bluuebunny/crossref_metadata_2025 huggingface dataset:
Disaggregation of the Copernicus Land Use/Land Cover (LULC) and Population Density Data to Fit Mesoscale Flood Risk Assessment Requirements in Partially Urbanized Catchments in Croatia
Authors: Horvat Bojana , Krvavica Nino
Published: November 2023

Flood risk assessment at the mesoscale requires data that are spatially and thematically detailed enough to provide reliable estimates at the catchment level. However, data availability and suitability are often contradictory: available data are rarely suitable at the required level of detail. To overcome this problem, numerous disaggregation methods have been proposed in recent decades, often based on somewhat generalised imperviousness characteristics derived from the available urban land use/land cover (LULC) nomenclature. To reduce generalisation, we propose a new disaggregation approach using a spatially distributed imperviousness density (IMD) layer at a very detailed spatial resolution of 10 m as ancillary data to improve the thematic detail of the urban classes of the available LULC datasets (Coastal Zones, Natura 2000) and the dasymetric mapping of the census data. The nomenclature of the urban classes and the impervious density thresholds were taken from the detailed Urban Atlas dataset. The disaggregation of the census data is then built on the resulting geometry of thematically improved residential classes. Assuming that IMD values indicate a built-up density, the proposed weighting scheme is IMD-dependent: it accounts for variability in the built-up density and, hence, variability in population. The approach was tested in three catchments in Croatia, each with a different degree of urbanisation. The resulting statistics (mean square error and percentage error) indicate that residential areas and population density depend on IMD. Using IMD as additional data therefore greatly improves the assessment of elements that are exposed to flooding and, consequently, the damage and flood risk assessment.
Correction: Brandolini et al. The Evolution of Historic Agroforestry Landscape in the Northern Apennines (Italy) and Its Consequences for Slope Geomorphic Processes. Land 2023, 12, 1054
Authors: Brandolini Filippo , Compostella Chiara , Pelfini Manuela , Turner Sam
Published: November 2023

In the original publication [...]
Preparing Size-Controlled Liposomes Modified with Polysaccharide Derivatives for pH-Responsive Drug Delivery Applications
Authors: Yanagihara Shin , Kitayama Yukiya , Yuba Eiji , Harada Atsushi
Published: November 2023

The liposome particle size is an important parameter because it strongly affects content release from liposomes as a result of different bilayer curvatures and lipid packing. Earlier, we developed pH-responsive polysaccharide-derivative-modified liposomes that induced content release from the liposomes under weakly acidic conditions. However, the liposome used in previous studies size was adjusted to 100–200 nm. The liposome size effects on their pH-responsive properties were unclear. For this study, we controlled the polysaccharide-derivative-modified liposome size by extrusion through polycarbonate membranes having different pore sizes. The obtained liposomes exhibited different average diameters, in which the diameters mostly corresponded to the pore sizes of polycarbonate membranes used for extrusion. The amounts of polysaccharide derivatives per lipid were identical irrespective of the liposome size. Introduction of cholesterol within the liposomal lipid components suppressed the size increase in these liposomes for at least three weeks. These liposomes were stable at neutral pH, whereas the content release from liposomes was induced at weakly acidic pH. Smaller liposomes exhibited highly acidic pH-responsive content release compared with those from large liposomes. However, liposomes with 50 mol% cholesterol were not able to induce content release even under acidic conditions. These results suggest that control of the liposome size and cholesterol content is important for preparing stable liposomes at physiological conditions and for preparing highly pH-responsive liposomes for drug delivery applications.
Improving Chest Monitoring through Magnetic Resonance Angiogram Image Contrast Enhancement
Authors: Arvinti Beatrice , Isar Alexandru
Published: November 2023

Magnetic resonance angiography is a medical procedure used to offer an image of the blood vessels and organs of the body. Given the worldwide spread of cardiovascular diseases, more and more resources are invested in treating them. One of the most modern treatments involves the acquisition of images of the heart. Sometimes the contrast of these images is not satisfactory. Injecting invasive enhancement substances to obtain a better view of the cardiac route is not advisable. However, software algorithms can solve the problem. This study proposes and tests a local adaptive contrast-adjustment algorithm using the dual-tree complex wavelet transform. The method has been tested with medical data from a public database to allow comparisons to other methods. The selected algorithm further improved the contrast of images. The performances are given for evaluation, both visually (to help doctors make accurate diagnoses) and in parametric form (to show engineers which parts of the algorithm might need improvement). Compared to other contrast enhancement methods, the proposed wavelet algorithm shows good results and greater stability. Thus, we aim to avoid future pointless complications due to unnecessary contrast substances.
Parametric Analysis of Tool Wear, Surface Roughness and Energy Consumption during Turning of Inconel 718 under Dry, Wet and MQL Conditions
Authors: Siddique M. Zeeshan , Faraz Muhammad Iftikhar , Butt Shahid Ikramullah , Khan Rehan , Petru Jana , Jaffery Syed Husain Imran , Khan Muhammad Ali , Tahir Abdul Malik
Published: November 2023

