bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Tannin Inclusion on the Enhancement of Rumen Undegradable Protein of Different Protein Sources
Authors: Loregian Kalista E. , Pereira David A. B. , Rigon Fernanda , Magnani Elaine , Marcondes Marcos I. , Baumel Eduardo A. , Branco Renata H. , Del Bianco Benedeti Pedro , Paula Eduardo M.
Published: November 2023

Tannins can be utilized to increase rumen undegradable protein (RUP) by their capacity to form complexes with diverse nutrients present in the feed. In that regard, high-performance ruminants demand elevated RUP levels. The objective of this study was to evaluate the effects of incorporating varying levels of tannin into three protein sources (cottonseed, peanut, and soybean meals) on ruminal kinetic parameters, ruminal fermentation, and intestinal digestibility. Thus, three in situ experiments were conducted to investigate the ruminal degradation kinetics, where Fraction A represents the soluble portion, Fraction B relates to the portion potentially degraded in the rumen, and kd denotes the degradation rate of Fraction B, for both dry matter (DM) and crude protein (CP) in the rumen. Additionally, the study assessed dry matter effective degradability (ED), rumen undegradable protein (RUP), and intestinal digestibility (ID). These experiments utilized three cannulated animals for the in situ incubations. Regarding cottonseed meal in terms of DM degradation kinetics, tannin inclusion had a quadratic effect on fraction A (p < 0.01), B (p = 0.10, trend), kd (p = 0.03), and ED (p < 0.01). Fraction A of CP had a cubic effect (p = 0.03), being greater for the control compared with the other treatments. The inclusion of tannin linearly increased RUP (p < 0.01). The RUP proportion increased 29, 33, and 45% when 20, 40, and 60 g/kg tannin were used, respectively, compared to the control. For peanut meal, the A fraction of protein and RUP responded quadratically as tannin was included in peanut meal (p < 0.01). However, tannin levels did not affect fraction B of protein and ID. Regarding soybean meal, fractions A and B of DM and ED had cubic effects (p < 0.01), being greater for the control compared with the other treatments, and responded quadratically as tannin increased. Also, tannin inclusion had a cubic effect on fractions A and B of protein, RUP, and ID (p < 0.01). The cubic behavior showed greater B fraction and ID and lower A fraction and RUP for the control compared other treatments (p < 0.01). Tannins offer a promising avenue for elevating RUP levels in diets featuring cottonseed and peanut meals. Nevertheless, no advantages were observed when treating soybean meal with tannin.
Physical Exertion Recognition Using Surface Electromyography and Inertial Measurements for Occupational Ergonomics
Authors: Concha-Pérez Elsa , Gonzalez-Hernandez Hugo G. , Reyes-Avendaño Jorge A.
Published: November 2023

By observing the actions taken by operators, it is possible to determine the risk level of a work task. One method for achieving this is the recognition of human activity using biosignals and inertial measurements provided to a machine learning algorithm performing such recognition. The aim of this research is to propose a method to automatically recognize physical exertion and reduce noise as much as possible towards the automation of the Job Strain Index (JSI) assessment by using a motion capture wearable device (MindRove armband) and training a quadratic support vector machine (QSVM) model, which is responsible for predicting the exertion depending on the patterns identified. The highest accuracy of the QSVM model was 95.7%, which was achieved by filtering the data, removing outliers and offsets, and performing zero calibration; in addition, EMG signals were normalized. It was determined that, given the job strain index’s purpose, physical exertion detection is crucial to computing its intensity in future work.
Microwave Resonators for Wearable Sensors Design: A Systematic Review
Authors: Royo Iris , Fernández-García Raúl , Gil Ignacio
Published: November 2023

