bluuebunny/crossref_metadata_2025 huggingface dataset:
Being There Even When You Are Not: Presence in Distance Preaching
Authors: Sensing Tim
Published: March 2023

Listening to sermons through digital media is not new. However, the pandemic pressed many preachers into a medium in unexpected ways. The need for immediate solutions did not allow time for preachers to reflect on what was happening theologically. This paper explores theological presence. The question of how one creates connection and presence when no one is even in the same room, city, or country is not a new one. The rhetorical concern of being “present while absent” shows up in the writings of ancient rhetoricians, opening the door for theological conversation. Recognizing there are ways to employ rhetorical techniques for non-virtuous ends, the pandemic also exposed that some virtuous attempts also failed the ecclesial need to build relationships online. Digital platforms and social media give churches and ministers opportunities to hold space for developing relationships and witnessing the Gospel.
A Statistical Study of Ion Upflow during Periods of Dawnside Auroral Polarization Streams and Subauroral Polarization Streams
Authors: Qian Chengyu , Wang Hui
Published: March 2023

We have investigated the effect of the dawnside subauroral polarization stream (SAPS) and dawnside auroral polarization stream (DAPS) on the ionospheric ion upflow in the Northern Hemisphere by using four years of Defense Meteorological Satellite Program (DMSP) observations. The occurrence rate of DAPS is higher than that of dawnside SAPS. Obvious ion upflow occurs in the topside ionosphere around the DAPS region. The effect of the dawnside SAPS on the vertical flow is weakened when geomagnetic activity increases. There are prominent upflow fluxes around the DAPS, while there are insignificant upflow fluxes around the SAPS region. The plasma density trough during SAPS periods becomes shallower with increasing magnetic activity. These variations with magnetic activity might be due to the weakened ion-neutral collision heating efficiency during highly disturbed periods. There is no obvious plasma density trough or ion temperature drop around the DAPS region at the altitude of ~800 km. The ion temperature around the SAPS area decreases, while the electron temperature increases around the DAPS and SAPS regions.
Estimation of Urban Evapotranspiration at High Spatiotemporal Resolution and Considering Flux Footprints
Authors: Zhou Lihao , Cheng Lei , Qin Shujing , Mai Yiyi , Lu Mingshen
Published: March 2023

Evapotranspiration (ET) estimations at high spatiotemporal resolutions in urban areas are crucial for extreme weather forecasting and water management. However, urban ET estimation remains a major challenge in current urban hydrology and regional climate research due to highly heterogeneous environments, human interference, and a lack of observations. In this study, an urban ET model, called the PT-Urban model, was proposed for half-hourly ET estimations at a 10 m resolution. The PT-Urban model was validated using observations from the Hotel Torni urban flux site during the 2018 growing season. The results showed that the PT-Urban model performed satisfactorily, with an R2 and root-mean-square error of 0.59 and 14.67 W m−2, respectively. Further analysis demonstrated that urban canopy heat storage and shading effects are essential for the half-hourly urban energy balance. Ignoring the shading effects led to a 38.7% urban ET overestimation. Modeling experiments further proved that flux footprint variations were critical for the accurate estimation of urban ET. The setting source areas either as an invariant 70% historical footprint or as a circle with a 1 km radius both resulted in poor performances. This study presents a practical method for the accurate estimation of urban ET with high spatiotemporal resolution and highlights the importance of real-time footprints in urban ET estimations.
Study on Attenuation Correction for the Reflectivity of X-Band Dual-Polarization Phased-Array Weather Radar Based on a Network with S-Band Weather Radar
Authors: Geng Fei , Liu Liping
Published: March 2023

X-band dual-polarization phased-array weather radars (X-PARs) have been used in South China extensively. Eliminating the attenuation and system bias of X-band radar data is the key to utilizing the advantage of X-PAR networks. In this paper, the disdrometer raindrop-size distribution (DSD) measurements are used to calculate the radar polarimetric variables and analyze the characteristics of precipitation attenuation. Furthermore, based on the network of S-band dual-polarization Doppler weather radar (S-POL) and X-PARs, an attenuation-correction method for X-PAR reflectivity is proposed with S-POL constraints in view of the radar-mosaic requirements of a multi-radar network. Linear programming is used to calculate the attenuation-correction parameters of different rainfall areas, which realizes the attenuation correction for X-PAR. The results show that the attenuation-correction parameters simulated based on the disdrometer DSD vary with different precipitation classification; the attenuation-corrected reflectivity of X-PARs is consistent with S-POL and can realize a more precise observation of the evolution of the convective system. Compared with previous attenuation-correction methods with constant correction parameters, the improved method can reduce the deviation between X-PAR reflectivity and that of S-POL in heavy rainfall areas and areas of strong attenuation. The method proposed in this paper is stable and effective. After effective quality control, it is found that the X-PAR network deployed in South China observes data accurately and is consistent with S-POL; thus, it is expected to achieve high temporal–spatial resolution within a radar mosaic.
ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation
Authors: Zhao Yang , Guo Peng , Sun Zihao , Chen Xiuwan , Gao Han
Published: March 2023

