bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning-Based Transfer Learning for Classification of Skin Cancer
Authors: Jain Satin , Singhania Udit , Tripathy Balakrushna , Nasr Emad Abouel , Aboudaif Mohamed K. , Kamrani Ali K.
Published: December 2021

One of the major health concerns for human society is skin cancer. When the pigments producing skin color turn carcinogenic, this disease gets contracted. A skin cancer diagnosis is a challenging process for dermatologists as many skin cancer pigments may appear similar in appearance. Hence, early detection of lesions (which form the base of skin cancer) is definitely critical and useful to completely cure the patients suffering from skin cancer. Significant progress has been made in developing automated tools for the diagnosis of skin cancer to assist dermatologists. The worldwide acceptance of artificial intelligence-supported tools has permitted usage of the enormous collection of images of lesions and benevolent sores approved by histopathology. This paper performs a comparative analysis of six different transfer learning nets for multi-class skin cancer classification by taking the HAM10000 dataset. We used replication of images of classes with low frequencies to counter the imbalance in the dataset. The transfer learning nets that were used in the analysis were VGG19, InceptionV3, InceptionResNetV2, ResNet50, Xception, and MobileNet. Results demonstrate that replication is suitable for this task, achieving high classification accuracies and F-measures with lower false negatives. It is inferred that Xception Net outperforms the rest of the transfer learning nets used for the study, with an accuracy of 90.48. It also has the highest recall, precision, and F-Measure values.
Development of a Novel Anesthesia Airway Management Robot
Authors: Ma Xuesong , Pan Bo , Song Tao , Sun Yanwen , Fu Yili
Published: December 2021

Non-invasive positive pressure ventilation has attracted increasing attention for air management in general anesthesia. This work proposes a novel robot equipped with two snake arms and a mask-fastening mechanism to facilitate trachea airway management for anesthesia as well as deep sedation and to improve surgical outcomes. The two snake arms with supporting terminals have been designed to lift a patient’s jaw with design optimization, and the mask-fastening mechanism has been utilized to fasten the mask onto a patient’s face. The control unit has been developed to implement lifting and fastening force control with safety and robustness. Loading experiments on the snake arm and tension experiments on the mask-fastening mechanism have been performed to investigate and validate the performances of the proposed anesthesia airway management robot. Experiments on a mock person have also been employed to further verify the effectiveness and reliability of the developed robot system. As an early study of an anesthesia airway management robot, it was verified as a valid attempt to perform mask non-invasive positive pressure ventilation technology by taking advantage of a robotic system.
Experimental Validation of LiDAR Sensors Used in Vehicular Applications by Using a Mobile Platform for Distance and Speed Measurements
Authors: Vasile Ionuț , Tudor Emil , Sburlan Ion-Cătălin , Gheți Marius-Alin , Popa Gabriel
Published: December 2021

LiDAR sensors are needed for use in vehicular applications, particularly due to their good behavior in low-light environments, as they represent a possible solution for the safety systems of vehicles that have a long braking distance, such as trams. The testing of long-range LiDAR dynamic responses is very important for vehicle applications because of the presence of difficult operation conditions, such as different weather conditions or fake targets between the sensor and the tracked vehicle. The goal of the authors in this paper was to develop an experimental model for indoor testing, using a scaled vehicle that can measure the distances and the speeds relative to a fixed or a moving obstacle. This model, containing a LiDAR sensor, was developed to operate at variable speeds, at which the software functions were validated by repeated tests. Once the software procedures are validated, they can be applied on the full-scale model. The findings of this research include the validation of the frontal distance and relative speed measurement methodology, in addition to the validation of the independence of the measurements to the color of the obstacle and to the ambient light.
Wireless Torque and Power Transfer Using Multiple Coils with LCC-S Topology for Implantable Medical Drug Pump
Authors: Rhee Jaewon , Shin Yujun , Woo Seongho , Lee Changmin , Kim Dongwook , Ahn Jangyong , Kim Haerim , Ahn Seungyoung
Published: December 2021

In this paper, we propose a method of wirelessly torque transfer (WTT) and power (WPT) to a drug pump, one of implantable medical devices. By using the magnetic field generated by the WPT system to transfer torque and power to the receiving coil at the same time, applications that previously used power from the battery can be operated without a battery. The proposed method uses a receiving coil with magnetic material as a motor, and can generate torque in a desired direction using the magnetic field from the transmitting coil. The WPT system was analyzed using a topology that generates a constant current for stable torque generation. In addition, a method for detecting the position of the receiving coil without using additional power was proposed. Through simulations and experiments, it was confirmed that WTT and WPT were possible at the same time, and in particular, it was confirmed that WTT was stably possible.
Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging
Authors: Esmaeili Nazila , Sharaf Esam , Gomes Ataide Elmer Jeto , Illanes Alfredo , Boese Axel , Davaris Nikolaos , Arens Christoph , Navab Nassir , Friebe Michael
Published: December 2021