Economy and productivity are the two most important elements of modern manufacturing systems. Economy is associated with energy-efficient operations, which results in an overall high input-to-output ratio, while productivity is related to quality and quantity. This specific work presents experimental investigations of the use of cooling conditions (dry, MQL and wet) as input variables alongside other input parameters, including depth of cut, feed and cutting speed. This research aimed to investigate the variation in output responses including tool wear, specific cutting energy, and surface roughness while machining Inconel 718, a nickel-based super alloy. For experimentation, three levels of depth of cut, feed, and cutting speed were chosen. The Taguchi method was used for the experimental design. The contribution ratio of each input parameter was ascertained through analysis of variance (ANOVA). Use of coolant showed a positive effect on process parameters, particularly MQL. By adapting the optimum machining conditions, specific cutting energy was improved by 27%, whereas surface roughness and tool wear were improved by 15% and 30%, respectively.
The Shape of a Compressible Drop on a Vibrating Solid Plate
Authors: Ivantsov Andrey , Lyubimova Tatyana , Khilko Grigoriy , Lyubimov Dmitry
Published: November 2023

The influence of high-frequency vibrations on the shape of a compressible drop placed on an oscillating solid substrate is studied in this paper. Due to the significant difference in characteristic temporal scales, the average and pulsating motions of the drop can be considered separately. For nearly hemispherical drop, the solution to the problem of pulsating motion is found in the form of series in Legendre polynomials. Frequencies of natural sound oscillations of hemispherical axisymmetric drop are obtained. Resonances of the acoustic mode of drop oscillations are found. The problem of forced oscillations of hemispherical drop in the limit of weakly compressible liquid is considered. It is found that drop oscillation amplitude grows with vibration intensity according to quadratic law, which is consistent with the solution of the pulsation problem for finite compressibility assumption. A variational principle for calculation of average drop shape is formulated based on minimization of energy functional for the case, so the compressibility of the liquid should be taken into account. It is shown that the functional (the sum of the kinetic and potential energies of the pulsating flow, the kinetic energy of the averaged flow, and the surface tension energy of the drop) decreases and reaches a minimum value at quasi-equilibrium state, in which the average shape of the drop becomes static. The influence of vibrations on the drop shape is studied for small values of the vibrational parameter. The surface of the drop in the absence of vibrations is assumed to be hemispherical. Calculations showed that under vibrations, drop height decreases, while the area of the base increases.
A Novel Real-Time Robust Controller of a Four-Wheel Independent Steering System for EV Using Neural Networks and Fuzzy Logic
Authors: Kosmidis Alexis , Ioannidis Georgios , Vokas Georgios , Kaminaris Stavros
Published: November 2023

In this study a four-wheel independent steering (4WIS) system for an electric vehicle (EV) steered by stepper motors is presented as a revolutionary real-time control technique employing neural networks in combination with fuzzy logic, where the use of the neural network greatly simplifies the computational process of fuzzy logic. The control of the four wheels is based on a variation of a Hopfield Neural Network (VHNN) method, in which the input is the error of each steering motor and the output is processed by a hyperbolic tangent function (HTF) feeding the fuzzy logic controller (FLC), which ultimately drives the stepper motor. The whole system consists of the four aforementioned blocks which work in sync and are inseparable from each other with the common goal of driving all the steering stepper motors at the same time. The novelty of this system is that each wheel monitors the condition of the others, so even in the case of the failure of one wheel, the vehicle does not veer off course. The results of the simulation show that the suggested control system is very resilient and workable at all angles and speeds.
A Client-Cloud-Chain Data Annotation System of Internet of Things for Semi-Supervised Missing Data
Authors: Yu Chao , Zhou Yang , Cui Xiaolong
Published: November 2023

With continuous progress in science and technology, a large amount of data are produced in all fields of the world at anytime and anywhere. These data are unmarked and lack marking information, while manual marking is time-consuming and laborious. Herein, this paper introduces a distributed semi-supervised labeling framework. This framework addresses the issue of missing data by proposing an attribute-filling method based on subspace learning. Furthermore, this paper presents a distributed semi-supervised learning strategy that trains sub-models (private models) within each sub-system. Finally, this paper develops a distributed graph convolutional neural network fusion technique with enhanced interpretability grounded on the attention mechanism. This paper assigns weights of importance to the edges of each layer in the graph neural network based on sub-models and public data, thereby enabling distributed and interpretable graph convolutional attention. Extensive experimentation using public datasets demonstrates the superiority of the proposed scheme over other state-of-the-art baselines, achieving a reduction in loss of 50% compared to the original approach.
Integrability Properties of the Slepyan–Palmov Model Arising in the Slepyan–Palmov Medium
Authors: Usman Muhammad , Hussain Akhtar , Zaman F. D. , Ibeas Asier , Almalki Yahya
Published: November 2023