The performance of a semantic segmentation model for remote sensing (RS) images pre-trained on an annotated dataset greatly decreases when testing on another unannotated dataset because of the domain gap. Adversarial generative methods, e.g., DualGAN, are utilized for unpaired image-to-image translation to minimize the pixel-level domain gap, which is one of the common approaches for unsupervised domain adaptation (UDA). However, the existing image translation methods face two problems when performing RS image translation: (1) ignoring the scale discrepancy between two RS datasets, which greatly affects the accuracy performance of scale-invariant objects; (2) ignoring the characteristic of real-to-real translation of RS images, which brings an unstable factor for the training of the models. In this paper, ResiDualGAN is proposed for RS image translation, where an in-network resizer module is used for addressing the scale discrepancy of RS datasets and a residual connection is used for strengthening the stability of real-to-real images translation and improving the performance in cross-domain semantic segmentation tasks. Combined with an output space adaptation method, the proposed method greatly improves the accuracy performance on common benchmarks, which demonstrates the superiority and reliability of ResiDualGAN. At the end of the paper, a thorough discussion is conducted to provide a reasonable explanation for the improvement of ResiDualGAN. Our source code is also available.
Exploring Contrastive Representation for Weakly-Supervised Glacial Lake Extraction
Authors: Zhao Hang , Wang Shuang , Liu Xuebin , Chen Fang
Published: March 2023

Against the background of the ongoing atmospheric warming, the glacial lakes that are nourished and expanded in High Mountain Asia pose growing risks of glacial lake outburst floods (GLOFs) hazards and increasing threats to the downstream areas. Effectively extracting the area and consistently monitoring the dynamics of these lakes are of great significance in predicting and preventing GLOF events. To automatically extract the lake areas, many deep learning (DL) methods capable of capturing the multi-level features of lakes have been proposed in segmentation and classification tasks. However, the portability of these supervised DL methods need to be improved in order to be directly applied to different data sources, as they require laborious effort to collect the labeled lake masks. In this work, we proposed a simple glacial lake extraction model (SimGL) via weakly-supervised contrastive learning to extend and improve the extraction performances in cases that lack the labeled lake masks. In SimGL, a Siamese network was employed to learn similar objects by maximizing the similarity between the input image and its augmentations. Then, a simple Normalized Difference Water Index (NDWI) map was provided as the location cue instead of the labeled lake masks to constrain the model to capture the representations related to the glacial lakes and the segmentations to coincide with the true lake areas. Finally, the experimental results of the glacial lake extraction on the 1540 Landsat-8 image patches showed that our approach, SimGL, offers a competitive effort with some supervised methods (such as Random Forest) and outperforms other unsupervised image segmentation methods in cases that lack true image labels.
Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning
Authors: Beloiu Mirela , Heinzmann Lucca , Rehush Nataliia , Gessler Arthur , Griess Verena C.
Published: March 2023

Automatic identification and mapping of tree species is an essential task in forestry and conservation. However, applications that can geolocate individual trees and identify their species in heterogeneous forests on a large scale are lacking. Here, we assessed the potential of the Convolutional Neural Network algorithm, Faster R-CNN, which is an efficient end-to-end object detection approach, combined with open-source aerial RGB imagery for the identification and geolocation of tree species in the upper canopy layer of heterogeneous temperate forests. We studied four tree species, i.e., Norway spruce (Picea abies (L.) H. Karst.), silver fir (Abies alba Mill.), Scots pine (Pinus sylvestris L.), and European beech (Fagus sylvatica L.), growing in heterogeneous temperate forests. To fully explore the potential of the approach for tree species identification, we trained single-species and multi-species models. For the single-species models, the average detection accuracy (F1 score) was 0.76. Picea abies was detected with the highest accuracy, with an average F1 of 0.86, followed by A. alba (F1 = 0.84), F. sylvatica (F1 = 0.75), and Pinus sylvestris (F1 = 0.59). Detection accuracy increased in multi-species models for Pinus sylvestris (F1 = 0.92), while it remained the same or decreased slightly for the other species. Model performance was more influenced by site conditions, such as forest stand structure, and less by illumination. Moreover, the misidentification of tree species decreased as the number of species included in the models increased. In conclusion, the presented method can accurately map the location of four individual tree species in heterogeneous forests and may serve as a basis for future inventories and targeted management actions to support more resilient forests.
Local Convergence Index-Based Infrared Small Target Detection against Complex Scenes
Authors: Cao Siying , Deng Jiakun , Luo Junhai , Li Zhi , Hu Junsong , Peng Zhenming
Published: March 2023

