bluuebunny/crossref_metadata_2025 huggingface dataset:
Phytochemical Compounds Involved in the Bone Regeneration Process and Their Innovative Administration: A Systematic Review
Authors: Hanga-Farcaș Alina , Miere (Groza) Florina , Filip Gabriela Adriana , Clichici Simona , Fritea Luminita , Vicaș Laura Grațiela , Marian Eleonora , Pallag Annamaria , Jurca Tunde , Filip Sanda Monica , Muresan Mariana Eugenia
Published: May 2023

Bone metabolism is a complex process which is influenced by the activity of bone cells (e.g., osteocytes, osteoblasts, osteoclasts); the effect of some specific biomarkers (e.g., parathyroid hormone, vitamin D, alkaline phosphatase, osteocalcin, osteopontin, osteoprotegerin, osterix, RANKL, Runx2); and the characteristic signaling pathways (e.g., RANKL/RANK, Wnt/β, Notch, BMP, SMAD). Some phytochemical compounds—such as flavonoids, tannins, polyphenols, anthocyanins, terpenoids, polysaccharides, alkaloids and others—presented a beneficial and stimulating effect in the bone regeneration process due to the pro-estrogenic activity, the antioxidant and the anti-inflammatory effect and modulation of bone signaling pathways. Lately, nanomedicine has emerged as an innovative concept for new treatments in bone-related pathologies envisaged through the incorporation of medicinal substances in nanometric systems for oral or local administration, as well as in nanostructured scaffolds with huge potential in bone tissue engineering.
Synthesis and Characterization of a New Molecularly Imprinted Polymer for Selective Extraction of Mandelic Acid Metabolite from Human Urine as a Biomarker of Environmental and Occupational Exposures to Styrene
Authors: Qronfla Murad. M. , Jamoussi Bassem , Chakroun Radhouane
Published: May 2023

4-Vinylpyridine molecularly imprinted polymer (4-VPMIP) microparticles for mandelic acid (MA) metabolite as a major biomarker of exposure to styrene (S) were synthesized by bulk polymerization with a noncovalent approach. A common mole ratio of 1:4:20 (i.e., metabolite template: functional monomer: cross-linking agent, respectively) was applied to allow the selective solid-phase extraction of MA in a urine sample followed by high-performance liquid chromatography–diode array detection (HPLC-DAD). In this research, the 4-VPMIP components were carefully selected: MA was used as a template (T), 4-Vinylpyridine (4-VP) as a functional monomer (FM), ethylene glycol dimethacrylate (EGDMA) as a cross-linker (XL), and azobisisobutyronitrile (AIBN) as an initiator (I) and acetonitrile (ACN) as a porogenic solvent. Non-imprinted polymer (NIP) which serves as a “control” was also synthesized simultaneously under the same condition without the addition of MA molecules. Fourier transform infrared (FT-IR) spectroscopy and scanning electron microscopy (SEM) were used to characterize the imprinted and nonimprinted polymer to explain the structural and morphological characteristics of the 4-VPMIP and surface NIP. The results obtained from SEM depicted that the polymers were irregularly shaped microparticles. Moreover, MIPs surfaces had cavities and were rougher than NIP. In addition, all particle sizes were less than 40 µm in diameter. The IR spectra of 4-VPMIPs before washing MA were a little different from NIP, while 4-VPMIP after elution had a spectrum that was almost identical to the NIP spectrum. The adsorption kinetics, isotherms, competitive adsorption, and reusability of 4-VPMIP were investigated. 4-VPMIP showed good recognition selectivity as well as enrichment and separation abilities for MA in the extract of human urine with satisfactory recoveries. The results obtained in this research imply that 4-VPMIP might be used as a sorbent for MA solid-phase extraction (MISPE), for the exclusive extraction of MA in human urine.
Conditions for Graviton Emission in the Recombination of a Delocalized Mass
Authors: Pesci Alessandro
Published: May 2023

In a known gedanken experiment, a delocalized mass is recombined while the gravitational field sourced by it is probed by another (distant) particle; in it, this is used to explore a possible tension between complementarity and causality in case the gravitational field entangles with the superposed locations, a proposed resolution being graviton emission from quadrupole moments. Here, we focus on the delocalized particle (forgetting about the probe and the gedanken experiment) and explore the conditions (in terms of mass, separation, and recombination time) for graviton emission. Through this, we find that the variations of quadrupole moments in the recombination are generically greatly enhanced if the field is entangled compared to if it is sourced instead by the energy momentum expectation value on the delocalized state (moment variation ∼md2 in the latter case, with m mass, d separation). In addition, we obtain the (upper) limit recombination time for graviton emission growing as m in place of the naive expectation m. In this, the Planck mass acts as threshold mass (huge, for delocalized objects): no graviton emission is possible below it, however fast the recombination occurs. If this is compared with the decay times foreseen in the collapse models of Diósi and Penrose (in their basic form), one finds that no (quadrupole) graviton emission from recombination is possible in them. Indeed, right when m becomes large enough to allow for emission, it also becomes too large for the superposition to survive collapse long enough to recombine.
The First Step and the Cob(II)alamin Cofactor Inactive Particles Reactivation in the Updated Mechanism of the Methionine Synthase Process
Authors: Spataru Tudor
Published: May 2023