This study investigates the Slepyan–Palmov (SP) model, which describes plane longitudinal waves propagating within a medium comprising a carrier medium and nonlinear oscillators. The primary objective is to analyze the integrability properties of this model. The research entails two key aspects. Firstly, the study explores the group invariant solution by utilizing reductions in symmetry subalgebras based on the optimal system. Secondly, the conservation laws are studied using the homotopy operator, which offers advantages over the conventional multiplier approach, especially when arbitrary functions are absent from both the equation and characteristics. This method proves advantageous in handling complex multipliers and yields significant outcomes.
Differential Expression Analysis Utilizing Condition-Specific Metabolic Pathways
Authors: Mattei Gianluca , Gan Zhuohui , Ramazzotti Matteo , Palsson Bernhard O. , Zielinski Daniel C.
Published: November 2023

Pathway analysis is ubiquitous in biological data analysis due to the ability to integrate small simultaneous changes in functionally related components. While pathways are often defined based on either manual curation or network topological properties, an attractive alternative is to generate pathways around specific functions, in which metabolism can be defined as the production and consumption of specific metabolites. In this work, we present an algorithm, termed MetPath, that calculates pathways for condition-specific production and consumption of specific metabolites. We demonstrate that these pathways have several useful properties. Pathways calculated in this manner (1) take into account the condition-specific metabolic role of a gene product, (2) are localized around defined metabolic functions, and (3) quantitatively weigh the importance of expression to a function based on the flux contribution of the gene product. We demonstrate how these pathways elucidate network interactions between genes across different growth conditions and between cell types. Furthermore, the calculated pathways compare favorably to manually curated pathways in predicting the expression correlation between genes. To facilitate the use of these pathways, we have generated a large compendium of pathways under different growth conditions for E. coli. The MetPath algorithm provides a useful tool for metabolic network-based statistical analyses of high-throughput data.
Experimental Validation of the Microplastic Index—Two Approaches to Understanding Microplastic Formation
Authors: Grigoriadi Kalouda , Nooijens Merel , Taşlı Ali , Vanhouttem Max , Henke Sieger , Parker Luke , Urbanus Jan , Boersma Arjen
Published: November 2023

The Microplastic Index (MPI) was presented in a previous paper as a method to assess the formation of microplastics during the application of impact and wear stresses, based on selected mechanical and physical properties of polymers. In this paper, the experimental validation of the MPI model is presented. A series of ten polymers was characterized to obtain the relevant parameters for the calculation of the MPI, i.e., the minimum particle size and volume of microplastics formed. The milling (addressing impact stress) and sanding experiments (addressing wear stress) resulted in particle sizes between 3 and 200 μm and 0.3 and 25 μm, respectively. These values were very well predicted by the MPI model, showing smaller particles for brittle polymers and larger ones for ductile polymers. In addition, the experimental-specific wear rates of impact and wear correlated well with the predicted ones, being 0.01–30 mm3/Nm for impact and 0.0002–0.012 mm3/Nm for wear. These results indicate that the MPI can be very well used to predict the tendency of a material to form microplastics. In the search for understanding and mitigating microplastic formation, the MPI can be used by both producers and end users to choose plastic grades that form fewer microplastics.
Recent Progress in Solution Structure Studies of Photosynthetic Proteins Using Small-Angle Scattering Methods
Authors: Golub Maksym , Pieper Jörg
Published: November 2023

Utilized for gaining structural insights, small-angle neutron and X-ray scattering techniques (SANS and SAXS, respectively) enable an examination of biomolecules, including photosynthetic pigment-protein complexes, in solution at physiological temperatures. These methods can be seen as instrumental bridges between the high-resolution structural information achieved by crystallography or cryo-electron microscopy and functional explorations conducted in a solution state. The review starts with a comprehensive overview about the fundamental principles and applications of SANS and SAXS, with a particular focus on the recent advancements permitting to enhance the efficiency of these techniques in photosynthesis research. Among the recent developments discussed are: (i) the advent of novel modeling tools whereby a direct connection between SANS and SAXS data and high-resolution structures is created; (ii) the employment of selective deuteration, which is utilized to enhance spatial selectivity and contrast matching; (iii) the potential symbioses with molecular dynamics simulations; and (iv) the amalgamations with functional studies that are conducted to unearth structure-function relationships. Finally, reference is made to time-resolved SANS/SAXS experiments, which enable the monitoring of large-scale structural transformations of proteins in a real-time framework.
Molecular Dynamics Simulations of Amylose- and Cellulose-Based Selectors and Related Enantioseparations in Liquid Phase Chromatography
Authors: Dallocchio Roberto , Dessì Alessandro , Sechi Barbara , Peluso Paola
Published: November 2023

