bluuebunny/crossref_metadata_2025 huggingface dataset:
5-Fluorouracil and Curcumin Combination Coated with Pectin and Its Strategy towards Titanium Dioxide, Dimethylhydrazine Colorectal Cancer Model with the Evaluation of the Blood Parameters
Authors: Karthika Chenmala , Rahman Md. Habibur , Sureshkumar Raman , Akter Rokeya , Khan Azmat Ali , Alanazi Amer M. , Azad Abul Kalam , Barai Paritosh , Barai Hasi Rani
Published: July 2022

Colorectal cancer is considered the third most common cancer and the second leading cause of death globally. It has been proven that titanium dioxide nanoparticles produce oxidative stress and can lead to chronic inflammation, which could turn into diseases like cancer, cardiovascular disorders, diabetes, and so on. To evaluate the effect of 5-fluorouracil (5-FU) curcumin (CUR) conjugate coated with pectin on colorectal cancer induced by titanium dioxide nanoparticles (TiO2-NPs) and dimethylhydrazine (DMH), male rats were administered TiO2-NPs (5 mg/kg) orally and DMH (1 mg/kg) peritoneally for 70 days and treated with 5-FU (60 mg/kg) and CUR (240 mg/kg) conjugate (1:4 ratio) coated with pectin. The bodyweight of the animals was evaluated, and the blood sugar level was calculated. Further blood and plasma analyses were conducted. Hematological parameters, antioxidant parameters, and biochemical estimation were taken into consideration. The TiO2-NPs level in the blood and colorectal region was also calculated. With the induction of colon cancer using TiO2-NPs and DMH, a significant increase in the body weight of the animals was seen; eventually, with treatment, it was reduced. The bodyweight increase was due to an increase in the blood sugar level. There were also significant changes in the hematological parameters and biochemical estimation reports when comparing those of the positive control, negative control, and treated groups. No significant effect on biochemical estimation reports was seen. Conclusions: These reports suggest that 5-FU CUR conjugate coated with pectin helps in the management of colorectal cancer induced by TiO2-NPs and DMH.
Application of Data Envelopment Analysis in the Ventilation and Cooling Efficiency Evaluation of Hot Development Headings
Authors: Xin Song , Han Xuefei , Li Sasa , Xiao Yue , Yang Wenyu
Published: July 2022

The thermal environment of the development headings in deep underground mines tends to be harsh. The design of a reliable auxiliary ventilation system is an important strategy to maintain the thermal environment and air quality in the workplace. Hence, it is of great significance to comprehensively evaluate the effects of different air supply conditions and explore the optimal solution for the auxiliary ventilation system. In this study, cooling efficiency (CE) and ventilation efficiency (VE), which are the two most widely used indices for evaluating the ventilation in public places, were introduced into the field of underground mine ventilation. Numerical simulations of multiple air supply conditions were carried out to obtain the CE and VE, respectively. A data envelopment analysis model was used innovatively to evaluate and rank the efficiency of the different conditions. In the evaluation model, the air supply temperature and air supply velocity were the inputs, whereas CE and VE were the outputs. The preliminary results showed that cases with either the highest VE or highest CE alone may not actually indicate efficient air supply conditions. The optimum efficiency was determined to be at an air supply temperature difference of 6 °C. Thereafter, the air supply rate could be determined using the psychrometric chart based on the cooling and moisture loads. These results can provide theoretical guidance for optimizing air supply conditions and energy-saving control of mining ventilation and cooling systems.
What Is Global Laozegetics?: Origins, Contents, and Significance
Authors: Tadd Misha
Published: July 2022

Mainstream scholarship on the Laozi or Daodejing generally focuses on the “original” text and its “original” meaning. However, the Chinese study of Laoxue 老學 (translated here with the author’s neologism “Laozegetics”) offers a valuable alternative, as it shifts focus to the hermeneutical and historical value of the 2185 Chinese, 430 Japanese, and 91 Korean relevant interpretations and commentaries on the classic. The inclusive perspective of Laozegetics has further inspired the author’s creation of the term “Global Laozegetics.” This even broader topic assumes both Laozi commentaries and translations (all 2051 in 97 languages) belong within a single field of research. To better introduce the study of Global Laozegetics to an English-language readership, this article will explore the history of the term Laoxue, review contemporary related research, and present the content and significance of applying the notion of Laozegetics to the globalized Laozi.
Experiential Avoidance Mediates the Relationship between Prayer Type and Mental Health before and through the COVID-19 Pandemic
Authors: Lowe Gabriel B. , Wang David C. , Chin Eu Gene
Published: July 2022

