bluuebunny/crossref_metadata_2025 huggingface dataset:
Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising
Authors: Carré Maxime , Jourlin Michel
Published: December 2021

Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the ability of the LIP laws to perform a precise simulation of exposure time variation. Such a simulation permits the enhancement of low-light images, but a denoising step is required, realized by using a CNN (Convolutional Neural Network). A main contribution of the paper consists of using rigorous tools (metrics) to estimate the enhancement reliability in terms of noise reduction, visual image quality, and color preservation. Thanks to these tools, it has been established that the standard exposure time can be significantly reduced, which considerably enlarges the use of a given sensor. Moreover, the contribution of the LIP enhancement and denoising step are evaluated separately.
Trends and Future Prospects of the Drowsiness Detection and Estimation Technology
Authors: Arakawa Toshiya
Published: December 2021

Drowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver’s drowsiness. Driver monitoring systems usually detect three types of information: biometric information, vehicle behavior, and driver’s graphic information. This review summarizes the research and development trends of drowsiness detection systems based on various methods. Drowsiness detection methods based on the three types of information are discussed. A prospect for arousal level detection and estimation technology for autonomous driving is also presented. In the case of autonomous driving levels 4 and 5, where the driver is not the primary driving agent, the technology will not be used to detect and estimate wakefulness for accident prevention; rather, it can be used to ensure that the driver has enough sleep to arrive comfortably at the destination.
Electrochemical Characterization of Modified Glassy Carbon Electrodes for Non-Enzymatic Glucose Sensors
Authors: Mazurków Julia Maria , Kusior Anna , Radecka Marta
Published: December 2021

The diversity of materials proposed for non-enzymatic glucose detection and the lack of standardized protocols for assessing sensor performance have caused considerable confusion in the field. Therefore, methods for pre-evaluation of working electrodes, which will enable their conscious design, are currently intensively sought. Our approach involved comprehensive morphologic and structural characterization of copper sulfides as well as drop-casted suspensions based on three different polymers—cationic chitosan, anionic Nafion, and nonionic polyvinylpyrrolidone (PVP). For this purpose, scanning electron microscopy (SEM), X-ray diffraction (XRD), and Raman spectroscopy were applied. Subsequently, comparative studies of electrochemical properties of bare glassy carbon electrode (GCE), polymer- and copper sulfides/polymer-modified GCEs were performed using electrochemical impedance spectroscopy (EIS) and voltammetry. The results from EIS provided an explanation for the enhanced analytical performance of Cu-PVP/GCE over chitosan- and Nafion-based electrodes. Moreover, it was found that the pH of the electrolyte significantly affects the electrocatalytic behavior of copper sulfides, indicating the importance of OHads in the detection mechanism. Additionally, diffusion was denoted as a limiting step in the irreversible electrooxidation process that occurs in the proposed system.
Sleep and COVID-19. A Case Report of a Mild COVID-19 Patient Monitored by Consumer-Targeted Sleep Wearables
Authors: Metlaine Arnaud , Sauvet Fabien , Chennaoui Mounir , Leger Damien , Elbaz Maxime
Published: December 2021

Since its first description in Wuhan, China, the novel Coronavirus (SARS-CoV-2) has spread rapidly around the world. The management of this major pandemic requires a close coordination between clinicians, scientists, and public health services in order to detect and promptly treat patients needing intensive care. The development of consumer wearable monitoring devices offers physicians new opportunities for the continuous monitoring of patients at home. This clinical case presents an original description of 55 days of SARS-CoV-2-induced physiological changes in a patient who routinely uses sleep-monitoring devices. We observed that sleep was specifically affected during COVID-19 (Total Sleep time, TST, and Wake after sleep onset, WASO), within a seemingly bidirectional manner. Sleep status prior to infection (e.g., chronic sleep deprivation or sleep disorders) may affect disease progression, and sleep could be considered as a biomarker of interest for monitoring COVID-19 progression. The use of habitual data represents an opportunity to evaluate pathologic states and improve clinical care.
Raman and Photoluminescence Spectroscopy with a Variable Spectral Resolution
Authors: Pavić Ivan , Šoda Joško , Gašparić Vlatko , Ivanda Mile
Published: December 2021