In the last few decades, theoretical and technical advancements in computer facilities and computational techniques have made molecular modeling a useful tool in liquid-phase enantioseparation science for exploring enantioselective recognition mechanisms underlying enantioseparations and for identifying selector–analyte noncovalent interactions that contribute to binding and recognition. Because of the dynamic nature of the chromatographic process, molecular dynamics (MD) simulations are particularly versatile in the visualization of the three-dimensional structure of analytes and selectors and in the unravelling of mechanisms at molecular levels. In this context, MD was also used to explore enantioseparation processes promoted by amylose and cellulose-based selectors, the most popular chiral selectors for liquid-phase enantioselective chromatography. This review presents a systematic analysis of the literature published in this field, with the aim of providing the reader with a comprehensive picture about the state of the art and what is still missing for modeling cellulose benzoates and the phenylcarbamates of amylose and cellulose and related enantioseparations with MD. Furthermore, advancements and outlooks, as well as drawbacks and pitfalls still affecting the applicability of MD in this field, are also discussed. The importance of integrating theoretical and experimental approaches is highlighted as an essential strategy for profiling mechanisms and noncovalent interaction patterns.
Catalyst-Free Trans-Selective Oxyiodination and Oxychlorination of Alkynes Employing N–X (Halogen) Reagents
Authors: Sun Jiaqiong , Guo Yunliang , Xia Jiuli , Zheng Guangfan , Zhang Qian
Published: November 2023

β-halogenated enol esters and ethers are versatile building blocks in organic synthesis, which has attracted increasing attention. In this study, we report the facile trans-oxyiodination and oxychlorination of alkynes, leading to the direct construction of versatile halogenated enol esters and ethers. This transformation features an easy operation, optimal atomic economy, a strong functional group tolerance, broad substrate scope, and excellent trans-selectivity. Employing highly electrophilic bifunctional N–X (halogen) reagents was the key to achieving broad reaction generality. To our knowledge, this transformation represents the first oxyhalogenation system employing N–X (halogen) reagents as both oxylation and halogenation sources.
Improved Short-Circuit Current and Fill Factor in PM6:Y6 Organic Solar Cells through D18-Cl Doping
Authors: Yang Jianjun , Wang Xiansheng , Yu Xiaobao , Liu Jiaxuan , Zhang Zhi , Zhong Jian , Yu Junsheng
Published: November 2023

Based on the PM6:Y6 binary system, a novel non-fullerene acceptor material, D18-Cl, was doped into the PM6:Y6 blend to fabricate the active layer. The effects of different doping ratios of D18-Cl on organic solar cells were investigated. The best-performing organic solar cell was achieved when the doping ratio of D18-Cl reached 20 wt%. It exhibited a short-circuit current of 28.13 mA/cm2, a fill factor of 70.25%, an open-circuit voltage (Voc) of 0.81 V, and a power conversion efficiency of 16.08%. The introduction of an appropriate amount of D18-Cl expanded the absorption spectrum of the active layer, improved the morphology of the active layer, reduced large molecular aggregation and defects, minimized bimolecular recombination, and optimized the collection efficiency of charge carriers. These results indicate the critical importance of selecting an appropriate third component in binary systems and optimizing the doping ratio to enhance the performance of ternary organic solar cells.
Direct Current Triboelectric Nanogenerators, a Perspective from Material Selections
Authors: Li Xiang , Wei Di , Wang Zhong Lin
Published: November 2023

With the global energy shortages, sustainable energy scavenging from the natural environment is desperately needed. Unlike solar cell or wind power, which depends heavily on weather conditions, triboelectric nanogenerator (TENG) has received extensive attention as an efficient all–weather energy–harvesting technology. Based on the coupling principle of contact electrification (CE) and electrostatic induction, conventional TENGs convert mechanical energy into an alternating current (AC) output. However, the typically distributed sensor systems in the ubiquitous Internet of Things (IoTs) request a direct current (DC) input. Direct current triboelectric nanogenerators (DC-TENGs) with the constant output characteristic are critical to satisfy the above requirements. Here, DC-TENGs were reviewed from the perspective of material selections. As device performance is mainly determined by material properties, the development of DC-TENGs could be divided into three categories based on dielectric materials, semiconductor materials, and materials for iontronic rectifications. The operating mechanism and influencing factors of various types of DC-TENG were summarized, representative applications were demonstrated, and the main challenges of future developments were also discussed.
The Role of Methionine-Rich Diet in Unhealthy Cerebrovascular and Brain Aging: Mechanisms and Implications for Cognitive Impairment
Authors: Ungvari Anna , Gulej Rafal , Csik Boglarka , Mukli Peter , Negri Sharon , Tarantini Stefano , Yabluchanskiy Andriy , Benyo Zoltan , Csiszar Anna , Ungvari Zoltan
Published: November 2023

