bluuebunny/crossref_metadata_2025 huggingface dataset:
The Internal Anatomy and Water Current System of Cambrian Archaeocyaths of South China
Authors: Wang Jiayue , Song Baopeng , Liang Yue , Liang Kun , Zhang Zhifei
Published: January 2024

Archaeocyaths are a group of extinct filter feeders that flourished in the early Cambrian period and occupied an important position in the evolution of basal fauna and the early marine ecosystem. However, the detailed morphological and anatomical information of this group are still unclear due to insufficient fossil material and limited experimental analyses. Here, we report exquisitely preserved phosphatized archaeocyathan fossil cups, ca. 515 million years old, from the top of the Shuijingtuo Formation (Series 2, Stage 3) and the Xiannüdong Formation (Series 2, Stage 3) of the Yangtze Platform, South China. Detailed observation of their external morphology via scanning electron microscopy (SEM) and micro-computed tomography (Micro-CT) analysis revealed detailed information of their internal structure. They have a typical double-walled cup, with the perforated inner and outer walls concentrically distributed, but the structure between the two walls differs. The inverted cone-shaped cups have radially distributed septa between the walls. Perforated septa connect the two walls. The low and columnar cups have canals between the two walls, forming the network. These pores and cavities constitute an important component of the water current system (pumping and filtering water with a network of canals and chambers) and influence the process of filtration in the cup. In comparison to traditional thin-section analysis, the combination of SEM and Micro-CT analysis on phosphatized archaeocyaths presented in this study further explored the detailed internal structure and finely reconstructed the microscopic overall morphology and anatomy, which provide important information to help us understand the systematic taxonomy, anatomy, and morphology of archaeocyaths during the Cambrian period.
Neuromodulation Techniques for Headache Management
Authors: Reffat Noora , Pusec Carolina , Price Scott , Gupta Mayank , Mavrocordatos Philippe , Abd-Elsayed Alaa
Published: January 2024

This narrative review aims to summarize evidence regarding the current utilization and future applications of neuromodulation in patients with headaches, with special attention paid to migraine and chronic cluster headache. A search was conducted in PubMed in August of 2023 to survey the current literature on neuromodulation for the treatment of headache. In total, the search yielded 1989 results, which were further filtered to include only systematic reviews published between 2022 to 2023 to capture the most up-to-date and comprehensive research on this topic. The citation lists of these articles were reviewed to find additional research on neuromodulation and supplement the results presented in this paper with primary literature. Research on the use of neuromodulation for the treatment of headache has predominantly focused on four neuromodulation techniques: peripheral nerve stimulation (PNS), transcranial magnetic stimulation (TMS), deep brain stimulation (DBS), and spinal cord stimulation (SCS). Outcome measures reported in this article include impact on migraine and headache frequency and/or pain intensity, adverse effects of the neuromodulation technique, and associated costs, when available. We found that neuromodulation has developed utility as an alternative treatment for both chronic cluster headaches and migraines, with a reduction in frequency and intensity of headache most elucidated from the articles mentioned in this review.
1-(2-(3,5-Di-tert-butyl-4-hydroxyphenyl)-2-oxoethyl) Quinolin-1-ium Bromide
Authors: Rusew Rusi , Iliev Kostadin , Kurteva Vanya , Shivachev Boris
Published: January 2024

The title compound 1-(2-(3,5-di-tert-butyl-4-hydroxyphenyl)-2-oxoethyl) quinolin-1-ium bromide was obtained in good yield by a facile one-pot, one-step synthetic procedure involving quinoline and an aromatic α-bromo ketone. The product was isolated using hot recrystallization from acetone/acetonitrile solution and characterized using powder and single-crystal XRD, 1D and 2D NMR, DSC, FT-IR, and HRMS analyses.
Ion Tracks and Nanohillocks Created in Natural Zirconia Irradiated with Swift Heavy Ions
Authors: Ishikawa Norito , Fukuda Shoma , Nakajima Toru , Ogawa Hiroaki , Fujimura Yuki , Taguchi Tomitsugu
Published: January 2024

Natural monoclinic zirconia (baddeleyite) was irradiated with 340 MeV Au ions, and the irradiation-induced nanostructures (i.e., ion tracks and nanohillocks) were observed using transmission electron microscopy. The diameter of the nanohillocks was approximately 10 nm, which was similar to the maximum molten region size calculated using the analytical thermal spike model. Ion tracks were imaged as strained regions that maintained their crystalline structure. The cross-sections of most of the ion tracks were imaged as rectangular contrasts as large as 10 nm. These results strongly indicated that the molten region was recrystallized anisotropically, reflecting the lattice structure. Furthermore, low-density track cores were formed in the center of the ion tracks. The formation of low-density track cores can be attributed to the ejection of molten matter toward the surface. A comparison of the ion tracks in the synthetic zirconia nanoparticles and those in larger natural zirconia samples showed that the interface between the strained track contrast and the matrix was less clear in the former than in the latter. These findings suggest that the recrystallization process was affected by the size of the irradiated samples.
Study on the Effect of Sn, In, and Se Co-Doping on the Thermoelectric Properties of GeTe
Authors: Guo Tao , Zhang Guangbing , Nan Bohang , Xu Guiying , Li Shuo , Ren Lingling
Published: January 2024