The practice of prayer has been shown to predict various mental health outcomes, with different types of prayer accounting for different outcomes. Considering the numerous stressors facing seminary students, which have only intensified throughout the COVID-19 pandemic, prayer may be a common coping strategy for students who study theology, spiritual growth, and leadership. The present study investigates the role that different types of prayer may have in reducing anxiety, depression, and work burnout among seminary students. Experiential avoidance is proposed as a mediator such that specific types of prayer contribute to greater spiritual and characterological formation through staying engaged in the midst of struggle. Longitudinal data was collected from 564 graduate seminary students from 17 institutions accredited by the Association of Theological Schools. Based on previous research, we hypothesized that experiential avoidance would mediate the relationship between colloquial, liturgical, meditative, and petitionary prayer types and the negative mental health outcomes of anxiety, depression, and work burnout. Results confirmed significant negative relationships between colloquial, liturgical, and meditative prayer types and all three mental health indicators, fully mediated by experiential avoidance. Petitionary prayer was not significantly related to lower levels of mental health. These results indicate that engaging in certain prayer practices may be a protective factor by facilitating experiential engagement.
A Quantitative Sustainability Assessment for Mine Closure and Repurposing Alternatives in Colorado, USA
Authors: Perdeli Demirkan Cansu , Smith Nicole M. , Duzgun Sebnem
Published: July 2022

Responsible mine closure and repurposing are key to contributing to sustainable development by ensuring successful environmental rehabilitation and reducing socioeconomic risks. However, mine closure has primarily focused on remediation and rehabilitation of mined lands with limited consideration of stakeholder perspectives and the broader social, economic, and cultural impacts of closure. In this paper, we use stakeholder input to evaluate and compare three different repurposing alternatives for the tailings dam area of a mine in the state of Colorado, USA, which is expected to close in the next twenty years. By using multi-attribute utility theory (MAUT), we determine which alternative better reflects stakeholder preferences and results in the most economically, environmentally, and socially sustainable outcome. Our results show that although stakeholder groups have different ideas about what constitutes sustainable development in the context of mine closure and repurposing, it is possible to identify to what extent different scenarios can address these perspectives. We argue that integrating stakeholder views into mine closure design and repurposing can lead to more responsible and sustainable mine closure that is unique to a particular setting and stakeholder needs, and we provide a methodology that mining companies may use to understand stakeholder priorities and preferences.
Multi-Product Productions from Malaysian Oil Palm Empty Fruit Bunch (EFB): Selection for Optimal Process and Transportation Mode
Authors: Abdulrazik Abdulhalim , Zailan Roziah , Elkamel Marwen , Elkamel Ali
Published: July 2022

In Malaysia, palm oil industries have played significant roles in the economic sectors and the nation’s developments. One aspect of these industries that is gaining growing interest is oil palm residue management and bio-based product generations. EFB has been identified to be a feasible raw material for the production of bio-energy, bio-chemicals, and bio-materials. In this paper, our previous deterministic mathematical programming model was extended to include decisions for selecting optimal transportation modes and processes at each level of the processing stage in the supply chain. The superstructure of alternatives was extended to show states of produced products whether solid, liquid, or gaseous, and for which truck, train, barge, or pipeline would be possible modes of transportation. The objective function was to maximize profit which accounts for associated costs including the emission treatment costs from production and transportation. The optimal profit was USD 1,561,106,613 per year for single ownership of all facilities in the supply chain.
Contributions of Climatic and Anthropogenic Drivers to Net Primary Productivity of Vegetation in the Mongolian Plateau
Authors: Yin Chaohua , Luo Min , Meng Fanhao , Sa Chula , Yuan Zhihui , Bao Yuhai
Published: July 2022

Global warming and intense human activity are altering the net primary productivity (NPP) of vegetation in arid and semi-arid regions where vegetation ecosystems are sensitive to climate change, including the Mongolian Plateau (MP). To deepen the understanding of the dynamics of vegetation and its driving factors on the MP, the actual NPP (ANPP) of the MP from 2000 to 2019 was estimated based on a modified Carnegie–Ames–Stanford Approach (CASA) model. The Thornthwaite Memorial and Guangsheng Zhou models were applied concurrently to estimate the potential NPP of the vegetation, and different scenarios were constructed to evaluate quantitatively the impact of climate change and human activity on the vegetation productivity of our study area. The results showed that the carbon sequestration capacities of various vegetation types in the MP differ, with forest > cropland > grassland > wetland. The NPP increased significantly during 2000–2019. Most areas showed a continuous and stable change in vegetation ANPP, with the current trend in variation mainly reflected in the continuous improvement of vegetation. In general, restoration of vegetation was prominent in the MP, and human activities affected more than 30% of vegetation restoration. The ANPP was positively correlated with temperature and precipitation, the latter of which had a more significant effect. Desertification management, restoration of cropland to forest and grassland, afforestation and reasonable grazing activities were the main human activities performed to restore vegetation. This study is expected to advance the theoretical understanding of ecological protection and sustainable development in the MP.
Machine Learning-Based Approach Using Open Data to Estimate PM2.5 over Europe
Authors: Ibrahim Saleem , Landa Martin , Pešek Ondřej , Brodský Lukáš , Halounová Lena
Published: July 2022