Raman and photoluminescence (PL) spectroscopy are important analytic tools in materials science that yield information on molecules’ and crystals’ vibrational and electronic properties. Here, we show results of a novel approach for Raman and PL spectroscopy to exploit variable spectral resolution by using zoom optics in a monochromator in the front of the detector. Our results show that the spectral intervals of interest can be recorded with different zoom factors, significantly reducing the acquisition time and changing the spectral resolution for different zoom factors. The smallest spectral intervals recorded at the maximum zoom factor yield higher spectral resolution suitable for Raman spectra. In contrast, larger spectral intervals recorded at the minimum zoom factor yield the lowest spectral resolution suitable for luminescence spectra. We have demonstrated the change in spectral resolution by zoom objective with a zoom factor of 6, but the perspective of such an approach is up to a zoom factor of 20. We have compared such an approach on the prototype Raman spectrometer with the high quality commercial one. The comparison was made on ZrO2 and TiO2 nanocrystals for Raman scattering and Al2O3 for PL emission recording. Beside demonstrating that Raman spectrometer can be used for PL and Raman spectroscopy without changing of grating, our results show that such a spectrometer could be an efficient and fast tool in searching for Raman and PL bands of unknown materials and, thereafter, spectral recording of the spectral interval of interest at an appropriate spectral resolution.
Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation
Authors: Ho Trang-Thi , Huang Yennun
Published: December 2021

Determining the price movement of stocks is a challenging problem to solve because of factors such as industry performance, economic variables, investor sentiment, company news, company performance, and social media sentiment. People can predict the price movement of stocks by applying machine learning algorithms on information contained in historical data, stock candlestick-chart data, and social-media data. However, it is hard to predict stock movement based on a single classifier. In this study, we proposed a multichannel collaborative network by incorporating candlestick-chart and social-media data for stock trend predictions. We first extracted the social media sentiment features using the Natural Language Toolkit and sentiment analysis data from Twitter. We then transformed the stock’s historical time series data into a candlestick chart to elucidate patterns in the stock’s movement. Finally, we integrated the stock’s sentiment features and its candlestick chart to predict the stock price movement over 4-, 6-, 8-, and 10-day time periods. Our collaborative network consisted of two branches: the first branch contained a one-dimensional convolutional neural network (CNN) performing sentiment classification. The second branch included a two-dimensional (2D) CNN performing image classifications based on 2D candlestick chart data. We evaluated our model for five high-demand stocks (Apple, Tesla, IBM, Amazon, and Google) and determined that our collaborative network achieved promising results and compared favorably against single-network models using either sentiment data or candlestick charts alone. The proposed method obtained the most favorable performance with 75.38% accuracy for Apple stock. We also found that the stock price prediction achieved more favorable performance over longer periods of time compared with shorter periods of time.
Next-Generation Electronics and Sensing Technology
Authors: Lee Trong-Yen , Chen Yen-Lin , Fan Yu-Cheng
Published: December 2021

This Special Issue is dedicated to several aspects of next-generation electronics and sensing technology and contains eight papers that focus on advanced sensing devices, sensing systems, and sensing circuits that focus on the state-of-the-art methods for sensing technologies [...]
A Development of the Self Shape Adjustment Cushion Mechanism for Improving Sitting Comfort
Authors: Choi Sooho , Kim Hyomin , Kim Hyungjoo , Yang Woosung
Published: December 2021

The seat comfort of automobiles is one of the significant factors for determining the driver’s fatigue, emotional experience, and individual space (which captures their individuality, rather than just a means of transportation in modern society). Conventional automobile seats could not provide seating comfort suitable for all drivers, in the form of seats that fit each driver’s body type and the difficulty of meeting individual needs. This study proposes self-shape adjustable (the SSA seats) seats that improve the sitting comfort, safety, and secure the stability, by adjusting shape fit to the driver’s body type. The SSA seats transforms the seat itself, in a way that improves the distribution of contact pressure and reduces sitting fatigue, with the pneumatic system. The transformed seats provide better sitting comfort and safety than the conventional automobile seat, by providing a seat shape suitable for the body shape of all users. It was verified that the SSA seats, proposed in this paper, have a uniform and improved pressure distribution, compared to the conventional seat, in various sitting postures; the contact area between the seat and user is enlarged, and the pressure concentrated on the ischial bone is lowered. In addition, it was proven (through user evaluation) that quantitative evaluation verification was the same as qualitative evaluation results.
Calibration of CO, NO2, and O3 Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring
Authors: Ionascu Marian-Emanuel , Castell Nuria , Boncalo Oana , Schneider Philipp , Darie Marius , Marcu Marius
Published: December 2021