The Methionine Synthase process, in principle, can take an unlimited number of turnovers in the presence of the AdoMet substrate. In the absence of this substrate, the Methionine Synthase process lasts only about 2000 turnovers. During 2000 turnovers, the entire amount of methylcob(II)alamin cofactor is converted into inactive cob(II)alamin particles. Nevertheless, the mechanism of the Methionine Synthase process determined previously lacks the presence of the AdoMet substrate. On the other hand, the first step of this mechanism was only mentioned earlier without its analysis. The CASSCF geometry optimization of the inactive cob(II)alamin cofactor particle plus the AdoMet ion substrate and of the methylcob(II)alamin cofactor particle plus homocysteine ion and histidine molecule joint models have been performed. CASSCF calculations show that the AdoMet particle transfers the methyl radical to the biologically inactive cob(II)alamin particle during their interaction, transforming it into the biologically active particle of methylcob(II)alamin. CASSCF geometry optimization of the second model leads to the Co-N bond’s full cleavage. The two processes take place in the absence of the total energy barrier. The fully updated mechanism of the Methionine Synthase process has been drawn.
A Deep-Learning Scheme for Hydrometeor Type Classification Using Passive Microwave Observations
Authors: Chen Ruiyao , Bennartz Ralf
Published: May 2023

This paper proposes a novel approach for hydrometeor classification using passive microwave observations. The use of passive measurements for this purpose has not been extensively explored, despite being available for over four decades. We utilize the Micro-Wave Humidity Sounder-2 (MWHS-2) to relate microwave brightness temperatures to hydrometeor types derived from the global precipitation measurement’s (GPM) dual-frequency precipitation radar (DPR), which are classified into liquid, mixed, and ice phases. To achieve this, we utilize a convolutional neural network model with an attention mechanism that learns feature representations of MWHS-2 observations from spatial and temporal dimensions. The proposed algorithm classified hydrometeors with 84.7% accuracy using testing data and captured the geographical characteristics of hydrometeor types well in most areas, especially for frozen precipitation. We then evaluated our results by comparing predictions from a different year against DPR retrievals seasonally and globally. Our global annual cycles of precipitation occurrences largely agreed with DPR retrievals with biases being 8.4%, −11.8%, and 3.4%, respectively. Our approach provides a promising direction for utilizing passive microwave observations and deep-learning techniques in hydrometeor classification, with potential applications in the time-resolved observations of precipitation structure and storm intensity with a constellation of smallsats (TROPICS) algorithm development.
An Efficient Cloud Classification Method Based on a Densely Connected Hybrid Convolutional Network for FY-4A
Authors: Wang Bo , Zhou Mingwei , Cheng Wei , Chen Yao , Sheng Qinghong , Li Jun , Wang Li
Published: May 2023

Understanding atmospheric motions and projecting climate changes depends significantly on cloud types, i.e., different cloud types correspond to different atmospheric conditions, and accurate cloud classification can help forecasts and meteorology-related studies to be more effectively directed. However, accurate classification of clouds is challenging and often requires certain manual involvement due to the complex cloud forms and dispersion. To address this challenge, this paper proposes an improved cloud classification method based on a densely connected hybrid convolutional network. A dense connection mechanism is applied to hybrid three-dimensional convolutional neural network (3D-CNN) and two-dimensional convolutional neural network (2D-CNN) architectures to use the feature information of the spatial and spectral channels of the FY-4A satellite fully. By using the proposed network, cloud categorization solutions with a high temporal resolution, extensive coverage, and high accuracy can be obtained without the need for any human intervention. The proposed network is verified using tests, and the results show that it can perform real-time classification tasks for seven different types of clouds and clear skies in the Chinese region. For the CloudSat 2B-CLDCLASS product as a test target, the proposed network can achieve an overall accuracy of 95.2% and a recall of more of than 82.9% for all types of samples, outperforming the other deep-learning-based techniques.
Regional Variability of Raindrop Size Distribution from a Network of Disdrometers over Complex Terrain in Southern China
Authors: Zhang Asi , Chen Chao , Wu Lin
Published: May 2023