Air pollution is currently considered one of the most serious problems facing humans. Fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) is a very harmful air pollutant that is linked with many diseases. In this study, we created a machine learning-based scheme to estimate PM2.5 using various open data such as satellite remote sensing, meteorological data, and land variables to increase the limited spatial coverage provided by ground-monitors. A space-time extremely randomised trees model was used to estimate PM2.5 concentrations over Europe, this model achieved good results with an out-of-sample cross-validated R2 of 0.69, RMSE of 5 μg/m3, and MAE of 3.3 μg/m3. The outcome of this study is a daily full coverage PM2.5 dataset with 1 km spatial resolution for the three-year period of 2018–2020. We found that air quality improved throughout the study period over all countries in Europe. In addition, we compared PM2.5 levels during the COVID-19 lockdown during the months March–June with the average of the previous 4 months and the following 4 months. We found that this lockdown had a positive effect on air quality in most parts of the study area except for the United Kingdom, Ireland, north of France, and south of Italy. This is the first study that depends only on open data and covers the whole of Europe with high spatial and temporal resolutions. The reconstructed dataset will be published under free and open license and can be used in future air quality studies.
RCA-LF: Dense Light Field Reconstruction Using Residual Channel Attention Networks
Authors: Salem Ahmed , Ibrahem Hatem , Kang Hyun-Soo
Published: July 2022

Dense multi-view image reconstruction has played an active role in research for a long time and interest has recently increased. Multi-view images can solve many problems and enhance the efficiency of many applications. This paper presents a more specific solution for reconstructing high-density light field (LF) images. We present this solution for images captured by Lytro Illum cameras to solve the implicit problem related to the discrepancy between angular and spatial resolution resulting from poor sensor resolution. We introduce the residual channel attention light field (RCA-LF) structure to solve different LF reconstruction tasks. In our approach, view images are grouped in one stack where epipolar information is available. We use 2D convolution layers to process and extract features from the stacked view images. Our method adopts the channel attention mechanism to learn the relation between different views and give higher weight to the most important features, restoring more texture details. Finally, experimental results indicate that the proposed model outperforms earlier state-of-the-art methods for visual and numerical evaluation.
ECG Approximate Entropy in the Elderly during Cycling Exercise
Authors: Liou Jiun-Wei , Wang Po-Shan , Wu Yu-Te , Lee Sheng-Kai , Chang Shen-Da , Liou Michelle
Published: July 2022

Approximate entropy (ApEn) is used as a nonlinear measure of heart-rate variability (HRV) in the analysis of ECG time-series recordings. Previous studies have reported that HRV can differentiate between frail and pre-frail people. In this study, EEGs and ECGs were recorded from 38 elderly adults while performing a three-stage cycling routine. Before and after cycling stages, 5-min resting-state EEGs (rs-EEGs) and ECGs were also recorded under the eyes-open condition. Applying the K-mean classifier to pre-exercise rs-ECG ApEn values and body weights revealed nine females with EEG power which was far higher than that of the other subjects in all cycling stages. The breathing of those females was more rapid than that of other subjects and their average heart rate was faster. Those females also presented higher degrees of asymmetry in the alpha and theta bands (stronger power levels in the right frontal electrode), indicating stressful responses during the experiment. It appears that EEG delta activity could be used in conjunction with a very low ECG frequency power as a predictor of bursts in the heart rate to facilitate the monitoring of elderly adults at risk of heart failure. A resting ECG ApEn index in conjunction with the subject’s weight or BMI is recommended for screening high-risk candidates prior to exercise interventions.
Multiple Attention Mechanism Graph Convolution HAR Model Based on Coordination Theory
Authors: Hu Kai , Ding Yiwu , Jin Junlan , Xia Min , Huang Huaming
Published: July 2022

Human action recognition (HAR) is the foundation of human behavior comprehension. It is of great significance and can be used in many real-world applications. From the point of view of human kinematics, the coordination of limbs is an important intrinsic factor of motion and contains a great deal of information. In addition, for different movements, the HAR algorithm provides important, multifaceted attention to each joint. Based on the above analysis, this paper proposes a HAR algorithm, which adopts two attention modules that work together to extract the coordination characteristics in the process of motion, and strengthens the attention of the model to the more important joints in the process of moving. Experimental data shows these two modules can improve the recognition accuracy of the model on the public HAR dataset (NTU-RGB + D, Kinetics-Skeleton).
Built-In Packaging for Single Terminal Devices
Authors: Gulsaran Ahmet , Bastug Azer Bersu , Kocer Samed , Rahmanian Sasan , Saritas Resul , Abdel-Rahman Eihab M. , Yavuz Mustafa
Published: July 2022

An alternative packaging method, termed built-in packaging, is proposed for single terminal devices, and demonstrated with an actuator application. Built-in packaging removes the requirements of wire bonding, chip carrier, PCB, probe station, interconnection elements, and even wires to drive single terminal devices. Reducing these needs simplifies operation and eliminates possible noise sources. A micro resonator device is fabricated and built-in packaged for demonstration with electrostatic actuation and optical measurement. Identical actuation performances are achieved with the most conventional packaging method, wire bonding. The proposed method offers a compact and cheap packaging for industrial and academic applications.
Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors
Authors: Linnhoff Clemens , Hofrichter Kristof , Elster Lukas , Rosenberger Philipp , Winner Hermann
Published: July 2022

