bluuebunny/crossref_metadata_2025 huggingface dataset:
Speech Emotion Recognition Based on Multiple Acoustic Features and Deep Convolutional Neural Network
Authors: Bhangale Kishor , Kothandaraman Mohanaprasad
Published: February 2023

Speech emotion recognition (SER) plays a vital role in human–machine interaction. A large number of SER schemes have been anticipated over the last decade. However, the performance of the SER systems is challenging due to the high complexity of the systems, poor feature distinctiveness, and noise. This paper presents the acoustic feature set based on Mel frequency cepstral coefficients (MFCC), linear prediction cepstral coefficients (LPCC), wavelet packet transform (WPT), zero crossing rate (ZCR), spectrum centroid, spectral roll-off, spectral kurtosis, root mean square (RMS), pitch, jitter, and shimmer to improve the feature distinctiveness. Further, a lightweight compact one-dimensional deep convolutional neural network (1-D DCNN) is used to minimize the computational complexity and to represent the long-term dependencies of the speech emotion signal. The overall effectiveness of the proposed SER systems’ performance is evaluated on the Berlin Database of Emotional Speech (EMODB) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets. The proposed system gives an overall accuracy of 93.31% and 94.18% for the EMODB and RAVDESS datasets, respectively. The proposed MFCC and 1-D DCNN provide greater accuracy and outpace the traditional SER techniques.
ARMOR: Differential Model Distribution for Adversarially Robust Federated Learning
Authors: Zhang Yanting , Liu Jianwei , Guan Zhenyu , Zhao Bihe , Leng Xianglun , Bian Song
Published: February 2023

In this work, we formalize the concept of differential model robustness (DMR), a new property for ensuring model security in federated learning (FL) systems. For most conventional FL frameworks, all clients receive the same global model. If there exists a Byzantine client who maliciously generates adversarial samples against the global model, the attack will be immediately transferred to all other benign clients. To address the attack transferability concern and improve the DMR of FL systems, we propose the notion of differential model distribution (DMD) where the server distributes different models to different clients. As a concrete instantiation of DMD, we propose the ARMOR framework utilizing differential adversarial training to prevent a corrupted client from launching white-box adversarial attack against other clients, for the local model received by the corrupted client is different from that of benign clients. Through extensive experiments, we demonstrate that ARMOR can significantly reduce both the attack success rate (ASR) and average adversarial transfer rate (AATR) across different FL settings. For instance, for a 35-client FL system, the ASR and AATR can be reduced by as much as 85% and 80% over the MNIST dataset.
Accuracy of Using a New Semi-Automated Software Package to Diagnose Osteoporotic Vertebral Fractures in Adults
Authors: Alqahtani Fawaz F. , Bromiley Paul A.
Published: February 2023

We evaluate the accuracy of a semi-automated software package for annotating landmark points on vertebral body outlines in dual-energy X-ray absorptiometry (DXA) images of adults. The aim of the study was to determine the accuracy with which a non-expert radiographer could use the software to annotate vertebrae in support of osteoporotic vertebral fracture diagnosis and grading. In this study, 71 GE Lunar iDXA vertebral fracture assessment (VFA) images were used. Annotations of landmark points on vertebral body outlines were performed by four observers. Annotations consisted of 33 points on each vertebra between T4 and L4 inclusive; 11 on the upper end-plate, 8 on the anterior side, 11 on the lower end-plate, and 3 on the pedicle (429 points for each image). There were a total of 19 (26%) cases in which the non-expert radiographer made vertebral level assignment errors. All of them were one level too high (with L1 identified as T12). Their median error for landmark annotation was 1.05 mm, comparable to the 0.8 mm error achieved by the expert radiographers. Normative mean vertebral body heights vary between approximately 22 mm at T4 and 36 mm at L4 in females. Mild, moderate, and severe vertebral fragility fractures are defined through vertebral body height reductions of 20%, 25%, and 40%, respectively. Therefore, the annotation accuracy of the software when used by a non-expert was 14–23% of the height reduction indicative of a mild fracture. We conclude that, even when used by non-experts, the software can annotate vertebral body outlines accurately enough to support vertebral fragility fracture diagnosis and grading.
Approximate Computing-Based Processing of MEA Signals on FPGA
Authors: Hassan Mohammad , Awwad Falah , Atef Mohamed , Hasan Osman
Published: February 2023

Microelectrode arrays (MEAs) are essential equipment in neuroscience for studying the nervous system’s behavior and organization. MEAs are arrays of parallel electrodes that work by sensing the extracellular potential of neurons in their proximity. Processing the data streams acquired from MEAs is a computationally intensive task requiring parallelization. It is performed using complex signal processing algorithms and architectural templates. In this paper, we propose using approximate computing-based algorithms on Field Programmable Gate Arrays (FPGAs), which can be very useful in custom implementations for processing neural signals acquired from MEAs. The motivation is to provide better performance gains in the system area, power consumption, and latency associated with real-time processing at the cost of reduced output accuracy within certain bounds. Three types of approximate adders are explored in different configurations to develop the signal processing algorithms. The algorithms are used to build approximate processing systems on FPGA and then compare them with the accurate system. All accurate and approximate systems are tested on real biological signals with the same settings. Results show an enhancement in processing speed of up to 37.6% in some approximate systems without a loss in accuracy. In other approximate systems, the area reduction is up to 14.3%. Other systems show the trade between processing speed, accuracy, and area.
ASSIST-IoT: A Modular Implementation of a Reference Architecture for the Next Generation Internet of Things
Authors: Szmeja Paweł , Fornés-Leal Alejandro , Lacalle Ignacio , Palau Carlos E. , Ganzha Maria , Pawłowski Wiesław , Paprzycki Marcin , Schabbink Johan
Published: February 2023