During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monitoring devices. The measured data quality must comply with regulations issued by national or international authorities in order to be used for regulatory purposes. This work discusses the challenges and methods suitable for calibrating a low-cost sensor platform developed by our group, Airify, that has a unit cost five times less expensive than the state-of-the-art solutions (approximately €1000). The evaluated platform can integrate a wide variety of sensors capable of measuring up to 12 parameters, including the regulatory pollutants defined in the European Directive. In this work, we developed new calibration models (multivariate linear regression and random forest) and evaluated their effectiveness in meeting the data quality objective (DQO) for the following parameters: carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). The experimental results show that the proposed calibration managed an improvement of 12% for the CO and O3 gases and a similar accuracy for the NO2 gas compared to similar state-of-the-art studies. The evaluated parameters had different calibration accuracies due to the non-identical levels of gas concentration at which the sensors were exposed during the model’s training phase. After the calibration algorithms were applied to the evaluated platform, its performance met the DQO criteria despite the overall low price level of the platform.
Attention-Guided Image Captioning through Word Information
Authors: Tang Ziwei , Yi Yaohua , Sheng Hao
Published: December 2021

Image captioning generates written descriptions of an image. In recent image captioning research, attention regions seldom cover all objects, and generated captions may lack the details of objects and may remain far from reality. In this paper, we propose a word guided attention (WGA) method for image captioning. First, WGA extracts word information using the embedded word and memory cell by applying transformation and multiplication. Then, WGA applies word information to the attention results and obtains the attended feature vectors via elementwise multiplication. Finally, we apply WGA with the words from different time steps to obtain previous word guided attention (PW) and current word attention (CW) in the decoder. Experiments on the MSCOCO dataset show that our proposed WGA can achieve competitive performance against state-of-the-art methods, with PW results of a 39.1 Bilingual Evaluation Understudy score (BLEU-4) and a 127.6 Consensus-Based Image Description Evaluation score (CIDEr-D); and CW results of a 39.1 BLEU-4 score and a 127.2 CIDER-D score on a Karpathy test split.
Differential Refractive Index Sensor Based on Coupled Plasmon Waveguide Resonance in the C-Band
Authors: Yang Qian , Gao Laixu , Zou Changwei , Xie Wei , Tian Canxin , Wang Zesong , Liang Feng , Ke Yihong , Zhou Xinmei , Li Songquan
Published: December 2021

We proposed a differential fiber-optic refractive index sensor based on coupled plasmon waveguide resonance (CPWR) in the C-band. The sensor head is a BK7 prism coated with ITO/Au/ITO/TiO2 film. CPWR is excited on the film by the S-polarized components of an incident light. The narrow absorption peak of CPWR makes it possible to realize dual-wavelength differential intensity (DI) interrogation by using only one incident point. To implement DI interrogation, we used a DWDM component to sample the lights with central wavelengths of 1529.55 and 1561.42 nm from the lights reflected back by the sensor head. The intensities of the dual-wavelength lights varied oppositely within the measurement range of refractive index, thus, a steep slope was produced as the refractive index of the sample increased. The experimental results show that the sensitivity is 32.15/RIUs within the measurement range from 1.3584 to 1.3689 and the resolution reaches 9.3 × 10−6 RIUs. Benefiting from the single incident point scheme, the proposed sensor would be easier to calibrate in bio-chemical sensing applications. Moreover, this sensing method is expected to be applied to retro-reflecting SPR sensors with tapered fiber tip to achieve better resolution than wavelength interrogation.
Design Optimization and Comparison of Cylindrical Electromagnetic Vibration Energy Harvesters
Authors: Phan Tra Nguyen , Aranda Jesus Javier , Oelmann Bengt , Bader Sebastian
Published: December 2021

Investigating the coil–magnet structure plays a significant role in the design process of the electromagnetic energy harvester due to the effect on the harvester’s performance. In this paper, the performance of four different electromagnetic vibration energy harvesters with cylindrical shapes constrained in the same volume were under investigation. The utilized structures are (i) two opposite polarized magnets spaced by a mild steel; (ii) a Halbach array with three magnets and one coil; (iii) a Halbach array with five magnets and one coil; and (iv) a Halbach array with five magnets and three coils. We utilized a completely automatic optimization procedure with the help of an optimization algorithm implemented in Python, supported by simulations in ANSYS Maxwell and MATLAB Simulink to obtain the maximum output power for each configuration. The simulation results show that the Halbach array with three magnets and one coil is the best for configurations with the Halbach array. Additionally, among all configurations, the harvester with two opposing magnets provides the highest output power and volume power density, while the Halbach array with three magnets and one coil provides the highest mass power density. The paper also demonstrates limitations of using the electromagnetic coupling coefficient as a metric for harvester optimization, if the ultimate goal is maximization of output power.
Muscle Synergies and Clinical Outcome Measures Describe Different Factors of Upper Limb Motor Function in Stroke Survivors Undergoing Rehabilitation in a Virtual Reality Environment
Authors: Maistrello Lorenza , Rimini Daniele , Cheung Vincent C. K. , Pregnolato Giorgia , Turolla Andrea
Published: December 2021