Safety validation of automated driving functions is a major challenge that is partly tackled by means of simulation-based testing. The virtual validation approach always entails the modeling of automotive perception sensors and their environment. In the real world, these sensors are exposed to adverse influences by environmental conditions such as rain, fog, snow, etc. Therefore, such influences need to be reflected in the simulation models. In this publication, a novel data set is introduced and analyzed. This data set contains lidar data with synchronized reference measurements of weather conditions from a stationary long-term experiment. Recorded weather conditions comprise fog, rain, snow, and direct sunlight. The data are analyzed by pairing lidar values, such as the number of detections in the atmosphere, with weather parameters such as rain rate in mm/h. This results in expectation values, which can directly be utilized for stochastic modeling or model calibration and validation. The results show vast differences in the number of atmospheric detections, range distribution, and attenuation between the different sensors of the data set.
Fair Facial Attribute Classification via Causal Graph-Based Attribute Translation
Authors: Kang Sunghun , Kim Gwangsu , Yoo Chang D.
Published: July 2022

Recent studies have raised concerns regarding racial and gender disparity in facial attribute classification performance. As these attributes are directly and indirectly correlated with the sensitive attribute in a complex manner, simple disparate treatment is ineffective in reducing performance disparity. This paper focuses on achieving counterfactual fairness for facial attribute classification. Each labeled input image is used to generate two synthetic replicas: one under factual assumptions about the sensitive attribute and one under counterfactual. The proposed causal graph-based attribute translation generates realistic counterfactual images that consider the complicated causal relationship among the attributes with an encoder–decoder framework. A causal graph represents complex relationships among the attributes and is used to sample factual and counterfactual facial attributes of the given face image. The encoder–decoder architecture translates the given facial image to have sampled factual or counterfactual attributes while preserving its identity. The attribute classifier is trained for fair prediction with counterfactual regularization between factual and corresponding counterfactual translated images. Extensive experimental results on the CelebA dataset demonstrate the effectiveness and interpretability of the proposed learning method for classifying multiple face attributes.
Human Lower Limb Motion Capture and Recognition Based on Smartphones
Authors: Duan Lin-Tao , Lawo Michael , Wang Zhi-Guo , Wang Hai-Ying
Published: July 2022

Human motion recognition based on wearable devices plays a vital role in pervasive computing. Smartphones have built-in motion sensors that measure the motion of the device with high precision. In this paper, we propose a human lower limb motion capture and recognition approach based on a Smartphone. We design a motion logger to record five categories of limb activities (standing up, sitting down, walking, going upstairs, and going downstairs) using two motion sensors (tri-axial accelerometer, tri-axial gyroscope). We extract the motion features and select a subset of features as a feature vector from the frequency domain of the sensing data using Fast Fourier Transform (FFT). We classify and predict human lower limb motion using three supervised learning algorithms: Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANNs). We use 670 lower limb motion samples to train and verify these classifiers using the 10-folder cross-validation technique. Finally, we design and implement a live detection system to validate our motion detection approach. The experimental results show that our low-cost approach can recognize human lower limb activities with acceptable accuracy. On average, the recognition rate of NB, KNN, and ANNs are 97.01%, 96.12%, and 98.21%, respectively.
Energy-Efficient Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks
Authors: Li Sihao , Kim Kyeong Soo , Zhang Linlin , Huan Xintao , Smith Jeremy
Published: July 2022

In a wireless sensor network (WSN), reducing the energy consumption of battery-powered sensor nodes is key to extending their operating duration before battery replacement is required. Message bundling can save on the energy consumption of sensor nodes by reducing the number of message transmissions. However, bundling a large number of messages could increase not only the end-to-end delays and message transmission intervals, but also the packet error rate (PER). End-to-end delays are critical in delay-sensitive applications, such as factory monitoring and disaster prevention. Message transmission intervals affect time synchronization accuracy when bundling includes synchronization messages, while an increased PER results in more message retransmissions and, thereby, consumes more energy. To address these issues, this paper proposes an optimal message bundling scheme based on an objective function for the total energy consumption of a WSN, which also takes into account the effects of packet retransmissions and, thereby, strikes the optimal balance between the number of bundled messages and the number of retransmissions given a link quality. The proposed optimal bundling is formulated as an integer nonlinear programming problem and solved using a self-adaptive global-best harmony search (SGHS) algorithm. The experimental results, based on the Cooja emulator of Contiki-NG, demonstrate that the proposed optimal bundling scheme saves up to 51.8% and 8.8% of the total energy consumption with respect to the baseline of no bundling and the state-of-the-art integer linear programming model, respectively.
Cost-Effective Open-Ended Coaxial Technique for Liquid Food Characterization by Using the Reflection Method for Industrial Applications
Authors: Iaccheri Eleonora , Varani Massimiliano , Ragni Luigi
Published: July 2022

