Mapping Using Sentinel-1 and Sentinel-2 Satellite Data
Authors: Marjani Mohammad , Mahdianpari Masoud , Mohammadimanesh Fariba , Gill Eric W.
Published: July 2024

Wetland mapping is a critical component of environmental monitoring, requiring advanced techniques to accurately represent the complex land cover patterns and subtle class differences innate in these ecosystems. This study aims to address these challenges by proposing CVTNet, a novel deep learning (DL) model that integrates convolutional neural networks (CNNs) and vision transformer (ViT) architectures. CVTNet uses channel attention (CA) and spatial attention (SA) mechanisms to enhance feature extraction from Sentinel-1 (S1) and Sentinel-2 (S2) satellite data. The primary goal of this model is to achieve a balanced trade-off between Precision and Recall, which is essential for accurate wetland mapping. The class-specific analysis demonstrated CVTNet’s proficiency across diverse classes, including pasture, shrubland, urban, bog, fen, and water. Comparative analysis showed that CVTNet outperforms contemporary algorithms such as Random Forest (RF), ViT, multi-layer perceptron mixer (MLP-mixer), and hybrid spectral net (HybridSN) classifiers. Additionally, the attention mechanism (AM) analysis and sensitivity analysis highlighted the crucial role of CA, SA, and ViT in focusing the model’s attention on critical regions, thereby improving the mapping of wetland regions. Despite challenges at class boundaries, particularly between bog and fen, and misclassifications of swamp pixels, CVTNet presents a solution for wetland mapping.
Integrating Sentinel 2 Imagery with High-Resolution Elevation Data for Automated Inundation Monitoring in Vegetated Floodplain Wetlands
Authors: Heath Jessica T. , Grimmett Liam , Gopalakrishnan Tharani , Thomas Rachael F. , Lenehan Joanne
Published: July 2024

Monitoring inundation in flow-dependent floodplain wetlands is important for understanding the outcomes of environmental water deliveries that aim to inundate different floodplain wetland vegetation types. The most effective way to monitor inundation across large landscapes is with remote sensing. Spectral water indices are often used to detect water in the landscape, but there are challenges in using them to map inundation within the complex vegetated floodplain wetlands. The current method used for monitoring inundation in the large floodplain wetlands that are targets for environmental water delivery in the New South Wales portion of the Murray–Darling Basin (MDB) in eastern Australia considers the complex mixing of water with vegetation and soil, but it is a time-consuming process focused on individual wetlands. In this study, we developed the automated inundation monitoring (AIM) method to enable an efficient process to map inundation in floodplain wetlands with a focus on the lower Lachlan floodplain utilising 25 Sentinel-2 image dates spanning from 2019 to 2023. A local adaptive thresholding (ATH) approach of a suite of spectral indices combined with best available DEM and a cropping layer were integrated into the AIM method. The resulting AIM maps were validated against high-resolution drone images, and vertical and oblique aerial images. Although instances of omission and commission errors were identified in dense vegetation and narrow creek lines, the AIM method showcased high mapping accuracy with overall accuracy of 0.8 measured. The AIM method could be adapted to other MDB wetlands that would further support the inundation monitoring across the basin.
A Novel Model for Instance Segmentation and Quantification of Bridge Surface Cracks—The YOLOv8-AFPN-MPD-IoU
Authors: Xiong Chenqin , Zayed Tarek , Jiang Xingyu , Alfalah Ghasan , Abelkader Eslam Mohammed
Published: July 2024

Surface cracks are alluded to as one of the early signs of potential damage to infrastructures. In the same vein, their detection is an imperative task to preserve the structural health and safety of bridges. Human-based visual inspection is acknowledged as the most prevalent means of assessing infrastructures’ performance conditions. Nonetheless, it is unreliable, tedious, hazardous, and labor-intensive. This state of affairs calls for the development of a novel YOLOv8-AFPN-MPD-IoU model for instance segmentation and quantification of bridge surface cracks. Firstly, YOLOv8s-Seg is selected as the backbone network to carry out instance segmentation. In addition, an asymptotic feature pyramid network (AFPN) is incorporated to ameliorate feature fusion and overall performance. Thirdly, the minimum point distance (MPD) is introduced as a loss function as a way to better explore the geometric features of surface cracks. Finally, the middle aisle transformation is amalgamated with Euclidean distance to compute the length and width of segmented cracks. Analytical comparisons reveal that this developed deep learning network surpasses several contemporary models, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and Mask-RCNN. The YOLOv8s + AFPN + MPDIoU model attains a precision rate of 90.7%, a recall of 70.4%, an F1-score of 79.27%, mAP50 of 75.3%, and mAP75 of 74.80%. In contrast to alternative models, our proposed approach exhibits enhancements across performance metrics, with the F1-score, mAP50, and mAP75 increasing by a minimum of 0.46%, 1.3%, and 1.4%, respectively. The margin of error in the measurement model calculations is maintained at or below 5%. Therefore, the developed model can serve as a useful tool for the accurate characterization and quantification of different types of bridge surface cracks.
ELA-Net: An Efficient Lightweight Attention Network for Skin Lesion Segmentation
Authors: Nie Tianyu , Zhao Yishi , Yao Shihong
Published: July 2024