(1) Background: Contact Endoscopy (CE) and Narrow Band Imaging (NBI) are optical imaging modalities that can provide enhanced and magnified visualization of the superficial vascular networks in the laryngeal mucosa. The similarity of vascular structures between benign and malignant lesions causes a challenge in the visual assessment of CE-NBI images. The main objective of this study is to use Deep Convolutional Neural Networks (DCNN) for the automatic classification of CE-NBI images into benign and malignant groups with minimal human intervention. (2) Methods: A pretrained Res-Net50 model combined with the cut-off-layer technique was selected as the DCNN architecture. A dataset of 8181 CE-NBI images was used during the fine-tuning process in three experiments where several models were generated and validated. The accuracy, sensitivity, and specificity were calculated as the performance metrics in each validation and testing scenario. (3) Results: Out of a total of 72 trained and tested models in all experiments, Model 5 showed high performance. This model is considerably smaller than the full ResNet50 architecture and achieved the testing accuracy of 0.835 on the unseen data during the last experiment. (4) Conclusion: The proposed fine-tuned ResNet50 model showed a high performance to classify CE-NBI images into the benign and malignant groups and has the potential to be part of an assisted system for automatic laryngeal cancer detection.
Triple Estimation of Fractional Variable Order, Parameters, and State Variables Based on the Unscented Fractional Order Kalman Filter
Authors: Sierociuk Dominik , Macias Michal
Published: December 2021

In this paper, a method for states, parameters, and fractional order estimation is presented. The proposed method is an extension of the traditional dual estimation method and uses three blocks of filters with appropriate data interconnections. As the main part of the estimation algorithm, the Fractional Unscented Kalman Filter was used. The proposed Triple Estimation algorithm might be treated as a convenient tool for estimation and analysis of a wide range of dynamical systems with fractional constants or variable order nature, especially when knowledge about the identified system is very restricted and both order and system parameters are unknown. In order to show the performance of the proposed algorithm, sets of numerical results are presented.
Deep Reinforcement Learning-Based Accurate Control of Planetary Soft Landing
Authors: Xu Xibao , Chen Yushen , Bai Chengchao
Published: December 2021

Planetary soft landing has been studied extensively due to its promising application prospects. In this paper, a soft landing control algorithm based on deep reinforcement learning (DRL) with good convergence property is proposed. First, the soft landing problem of the powered descent phase is formulated and the theoretical basis of Reinforcement Learning (RL) used in this paper is introduced. Second, to make it easier to converge, a reward function is designed to include process rewards like velocity tracking reward, solving the problem of sparse reward. Then, by including the fuel consumption penalty and constraints violation penalty, the lander can learn to achieve velocity tracking goal while saving fuel and keeping attitude angle within safe ranges. Then, simulations of training are carried out under the frameworks of Deep deterministic policy gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor Critic (SAC), respectively, which are of the classical RL frameworks, and all converged. Finally, the trained policy is deployed into velocity tracking and soft landing experiments, results of which demonstrate the validity of the algorithm proposed.
Completion of Metal-Damaged Traces Based on Deep Learning in Sinogram Domain for Metal Artifacts Reduction in CT Images
Authors: Zhu Linlin , Han Yu , Xi Xiaoqi , Li Lei , Yan Bin
Published: December 2021

In computed tomography (CT) images, the presence of metal artifacts leads to contaminated object structures. Theoretically, eliminating metal artifacts in the sinogram domain can correct projection deviation and provide reconstructed images that are more real. Contemporary methods that use deep networks for completing metal-damaged sinogram data are limited to discontinuity at the boundaries of traces, which, however, lead to secondary artifacts. This study modifies the traditional U-net and adds two sinogram feature losses of projection images—namely, continuity and consistency of projection data at each angle, improving the accuracy of the complemented sinogram data. Masking the metal traces also ensures the stability and reliability of the unaffected data during metal artifacts reduction. The projection and reconstruction results and various evaluation metrics reveal that the proposed method can accurately repair missing data and reduce metal artifacts in reconstructed CT images.
Rotating Machinery Fault Diagnosis Method by Combining Time-Frequency Domain Features and CNN Knowledge Transfer
Authors: Ye Lihao , Ma Xue , Wen Chenglin
Published: December 2021

Aiming at the problem of fault diagnosis when there are only a few labeled samples in the large amount of data collected during the operation of rotating machinery, this paper proposes a fault diagnosis method based on knowledge transfer in deep learning. First, we describe the data collected during the operation as a two-dimensional image with both time and frequency-domain characteristics. Second, we transform the trained source domain model into a shallow model suitable for small samples in the target domain, and we train the shallow model with small samples with labels. Third, we input a large number of unlabeled samples into the shallow model, and the output result of the system is regarded as the label of the input sample. Fourth, we combine the original data and the data annotated by the shallow model to train the new deep CNN fault diagnosis model so as to realize the migration of knowledge from the expert system to the deep CNN. The newly built deep CNN model is used for the online fault diagnosis of rotating machinery. The FFCNN-SVM shallow model tagger method proposed in this paper compares the fault diagnosis results with other transfer learning methods at this stage, and its correct rate has been greatly improved. This method provides new ideas for future fault diagnosis under small samples.
An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing
Authors: Reimer Tyson , Pistorius Stephen
Published: December 2021