A cheap technique based on an open-ended coaxial probe together with a vector network analyzer was set up. The vector network analyzer NanoVNA, a very tiny handheld device, is the affordable component that gives the instrumental chain a cost-effective perspective. The open-ended coaxial probe is a cable with an SMA gold-plated termination. User-friendly programs can be used to calibrate the instrument, carry out the measurements, and save data on PC. Simple liquid solutions (sodium chloride, citric acids, and saccharose) and more complex liquid food (milk, egg products, and fruit juice) were investigated. In addition, the temperature on the electric measurement of milk was measured to evaluate a possible influence for refrigerated storage products. The reflection parameters, such as the real and imaginary parts of S11, were used to build univariate and multivariate models. The best results in terms of coefficient of determination and related error were 0.997 (RMSE 0.05%) for sodium chloride and 0.965 (RMSE 0.71 °Brix) for fruit juice considering the univariate model, and 0.997 (RMSE 0.04%) for sodium chloride and 0.981 (RMSE 4.44%) for yolk using multivariate analysis. The proposed solution is non-destructive, cheap, rapid, and very attractive for potential lab and industrial applications.
A Room-Temperature Surface Acoustic Wave Ammonia Sensor Based on rGO/DPP2T-TT Composite Films
Authors: Hung Tien-Tsan , Chung Mei-Hui , Wu Jiun-Yi , Shen Chi-Yen
Published: July 2022

Surface acoustic wave (SAW) sensors based on reduced graphene oxide/poly (diketopyrrolopyrrolethiophene-thieno [3,2-b]thiophene-thiophene) (rGO/DPP2T-TT) composite sensing films for the detection of ammonia were investigated at room temperature in this study. The rGO/DPP2T-TT composite films were deposited onto ST-X quartz SAW resonators by a drop-casting method. FESEM, EDS, and XRD characterizations showed that the rGO/DPP2T-TT composite film was successfully synthesized and exhibited numerous wrinkles and a rough structure, which are crucial for gas adsorption. The frequency response to 500–1400 ppb ammonia shown by the prepared SAW sensor coated with rGO/DPP2T-TT composite film increased linearly as the ammonia concentration increased. The sensor based on a rGO/DPP2T-TT composite film exhibited a positive frequency shift of 55 Hz/ppm, and its frequency response to 500 ppb ammonia was 35 Hz. The sensors thus show promising potential in detecting sub-ppm concentration levels of NH3 at room temperature, which opens up possibilities for applications in the noninvasive detection of NH3 in the breath. As a result, the rGO/DPP2T-TT composite sensor can be a good candidate for in situ medical diagnosis and indoor/outdoor environment monitoring.
A Formal and Visual Data-Mining Model for Complex Ship Behaviors and Patterns
Authors: Suo Yongfeng , Ji Yuxiang , Zhang Zhenye , Chen Jinhai , Claramunt Christophe
Published: July 2022

The successful emergence of real-time positioning systems in the maritime domain has favored the development of data infrastructures that provide valuable monitoring and decision-aided systems. However, there is still a need for the development of data mining approaches oriented to the detection of specific patterns such as unusual ship behaviors and collision risks. This research introduces a CSBP (complex ship behavioral pattern) mining model aiming at the detection of ship patterns. The modeling approach first integrates ship trajectories from automatic identification system (AIS) historical data, then categorizes different vessels’ navigation behaviors, and introduces a visual-oriented framework to characterize and highlight such patterns. The potential of the model is illustrated by a case study applied to the Jiangsu and Zhejiang waters in China. The results show that the CSBP mining model can highlight complex ships’ behavioral patterns over long periods, thus providing a valuable environment for supporting ship traffic management and preventing maritime accidents.
A Novel Memory and Time-Efficient ALPR System Based on YOLOv5
Authors: Batra Piyush , Hussain Imran , Ahad Mohd Abdul , Casalino Gabriella , Alam Mohammad Afshar , Khalique Aqeel , Hassan Syed Imtiyaz
Published: July 2022

With the rapid development of deep learning techniques, new innovative license plate recognition systems have gained considerable attention from researchers all over the world. These systems have numerous applications, such as law enforcement, parking lot management, toll terminals, traffic regulation, etc. At present, most of these systems rely heavily on high-end computing resources. This paper proposes a novel memory and time-efficient automatic license plate recognition (ALPR) system developed using YOLOv5. This approach is ideal for IoT devices that usually have less memory and processing power. Our approach incorporates two stages, i.e., using a custom transfer learned model for license plate detection and an LSTM-based OCR engine for recognition. The dataset that we used for this research was our dataset consisting of images from the Google open images dataset and the Indian License plate dataset. Along with training YOLOv5 models, we also trained YOLOv4 models on the same dataset to illustrate the size and performance-wise comparison. Our proposed ALPR system results in a 14 megabytes model with a mean average precision of 87.2% and 4.8 ms testing time on still images using Nvidia T4 GPU. The complete system with detection and recognition on the other hand takes about 85 milliseconds.
Towards a Democratization of Data in the Context of Industry 4.0
Authors: Harland Tobias , Hocken Christian , Schröer Tobias , Stich Volker
Published: July 2022