Next Generation Internet of Things (NGIoT) addresses the deployment of complex, novel IoT ecosystems. These ecosystems are related to different technologies and initiatives, such as 5G/6G, AI, cybersecurity, and data science. The interaction with these disciplines requires addressing complex challenges related with the implementation of flexible solutions that mix heterogeneous software and hardware, while providing high levels of customisability and manageability, creating the need for a blueprint reference architecture (RA) independent of particular existing vertical markets (e.g., energy, automotive, or smart cities). Different initiatives have partially dealt with the requirements of the architecture. However, the first complete, consolidated NGIoT RA, covering the hardware and software building blocks, and needed for the advent of NGIoT, has been designed in the ASSIST-IoT project. The ASSIST-IoT RA delivers a layered and modular design that divides the edge-cloud continuum into independent functions and cross-cutting capabilities. This contribution discusses practical aspects of implementation of the proposed architecture within the context of real-world applications. In particular, it is shown how use of cloud-native concepts (microservices and applications, containerisation, and orchestration) applied to the edge-cloud continuum IoT systems results in bringing the ASSIST-IoT concepts to reality. The description of how the design elements can be implemented in practice is presented in the context of an ecosystem, where independent software packages are deployed and run at the selected points in the hardware environment. Both implementation aspects and functionality of selected groups of virtual artefacts (micro-applications called enablers) are described, along with the hardware and software contexts in which they run.
A Survey of Recent Advances in Quantum Generative Adversarial Networks
Authors: Ngo Tuan A. , Nguyen Tuyen , Thang Truong Cong
Published: February 2023

Quantum mechanics studies nature and its behavior at the scale of atoms and subatomic particles. By applying quantum mechanics, a lot of problems can be solved in a more convenient way thanks to its special quantum properties, such as superposition and entanglement. In the current noisy intermediate-scale quantum era, quantum mechanics finds its use in various fields of life. Following this trend, researchers seek to augment machine learning in a quantum way. The generative adversarial network (GAN), an important machine learning invention that excellently solves generative tasks, has also been extended with quantum versions. Since the first publication of a quantum GAN (QuGAN) in 2018, many QuGAN proposals have been suggested. A QuGAN may have a fully quantum or a hybrid quantum–classical architecture, which may need additional data processing in the quantum–classical interface. Similarly to classical GANs, QuGANs are trained using a loss function in the form of max likelihood, Wasserstein distance, or total variation. The gradients of the loss function can be calculated by applying the parameter-shift method or a linear combination of unitaries in order to update the parameters of the networks. In this paper, we review recent advances in quantum GANs. We discuss the structures, optimization, and network evaluation strategies of QuGANs. Different variants of quantum GANs are presented in detail.
A Secure Internet of Medical Things Framework for Breast Cancer Detection in Sustainable Smart Cities
Authors: Aldhyani Theyazn H. H. , Khan Mohammad Ayoub , Almaiah Mohammed Amin , Alnazzawi Noha , Hwaitat Ahmad K. Al , Elhag Ahmed , Shehab Rami Taha , Alshebami Ali Saleh
Published: February 2023

Computational intelligence (CI) and artificial intelligence (AI) have incredible roles to play in the development of smart and sustainable healthcare systems by facilitating the integration of smart technologies with conventional medical procedures. The Internet of Things (IoT) and CI healthcare systems rely heavily on data collection and machine learning since miniature devices represent the foundation and paradigm shift to sustainable healthcare. With these advancements in AI techniques, we can reduce our environmental impact, while simultaneously enhancing the quality of our services. Widespread use of these devices for innovative IoT applications, however, generates massive amounts of data, which can significantly strain processing power. There is still a need for an efficient and sustainable model in the area of disease predictions, such as lung cancer, blood cancer, and breast cancer. The fundamental purpose of this research is to prove the efficacy of a secure Internet of Medical Things (IoMT) in the detection and management of breast cancer via the use of gated recurrent units (GRUs), which are a more recent version of recurrent neural networks (RNNs). The blockchain has been employed to achieve the secure IoMT. Unlike long short-term memory units, they do not have a cell state of their own. Therefore, we have combined GRU with RNN to achieve the best results. When training a GRU-RNN classifier, it is typically necessary to collect tagged IoT data from many sources, which raises significant concerns about the confidentiality of the data. To verify the model, the experiment is performed on Wisconsin Diagnostic Breast Cancer (WDBC). The experimental result shows that the GRU-RNN has been archived 95% in terms of the accuracy metric, and the efficacy of the proposed IoMT model is superior to the existing approach in terms of accuracy, precision, and recall.
The Importance of Laminae for China Lacustrine Shale Oil Enrichment: A Review
Authors: Xu Shang , Gou Qiyang
Published: February 2023