Breast microwave sensing (BMS) has been studied as a potential technique for cancer detection due to the observed microwave properties of malignant and healthy breast tissues. This work presents a novel radar-based image reconstruction algorithm for use in BMS that reframes the radar image reconstruction process as an optimization problem. A gradient descent optimizer was used to create an optimization-based radar reconstruction (ORR) algorithm. Two hundred scans of MRI-derived breast phantoms were performed with a preclinical BMS system. These scans were reconstructed using the ORR, delay-and-sum (DAS), and delay-multiply-and-sum (DMAS) beamformers. The ORR was observed to improve both sensitivity and specificity compared to DAS and DMAS. The estimated sensitivity and specificity of the DAS beamformer were 19% and 44%, respectively, while for ORR, they were 27% and 56%, representing a relative increase of 42% and 27%. The DAS reconstructions also exhibited a hot-spot image artifact, where a localized region of high intensity that did not correspond to any physical phantom feature would be present in an image. This artifact appeared like a tumour response within the image and contributed to the lower specificity of the DAS beamformer. This artifact was not observed in the ORR reconstructions. This work demonstrates the potential of an optimization-based conceptualization of the radar image reconstruction problem in BMS. The ORR algorithm implemented in this work showed improved diagnostic performance and fewer image artifacts compared to the widely employed DAS algorithm.
Does the Score on the MRC Strength Scale Reflect Instrumented Measures of Maximal Torque and Muscle Activity in Post-Stroke Survivors?
Authors: Kiper Pawel , Rimini Daniele , Falla Deborah , Baba Alfonc , Rutkowski Sebastian , Maistrello Lorenza , Turolla Andrea
Published: December 2021

It remains unknown whether variation of scores on the Medical Research Council (MRC) scale for muscle strength is associated with operator-independent techniques: dynamometry and surface electromyography (sEMG). This study aimed to evaluate whether the scores of the MRC strength scale are associated with instrumented measures of torque and muscle activity in post-stroke survivors with severe hemiparesis both before and after an intervention. Patients affected by a first ischemic or hemorrhagic stroke within 6 months before enrollment and with complete paresis were included in the study. The pre- and post-treatment assessments included the MRC strength scale, sEMG, and dynamometry assessment of the triceps brachii (TB) and biceps brachii (BB) as measures of maximal elbow extension and flexion torque, respectively. Proprioceptive-based training was used as a treatment model, which consisted of multidirectional exercises with verbal feedback. Each treatment session lasted 1 h/day, 5 days a week for a total 15 sessions. Nineteen individuals with stroke participated in the study. A significant correlation between outcome measures for the BB (MRC and sEMG p = 0.0177, ρ = 0.601; MRC and torque p = 0.0001, ρ = 0.867) and TB (MRC and sEMG p = 0.0026, ρ = 0.717; MRC and torque p = 0.0001, ρ = 0.873) were observed post intervention. Regression models revealed a relationship between the MRC score and sEMG and torque measures for both the TB and BB. The results confirmed that variation on the MRC strength scale is associated with variation in sEMG and torque measures, especially post intervention. The regression model showed a causal relationship between MRC scale scores, sEMG, and torque assessments.
Characterization and Improvement of Heat Resistance of a Polymer-Ceramic Pressure-Sensitive Paint at High Temperatures
Authors: Furuya Takenori , Nakai Takumi , Imai Masato , Kameda Masaharu
Published: December 2021

Degradation of fast response pressure-sensitive paints (PSP) above room temperature is a serious problem for PSP measurements in high-temperature environments. A standard polymer-ceramic PSP (PC-PSP) composed of platinum(II)-5,10,15,20-tetrakis-(2,3,4,5,6-pentafluorphenyl)-porphyrin (PtTFPP), titania particles and poly(isobutyl methacrylate) (polyIBM) was characterized to elucidate the degradation mechanism. Applying a two-gate lifetime-based method, the PC-PSP has sufficient pressure and temperature sensitivities even at 100 °C, while the luminescence intensity significantly decreases during the test. Subsequent measurements on thermal and photostability as well as luminescence spectra reveal that the main cause of the degradation is the photodegradation of PtTFPP due to direct exposure of the dye molecules to the atmosphere. In order to suppress such degradation, a small amount of urethane resin is added to the dye solution as a simple additional step in the preparation of PC-PSP. The addition of the urethane resin significantly reduces the degradation of the PSP, although its time response is slightly slower than that of the standard PC-PSP.
GC-MS Method for Quantification and Pharmacokinetic Study of Four Volatile Compounds in Rat Plasma after Oral Administration of Commiphora myrrh (Nees) Engl. Resin and In Vitro Cytotoxic Evaluation
Authors: Alqahtani Ali S. , Herqash Rashed N. , Alqahtani Faleh , Ahamad Syed Rizwan , Nasr Fahd A. , Noman Omar M.
Published: December 2021