Data-driven transparency in end-to-end operations in real-time is seen as a key benefit of the fourth industrial revolution. In the context of a factory, it enables fast and precise diagnoses and corrections of deviations and, thus, contributes to the idea of an agile enterprise. Since a factory is a complex socio-technical system, multiple technical, organizational and cultural capabilities need to be established and aligned. In recent studies, the underlying broad accessibility of data and corresponding analytics tools are called “data democratization”. In this study, we examine the status quo of the relevant capabilities for data democratization in the manufacturing industry. (1) and outline the way forward. (2) The insights are based on 259 studies on the digital maturity of factories from multiple industries and regions of the world using the acatech Industrie 4.0 Maturity Index as a framework. For this work, a subset of the data was selected. (3) As a result, the examined factories show a lack of capabilities across all dimensions of the framework (IT systems, resources, organizational structure, culture). (4) Thus, we conclude that the outlined implementation approach needs to comprise the technical backbone for a data pipeline as well as capability building and an organizational transformation.
Teachers’ Innovative Work Behavior Scale: Psychometric Properties of the Greek Version and Measurement Invariance across Genders
Authors: Gkontelos Angelos , Vaiopoulou Julie , Stamovlasis Dimitrios
Published: July 2022

Innovation refers to the implementation of creative ideas into practice. In that sense, innovative work behavior (IWB) is a type of behavior and a multidimensional construct that involves four factors: the generation, promotion, realization, and sustainability of new ideas for the whole organizations’ benefit. Thus, the development of instruments for measuring and singling IWB is an interesting and necessary endeavor. The present study appraises the psychometric properties of the Greek version of the innovative work behavior scale (IWBS-G), a 44-item self-reported instrument, using data collected from two studies with in-service teachers. In the first study dataset (N = 485), exploratory factor analysis was applied, which, by implementing scree plot with parallel analysis, revealed the dimensionality of four factors, namely: Idea Generation (IG), Idea Promotion (IP), Idea Realization (IR), and Idea Sustainability (IS). The corresponding reliability measures using Cronbach’s alpha and McDonald’s omega ranged between 0.917 and 0.944. In the second study dataset (N = 964), confirmatory factor analysis validated a sufficient fit of the measurement model [χ2 = 396.85, df = 203, p < 0.001; CFI = 0.995; TLI = 0.994; RMSEA = 0.031], while the corresponding internal consistency measures ranged between 0.814 and 0.914. Furthermore, measurement invariance was conducted, which demonstrated insignificant differences between genders. Discussion on the significance of innovative work behavior and the potential implementation of the IWBS instrument in educational research is provided.
Social Sciences Teaching: Building a Holistic Approach from Student Teachers’ Social Representations
Authors: Pérez-Guilarte Yamilé , Armas-Quintá Francisco Xosé , Macía-Arce Xosé Carlos
Published: July 2022

The dynamics that today’s society is facing require critical citizens capable of understanding the complexity of problems from their various dimensions. This work aimed to investigate the social representations that future primary school teachers have about socio-environmental problems, sustainability, purposes of socio-environmental education, and strategies to train in Global Citizenship Education and in Education for Sustainable Development. An exploratory case study was carried out, framed in an action-research approach in the classes of Social Sciences Teaching of the Primary Education Degree of two universities in Galicia (Spain): the University of A Coruña and the University of Santiago de Compostela. A quantitative and qualitative analysis was developed by applying a questionnaire to a sample of 200 students. The results showed that students focus on problems such as climate change or environmental pollution, leaving aside issues such as social inequalities, poverty, or gender issues. In addition, they have difficulties in recognizing the strategies by which teachers can provide their students with critical thinking that leads to social transformation. Significant differences were observed in some researched aspects according to the university of origin, the grade, and the gender of the students, with gender being the one that influenced the greatest number of questions.
Ground-Mounted Photovoltaic and Crop Cultivation: A Comparative Analysis
Authors: Sacchelli Sandro , Havrysh Valerii , Kalinichenko Antonina , Suszanowicz Dariusz
Published: July 2022

Human civilization depends on energy sources, mainly fossil fuels. An increase in the prices of fossil fuels and their exhaustibility limit economic growth. Carbon dioxide emission causes global environmental problems. Global crises (including COVID-19) have sharpened food and energy supply problems. The decentralized energy supply systems as well as the expedition of the application of renewable energy may solve these challenges. The economic shift to renewable power generation intensifies the competition between food crop production and green energy for land. This paper applied an open-source spatial-based model to quantify the solar power generation (the ground-mounted photovoltaic panels) for the southern regions of Poland (the Opole region) and Ukraine (the Mykolaiv region). The model used technical, economic, and legal constraints. This study compared economic indicators of the solar power generation and the crop production projects for rain-fed land. The net present value (NPV) and the profitability index (PI) were used for the economic evaluation. Additionally, the coefficients of variation were determined to assess investment risks. The use of r.green.solar model to find the spatial distribution of the reduction of carbon dioxide emission was the novelty of this study. The analysis revealed that the PV projects have higher NPV, but lower PI compared to the crop production. The PV projects have lower coefficients of variation. This fact testifies that these projects are less risky.
Compulsive Study Behaviors Are Associated with Eating Disorders and Have Independent Negative Effects on Well-Being: A Structural Equation Model Study among Young Musicians
Authors: Woropay-Hordziejewicz Natalia A. , Buźniak Aleksandra , Lawendowski Rafał , Atroszko Paweł A.
Published: July 2022