Recent studies have investigated muscle synergies as biomarkers for stroke, but it remains controversial if muscle synergies and clinical observation convey the same information on motor impairment. We aim to identify whether muscle synergies and clinical scales convey the same information or not. Post-stroke patients were administered an upper limb treatment. Before (T0) and after (T1) treatment, we assessed motor performance with clinical scales and motor output with EMG-derived muscle synergies. We implemented an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) to identify the underlying relationships among all variables, at T0 and T1, and a general linear regression model to infer any relationships between the similarity between the affected and unaffected synergies (Median-sp) and clinical outcomes at T0. Clinical variables improved with rehabilitation whereas muscle-synergy parameters did not show any significant change. EFA and CFA showed that clinical variables and muscle-synergy parameters (except Median-sp) were grouped into different factors. Regression model showed that Median-sp could be well predicted by clinical scales. The information underlying clinical scales and muscle synergies are therefore different. However, clinical scales well predicted the similarity between the affected and unaffected synergies. Our results may have implications on personalizing rehabilitation protocols.
Recent Progress in Devices Based on Magnetoelectric Composite Thin Films
Authors: Patil Deepak Rajaram , Kumar Ajeet , Ryu Jungho
Published: December 2021

The strain-driven interfacial coupling between the ferromagnetic and ferroelectric constituents of magnetoelectric (ME) composites makes them potential candidates for novel multifunctional devices. ME composites in the form of thin-film heterostructures show promising applications in miniaturized ME devices. This article reports the recent advancement in ME thin-film devices, such as highly sensitive magnetic field sensors, ME antennas, integrated tunable ME inductors, and ME band-pass filters, is discussed. (Pb1−xZrx)TiO3 (PZT), Pb(Mg1/3Nb2/3)O3-PbTiO3 (PMN-PT), Aluminium nitride (AlN), and Al1−xScxN are the most commonly used piezoelectric constituents, whereas FeGa, FeGaB, FeCo, FeCoB, and Metglas (FeCoSiB alloy) are the most commonly used magnetostrictive constituents in the thin film ME devices. The ME field sensors offer a limit of detection in the fT/Hz1/2 range at the mechanical resonance frequency. However, below resonance, different frequency conversion techniques with AC magnetic or electric fields or the delta-E effect are used. Noise floors of 1–100 pT/Hz1/2 at 1 Hz were obtained. Acoustically actuated nanomechanical ME antennas operating at a very-high frequency as well as ultra-high frequency (0.1–3 GHz) range, were introduced. The ME antennas were successfully miniaturized by a few orders smaller in size compared to the state-of-the-art conventional antennas. The designed antennas exhibit potential application in biomedical devices and wearable antennas. Integrated tunable inductors and band-pass filters tuned by electric and magnetic field with a wide operating frequency range are also discussed along with miniaturized ME energy harvesters.
Volitional EMG Estimation Method during Functional Electrical Stimulation by Dual-Channel Surface EMGs
Authors: Jung Joonyoung , Lee Dong-Woo , Son Yong Ki , Kim Bae Sun , Shin Hyung Cheol
Published: December 2021

We propose a novel dual-channel electromyography (EMG) spatio-temporal differential (DESTD) method that can estimate volitional electromyography (vEMG) signals during time-varying functional electrical stimulation (FES). The proposed method uses two pairs of EMG signals from the same stimulated muscle to calculate the spatio-temporal difference between the signals. We performed an experimental study with five healthy participants to evaluate the vEMG signal estimation performance of the DESTD method and compare it with that of the conventional comb filter and Gram–Schmidt methods. The normalized root mean square error (NRMSE) values between the semi-simulated raw vEMG signal and vEMG signals which were estimated using the DESTD method and conventional methods, and the two-tailed t-test and analysis of variance were conducted. The results showed that under the stimulation of the gastrocnemius muscle with rapid and dynamically modulated stimulation intensity, the DESTD method had a lower NRMSE compared to the conventional methods (p < 0.01) for all stimulation intensities (maximum 5, 10, 15, and 20 mA). We demonstrated that the DESTD method could be applied to wearable EMG-controlled FES systems because it estimated vEMG signals more effectively compared to the conventional methods under dynamic FES conditions and removed unnecessary FES-induced EMG signals.
A Method for Behavior Change Support by Controlling Psychological Effects on Walking Motivation Caused by Step Count Log Competition System
Authors: Futami Kyosuke , Terada Tsutomu , Tsukamoto Masahiko
Published: December 2021