A rapid, simple, and sensitive gas chromatography–tandem mass spectrometry (GC–MS) method was established and validated for simultaneous determination of four volatile compounds, namely curzerene, methoxyfuranodiene, β-elemene, and α-pinene in rat plasma samples after oral administration of the resin extract of Commiphora myrrh using limonene as an internal standard (IS). Liquid-liquid extraction using hexane and ethyl acetate (1:1) mixture as an extracting agent was used for the samples extraction procedure. The GC–MS system was operated under selective ion monitoring (SIM) mode using Perkin Elmer Elite 5MS column (30 m × 0.25 mm × 0.25 µm film thickness). Specificity, linearity, precision, accuracy, extraction recovery, and stability were used to validate the developed method. The assay showed good linearity (r2 ≥ 0.998), and the lowest limits of quantification (LLOQ) were 3.97–21.38 ng/mL for the four analytes. This assay was successfully applied to pharmacokinetic studies of the four volatile compounds in rat plasma. The antiproliferative activity of these volatile compounds was evaluated against lung carcinoma (A549) and colon (LoVo) cell lines, were each compound caused variable inhibition on cells proliferation and methoxyfuranodiene exerted the strong antiproliferative activity against both cell line according to IC50 values.
Effect of Exposure Time on Thermal Behaviour: A Psychophysiological Approach
Authors: Kobas Bilge , Koth Sebastian Clark , Nkurikiyeyezu Kizito , Giannakakis Giorgos , Auer Thomas
Published: December 2021

This paper presents the findings of a 6-week long, five-participant experiment in a controlled climate chamber. The experiment was designed to understand the effect of time on thermal behaviour, electrodermal activity (EDA) and the adaptive behavior of occupants in response to a thermal non-uniform indoor environment were continuously logged. The results of the 150 h-long longitudinal study suggested a significant difference in tonic EDA levels between “morning” and “afternoon” clusters although the environmental parameters were the same, suggesting a change in the human body’s thermal reception over time. The correlation of the EDA and temperature was greater for the afternoon cluster (r = 0.449, p < 0.001) in relation to the morning cluster (r = 0.332, p < 0.001). These findings showed a strong temporal dependency of the skin conductance level of the EDA to the operative temperature, following the person’s circadian rhythm. Even further, based on the person’s chronotype, the beginning of the “afternoon” cluster was observed to have shifted according to the person’s circadian rhythm. Furthermore, the study is able to show how the body reacts differently under the same PMV values, both within and between subjects; pointing to the lack of temporal parameter in the PMV model.
Development of Ti PVD Films to Limit the Carburization of Metal Powders during SPS Process
Authors: Ardigo-Besnard Maria-Rosa , Besnard Aurélien , Moser Mathias , Bussière Florian
Published: December 2021

Spark plasma sintering technique is used for the fabrication of dense materials with a fine-grained microstructure. In this process, a powder is placed into a graphite mold and a uniaxial pressure is applied by two graphite punches. A graphite foil is inserted between the punches and the powder and between the mold and the powder to ensure good electrical, physical and thermal contact. One of the major drawbacks during sintering of metal powders is the carburization of the powder in contact with the graphite foils. In this study, a PVD coating of titanium was applied on the graphite foils in contact with the metal powder (pure iron). The results are promising, as the investigations show that the application of a Ti PVD film of 1.5 and 1.1 µm thickness is effective to completely avoid the carburization of iron powder. Carbon diffuses inside the PVD film during sintering. In parallel, iron diffusion was revealed inside the Ti coating of 1.5 µm thickness. On the other hand, a Ti PVD film of 0.5 µm thickness provides a protection against carbon diffusion just on the sides in contact with the mold, proving that the coating thickness represents an important parameter to consider.
Education for Citizenship: The Meanings Chilean Teachers Convey in the Neoliberal Context
Authors: Redon Pantoja Silvia , Vallejos Silva Natalia , Angulo Rasco José Félix
Published: December 2021

This article presents the results from research into education for citizenship in which Chilean teachers participated. Ninety-nine interviews and two focal groups that included questions on knowledge, beliefs, values and practices related to education for citizenship were carried out. NVivo 12 software was used for the analysis of the discourses, following the direction similar to the grounded theory that considers elaborating free nodes, structuring categories and configuring categorical trees, according to the school’s administrative dependency. The results yield six macro-categories: School, Authoritarianism, Neoliberalism, Curriculum, Teacher Role and Citizenship. The present article analyses the Neoliberalism macro-category formed, in turn, by the following subcategories: (1) subject and resistance, (2) competitiveness and individualism in a subject that is instrumental, consumer and reproducer of the establishment, (3) commodified schools, where the economic value regards students and families as clients, (4) a culture of bureaucratization and accountability, and (5) lack of a sense of communality as a collective, supportive body. In all of them, teachers show themselves eloquently critical of the neoliberal system and of the obstacles it poses to rights, justice and democracy in the current capitalist citizenship and school.
Spatial Disparities and Correlated Variables of Community Care Facility Accessibility in Rural Areas of China
Authors: Yu Yang , Wu Yijin , Xu Xin , Chen Yun , Tian Xiaobo , Wang Li , Chen Siyun
Published: December 2021