The laminar structure of shale system has an important influence on the evaluation of hydrocarbon source rock quality, reservoir quality, and engineering quality, and it is receiving increasing attention. A systematic study of the lamina structure is not only of great scientific significance but also of vital practical importance for shale oil production. In this paper, the identification and description classification of shale laminae are first reviewed. Multiple scales and types indicate that a combination of different probe techniques is the basis for an accurate evaluation of shale laminar characteristics. The influence of laminae on shale reservoir, oil-bearing, mobility, and fracability properties is discussed systematically. A comparative analysis shows that shale systems with well-developed lamination facilitate the development of bedding fractures, thus improving the shale storage space. The average pore size and pore connectivity are also enhanced. These factors synergistically control the superior retention and flow capacity of shale oil in laminated shales. In such conditions, the high production of shale oil wells can still be achieved even if complex networks of fracturing cracks are difficult to form in shale systems with well-developed lamination. This work is helpful to reveal the enrichment mechanism of shale oil and clarify the high-yield law of hydrocarbons, so as to guide the selection of sweet spots.
Effects of Different Afforestation Years on Soil Properties and Quality
Authors: Yao Wenxiu , Nan Fusen , Li Yage , Li Yuchen , Liang Pengfei , Zhao Changming
Published: February 2023

As an important means of curbing soil degradation, afforestation has a profound impact on regional soil properties and quality. However, it is still unclear regarding how to conduct a systematic assessment of soil properties and soil quality and the impact of vegetation characteristics and plant functional traits in leguminous plantations with different afforestation years in drylands. Therefore, we investigated the vegetation characteristics and determined the functional traits of leaves and roots and the soil physicochemical properties of Caragana korshinskii plantations with 13, 35, and 55 years. The results showed that tree height; crown diameter (CD); root dry matter content; root water content; soil clay, silt, and sand contents; bulk density (BD); soil water content; soil organic carbon (SOC); total nitrogen (TN); available nitrogen; total phosphorus (TP); available phosphorus (AP); and soil quality index (SQI) changed significantly with an increase in afforestation years. Although the specific leaf area did not show a significant variation, it had a significant negative effect on soil properties and SQI, except for soil sand and BD. Soil sand and BD decreased with the afforestation succession, but the succession pattern of soil clay, silt, SOC, TN, TP, and AP was 13 years < 35 years < 55 years, and SQI increased from 0.20 (13 years) to 0.77 (55 years). This indicated that long-term legume afforestation led to the transformation of soil texture from silty loam to silt and significantly improved the soil properties and quality in the study area.
Field Efficacy of Fluopyram Suspension Concentrate against Pine Wilt Disease and Its Distribution and Persistence in Pine Tree Tissues
Authors: Guo Yajie , Ma Jiayi , You Ziyi , Weng Mingqing , Carballar-Lejarazú Rebeca , Jiao Wenlong , Wu Jieqin , Hu Xia , Wang Rong , Zhang Feiping , Wu Songqing
Published: February 2023

Fluopyram is a novel nematicide compound with systemic activity, and it is a potential chemical agent for controlling pine wilt disease, which is devastating pine trees worldwide. Here, a fluopyram suspension concentrate (SC) was used to determine its efficacy in controlling pine wilt disease, its distribution in pine tree tissues, and its environmental degradation. Fluopyram SC had an evident inhibitory effect on Pestalotiopsis sp. and showed a good preventive effect on pine wilt disease in healthy pine seedlings. In the field application, after spraying onto the canopy of pine trees, fluopyram was absorbed and translocated downward to all parts of the tree via the branches and needles, and its distribution was stable at 30 days post-application (all samples’ mean residual concentration = 1.166 mg/kg). In addition, the maximum concentration of fluopyram residue in soil and water was 1.139 and 0.099 mg/kg, respectively, at 30 days post-application when fluopyram was significantly degraded. The control efficiency of fluopyram SC in the forest reached 90.48% when the formulation was sprayed by agricultural unmanned aerial vehicles. The half-life of fluopyram SC was 346.6 days, with a persistence of 3 years. These results indicate that spraying fluopyram SC could effectively reduce the number of pine trees dying from pine wilt disease.
Casting Light on the Micro-Organisms in Digestate: Diversity and Untapped Potential
Authors: Roopnarain Ashira , Akindolire Muyiwa Ajoke , Rama Haripriya , Ndaba Busiswa
Published: February 2023