The field of flexible electronics is undergoing an exponential evolution due to the demand of the industry for wearable devices, wireless communication devices and networks, healthcare sensing devices and the technology around the Internet of Things (IoT) framework. E-tex tiles are attracting attention from within the healthcare areas, amongst others, for providing the possibility of developing continuous patient monitoring solutions and customized devices to accommodate each patient’s specific needs. This review paper summarizes multiple approaches investigated in the literature for wearable/flexible resonators working as antenna-based systems, sensors and filters with special attention paid to the integration to flexible materials, especially textiles. This review manuscript provides a general overview of the flexible resonators’ advantages and drawbacks, materials, fabrication techniques and processes and applications. Finally, the main challenges and future prospects of wearable resonators are discussed.
Driving Sustainability: Carbon Footprint, 3D Printing, and Legislation concerning Electric and Autonomous Vehicles
Authors: Jovanović Mihailo , Mateo Sanguino Tomás de J. , Damjanović Milanko , Đukanović Milena , Thomopoulos Nikolas
Published: November 2023

In recent years, there has been a remarkable development in the technology and legislation related to electric and autonomous vehicles (i.e., EVs/AVs). This technological advancement requires the deployment of the most up-to-date supporting infrastructure to achieve safe operation. Further infrastructure is needed for Level 5 vehicles, namely the introduction of super-fast wireless 5G technology. To achieve harmony between the rapid technological advancement of EVs/AVs and environmental preservation, enacting legislation related to their sustainable use is vital. Thus, this manuscript provides a review of the technological development of EVs/AVs, with a special focus on carbon footprints and the implementation of additive manufacturing using recycled materials. While EVs have a 12.13% increased carbon footprint compared to conventional vehicles, AVs with basic and advanced intelligence features have an increased carbon footprint of 41.43% and 99.65%, respectively. This article emphasizes that the integration of 3D-printed components has the potential to offset this impact with a substantial 60% reduction. As a result, custom-made solutions involving 3D printing are explored, leading to greater speed, customization, and cost-effectiveness for EVs/AVs. This article also lists the advantages and disadvantages of the existing legislation in Spain, the United Kingdom, and the western Balkans, demonstrating various approaches to promoting electric mobility and the development of autonomous vehicles. In Spain, initiatives like the MOVES program incentivize EV adoption, while the UK focuses on expanding the EV market and addressing concerns about EVs’ quiet operation. In the western Balkans, the adoption of legislation lags behind, with limited incentives and infrastructure for EVs. To boost sales, legal mechanisms are necessary to reduce costs and improve accessibility, in addition to offering subsidies for the purchase of EVs. To this end, an analysis of the incentive measures proposed for the development and use of renewable power sources for the supply of energy for EVs/AVs is presented.
NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States
Authors: He Qinghai , Zhang Haowen , Li Tianhua , Zhang Xiaojia , Li Xiaoli , Dong Chunwang
Published: November 2023

Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision agriculture in tea plantations. In recent years, spectral data have become an important tool for the non-destructive testing of soil physicochemical properties. In this study, a support vector regression (SVR) model was constructed to model the hydrolyzed nitrogen, available potassium, and effective phosphorus in tea plantation soils of different grain sizes. Then, the successful projections algorithm (SPA) and least-angle regression (LAR) and bootstrapping soft shrinkage (BOSS) variable importance screening methods were used to optimize the variables in the soil physicochemical properties. The findings demonstrated that soil particle sizes of 0.25–0.5 mm produced the best predictions for all three physicochemical properties. After further using the dimensionality reduction approach, the LAR algorithm (R2C = 0.979, R2P = 0.976, RPD = 6.613) performed optimally in the prediction model for hydrolytic nitrogen at a soil particle size of 0.25~0.5. The models using data dimensionality reduction and those that used the BOSS method to estimate available potassium (R2C = 0.977, R2P = 0.981, RPD = 7.222) and effective phosphorus (R2C = 0.969, R2P = 0.964, RPD = 5.163) had the best accuracy. In order to offer a reference for the accurate detection of soil physicochemical properties in tea plantations, this study investigated the modeling effect of each physicochemical property under various soil particle sizes and integrated the regression model with various downscaling strategies.
A CMOS Image Sensor Dark Current Compensation Using In-Pixel Temperature Sensors
Authors: Abarca Accel , Theuwissen Albert
Published: November 2023