Raindrop size distribution (DSD) over the complex terrain of Guangdong Province, southern China, was studied using six disdrometers operated by the Guangdong Meteorology Service during the period 1 March 2018 to 30 August 2022 (~5 years). To analyze the long-term DSD characteristics over complex topography in southern China, three stations on the windward side, Haifeng, Enping and Qingyuan, and three stations on the leeward side, Meixian, Luoding and Xuwen, were utilized. The median mass-weighted diameter (Dm) value was higher on the windward than on the leeward side, and the windward-side stations also showed greater Dm variability. With regard to the median generalized intercept (log10Nw) value, the log10Nw values decreased from coastal to mountainous areas. Although there were some differences in Dm, log10Nw and liquid water content (LWC) frequency between the six stations, there were still some similarities, with the Dm, log10Nw and LWC frequency all showing a single-peak curve. In addition, the diurnal variation of the mean log10Nw had a negative relationship with Dm diurnal variation although the inverse relationship was not particularly evident at the Haifeng site. The diurnal mean rainfall rate also peaked in the afternoon and exceeded the maximum at night which indicated that strong land heating in the daytime significantly influenced the local DSD variation. What is more, the number concentration of drops, N(D), showed an exponential shape which decreased monotonically for all rainfall rate types at the six observation sites, and an increase in diameter caused by increases in the rainfall rate was also noticeable. As the rainfall rate increased, the N(D) for sites on the windward side (i.e., Haifeng, Enping and Qingyuan) were higher than for the sites on the leeward side (i.e., Meixian, Luoding and Xuwen), and the difference between them also became distinct. The abovementioned DSD characteristic differences also showed appreciable variability in convective precipitation between stations on the leeward side (i.e., Meixian, Luoding and Xuwen) and those on the windward side (Haifeng and Enping, but not Qingyuan). This study enhances the precision of numerical weather forecast models in predicting precipitation and verifies the accuracy of measuring precipitation through remote sensing instruments, including weather radars located on the ground.
Large-Scale Urban Heating and Pollution Domes over the Indian Subcontinent
Authors: Chakraborty Trisha , Das Debashish , Hamdi Rafiq , Khan Ansar , Niyogi Dev
Published: May 2023

The unique geographical diversity and rapid urbanization across the Indian subcontinent give rise to large-scale spatiotemporal variations in urban heating and air emissions. The complex relationship between geophysical parameters and anthropogenic activity is vital in understanding the urban environment. This study analyses the characteristics of heating events using aerosol optical depth (AOD) level variability, across 43 urban agglomerations (UAs) with populations of a million or more, along with 13 industrial districts (IDs), and 14 biosphere reserves (BRs) in the Indian sub-continent. Pre-monsoon average surface heating was highest in the urban areas of the western (42 °C), central (41.9 °C), and southern parts (40 °C) of the Indian subcontinent. High concentration of AOD in the eastern part of the Indo-Gangetic Plain including the megacity: Kolkata (decadal average 0.708) was noted relative to other UAs over time. The statistically significant negative correlation (−0.51) between land surface temperature (LST) and AOD in urban areas during pre-monsoon time illustrates how aerosol loading impacts the surface radiation and has a net effect of reducing surface temperatures. Notable interannual variability was noted with, the pre-monsoon LST dropping in 2020 across most of the selected urban regions (approx. 89% urban clusters) while it was high in 2019 (for approx. 92% urban clusters) in the pre-monsoon season. The results indicate complex variability and correlations between LST and urban aerosol at large scales across the Indian subcontinent. These large-scale observations suggest a need for more in-depth analysis at city scales to understand the interplay and combined variability between physical and anthropogenic atmospheric parameters in mesoscale and microscale climates.
A Novel Edge Detection Method for Multi-Temporal PolSAR Images Based on the SIRV Model and a SDAN-Based 3D Gaussian-like Kernel
Authors: Zheng Xiaolong , Guan Dongdong , Li Bangjie , Chen Zhengsheng , Pan Lefei
Published: May 2023

Edge detection for PolSAR images has demonstrated its importance in various applications such as segmentation and classification. Although there are many edge detectors which have demonstrated an impressive ability to achieve accurate edge detection results, these methods only focus on edge detection in a single-date PolSAR image. However, a single-date PolSAR image cannot fully characterize the changes in scattering mechanisms of land cover in different growth cycles, resulting in some omissions of the true edges. In this paper, we propose a novel edge detection method for multi-temporal PolSAR images based on the SIRV model and an SDAN-based 3D Gaussian-like kernel. The spherically invariant random vector (SIRV) and span-driven adaptive neighborhood (SDAN) improve the estimation accuracy of the average covariance matrix (ACM) in terms of data representation and spatial support, respectively. We propose an SDAN-based 2D Gaussian kernel to accurately extract the edge strength of single-date PolSAR images. Then, we design a 1D convolution kernel in the temporal dimension to smooth fluctuations in the edge strength of multi-temporal PolSAR images. The SDAN-based 2D Gaussian kernels in the X- and Y-directions are combined with the 1D convolution kernel in the Z-direction to form an SDAN-based 3D Gaussian-like kernel. In addition, we design an adaptive hysteresis threshold method to optimize the edge map. The performance of our proposed method is presented and analyzed on two real multi-temporal PolSAR datasets, and the results demonstrate that the proposed edge detector achieves a better performance than other edge detectors, particularly for crop regions with time-varying scattering mechanisms.
Fittings Detection Method Based on Multi-Scale Geometric Transformation and Attention-Masking Mechanism
Authors: Wang Ning , Zhang Ke , Zhu Jinwei , Zhao Liuqi , Huang Zhenlin , Wen Xing , Zhang Yuheng , Lou Wenshuo
Published: May 2023