As aging societies in the western world face a growing prevalence of vascular cognitive impairment and Alzheimer’s disease (AD), understanding their underlying causes and associated risk factors becomes increasingly critical. A salient concern in the western dietary context is the high consumption of methionine-rich foods such as red meat. The present review delves into the impact of this methionine-heavy diet and the resultant hyperhomocysteinemia on accelerated cerebrovascular and brain aging, emphasizing their potential roles in cognitive impairment. Through a comprehensive exploration of existing evidence, a link between high methionine intake and hyperhomocysteinemia and oxidative stress, mitochondrial dysfunction, inflammation, and accelerated epigenetic aging is drawn. Moreover, the microvascular determinants of cognitive deterioration, including endothelial dysfunction, reduced cerebral blood flow, microvascular rarefaction, impaired neurovascular coupling, and blood–brain barrier (BBB) disruption, are explored. The mechanisms by which excessive methionine consumption and hyperhomocysteinemia might drive cerebromicrovascular and brain aging processes are elucidated. By presenting an intricate understanding of the relationships among methionine-rich diets, hyperhomocysteinemia, cerebrovascular and brain aging, and cognitive impairment, avenues for future research and potential therapeutic interventions are suggested.
Lifestyle Interventions to Prevent Type 2 Diabetes in Women with a History of Gestational Diabetes: A Systematic Review and Meta-Analysis through the Lens of Health Equity
Authors: Ukke Gebresilasea Gendisha , Boyle Jacqueline A. , Reja Ahmed , Lee Wai Kit , Chen Mingling , Ko Michelle Shi Min , Alycia Chelsea , Kwon Jane , Lim Siew
Published: November 2023

Background: Type 2 diabetes mellites is one of the health problems disproportionally affecting people with low socioeconomic statuses. Gestational diabetes mellites increases the risk of type 2 diabetes by up to ten-fold for women. Lifestyle interventions prevent type 2 diabetes in women with prior gestational diabetes. However, it is unknown if similar effectiveness can be expected for all population subgroups. Objective: This study aims to assess the prevention of type 2 diabetes in women with prior gestational diabetes using population characteristics according to the PROGRESS (place of residence, race/ethnicity/culture/language, occupation, gender/sex, religion, education, socioeconomic status, and social capital) criteria. Methods: MEDLINE, CINAHL, EMBASE, PubMed, PsycINFO, Web of Science, and EBM Reviews databases were searched for interventional studies of diet, physical activity, or behavioural interventions published up to 21 February 2023. Random effects subgroup meta-analysis was conducted to evaluate the association of population characteristics and intervention effects. Results: All studies were conducted in high-income countries or middle-income countries. Two-thirds of the studies reported on race/ethnicity and education level. Less than one-third reported on place (urban/rural), occupation, and socioeconomic status. None reported on religion or social capital. Studies from high-income countries (MD = −1.46; 95% CI: −2.27, −0.66, I2 = 70.46, p < 0.001) showed a greater reduction in bodyweight compared with the studies conducted in middle-income countries (MD = −0.11; 95% CI: −1.12, 0.89, I2 = 69.31, p < 0.001) (p for subgroup difference = 0.04). Conclusion: There are significant equity gaps in the evidence for the prevention of type 2 diabetes in women with prior gestational diabetes due to reports on population characteristics being poor. Interventions may be less effective in reducing bodyweight in women from middle-income countries compared to high-income countries. Collecting and analysing data related to equity is needed to understand the effect of lifestyle interventions on type 2 diabetes for different population subgroups.
Breaking the Stigma in Mental Health Nursing through High-Fidelity Simulation Training
Authors: Simonelli-Muñoz Agustín Javier , Jiménez-Rodríguez Diana , Arrogante Oscar , Plaza del Pino Fernando Jesús , Gallego-Gómez Juana Inés
Published: November 2023

The social stigma toward individuals with mental health problems is habitual among nursing students, which can lead to poor quality of health care services for patients with mental illnesses. The purpose of the present study was to learn about nursing students’ perceptions of providing care to patients with severe mental disorders before and after participating in a simulated student clinical case. A descriptive qualitative study was conducted through 39 interviews. The difficulties expected by the students and their perceptions about patients were explored before the simulation training. Their perceptions about the use of clinical simulation for learning about the adequate management of these patients were analyzed afterwards. Results: Before the simulation training, the students assessed the case as being complicated, expressed their lack of specific training, and felt fear and insecurity, thus reproducing the stigma towards mental health patients. After this training, they positively valued the usefulness of the clinical simulation for gaining confidence and overcoming the stigma. Discussion: The use of high-fidelity simulation offers nursing students the opportunity to approach patients with mental health conditions, overcoming their fears and normalizing mental disorders. Simulation training allows nursing students to analyze the reasoning of clinical judgment and to detect the influence of previous prejudices about mental illness in their clinical decision. This study was not registered.
Ticking off the Tick Vectors: Rhipicephalus microplus Fails to Transmit Theileria orientalis
Authors: Ghafar Abdul , Cabezas-Cruz Alejandro
Published: November 2023

Theileria (T [...]
Isolation and Characterization of Erianthus arundinaceus Phosphate Transporter 1 (PHT1) Gene Promoter and 5′ Deletion Analysis of Transcriptional Regulation Regions under Phosphate Stress in Transgenic Tobacco
Authors: Naveenarani Murugan , Swamy Huskur , Surya Krishna Sakthivel , Mahadevaiah Channappa , Valarmathi Ramanathan , Manickavasagam Markandan , Arun Muthukrishnan , Hemaprabha Govindakurup , Appunu Chinnaswamy
Published: November 2023