Systems presenting information that encourages competition by using rankings and scores (hereafter referred to as competition information) have become widespread to support behavioral change. However, users without high levels of motivation, such as behavior change support targets, do not necessarily benefit from competition information. In this study, we propose a method to control the psychological effects caused by competition information to support behavior change. We implemented a competition information presentation system using step counts logs to support increasing one’s daily steps. We designed two patterns of competition information considering psychological effects. One is likely to have good effects, using three mechanisms to easily obtain results corresponding to the effort, make closely matched rivals with similar abilities, and pay attention to a small number of rivals. The other is unlikely to have positive effects and may potentially even have negative ones, using a mechanism that brings about the opposite results of the former pattern. We evaluated 42 participants with low levels of motivation over six weeks. The results showed that the former information pattern increased participants’ step counts by about 1000 steps per day, and the latter information pattern did not lead to an increase. We confirmed the feasibility of the proposed method and discussed the possibility of the appropriate use and potential abuse of such techniques for manipulating motivation. Our study can be helpful in designing a competition information presentation system considering psychological effects.
Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine
Authors: Zamry Nurfazrina M. , Zainal Anazida , Rassam Murad A. , Alkhammash Eman H. , Ghaleb Fuad A. , Saeed Faisal
Published: December 2021

Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with O(nd) memory utilization and no communication overhead.
Vision-Based Driver’s Cognitive Load Classification Considering Eye Movement Using Machine Learning and Deep Learning
Authors: Rahman Hamidur , Ahmed Mobyen Uddin , Barua Shaibal , Funk Peter , Begum Shahina
Published: December 2021

Due to the advancement of science and technology, modern cars are highly technical, more activity occurs inside the car and driving is faster; however, statistics show that the number of road fatalities have increased in recent years because of drivers’ unsafe behaviors. Therefore, to make the traffic environment safe it is important to keep the driver alert and awake both in human and autonomous driving cars. A driver’s cognitive load is considered a good indication of alertness, but determining cognitive load is challenging and the acceptance of wire sensor solutions are not preferred in real-world driving scenarios. The recent development of a non-contact approach through image processing and decreasing hardware prices enables new solutions and there are several interesting features related to the driver’s eyes that are currently explored in research. This paper presents a vision-based method to extract useful parameters from a driver’s eye movement signals and manual feature extraction based on domain knowledge, as well as automatic feature extraction using deep learning architectures. Five machine learning models and three deep learning architectures are developed to classify a driver’s cognitive load. The results show that the highest classification accuracy achieved is 92% by the support vector machine model with linear kernel function and 91% by the convolutional neural networks model. This non-contact technology can be a potential contributor in advanced driver assistive systems.
Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses
Authors: Nor Ahmad Kamal Mohd , Pedapati Srinivasa Rao , Muhammad Masdi , Leiva Víctor
Published: December 2021

Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials, fintech management, medicine, neurorobotics, and psychology, among others. Prognostics and health management (PHM) is the discipline that links the studies of failure mechanisms to system lifecycle management. There is a need, which is still absent, to produce an analytical compilation of PHM-XAI works. In this paper, we use preferred reporting items for systematic reviews and meta-analyses (PRISMA) to present a state of the art on XAI applied to PHM of industrial assets. This work provides an overview of the trend of XAI in PHM and answers the question of accuracy versus explainability, considering the extent of human involvement, explanation assessment, and uncertainty quantification in this topic. Research articles associated with the subject, since 2015 to 2021, were selected from five databases following the PRISMA methodology, several of them related to sensors. The data were extracted from selected articles and examined obtaining diverse findings that were synthesized as follows. First, while the discipline is still young, the analysis indicates a growing acceptance of XAI in PHM. Second, XAI offers dual advantages, where it is assimilated as a tool to execute PHM tasks and explain diagnostic and anomaly detection activities, implying a real need for XAI in PHM. Third, the review shows that PHM-XAI papers provide interesting results, suggesting that the PHM performance is unaffected by the XAI. Fourth, human role, evaluation metrics, and uncertainty management are areas requiring further attention by the PHM community. Adequate assessment metrics to cater to PHM needs are requested. Finally, most case studies featured in the considered articles are based on real industrial data, and some of them are related to sensors, showing that the available PHM-XAI blends solve real-world challenges, increasing the confidence in the artificial intelligence models’ adoption in the industry.
Health and Lifestyle, Safety, Relationship and Personality Factors Influence Gender, Sex and Sexuality Issues among Youth—A Case Record Analysis from Youth Mental Health Promotion Clinics in Karnataka, India
Authors: Banandur Pradeep , Shahane Swati , Velu Sathya , Bhargav Sathrajith , Thakkar Aditi , Hasiruvalli Gangappa Virupaksha , Naik Vani , Arelingaiah Mutharaju , Garady Lavanya , Koujageri Jyoti , Sajjanar Sateesh L. , Kashipalli Lakshminarayan Subhash Chandra , Rajneesh Shalini , Gopalkrishna Gururaj
Published: December 2021