Infrared small target detection (ISTD) plays a crucial role in precision guidance, anti-missile interception, and military early-warning systems. Existing approaches suffer from high false alarm rates and low detection rates when detecting dim and small targets in complex scenes. A robust scheme for automatically detecting infrared small targets is proposed to address this problem. First, a gradient weighting technique with high sensitivity was used for extracting target candidates. Second, a new collection of features based on local convergence index (LCI) filters with a strong representation of dim or arbitrarily shaped targets was extracted for each candidate. Finally, the collective set of features was inputted to a random undersampling boosting classifier (RUSBoost) to discriminate the real targets from false-alarm candidates. Extensive experiments on public datasets NUDT-SIRST and NUAA-SIRST showed that the proposed method achieved competitive performance with state-of-the-art (SOTA) algorithms. It is also important to note that the average processing time was as low as 0.07 s per frame with low time consumption, which is beneficial for practical applications.
Using Digital Human Modelling to Evaluate the Risk of Musculoskeletal Injury for Workers in the Healthcare Industry
Authors: Ji Xiaoxu , Hettiarachchige Ranuki O. , Littman Alexa L. E. , Piovesan Davide
Published: March 2023

Background: Hospital nurses and caregivers are reported to have the highest number of workplace injuries every year, which directly leads to missed days of work, a large amount of compensation costs, and staff shortage issues in the healthcare industry. Hence, this research study provides a new technique to evaluate the risk of injuries for healthcare workers using a combination of unobtrusive wearable devices and digital human technology. The seamless integration of JACK Siemens software and the Xsens motion tracking system was used to determine awkward postures adopted for patient transfer tasks. This technique allows for continuous monitoring of the healthcare worker’s movement which can be obtained in the field. Methods: Thirty-three participants underwent two common tasks: moving a patient manikin from a lying position to a sitting position in bed and transferring the manikin from a bed to a wheelchair. By identifying, in these daily repetitive patient-transfer tasks, potential inappropriate postures that can be conducive to excessive load on the lumbar spine, a real-time monitoring process can be devised to adjust them, accounting for the effect of fatigue. Experimental Result: From the results, we identified a significant difference in spinal forces exerted on the lower back between genders at different operational heights. Additionally, we revealed the main anthropometric variables (e.g., trunk and hip motions) that are having a large impact on potential lower back injury. Conclusions: These results will lead to implementation of training techniques and improvements in working environment design to effectively reduce the number of healthcare workers experiencing lower back pain, which can be conducive to fewer workers leaving the healthcare industry, better patient satisfaction and reduction of healthcare costs.
Dynamic Deformation Behaviors of the Levitation Electromagnets of High-Speed Maglev Vehicle Negotiating a Sharp Horizontal Curve
Authors: Yu Qingsong , Li Xiaoqing , Shao Qing , Han Tian , Zhao Chunfa , He Feng
Published: March 2023

The elastic deformation of the levitation electromagnet (LM) of the high-speed maglev vehicle brings uneven levitation gaps and displacement differences between measured gap signals and the real gap in the middle of the LM, and then reduces dynamic performances of the electromagnetic levitation unit. However, most of the published literature has paid little attention to the dynamic deformation of the LM under complex line conditions. In this paper, considering the flexibility of the LM and the levitation bogie, a rigid-flexible coupled dynamic model is established to simulate deformation behaviors of the LMs of the maglev vehicle passing through the 650 m radius horizontal curve. Simulated results indicate that the deflection deformation direction of the same LM on the front transition curve is always opposite to that on the rear transition curve. Similarly, the deflection deformation direction of a left LM on the transition curve is opposite to that of the corresponding right LM. Furthermore, deflection deformation amplitudes of the LMs in the middle of the vehicle are always very small (less than 0.2 mm). However, the deflection deformation of the LMs at both ends of the vehicle is considerably large, and the maximum deflection deformation is about 0.86 mm when the vehicle passes at the balance speed. This forms a considerable displacement disturbance for the nominal levitation gap of 10 mm. It is necessary to optimize the supporting structure of the LM at the end of the maglev train in the future.
Wave Equation Modeling via Physics-Informed Neural Networks: Models of Soft and Hard Constraints for Initial and Boundary Conditions
Authors: Alkhadhr Shaikhah , Almekkawy Mohamed
Published: March 2023

Therapeutic ultrasound waves are the main instruments used in many noninvasive clinical procedures. They are continuously transforming medical treatments through mechanical and thermal effects. To allow for effective and safe delivery of ultrasound waves, numerical modeling methods such as the Finite Difference Method (FDM) and the Finite Element Method (FEM) are used. However, modeling the acoustic wave equation can result in several computational complications. In this work, we study the accuracy of using Physics-Informed Neural Networks (PINNs) to solve the wave equation when applying different combinations of initial and boundary conditions (ICs and BCs) constraints. By exploiting the mesh-free nature of PINNs and their prediction speed, we specifically model the wave equation with a continuous time-dependent point source function. Four main models are designed and studied to monitor the effects of soft or hard constraints on the prediction accuracy and performance. The predicted solutions in all the models were compared to an FDM solution for prediction error estimation. The trials of this work reveal that the wave equation modeled by a PINN with soft IC and BC (soft–soft) constraints reflects the lowest prediction error among the four combinations of constraints.
A New Generation of OPM for High Dynamic and Large Bandwidth MEG: The 4He OPMs—First Applications in Healthy Volunteers
Authors: Gutteling Tjerk , Bonnefond Mathilde , Clausner Tommy , Daligault Sébastien , Romain Rudy , Mitryukovskiy Sergey , Fourcault William , Josselin Vincent , Le Prado Matthieu , Palacios-Laloy Agustin , Labyt Etienne , Jung Julien , Schwartz Denis
Published: March 2023