Overhead transmission lines are important lifelines in power systems, and the research and application of their intelligent patrol technology is one of the key technologies for building smart grids. The main reason for the low detection performance of fittings is the wide range of some fittings’ scale and large geometric changes. In this paper, we propose a fittings detection method based on multi-scale geometric transformation and attention-masking mechanism. Firstly, we design a multi-view geometric transformation enhancement strategy, which models geometric transformation as a combination of multiple homomorphic images to obtain image features from multiple views. Then, we introduce an efficient multiscale feature fusion method to improve the detection performance of the model for targets with different scales. Finally, we introduce an attention-masking mechanism to reduce the computational burden of model-learning multiscale features, thereby further improving model performance. In this paper, experiments have been conducted on different datasets, and the experimental results show that the proposed method greatly improves the detection accuracy of transmission line fittings.
Object Detection of Flexible Objects with Arbitrary Orientation Based on Rotation-Adaptive YOLOv5
Authors: Wu Jiajun , Su Lumei , Lin Zhiwei , Chen Yuhan , Ji Jiaming , Li Tianyou
Published: May 2023

It is challenging to accurately detect flexible objects with arbitrary orientation from monitoring images in power grid maintenance and inspection sites. This is because these images exhibit a significant imbalance between the foreground and background, which can lead to low detection accuracy when using a horizontal bounding box (HBB) as the detector in general object detection algorithms. Existing multi-oriented detection algorithms that use irregular polygons as the detector can improve accuracy to some extent, but their accuracy is limited due to boundary problems during the training process. This paper proposes a rotation-adaptive YOLOv5 (R_YOLOv5) with a rotated bounding box (RBB) to detect flexible objects with arbitrary orientation, effectively addressing the above issues and achieving high accuracy. Firstly, a long-side representation method is used to add the degree of freedom (DOF) for bounding boxes, enabling accurate detection of flexible objects with large spans, deformable shapes, and small foreground-to-background ratios. Furthermore, the further boundary problem induced by the proposed bounding box strategy is overcome by using classification discretization and symmetric function mapping methods. Finally, the loss function is optimized to ensure training convergence for the new bounding box. To meet various practical requirements, we propose four models with different scales based on YOLOv5, namely R_YOLOv5s, R_YOLOv5m, R_YOLOv5l, and R_YOLOv5x. Experimental results demonstrate that these four models achieve mean average precision (mAP) values of 0.712, 0.731, 0.736, and 0.745 on the DOTA-v1.5 dataset and 0.579, 0.629, 0.689, and 0.713 on our self-built FO dataset, exhibiting higher recognition accuracy and a stronger generalization ability. Among them, R_YOLOv5x achieves a mAP that is about 6.84% higher than ReDet on the DOTAv-1.5 dataset and at least 2% higher than the original YOLOv5 model on the FO dataset.
Hybrid Digital-Droplet Microfluidic Chip for Applications in Droplet Digital Nucleic Acid Amplification: Design, Fabrication and Characterization
Authors: Coelho Beatriz , Neto Joana , Sieira Bárbara , Moura André , Fortunato Elvira , Martins Rodrigo , Baptista Pedro , Igreja Rui , Águas Hugo
Published: May 2023

Microfluidic-based platforms have become a hallmark for chemical and biological assays, empowering micro- and nano-reaction vessels. The fusion of microfluidic technologies (digital microfluidics, continuous-flow microfluidics, and droplet microfluidics, just to name a few) presents great potential for overcoming the inherent limitations of each approach, while also elevating their respective strengths. This work exploits the combination of digital microfluidics (DMF) and droplet microfluidics (DrMF) on a single substrate, where DMF enables droplet mixing and further acts as a controlled liquid supplier for a high-throughput nano-liter droplet generator. Droplet generation is performed at a flow-focusing region, operating on dual pressure: negative pressure applied to the aqueous phase and positive pressure applied to the oil phase. We evaluate the droplets produced with our hybrid DMF–DrMF devices in terms of droplet volume, speed, and production frequency and further compare them with standalone DrMF devices. Both types of devices enable customizable droplet production (various volumes and circulation speeds), yet hybrid DMF–DrMF devices yield more controlled droplet production while achieving throughputs that are similar to standalone DrMF devices. These hybrid devices enable the production of up to four droplets per second, which reach a maximum circulation speed close to 1540 µm/s and volumes as low as 0.5 nL.
Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices
Authors: Han Noel , Kim Il-Min , So Jaewoo
Published: May 2023

As the number of Internet of things (IoT) devices increases exponentially, scheduling and managing the radio resources for IoT devices has become more important. To efficiently allocate radio resources, the base station (BS) needs the channel state information (CSI) of devices every time. Hence, each device needs to periodically (or aperiodically) report its channel quality indicator (CQI) to the BS. The BS determines the modulation and coding scheme (MCS) based on the CQI reported by the IoT device. However, the more a device reports its CQI, the more the feedback overhead increases. In this paper, we propose a long short-term memory (LSTM)-based CQI feedback scheme, where the IoT device aperiodically reports its CQI relying on an LSTM-based channel prediction. Additionally, because the memory capacity of IoT devices is generally small, the complexity of the machine learning model must be reduced. Hence, we propose a lightweight LSTM model to reduce the complexity. The simulation results show that the proposed lightweight LSTM-based CSI scheme dramatically reduces the feedback overhead compared with that of the existing periodic feedback scheme. Moreover, the proposed lightweight LSTM model significantly reduces the complexity without sacrificing performance.
Spurious Resonance of the QCM Sensor: Load Analysis Based on Impedance Spectroscopy
Authors: Burda Ioan
Published: May 2023

