bluuebunny/crossref_metadata_2025 huggingface dataset:
A Bilayer Microarray Patch (MAP) for HIV Pre-Exposure Prophylaxis: The Role of MAP Designs and Formulation Composition in Enhancing Long-Acting Drug Delivery
Authors: Vora Lalitkumar K. , Tekko Ismaiel A. , Zanutto Fabiana Volpe , Sabri Akmal , Choy Robert K. M. , Mistilis Jessica , Kwarteng Priscilla , Jarrahian Courtney , McCarthy Helen O. , Donnelly Ryan F.
Published: January 2024

Microarray patches (MAPs) have shown great potential for efficient and patient-friendly drug delivery through the skin; however, improving their delivery efficiency for long-acting drug release remains a significant challenge. This research provides an overview of novel strategies aimed at enhancing the efficiency of MAP delivery of micronized cabotegravir sodium (CAB Na) for HIV pre-exposure prophylaxis (PrEP). The refinement of microneedle design parameters, including needle length, shape, density, and arrangement, and the formulation properties, such as solubility, viscosity, polymer molecular weight, and stability, are crucial for improving penetration and release profiles. Additionally, a bilayer MAP optimization step was conducted by diluting the CAB Na polymeric mixture to localize the drug into the tips of the needles to enable rapid drug deposition into the skin following MAP application. Six MAP designs were analyzed and investigated with regard to delivery efficiency into the skin in ex vivo and in vivo studies. The improved MAP design and formulations were found to be robust and had more than 30% in vivo delivery efficiency, with plasma levels several-fold above the therapeutic concentration over a month. Repeated weekly dosing demonstrated the robustness of MAPs in delivering a consistent and sustained dose of CAB. In summary, CAB Na MAPs were able to deliver therapeutically relevant levels of drug.
RETRACTED: Khan et al. Poly (N-vinylcaprolactam-grafted-sodium alginate) Based Injectable pH/Thermo Responsive In Situ Forming Depot Hydrogels for Prolonged Controlled Anticancer Drug Delivery; In Vitro, In Vivo Characterization and Toxicity Evaluation. Pharmaceutics 2022, 14, 1050
Authors: Khan Samiullah , Minhas Muhammad Usman , Aqeel Muhammad Tahir , Shah Ihsan , Khan Shahzeb , Kazi Mohsin , Warnken Zachary N.
Published: January 2024

The journal retracts the article, “Poly (N-vinylcaprolactam-grafted-sodium alginate) Based Injectable pH/Thermo Responsive In Situ Forming Depot Hy-drogels for Prolonged Controlled Anticancer Drug Delivery; In Vitro, In Vivo Characterization and Toxicity Evaluation” [...]
Gastroprotective Chitosan Nanoparticles Loaded with Oleuropein: An In Vivo Proof of Concept
Authors: Abd-Allah Hend , Youshia John , Abdel Jaleel Gehad A. , Hassan Azza , El Madani Mevidette , Nasr Maha
Published: January 2024

Oleuropein is the main constituent of olive leaf extract, and it has shown antioxidant and gastroprotective properties against gastric ulcers. Chitosan nanoparticles are known for their mucoadhesive abilities, and consequently, they can increase the retention time of drugs in the gastrointestinal tract. Therefore, loading oleuropein onto chitosan nanoparticles is expected to enhance its biological efficiency. Oleuropein-loaded chitosan nanoparticles were prepared and characterized for particle size, surface charge, in vitro release, and anti-inflammatory activity. Their in vivo efficacy was assessed by measuring specific inflammatory and protective biomarkers, along with histopathological examination. The optimum oleuropein chitosan nanoparticles were cationic, had a size of 174.3 ± 2.4 nm and an entrapment efficiency of 92.81%, and released 70% of oleuropein within 8 h. They recorded a lower IC50 in comparison to oleuropein solutions for membrane stabilization of RBCs (22.6 vs. 25.6 µg/mL) and lipoxygenase inhibition (7.17 vs. 15.6 µg/mL). In an ethanol-induced gastric ulcer in vivo model, they decreased IL-1β, TNF-α, and TBARS levels by 2.1, 1.7, and 1.3 fold, respectively, in comparison to increments caused by exposure to ethanol. Moreover, they increased prostaglandin E2 and catalase enzyme levels by 2.4 and 3.8 fold, respectively. Immunohistochemical examination showed that oleuropein chitosan nanoparticles markedly lowered the expression of IL-6 and caspase-3 in gastric tissues in comparison to oleuropein solution. Overall, oleuropein chitosan nanoparticles showed superior gastroprotective effects to oleuropein solution since comparable effects were demonstrated at a 12-fold lower drug dose, delineating that chitosan nanoparticles indeed enhanced the potency of oleuropein as a gastroprotective agent.
Chitosan-Functionalized Poly(β-Amino Ester) Hybrid System for Gene Delivery in Vaginal Mucosal Epithelial Cells
Authors: Gao Xueqin , Dong Dirong , Zhang Chong , Deng Yuxing , Ding Jiahui , Niu Shiqi , Tan Songwei , Sun Lili
Published: January 2024