MagnetoEncephaloGraphy (MEG) provides a measure of electrical activity in the brain at a millisecond time scale. From these signals, one can non-invasively derive the dynamics of brain activity. Conventional MEG systems (SQUID-MEG) use very low temperatures to achieve the necessary sensitivity. This leads to severe experimental and economical limitations. A new generation of MEG sensors is emerging: the optically pumped magnetometers (OPM). In OPM, an atomic gas enclosed in a glass cell is traversed by a laser beam whose modulation depends on the local magnetic field. MAG4Health is developing OPMs using Helium gas (4He-OPM). They operate at room temperature with a large dynamic range and a large frequency bandwidth and output natively a 3D vectorial measure of the magnetic field. In this study, five 4He-OPMs were compared to a classical SQUID-MEG system in a group of 18 volunteers to evaluate their experimental performances. Considering that the 4He-OPMs operate at real room temperature and can be placed directly on the head, our assumption was that 4He-OPMs would provide a reliable recording of physiological magnetic brain activity. Indeed, the results showed that the 4He-OPMs showed very similar results to the classical SQUID-MEG system by taking advantage of a shorter distance to the brain, despite having a lower sensitivity.
Advantageous Strain Sensing Performances of FBG Strain Sensors Equipped with Planar UV-Curable Resin
Authors: Li Xiaojin , Zheng Min , Hou Dan , Wen Qiao
Published: March 2023

The existing optical strain sensors based on fiber Bragg grating (FBG) have limitations, such as a complex structure, a limited strain range (±200 με) and poor linearity performance (R-squared value < 0.9920); these limitations affect their potential practical applications. Here, four FBG strain sensors equipped with planar UV-curable resin are investigated. The proposed FBG strain sensors have a simple structure, a large strain range (±1800 με) and excellent linearity performance (R-squared value ≥ 0.9998); they further produce the following performances: (1) good optical properties, including an undistorted Bragg peak shape, narrow bandwidth (−3 dB bandwidth ≤ 0.65 nm) and a high side mode suppression ratio (SMSR, the absolute value of SMSR ≥ 15 dB); (2) good temperature sensing properties with high temperature sensitivities (≥47.7 pm/°C) and a good linearity performance (R-squared value ≥ 0.9990); and (3) excellent strain sensing properties with no hysteresis behavior (hysteresis error ≤ 0.058%) and excellent repeatability (repeatability error ≤ 0.045%). Based on their excellent properties, the proposed FBG strain sensors are expected to be applied as high-performance strain sensing devices.
A New Deep-Learning Method for Human Activity Recognition
Authors: Vrskova Roberta , Kamencay Patrik , Hudec Robert , Sykora Peter
Published: March 2023

Currently, three-dimensional convolutional neural networks (3DCNNs) are a popular approach in the field of human activity recognition. However, due to the variety of methods used for human activity recognition, we propose a new deep-learning model in this paper. The main objective of our work is to optimize the traditional 3DCNN and propose a new model that combines 3DCNN with Convolutional Long Short-Term Memory (ConvLSTM) layers. Our experimental results, which were obtained using the LoDVP Abnormal Activities dataset, UCF50 dataset, and MOD20 dataset, demonstrate the superiority of the 3DCNN + ConvLSTM combination for recognizing human activities. Furthermore, our proposed model is well-suited for real-time human activity recognition applications and can be further enhanced by incorporating additional sensor data. To provide a comprehensive comparison of our proposed 3DCNN + ConvLSTM architecture, we compared our experimental results on these datasets. We achieved a precision of 89.12% when using the LoDVP Abnormal Activities dataset. Meanwhile, the precision we obtained using the modified UCF50 dataset (UCF50mini) and MOD20 dataset was 83.89% and 87.76%, respectively. Overall, our work demonstrates that the combination of 3DCNN and ConvLSTM layers can improve the accuracy of human activity recognition tasks, and our proposed model shows promise for real-time applications.
AI-Enabled Smart Wristband Providing Real-Time Vital Signs and Stress Monitoring
Authors: Mitro Nikos , Argyri Katerina , Pavlopoulos Lampros , Kosyvas Dimitrios , Karagiannidis Lazaros , Kostovasili Margarita , Misichroni Fay , Ouzounoglou Eleftherios , Amditis Angelos
Published: March 2023