A research topic of equal importance to technological and application fields related to quartz crystal is the presence of unwanted responses known as spurious resonances. Spurious resonances are influenced by the surface finish of the quartz crystal, its diameter and thickness, and the mounting technique. In this paper, spurious resonances associated with fundamental resonance are studied by impedance spectroscopy to determine their evolution under load conditions. Investigation of the response of these spurious resonances provides new insights into the dissipation process at the QCM sensor surface. The significant increase of the motional resistance for spurious resonances at the transition from air to pure water is a specific situation revealed experimentally in this study. It has been shown experimentally that in the range between the air and water media, spurious resonances are much more attenuated than the fundamental resonance, thus providing support for investigating the dissipation process in detail. In this range, there are many applications in the field of chemical sensors or biosensors, such as VOC sensors, humidity sensors, or dew point sensors. The evolution of D factor with increasing medium viscosity is significantly different for spurious resonances compared to fundamental resonance, suggesting the usefulness of monitoring them in liquid media.
A Cross-Source Point Cloud Registration Algorithm Based on Trigonometric Mutation Chaotic Harris Hawk Optimisation for Rockfill Dam Construction
Authors: Ren Bingyu , Zhao Hao , Han Shuyang
Published: May 2023

A high-precision three-dimensional (3D) model is the premise and vehicle of digitalising hydraulic engineering. Unmanned aerial vehicle (UAV) tilt photography and 3D laser scanning are widely used for 3D model reconstruction. Affected by the complex production environment, in a traditional 3D reconstruction based on a single surveying and mapping technology, it is difficult to simultaneously balance the rapid acquisition of high-precision 3D information and the accurate acquisition of multi-angle feature texture characteristics. To ensure the comprehensive utilisation of multi-source data, a cross-source point cloud registration method integrating the trigonometric mutation chaotic Harris hawk optimisation (TMCHHO) coarse registration algorithm and the iterative closest point (ICP) fine registration algorithm is proposed. The TMCHHO algorithm generates a piecewise linear chaotic map sequence in the population initialisation stage to improve population diversity. Furthermore, it employs trigonometric mutation to perturb the population in the development stage and thus avoid the problem of falling into local optima. Finally, the proposed method was applied to the Lianghekou project. The accuracy and integrity of the fusion model compared with those of the realistic modelling solutions of a single mapping system improved.
Development of Path Generation and Algorithm for Autonomous Combine Harvester Using Dual GPS Antenna
Authors: Lee Kyuho , Choi Hyohyuk , Kim Junghun
Published: May 2023

Research on autonomous driving technology is actively underway to solve the facing problems in the agricultural field. Combine harvesters used in East Asian countries, including Korea, are tracked-type vehicles. The steering control system of the tracked vehicle has different characteristics from the wheeled vehicle used in the agricultural tractor. In this paper, a dual GPS antenna-based autonomous driving system and path tracking algorithm were developed for a robot combine harvester. An α-turn-type work path generation algorithm and a path tracking algorithm were developed. The developed system and algorithm were verified through experiments using actual combine harvesters. The experiment consisted of an experiment with harvesting work and an experiment without harvesting work. In the experiment without harvesting work, an error of 0.052 m occurred during working driving and 0.207 m during turning driving. In the experiment where the harvesting work was carried out, an error of 0.038 m occurred during work driving and 0.195 m during turning driving. As a result of comparing the non-work area and driving time to the results of manual driving, the self-driving experiment with harvesting work showed an efficiency of 76.7%.
TiS3 Nanoribbons: A Novel Material for Ultra-Sensitive Photodetection across Extreme Temperature Ranges
Authors: Talib Mohammad , Tripathi Nishant , Manzoor Samrah , Sharma Prachi , Pavelyev Vladimir , Volkov Valentyn S. , Arsenin Aleksey V. , Novikov Sergey M. , Mishra Prabhash
Published: May 2023