This paper presents a novel technique for dark current compensation of a CMOS image sensor (CIS) by using in-pixel temperature sensors (IPTSs) over a temperature range from −40 °C to 90 °C. The IPTS makes use of the 4T pixel as a temperature sensor. Thus, the 4T pixel has a double functionality, either as a pixel or as a temperature sensor. Therefore, the dark current compensation can be carried out locally by generating an artificial dark reference frame from the temperature measurements of the IPTSs and the temperature behavior of the dark current (previously calibrated). The artificial dark current frame is subtracted from the actual images to reduce/cancel the dark signal level of the pictures. In a temperature range from −40 °C to 90 °C, results show that the temperature sensors have an average temperature coefficient (TC) of 1.15 mV/°C with an inaccuracy of ±0.55 °C. Parameters such as conversion gain, gain of the amplifier, and ADC performance have been analyzed over temperature. The dark signal can be compensated in the order of 80% in its median value, and the nonuniformity is reduced in the order of 55%.
Hierarchical Controlled Hybrid Quantum Communication Based on Six-Qubit Entangled States in IoT
Authors: Hua Xiaoyu , Li Dongfen , Fu You , Zhu Yonghao , Jiang Yangyang , Zhou Jie , Yang Xiaolong , Tan Yuqiao
Published: November 2023

The rapid development and extensive application of the Internet of Things (IoT) have brought new challenges and opportunities to the field of communication. By integrating quantum secure communication with the IoT, we can provide a higher level of security and privacy protection to counteract security threats in the IoT. In this paper, a hybrid quantum communication scheme using six-qubit entangled states as a channel is proposed for specific IoT application scenarios. This scheme achieves hierarchical control of communication protocols on a single quantum channel. In the proposed scheme, device A transmits data to device B through quantum teleportation, while device B issues control commands to device A through remote quantum state preparation technology. These two tasks are controlled by control nodes C and D, respectively. The transmission of information from device A to device B is a relatively less important task, which can be solely controlled by control node C. On the other hand, issuing control commands from device B to device A is a more crucial task requiring joint control from control nodes C and D. This paper describes the proposed scheme and conducts simulation experiments using IBM’s Qiskit Aer quantum computing simulator. The results demonstrate that the fidelity of the quantum teleportation protocol (QTP) and the remote state preparation protocol (RSP) reach an impressive value of 0.999, fully validating the scheme’s feasibility. Furthermore, the factors affecting the fidelity of the hybrid communication protocol in an IoT environment with specific quantum noise are analyzed. By combining the security of quantum communication with the application scenarios of the IoT, this paper presents a new possibility for IoT communication.
Research on Recognition of Coal and Gangue Based on Laser Speckle Images
Authors: Li Hequn , Wang Qiong , Ling Ling , Lv Ziqi , Liu Yun , Jiao Mingxing
Published: November 2023