This work introduces the design, architecture, implementation, and testing of a low-cost and machine-learning-enabled device to be worn on the wrist. The suggested wearable device has been developed for use during emergency incidents of large passenger ship evacuations, and enables the real-time monitoring of the passengers’ physiological state, and stress detection. Based on a properly preprocessed PPG signal, the device provides essential biometric data (pulse rate and oxygen saturation level) and an efficient unimodal machine learning pipeline. The stress detecting machine learning pipeline is based on ultra-short-term pulse rate variability, and has been successfully integrated into the microcontroller of the developed embedded device. As a result, the presented smart wristband is able to provide real-time stress detection. The stress detection system has been trained with the use of the publicly available WESAD dataset, and its performance has been tested through a two-stage process. Initially, evaluation of the lightweight machine learning pipeline on a previously unseen subset of the WESAD dataset was performed, reaching an accuracy score equal to 91%. Subsequently, external validation was conducted, through a dedicated laboratory study of 15 volunteers subjected to well-acknowledged cognitive stressors while wearing the smart wristband, which yielded an accuracy score equal to 76%.
Trunk Velocity Changes in Response to Physical Perturbations Are Potential Indicators of Gait Stability
Authors: Fallahtafti Farahnaz , Bruijn Sjoerd , Mohammadzadeh Gonabadi Arash , Sangtarashan Mohammad , Boron Julie Blaskewicz , Curtze Carolin , Siu Ka-Chun , Myers Sara A. , Yentes Jennifer
Published: March 2023

Response to challenging situations is important to avoid falls, especially after medial perturbations, which require active control. There is a lack of evidence on the relationship between the trunk’s motion in response to perturbations and gait stability. Eighteen healthy adults walked on a treadmill at three speeds while receiving perturbations of three magnitudes. Medial perturbations were applied by translating the walking platform to the right at left heel contact. Trunk velocity changes in response to the perturbation were calculated and divided into the initial and the recovery phases. Gait stability after a perturbation was assessed using the margin of stability (MOS) at the first heel contact, MOS mean, and standard deviation for the first five strides after the perturbation onset. Faster speed and smaller perturbations led to a lower deviation of trunk velocity from the steady state, which can be interpreted as an improvement in response to the perturbation. Recovery was quicker after small perturbations. The MOS mean was associated with the trunk’s motion in response to perturbations during the initial phase. Increasing walking speed may increase resistance to perturbations, while increasing the magnitude of perturbation leads to greater trunk motions. MOS is a useful marker of resistance to perturbations.
Pre-Trained Joint Model for Intent Classification and Slot Filling with Semantic Feature Fusion
Authors: Chen Yan , Luo Zhenghang
Published: March 2023

The comprehension of spoken language is a crucial aspect of dialogue systems, encompassing two fundamental tasks: intent classification and slot filling. Currently, the joint modeling approach for these two tasks has emerged as the dominant method in spoken language understanding modeling. However, the existing joint models have limitations in terms of their relevancy and utilization of contextual semantic features between the multiple tasks. To address these limitations, a joint model based on BERT and semantic fusion (JMBSF) is proposed. The model employs pre-trained BERT to extract semantic features and utilizes semantic fusion to associate and integrate this information. The results of experiments on two benchmark datasets, ATIS and Snips, in spoken language comprehension demonstrate that the proposed JMBSF model attains 98.80% and 99.71% intent classification accuracy, 98.25% and 97.24% slot-filling F1-score, and 93.40% and 93.57% sentence accuracy, respectively. These results reveal a significant improvement compared to other joint models. Furthermore, comprehensive ablation studies affirm the effectiveness of each component in the design of JMBSF.
A Dew-Condensation Sensor Exploiting Local Variations in the Relative Refractive Index on the Dew-Friendly Surface of a Waveguide
Authors: Hwa Subin , Sim Eun-Seon , Na Jun-Hee , Jang Ik-Hoon , Kwon Jin-Hyuk , Kim Min-Hoi
Published: March 2023

We propose a sensor technology for detecting dew condensation, which exploits a variation in the relative refractive index on the dew-friendly surface of an optical waveguide. The dew-condensation sensor is composed of a laser, waveguide, medium (i.e., filling material for the waveguide), and photodiode. The formation of dewdrops on the waveguide surface causes local increases in the relative refractive index accompanied by the transmission of the incident light rays, hence reducing the light intensity inside the waveguide. In particular, the dew-friendly surface of the waveguide is obtained by filling the interior of the waveguide with liquid H2O, i.e., water. A geometric design for the sensor was first carried out considering the curvature of the waveguide and the incident angles of the light rays. Moreover, the optical suitability of waveguide media with various absolute refractive indices, i.e., water, air, oil, and glass, were evaluated through simulation tests. In actual experiments, the sensor with the water-filled waveguide displayed a wider gap between the measured photocurrent levels under conditions with and without dew, than those with the air- and glass-filled waveguides, as a result of the relatively high specific heat of the water. The sensor with the water-filled waveguide exhibited excellent accuracy and repeatability as well.
Natural Intelligence as the Brain of Intelligent Systems
Authors: Naghshvarianjahromi Mahdi , Kumar Shiva , Deen Mohammed Jamal
Published: March 2023