GeTe and Ge0.99-xIn0.01SnxTe0.94Se0.06 (x = 0, 0.01, 0.03, and 0.06) samples were prepared by vacuum synthesis combined with spark plasma sintering (SPS). The thermoelectric properties of GeTe were coordinated by multiple doping of Sn, In, and Se. In this work, a maximum zT(zT = S2σT/κ) of 0.9 and a power factor (PF = S2σ) of 3.87 μWmm−1 K−2 were obtained in a sample of Ge0.99In0.01Te0.94Se0.06 at 723K. The XRD results at room temperature show that all samples are rhombohedral phase structures. There is a peak (~27°) of the Ge element in GeTe and the sample (x = 0), but it disappears after Sn doping, indicating that Sn doping can promote the dissolution of Ge. The scattering mechanism of the doped samples was calculated by the conductivity ratio method. The results show that phonon scattering Is dominant in all samples, and alloy scattering is enhanced with the increase in the Sn doping amount. In doping can introduce resonance energy levels and increase the Seebeck coefficient, and Se doping can introduce point defects to suppress phonon transmission and reduce lattice thermal conductivity. Therefore, the thermoelectric properties of samples with x = 0 improved. Although Sn doping will promote the dissolution of Ge precipitation, the phase transition of the samples near 580 K deteriorates the thermoelectric properties. The thermoelectric properties of Sn-doped samples improved only at room temperature to ~580 K compared with pure GeTe. The synergistic effect of multi-element doping is a comprehensive reflection of the interaction between elements rather than the sum of all the effects of single-element doping.
Advances in Non-Destructive Testing Methods
Authors: Peruń Grzegorz
Published: January 2024

Non-destructive testing (NDT) methods are a group of tests allowing one to detect external (surface) as well as internal defects of a structure [...]
The Construction of a Lattice Image and Dislocation Analysis in High-Resolution Characterizations Based on Diffraction Extinctions
Authors: Ni Kun , Wang Hanyu , Guo Qianying , Wang Zumin , Liu Wenxi , Huang Yuan
Published: January 2024

This paper introduces a method for high-resolution lattice image reconstruction and dislocation analysis based on diffraction extinction. The approach primarily involves locating extinction spots in the Fourier transform spectrum (reciprocal space) and constructing corresponding diffraction wave functions. By the coherent combination of diffraction and transmission waves, the lattice image of the extinction planes is reconstructed. This lattice image is then used for dislocation localization, enabling the observation and analysis of crystal planes that exhibit electron diffraction extinction effects and atomic jump arrangements during high-resolution transmission electron microscopy (HRTEM) characterization. Furthermore, due to the method’s effectiveness in localizing dislocations, it offers a unique advantage when analyzing high-resolution images with relatively poor quality. The feasibility of this method is theoretically demonstrated in this paper. Additionally, the method was successfully applied to observed edge dislocations, such as 1/6[211−], 1/6[2−11−], and 1/2[01−1], which are not easily observable in conventional HRTEM characterization processes, in electro-deposited Cu thin films. The Burgers vectors were determined. Moreover, this paper also attempted to observe screw dislocations that are challenging to observe in high-resolution transmission electron microscopy. By shifting a pair of diffraction extinction spots and superimposing the reconstructed images before and after the shift, screw dislocations with a Burgers vector of 1/2[011−] were successfully observed in electro-deposited Cu thin films.
Capacitance Determination for the Evaluation of Electrochemically Active Surface Area in a Catalyst Layer of NiFe-Layered Double Hydroxides for Anion Exchange Membrane Water Electrolyser
Authors: Xie Zhong , Qu Wei , Fisher Elizabeth A. , Fahlman Jason , Asazawa Koichiro , Hayashi Takao , Shirataki Hiroshi , Murase Hideaki
Published: January 2024

The determination of the electrochemically active surface area (ECSA) of a catalyst layer (CL) of a non-precious metal catalyst is of fundamental importance in optimizing the design of a durable CL for anion exchange membrane (AEM) water electrolysis, but has yet to be developed. Traditional double layer capacitance (Cdl), measured by cyclic voltammetry (CV), is not suitable for the estimation of the ECSA due to the nonconductive nature of Ni-based oxides and hydroxides in the non-Faradaic region. This paper analyses the applicability of electrochemical impedance spectroscopy (EIS) compared to CV in determining capacitances for the estimation of the ECSA of AEM-based CLs in an aqueous KOH electrolyte solution. A porous electrode transmission line (TML) model was employed to obtain the capacitance–voltage dependence from 1.0 V to 1.5 V at 20 mV intervals, covering both non-Faradic and Faradic regions. This allows for the identification of the contribution of a NiFe-layered double hydroxide (LDH) catalyst and supports in a CL, to capacitances in both non-Faradic and Faradic regions. A nearly constant double layer capacitance (Qdl) observed in the non-Faradic region represents the interfaces between catalyst supports and electrolytes. The capacitance determined in the Faradic region by EIS experiences a peak capacitance (QF), which represents the maximum achievable ECSA in an AEMCL during reactions. The EIS method was additionally validated in durability testing. An approximate 30% loss of QF was noted while Qdl remained unchanged following an eight-week test at 1 A/cm2 constant current density, implying that QF, determined by EIS, is sensitive to and therefore suitable for assessing the loss of ECSA. This universal method can provide a reasonable estimate of catalyst utilization and enable the monitoring of catalyst degradation in CLs, in particular in liquid alkaline electrolyte water electrolysis systems.
Femtosecond Laser Direct Writing of Flexible Electronic Devices: A Mini Review
Authors: Wang Shutong , Yang Junjie , Deng Guoliang , Zhou Shouhuan
Published: January 2024