Anaerobic digestion (AD) is an established process for waste conversion to bioenergy. However, for the AD process to be viable, it is imperative that all products be adequately valorized to maximize the benefits associated with the technology and in turn promote economic feasibility and technology uptake. Digestate is a byproduct of the AD process that is oftentimes overshadowed by the primary product, biogas, however the potential of digestate is vast. Digestate is composed of undigested organic matter, inorganic matter, and microorganisms. Whilst digestate has frequently been utilized as a soil amendment due to its abundance of readily available plant nutrients, the microbial content of digestate is oftentimes neglected or undermined. The array of microbes prevalent in digestate may contribute to expanding its potential applications. This microbial composition is shaped by several factors including resident microbial communities in inoculum and feedstock, feedstock composition, temperature of the AD system, AD additives and augmenting agents as well as post-treatment strategies, amongst others. Hence, it is hypothesized that digestate microbial content can be manipulated to target particular downstream applications by altering the above-mentioned factors. In so doing, the value of the produced digestate may be improved, which may even lead to digestate becoming the most lucrative product of the AD process. This review provides a holistic overview of the factors influencing the microbial community structure of digestate, the microorganisms in digestate from diverse AD systems and the associated microbial functionality as well as the potential applications of the digestate from a perspective of the resident microflora. The aim of the paper is to highlight the vast potential of microorganisms in digestate so as to broaden its applicability and value.
Kinetic Study of Pyrolysis of Coniferous Bark Wood and Modified Fir Bark Wood
Authors: Fetisova Olga Yu. , Mikova Nadezhda M. , Chudina Anna I. , Kazachenko Aleksandr S.
Published: February 2023

We report on the kinetics of pyrolysis of bark wood of four coniferous tree species: fir (Abies sibirica), larch (Larix sibirica), spruce (Picea obovata), and cedar (Pinus sibirica) denoted as FB, LB, SB, and CB, respectively. Thermogravimetry (TG) and differential scanning calorimetry (DSC) methods were used to study the influence of KCl and K3PO4 compounds on the process of thermal decomposition of fir bark and determine the main thermal effects accompanying this process. As a result of the studies carried out, it was found that KCl additives practically do not affect the decomposition of hemicelluloses, but they shift the maximum decomposition of the cellulose peak in the direction of decreasing temperature to 340.9 °C compared to untreated bark (357.5 °C). K3PO4 promotes the simultaneous decomposition of hemicelluloses and cellulose in the temperature range with a maximum of 277.8 °C. In both cases, the additions of KCl and K3PO4 reduce the maximum rate of weight loss, which leads to a higher yield of carbon residues: the yield of char from the original fir bark is 28.2%, in the presence of K3PO4 and KCl it is 52.6 and 65.0%, respectively. Using the thermogravimetric analysis in the inert atmosphere, the reaction mechanism has been established within the Criado model. It is shown that the LB, SB, and CB thermal decomposition can be described by a two-dimensional diffusion reaction (D2) in a wide range (up to 0.5) of conversion values followed by the reactions with orders of three (R3). The thermal decomposition of the FB occurs somewhat differently. The diffusion mechanism (D2) of the FB thermal decomposition continues until a conversion value of 0.6. As the temperature increases, the degradation of the FB sample tends to R3. It has been found by the thermogravimetric analysis that the higher cellulose content prevents the degradation of wood. The bark wood pyrolysis activation energy has been calculated within the Coats–Redfern and Arrhenius models. The activation energies obtained within these models agree well and can be used to understand the complexity of biomass decomposition.
Bluegill Population Demographics as Related to Abiotic and Biotic Factors in Florida Lakes
Authors: Carlson Andrew K. , Hoyer Mark V.
Published: February 2023

Research on Bluegills, Lepomis macrochirus R., is abundant but typically focuses on water bodies with similar environmental conditions. We assessed Bluegill density, relative abundance (catch per unit effort [CPUE] by electrofishing), growth, and size structure in 60 lakes with wide-ranging surface areas (2–12,412 ha), trophic states (oligotrophic–hypereutrophic), and macrophyte abundances (0.3–100 percent of lake volume inhabited [PVI]) across Florida, USA. Bluegill density and CPUE increased with lake productivity and decreased with macrophyte abundance. Bluegill growth increased with lake productivity and CPUE of stock-length Florida Bass, Micropterus floridanus L., a Bluegill predator. Bluegill size structure increased with lake productivity and decreased with Bluegill density. Results indicate that Bluegill fisheries with abundant individuals of quality size (≥150 mm) require productive (>25 μg/L chlorophyll-a concentration) lakes with moderate to high macrophyte coverage (PVI 50–100), abundant stock-length Florida Bass (>40 fish/h of electrofishing), and Bluegill densities <300 fish/ha. This study provides an approach to predict Bluegill population demographics based on abiotic and biotic factors, establish fisheries management expectations, and develop regional and lake-specific management tools.
Ideal Reactors as an Illustration of Solving Transport Phenomena Problems in Engineering
Authors: Laín Santiago , Gandini Mario A.
Published: February 2023

This contribution aims at emphasizing the importance of ideal reactors in the field of environmental engineering and in the education of the corresponding engineers. The exposition presents the mass flow governing equations of the ideal reactors (batch, completely mixed flow, and plug flow reactors) as particular cases derived from the integral version of the conservation of mass of a chemical/biological species. In the case of transient problems and simple kinetics, such expressions result in first-order ordinary differential equations amenable to be solved analytically when they are linear. In this article, it is shown that when they are non-linear, due to the presence of a second-order kinetics reaction, an analytical solution is also possible, a situation not dealt with in the textbooks. Finally, the previous findings are integrated into a teaching proposal addressed to help undergraduate students to solve more efficiently ideal reactor problems.
In Vitro Inhibitory Effects of Polyphenols from Flos sophorae immaturus on α-Glucosidase: Action Mechanism, Isothermal Titration Calorimetry and Molecular Docking Analysis
Authors: Gong Yuhong , Li Jun , Li Jinwei , Wang Li , Fan Liuping
Published: February 2023