This article discusses the concept and applications of cognitive dynamic systems (CDS), which are a type of intelligent system inspired by the brain. There are two branches of CDS, one for linear and Gaussian environments (LGEs), such as cognitive radio and cognitive radar, and another one for non-Gaussian and nonlinear environments (NGNLEs), such as cyber processing in smart systems. Both branches use the same principle, called the perception action cycle (PAC), to make decisions. The focus of this review is on the applications of CDS, including cognitive radios, cognitive radar, cognitive control, cyber security, self-driving cars, and smart grids for LGEs. For NGNLEs, the article reviews the use of CDS in smart e-healthcare applications and software-defined optical communication systems (SDOCS), such as smart fiber optic links. The results of implementing CDS in these systems are very promising, with improved accuracy, performance, and lower computational costs. For example, CDS implementation in cognitive radars achieved a range estimation error that is as good as 0.47 (m) and a velocity estimation error of 3.30 (m/s), outperforming traditional active radars. Similarly, CDS implementation in smart fiber optic links improved the quality factor by 7 dB and the maximum achievable data rate by 43% compared to those of other mitigation techniques.
The Digital Calibration Certificate (DCC) for an End-to-End Digital Quality Infrastructure for Industry 4.0
Authors: Hackel Siegfried , Schönhals Shanna , Doering Lutz , Engel Thomas , Baumfalk Reinhard
Published: March 2023

This article depicts the role of the Digital Calibration Certificate (DCC) for an end-to-end digital quality infrastructure and as the basis for developments that are designated by the keyword “Industry 4.0”. Furthermore, it describes the impact the DCC has on increasing productivity in the manufacturing of products and in global trade. The DCC project is international in its scope. Calibration certificates document the measurement capability of a measurement system. They do this independently and by providing traceability to measurement standards. Therefore, they do not only play an important role in the world of metrology, but they also make it possible for manufacturing and commercial enterprises to exchange measurement values reliably and correctly at the national and at the international level. Thus, a DCC concept is urgently needed for the end-to-end digitalization of industry for the era of Industry 4.0 and for Medicine 4.0. A DCC brings about important advantages for issuers and for users. The DCC leads to the stringent, end-to-end, traceable and process-oriented organization of manufacturing and trading. Digitalization is thus a key factor in the field of calibration as it enables significant improvements in product and process quality. The reason for this is that the transmission of errors will be prevented, and consequently, costs will be saved as the time needed for distributing and disseminating the DCCs and the respective calibration objects will be reduced. Furthermore, it will no longer be necessary for the test equipment administration staff to update the data manually, which is a time-consuming, tedious and error-prone process.
Characterization of the Aroma Profile of Food Smoke at Controllable Pyrolysis Temperatures
Authors: Rigling Marina , Höckmeier Laura , Leible Malte , Herrmann Kurt , Gibis Monika , Weiss Jochen , Zhang Yanyan
Published: March 2023

Smoking is used to give food its typical aroma and to obtain the desired techno-functional properties of the product. To gain a deeper knowledge of the whole process of food smoking, a controllable smoking process was developed, and the influence of wood pyrolysis temperature (150–900 °C) on the volatile compounds in the smoking chamber atmosphere was investigated. The aroma profile of smoke was decoded by headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Subsequently, the correlations in the most important substance classes, as well as in individual target components, were investigated by the Pearson test. Phenols and lactones showed an increase over the entire applied temperature range (rT = 0.94 and rT = 0.90), whereas furans and carbonyls showed no strict temperature dependence (rT < 0.6). Investigations on single aroma compounds showed that not all compounds of one substance class showed the same behavior, e.g., guaiacol showed no significant increase over the applied pyrolysis temperature, whereas syringol and hydoxyacetone showed a plateau after 450 °C, and phenol and cyclotene increased linear over the applied temperature range. These findings will help to better understand the production of aroma-active compounds during smoke generation in order to meet consumers preferences.
An Overview of Herbal Nutraceuticals, Their Extraction, Formulation, Therapeutic Effects and Potential Toxicity
Authors: Bommakanti Vaishnavi , Puthenparambil Ajikumar Amruthamol , Sivi Chelssa , Prakash Geethika , Mundanat Anjaly , Ahmad Faraz , Haque Shafiul , Prieto Miguel , Rana Sandeep
Published: March 2023