Phosphorus deficiency highly interferes with plant growth and development. Plants respond to persistent P deficiency by coordinating the expression of genes involved in the alleviation of stress. Promoters of phosphate transporter genes are a great choice for the development of genetically modified plants with enhanced phosphate uptake abilities, which improve crop yields in phosphate-deficient soils. In our previous study, the sugarcane phosphate transporter PHT1;2 gene showed a significantly high expression under salinity stress. In this study, the Erianthus arundinaceus EaPHT1;2 gene was isolated and characterized using various in silico tools. The deduced 542 amino acid residues have 10 transmembrane domains, with a molecular weight and isoelectric point of 58.9 kDa and 9.80, respectively. They displayed 71–96% similarity with Arabidopsis thaliana, Zea mays, and the Saccharum hybrid. To elucidate the function of the 5′ regulatory region, the 1.1 kb promoter was isolated and validated in tobacco transgenics under Pi stress. The EaPHT1;2 promoter activity was detected using a β-glucuronidase (GUS) assay. The EaPHT1;2 promoter showed 3- to 4.2-fold higher expression than the most widely used CaMV35S promoter. The 5′ deletion analysis with and without 5′ UTRs revealed a small-sized 374 bp fragment with the highest promoter activity among 5′ truncated fragments, which was 2.7 and 4.2 times higher than the well-used CaMV35S promoter under normal and Pi deprivation conditions, respectively. The strong and short promoter of EaPHT1;2 with 374 bp showed significant expression in low-Pi-stress conditions and it could be a valuable source for the development of stress-tolerant transgenic crops.
Potential of Trichoderma virens HZA14 in Controlling Verticillium Wilt Disease of Eggplant and Analysis of Its Genes Responsible for Microsclerotial Degradation
Authors: Tomah Ali Athafah , Alamer Iman Sabah Abd , Khattak Arif Ali , Ahmed Temoor , Hatamleh Ashraf Atef , Al-Dosary Munirah Abdullah , Ali Hayssam M. , Wang Daoze , Zhang Jingze , Xu Lihui , Li Bin
Published: November 2023

Verticillium dahliae is a soilborne fungal pathogen that causes vascular wilt diseases in a wide range of economically important crops, including eggplant. Trichoderma spp. are effective biological control agents that suppress a wide range of plant pathogens through a variety of mechanisms, including mycoparasitism. However, the molecular mechanisms of mycoparasitism of Trichoderma spp. in the degradation of microsclerotia of V. dahliae are not yet fully understood. In this study, the ability of 15 isolates of Trichoderma to degrade microsclerotia of V. dahliae was evaluated using a dual culture method. After 15 days, isolate HZA14 showed the greatest potential for microsclerotial degradation. The culture filtrate of isolate HZA14 also significantly inhibited the mycelial growth and conidia germination of V. dahliae at different dilutions. Moreover, this study showed that T. virens produced siderophores and indole-3-acetic acid (IAA). In disease control tests, T. virens HZA14 reduced disease severity in eggplant seedlings by up to 2.77%, resulting in a control efficacy of 96.59% at 30 days after inoculation. Additionally, inoculation with an HZA14 isolate increased stem and root length and fresh and dry weight, demonstrating plant growth promotion efficacy. To further investigate the mycoparasitism mechanism of T. virens HZA14, transcriptomics sequencing and real-time fluorescence quantitative PCR (RT-qPCR) were used to identify the differentially expressed genes (DEGs) of T. virens HZA14 at 3, 6, 9, 12, and 15 days of the interaction with microsclerotia of V. dahliae. In contrast to the control group, the mycoparasitic process of T. virens HZA14 exhibited differential gene expression, with 1197, 1758, 1936, and 1914 genes being up-regulated and 1191, 1963, 2050, and 2114 genes being down-regulated, respectively. Among these genes, enzymes associated with the degradation of microsclerotia, such as endochitinase A1, endochitinase 3, endo-1,3-beta-glucanase, alpha-N-acetylglucosaminidase, laccase-1, and peroxidase were predicted based on bioinformatics analysis. The RT-qPCR results confirmed the RNA-sequencing data, showing that the expression trend of the genes was consistent. These results provide important information for understanding molecular mechanisms of microsclerotial degradation and integrated management of Verticillium wilt in eggplant and other crops.
Religions in al-Ḥarāllī’s Sufi Hermeneutics: An Apolemical Understanding of the Qurʾān
Authors: Mokrani Adnane
Published: November 2023

This article analyzes the three introductory epistles to Sufi hermeneutics written by al-Ḥarāllī, a mystic of Andalusian origin from the seventh/thirteenth century. According to this author, the objective of the Scriptures is to explain human beings to themselves. The revelation, received through a particular understanding called fahm, contains transformative knowledge that can change the life of the reader. In this foundational work, al-Ḥarāllī explains the relationship between the Qurʾān and preceding Scriptures, recognizing in the Qurʾān their unity and fulfillment. Dedicating the final chapter of the third epistle to the seven religions mentioned in the Qurʾān, which are, rather, ethical prototypes, he seeks to internalize the Qurʾānic critique expounded on each of these categories. In a reading that goes beyond polemics and offers significant elements to the Islamic theology of religions, al-Ḥarāllī demonstrates how the aim of the Qurʾānic narratives is not to present information about the past but rather to provide moral education for the Islamic community and the reader.
A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
Authors: Liu Pengfei , Guo Lishu , Zhao Hang , Shang Peng , Chu Ziyue , Lu Xiaochun
Published: November 2023