By virtue of its narrow pulse width and high peak power, the femtosecond pulsed laser can achieve high-precision material modification, material additive or subtractive, and other forms of processing. With additional good material adaptability and process compatibility, femtosecond laser-induced application has achieved significant progress in flexible electronics in recent years. These advancements in the femtosecond laser fabrication of flexible electronic devices are comprehensively summarized here. This review first briefly introduces the physical mechanism and characteristics of the femtosecond laser fabrication of various electronic microdevices. It then focuses on effective methods of improving processing efficiency, resolution, and size. It further highlights the typical progress of applications, including flexible energy storage devices, nanogenerators, flexible sensors, and detectors, etc. Finally, it discusses the development tendency of ultrashort pulse laser processing. This review should facilitate the precision manufacturing of flexible electronics using a femtosecond laser.
Development of a Compact Incremental Forming Machine
Authors: Resende Tatiana P. , Carmo Gustavo P. , Afonso Daniel G. , Alves de Sousa Ricardo J.
Published: January 2024

Since the beginning of the 21st century, incremental sheet-metal-forming processes, such as single-point incremental forming (SPIF), have been the subject of extensive research. The SPIF process is highlighted as an efficient and cost-effective solution for producing complex parts with different materials and scales, surpassing conventional methods and being ideal for small series and customized products. Various machines can be used to implement SPIF, such as adapted milling machines, serial robots, and dedicated machines, each with its own advantages. However, although it requires a higher initial investment, a dedicated machine offers superior performance. The objective of this project was the creation of a compact and portable dedicated machine, which included the design of suitable kinematics, a mechanical project, and numerical control. The structural design led to the optimization of the dimensions of the robot arms. Direct and indirect kinematics were analyzed. Finally, the careful selection and adaptation of components were carried out, bearing in mind the support system of the forming punch, including the selection and sizing of motors, reducers, and linear actuators. A functional early prototype was successfully built and tested.
Finite Element Analysis of Different Transverse Flux Linear Induction Motor Models to Improve the Performance of the Main Magnetic Circuit
Authors: Domínguez Juan Antonio , Duro Natividad , Gaudioso Elena
Published: January 2024

This paper delves into the knowledge of transverse flux linear induction motors using three-dimensional finite element simulation tools. Original linear induction motors have a useful magnetic flux perpendicular to the movement. We propose some geometric changes to improve the main magnetic circuit of the machine and to ensure simultaneous operation between longitudinal and transverse magnetic fluxes. To obtain the main parameters of the equivalent electrical circuit in a steady state, we propose two steps. Firstly, replicate the classic indirect tests used in rotating machines. This represents a significant advantage since it allows several models to be experimentally tested to obtain the values of electrical parameters. Secondly, use the data from these tests to solve a particular system of equations using numerical methods. The solution provides the electrical elements necessary to generate the equivalent circuit proposed by the authors. A quantitative analysis of the main electrical parameters is also carried out, confirming the advantages of the changes introduced. With them, a significant improvement in thrust force is obtained, especially in stationary conditions and low speeds. Finally, we study, in detail, a set of specific phenomena of linear machines using two parameters: the secondary equivalent air gap and the secondary equivalent conductivity.
Investigation on the Dynamic Characteristics of Non-Orthogonal Helical Face Gears with Higher-Order Tooth Surface Modification
Authors: Jia Chao , Zhang Ge
Published: January 2024

A study on the dynamic characteristics of non-orthogonal helical face gears with higher-order tooth surface modification is presented in this article. The method of designing the non-orthogonal helical face gears with higher-order tooth surface modification is described. First, MATLAB programming that can be used for the parameterized 3D mesh calculations of non-orthogonal helical face gears with higher-order tooth surface modification are completed. Second, the calculated grid nodes from the MATLAB programming are imported into ABAQUS to generate a three-dimensional mode. The meshing stiffness of the gear pair is then estimated using finite element analysis. Ultimately, a dynamic model of a non-orthogonal helical face gear pair involving second-order and higher-order tooth surface modifications is established. One example is presented to study the dynamic characteristics of non-orthogonal helical face gear pairs with second-order and higher-order tooth surface modifications. The results show that the dynamic response from the second-order tooth surface modification has a higher peak-to-peak amplitude than that of the higher-order modification.
Enhanced Sea Horse Optimization Algorithm for Hyperparameter Optimization of Agricultural Image Recognition
Authors: Li Zhuoshi , Qu Shizheng , Xu Yinghang , Hao Xinwei , Lin Nan
Published: January 2024