Coal gangue image recognition is a critical technology for achieving automatic separation in coal processing, characterized by its rapid, environmentally friendly, and energy-saving nature. However, the response characteristics of coal and gangue vary greatly under different illuminance conditions, which poses challenges to the stability of feature extraction and recognition, especially when strict illuminance requirements are necessary. This leads to fluctuating coal gangue recognition accuracy in industrial environments. To address these issues and improve the accuracy and stability of image recognition under variable illuminance conditions, we propose a novel coal gangue recognition method based on laser speckle images. Firstly, we studied the inter-class separability and intra-class compactness of the collected laser speckle images of coal and gangue by extracting gray and texture features from the laser speckle images, and analyzed the performance of laser speckle images in representing the differences between coal and gangue minerals. Subsequently, coal gangue recognition was achieved using an SVM classifier based on the extracted features from the laser speckle images. The fusion feature approach achieved a recognition accuracy of 94.4%, providing further evidence of the feasibility of this method. Lastly, we conducted a comparative experiment between natural images and laser speckle images for coal gangue recognition using the same features. The average accuracy of coal gangue laser speckle image recognition under various lighting conditions is 96.7%, with a standard deviation of the recognition accuracy of 1.7%. This significantly surpasses the recognition accuracy obtained from natural coal and gangue images. The results showed that the proposed laser speckle image features can facilitate more stable coal gangue recognition with illumination factors, providing a new, reliable method for achieving accurate classification of coal and gangue in the industrial environment of mines.
User Perspectives of Geriatric German Patients on Smart Sensor Technology in Healthcare
Authors: Orzechowski Marcin , Skuban-Eiseler Tobias , Ajlani Anna , Lindemann Ulrich , Klenk Jochen , Steger Florian
Published: November 2023

With consideration of the progressing aging of our societies, the introduction of smart sensor technology can contribute to the improvement of healthcare for older patients and to reductions of the costs of care. From the clinical and medico-ethical points of view, the advantages of smart sensor technology are copious. However, any ethical evaluation of an introduction of a new technology in medical practice requires an inclusion of patients’ perspectives and their assessments. We have conducted qualitative, semi-structured, exploratory interviews with 11 older patients in order to gain their subjective opinions on the use of smart sensor devices for rehabilitation purposes. The interviews were analyzed using methods of qualitative content and thematic analyses. In our analysis, we have focused on ethical aspects of adoption of this technology in clinical practice. Most of the interviewees expressed their trust in this technology, foremost because of its accuracy. Several respondents stated apprehension that the use of smart sensors will lead to a change in the patient–healthcare professional relationship. Regarding costs of introduction of smart sensors into healthcare, interviewees were divided between health insurance bearing the costs and individual participation in corresponding costs. Most interviewees had no concerns about the protection of their privacy or personal information. Considering these results, improvement of users’ technology literacy regarding possible threats connected with putting smart sensors into clinical practice is a precondition to any individual application of smart sensors. This should occur in the form of extended and well-designed patient information adapted to individual levels of understanding. Moreover, application of smart sensors needs to be accompanied with careful anamnesis of patient’s needs, life goals, capabilities, and concerns.
VEPdgets: Towards Richer Interaction Elements Based on Visually Evoked Potentials
Authors: Wolf Philipp , Götzelmann Timo
Published: November 2023

For brain–computer interfaces, a variety of technologies and applications already exist. However, current approaches use visual evoked potentials (VEP) only as action triggers or in combination with other input technologies. This paper shows that the losing visually evoked potentials after looking away from a stimulus is a reliable temporal parameter. The associated latency can be used to control time-varying variables using the VEP. In this context, we introduced VEP interaction elements (VEP widgets) for a value input of numbers, which can be applied in various ways and is purely based on VEP technology. We carried out a user study in a desktop as well as in a virtual reality setting. The results for both settings showed that the temporal control approach using latency correction could be applied to the input of values using the proposed VEP widgets. Even though value input is not very accurate under untrained conditions, users could input numerical values. Our concept of applying latency correction to VEP widgets is not limited to the input of numbers.
Instance Segmentation and Ensemble Learning for Automatic Temperature Detection in Multiparous Sows
Authors: Xue Hongxiang , Shen Mingxia , Sun Yuwen , Tian Haonan , Liu Zihao , Chen Jinxin , Xu Peiquan
Published: November 2023