With the increasing aging of the world’s population, research on the equitable allocation of elderly care facilities has received increasing attention, but measuring the accessibility of community care facilities (CCFs) in rural areas has received little attention. In this study, which covered 7985 CCFs in 223,877 villages, we measured the accessibility of CCFs in rural areas of Hubei Province by using the nearest distance method. Based on the accessibility calculation, the spatial disparities and agglomeration characteristics of spatial accessibility were analyzed, and the correlated variables related to the accessibility were analyzed from both natural environment and socioeconomic aspects by employing a geographically weighted regression (GWR) model. Our results show that 87% of villages have a distance cost of less than 7121 m and 81% of townships have a distance cost of less than 5114 m; good spatial accessibility is present in the eastern and central regions, while poor spatial accessibility is shown in a small number of areas in the west. The results from the clustering analysis show that the hot spot areas are mainly clustered in the western mountainous areas and that the cold spot areas are mainly clustered around Wuhan city. We also observed that area, elevation, population aged 65 and above, and number of villages are significantly correlated with accessibility. The results of this study can be used to provide a reference for configuration optimization and layout planning of elderly care facilities in rural areas.
Energy Transition and Climate Change in Decision-Making Processes
Authors: Tsantopoulos Georgios , Karasmanaki Evangelia
Published: December 2021

Humans have been using fossil fuels for centuries, and the development of fossil fuel technology reshaped society in lasting ways [...]
Key Issues and Technical Applications in the Study of Power Markets as the System Adapts to the New Power System in China
Authors: Dong Jun , Liu Dongran , Dou Xihao , Li Bo , Lv Shiyao , Jiang Yuzheng , Ma Tongtao
Published: December 2021

To reach the “30·60” decarbonization target (where carbon emissions start declining in 2030 and reach net zero in 2060), China is restructuring its power system to a new energy-based one. Given this new situation, this paper reviews previous studies on the power market and highlights key issues for future research as we seek to adapt to the new power system (NPS). Based on a systematic literature review, papers on the operational efficiency of the power market, participants’ bidding strategies and market supervision were identified. In a further step, papers with high relevance were analyzed in more detail. Then, key studies that focused on market trading under China’s new power system were picked out for further discussion. New studies were searched for that pertained to new energy mechanisms and bidding, the transition from coal-fired power, flexible resources and the technical applications of simulations. The quantitative analysis supports the construction of a basic paradigm for the study of power markets that is suitable for the new power system. Finally, the theoretical basis and application suggestions for power market simulations are introduced. This study summarized the existing research on the power market and further explored the key issues relating to the power market as it adapts to the NPS, hoping to inspire better research into China’s power sector, and promote safe, low-carbon, and sustainable development in China’s power industry.
Pairing Co-Creation with Food and Wine Experiences—A Holistic Perspective of Tourist Experiences in Dão, a Portuguese Wine Region
Authors: Carvalho Mariana , Kastenholz Elisabeth , Carneiro Maria João
Published: December 2021

The literature increasingly recognises the value of food and wine tourism for destinations’ competitiveness. Given the scarcity of conceptual and empirical studies on co-creation within this field of special interest tourism, this paper aims to enhance the understanding of how visitors and supply agents co-create value in food and wine experiences, by analysing such experiences in the Portuguese wine region Dão. For this purpose, a qualitative study was undertaken, analysing visitors’ and tourism agents’ perceptions regarding five food and wine experiences: food and wine pairing, wine tasting with food pairing, harvesting, a culinary workshop and a wine workshop. The discourse obtained via in-depth semi-structured interviews from sixteen visitors and three supply agents was content analysed, supported by QSR NVivo 12. The results show that dimensions of the conceptually defined co-creation experience were, indeed, perceived in the visitors’ discourse, namely interaction (the most prominent in wine tasting and harvesting), active participation, engagement and personalization (the latter least reported). Sensorial engagement emerged from the discourse as an additional dimension that deserved attention. The agents’ perspective confirmed the importance of these dimensions in experience design. This paper identifies theoretical and managerial contributions for destination management organisations, wine tourism agents and marketers, as well as relevant paths for future research in this field.
Sustainable Circular Micro Index for Evaluating Virtual Substitution Using Machine Learning with the Path Planning Problem as a Case Study
Authors: Maldonado-Romo Javier , Aldape-Pérez Mario
Published: December 2021