Flos sophorae immaturus (FSI) is considered to be a natural hypoglycemic product with the potential for a-glucosidase inhibitory activity. In this work, the polyphenols with α-glucosidase inhibition in FSI were identified, and then their potential mechanisms were investigated by omission assay, interaction, type of inhibition, fluorescence spectroscopy, circular dichroism, isothermal titration calorimetry and molecular docking analysis. The results showed that five polyphenols, namely rutin, quercetin, hyperoside, quercitrin and kaempferol, were identified as a-glucosidase inhibitors with IC50 values of 57, 0.21, 12.77, 25.37 and 0.55 mg/mL, respectively. Quercetin plays a considerable a-glucosidase inhibition role in FSI. Furthermore, the combination of quercetin with kaempferol generated a subadditive effect, and the combination of quercetin with rutin, hyperoside and quercitrin exhibited an interference effect. The results of inhibition kinetics, fluorescence spectroscopy, isothermal titration calorimetry and molecular docking analysis showed that the five polyphenols were mixed inhibitors and significantly burst the fluorescence intensity of α-glucosidase. Moreover, the isothermal titration calorimetry and molecular docking analysis showed that the binding to α-glucosidase was a spontaneous heat-trapping process, with hydrophobic interactions and hydrogen bonding being the key drivers. In general, rutin, quercetin, hyperoside, quercitrin and kaempferol in FSI are potential α-glucosidase inhibitors.
Characterization of Lactic Acid Bacteria Isolated from Spontaneously Fermented Sausages: Bioprotective, Technological and Functional Properties
Authors: Nikodinoska Ivana , Tabanelli Giulia , Baffoni Loredana , Gardini Fausto , Gaggìa Francesca , Barbieri Federica , Di Gioia Diana
Published: February 2023

Fermentation is one of the most ancient strategies to improve safety and extend shelf-life of the products. Starter cultures are mainly represented by lactic acid bacteria (LAB), which may also be bioprotective agents controlling the fermentation process, the native microbiota and pathogen outgrowth. This work aimed to select new LAB strains from spontaneously fermented sausages produced in different areas of Italy, which can be effective as starter cultures and bioprotective agents in fermented salami. The strains, mainly belonging to the Latilactobacillus sakei species, were characterized for their ability to inhibit major meat pathogens, the presence of antibiotic resistances and amine production. Moreover, technological performances, such as growth and acidification kinetics at increasing NaCl concentrations, were studied. As a result, new autochthonous Lat. sakei strains were obtained, lacking antibiotic resistance, possessing antimicrobial activity against Clostridium sporogenes, Listeria monocytogenes, Salmonella and Escherichia coli and with high growth performance under osmotic pressure. These strains have the potential for future application to improve the safety of fermented meats, even under conditions in which chemical preservatives are reduced or eliminated. Moreover, studies on autochthonous cultures are pivotal for guaranteeing specific characteristics of traditional products that represent an important cultural heritage.
A Systematical Rheological Study of Maize Kernel
Authors: Sheng Shaoyang , Shi Aimin , Xing Junjie
Published: February 2023

In this study, the rheological behavior of maize kernel was systematically investigated using a dynamic mechanical analyzer. The loss in toughness caused by drying resulted in a downward shift in the relaxation curve and an upward shift in the creep curve. The long relaxation behavior became obvious when the temperature was above 45 °C, resulting from the weakening of hydrogen bonds with temperature. The maize kernel relaxed more rapidly at high temperatures, caused by a reduction in the cell wall viscosity and polysaccharide tangles. The Deborah numbers were all much smaller than one, suggesting that the Maxwell elements showed viscous behavior. Maize kernel, as a viscoelastic material, showed a dominant viscous property at high temperatures. The decline in β with increasing drying temperature indicated an increase in the width of the relaxation spectrum. A Hookean spring elastic portion made up the majority of the maize kernel creep strain. The order–disorder transformation zone of maize kernel was about 50–60 °C. Due to the complexity of maize kernel, the William–Landel–Ferry constants differed from the universal values; these constants should be ascertained through experiments. Time-temperature superposition was successfully used to describe the rheological behavior. The results show that maize kernel is a thermorheologically simple material. The data acquired in this study can be used for maize processing and storage.
Effects of Citrus Fiber on the Gel Properties of Mutton Myofibrillar Protein
Authors: Zhu Chenyan , Wang Shouwei , Bai Yanhong , Zhang Shunliang , Zhang Xin , Wu Qianrong , He Xiangli
Published: February 2023