Deep learning technology has made significant progress in agricultural image recognition tasks, but the parameter adjustment of deep models usually requires a lot of manual intervention, which is time-consuming and inefficient. To solve this challenge, this paper proposes an adaptive parameter tuning strategy that combines sine–cosine algorithm with Tent chaotic mapping to enhance sea horse optimization, which improves the search ability and convergence stability of standard sea horse optimization algorithm (SHO). Through adaptive optimization, this paper determines the best parameter configuration in ResNet-50 neural network and optimizes the model performance. The improved ESHO algorithm shows superior optimization effects than other algorithms in various performance indicators. The improved model achieves 96.7% accuracy in the corn disease image recognition task, and 96.4% accuracy in the jade fungus image recognition task. These results show that ESHO can not only effectively improve the accuracy of agricultural image recognition, but also reduce the need for manual parameter adjustment.
Diversified Chemical Structures and Bioactivities of the Chemical Constituents Found in the Brown Algae Family Sargassaceae
Authors: Peng Yan , Yang Xianwen , Huang Riming , Ren Bin , Chen Bin , Liu Yonghong , Zhang Hongjie
Published: January 2024

Sargassaceae, the most abundant family in Fucales, was recently formed through the merging of the two former families Sargassaceae and Cystoseiraceae. It is widely distributed in the world’s oceans, notably in tropical coastal regions, with the exception of the coasts of Antarctica and South America. Numerous bioactivities have been discovered through investigations of the chemical diversity of the Sargassaceae family. The secondary metabolites with unique structures found in this family have been classified as terpenoids, phlorotannins, and steroids, among others. These compounds have exhibited potent pharmacological activities. This review describes the new discovered compounds from Sargassaceae species and their associated bioactivities, citing 136 references covering from March 1975 to August 2023.
The Potential Harmful Effects of Genetically Engineered Microorganisms (GEMs) on the Intestinal Microbiome and Public Health
Authors: Lerner Aaron , Benzvi Carina , Vojdani Aristo
Published: January 2024

Gut luminal dysbiosis and pathobiosis result in compositional and biodiversified alterations in the microbial and host co-metabolites. The primary mechanism of bacterial evolution is horizontal gene transfer (HGT), and the acquisition of new traits can be achieved through the exchange of mobile genetic elements (MGEs). Introducing genetically engineered microbes (GEMs) might break the harmonized balance in the intestinal compartment. The present objectives are: 1. To reveal the role played by the GEMs’ horizontal gene transfers in changing the landscape of the enteric microbiome eubiosis 2. To expand on the potential detrimental effects of those changes on the human genome and health. A search of articles published in PubMed/MEDLINE, EMBASE, and Scielo from 2000 to August 2023 using appropriate MeSH entry terms was performed. The GEMs’ horizontal gene exchanges might induce multiple human diseases. The new GEMs can change the long-term natural evolution of the enteric pro- or eukaryotic cell inhabitants. The worldwide regulatory authority’s safety control of GEMs is not enough to protect public health. Viability, biocontainment, and many other aspects are only partially controlled and harmful consequences for public health should be avoided. It is important to remember that prevention is the most cost-effective strategy and primum non nocere should be the focus.
Orally Administered Drugs and Their Complicated Relationship with Our Gastrointestinal Tract
Authors: Bashiardes Stavros , Christodoulou Christina
Published: January 2024

Orally administered compounds represent the great majority of all pharmaceutical compounds produced for human use and are the most popular among patients since they are practical and easy to self-administer. Following ingestion, orally administered drugs begin a “perilous” journey down the gastrointestinal tract and their bioavailability is modulated by numerous factors. The gastrointestinal (GI) tract anatomy can modulate drug bioavailability and accounts for interpatient drug response heterogeneity. Furthermore, host genetics is a contributor to drug bioavailability modulation. Importantly, a component of the GI tract that has been gaining notoriety with regard to drug treatment interactions is the gut microbiota, which shares a two-way interaction with pharmaceutical compounds in that they can be influenced by and are able to influence administered drugs. Overall, orally administered drugs are a patient-friendly treatment option. However, during their journey down the GI tract, there are numerous host factors that can modulate drug bioavailability in a patient-specific manner.
Functional Groups Dominate Aboveground Net Primary Production under Long-Term Nutrient Additions in a Tibetan Alpine Meadow
Authors: Chen Xueying , Hou Ge , Shi Peili , Zong Ning , Yu Jialuo
Published: January 2024