Gene therapy displays great promise in the treatment of cervical cancer. The occurrence of cervical cancer is highly related to persistent human papilloma virus (HPV) infection. The HPV oncogene can be cleaved via gene editing technology to eliminate carcinogenic elements. However, the successful application of the gene therapy method depends on effective gene delivery into the vagina. To improve mucosal penetration and adhesion ability, quaternized chitosan was introduced into the poly(β-amino ester) (PBAE) gene-delivery system in the form of quaternized chitosan-g-PBAE (QCP). At a mass ratio of PBAE:QCP of 2:1, the polymers exhibited the highest green fluorescent protein (GFP) transfection efficiency in HEK293T and ME180 cells, which was 1.1 and 5.4 times higher than that of PEI 25 kD. At this mass ratio, PBAE–QCP effectively compressed the GFP into spherical polyplex nanoparticles (PQ–GFP NPs) with a diameter of 255.5 nm. In vivo results indicated that owing to the mucopenetration and adhesion capability of quaternized CS, the GFP transfection efficiency of the PBAE–QCP hybrid system was considerably higher than those of PBAE and PEI 25 kD in the vaginal epithelial cells of Sprague–Dawley rats. Furthermore, the new system demonstrated low toxicity and good safety, laying an effective foundation for its further application in gene therapy.
Combatting Sugar Beet Root Rot: Streptomyces Strains’ Efficacy against Fusarium oxysporum
Authors: Abdelghany Walaa R. , Yassin Abeer S. , Abu-Ellail Farrag F. B. , Al-Khalaf Areej A. , Omara Reda I. , Hozzein Wael N.
Published: January 2024

Sugar beet root rot disease triggered by Fusarium oxysporum f. sp. radicis-betae is a destructive disease and dramatically affects the production and quality of the sugar beet industry. Employing beneficial microorganisms as a biocontrol strategy represents an eco-friendly and sustainable approach to combat various plant diseases. The distinct aspect of this study was to assess the antifungal and plant growth-promoting capabilities of recently isolated Streptomyces to treat sugar beet plants against infection with the phytopathogen F. oxysporum. Thirty-seven actinobacterial isolates were recovered from the rhizosphere of healthy sugar beet plants and screened for their potential to antagonize F. oxysporum in vitro. Two isolates SB3-15 and SB2-23 that displayed higher antagonistic effects were morphologically and molecularly identified as Streptomyces spp. Seed treatment with the fermentation broth of the selected Streptomyces strains SB3-15 and SB2-23 significantly reduced disease severity compared to the infected control in a greenhouse experiment. Streptomyces SB2-23 exhibited the highest protective activity with high efficacy ranging from 91.06 to 94.77% compared to chemical fungicide (86.44 to 92.36%). Furthermore, strain SB2-23 significantly increased plant weight, root weight, root length, and diameter. Likewise, it improves sucrose percentage and juice purity. As a consequence, the strain SB2-23’s intriguing biocontrol capability and sugar beet root growth stimulation present promising prospects for its utilization in both plant protection and enhancement strategies.
Patterns of Leaf and Fruit Morphological Variation in Marginal Populations of Acer tataricum L. subsp. tataricum
Authors: Poljak Igor , Vidaković Antonio , Benić Luka , Tumpa Katarina , Idžojtić Marilena , Šatović Zlatko
Published: January 2024

Marginal populations are usually smaller and more isolated and grow in less favourable conditions than those at the distribution centre. The variability of these populations is of high importance, as it can support the adaptations needed for the conditions that they grow in. In this research, the morphological variability of eight Tatar maple (Acer tataricum L. subsp. tataricum) populations was analysed. Tatar maple is an insect-pollinated and wind-dispersed shrub/tree, whose northwestern distribution edge is in southeastern Europe. Morphometric methods were used to analyse the variability of the populations using leaf and fruit morphology. The research revealed significant differences between and within populations. Furthermore, differences in the distribution of the total variability were noted, which suggest that different evolutionarily factors affect different plant traits. Correlation analysis confirmed a weak dependency between the vegetative and generative traits. In addition, no evidence was found for the presence of isolation by environment (IBE). However, the Mantel test for isolation by distance (IBD) was significant for the leaf morphometric traits and non-significant for the fruit morphometric traits. Being the marginal leading-edge populations, they are younger and were less likely to have had time for adaptation to local environments, which would have resulted in the development of IBE. Overall, edge populations of Tatar maple were characterised by great morphological variability, which helps these populations in their response to the intensive selective pressures they face in their environment.
Antifungal Activity of Cedrol from Cunninghamia lanceolate var. konishii against Phellinus noxius and Its Mechanism
Authors: Hsiao Wen-Wei , Lau Ka-Man , Chien Shih-Chang , Chu Fang-Hua , Chung Wen-Hsin , Wang Sheng-Yang
Published: January 2024