This work investigated the effects of different additions of water-soluble citrus fiber (SCF) and water-insoluble citrus fiber (ICF) on the gel properties of the mutton myofibrillar protein (MP). The key parameters of water-holding capacity (WHC), rheological properties, and microstructure were evaluated. The addition of 2.5–10% of SCF and ICF significantly improved (p < 0.05) the WHC and gel strength of mutton MP gel. The rheological results showed that the viscoelasticity of MP with 5% SCF was the best, and the T2 relaxation time of the gel was significantly shortened. SEM results showed SCF reduced the number of pores in the MP gel, forming a more compact network structure. ICF stabilized the MP gel network structure as a filler after water absorption and expansion. However, the gel lost moisture under the action of strong external force (freeze-drying), which left large pores. These data confirmed that SCF and ICF could effectively improve the gel properties of meat products.
Fluoride Levels in Supply Water from the Canary Islands Region
Authors: Revelo-Mejía Inés A. , Alejandro-Vega Samuel , Paz-Montelongo Soraya , Niebla-Canelo Daniel , Cerdán-Pérez Santiago , Rubio-Armendáriz Carmen , Gutiérrez-Fernández Ángel J. , Hardisson Arturo , Rodríguez-Díaz Rubí , Hernández-Sánchez Cintia
Published: February 2023

The Canary Islands, located in the Atlantic Ocean, are an archipelago of volcanic origin which, for decades, has been affected by natural fluoride contamination in the water supply of some of its islands, mainly the island of Tenerife. In addition, recent volcanic eruptions in the archipelago and the increased demand for water supply have led to an increase in the fluoride content in other areas which, historically, were not affected. Fluoride content was determined in 274 water supply samples from the most populated islands of the Canary Islands (Tenerife and Gran Canaria) collected during the months of June 2021 to May 2022. The samples were analysed by fluoride ion selective potentiometry. The highest concentrations in Tenerife were found in the municipalities of Sauzal (7.00 mg/L) and Tegueste (5.39 mg/L), both water samples are over the parametric value of 1.5 mg/L set in the supply water legislation. In the Gran Canaria Island, the highest fluoride levels were found in Valsequillo and Mogán with 1.44 mg/L in both locations, but under the parametric fluoride value abovementioned. Consumption of just 1 L of water per day in the El Sauzal area would result in a contribution rate of 77% for adults and children over 15 years of age (Upper Level value of 7 mg/day) and 108% for children 9–14 years of age (UL value of 5 mg/day). The contribution rates increase considerably, reaching or exceeding 100% of the reference value (UL) with increasing consumption of 1 to 2 L of water per day. Therefore, it is considered that there is a health risk of overexposure to fluoride on the island of Tenerife. In the case of the island of Gran Canaria, it has been shown that even the consumption of 2 litres of water per day does not confer contribution rates that pose a health risk.
Extraction Optimization and Characterization of Cellulose Nanocrystals from Apricot Pomace
Authors: Dinçel Kasapoğlu Ekin , Kahraman Sibel , Tornuk Fatih
Published: February 2023

Apricot pomace (AP) is lignocellulosic agro-industrial waste that could be considered a good source for cellulose-based, value-added compounds. In this study, conditions for cellulose nanocrystals’ (CNCs) extraction from apricot pomace (AP) were optimized using Response Surface Methodology (RSM) based on the extraction yield, and the resulting CNC was characterized using Fourier transform infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), Transmittance Electron Microscopy (TEM), Thermogravimetric Analysis (TGA), and X-Ray Diffraction (XRD). The maximum CNC yield (34.56%) was obtained at a sulfuric acid concentration of 9.5 M within 60 min. FTIR analysis showed that noncellulosic components were gradually removed from the pomace. A morphological analysis of the nanocrystal was performed using SEM and TEM. CNCs were in the range of 5–100 µm in diameter and appeared as individual fibers. TGA analysis of the CNC sample revealed good thermal stability around 320°C. The crystalline index (%CI) of the CNC obtained from AP was determined to be 67.2%. In conclusion, this study demonstrated that AP could be considered a sustainable source for value-added compounds such as CNCs to contribute to a circular economy.
Involving Forensic Students in Integrative Learning—A Project Proposal
Authors: Teixeira A. , Azevedo A. , Pérez-Mongiovi D. , Caldas I. M. , Costa-Rodrigues J.
Published: February 2023

In our experience, university students enrolling in health science and forensic science degrees show difficulty in retaining and integrating basic scientific knowledge learned in their first academic year. Furthermore, in the forensic sciences case, many students have oversimplified and unrealistic expectations as a result of the exposure to crime TV shows, internet blogs, and other social media platforms. Our pedagogical proposal is focused on second-year university students, aiming at promoting effective learning and the integration of scientific knowledge from previous courses, in this particular example, molecular and cell biology and biochemistry, with more advanced forensic courses, such as forensic anthropology and odontology. Teams composed of students and tutors from the teaching staff, with the help of dichotomous keys, are challenged to analyze a crime scene and choose the relevant evidence to further investigate, determine the scientific approach, execute the experimental work, interpret the results and, finally, resolve the case. To assess the pedagogical advantages and the receptivity of this project, a survey is to be carried out among students, and respective statistical analysis is also proposed. Finally, we hope this project outline may be adapted to other subjects, and, therefore, be used to address different pedagogical questions in forensic studies.
Some New Fractal Milne-Type Integral Inequalities via Generalized Convexity with Applications
Authors: Meftah Badreddine , Lakhdari Abdelghani , Saleh Wedad , Kiliçman Adem
Published: February 2023