Photodetectors that can operate over a wide range of temperatures, from cryogenic to elevated temperatures, are crucial for a variety of modern scientific fields, including aerospace, high-energy science, and astro-particle science. In this study, we investigate the temperature-dependent photodetection properties of titanium trisulfide (TiS3)- in order to develop high-performance photodetectors that can operate across a wide range of temperatures (77 K–543 K). We fabricate a solid-state photodetector using the dielectrophoresis technique, which demonstrates a quick response (response/recovery time ~0.093 s) and high performance over a wide range of temperatures. Specifically, the photodetector exhibits a very high photocurrent (6.95 × 10−5 A), photoresponsivity (1.624 × 108 A/W), quantum efficiency (3.3 × 108 A/W·nm), and detectivity (4.328 × 1015 Jones) for a 617 nm wavelength of light with a very weak intensity (~1.0 × 10−5 W/cm2). The developed photodetector also shows a very high device ON/OFF ratio (~32). Prior to fabrication, the TiS3 nanoribbons were synthesized using the chemical vapor technique and characterized according to their morphology, structure, stability, and electronic and optoelectronic properties; this was performed using scanning electron microscopy (SEM), transmission electron microscopy (TEM), Raman spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis (TGA), and a UV–Visible–NIR spectrophotometer. We anticipate that this novel solid-state photodetector will have broad applications in modern optoelectronic devices.
Machine Learning-Based Aggression Detection in Children with ADHD Using Sensor-Based Physical Activity Monitoring
Authors: Park Catherine , Rouzi Mohammad Dehghan , Atique Md Moin Uddin , Finco M. G. , Mishra Ram Kinker , Barba-Villalobos Griselda , Crossman Emily , Amushie Chima , Nguyen Jacqueline , Calarge Chadi , Najafi Bijan
Published: May 2023

Aggression in children is highly prevalent and can have devastating consequences, yet there is currently no objective method to track its frequency in daily life. This study aims to investigate the use of wearable-sensor-derived physical activity data and machine learning to objectively identify physical-aggressive incidents in children. Participants (n = 39) aged 7 to 16 years, with and without ADHD, wore a waist-worn activity monitor (ActiGraph, GT3X+) for up to one week, three times over 12 months, while demographic, anthropometric, and clinical data were collected. Machine learning techniques, specifically random forest, were used to analyze patterns that identify physical-aggressive incident with 1-min time resolution. A total of 119 aggression episodes, lasting 7.3 ± 13.1 min for a total of 872 1-min epochs including 132 physical aggression epochs, were collected. The model achieved high precision (80.2%), accuracy (82.0%), recall (85.0%), F1 score (82.4%), and area under the curve (89.3%) to distinguish physical aggression epochs. The sensor-derived feature of vector magnitude (faster triaxial acceleration) was the second contributing feature in the model, and significantly distinguished aggression and non-aggression epochs. If validated in larger samples, this model could provide a practical and efficient solution for remotely detecting and managing aggressive incidents in children.
Magnetic Signatures and Magnetization Mechanisms for Grinding Burns Detection and Evaluation
Authors: Ducharne Benjamin , Sebald Gael , Petitpré Hélène , Lberni Hicham , Wasniewski Eric , Zhang Fan
Published: May 2023

Grinding thermal damages, commonly called grinding burns occur when the grinding energy generates too much heat. Grinding burns modify the local hardness and can be a source of internal stress. Grinding burns will shorten the fatigue life of steel components and lead to severe failures. A typical way to detect grinding burns is the so-called nital etching method. This chemical technique is efficient but polluting. Methods based on the magnetization mechanisms are the alternative studied in this work. For this, two sets of structural steel specimens (18NiCr5-4 and X38Cr-Mo16-Tr) were metallurgically treated to induce increasing grinding burn levels. Hardness and surface stress pre-characterizations provided the study with mechanical data. Then, multiple magnetic responses (magnetic incremental permeability, magnetic Barkhausen noise, magnetic needle probe, etc.) were measured to establish the correlations between the magnetization mechanisms, the mechanical properties, and the grinding burn level. Owing to the experimental conditions and ratios between standard deviation and average values, mechanisms linked to the domain wall motions appear to be the most reliable. Coercivity obtained from the Barkhausen noise, or magnetic incremental permeability measurements, was revealed as the most correlated indicator (especially when the very strongly burned specimens were removed from the tested specimens list). Grinding burns, surface stress, and hardness were found to be weakly correlated. Thus, microstructural properties (dislocations, etc.) are suspected to be preponderant in the correlation with the magnetization mechanisms.
Identification of Abnormal Data for Synchronous Monitoring of Transformer DC Bias Based on Multiple Criteria
Authors: Kou Zhongqing , Lin Sheng , Wang Aimin , He Yuanda , Chen Long
Published: May 2023

Seriously abnormal data exist in the synchronous monitoring data of transformer DC bias, which causes serious data feature contamination and even affects the identification of transformer DC bias. For this reason, this paper aims to ensure the reliability and validity of synchronous monitoring data. This paper proposes an identification of abnormal data for the synchronous monitoring of transformer DC bias based on multiple criteria. By analyzing the abnormal data of different types, the characteristics of abnormal data are obtained. Based on this, the abnormal data identification indexes are introduced, including gradient, sliding kurtosis and Pearson correlation coefficient. Firstly, the Pauta criterion is used to determine the threshold of the gradient index. Then, gradient is used to identify the suspected abnormal data. Finally, the sliding kurtosis and Pearson correlation coefficient are used to identify the abnormal data. Data for synchronous monitoring of transformer DC bias in a certain power grid are used to verify the proposed method. The results show that the accuracy of the proposed method in identifying mutated abnormal data and zero-value abnormal data is claimed to be 100%. Compared with traditional abnormal data identification methods, the accuracy of the proposed method is significantly improved.
Prediction of the Physical Activity Level of Community-Dwelling Older Japanese Adults with a Triaxial Accelerometer Containing a Classification Algorithm for Ambulatory and Non-Ambulatory Activities
Authors: Tanaka Shigeho , Ishikawa-Takata Kazuko , Nakae Satoshi , Sasaki Satoshi
Published: May 2023