Phellinus noxius is a highly destructive fungus that causes brown root disease in trees, leading to decay and death. In Taiwan, five prized woods—Taiwania cryptomerioides, Calocedrus macrolepis var. formosana, Cunninghamia lanceolata var. konishii, Chamaecyparis formosensis, and Chamaecyparis obtusa var. formosana—are known for their fragrance and durability. This study aims to explore the anti-brown-root-rot-fungus activity of Cunninghamia lanceolata var. konishii (CL) essential oil (CLOL) and its primary components, while also delving into their mechanisms of action and inhibition pathways. The essential oil (CLOL) from CL wood demonstrated significant efficacy against P. noxius, with an inhibitory concentration (IC50) of 37.5 µg/mL. Cedrol, the major component (78.48%) in CLOL, emerged as a potent antifungal agent, surpassing the reference drug triflumizole. Further assays with cedrol revealed a stronger anti-brown-root-disease activity (IC50 = 15.7 µg/mL) than triflumizole (IC50 = 32.1 µg/mL). Scanning electron microscopy showed deformation and rupture of fungal hyphae treated with CLOL and cedrol, indicating damage to the fungal cell membrane. Cedrol-induced oxidative stress in P. noxius was evidenced by increased reactive oxygen species (ROS) levels, leading to DNA fragmentation, mitochondrial membrane potential reduction, and fungal apoptosis through the mitochondrial pathway. Gel electrophoresis confirmed cedrol-induced DNA fragmentation, whereas TUNEL staining demonstrated increased apoptosis with rising cedrol concentrations. Moreover, protein expression analysis revealed cedrol-triggered release of cytochrome c, activation of caspase-9, and subsequent caspase-3 activation, initiating a caspase cascade reaction. This groundbreaking study establishes cedrol as the first compound to induce apoptosis in P. noxius while inhibiting its growth through oxidative stress, an increase in mitochondrial membrane permeability, and activation of the mitochondrial pathway. The findings offer compelling evidence for cedrol’s potential as an effective antifungal agent against the destructive brown root disease caused by P. noxius.
Evaluation of Starch–Garlic Husk Polymeric Composites through Mechanical, Thermal, and Thermo-Mechanical Tests
Authors: Flores-Hernández Cynthia Graciela , López-Barroso Juventino , Salazar-Cruz Beatriz Adriana , Saucedo-Rivalcoba Verónica , Almendarez-Camarillo Armando , Rivera-Armenta José Luis
Published: January 2024

The present work evaluates the influence of different properties of composite materials from natural sources. Films were prepared using the evaporative casting technique from corn starch reinforced with a waste material such as garlic husk (GH), using glycerin as a plasticizer. The results of the syntheses carried out demonstrated the synergy between these materials. In the morphological analysis, the compatibility and adequate dispersion of the reinforcer in the matrix were confirmed. Using Fourier transform infrared spectroscopy (FTIR), the interaction and formation of bonds between the matrix and the reinforcer were confirmed by the presence of some signals such as S-S and C-S. Similarly, thermogravimetric analysis (TGA) revealed that even at low concentrations, GH can slightly increase the decomposition temperature. Finally, from the results of dynamic mechanical analysis (DMA), it was possible to identify that the storage modulus increases significantly, up to 115%, compared to pure starch, especially at low concentrations of the reinforcer.
Differences in the Residual Behavior of a Bumetrizole-Type Ultraviolet Light Absorber during the Degradation of Various Polymers
Authors: Nakatani Hisayuki , Uchiyama Taishi , Motokucho Suguru , Dao Anh Thi Ngoc , Kim Hee-Jin , Yagi Mitsuharu , Kyozuka Yusaku
Published: January 2024

The alteration of an ultraviolet light absorber (UVA: UV-326) in polymers (PP, HDPE, LDPE, PLA, and PS) over time during degradation was studied using an enhanced degradation method (EDM) involving sulfate ion radicals in seawater. The EDM was employed to homogeneously degrade the entire polymer samples containing the UVA. The PP and PS samples containing 5-phr (phr: per hundred resin) UVA films underwent rapid whitening, characterized by the formation of numerous grooves or crushed particles. Notably, the UVA loss rate in PS, with the higher glass transition temperature (Tg), was considerably slower. The behavior of crystalline polymers, with the exception of PS, was analogous in terms of the change in UVA loss rate over the course of degradation. The significant increase in the initial loss rate observed during EDM degradation was due to microplasticization. A similar increase in microplasticization rate occurred with PS; however, the intermolecular interaction between UVA and PS did not result in as pronounced an increase in loss rate as observed in other polymers. Importantly, the chemical structure of UVA remained unaltered during EDM degradation. These findings revealed that the primary cause of UVA loss was leaching from the polymer matrix.
Synthesis and Characterization of DOPO-Containing Poly(2,6-dimethyl-1,4-phenylene oxide)s by Oxidative Coupling Polymerization
Authors: Lu Cheng-Hao , Chang Chi , Huang Yu-Chen , You Jun-Xiang , Liang Mong
Published: January 2024

A set of polyphenylene oxides incorporating DOPO (9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide) functionality, denoted as DOPO−R−PPO, was synthesized by copolymerization of 2,6-dimethylphenol (2,6-DMP) with various DOPO-substituted tetramethyl bisphenol monomers. In the initial step, a Friedel–Crafts acylation reaction was employed to react 2,6-DMP with different acyl chlorides, leading to the formation of ketone derivatives substituted with 2,6-dimethylphenyl groups. Subsequently, the ketones, along with DOPO and 2,6-DMP, underwent a condensation reaction to yield a series of DOPO-substituted bisphenol derivatives. Finally, polymerizations of 2,6-dimethylphenol with these DOPO-substituted bisphenols were carried out in organic solvents using copper(I) bromide/N-butyldimethylamine catalysts (CuBr/DMBA) under a continuous flow of oxygen, yielding telechelic PPO oligomers with DOPO moieties incorporated into the polymer backbone. The chemical structures of the synthesized compounds were characterized using various analytical techniques, including Fourier transform infrared spectroscopy (FTIR), proton nuclear magnetic resonance (1H NMR), phosphorus nuclear magnetic resonance (31P NMR), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). When compared to conventional poly(2,6-dimethyl-1,4-phenylene oxide)s with a similar molecular weight range, all DOPO−PPOs exhibited higher glass transition temperatures, enhanced thermal degradability, and increased char yield formation at 800 °C without compromising solubility in organic solvents.
In Silico and In Vitro Analyses of Multiple Terpenes Predict Cryptotanshinone as a Potent Inhibitor of the Omicron Variant of SARS-CoV-2
Authors: Shrestha Asmita , Upadhyaya Siddha Raj , Raut Bimal K. , Bhattarai Salyan , Sharma Khaga Raj , Parajuli Niranjan , Sohng Jae Kyung , Regmi Bishnu P.
Published: January 2024