Specific emitter identification (SEI) is a professional technology to recognize different emitters by measuring the unique features of received signals. It has been widely used in both civilian and military fields. Recently, many SEI methods based on deep learning have been proposed, most of which assume that the training set and testing set have the same data distribution. However, in reality, the testing set is generally used later than the training set and lacks labels. The long time span may change the signal transmission environment and fingerprint features. These changes result in considerable differences in data distribution between the training and testing sets, thereby affecting the recognition and prediction abilities of the model. Therefore, the existing works cannot achieve satisfactory results for a long time span SEI. To address this challenge and obtain stable fingerprints, we transform the long time span SEI problem into a domain adaptive problem and propose an unsupervised domain adaptive method called LTS-SEI. Noteworthily, LTS-SEI uses a multilayer convolutional feature extractor to learn feature knowledge and confronts a domain discriminator to generate domain-invariant shallow fingerprints. The classifier of LTS-SEI applies feature matching to source domain samples and target domain samples to achieve the domain alignment of deep fingerprints. The classifier further reduces the intraclass diversity of deep features to alleviate the misclassification problem of edge samples in the target domain. To confirm the effectiveness and reliability of LTS-SEI, we collect multiple sets of real satellite navigation signals using two antennas with 13 m- and 40 m-large apertures, respectively, and construct two available datasets. Numerous experiments demonstrate that LTS–SEI can considerably increase the recognition accuracy of the long time span SEI and is superior to the other existing methods.
MADANet: A Lightweight Hyperspectral Image Classification Network with Multiscale Feature Aggregation and a Dual Attention Mechanism
Authors: Cui Binge , Wen Jiaxiang , Song Xiukai , He Jianlong
Published: November 2023

Hyperspectral remote sensing images, with their continuous, narrow, and rich spectra, hold distinct significance in the precise classification of land cover. Deep convolutional neural networks (CNNs) and their variants are increasingly utilized for hyperspectral classification, but solving the conflict between the number of model parameters, performance, and accuracy has become a pressing challenge. To alleviate this problem, we propose MADANet, a lightweight hyperspectral image classification network that combines multiscale feature aggregation and a dual attention mechanism. By employing depthwise separable convolution, multiscale features can be extracted and aggregated to capture local contextual information effectively. Simultaneously, the dual attention mechanism harnesses both channel and spatial dimensions to acquire comprehensive global semantic information. Ultimately, techniques such as global average pooling (GAP) and full connection (FC) are employed to integrate local contextual information with global semantic knowledge, thereby enabling the accurate classification of hyperspectral pixels. The results from the experiments conducted on representative hyperspectral images demonstrate that MADANet not only attains the highest classification accuracy but also maintains significantly fewer parameters compared to the other methods. Experimental results show that our proposed framework significantly reduces the number of model parameters while still achieving the highest classification accuracy. As an example, the model has only 0.16 M model parameters in the Indian Pines (IP) dataset, but the overall accuracy is as high as 98.34%. Similarly, the framework achieves an overall accuracy of 99.13%, 99.17%, and 99.08% on the University of Pavia (PU), Salinas (SA), and WHU Hi LongKou (LongKou) datasets, respectively. This result exceeds the classification accuracy of existing state-of-the-art frameworks under the same conditions.
A Novel MIMO Radar Orthogonal Waveform Design Algorithm Based on the Multi-Objective Improved Archimedes Optimization Algorithm
Authors: Wang Yanjiao , Chen Mingchi
Published: November 2023

Realization and enhancement of detection techniques for multiple-input–multiple-output (MIMO) radar systems require polyphase code sequences with excellent orthogonality characteristics. Therefore, orthogonal waveform design is the key to realizing MIMO radar. Conventional orthogonal waveform design methods fail to ensure acceptable orthogonal characteristics by individually optimizing the autocorrelation sidelobe peak level and the cross-correlation sidelobe peak level. In this basis, the multi-objective Archimedes optimization algorithm (MOIAOA) is proposed for orthogonal waveform optimization while simultaneously minimizing the total autocorrelation sidelobe peak energy and total cross-correlation peak energy. A novel optimal individual selection method is proposed to select those individuals that best match the weight vectors and lead the evolution of these individuals to their respective neighborhoods. Then, new exploration and development phases are introduced to improve the algorithm’s ability to increase its convergence speed and accuracy. Subsequently, novel incentive functions are formulated based on distinct evolutionary phases, followed by the introduction of a novel environmental selection method aimed at comprehensively enhancing the algorithm’s convergence and distribution. Finally, a weight updating method based on the shape of the frontier surface is proposed to dynamically correct the shape of the overall frontier, further enhancing the overall distribution. The results of experiments on the orthogonal waveform design show that the multi-objective improved Archimedes optimization algorithm (MOIAOA) achieves superior orthogonality, yielding lower total autocorrelation sidelobe peak energy and total cross-correlation peak energy than three established methods.
Velocity Dealiasing for 94 GHz Vertically Pointing MMCR with Dual-PRF Technique
Authors: Lin Hai , Wang Jie , Ge Junxiang
Published: November 2023