The core body temperature serves as a pivotal physiological metric indicative of sow health, with rectal thermometry prevailing as a prevalent method for estimating core body temperature within sow farms. Nonetheless, employing contact thermometers for rectal temperature measurement proves to be time-intensive, labor-demanding, and hygienically suboptimal. Addressing the issues of minimal automation and temperature measurement accuracy in sow temperature monitoring, this study introduces an automatic temperature monitoring method for sows, utilizing a segmentation network amalgamating YOLOv5s and DeepLabv3+, complemented by an adaptive genetic algorithm-random forest (AGA-RF) regression algorithm. In developing the sow vulva segmenter, YOLOv5s was synergized with DeepLabv3+, and the CBAM attention mechanism and MobileNetv2 network were incorporated to ensure precise localization and expedited segmentation of the vulva region. Within the temperature prediction module, an optimized regression algorithm derived from the random forest algorithm facilitated the construction of a temperature inversion model, predicated upon environmental parameters and vulva temperature, for the rectal temperature prediction in sows. Testing revealed that vulvar segmentation IoU was 91.50%, while the predicted MSE, MAE, and R2 for rectal temperature were 0.114 °C, 0.191 °C, and 0.845, respectively. The automatic sow temperature monitoring method proposed herein demonstrates substantial reliability and practicality, facilitating an autonomous sow temperature monitoring.
A Method for Point Cloud Accuracy Analysis Based on Intensity Information
Authors: Li Siyuan , Zheng Dehua , Yue Dongjie , Hu Chuang , Ma Xinjiang
Published: November 2023

Three-dimensional laser scanning has emerged as a prevalent measurement method in numerous high-precision applications, and the precision of the obtained data is closely related to the intensity information. Comprehending the association between intensity and point cloud accuracy facilitates scanner performance assessment, optimization of data acquisition strategies, and evaluation of point cloud precision, thereby ensuring data reliability for high-precision applications. In this study, we investigated the correlation between point cloud accuracy and two distinct types of intensity information. In addition, we presented methods for assessing point cloud accuracy using these two forms of intensity information, along with their applicable scopes. By examining the percentage intensity, we analyzed the reflectance properties of the scanned object’s surface employing the Lambertian model. Our findings indicate that the Lambertian circle fitting radius is inversely correlated with the scanner’s ranging error at a constant scanning distance. Experimental outcomes substantiate that modifying the surface characteristics of the object enables the attainment of higher-precision point cloud data. By constructing a model associating the raw reflectance intensity with ranging errors, we developed a single-point error ellipsoid model to assess the accuracy of individual points within the point cloud. The experiments revealed that the ranging error model based on the raw intensity is solely applicable to point cloud data unaffected by specular reflectance properties. Moreover, the devised single-point error ellipsoid model accurately evaluates the measurement error of individual points. Both analytical methods can be utilized to evaluate the performance of the scanner as well as the accuracy of the acquired point cloud data, providing reliable data support for various high-precision applications.
Tunnel Crack Detection Method and Crack Image Processing Algorithm Based on Improved Retinex and Deep Learning
Authors: Wu Jie , Zhang Xiaoqian
Published: November 2023

Tunnel cracks are the main factors that cause damage and collapse of tunnel structures. How to detect tunnel cracks efficiently and avoid safety accidents caused by tunnel cracks effectively is a research hotspot at present. In order to meet the need for efficient detection of tunnel cracks, the tunnel crack detection method based on improved Retinex and deep learning is proposed in this paper. The tunnel crack images collected by optical imaging equipment are used to improve the contrast information of tunnel crack images using the image enhancement algorithm, and this image enhancement algorithm has the function of multi-scale Retinex decomposition with improved central filtering. An improved VGG19 network model is constructed to achieve efficient segmentation of tunnel crack images through deep learning methods and then form the segmented binary image. The Zhang–Suen fast parallel-thinning method is used to obtain the skeleton map of the single-layer pixel, and the length and width information of the tunnel cracks are obtained. The feasibility and effectiveness of the proposed method are verified by experiments. Compared with other methods in the literature, the maximum deviation in the length of the tunnel crack is about 5 mm, and the maximum deviation in the width of the tunnel crack is about 0.8 mm. The experimental results show that the proposed method has a shorter detection time and higher detection accuracy. The research results of this paper can provide a strong basis for the health evaluation of tunnels.
Correction: Lagrois et al. Low-to-Mid-Frequency Monopole Source Levels of Underwater Noise from Small Recreational Vessels in the St. Lawrence Estuary Beluga Critical Habitat. Sensors 2023, 23, 1674
Authors: Lagrois Dominic , Kowalski Camille , Sénécal Jean-François , Martins Cristiane C. A. , Chion Clément
Published: November 2023