Herbal nutraceuticals are foods derived from plants and/or their derivatives, such as oils, roots, seeds, berries, or flowers, that support wellness and combat acute and chronic ailments induced by unhealthful dietary habits. The current review enlists various traditional as well as unexplored herbs including angelica, burnet, caraway, laurel, parsley, yarrow, and zedoary, which are rich sources of bioactive components, such as aloesin, angelicin, trans-anethole, and cholesteric-7-en-3β-ol. The review further compares some of the extraction and purification techniques, namely, Soxhlet extraction, ultrasound assisted extraction, microwave assisted extraction, supercritical fluid extraction, accelerated solvent extraction, hydro-distillation extraction, ultra-high-pressure extraction, enzyme assisted extraction, pulsed electric field extraction, bio affinity chromatography, cell membrane chromatography, and ligand fishing. Herbal nutraceuticals can be purchased in varied formulations, such as capsules, pills, powders, liquids, and gels. Some of the formulations currently available on the market are discussed here. Further, the significance of herbal nutraceuticals in prevention and cure of diseases, such as diabetes, obesity, dementia, hypertension, and hypercholesterolemia; and as immunomodulators and antimicrobial agents has been discussed. Noteworthy, the inappropriate use of these herbal nutraceuticals can lead to hepatotoxicity, pulmonary toxicity, cytotoxicity, carcinogenicity, nephrotoxicity, hematotoxicity, and cardiac toxicity. Hence, this review concludes with a discussion of various regulatory aspects undertaken by the government agencies in order to minimize the adverse effects associated with herbal nutraceuticals.
Car-Sharing Systems in Smart Cities: A Review of the Most Important Issues Related to the Functioning of the Systems in Light of the Scientific Research
Authors: Turoń Katarzyna
Published: March 2023

Currently, short-term car rental services (car-sharing) are a solution that is an alternative to individual motorization, which can be used in cities. With the level of sophistication of the relevant systems, there are more and more problems with their proper functioning. However, to understand the essence of some of the problems or properly introduce a new system to the market, it is important to understand how the systems work and the main relationships between the factors affecting car-sharing. This work aims to comprehensively present the most important aspects related to the functioning of car-sharing in light of the current research, as a form of compendium of knowledge about car-sharing. A review of the literature indicates that the leading areas of research in the field of car-sharing are seven topics related to the origin and history of services, the analysis of the functioning of the services market and criteria affecting the development of the relevant systems, the management of vehicle systems and location, issues related to the sustainable development of transport, IT systems, electromobility in car-sharing, and service optimization and modeling. A detailed analysis of the work carried out in this area allowed for the identification of 15 leading factors influencing car-sharing. This article supports the implementation and improvement of car-sharing services. In addition, it supports researchers during literature reviews and the search for a coherent list of the factors influencing car-sharing.
From Left-Behind Children to Youth Labor Migrants: The Impact of Household Networks, Gendered Migration, and Relay Migration in Southeast Asia
Authors: Chow Cheng , Zhou Xiaochen , Fu Yao , Jampaklay Aree , Jordan Lucy P.
Published: March 2023

Do children with migrant parents or migrant family members have a greater likelihood of migration as they reach adulthood? Three possible patterns of youth migration are examined in this study: (1) network migration, when families migrating first pave the way for subsequent generations to follow; (2) gendered migration, where the gender of migrant parents and left-behind children influences the probability of youth migration; and (3) relay migration, which involves transgenerational migration switching within a family. We use data collected from Thailand in 2008/2010 (Wave 1) and tracked in 2019 (Wave 2) to understand how the migration of parents and other family members influenced youth migration in 2019. Within the network effect, household migration was one of the driving forces behind youth migration, while mother-involved migration appeared to be strongly associated with youth labor migration, especially among males. There is, however, no evidence that return migration, whether parental or non-parental in the same household, was associated with an increased likelihood of youth labor migration. This study demonstrates the relative strength of network effects of household migration when compared to relay migration on youth migration. The findings also highlight the complexity of gender-based migration in the Thai context. This research contributes to the larger field of left-behind children and youth migration by establishing the transition between these two roles and illustrating different reasons for migration in sending countries.
Exploring the Effect of Perceived Transaction Costs on Farmers’ Attitudes toward Participation in Agri-Environment-Climate Measures (AECMs)
Authors: Riccioli Francesco , Espinosa Diaz Salomon , Di Iacovo Francesco , Moruzzo Roberta
Published: March 2023

There is growing interest in research and policy to provide alternatives for developing and supporting sustainable business models in the agricultural sector. Agri-environment–climate measures (AECMs) were devised with the intention of compensating land users for potential income losses generated from the uptake of more appropriate, less intensive management practices in areas considered environmentally sensitive. Nonetheless, the effectiveness of these public initiatives is influenced by farmers’ decision to participate, which in turn is influenced by the level of transaction costs (TC) they must bear when engaging in these programs. Most studies have approached this topic from a quantitative perspective, trying to measure and estimate transaction costs; however, there is a lack of qualitative studies exploring the perceptions of farmers around these costs and analyzing how such perceptions influence their attitude toward AECM participation. This study presents the results of thirty semi-structured interviews conducted among farmers involved in different AECMs across seven European countries, in which their perceptions about transaction costs were explored through the assessment of the time required to perform certain activities. The objective was to gain deeper insights into the way in which the perceived level of TC can affect farmers’ attitudes and decisions toward the implementation of environment-friendly management practices through AECMs. Results showed that such perceptions are influenced by several factors, such as the presence or absence of intermediaries helping farmers to carry out some of the most time-consuming activities, the organizational structure of the contract, and the experience farmers acquire through repeated participation.
Are Women’s and Men’s Pay Increase Trajectories Different in Nonprofit and For-Profit Human Services Organizations?
Authors: Zhao Rong
Published: March 2023