Promoting positive identity and seeking early support for gender, sex and sexuality (GSS) issues among youth is vital. Understanding and addressing factors associated withGSS among them is critical. We assessed four-year case records (January 2017–December 2020) of all first visit youth mental health promotion clinic (YMHPC) clients (15–35 years) for factors associated with GSS issues in Karnataka. Overall, prevalence of GSS issues was 1.8% (189/10,340). Increased risk of GSS issues was observed among clients reporting suicidality (AOR = 4.27, 95% CI = 2.70–6.74) and relationship issues (AOR = 3.63, 95% CI = 2.36–5.57), followed by issues of safety (AOR = 2.56, 95% CI = 1.72–3.81), personality (AOR = 2.48, 95% CI = 1.60–3.85), health and lifestyle (AOR = 2.27, 95% CI = 1.77–4.19), smokers (AOR = 2.30, 95% CI = 1.24–4.27), and those who felt depressed (AOR = 2.10, 95% CI = 1.43–3.09) and worthless (AOR = 2.08, 95% CI = 1.28–3.39). Clients aged 21–25 years (AOR = 1.80,95% CI = 1.27–2.54), male (AOR = 1.72, 95% CI = 1.20–2.46) and who had been married (AOR = 2.32, 95% CI = 1.51–3.57) had a higher risk of GSS issues than those aged 15–20 years and other counterparts, respectively. Clients who drank alcohol (AOR = 0.49, 95% CI = 0.30–0.81) had reduced risk of GSS issues. The findings re-iterate the importance of early recognition of factors (essential precursors) of GSS issues among youth. The study highlights the importance of promoting awareness and improving primordial prevention of possible GSS issues in later life. This study has important implications on youth mental health promotion programs, especially in countries like India.
The Potentiating Response to Accentuated Eccentric Loading in Professional Football Players
Authors: Godwin Mark Steven , Fearnett Tim , Newman Mark Ashton
Published: December 2021

The purpose of this study was to assess the acute effect of Accentuated Eccentric Loading (AEL) on countermovement jump (CMJ) height, peak power output (PPO) and peak velocity in male professional footballers using loads of 20% or 40% of body mass (AEL20 or AEL40, respectively). Twenty-three male professional football players (age 24 ± 4.5 years, range 18–34 years; body mass 80.21 ± 8.4 kg; height 178.26 ± 7.62 cm) took part in a randomised, cross-over design to test the potentiating responses of two AEL conditions (AEL20 and AEL40) versus a body weight control group (CON). Mean loads for the two conditions were 15.84 ± 1.70 kg (AEL20) and 31.67 ± 3.40 kg (AEL40). There was no significant difference between the three conditions for jump height (p = 0.507, η2G = 0.022). There were significant differences in peak power between the groups (p = 0.001, η2G = 0.154). Post hoc analysis with Bonferroni adjustment showed significantly higher peak power for both AEL conditions compared to the control group, but no significant differences between AEL conditions (CON vs. AEL20, p = 0.029, 95% CI −1016.735, −41.815, Cohen’s d = −0.56; CON vs. AEL40, p = 0.001, 95% CI −1244.995, −270.075, Cohen’s d = −0.81; AEL20 vs. AEL40, p = 0.75, 95% CI −715.720, 259.201, Cohen’s d = −0.24). There was no significant difference between the three conditions for peak velocity (p = 0.269, η2G = 0.046). AEL using either 20% or 40% of body mass may be used to increase peak power in the countermovement jump in well-trained professional football players.
Creating Legitimacy in the ISO/CEN Standard for Sustainable and Traceable Cocoa: An Exploratory Case Study Integrating Normative and Empirical Legitimacy
Authors: Kusnezowa Dina , Vang Jan
Published: December 2021

The paper assesses the legitimacy of the ISO/CEN standard for sustainable and traceable cocoa during the standard-setting process and thereby to establish the degree of legitimacy achieved and to explore new sources of legitimacy in the development of sustainability standards for agricultural commodities. The paper examines the normative and empirical legitimacy concerns involved in the development of the ISO/CEN standard for sustainable and traceable cocoa (ISO 34101 series). The findings suggest that while the standard-setting organisation is establishing normative legitimacy, empirical legitimacy is lacking. Absence of empirical legitimacy is a serious concern for a successful and just implementation.
Digitalizing Higher Education in Light of Sustainability and Rebound Effects—Surveys in Times of the COVID-19 Pandemic
Authors: Arnold Marlen Gabriele , Vogel Alina , Ulber Martin
Published: December 2021