There was an error in the original publication [...]
Research and Implementation of a Demodulation Switch Signal Phase Alignment System in Dynamic Environments
Authors: Xue Ke , Yu Tao , Sui Yanlin , Chen Yongkun , Wang Longqi , Wang Zhi , Zhou Jun , Chen Yuzhu , Liu Xin
Published: November 2023

In the space gravitational wave detection mission, inertial sensors play the role of providing an inertial reference for the laser interferometric measurement system. Among them, the capacitance sensor serves as the core key technology of the inertial sensor, used to measure the relative position of the test mass (TM) in the electrode cage. The capacitance sensor utilizes synchronous demodulation technology to extract signals from the AC induction signal. When the phase of the demodulation switch signal is aligned, the synchronous demodulator can most effectively filter out noise, thus directly influencing the performance of the capacitance sensor. However, since the TM is in a suspended state, the information read by the capacitance sensor is dynamic, which increases the difficulty of demodulation phase alignment. In light of this, a method is proposed for achieving the phase alignment of the demodulation switch signal in a dynamic environment. This is accomplished by adjusting the phase of the demodulation switch signal, and subsequently computing the phase difference between the AC induction signal and the demodulation switch signal. At the same time, a measurement and evaluation method for phase deviation is also proposed. Ultimately, an automatic phase alignment system for the demodulation switch signal in dynamic environments is successfully implemented on an FPGA platform, and tests are conducted on a hexapod PI console platform to simulate dynamic environments. The experimental results demonstrate that the system accurately achieves phase alignment in the static environment, with a phase deviation of 0.1394 rad. In the simulated dynamic environment, the phase deviation is 0.1395 rad.
Experimental Analysis and Optimization Approach of Self-Clocked Rate Adaptation for Multimedia Congestion Control Algorithm in Emulated 5G Environment
Authors: Zubaydi Haider Dhia , Jagmagji Ahmed Samir , Molnár Sándor
Published: November 2023

The congestion problem has driven many researchers to address it, among other networking issues. In a packet-switched network, congestion is essential; it leads to a high response time to deliver packets due to heavy traffic, which eventually causes packet loss. Hence, congestion control mechanisms are utilized to prevent such cases. Several interesting algorithms are proposed to focus on this dilemma, such as the Self-Clocked Rate Adaptation for Multimedia (SCReAM) designed for interactive real-time video streaming applications. One of the main issues of SCReAM is the high design complexity due to the large size of its documentation and coding. Furthermore, there is a considerable number of parameters that can be adjusted to accomplish the desired performance. This study proposes a guided parameters’ tuning approach to assess and optimize the SCReAM algorithm in an emulated 5G environment through a detailed exploration of its parameters. The proposed approach consists of three phases, namely, the initialization phase, the standalone experimentation phase, and the hybrid experimentation phase. In the first phase, we illustrate the method of initializing and implementing the environment, followed by specifying the investigated parameters’ settings, testing, and validation. The second phase aims to investigate SCReAM parameters in isolation to identify the effect on the performance in relation to network queue delay, smoothed Round Trip Time (sRTT), and throughput. The final phase discusses the possibility of achieving the optimum performance by combining various sets to provide researchers with clear and explicit guidelines to establish an adequate SCReAM behavior for the desired application. To the best of our knowledge, this is the first study that proposes a preliminary and comprehensive analysis of the SCReAM algorithm. Based on the proposed approach, when L4S/ECN is disabled, we reduced the network queue delay by 63.36% and increased the network throughput by 48.6% as compared to the results generated by the original design. In L4S/ECN-enabled mode, the network queue delay is reduced by 16.17% while the network throughput increased by 93%.
A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm
Authors: Manasa B. M. R. , Pakala Venugopal , Chinthaginjala Ravikumar , Ayadi Manel , Hamdi Monia , Ksibi Amel
Published: November 2023