Anthropogenic nutrient additions are influencing the structure and function of alpine grassland ecosystems. However, the underlying mechanisms of the direct and indirect effects of nutrient additions on aboveground net primary productivity (ANPP) are not well understood. In this study, we conducted an eight-year field experiment to explore the ecological consequences of nitrogen (N) and/or phosphorous (P) additions on the northern Tibetan Plateau. ANPP, species diversity, functional diversity, and functional groups were used to assess species’ responses to increasing nutrients. Our results showed that nutrient additions significantly increased ANPP due to the release in nutrient limitations. Although N addition had a significant effect on species richness and functional richness, and P and N + P additions altered functional diversity, it was functional groups rather than biodiversity that drove changes in ANPP in the indirect pathways. We identified the important roles of N and P additions in begetting the dominance of grasses and forbs, respectively. The study highlights that the shift of functional groups should be taken into consideration to better predict the structure, function, and biodiversity–ANPP relationship in grasslands, particularly under future multifaceted global change.
Development and Optimization of a SPME-GC-FID Method for Ethanol Detection
Authors: Costa Nuna G. , Freitas David S. , Barros Aline , Silva Carla , Antunes Joana C. , Rocha Ana M.
Published: January 2024

A solid-phase microextraction (SPME) injection gas chromatography was validated with the flame ionization detection method (GC-FID) using a capillary column to detect ethanol. The method was used to determine ethanol in fluids with biomedical, clinical, and forensic importance, including water, phosphate-buffered saline (PBS), and artificial sweat. The strategy produced good peak resolution and showed a linear correlation between the concentration and peak areas for ethanol in all matrices. The inter- and intra-day precisions of the method were below 15.5% and 6.5%, respectively, varying according to the matrix. The method achieved detection limits below 1.3 mg/L, varying according to the matrix. Lower limits were obtained for the aqueous solution (0.22 mg/L), followed by the PBS solution (0.96 mg/L), and finally, the sweat solution (1.29 mg/L). This method is easy to perform and suitable for use in routine clinical biochemistry and forensic laboratories, allowing ethanol detection at lower concentrations using lower temperature and time of extraction, when compared with other studies.
DInSAR Multi-Temporal Analysis for the Characterization of Ground Deformations Related to Tectonic Processes in the Region of Bucaramanga, Colombia
Authors: Valencia Ortiz Joaquín Andrés , Martínez-Graña Antonio Miguel , Cabero Morán María Teresa
Published: January 2024

The analysis of the degree of surface deformation can be a relevant aspect in the study of surface stability conditions, as it provides added value in the construction of risk management plans. This analysis provides the opportunity to establish the behaviors of the internal dynamics of the earth and its effects on the surface as a prediction tool for possible future effects. To this end, this study was approached through the analysis of Synthetic Aperture Radar (SAR) images using the Differential Interferometry (DInSAR) technique, which, in turn, is supported by the Small Baseline Subset (SBAS) technique to take advantage of the orbital separation of the Sentinel-1 satellite images in ascending and descending trajectory between the years 2014 and 2021. As a result, a time series was obtained in which there is a maximum uplift of 117.5 mm (LOS-ascending) or 49.3 mm (LOS-descending) and a maximum subsidence of −86.2 mm (LOS-ascending) or −71.5 mm (LOS-descending), with an oscillating behavior. These deformation conditions are largely associated with the kinematics of the Bucaramanga Fault, but a recurrent action of deep seismic activity from the Bucaramanga Seismic Nest was also observed, generating a surface deformation of ±20 mm for the period evaluated. These deformations have a certain degree of impact on the generation of mass movements, evaluated by the correlation with the LOS-descending images. However, their action is more focused as an inherent factor of great weight, which makes it possible to respond to early care and allows real-time follow-up, giving positive feedback to the system.
IMU-Based Fitness Activity Recognition Using CNNs for Time Series Classification
Authors: Müller Philipp Niklas , Müller Alexander Josef , Achenbach Philipp , Göbel Stefan
Published: January 2024

Mobile fitness applications provide the opportunity to show users real-time feedback on their current fitness activity. For such applications, it is essential to accurately track the user’s current fitness activity using available mobile sensors, such as inertial measurement units (IMUs). Convolutional neural networks (CNNs) have been shown to produce strong results in different time series classification tasks, including the recognition of daily living activities. However, fitness activities can present unique challenges to the human activity recognition task (HAR), including greater similarity between individual activities and fewer available data for model training. In this paper, we evaluate the applicability of CNNs to the fitness activity recognition task (FAR) using IMU data and determine the impact of input data size and sensor count on performance. For this purpose, we adapted three existing CNN architectures to the FAR task and designed a fourth CNN variant, which we call the scaling fully convolutional network (Scaling-FCN). We designed a preprocessing pipeline and recorded a running exercise data set with 20 participants, in which we evaluated the respective recognition performances of the four networks, comparing them with three traditional machine learning (ML) methods commonly used in HAR. Although CNN architectures achieve at least 94% test accuracy in all scenarios, two traditional ML architectures surpass them in the default scenario, with support vector machines (SVMs) achieving 99.00 ± 0.34% test accuracy. The removal of all sensors except one foot sensor reduced the performance of traditional ML architectures but improved the performance of CNN architectures on our data set, with our Scaling-FCN reaching the highest accuracy of 99.86 ± 0.11% on the test set. Our results suggest that CNNs are generally well suited for fitness activity recognition, and noticeable performance improvements can be achieved if sensors are dropped selectively, although traditional ML architectures can still compete with or even surpass CNNs when favorable input data are utilized.
A Tree Attenuation Factor Model for a Low-Power Wide-Area Network in a Ruby Mango Plantation
Authors: Phaiboon Supachai , Phokharatkul Pisit
Published: January 2024