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant (B.1.1.529) underwent a substantial number of alterations, and the accompanying structural mutations in the spike protein prompted questions about the virus’s propensity to evade the antibody neutralization produced by prior infection or vaccination. New mutations in SARS-CoV-2 have raised serious concerns regarding the effectiveness of drugs and vaccines against the virus; thus, identifying and developing potent antiviral medications is crucial to combat viral infections. In the present study, we conducted a detailed in silico investigation that involves molecular docking, density functional (DFT) analysis, molecular dynamics (MD) simulations, and pharmacological analysis followed by an in vitro study with the spike protein. Among fifty terpenes screened, cryptotanshinone and saikosaponin B2 were found to be potent S1-RBD spike protein inhibitors, displaying considerable hydrogen bond interactions with key binding site residues, significant binding affinity, and high reactivity attributed to band gap energy. In addition, 100 ns molecular dynamics (MD) simulations further substantiated these findings, showcasing the stability of the compounds within a biological environment. With favorable pharmacokinetic properties and a low half inhibitory concentration (IC50) of 86.06 ± 1.56 μM, cryptotanshinone inhibited S1-RBD of the SARS-CoV-2 Omicron variant. Our findings account for in-depth research on cryptotanshinone as a SARS-CoV-2 inhibitor.
Leveraging Dion–Jacobson Interface Hierarchies for Defect Alleviation in High-Efficiency and Durable Perovskite Solar Cells
Authors: Bian Jianxiao , Zhang Yuncong , Liu Yang , Pan Xiaonan
Published: January 2024

The noteworthy stability of Dion–Jacobson (DJ) phase two-dimensional perovskites marks them as potential contenders for use in optoelectronic applications. Nonetheless, their proliferation is considerably stymied by the constrained charge transport properties inherent to them. This bottleneck is adeptly navigated by deploying 2D-DJ perovskite top layers, seamlessly integrated on 3D perovskite films. We unveil a novel organic cation salt, 4-(Aminomethyl)piperidine (4AMP), as a potent facilitator for treating perovskite photovoltaic films. By employing the annealing technique, we facilitated the in situ creation of a hybrid 2D/3D architecture. Contrasted with conventional 3D architectures, the delineated perovskite heterojunctions with a 2D/3D structure exhibit superior enhanced charge separation, and mitigate photovoltaic losses by proficiently passivating intrinsic defects. The size-graded perovskite 2D/3D structure engineered herein significantly elevates the charge transfer performance, concurrently attenuating the excess lead iodide induced by bulk defects. This precise method resulted in a significant increase in Power Conversion Efficiency, reaching 23.08%, along with an open-circuit voltage (Voc) of 1.17 V. Remarkably, the unpackaged modified device robustly retains 92% of its initial PCE post a 3000 h sojourn under ambient conditions. This discourse propounds a novel paradigm for constructing stable planar PSC 2D/3D heterojunctions, thereby enriching the blueprint for advanced perovskite-based photovoltaic systems.
Contested Professionalism and Spiritual Legitimization: Catholic Religious Education Teachers and the Theme of Spirituality in Contemporary Italian Schools
Authors: Silhol Guillaume
Published: January 2024

Based on observations and in-depth interviews with Catholic Religious Education teachers in Italy, this sociological study tackles “spirituality” as a register of legitimization in their professional settings. Compared with more established topics of “religious culture”, the motives of “spirituality” appear as a lesser category of justification in teachers’ discourse in two significant aspects: teaching about “spirituality” as a necessary component of human experience, and talking about their own relationship to “spirituality” as proof of sincere commitment and/or professionalism. Thus, in the context of teachers’ labor, “spirituality” constitutes an ambivalent category that can serve the purposes of Catholic institutions as well as forms of criticism of authority.
Nurturing Faith and Enlightening Minds: Assumptionist Education in the Ottoman Empire
Authors: Hazir Ediz
Published: January 2024