The COVID-19 pandemic has focused attention not only on health and social issues, but on the issue of digital transformation as well. Within a very short time, universities had to convert their courses to digital formats and university life was reduced to a minimum. To shed light on how the COVID-19 pandemic has affected universities, we investigated the following questions: How was this transformation accomplished? What advantages and disadvantages did it bring with it? How sustainable was this transformation? and What can the future of higher education look like? This study is based on the responses to two questionnaires for university staff and students conducted at the Chemnitz University of Technology between mid-July and September, 2020 (n = 369), and between February and March, 2021 (n = 252). Both questionnaires were analysed using descriptive statistics and qualitative content analysis. The results show wide variations in response to digital teaching and learning. Digital teaching and working/learning from home have brought both multiple benefits and multiple challenges at the same time. Working and learning from home was perceived as both enriching and overwhelming—even for the same individual. Respondents appreciated the flexibility associated with digital teaching, even though digital teaching was perceived as imposing excessive demands. This study reveals striking gaps in our knowledge and our actions linking digital transformation and sustainability and highlights how digital teaching can be further developed.
Corporate Social Responsibility, CEO Compensation Structure, and Corporate Innovation Activities
Authors: Choi Bu-Kyung , Ahn Ji-Young , Choi Myeong-Cheol
Published: December 2021

This study empirically investigated the economic effect of CSR initiatives on innovation by examining Korean firms. Our primary objective of this study was to explore how a CEO compensation system can affect the CSR-innovation relationship. An integrated model of the impact of CSR on innovation activities was developed through analyzing various CEO compensation components such as structure, type, mix, and distribution. We identified the CEO compensation system that more suitably supports CSR in driving innovation performance improvement, and empirically examined a compensation system that enhances corporate innovation by creating a good alignment with CSR. Using a longitudinal data, we empirically tested the interactive effect of a CSR and compensation system of CEO in Korean publicly traded companies. Our empirical findings concerning the interaction between CSR strategies and CEO compensation schemes hold practical implications for establishing and implementing a suitable human resource system to improve organizational competitiveness.
The Crossroads of Ecotourism Dependency, Food Security and a Global Pandemic in Galápagos, Ecuador
Authors: Burke Adam
Published: December 2021

International esteem for Galápagos’ natural wonders and the democratization of travel have contributed to a 300% increase in annual tourist entries to the archipelago from 2000 (68,989) to 2018 (275,817). The attendant spike in tourism-related anthropogenic impact coupled with deficient infrastructure development has put the archipelago’s natural capital and carrying capacity at risk. The complex nature of Galápagos’ food insecurity is linked to the archipelago’s geographic isolation, its diminishing agricultural workforce, international tourists’ demand for recognizable food, and a lack of investment in sustainable and innovative agricultural futures. Food security is key to the long-term well-being of Galapagueños, who sustain Galápagos’ tourism industry. However, the COVID-19 pandemic has further exposed the vulnerability of human systems in Galápagos, especially the fragility of Galápagos’ ecotourism dependency. Galapagueños’ struggle to endure the tourism sector’s slow rebound following the 2020 travel restrictions points to an urgent need to implement food security measures as an indispensable component of the archipelago’s long-term sustainability plan. This article presents ethnographic data to discuss the tourism sector’s impact on local food systems, Galapagueños’ right to food sovereignty, efforts to increase agricultural production, and why strengthening institutional partnerships is vital to Galápagos’ food self-sufficiency.
Characteristics and Evolution of China’s Industry–University–Research Collaboration to Promote the Sustainable Development: Based on Policy Text Analysis
Authors: Yao Xiaojie , Hu Yuan , Gong Huaping , Chen Dongyou
Published: December 2021

Collaborative innovation is an effective way to realize national innovation and sustainable development. The Chinese government has issued a series of Industry–University–Research (IUR) policies and regulations in recent decades to effectively promote the development of national scientific and technological innovation. Exploring the characteristics and evolution of IUR collaborative policy is critical for the healthy development of IUR and subsequent policy formulation. In this study, we collected IUR policy texts at the national level of China from 1992 to 2020 as the research object. On the basis of policy tool theory, a three-dimensional analysis framework of “Policy tool–Policy theme–Evolution stage” was constructed and studied using content analysis and social network analysis methods. Through the quantitative statistical analysis, we find that China’s IUR policies have experienced four development stages. Among all policy tools, the supply-side IUR ones are sufficient, whereas demand-side policy tools are insufficient. The service system policy theme is lacking relative to other themes. In addition, the application of information technology (IT) policies is prominent. Therefore, we suggest optimizing the policy structure in combination with social characteristics and strengthening the establishment of service system innovation. Enhancing the role of IT to promote innovation policies is also recommended.
An Explorative Study of Korean Venture Companies: Do CSR and Company Competitiveness Improve Non-Financial and Financial Performance?
Authors: Kim Boine , Kim Byoung-Goo
Published: December 2021