In clinical conditions limited by equipment, attaining lightweight skin lesion segmentation is pivotal as it facilitates the integration of the model into diverse medical devices, thereby enhancing operational efficiency. However, the lightweight design of the model may face accuracy degradation, especially when dealing with complex images such as skin lesion images with irregular regions, blurred boundaries, and oversized boundaries. To address these challenges, we propose an efficient lightweight attention network (ELANet) for the skin lesion segmentation task. In ELANet, two different attention mechanisms of the bilateral residual module (BRM) can achieve complementary information, which enhances the sensitivity to features in spatial and channel dimensions, respectively, and then multiple BRMs are stacked for efficient feature extraction of the input information. In addition, the network acquires global information and improves segmentation accuracy by putting feature maps of different scales through multi-scale attention fusion (MAF) operations. Finally, we evaluate the performance of ELANet on three publicly available datasets, ISIC2016, ISIC2017, and ISIC2018, and the experimental results show that our algorithm can achieve 89.87%, 81.85%, and 82.87% of the mIoU on the three datasets with a parametric of 0.459 M, which is an excellent balance between accuracy and lightness and is superior to many existing segmentation methods.
Using Support Vector Machines to Classify Road Surface Conditions to Promote Safe Driving
Authors: Moon Jaepil , Park Wonil
Published: July 2024

Accurate detection of road surface conditions in adverse winter weather is essential for traffic safety. To promote safe driving and efficient road management, this study presents an accurate and generalizable data-driven learning model for the estimation of road surface conditions. The machine model was a support vector machine (SVM), which has been successfully applied in diverse fields, and kernel functions (linear, Gaussian, second-order polynomial) with a soft margin classification technique were also adopted. Two learner designs (one-vs-one, one-vs-all) extended their application to multi-class classification. In addition to this non-probabilistic classifier, this study calculated the posterior probability of belonging to each group by applying the sigmoid function to the classification scores obtained by the trained SVM. The results indicate that the classification errors of all the classifiers, excluding the one-vs-all linear learners, were below 3%, thereby accurately classifying road surface conditions, and that the generalization performance of all the one-vs-one learners was within an error rate of 4%. The results also showed that the posterior probabilities can analyze certain atmospheric and road surface conditions that correspond to a high probability of hazardous road surface conditions. Therefore, this study demonstrates the potential of data-driven learning models in classifying road surface conditions accurately.
Improvement of Dynamic Characteristics of Purpose-Built Vehicles Using Semi-Active Suspension System
Authors: Kim Minyoung , Lee Chunhwan
Published: July 2024

The diversification of mobility into services such as smart stores and conference rooms has accelerated the development of purpose-built vehicles (PBVs)—vehicles designed for specific purposes that utilize an extended electric vehicle chassis and autonomous driving technology. Despite the standards on speed bump dimensions stipulated by the National Land Transportation Act of the Republic of Korea, real-world speed bumps feature varying widths and heights that deviate from these standards. In this study, a velocity equation was derived via regression analysis to achieve the desired dynamic characteristics for a PBV passing over speed bumps with varying shapes through two types of semi-active suspension control: proportional–integral–differential (PID) and linear–quadratic–regulator (LQR). For a cargo-transport PBV, the PID and LQR controllers increased the velocity by 23.74% and 50.74%, respectively, under different speed bump widths and by 19.44% and 38.31%, respectively, under different speed bump heights. Moreover, an analysis of the vibration dose value (VDV), an indicator of ride comfort, revealed that the VDVs calculated using the velocity equation were within an acceptable error range of 10% above the target VDV. These findings provide insights into the speed control required for different types of autonomous PBVs to ensure ride comfort, as well as minimize the driving duration, depending on the specific purpose of the vehicle.