Accurate methods for the prediction of the total energy expenditure and physical activity level (PAL) in community-dwelling older adults have not been established. Therefore, we examined the validity of estimating the PAL using an activity monitor (Active style Pro HJA-350IT, [ASP]) and proposed correction formulae for such populations in Japan. Data for 69 Japanese community-dwelling adults aged 65 to 85 years were used. The total energy expenditure in free-living conditions was measured with the doubly labeled water method and the measured basal metabolic rate. The PAL was also estimated from metabolic equivalent (MET) values obtained with the activity monitor. Adjusted MET values were also calculated with the regression equation of Nagayoshi et al. (2019). The observed PAL was underestimated, but significantly correlated, with the PAL from the ASP. When adjusted using the Nagayoshi et al. regression equation, the PAL was overestimated. Therefore, we developed regression equations to estimate the actual PAL (Y) from the PAL obtained with the ASP for young adults (X) as follows: women: Y = 0.949 × X + 0.205, mean ± standard deviation of the prediction error = 0.00 ± 0.20; men: Y = 0.899 × X + 0.371, mean ± standard deviation of the prediction error = 0.00 ± 0.17.
Comparing Machine Learning Techniques for Predictions of Motorway Segment Crash Risk Level
Authors: Nikolaou Dimitrios , Ziakopoulos Apostolos , Dragomanovits Anastasios , Roussou Julia , Yannis George
Published: May 2023

Motorways are typically the safest road environment in terms of injury crashes per million vehicle kilometres; however, given the high severity of crashes occurring therein, there is still space for road safety improvements. The objective of this study is to compare the classification performance of five machine learning techniques for predictions of crash risk levels of motorway segments. To that end, data on crash risk levels, driving behaviour metrics, and road geometry characteristics of 668 motorway segments were exploited. The utilized dataset was divided into training and test subsets, with a proportion of 75% and 25%, respectively. The training subset was used to train the models, whereas the test subset was used for the evaluation of their performance. The response variable of the models was the crash risk level of the considered motorway segments, while the predictors were various road design characteristics and naturalistic driving behaviour metrics. The techniques considered were Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and K-Nearest Neighbours. Among the five techniques, the Random Forest model achieved the best classification performance (overall accuracy: 89.3%, macro-averaged precision: 89.0%, macro-averaged recall: 88.4%, macro-averaged F1 score: 88.6%). Moreover, the Shapley additive explanations were calculated in order to assist with the interpretation of the model’s outcomes. The findings of this study are particularly useful as the Random Forest model could be used as a highly promising proactive road safety tool for identifying potentially hazardous motorway segments.
How Sustainable Is Our Urban Social-Sustainability Theory?
Authors: Bigon Liora , Langenthal Edna
Published: May 2023

This short opinion article critically comments on some current mainstream trends, characteristics, and biases in urban social sustainability research literature. Through identifying some gaps regarding geography, sub-topics, and study approaches, and through considering “off-the-map” southern urban realities, it calls for the need to refocus and reshape some of the basic notions and presumptions that currently stand behind urban social sustainability theory, concepts, and policy design. Enhancing our sensitivity to truly global urban conditions, argue the authors, would result in less expected and generic (Eurocentric) approaches regarding urban social sustainability and would contribute to its more meaningful and comprehensive understanding. A bias towards qualitative, place-based, and context-sensitive analysis is a necessary step in rendering urban social sustainability truly global as well as in the making of more place-intelligent and place-responsive planning interventions.
Rural Population Aging, Capital Deepening, and Agricultural Labor Productivity
Authors: Shen Danhong , Liang Haimeng , Shi Wangfang
Published: May 2023

Whether the aging of rural population will affect the high-quality development of China’s agriculture is an important issue facing the construction of China’s characteristic modern agriculture. Using panel data from 30 provinces in China from 1999 to 2020, this paper used fixed effects models and mediation models for econometric regressions to explore the relationship between rural population aging and agricultural development, as well as the intermediate mechanisms involved. The study found that there was a positive relationship between rural population aging and agricultural labor productivity, and aging can stimulate agricultural development. Mechanism analysis revealed that rural population aging can promote the adjustment of the structure of agricultural factors, drive the deepening accumulation of capital and other factors, promote the modernization of agricultural production methods, and ultimately achieve an increase in agricultural labor productivity. Further examination showed that the positive relationship between rural population aging and agricultural productivity gradually weakened as the degree of rural population aging deepened. The study provides empirical evidence for sustainable agricultural development in the context of population aging.
Effective Speed: Factors That Influence the Attractiveness of Cost Effective and Sustainable Modes of Transport in Cities
Authors: Schnieder Maren
Published: May 2023