The text explores the educational activities of French Roman Catholic missions in the nineteenth century, as they evolved from serving local Catholic needs to becoming crucial assets in advancing France’s religious–cultural influences and the Holy See’s efforts to unify Eastern Christian Churches under Rome. Focused on the Mission d’Orient, initiated during Pius IX’s papacy, this study delves into the Assumptionists’ educational activities in the Ottoman Empire (1863–1914), which aimed to inculturate the Christian communities of the Ottoman Empire, achieve union with Rome, and build a bridge of knowledge between the Ottoman Orient and Europe. Employing a transnational historical approach, this research utilizes primary sources from the Holy See and the Assumptionist Order, examining religious and educational interactions with Ottoman millets. This article argues that Assumptionist institutions succeeded in inculturation and acted as bridges for cultural exchange. The context includes the French protectorate of the Ottoman Empire, the protégé system, and the Capitulations of 1740, demonstrating the Holy See’s use of political and religious alliances. The Assumptionists, influential in advancing the Holy See’s interests, are studied regarding their engagement in France and the Orient. Despite valuable insights from existing research, this article seeks to fill gaps by using Assumptionists as a case study, exploring the specific impacts of their education on various religious groups within the context of France’s religious–cultural imperialism.
Comparison of Mixed and Door-to-Door Systems for Urban Waste Collection in Terms of Effectiveness and Greenhouse Gas Emissions: A Case Study from Two Mountainous Italian Valleys
Authors: Baltrocchi Alberto Pietro Damiano , Maggi Lucrezia , Carnevale Miino Marco , Torretta Vincenzo , Rada Elena Cristina
Published: January 2024

Collecting urban solid waste (USW) is a critical and essential phase for proper waste management and valorization. To date, many data are available for large cities, but few studies have focused on low-density areas, such as mountainous regions. Considering this lack in the literature, this study aimed to compare two separate waste collection systems in two mountainous valleys in terms of effectiveness and GHG emissions. In the first scenario, a mixed collection system (door-to-door + centers) was used, while in the other, waste was separately collected by a full door-to-door system. The results suggested that the full door-to-door system achieved better performance than the mixed collection system (door-to-door + centers), with a fraction of separate waste collection compared to the unit equals (0.84 ± 0.01 vs. 0.79 ± 0.02). Moreover, the full door-to-door system represented the best option for collecting separate waste in mountainous areas in terms of GHG emissions, with 11.21 kgCO2, eq twaste−1 emitted vs. 15.62 kgCO2, eq twaste−1 in the case of the mixed system. Despite utilities emitting a higher amount of GHGs in the door-to-door scenario (4.66 kgCO2, eq inh−1 y−1), they were fully compensated for by the low GHG emissions from citizens in the mixed scenario (1.77 kgCO2, eq inh−1 y−1 vs. 6.65 kgCO2, eq inh−1 y−1). Given the low amount of data on this topic, this work could be considered as a pioneer study of waste management in mountainous areas by comparing the results of two systems regarding effectiveness and GHG emissions. The outcomes of this study could be helpful for waste utilities, institutional agencies, and local communities and also serve as a tool for decision-making in the case of comparing the different options for USW collection systems.
Analyzing the Impact of Carbon Risk on Firms’ Creditworthiness in the Context of Rising Interest Rates
Authors: Batoon Aimee Jean , Rroji Edit
Published: January 2024

Carbon risk, a type of climate risk, is expected to have a crucial impact, especially on high-carbon-emitting, “polluting” firms as opposed to less carbon-intensive, “clean” ones. With a rising number of actions and policies being continuously proposed to mitigate these concerns and an increasing number of investors demanding more climate adaptation initiatives, this transition risk will certainly need to be incorporated into a firm’s credit risk assessment. In this paper, we explore the impact of the carbon risk factor, constructed as the daily median difference in default protection between polluting and clean European firms, on firm creditworthiness using quantile regressions on the tail distribution of credit default swap spreads for different maturities between 2020 and 2023. In particular, the recent European interest rate hikes lead to unexpected conclusions about when the carbon risk factor affects firm creditworthiness and how rapidly the net-zero economy transition must occur. Contrary to the previous literature, we find that investors are expecting the transition to occur in the medium-to-long term.
Soil Classification Mapping Using a Combination of Semi-Supervised Classification and Stacking Learning (SSC-SL)
Authors: Zhu Fubin , Zhu Changda , Lu Wenhao , Fang Zihan , Li Zhaofu , Pan Jianjun
Published: January 2024

In digital soil mapping, machine learning models have been widely applied. However, the accuracy of machine learning models can be limited by the use of a single model and a small number of soil samples. This study introduces a novel method, semi-supervised classification combined with stacking learning (SSC-SL), to enhance soil classification mapping in hilly and low-mountain areas of Northern Jurong City, Jiangsu Province, China. This study incorporated Gaofen-2 (GF-2) remote sensing imagery along with its associated remote sensing indices, the ALOS Digital Elevation Model (DEM) and their derived topographic factors, and soil parent material data in its modelling process. We first used three base learners, Ranger, Rpart, and XGBoost, to construct the SL model. In addition, we employed the fuzzy c-means clustering algorithm (FCM) to construct a clustering map. To fully leverage the information from a multitude of environmental variables, understand the distribution of data, and enhance the effectiveness of the classification, we selected unlabelled samples near the boundaries of the patches on the clustering map. The SSC-SL model demonstrated superior stability and performance, with optimal accuracy at a 0.9 confidence level, achieving an overall accuracy of 0.77 and a kappa coefficient of 0.73. These metrics exceeded those of the highest performing base learner (Ranger model) by 10.4% and 12.3%, respectively, and they outperformed the least effective base learner (Rpart model) by 27.3% and 32.9%. It notably improves the spatial distribution accuracy of soil types. Key environmental variables influencing soil type distribution include soil parent material (SPM), land use (LU), the multi-resolution valley bottom flatness index (MRVBF), and Elevation (Ele). In conclusion, the SSC-SL model offers a novel and effective approach for enhancing the predictive accuracy of soil classification mapping.
Identification of Complex Slope Subsurface Strata Using Ground-Penetrating Radar
Authors: Wang Tiancheng , Zhang Wensheng , Li Jinhui , Liu Da , Zhang Limin
Published: January 2024