Ruby mangoes are a cultivar with a thick skin, firm texture, red color, no splinters, and thin seeds that is grown in eastern Thailand for export. Implementing a low-power wide-area network (LPWAN) for smart agriculture applications can help increase the crop quality or yield. In this study, empirical path loss models were developed to help plan a LPWAN, operating at 433 MHz, of a Ruby mango plantation in Sakaeo, eastern Thailand. The proposed models take advantage of the symmetric pattern of Ruby mango trees cultivated in the plantation by using tree attenuation factors (TAFs) to consider the path loss at the trunk and canopy levels. A field experiment was performed to collect received signal strength indicator (RSSI) measurements and compare the performance of the proposed models with those of conventional models. The proposed models demonstrated a high prediction accuracy for both line-of-sight and non-line-of-sight routes and performed better than the other models.
Misalignment-Tolerant Planar Spiral Coil Pair Design for 13.56 MHz Inductive Coupling of Wireless Resistive Analog Passive Sensors
Authors: Noroozi Babak , Morshed Bashir I.
Published: January 2024

Long-term daily-life body signal monitoring offers numerous advantages, such as timely response to health alerts, diseases monitoring, and reducing time and expenses related to clinical trials. Access to physiological data can be achieved with low-cost and comfortable wireless wearable sensors. In our previous publication, we reported a low-cost, easy to implement, and unobtrusive wireless resistive analog passive (WRAP) sensor to provide a feasible bio-signal monitoring technique by using a pair of printed spiral coils (PSC) in a near field connection. Sensitivity, defined as the response to the transducer, is a critical feature in the establishment of a reliable system. In the previous publication, we presented the utilization of a Genetic Algorithm to design a pair of coils and related components to maximize sensitivity. Although the coils’ misalignment can significantly affect the optimized sensitivity, it was not incorporated into the optimization process. This paper focuses on optimizing the coils and components in order to maximize both their sensitivity and their resilience against movements of the PSC pair. In a square-shaped pair comprising a primary coil of 60 mm and a secondary coil of 20 mm dimensions, we found that the sensitivity is maximized at 1.3 mƱ for a 16 mm axial distance. Additionally, it remains above 0.65 mƱ within ±11.25 mm lateral and +14 mm axial displacements.
A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction
Authors: Hossain Md. Biddut , Shinde Rupali Kiran , Oh Sukhoon , Kwon Ki-Chul , Kim Nam
Published: January 2024

Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI reconstruction is essential for ensuring accurate diagnosis, supporting clinical decision-making, enhancing patient safety, facilitating efficient workflows, and contributing to the validity of research studies and clinical trials. Recently, deep learning has demonstrated several advantages over conventional MRI reconstruction methods. Conventional methods rely on manual feature engineering to capture complex patterns and are usually computationally demanding due to their iterative nature. Conversely, DL methods use neural networks with hundreds of thousands of parameters and automatically learn relevant features and representations directly from the data. Nevertheless, there are some limitations to DL-based techniques concerning MRI reconstruction tasks, such as the need for large, labeled datasets, the possibility of overfitting, and the complexity of model training. Researchers are striving to develop DL models that are more efficient, adaptable, and capable of providing valuable information for medical practitioners. We provide a comprehensive overview of the current developments and clinical uses by focusing on state-of-the-art DL architectures and tools used in MRI reconstruction. This study has three objectives. Our main objective is to describe how various DL designs have changed over time and talk about cutting-edge tactics, including their advantages and disadvantages. Hence, data pre- and post-processing approaches are assessed using publicly available MRI datasets and source codes. Secondly, this work aims to provide an extensive overview of the ongoing research on transformers and deep convolutional neural networks for rapid MRI reconstruction. Thirdly, we discuss several network training strategies, like supervised, unsupervised, transfer learning, and federated learning for rapid and efficient MRI reconstruction. Consequently, this article provides significant resources for future improvement of MRI data pre-processing and fast image reconstruction.
Spacecraft Attitude Measurement and Control Using VSMSCSG and Fractional-Order Zeroing Neural Network Adaptive Steering Law
Authors: Li Lei , Ren Yuan , Wang Weijie , Pang Weikun
Published: January 2024

In order to improve the accuracy and convergence speed of the steering law under the conditions of high dynamics, high bandwidth, and a small deflection angle, and in an effort to improve attitude measurement and control accuracy of the spacecraft, a spacecraft attitude measurement and control method based on variable speed magnetically suspended control sensitive gyroscopes (VSMSCSGs) and the fractional-order zeroing neural network (FO-ZNN) steering law is proposed. First, a VSMSCSG configuration is designed to realize attitude measurement and control integration in which the VSMSCSGs are employed as both actuators and attitude-rate sensors. Second, a novel adaptive steering law using FO-ZNN is designed. The matrix pseudoinverses are replaced by FO-ZNN outputs, which solves the problem of accuracy degradation in the traditional pseudoinverse steering laws due to the complexity of matrix pseudoinverse operations under high dynamics conditions. In addition, the convergence and robustness of the FO-ZNN are proven. The results show that the proposed FO-ZNN converges faster than the traditional zeroing neural network under external disturbances. Finally, a new weighting function containing rotor deflection angles is added to the steering law to ensure that the saturation of the rotor deflection angles can be avoided. Semi-physical simulation results demonstrate the correctness and superiority of the proposed method.
Empowering Undergraduate Women in Science, Technology, Engineering, Mathematics, and Medicine: Exploring Experiences, Fostering Motivation, and Advancing Gender Equity
Authors: Phillips Matthew James
Published: January 2024