Due to the problems resulting from the COVID-19 pandemic, for example, semiconductor supply shortages impacting the technology industry, micro-, small-, and medium-sized enterprises have been affected because the profitability of their business models depends on market stability. Therefore, it is essential to propose alternatives to mitigate the various consequences, such as the high costs. One attractive alternative is to replace the physical elements using resource-limited devices powered by machine learning. Novel features can improve the embedded devices’ (such as old smartphones) ability to perceive an environment and be incorporated in a circular model. However, it is essential to measure the impact of substituting the physical elements employing an approach of a sustainable circular economy. For this reason, this paper proposes a sustainable circular index to measure the impact of the substitution of a physical element by virtualization. The index is composed of five dimensions: economic, social, environmental, circular, and performance. In order to describe this index, a case study was employed to measure the path-planning generator for micro aerial vehicles developed using virtual simulation using machine-learning methods. The proposed index allows considering virtualization to extend the life cycle of devices with limited resources based on suggested criteria. Thus, a smartphone and the Jetson nano board were analyzed as replacements of specialized sensors in controlled environments.
Fermentation of Washed Rice Water Increases Beneficial Plant Bacterial Population and Nutrient Concentrations
Authors: Nabayi Abba , Sung Christopher Teh Boon , Zuan Ali Tan Kee , Paing Tan Ngai
Published: December 2021

Washed rice water (WRW) is said to be a beneficial plant fertilizer because of its nutrient content. However, rigorous scientific studies to ascertain its efficiency are lacking. The purpose of this study was to determine the effect of fermenting WRW on the bacterial population and identification, and to measure how fermentation affects the nutrient composition of WRW. Rice grains were washed in a volumetric water-to-rice ratio of 3:1 and at a constant speed of 80 rpm for all treatments. The treatments were WRW fermented at 0 (unfermented), 3, 6, and 9 days. Bacterial N fixation and P and K solubilization abilities in the fermented WRW were assessed both qualitatively and quantitatively. The isolated bacterial strains and the WRW samples were also tested for catalase and indole acetic acid (IAA) production ability. Significantly greater N fixation, P and K solubilization, and IAA production were recorded after 3 days of fermentation compared with other fermentation periods, with increases of 46.9–83.3%, 48.2–84.1%, 73.7–83.6%, and 13.3–85.5%, respectively, in addition to the highest (2.12 × 108 CFU mL−1) total bacterial population. Twelve bacteria strains were isolated from the fermented WRW, and the gene identification showed the presence of beneficial bacteria Bacillus velezensis, Enterobacter spp., Pantoea agglomerans, Klebsiella pneumoniae and Stenotrophomonas maltophilia at the different fermentation periods. All the identified microbes (except Enterobacter sp. Strain WRW-7) were positive for catalase production. Similarly, all the microbes could produce IAA, with Enterobacter spp. strain WRW-10 recording the highest IAA of up to 73.7% higher than other strains. Generally, with increasing fermentation periods, the nutrients N, S, P, K, Mg, NH4+, and NO3− increased, while pH, C, and Cu decreased. Therefore, fermentation of WRW can potentially increase plant growth and enhance soil health because of WRW’s nutrients and microbial promotional effect, particularly after 3 days of fermentation.
A GIS-Based Approach for the Quantitative Assessment of Soil Quality and Sustainable Agriculture
Authors: Abdellatif Mostafa A. , El Baroudy Ahmed A. , Arshad Muhammad , Mahmoud Esawy K. , Saleh Ahmed M. , Moghanm Farahat S. , Shaltout Kamal H. , Eid Ebrahem M. , Shokr Mohamed S.
Published: December 2021

Assessing soil quality is considered one the most important indicators to ensure planned and sustainable use of agricultural lands according to their potential. The current study was carried out to develop a spatial model for the assessment of soil quality, based on four main quality indices, Fertility Index (FI), Physical Index (PI), Chemical Index (CI), and Geomorphologic Index (GI), as well as the Geographic Information System (GIS) and remote sensing data (RS). In addition to the GI, the Normalized Difference Vegetation Index (NDVI) parameter were added to assess soil quality in the study area (western part of Matrouh Governorate, Egypt) as accurately as possible. The study area suffers from a lack of awareness of agriculture practices, and it depends on seasonal rain for cultivation. Thus, it is very important to assess soil quality to deliver valuable data to decision makers and regional governments to find the best ways to improve soil quality and overcome the food security problem. We integrated a Digital Elevation Model (DEM) with Sentinel-2 satellite images to extract landform units of the study area. Forty-eight soil profiles were created to represent identified geomorphic units of the investigated area. We used the model builder function and a geostatistical approach based on ordinary kriging interpolation to map the soil quality index of the study area and categorize it into different classes. The soil quality (SQ) of the study area, classified into four classes (i.e., high quality (SQ2), moderate quality (SQ3), low quality (SQ4), and very low quality (SQ5)), occupied 0.90%, 21.87%, 22.22%, and 49.23% of the total study area, respectively. In addition, 5.74% of the study area was classified as uncultivated area as a reference. The developed soil quality model (DSQM) shows substantial agreement (0.67) with the weighted additive model, according to kappa coefficient statics, and significantly correlated with land capability R2 (0.71). Hence, the model provides a full overview of SQ in the study area and can easily be implemented in similar environments to identify soil quality challenges and fight the negative factors that influence SQ, in addition to achieving environmental sustainability.
Examining Teachers’ Perspectives on School Principals’ Digital Leadership Roles and Technology Capabilities during the COVID-19 Pandemic
Authors: Karakose Turgut , Polat Hakan , Papadakis Stamatios
Published: December 2021