Identification of slope subsurface strata for natural soil slopes is essential to assess the stability of potential landslides. The highly variable strata in a slope are hard to characterize by traditional boreholes at limited locations. Ground-penetrating radar (GPR) is a non-destructive method that is capable of capturing continuous subsurface information. However, the accuracy of subsurface identification using GPRs is still an open issue. This work systematically investigates the capability of the GPR technique to identify different strata via both laboratory experiments and on-site examination. Six large-scale models were constructed with various stratigraphic interfaces (i.e., sand–rock, clay–rock, clay–sand, interbedded clay, water table, and V–shaped sand–rock). The continuous interfaces of the strata in these models were obtained using a GPR, and the depths at different points of the interfaces were interpreted. The interpreted depths along the interface were compared with the measured values to quantify the interpretation accuracy. Results show that the depths of interfaces should be interpreted with the relative permittivity, back-calculated using on-site borehole information instead of empirical values. The relative errors of the depth of horizontal interfaces of different strata range within ±5%. The relative and absolute errors of the V–shaped sand–rock interface depths are in the ranges of [−9.9%, 10.5%] and [−107, 119] mm, respectively. Finally, the GPR technique was used in the field to identify the strata of a slope from Tanglang Mountain in China. The continuous profile of the subsurface strata was successfully identified with a relative error within ±5%.
SA-Pmnet: Utilizing Close-Range Photogrammetry Combined with Image Enhancement and Self-Attention Mechanisms for 3D Reconstruction of Forests
Authors: Yan Xuanhao , Chai Guoqi , Han Xinyi , Lei Lingting , Wang Geng , Jia Xiang , Zhang Xiaoli
Published: January 2024

Efficient and precise forest surveys are crucial for in-depth understanding of the present state of forest resources and conducting scientific forest management. Close-range photogrammetry (CRP) technology enables the convenient and fast collection of highly overlapping sequential images, facilitating the reconstruction of 3D models of forest scenes, which significantly improves the efficiency of forest surveys and holds great potential for forestry visualization management. However, in practical forestry applications, CRP technology still presents challenges, such as low image quality and low reconstruction rates when dealing with complex undergrowth vegetation or forest terrain scenes. In this study, we utilized an iPad Pro device equipped with high-resolution cameras to collect sequential images of four plots in Gaofeng Forest Farm in Guangxi and Genhe Nature Reserve in Inner Mongolia, China. First, we compared the image enhancement effects of two algorithms: histogram equalization (HE) and median–Gaussian filtering (MG). Then, we proposed a deep learning network model called SA-Pmnet based on self-attention mechanisms for 3D reconstruction of forest scenes. The performance of the SA-Pmnet model was compared with that of the traditional SfM+MVS algorithm and the Patchmatchnet network model. The results show that histogram equalization significantly increases the number of matched feature points in the images and improves the uneven distribution of lighting. The deep learning networks demonstrate better performance in complex environmental forest scenes. The SA-Pmnet network, which employs self-attention mechanisms, improves the 3D reconstruction rate in the four plots to 94%, 92%, 94%, and 96% by capturing more details and achieves higher extraction accuracy of diameter at breast height (DBH) with values of 91.8%, 94.1%, 94.7%, and 91.2% respectively. These findings demonstrate the potential of combining of the image enhancement algorithm with deep learning models based on self-attention mechanisms for 3D reconstruction of forests, providing effective support for forest resource surveys and visualization management.
A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing
Authors: Wang Rongfang , Zhang Chenchen , Chen Chao , Hao Hongxia , Li Weibin , Jiao Licheng
Published: January 2024

Water area segmentation in remote sensing is of great importance for flood monitoring. To overcome some challenges in this task, we construct the Water Index and Polarization Information (WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net (MFGF-UNet) convolutional neural network. The WIPI dataset can enhance the water information while reducing the data dimensionality: specifically, the Cloud-Free Label provided in the dataset can effectively alleviate the problem of labeled sample scarcity. Since a single form or uniform kernel size cannot handle the variety of sizes and shapes of water bodies, we propose the Gated Multi-Filter Inception (GMF-Inception) module in our MFGF-UNet. Moreover, we utilize an attention mechanism by introducing a Gated Channel Transform (GCT) skip connection and integrating GCT into GMF-Inception to further improve model performance. Extensive experiments on three benchmarks, including the WIPI, Chengdu and GF2020 datasets, demonstrate that our method achieves favorable performance with lower complexity and better robustness against six competing approaches. For example, on the WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 scores of 0.9191, 0.7410 and 0.8421, respectively, with the average F1 score on the three datasets 0.0045 higher than that of the U-Net model; likewise, GFLOPS were reduced by 62% on average. The new WIPI dataset, the code and the trained models have been released on GitHub.
Enhancing Satellite Image Sequences through Multi-Scale Optical Flow-Intermediate Feature Joint Network
Authors: Shi Keli , Liu Zhi-Qiang , Zhang Weixiong , Tang Ping , Zhang Zheng
Published: January 2024