The representation and success of women in Science, Technology, Engineering, Mathematics, and Medicine (STEMM) have sparked discussions, given their underrepresentation in these traditionally male-dominated fields. While women comprise 40% of STEMM undergraduates, senior-level positions see this figure drop below 20%. Research suggests that gender disparities in STEMM participation result from motivational differences, rather than competence. Exploring the reasons for these disparities are important as they can have significant consequences for the acknowledgement of women in STEMM, the maintenance of their STEMM discipline, and future careers. As such, this study explored how the experiences of 13 female undergraduates in Australian STEMM programs impacted their motivation and persistence in their degree. Through face-to-face semi-structured interviews, analysed via reflexive thematic analysis, five themes were constructed, encapsulating diverse experiences shaping female undergraduates in STEMM, influencing self-efficacy, determination, and well-being. Early exposure and environment were pivotal, acting as motivators or deterrents. Additionally, peer support was suggested as being crucial, fostering belongingness in the male-dominated space. Participants also grappled with recognition challenges, influenced by gender imbalances and a lack of role models. Intimidation was evident, leading to thoughts of dropping out, but passion drove persistence amid these challenges. The research increases awareness and understanding of the challenges faced by female undergraduates in STEMM, offering valuable insights for developing strategies to enhance their university experiences and promote success in future STEMM careers.
Psychological Determinants in Biathlon Performance: A U23 National Team Case Study
Authors: Abrahamsen Frank Eirik , Kvam Andreas , Sæther Stig Arve
Published: January 2024

Background: The present investigation examined what psychological factors athletes perceived to impact their competition performance and what training strategies and focus the athletes considered to be the most important. Methods: We recruited six participants (three females, three males) from the Norwegian Biathlon Federation’s national U23 and junior teams, and all participated. We used semi-structured interviews to gather the data and used thematic analyses to examine our findings. Results: The findings centered around the intricate relationship between psychological factors, particularly self-efficacy, anxiety, attention control, and performance, in biathlon shooting. Conclusions: Implementing a holistic approach to biathlon training entails harmonizing physical and psychological elements with personalized psychological training regimens.
Design and Performance Evaluation of a Photovoltaic Greenhouse as an Energy Hub with Battery Storage and an Electric Vehicle Charger
Authors: Torres Miguel A. , Muñoz Diego , Burgos Claudio , Casagrande Daniel , Ortiz Javier , Reyes Hernán
Published: January 2024

This work presents a photovoltaic greenhouse’s design and performance evaluation as an energy hub in modern agriculture that integrates battery energy storage, an electric vehicle charging station, and non-controlled loads. The greenhouse roof comprises 48 semi-transparent photovoltaic panels with nominal transparency of 20% and 110 W capacity. The control of the photovoltaic greenhouse as an energy hub was approached as an optimization problem with the aim of minimizing the energy purchased from the grid. The simulation results indicate that the system is capable of balancing power transactions within the microgrid, thus enabling electromobility and, at the same time, achieving an average energy saving of up to 41%. Furthermore, it was found that the case of slow charging of the electric vehicle at night was less demanding on the battery system than fast charging during the day in terms of abrupt power transitions and average state of charge of the battery system, 61% vs. 53%, respectively. Empirical results also demonstrated the negative impact of soiling generated by agricultural activity on the performance of solar panels. For a period analyzed of three years, an average annual production loss of 6.8% was calculated.
Construction and Application of an Intelligent Prediction Model for the Coal Pillar Width of a Fully Mechanized Caving Face Based on the Fusion of Multiple Physical Parameters
Authors: Yan Zhenguo , Wang Huachuan , Xu Huicong , Fan Jingdao , Ding Weixi
Published: January 2024

The scientific and reasonable width of coal pillars is of great significance to ensure safe and sustainable mining in the western mining area of China. To achieve a precise analysis of the reasonable width of coal pillars in fully mechanized caving face sections of gently inclined coal seams in western China, this paper analyzes and studies various factors that affect the retention of coal pillars in the section, and calculates the correlation coefficients between these influencing factors. We selected parameters with good universality and established a data set of gently inclined coal seams based on 106 collected engineering cases. We used the LSTM algorithm loaded with a simulated annealing algorithm for training, and constructed a coal pillar width prediction model. Compared with other prediction algorithms such as the original LSTM algorithm, the residual sum of squares and root mean square error were reduced by 27.2% and 24.2%, respectively, and the correlation coefficient was increased by 12.6%. An engineering case analysis was conducted using the W1123 working face of the Kuangou Coal Mine. The engineering verification showed that the SA-CNN-LSTM coal pillar width prediction model established in this paper has good stability and accuracy for multi-parameter nonlinear coupling prediction results. We have established an effective solution for achieving the accurate reservation of coal pillar widths in the fully mechanized caving faces of gently inclined coal seams.
Assessment of Design Consistency for Two-Lane Rural Highways with Low Tortuosity Alignment
Authors: Cefalo Raffaela , Sluga Tatiana , Ossich Giulio , Roberti Roberto
Published: January 2024