Compulsive overworking and eating disorders (EDs) show considerable similarities in terms of risk factors (e.g., rigid perfectionism), clinical manifestation (e.g., excessive controlling behaviors), and consequences (e.g., physical exhaustion and depression). This study aimed to examine the hypotheses that compulsive study behaviors (conceptualized as study addiction) are related to EDs and that they have independent negative effects on well-being among young musicians, who constitute a highly vulnerable population for these types of problematic behaviors. The relatively high prevalence of study addiction and its pronounced negative relationship with psychosocial functioning make it a pending challenge for sustainable education. A total of 255 students from various music academies in Poland took part in the study. The Bergen Study Addiction Scale, assessing compulsive studying (conceptualized as addictive behavior), the Eating Attitude Test-26 (EAT-26), the Perceived Stress Scale, the Hospital Anxiety and Depression Scale, and the quality-of-life measure were used. A structural equation model was investigated. Study addiction was positively related to the general factor of EDs and the social pressure component. Both problematic behaviors showed negative and independent effects on the well-being of young musicians. EDs may be 8 to 16 times more prevalent among the students of music academies who are addicted to studying than among the general population. About 80% of those students showing all seven symptoms of study addiction exhibited at least mild depression, while more than half had clinically significant levels of depression. Almost 90% had clinically significant levels of anxiety. Without addressing co-occurring study addiction and eating disorders, including their commonalities and idiosyncrasies, their prevention and treatment cannot be effective and it will substantially affect the sustainability of education and work.
The “Adaptation Paradox” and Citizen Ambiguity over Government Climate Policies: Survey Findings from Bangladesh
Authors: Eisenstadt Todd A. , Haque Tawfique , Toman Michael , Wright Matthew
Published: July 2022

National governments in the world’s most climate-vulnerable nations are using domestically sourced and international funding and expertise. However, local governments are where citizens in many developing countries turn to solve problems. Using results from a nationally representative sample in Bangladesh, one of the most climate-vulnerable nations in the world, we examine citizens’ perspectives about the responsibility of different levels of government to address climate change problems. Inasmuch as Bangladeshi survey respondents do draw distinctions, they trust local governments more than the national one. However, local governments tend to be relatively weak vis-à-vis the national government: political and financial resources are concentrated there, and the national government has access to the resources of international financial institutions. Furthermore, respondents tend to view local officials as embedded community networks more than as formal government agents. We conclude that better public communications across levels of government with vulnerable communities are needed if these communities are to protect themselves from extreme weather events, access services, and reap the benefits of “polycentric” climate adaptation governance across a full range of levels.
Graduating during the COVID-19 Pandemic: Digital Media Practices and Learning Spaces among Pupils Taking Their School-Leaving Exams
Authors: Mahlknecht Belinda , Kempert Richard , Bork-Hüffer Tabea
Published: July 2022

The COVID-19 pandemic has profoundly changed educational and qualification experiences among young people. When the pandemic spread in 2020, schools worldwide were required to switch to remote learning. Through a qualitative multi-method, partly mobile, in-situ research approach, we accompanied pupils in the final year of their secondary education as they prepared for and finalized their school-leaving exams to investigate the following questions: What did pupils’ socio-material-technological learning spaces look like during this period? How did they adapt their digital media practices to cope with learning remotely? How did their situatedness in these learning spaces influence their learning experiences? Building on existing research in the field of digital and children’s geographies as well as learning spaces, through a combined content and narrative analysis, this article situates pupils’ learning spaces and experiences of graduating during the pandemic in the context of family relations, socio-material home spaces, polymediated learning environments and the accessibility of outdoor spaces. We debate the wide spectrum of media practices—ranging from indulgence in digital media, to balanced media use, to attempting to withdraw from using digital media—used by pupils to navigate through inextricably entangled socio-material-technological spaces during the pandemic. The further digitization of education prompted by the pandemic must be used in ways that empower pupils to engage in responsible and active use of digital media, thus allowing them to become mature and resilient digital participants in society.
Long-Term Nutrient Supply Options: Strategies to Improve Soil Phosphorus Availability in the Rice-Wheat System
Authors: Meena Sunita Kumari , Dwivedi Brahma Swaroop , Meena Mahesh Chand , Datta Saba Prasad , Singh Vinod Kumar , Mishra Rajendra Prasad , Chakraborty Debashish , Dey Abir , Meena Vijay Singh
Published: July 2022