Satellite time-series data contain information in three dimensions—spatial, spectral, and temporal—and are widely used for monitoring, simulating, and evaluating Earth activities. However, some time-phase images in the satellite time series data are missing due to satellite sensor malfunction or adverse atmospheric conditions, which prevents the effective use of the data. Therefore, we need to complement the satellite time series data with sequence image interpolation. Linear interpolation methods and deep learning methods that have been applied to sequence image interpolation lead to large errors between the interpolation results and the real images due to the lack of accurate estimation of pixel positions and the capture of changes in objects. Inspired by video frame interpolation, we combine optical flow estimation and deep learning and propose a method named Multi-Scale Optical Flow-Intermediate Feature Joint Network. This method learns pixel occlusion and detailed compensation information for each channel and jointly refines optical flow and intermediate features at different scales through an end-to-end network together. In addition, we set a spectral loss function to optimize the network’s learning of the spectral features of satellite images. We have created a time-series dataset using Landsat-8 satellite data and Sentinel-2 satellite data and then conducted experiments on this dataset. Through visual and quantitative evaluation of the experimental results, we discovered that the interpolation results of our method retain better spectral and spatial consistency with the real images, and that the results of our method on the test dataset have a 7.54% lower Root Mean Square Error than other approaches.
Characterization of Walking in Mild Parkinson’s Disease: Reliability, Validity and Discriminant Ability of the Six-Minute Walk Test Instrumented with a Single Inertial Sensor
Authors: Bailo Gaia , Saibene Francesca Lea , Bandini Virginia , Arcuri Pietro , Salvatore Anna , Meloni Mario , Castagna Anna , Navarro Jorge , Lencioni Tiziana , Ferrarin Maurizio , Carpinella Ilaria
Published: January 2024

Although the 6-Minute Walk Test (6MWT) is among the recommended clinical tools to assess gait impairments in individuals with Parkinson’s disease (PD), its standard clinical outcome consists only of the distance walked in 6 min. Integrating a single Inertial Measurement Unit (IMU) could provide additional quantitative and objective information about gait quality complementing standard clinical outcome. This study aims to evaluate the test–retest reliability, validity and discriminant ability of gait parameters obtained by a single IMU during the 6MWT in subjects with mild PD. Twenty-two people with mild PD and ten healthy persons performed the 6MWT wearing an IMU placed on the lower trunk. Features belonging to rhythm and pace, variability, regularity, jerkiness, intensity, dynamic instability and symmetry domains were computed. Test–retest reliability was evaluated through the Intraclass Correlation Coefficient (ICC), while concurrent validity was determined by Spearman’s coefficient. Mann–Whitney U test and the Area Under the receiver operating characteristic Curve (AUC) were then applied to assess the discriminant ability of reliable and valid parameters. Results showed an overall high reliability (ICC ≥ 0.75) and multiple significant correlations with clinical scales in all domains. Several features exhibited significant alterations compared to healthy controls. Our findings suggested that the 6MWT instrumented with a single IMU can provide reliable and valid information about gait features in individuals with PD. This offers objective details about gait quality and the possibility of being integrated into clinical evaluations to better define walking rehabilitation strategies in a quick and easy way.
A GRU-Based Model for Detecting Common Accidents of Construction Workers
Authors: Dzeng Ren-Jye , Watanabe Keisuke , Hsueh Hsien-Hui , Fu Chien-Kai
Published: January 2024

Fall accidents in the construction industry have been studied over several decades and identified as a common hazard and the leading cause of fatalities. Inertial sensors have recently been used to detect accidents of workers in construction sites, such as falls or trips. IMU-based systems for detecting fall-related accidents have been developed and have yielded satisfactory accuracy in laboratory settings. Nevertheless, the existing systems fail to uphold consistent accuracy and produce a significant number of false alarms when deployed in real-world settings, primarily due to the intricate nature of the working environments and the behaviors of the workers. In this research, the authors redesign the aforementioned laboratory experiment to target situations that are prone to false alarms based on the feedback obtained from workers in real construction sites. In addition, a new algorithm based on recurrent neural networks was developed to reduce the frequencies of various types of false alarms. The proposed model outperforms the existing benchmark model (i.e., hierarchical threshold model) with higher sensitivities and fewer false alarms in detecting stumble (100% sensitivity vs. 40%) and fall (95% sensitivity vs. 65%) events. However, the model did not outperform the hierarchical model in detecting coma events in terms of sensitivity (70% vs. 100%), but it did generate fewer false alarms (5 false alarms vs. 13).
Camera-Based Net Avoidance Controls of Underwater Robots
Authors: Kim Jonghoek
Published: January 2024

Fishing nets are dangerous obstacles for an underwater robot whose aim is to reach a goal in unknown underwater environments. This paper proposes how to make the robot reach its goal, while avoiding fishing nets that are detected using the robot’s camera sensors. For the detection of underwater nets based on camera measurements of the robot, we can use deep neural networks. Passive camera sensors do not provide the distance information between the robot and a net. Camera sensors only provide the bearing angle of a net, with respect to the robot’s camera pose. There may be trailing wires that extend from a net, and the wires can entangle the robot before the robot detects the net. Moreover, light, viewpoint, and sea floor condition can decrease the net detection probability in practice. Therefore, whenever a net is detected by the robot’s camera, we make the robot avoid the detected net by moving away from the net abruptly. For moving away from the net, the robot uses the bounding box for the detected net in the camera image. After the robot moves backward for a certain distance, the robot makes a large circular turn to approach the goal, while avoiding the net. A large circular turn is used, since moving close to a net is too dangerous for the robot. As far as we know, our paper is unique in addressing reactive control laws for approaching the goal, while avoiding fishing nets detected using camera sensors. The effectiveness of the proposed net avoidance controls is verified using simulations.
A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN
Authors: Xiao Xianpu , Li Taifeng , Lin Feng , Li Xinzhi , Hao Zherui , Li Jiashen
Published: January 2024