One technique employed to enhance road safety involves assessing the alignment’s consistency. A prevalent measure of consistency is evaluating speed variations along the alignment. A key consideration in this assessment is determining the speed upon which the road alignment should be based. This research reveals that on two-lane rural highways with low tortuosity alignments, operating speeds on horizontal curves and tangents consistently exceeded not only the design speeds but also the maximum permissible design speed for the road category. Consequently, using the design speed to assess consistency on these roads is deemed impractical, and utilizing operating speed poses challenges due to speeds exceeding the maximum permissible limit. The objectives of this paper are twofold: to explore the relationship between design consistency and safety levels on two-lane rural highways with low tortuosity alignments (which have been insufficiently covered in research) and to propose speed-control measures to limit the maximum operating speed to the maximum permissible speed. The study findings suggest that on roads with a low tortuosity alignment, operating speeds depend much more on the general characteristics of the alignment (evaluated in the operating speed models through the desired speed). Further, assessing speed consistency is feasible only with a rigorous control of the maximum operating speed (desired speed). Additionally, a specific type of speed control is recommended, achieved by limiting the curvature change rate (CCR) of the road section based on the desired speed (environmental speed), whose evaluation becomes a crucial factor.
Roles of Urban Green Spaces for Children in High-Density Metropolitan Areas during Pandemics: A Systematic Literature Review
Authors: Wang Yunjin , Desha Cheryl , Caldera Savindi , Beer Tanja
Published: January 2024

For children living in one of the 30 megacities around the world, green spaces may be far away or tokenistic, with growing accounts of youth exhibiting a variety of nature deficit disorders. Amidst increasing pressure for more high-density metropolitan areas (HDMAs), international advocacy agencies are calling for policy makers and service providers to ensure good quality urban green spaces (UGSs) for all children. Furthermore, these green spaces need to be purposefully designed to cater to children’s needs, which continue to evolve amidst extreme weather such as heat waves. Recently, pandemics have placed additional constraints on how far children can travel and their time spent outdoors, making the quality of this experience for children even more important. This systematic literature review explored the roles of HDMA-UGSs for children during pandemics. From 551 studies retrieved and curated using the PRISMA methodology, 40 papers were subsequently thematically analysed. Four roles were synthesised for HDMA-UGSs in supporting children’s experiences during pandemics, which also benefit children during ‘normal’ day-to-day life’: (1) sustaining growth and development, (2) bridging social stratifications, (3) encouraging self-agency, and (4) facilitating independent mobility. Thirteen principles were distilled to support decision making in enabling these outcomes in new-build and refurbishment projects. The results are of immediate use for decision makers who are responsible for HDMA-UGS planning, procurement, installation, and maintenance. The UGS role descriptions enable targeted design interventions for supporting children’s health and well-being, including during times of constrained outdoor time.
Entrepreneurship among Social Workers: Implications for the Sustainable Development Goals
Authors: Pereira João , Rodrigues Ricardo Gouveia , Veiga Pedro Mota
Published: January 2024

The study’s objective is to analyze the entrepreneurial intentions of social workers and their impact on the SDGs. Furthermore, it seeks to explore social workers’ perceptions and attitudes towards entrepreneurship. Thus, this research aims to answer the following research questions: How do entrepreneurial intentions among social workers influence their contribution to achieving the Sustainable Development Goals (SDGs), and what are the implications of these intentions for social work practices and policies within the framework of sustainable development? Based on a quantitative approach, this study leverages microdata from the 2018 Global Entrepreneurship Monitor (GEM) survey. The dataset includes responses from 4545 social workers across 30 high-income countries. The study employs logistic regression analysis as the primary statistical method to delve into the determinants of entrepreneurial intentions among social workers within this diverse international sample. Key findings indicate that the aspiration to establish a future business is a significant factor, underscoring the importance of entrepreneurial intention in this context. Additionally, social workers in the private sector exhibit a greater predisposition to entrepreneurship compared to their public sector counterparts. The study’s implications for the SDGs are substantial, as promoting entrepreneurship among social workers can contribute to economic growth, poverty reduction, and reduced inequalities (SDGs 1, 8, and 10). It also emphasizes the importance of access to entrepreneurial education, sustainable job creation (SDG 4 and 8), and the development of partnerships among academic institutions, social assistance organizations, and entrepreneurial entities (SDG 17). Furthermore, the research highlights the significance of gender equality and access to entrepreneurial resources (SDGs 5 and 9) in fostering entrepreneurship among social workers.