The current study investigates the perspectives and experiences of teachers regarding their school principal’s digital leadership roles and technology capabilities during the COVID-19 pandemic. The research was conducted using a case study-based qualitative approach, and with a study group consisting of 89 teachers holding a Master’s degree. Maximum diversity sampling, one of the purposive sampling methods, was preferred in the determination of the study group, and the data obtained from the research were analyzed through content analysis. The five main themes determined based on the perceptions and experiences of the participants are listed as: “Digital technology usage, support for the digital transformation, support for technology-based professional development, support for digital learning culture, and digital leadership skills”. The results of the research revealed that the level of use of digital technologies by school principals during the COVID-19 pandemic was perceived as adequate by teachers. In addition, it was determined that school principals support digital transformation and technology-based professional development in schools. Furthermore, within the scope of the research, it was determined that school principals contribute to the construction of a digital learning culture in schools. The results of the study revealed that school principals’ digital leadership skills were clustered under three categories: technology use, managerial skills, and individual skills. As a result, in order to realize digital transformation within the context of K-12 education, school principals must first demonstrate their digital leadership and actively support the establishment of a digital learning culture in their schools.
The Valorization of Biolignin from Esparto Grass (Stipa tenacissima L.) Produced by Green Process CIMV (Compagnie Industrielle de la Matière Végétale) for Fertilization of Algerian Degraded Soil: Impact on the Physicochemical and Biological Properties
Authors: Bendouma Amal , Houyou Zohra , Gherib Abdelaziz , Gouzi Hicham
Published: December 2021

This study proposes a new use for a paper industry waste material, lignin, in agriculture and agronomy as a fertilizer for arid soils, while following a strategy aiming to both increase the amount of organic matter in these soils and decrease the impact of pollution caused by industrial discharges that contain organic and/or inorganic pollutants generated by the paper industry. In fact, this method works to improve soil quality through a new carbon-rich bioorganic fertilizer (biolignin) that results from a green method called CIMV, a targeted depollution objective of the paper industry. Over the course of 180 days, we monitored the physicochemical and biological characteristics of degraded soils treated with three different biolignin treatments of 0 (D0), 2 (D1), and 4 (D2) g/kg. The humification was then evaluated by the equation E4/E6. A remarkable variation of the physicochemical and biological parameters was observed in D1 and D2: temperature 12–38 °C, humidity 9–29%, and pH 7.06–8.73. The C/N ratio decreased from 266 to 49. After 180 days, the improvement in soil carbon content for the three treatments D0, D1, and D2 was 14%, 19%, and 24%, respectively. A maximum bacterial biomass of 152 (CFU/g soil) was observed on the 30th day for D1. Maximum laccase activity for D2 was observed on the 120th day. D1 and D2 recorded a significant degree of humification compared to D0. The best indicator of humification E4/E6 was observed in D1, where the value reached 2.66 at the end of the treatment period. The D2 treatment showed a remarkable effect improving the fertility of the degraded soil, which confirms that biolignin is a good fertilizer.
Waste Strategies Development in the Framework of Circular Economy
Authors: Zorpas Antonis A. , Doula Maria K. , Jeguirim Mejdi
Published: December 2021

Current studies proposed that more that 2bn tons of solid waste/year are produced globally [...]
The Impact of Succession Management on Small and Medium Enterprises’ Sustainability in Lagos State, Nigeria
Authors: Monyei Francis Ezieshi , Ukpere Wilfred I. , Agbaeze Emmanuel Kalu , Omonona Solomon , Kelvin-Iloafu Lovlyn Ekeowa , Obi-Anike Happiness Ozioma
Published: December 2021

This study investigated the impact of succession management on small and medium enterprises’ (SMEs’) sustainability in Lagos, Nigeria. The study adopted a descriptive survey research method and its participants were selected from five SMEs across Lagos State, based on their number of years of operating, staff strength, and branch locations. The enterprises included Gnakk Enterprises, Dreamville School, Vitus Cosmetics, Osi Ventures, and Chijioke Frozen Foods. The Pearson product moment correlation coefficient was used to analyze the hypotheses, which revealed that succession management had a positive correlation (r = 0.934, p-value < 0.05) with corporate sustainability amongst SMEs in Lagos State, Nigeria. Hence, the study concluded that in respect of strategic recruitment, succession management attracts and assists to retain a high performing skilled labour force.
Could Government Data Openness Enhance Urban Innovation Capability? An Evaluation Based on Multistage DID Method
Authors: Luo Yi , Tang Zhiwei , Fan Peiqi
Published: December 2021