Velocity aliasing is unavoidable for millimeter-wave cloud radar (MMCR) due to its short wavelength. In the vertically pointing MMCR, a special aliasing state called half-folding will cause the traditional postprocessing dealiasing methods used for weather radar, including the dual-PRF method, to fail. In this paper, we propose a method that applies the dual-PRF technique to spectral dealiasing. By utilizing the property that the true velocity difference between peaks should be the same in both PRFs, our method is able to solve a special case of half-folding caused by multiple peaks, which is ignored by other spectral dealiasing methods. The special case, which we call implicit half-folding, occurs in the presence of multiple peaks in a Doppler power spectrum, where none of the peaks are folded, and they appear to be in the same Nyquist interval, whereas the peaks are actually not in the same Nyquist interval. Observations from a 94 GHz vertically pointing MMCR called TJ-II were used to demonstrate various aliasing cases, including the implicit half-folding case. As a result, our method successfully solved all aliasing cases while the other method failed when the implicit half-folding case occurred.
Open-Air Testing of Dual-Comb Time-of-Flight Measurement
Authors: Kim Wooram , Yang Jaewon , Jang Jaeyoung , Oh Jeong Seok , Han Seongheum , Kim Seungman , Jang Heesuk , Kim Young-Jin , Kim Seung-Woo
Published: November 2023

We configured a long-distance ranging apparatus to test the principle of dual-comb time-of-flight measurement using ultrashort lasers. Emphasis was given to the evaluation of open-air performance quantitatively in terms of the measurement resolution and stability. The test results revealed that our dual-comb asynchronous optical pulse sampling permits micrometer-resolved ranging with a repeatability of 2.05 μm over a 648 m distance in dry weather conditions. Further atmospheric effects were evaluated in three different weather conditions with corresponding Allan deviations. Finally, the capability of simultaneous determination of multiple targets was verified with the potential of advanced industrial applications, such as manufacturing, surveying, metrology, and geodesy.
An Integrated Approach for Real-Time Monitoring of Knee Dynamics with IMUs and Multichannel EMG
Authors: Malesevic Nebojsa , Svensson Ingrid , Hägglund Gunnar , Antfolk Christian
Published: November 2023

Measuring human joint dynamics is crucial for understanding how our bodies move and function, providing valuable insights into biomechanics and motor control. Cerebral palsy (CP) is a neurological disorder affecting motor control and posture, leading to diverse gait abnormalities, including altered knee angles. The accurate measurement and analysis of knee angles in individuals with CP are crucial for understanding their gait patterns, assessing treatment outcomes, and guiding interventions. This paper presents a novel multimodal approach that combines inertial measurement unit (IMU) sensors and electromyography (EMG) to measure knee angles in individuals with CP during gait and other daily activities. We discuss the performance of this integrated approach, highlighting the accuracy of IMU sensors in capturing knee joint movements when compared with an optical motion-tracking system and the complementary insights offered by EMG in assessing muscle activation patterns. Moreover, we delve into the technical aspects of the developed device. The presented results show that the angle measurement error falls within the reported values of the state-of-the-art IMU-based knee joint angle measurement devices while enabling a high-quality EMG recording over prolonged periods of time. While the device was designed and developed primarily for measuring knee activity in individuals with CP, its usability extends beyond this specific use-case scenario, making it suitable for applications that involve human joint evaluation.
Gender Recognition Based on Gradual and Ensemble Learning from Multi-View Gait Energy Images and Poses
Authors: Leung Tak-Man , Chan Kwok-Leung
Published: November 2023

Image-based gender classification is very useful in many applications, such as intelligent surveillance, micromarketing, etc. One common approach is to adopt a machine learning algorithm to recognize the gender class of the captured subject based on spatio-temporal gait features extracted from the image. The image input can be generated from the video of the walking cycle, e.g., gait energy image (GEI). Recognition accuracy depends on the similarity of intra-class GEIs, as well as the dissimilarity of inter-class GEIs. However, we observe that, at some viewing angles, the GEIs of both gender classes are very similar. Moreover, the GEI does not exhibit a clear appearance of posture. We postulate that distinctive postures of the walking cycle can provide additional and valuable information for gender classification. This paper proposes a gender classification framework that exploits multiple inputs of the GEI and the characteristic poses of the walking cycle. The proposed framework is a cascade network that is capable of gradually learning the gait features from images acquired in multiple views. The cascade network contains a feature extractor and gender classifier. The multi-stream feature extractor network is trained to extract features from the multiple input images. Features are then fed to the classifier network, which is trained with ensemble learning. We evaluate and compare the performance of our proposed framework with state-of-the-art gait-based gender classification methods on benchmark datasets. The proposed framework outperforms other methods that only utilize a single input of the GEI or pose.