The indiscriminate use of chemical fertilizers can deteriorate soil, grain, and environmental quality; still, these can be restored if integrated nutrient management options with inclusion of legumes in the cropping system are adopted. A long-term (19 year) rice-wheat system experiment was examined to find out the best nutrient management practices (BNMP) through recommended dose of fertilizers (RDF), integrated plant nutrition system (IPNS), soil test crop response (STCR), farmyard manure (FYM), along with the inclusion of pulses (berseem and cowpea). Seven nutrient management practices were applied in combination of organic and chemical fertilizer in the rice-wheat system. Results showed that a significant variation was seen in phosphorus (P) fractions among the treatments and soil depths. The results showed a significantly (p < 0.05) higher contribution to phosphorus availability by Residual-P followed by NaHCO3-Po > NaOH-Pi > NaOH-Po > HCl-P > NaHCO3-Pi > available P and lowest in WSP under different long-term management options in rice-wheat system after completing 19 crop cycles. Variations in soil P-fractions with depth were compared to different treatment combination, and a considerable increase in all the major P-fractions was noticed. The continuous application of various IPNS options as organic farming (OF), RDF, STCR, and the inclusion of pulses (berseem and cowpea) significantly improved all P fractions in the soil system and offered an added benefit in terms of sustainability of production and soil health compared to the solo application of chemical fertilizers. Overall, results showed that IPNS options (berseem and cowpea) showed its superiority over the rest of the treatment. This study suggests that the inclusion of pulses would increase P-availability in soil system.
Traditional Subsistence Farming of Smallholder Agroforestry Systems in Indonesia: A Review
Authors: Achmad Budiman , Sanudin  , Siarudin Mohamad , Widiyanto Ary , Diniyati Dian , Sudomo Aris , Hani Aditya , Fauziyah Eva , Suhaendah Endah , Widyaningsih Tri Sulistyati , Handayani Wuri , Maharani Dewi , Suhartono  , Palmolina Maria , Swestiani Dila , Budi Santoso Sulistiadi Harry , Winara Aji , Nur Yudha Hadian , Diana Muthya , Gartika Dewi , Ruswandi Agus
Published: July 2022

Agroforestry has been practiced for decades and is undoubtedly an important source of income for Indonesian households living near forests. However, there are still many cases of poverty among farmers due to a lack of ability to adopt advanced technology. This literature review aims to identify the characteristics and factors causing the occurrence of agricultural subsistence and analyze its implications for the level of farmer welfare and the regional forestry industry. The literature analysis conducted reveals that small land tenure, low literacy rates, and lack of forest maintenance are the main causes of the subsistence of small agroforestry farmers. Another reason is that subsistence-oriented agroforestry practices are considered a strong form of smallholder resilience. All of these limitations have implications for low land productivity and high-sawn timber waste from community forests. To reduce the subsistence level of farmers, government intervention is needed, especially in providing managerial assistance packages, capital assistance, and the marketing of forest products. Various agroforestry technologies are available but have not been implemented consistently by farmers. Therefore, it is necessary to develop an integrated collaboration between researchers, farmers, and regionally owned enterprises (BUMD) to increase access to technology and markets. Although it is still difficult to realize, forest services, such as upstream–downstream compensation and carbon capture, have the potential to increase farmer income.
The Impact of Board Size on Green Innovation in China’s Heavily Polluting Enterprises: The Mediating Role of Innovation Openness
Authors: Zhao Jianfei , Pongtornkulpanich Anan , Cheng Wenjin
Published: July 2022

Among the many reasons to encourage enterprises to implement green innovation, external factors such as market mechanisms and policy regulation both have the greatest probability of failure. Therefore, the idea of exploring ways to promote green innovation from internal factors has gradually attracted attention. This study is based on an internal perspective to explore the relationship between board size, openness, and green innovation by using data from the heavily polluting enterprises listed in China’s A-share market from 2015 to 2020. The regression results show that board size has a significant positive impact on green innovation, and the openness breadth plays a partial mediating role. This indicates that more board members are conducive to the implementation of green innovation strategies, as well as expanding the innovation openness breadth, so as to obtain external knowledge and resources to promote green innovation. Through further heterogeneity analysis, we found that the above relationship is more significant in state-owned enterprises. Finally, this study provides new theoretical evidence for the debate over whether board size promotes or inhibits green innovation. Furthermore, it provides a path and practical guidance for enterprises to implement green innovation more effectively by relying on their directors’ networks and increasing their own openness.
A Parametric Resonance for the Nonlocal Hirota–Maccari Equation
Authors: Maccari Attilio
Published: July 2022

The nonlocal Hirota–Maccari equation is considered when a parametric excitation is acting over the frequency of a generic mode. Using the well-known asymptotic perturbation (AP) method, two coupled equations for the amplitude and phase can be obtained. We discovered the existence of an infinite-period bifurcation when the parametric force increases its value. Moreover, symmetry considerations suggest performing a global analysis of the two couples, in such a way that we find an energy-like function and corroborate and verify the existence of this infinite period bifurcation.