This study aims to construct some new Milne-type integral inequalities for functions whose modulus of the local fractional derivatives is convex on the fractal set. To that end, we develop a novel generalized integral identity involving first-order generalized derivatives. Finally, as applications, some error estimates for the Milne-type quadrature formula and new inequalities for the generalized arithmetic and p-Logarithmic means are derived. This paper’s findings represent a significant improvement over previously published results. The paper’s ideas and formidable tools may inspire and motivate further research in this worthy and fascinating field.
Application on Similarity Relation and Pretopology
Authors: Azzam A. A. , Nasr Asmaa. M. , ElGhawalby Hewayda , Mareay R.
Published: February 2023

Due to the tremendous use of computers, the need for dealing with digital information continues to grow. This has led to the need for constructing a structure for saving information in a way that eases data retrieval and processing. An information system is a table form that contains all the data needed for a user to reach a decision. Information systems are the most common form used for knowledge and data representation, and attribute reduction plays an important role in data processing. This paper is devoted to proposing a strategy for attributes reduction based on the similarity relation and pretopological concepts. Moreover, new types of pretopological spaces are to be constructed from the information system. Furthermore, a comparison between pretopologies and their pre-interiors constructed from the information system, as well as the different types of pretopological spaces, is investigated. Finally, the concept of the cover pretopology is applied to the information system.
A Novel Regression Model for Fractiles: Formulation, Computational Aspects, and Applications to Medical Data
Authors: Leiva Víctor , Mazucheli Josmar , Alves Bruna
Published: February 2023

Covariate-related response variables that are measured on the unit interval frequently arise in diverse studies when index and proportion data are of interest. A regression on the mean is commonly used to model this relationship. Instead of relying on the mean, which is sensitive to atypical data and less general, we can estimate such a relation using fractile regression. A fractile is a point on a probability density curve such that the area under the curve between that point and the origin is equal to a specified fraction. Fractile or quantile regression modeling has been considered for some statistical distributions. Our objective in the present article is to formulate a novel quantile regression model which is based on a parametric distribution. Our fractile regression is developed reparameterizing the initial distribution. Then, we introduce a functional form based on regression through a link function. The main features of the new distribution, as well as the density, distribution, and quantile functions, are obtained. We consider a brand-new distribution to model the fractiles of a continuous dependent variable (response) bounded to the interval (0, 1). We discuss an R package with random number generators and functions for probability density, cumulative distribution, and quantile, in addition to estimation and model checking. Instead of the original distribution-free quantile regression, parametric fractile regression has lately been employed in several investigations. We use the R package to fit the model and apply it to two case studies using COVID-19 and medical data from Brazil and the United States for illustration.
New Complex Wave Solutions and Diverse Wave Structures of the (2+1)-Dimensional Asymmetric Nizhnik–Novikov–Veselov Equation
Authors: Wu Guojiang , Guo Yong
Published: February 2023

In this paper, we use a new, extended Jacobian elliptic function expansion method to explore the exact solutions of the (2+1)-dimensional asymmetric Nizhnik–Novikov–Veselov (aNNV) equation, which is a nonlinear physical model to describe an incompressible fluid. Combined with the mapping method, many new types of exact Jacobian elliptic function solutions are obtained. As we use two new forms of transformation, most of the solutions obtained are not found in previous studies. To show the complex nonlinear wave phenomena, we also provide various wave structures of a group of solutions, including periodic wave and solitary wave structures of ordinary traveling wave solutions, horseshoe-type wave, s-type wave and breaker-wave structures superposed by two kinds of waves: chaotic wave structures with chaotic behavior and spiral wave structures. The results show that this method is effective and powerful and can be used to construct various exact solutions for a wide range of nonlinear models and complex nonlinear wave phenomena in mathematical and physical research.
A Synergistic Antibacterial Platform Combining Low–Temperature Photothermal Therapy and Antibiotic Therapy
Authors: Zhang Qiming , Chang Lei , Sun Caixia , Zuo Wanchao , Zhang Shibo , Liu Cong , Deng Shuyue , Wu Pengcheng , Dai Panpan , Dai Jianjun , Ju Yanmin
Published: February 2023

Antimicrobial resistance has brought great burden to global public health. Alternative strategies are needed to reduce the development of drug resistance. Herein, we have developed an effective synergistic antibacterial strategy combining low–temperature photothermal therapy (LT–PTT) with antibiotic therapy, improving the bactericidal efficiency to avoid antimicrobial resistance. Copper sulfide templated with bovine serum albumin (CuS–BSA) nanoparticles were selected as the photothermal agent, and co–loaded into the hydrogel (Gel) with mupirocin. The Gel could slow down the release rate of CuS–BSA and mupirocin, thereby prolonging the effective drug reaction time. More importantly, when applying near–infrared laser irradiation, the antibacterial activity of the platform could be enhanced greatly by LT–PTT effect of CuS–BSA nanoparticles. In vitro and in vivo results both confirmed that the antibacterial efficacy of the synergistic therapeutic strategy was improved greatly with complete bacterial removal. Overall, this platform has posed a potential strategy to reduce the development of drug resistance and improve patient compliance.
Effect of Storage Time and Temperature on Digestibility, Thermal, and Rheological Properties of Retrograded Rice
Authors: Chakraborty Ishita , Govindaraju Indira , Kunnel Steffi , Managuli Vishwanath , Mazumder Nirmal
Published: February 2023