Using the Survey of Income and Program Participation panel data, this study compares women’s and men’s pay increase trajectories and patterns of job mobility in the nonprofit and for-profit sectors. As recent studies suggested that industry-specific rather than economy-wide analysis is more appropriate in nonprofit/for-profit comparison, this study only focuses on the human services field. The results indicate that: (1) there was a selection in workers’ choices regarding staying or changing sector of employment; (2) nonprofit workers who chose to move to the for-profit sector tended to be those worse off in the nonprofit sector, whereas for-profit workers who chose to move to the nonprofit sector tended to be those better off in the for-profit sector, and both of the mover groups gained by moving; (3) in both nonprofits and for-profits, men enjoyed a steeper pay increase curve by occupying more managerial positions, having higher levels of education, and working longer hours; (4) on average, there was no statistically significant difference in pay increase trajectories between workers who chose to stay in their sector of employment. Consistent with previous studies, the regression analyses further support the argument that, within human services, there is not necessarily a sectoral difference in the gender pay gap—in other words, nonprofits are not necessarily more equitable than for-profits.
Key Determinants of Job Satisfaction among University Lecturers
Authors: Kim Long , Pongsakornrungsilp Pimlapas , Pongsakornrungsilp Siwarit , Horam Ngachonpam , Kumar Vikas
Published: March 2023

Creating job satisfaction for employees can help organizations maintain their employees and save more on costs from searching for new ones. Therefore, a primary goal of this research was to investigate how work–family conflict, workload, and job stress influenced university lecturers’ work satisfaction. To accomplish the current aim, researchers invited 450 respondents who were holding positions as lecturers at any university in Thailand. Next, a structural equation model was employed to analyze 387 valid data points. In gender statistics, 45.2% were male respondents while 54.8% were female respondents. Moreover, gender obtained mean scores (1.54) with standard deviation scores (0.49). Based on age statistics, most of the respondents who joined this research were between 20 and 30 years old (41.3%) followed by 31–41 years (24.5%), 41–50 years (19.9%), and above 50 years (14.2%). Meanwhile, mean scores were 2.07 with standard deviation 1.09. According to results of this research, increasing work–family conflict and workload caused lecturers to receive more stress from their work. Moreover, the lecturers found themselves happy once certain degrees of stress and work–family conflict, except workload, diminished. Meanwhile, stress among university lecturers significantly mediated their workloads and work satisfaction. This result highlights a side effect of a certain amount of workload influencing lecturers’ stress levels, which in turn increased the significant role of job stress in further influencing lecturers’ work satisfaction.
From Geomimetic to Biomimetic Manufacturing: Digitally Transforming Industry for Sustainability
Authors: Unruh Gregory C.
Published: March 2023

Digital technologies and Industry 4.0 hold the prospect of improving the sustainability performance of manufacturing, but the environmental implications of this transformation are uncertain. To contribute to resolving the environmental impacts of production, Industry 4.0 needs to be guided by sustainable manufacturing principles. This article asserts that we have access to only one functioning example of sustainable production on planet Earth, which is nature, and that Industry 4.0 guided by natural biomimetic principles can advance sustainable production goals. It first contends that industry to date has been guided geomimetic principles—which is the industrial mimicking of physical geologic processes—and that geomimicry is a source of many environmental externalities arising from industrial production. The paper then introduces a series of nature-inspired, biomimetic principles that can be facilitated by the unique capabilities inherent in emerging digital production technologies.
The Moderating Effect of Athletes’ Personal Values on the Relationship between Coaches’ Leadership Behaviors and the Personal and Social Skills of Young Basketball Players
Authors: Corti Juan Facundo , Raimundi María Julia , Celsi Ignacio , Alvarez Octavio , Castillo Isabel
Published: March 2023

Transformational coaching has been shown to increase the personal and social skills of adolescent athletes. Nevertheless, the latter’s dispositional characteristics, such us personal values, could have a moderating effect on this relationship. The main objective of this work was to examine perceptions of coaches’ transformational behaviors and the modulation of athletes’ personal values in their relationship with personal and social skills development. Adolescents (n = 309) of both genders (81.9% male) aged 13–19 years (M = 16.10; SD = 1.70) from 16 different basketball clubs participated in the study. All participants completed a socio-demographic questionnaire, the Transformational Coaching Questionnaire, the Portrait Values Questionnaire—Revised, and the Youth Experiences Survey for Sport. Results showed that, controlling for age and gender, transformational behaviors that highlight individual differences within the team, especially through fostering autonomy and creative thinking (i.e., intellectual stimulation), create contexts in which their athletes can develop personal and social skills to their fullest. However, in players with high self-transcendence and low self-enhancement, the increase in intellectual stimulation was not associated with higher personal and social skills, but social focus transformational behaviors (i.e., idealized influence and inspirational motivation) were. This study contributes to the field of positive adolescent development by showing evidence of both the individual and the social focus of transformational leadership behaviors to maximize personal and social skills, depending on which values their athletes consider important.