In wireless communication, multiple signals are utilized to receive and send information in the form of signals simultaneously. These signals consume little power and are usually inexpensive, with a high data rate during data transmission. An Multi Input Multi Output (MIMO) system uses numerous antennas to enhance the functionality of the system. Moreover, system intricacy and power utilization are difficult and highly complicated tasks to achieve in an Analog to Digital Converter (ADC) at the receiver side. An infinite number of MIMO channels are used in wireless networks to improve efficiency with Cross Entropy Optimization (CEO). ADC is a serious issue because the data of the accepted signal are completely lost. ADC is used in the MIMO channels to overcome the above issues, but it is very hard to implement and design. So, an efficient way to enhance the estimation of channels in the MIMO system is proposed in this paper with the utilization of the heuristic-based optimization technique. The main task of the implemented channel prediction framework is to predict the channel coefficient of the MIMO system at the transmitter side based on the receiver side error ratio, which is obtained from feedback information using a Hybrid Serial Cascaded Network (HSCN). Then, this multi-scaled cascaded autoencoder is combined with Long Short Term Memory (LSTM) with an attention mechanism. The parameters in the developed Hybrid Serial Cascaded Multi-scale Autoencoder and Attention LSTM are optimized using the developed Hybrid Revised Position-based Wild Horse and Energy Valley Optimizer (RP-WHEVO) algorithm for minimizing the “Root Mean Square Error (RMSE), Bit Error Rate (BER) and Mean Square Error (MSE)” of the estimated channel. Various experiments were carried out to analyze the accomplishment of the developed MIMO model. It was visible from the tests that the developed model enhanced the convergence rate and prediction performance along with a reduction in the computational costs.
Isolation and Phenotypic Microarray Profiling of Different Pseudomonas Strains Isolated from the Rhizosphere of Curcuma longa L.
Authors: Pathak Parul , Singh Monika , Naskar Ananya , Singh Sandeep Kumar , Bhardwaj Nikunj , Kumar Ajay
Published: November 2023

In the present study, different Pseudomonas strains were isolated from the rhizospheric soil of Curcuma longa (turmeric) and further identified and characterized based on morphological, biochemical, and molecular characteristics through the 16S rRNA gene sequencing analysis. The identified bacterial strains belong to the Pseudomonas genus viz. Pseudomonas sp. CL10, Pseudomonas sp. CL11, and P. fluorescence CLI4. However, the isolated strains tested positive for IAA production, siderophore production, and the solubilization of tricalcium phosphate during plant growth promoting traits analysis. Further phenotype microArray (PM) technology was used to evaluate the antibiotic and chemical sensitivity of the isolated bacterial strains. The antibiotics phleomycin, oxacillin, vancomycin, novobiocin, spiramycin, and rifampicin, as well as chemicals like, 5-7 dichloro-8-hydroxy quanaldine, 5-7 dichloro-8-hydroxyquinoline, domophenbrobide, and 3-5 dimethoxy benzyl alcohol, showed resistance in all the rhizobacterial strains. However, upon further detailed study, Pseudomonas sp. CL10 exhibited resistance to thirteen antibiotics, CL11 to fourteen, and CL14 showed resistance against seventeen antibiotics and chemical classes. The results of the study indicate that some of these strains can be used as bioinoculum to enhance the plant growth and health.
How to Shape Local Public Acceptance of Not-in-My-Backyard Infrastructures? A Social Cognitive Theory Perspective
Authors: Bao Wenling , Chen Yu , Cui Caiyun , Xia Bo , Ke Yongjian , Skitmore Martin , Liu Yong
Published: November 2023