Retrogradation is defined as the recrystallization or realignment of amylose and amylopectin chains upon cooling of gelatinization starch gels. The storage conditions such as the storage time and temperature are crucial factors that influence and govern the degree of retrogradation and in turn, affect the formation of resistant starch and alteration of thermal and rheological properties. This article investigates the effect of storage time and temperature on the properties of retrograded rice starch. Rice kernels of five different indigenous varieties, namely Diasang lahi, Khaju lahi, Dhusuri bao, Omkar, and Bili rajamudi were cooked by boiling in water and stored at 4 °C and −20 °C for 6 and 12 h, respectively. Differential scanning calorimetry (DSC) studies revealed in raw form that Bili rajamudi exhibited the highest peak gelatinization temperature (Tp, °C) at 79.05 °C whereas Diasang lahi showed the least Tp at 56.12 °C. Further, it was indicated that the Tp and degree of retrogradation (DR%) also increase with increasing time and decreasing temperature of storage. All samples stored at −20 °C for 12 h exhibited the highest degree of retrogradation DR%. Amongst all five varieties stored at −20 °C for 12 h, Omkar exhibited the highest %DR, followed by Bili rajamudi, Khaju lahi, Dhusuri bao, and Diasang lahi. A negative correlation was also established between Tp and resistant starch content (RS%). It was also observed that the resistant starch (RS%) content increased with the increasing time and decreasing temperature of storage. A strong negative correlation was observed between RS% and non-resistant starch (NRS%). Further, rheological studies indicated that retrogradation also affects the viscosity and dynamic rheological properties of starch. In this study, it was evident that extending storage duration from 6 to 12 h and lowering temperature from 4 to −20 °C impact retrogradation of rice starch, which in turn affects the starch’s gelatinization, digestibility, and rheology. Rice starch retrograded at lower temperatures for a longer period could prove to be extremely beneficial for development of food products with better textural properties and high RS content or low glycemic index.
Automatic Detection and Measurement of Renal Cysts in Ultrasound Images: A Deep Learning Approach
Authors: Kanauchi Yurie , Hashimoto Masahiro , Toda Naoki , Okamoto Saori , Haque Hasnine , Jinzaki Masahiro , Sakakibara Yasubumi
Published: February 2023

Ultrasonography is widely used for diagnosis of diseases in internal organs because it is nonradioactive, noninvasive, real-time, and inexpensive. In ultrasonography, a set of measurement markers is placed at two points to measure organs and tumors, then the position and size of the target finding are measured on this basis. Among the measurement targets of abdominal ultrasonography, renal cysts occur in 20–50% of the population regardless of age. Therefore, the frequency of measurement of renal cysts in ultrasound images is high, and the effect of automating measurement would be high as well. The aim of this study was to develop a deep learning model that can automatically detect renal cysts in ultrasound images and predict the appropriate position of a pair of salient anatomical landmarks to measure their size. The deep learning model adopted fine-tuned YOLOv5 for detection of renal cysts and fine-tuned UNet++ for prediction of saliency maps, representing the position of salient landmarks. Ultrasound images were input to YOLOv5, and images cropped inside the bounding box and detected from the input image by YOLOv5 were input to UNet++. For comparison with human performance, three sonographers manually placed salient landmarks on 100 unseen items of the test data. These salient landmark positions annotated by a board-certified radiologist were used as the ground truth. We then evaluated and compared the accuracy of the sonographers and the deep learning model. Their performances were evaluated using precision–recall metrics and the measurement error. The evaluation results show that the precision and recall of our deep learning model for detection of renal cysts are comparable to standard radiologists; the positions of the salient landmarks were predicted with an accuracy close to that of the radiologists, and in a shorter time.
A New Approach for Extending Shelf-Life of Pomegranate Arils with Combined Application of Salicylic Acid and Methyl Jasmonate
Authors: El-Beltagi Hossam S. , Al-Otaibi Hala Hazam , Ali Marwa R.
Published: February 2023

The consumption of fresh-cut pomegranate fruits (arils) has risen recently due to their bioactive compounds and benefits for consumers. However, pomegranate arils have a limited shelf-life and vastly lose their valuable compounds. Therefore, in this study, we investigated the effects of exogenous postharvest treatment with salicylic acid (SA), methyl Jasmonate (MeJA), and their combination on the shelf-life and chemical composition of pomegranate arils under refrigerated storage (5 °C and 90 ± 2% relative humidity) for 15 days. The results indicated that individual or combined application of SA at 2 mM + MeJA at 0.5 mM decreased weight loss, respiration rate, hue angle (h°), and soluble solids content (SSC) compared to the control. All treatments maintained vitamin C, titratable acidity (TA), anthocyanin content, flavonoids, phenolic compounds, and antioxidant capacity under cold conditions compared to the control. The combined application was more effective than the individual application. In conclusion, SA + MeJA application could be applied during the preparation of fresh-cut pomegranate for maintaining quality and bioactive compounds.