For the sustainable growth of venture companies, this explorative study aimed to comprehensively analyze the factors affecting their performance. In addition, this study attempted to verify whether different or similar performance management should be performed according to the difference in characteristics of venture companies. In this study, corporate performance was classified into non-financial and financial performance and analyzed by dividing it into quantitative and qualitative growth. As factors influencing performance, this study analyzed corporate competitiveness compared to competitors and the number of CSR types in which companies participate. In addition, it intended to provide realistic implications and academic contributions to the performance management of venture companies by verifying whether differences in characteristics such as a company’s start-up business year, growth stage, and industry should be reflected in corporate performance management as control variables.
Evaluation of the Effects of Magnetically Treated Saline Water on Physiological, Antioxidant and Agronomic Traits of Jojoba [Simmondsia chinensis (Link) Schneider]
Authors: Alghabari Fahad
Published: December 2021

Salinity poses a serious challenge to agriculture across the globe. In the Middle East, countries such as Saudi Arabia are facing potential problems of salinity due to the use of processed saline Red Sea water for agriculture. To tackle this challenge, the current study was conducted with the objective of assessing the effects of magnetically (1.80 mT) treated normal agriculture water (NW = 2.11 DSm−1) and different concentrations of Red Sea water (RSWC1 = 5.61 DSm−1 and RSWC2 = 7.01 DSm−1) on the physiological traits (chlorophyll, photosynthesis rate, transpiration rate, stomatal conductance and membrane damage), antioxidant enzymes (superoxide dismutase, catalase and peroxidase), proline and agronomic characteristics (germination percentage, germination rate, shoot length and root length) of jojoba (Simmondsia chinensis) seedlings. The experiment was set in a glasshouse with three replicates, using RCBD with two factorial arrangements. The data were collected and subjected to statistical analysis using statistix8.1 and R-program. All magnetically treated concentrations of saline water showed significant improvements in all traits compared with their respective controls, except proline, membrane damage (MD) and germination rate (GR). However, the response of these all traits was more significant at NW compared with RSWC1 and RSWC2. Furthermore, correlation, PCA and heat map analysis revealed that all traits are significantly interlinked in determining the jojoba response to different concentrations of salinity, both in the presence and absence of MF.
Reimagining African Women Youth Climate Activism: The Case of Vanessa Nakate
Authors: Barnes Brendon R.
Published: December 2021

African women youth climate activists are marginalised in mainstream climate activism. There is very little scholarly work done on this group, specifically on how their agency is deployed in the context of extreme undermining. Based on a case study of the activism of Vanessa Nakate, this paper analyses online interviews, media reports and social media interactions. The text was analysed thematically. The paper identifies three social binds (location, gender, and youth) that limit her activism. Importantly, the findings show how she deploys context-dependent agency to overcome those binds. The paper offers practical and theoretical insights for the study of African women climate activism. I argue that understanding and developing personal and political agency is essential for the sustainability of African women youth climate activism.
Coupling and Coordination Analysis of Thermal Power Carbon Emission Efficiency under the Background of Clean Energy Substitution
Authors: Liu Yujing , Niu Dongxiao
Published: December 2021

With the proposed goals of reaching its “carbon peak” by 2030 and becoming “carbon neutral” by 2060, China will comprehensively build a diversified, efficient and clean energy system. The differences in China’s resource endowments have made the development of carbon emission reduction in the thermal power industry uncoordinated in various regions. Therefore, it is necessary to optimize the method for measuring thermal power carbon emission efficiency and determine the impact of regional development imbalances on the carbon emission efficiency of thermal power. For this article, we used the stochastic frontier analysis method and selected a variety of influencing factors as technical inefficiency items. After that, we measured the thermal power carbon emission efficiency in 30 provinces and municipalities (autonomous regions) in China in the past 10 years, and it was found that the efficiency was increasing yearly and showed obvious spatial differences. The impact of the clean energy substitution effect on the thermal power carbon emission efficiency cannot be ignored. After performing a coupled and coordinated analysis on the efficiency of thermal carbon emission in various regions and its influencing factors, the three indicators of power consumption intensity, urbanization level and clean energy substitution effect were selected. The weight of the indicator subsystem was determined in view of the estimation of the technical inefficiency. The results of the coupling and coordination analysis show that the degree of coupling and coordination of thermal power carbon emission efficiency is increasing yearly and presents a distribution of “high in the eastern region and low in the western region”. Therefore, all provinces need to vigorously carry out clean replacement work to enhance the coordinated development of carbon emission reduction in the thermal power industry and the level of regional economic development.