Background: People invest hours of their working day to pay for their vehicle. Is this a worthwhile use of their time? Wouldn’t public transport be a more economical solution for those on low income? Which demographic group typically chooses the most effective mode of transport? Aim: The two studies presented in this paper answer those fundamental questions based on the effective speed concept. This is calculated by dividing the distance travelled by time (i.e., minutes spent travelling and earning the money to afford it). Methodology: A digital model of the residents in Berlin is created to calculate the ‘effective speed’ for everyone. In the second study, based on a survey of residents in New York City, the best mode of transport for each respondent based on their effective speed has been identified and compared to the mode chosen. Results: The results of the Berlin case study show that the mode share for cars would be less than 6% if people choose the mode of transport with the highest effective speed. A similar picture is seen in NYC. Factors that significantly affect whether people who should use public transport, do so, include: age, gender, education, place of work or home.
Education for Sustainable Development: Mapping the SDGs to University Curricula
Authors: Adams Thomas , Jameel Syed Muslim , Goggins Jamie
Published: May 2023

Education for sustainable development (ESD) is a growing research field, particularly over the last decade. Measuring the level of ESD that is currently embedded in curricula is useful for planning the further implementation of sustainability-related teaching. The Sustainable Development Goals (SDGs) are a useful benchmark for sustainability topics and so this paper follows a methodology in which a keyword scanning tool was created to quantify the level of SDG coverage within a list of learning outcomes for a module. The aim of the research is to further develop this methodology and compare the results from the keyword tool with results from a survey of the academic staff who deliver the modules. SDG-related keyword lists were collected from multiple sources for a meta-analysis, examining the performance of various lists. These lists were then compiled into one list of over 12,000 SDG keywords and a team of reviewers conducted a critical analysis on the relevancy of the context in which the keywords were found when scanned. This process reduced the list to 222 “crucial keywords” and gave the keywords a relevancy label based on the STARS definitions, the sustainability tracking assessment and rating system. Finally, ChatGPT was also investigated as a method of enriching the critically analysed list with contextually relevant synonyms. A survey was carried out within the College of Science and Engineering at the University of Galway. It asked staff to rate the level of SDG coverage within their own modules, in their own opinion. This gave results which could be compared with the keyword scanning tool. The findings show success in improving the accuracy of the SDG keywords. ChatGPT added synonyms to the crucial keywords identified and this list was the most accurate out of all keyword lists used in the study. Using these keywords and the modules that staff rated in the survey, a correlation was found in the SDG trend.
Factors Affecting Entrepreneurship of Somali Students Studying Agriculture
Authors: Farah Kalif Mohamud , Taşcıoğlu Yavuz
Published: May 2023

The present study investigated the factors that affect the post-educational agricultural sector entrepreneurship of Somali university students studying agriculture in Turkey. The study data were obtained via an online survey from 77 Somali students carrying out their undergraduate and graduate education. The factor analysis technique was used for the analysis of the data. The hypothesis question of the research was “What are the factors affecting the entrepreneurship of individuals who receive agricultural education?”. The factors; i. Ensuring the structural change of Somalia’s economic structure, agricultural sector, and rural areas, ii. Having information about production costs and marketing in the agricultural sector in Somalia, iii. Being an entrepreneur and risk-taking in the agricultural sector in Somalia, iv. Being an entrepreneur in rural areas in Somalia and being able to benefit from support, v. Entrepreneurship in the agricultural sector in Somalia, the ability to invest in the agricultural sector, and having a business idea, vi. Availability of national and international support to the agricultural sector in Somalia, vii. Having entrepreneurship ideas in rural areas in Somalia, and viii. Increasing youth participation in the Economy and agriculture sector in Somalia was determined to be effective. Entrepreneurial activity is linked to the social structure and should be supported by education. The entrepreneurial willingness of young people, especially in higher education, will positively affect the economic structure of countries regarding the development and sustainability of the agricultural sector.
An Integrated Model to Improve Job Satisfaction: A Case for a Sustainable Construction Industry
Authors: Khahro Qasim Hussain , Zainun Noor Yasmin , Khahro Shabir Hussain , Sultan Basel
Published: May 2023

In the last few years, the fields of management, social psychology, and business operations have all paid a large amount of attention to the academic idea of job satisfaction. This paper looks at more than a decade of research into what makes people happy at work and what happens to them as a result. Companies have started to realize that their employees are their most valuable asset in this time of rapid change. There is no specific model of the job satisfaction of construction workers in developing countries. Thus, this paper evaluates the different job satisfaction models and also proposes an integrated job satisfaction model for construction industry workers. The data were collected from experts in the construction industry using a questionnaire survey and almost 290 experts participated in this research to assist in the development of the model. The data were analyzed using SPSS. The model was developed and validated using Smart PLS. Eight key aspects were found to be very important to improving the job satisfaction of construction workers in developing countries. Job compensation packages given to construction workers, work–life balance, career growth, and job security are the top key features investigated in this study. It is concluded that satisfied and motivated employees are imperative for the construction business, and that this is also a key factor that separates successful companies from others. The findings of this paper contribute to UN-SDG 8 “Decent Work and Economic Growth” and UN-SDG 9 “Industry, Innovation, and Infrastructure”.