To address the uncertainty of optimal vibratory frequency fov of high-speed railway graded gravel (HRGG) and achieve high-precision prediction of the fov, the following research was conducted. Firstly, commencing with vibratory compaction experiments and the hammering modal analysis method, the resonance frequency f0 of HRGG fillers, varying in compactness K, was initially determined. The correlation between f0 and fov was revealed through vibratory compaction experiments conducted at different vibratory frequencies. This correlation was established based on the compaction physical–mechanical properties of HRGG fillers, encompassing maximum dry density ρdmax, stiffness Krd, and bearing capacity coefficient K20. Secondly, the gray relational analysis algorithm was used to determine the key feature influencing the fov based on the quantified relationship between the filler feature and fov. Finally, the key features influencing the fov were used as input parameters to establish the artificial neural network prediction model (ANN-PM) for fov. The predictive performance of ANN-PM was evaluated from the ablation study, prediction accuracy, and prediction error. The results showed that the ρdmax, Krd, and K20 all obtained optimal states when fov was set as f0 for different gradation HRGG fillers. Furthermore, it was found that the key features influencing the fov were determined to be the maximum particle diameter dmax, gradation parameters b and m, flat and elongated particles in coarse aggregate Qe, and the Los Angeles abrasion of coarse aggregate LAA. Among them, the influence of dmax on the ANN-PM predictive performance was the most significant. On the training and testing sets, the goodness-of-fit R2 of ANN-PM all exceeded 0.95, and the prediction errors were small, which indicated that the accuracy of ANN-PM predictions was relatively high. In addition, it was clear that the ANN-PM exhibited excellent robust performance. The research results provide a novel method for determining the fov of subgrade fillers and provide theoretical guidance for the intelligent construction of high-speed railway subgrades.
A Dynamic Framework for Internet-Based Network Time Protocol
Authors: Gamage Kelum A. A. , Sajid Asher , Sonbul Omar S. , Rashid Muhammad , Jaffar Amar Y.
Published: January 2024

Time synchronization is vital for accurate data collection and processing in sensor networks. Sensors in these networks often operate under fluctuating conditions. However, an accurate timekeeping mechanism is critical even in varying network conditions. Consequently, a synchronization method is required in sensor networks to ensure reliable timekeeping for correlating data accurately across the network. In this research, we present a novel dynamic NTP (Network Time Protocol) algorithm that significantly enhances the precision and reliability of the generalized NTP protocol. It incorporates a dynamic mechanism to determine the Round-Trip Time (RTT), which allows accurate timekeeping even in varying network conditions. The proposed approach has been implemented on an FPGA and a comprehensive performance analysis has been made, comparing three distinct NTP methods: dynamic NTP (DNTP), static NTP (SNTP), and GPS-based NTP (GNTP). As a result, key performance metrics such as variance, standard deviation, mean, and median accuracy have been evaluated. Our findings demonstrate that DNTP is markedly superior in dynamic network scenarios, a common characteristic in sensor networks. This adaptability is important for sensors installed in time-critical networks, such as real-time industrial IoTs, where precise and reliable time synchronization is necessary.
Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning
Authors: Dgani Ben , Cohen Israel
Published: January 2024

This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have received limited attention in previous works. The simulation results show that the proposed method performs well with different signal-to-noise ratios (SNRs) and channel conditions. The classifier’s performance is superior to that of complex deep learning methods, making it suitable for deployment in CR networks’ end units, especially in military and emergency service applications. The proposed method offers a cost-effective and high-quality solution for AMC that meets the strict demands of these critical applications.
Cooperative Safe Trajectory Planning for Quadrotor Swarms
Authors: Zhang Yahui , Yi Peng , Hong Yiguang
Published: January 2024

In this paper, we propose a novel distributed algorithm based on model predictive control and alternating direction multiplier method (DMPC-ADMM) for cooperative trajectory planning of quadrotor swarms. First, a receding horizon trajectory planning optimization problem is constructed, in which the differential flatness property is used to deal with the nonlinear dynamics of quadrotors while we design a relaxed form of the discrete-time control barrier function (DCBF) constraint to balance feasibility and safety. Then, we decompose the original trajectory planning problem by ADMM and solve it in a fully distributed manner with peer-to-peer communication, which induces the quadrotors within the communication range to reach a consensus on their future trajectories to enhance safety. In addition, an event-triggered mechanism is designed to reduce the communication overhead. The simulation results verify that the trajectories generated by our method are real-time, safe, and smooth. A comprehensive comparison with the centralized strategy and several other distributed strategies in terms of real-time, safety, and feasibility verifies that our method is more suitable for the trajectory planning of large-scale quadrotor swarms.
Advancing Qualitative Research: Insights from the 7th World Conference on Qualitative Research
Authors: Petre Gianina-Estera , Costa António Pedro
Published: January 2024

The 7th World Conference on Qualitative Research convened scholars, researchers, and practitioners across various domains to exchange insights into the wide-ranging utilisation of qualitative research [...]