The wave of government data opening has gradually swept the world since it rose from the United States in 2009. The purpose is not to open government data, but to release data value and drive economic and social development through data accessibility. At present, the impact of academic circles on government open data mostly stays in theoretical discussion, especially due to the lack of empirical tests. Using the multistage difference-in-difference (DID) model, this paper analyzes the panel data from 2009 to 2016 by taking two batches of Chinese cities with open data released in 2014 and 2105 as samples to test the impact of government data opening on urban innovation ability. The results show that the opening of government data significantly improves urban innovation abilities. After considering the heterogeneity and fixed effects of urban characteristics, the opening of government data still significantly improves urban innovation ability and shows a greater innovation driving role in cities with high levels of economic development, human capital, and infrastructure. Based on this, this paper believes that we should continue to promote the opening of government data, release the value of data, and pay attention to the Matthew effect between cities that may appear in the era of big data.
Compressive Strength of Sustainable Geopolymer Concrete Composites: A State-of-the-Art Review
Authors: Ahmed Hemn Unis , Mohammed Azad A. , Rafiq Serwan , Mohammed Ahmed S. , Mosavi Amir , Sor Nadhim Hamah , Qaidi Shaker M. A.
Published: December 2021

The building industry, which emits a significant quantity of greenhouse gases, is under tremendous pressure due to global climate change and its consequences for communities. Given the environmental issues associated with cement production, geopolymer concrete has emerged as a sustainable construction material. Geopolymer concrete is an eco-friendly construction material that uses industrial or agricultural by-product ashes as the principal binder instead of Portland cement. Fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin, and palm oil fuel ash were all employed as binders in geopolymer concrete, with fly ash being the most frequent. The most important engineering property for all types of concrete composites, including geopolymer concrete, is the compressive strength. It is influenced by different parameters such as the chemical composition of the binder materials, alkaline liquid to binder ratio, extra water content, superplasticizers dosages, binder content, fine and coarse aggregate content, sodium hydroxide and sodium silicate content, the ratio of sodium silicate to sodium hydroxide, the concentration of sodium hydroxide (molarity), curing temperature, curing durations inside oven, and specimen ages. In order to demonstrate the effects of these varied parameters on the compressive strength of the fly ash-based geopolymer concrete, a comprehensive dataset of 800 samples was gathered and analyzed. According to the findings, the curing temperature, sodium silicate content, and alkaline solution to binder ratio are the most significant independent parameters influencing the compressive strength of the fly ash-based geopolymer concrete (FA-BGPC) composites.
Xylan Hemicellulose: A Renewable Material with Potential Properties for Food Packaging Applications
Authors: Nechita Petronela , Mirela Roman , Ciolacu Florin
Published: December 2021

Xylan hemicelluloses are considered the second most abundant class of polysaccharides after cellulose which has good natural barrier properties necessary for foods packaging papers and films. Xylan exists today as a natural polymer, but its utilisation in packaging applications is limited and not sufficiently analysed. In this study, the performances of hardwood xylan hemicellulose in forming uniform films and as biopolymer for paper coatings were analysed. The xylan-coated paper and film samples were tested regarding their water, air, and water vapour permeability, water solubility, mechanical strength, and antimicrobial activity against pathogenic bacteria. Structural analyses of xylan hemicelluloses emphasised a high number of hydroxyl groups with high water affinity. This affects the functional properties of xylan-coated papers but can facilitate the chemical modification of xylan in order to improve their hydrophobic properties and extend their areas of application. The obtained results unveil a promising starting point for using this material in food packaging applications as a competitive and sustainable alternative to petroleum-based polymers.
Flood-Prone Area Delineation in Urban Subbasins Based on Stream Ordering: Culiacan Urban Basin as a Study Case
Authors: Sanhouse-García Antonio J. , Rangel-Peraza Jesús Gabriel , Rentería-Guevara Sergio A. , Bustos-Terrones Yaneth A. , Mora-Félix Zuriel D. , Plata-Rocha Wenseslao , Monjardin-Armenta Sergio Alberto
Published: December 2021

Urban development decreases infiltration, increases the runoff velocity, and reduces the concentration times. This situation increases the flood risk in urban watersheds, which represent a management challenge for urban communities and authorities. To increase the resilience of communities due to modifications of the hydrological cycle produced by climate change and urban development, a methodology is proposed to delineate flood-prone areas in urban basins. This methodology is implemented in an urban subbasin of Culiacan, Mexico, and is based on stream order. A high-resolution digital elevation model was used, which was validated independently through a photogrammetric flight with an unmanned aerial vehicle and ground control points obtained with GNSS (global navigation satellite systems) receivers. Morphometric parameters related to geometry, shape, relief, and drainage network aspects of the subbasin were determined and analyzed. Then, flood-prone area zonation was carried out based on stream-order classification and flow direction. Fieldwork was also carried out for the inspection of the sewage network conditions. This methodology simplifies the identification of the flood-prone areas in urban subbasins without carrying out complex hydraulic calculations.