Acceptance by the local public is the key determinant for the successful implementation of NIMBY (Not-In-My-Backyard) infrastructures and may be shaped in different ways among different infrastructure types. Based on social cognitive theory (SCT), this study clarifies the specific mechanism shaping local public acceptance of NIMBY facilities with two types of hazardous effects (i.e., pollution and psychological exclusion) using a large-scale questionnaire survey and structural equation model. The results reveal that, firstly, SCT provides a solid theoretical basis for exploring the mechanism under the joint action of environmental and personal factors. Secondly, it is verified that self-efficacy indirectly predicts local public acceptance by influencing perceived risk. The effect of the positive affect tag is mediated by perceived risk in shaping acceptance of polluting facilities but not of psychologically excluded facilities. In general, people tend to have a lower perceived risk, higher perceived benefit, stronger sense of self-efficacy, and more positive attitude when faced with the siting of psychologically excluded NIMBY facilities over polluting ones. These findings are helpful for planning and decision-making of NIMBY facilities with different types of hazardous impacts, reducing NIMBY conflicts and promoting the construction of NIMBY infrastructures. Furthermore, it contributes to the achievement of Sustainable Development Goal (SDG) 16 (promoting peaceful and inclusive societies for sustainable development) and (SDG) 11 (building inclusive, safe, resilient and sustainable cities and human settlements).
Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi
Authors: Ma Sheng , Chen Jian , Wu Saier , Li Yurou
Published: November 2023

Landslide susceptibility prediction (LSP) is the basis for risk management and plays an important role in social sustainability. However, the modeling process of LSP is constrained by various factors. This paper approaches the effect of landslide data integrity, machine-learning (ML) models, and non-landslide sample-selection methods on the accuracy of LSP, taking the Yinghu Lake Basin in Ankang City, Shaanxi Province, as an example. First, previous landslide inventory (totaling 46) and updated landslide inventory (totaling 46 + 176) were established through data collection, remote-sensing interpretation, and field investigation. With the slope unit as the mapping unit, twelve conditioning factors, including elevation, slope, aspect, topographic relief, elevation variation coefficient, slope structure, lithology, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), distance to road, distance to river, and rainfall were selected. Next, the initial landslide susceptibility mapping (LSM) was obtained using the K-means algorithm, and non-landslide samples were determined using two methods: random selection and semi-supervised machine learning (SSML). Finally, the random forest (RF) and artificial neural network (ANN) machine-learning methods were used for modeling. The research results showed the following: (1) The performance of supervised machine learning (SML) (RF, ANN) is generally superior to unsupervised machine learning (USML) (K-means). Specifically, RF in the SML model has the best prediction performance, followed by ANN. (2) The selection method of non-landslide samples has a significant impact on LSP, and the accuracy of the SSML-based non-landslide selection method is controlled by the ratio of the number of landslide samples to the number of mapping units. (3) The quantity of landslides has an impact on how reliably the results of LSM are obtained because fewer landslides result in a smaller sample size for LSM, which deviates from reality. Although the results in this dataset are satisfactory, the zoning results cannot reliably anticipate the recently added landslide data discovered by the interpretation of remote-sensing data and field research. We propose that the landslide inventory can be increased by remote sensing in order to achieve accurate and impartial LSM since the LSM of adequate landslide samples is more reasonable. The research results of this paper will provide a reference basis for uncertain analysis of LSP and regional landslide risk management.
Metaversity: Beyond Emerging Educational Technology
Authors: Laurens-Arredondo Luis Alberto , Laurens Lilibeth
Published: November 2023

The pandemic caused by the COVID-19 virus forced the virtualization of educational processes worldwide, which has generated a series of challenges in terms of teaching quality due to the improvisation of its implementation. However, the emergence of the metaverse as a new social scenario has opened new opportunities to overcome the problems inherited by education during this pandemic. The main objective is to explore the use of the metaverse in universities through the new concept of the metaversity. A methodology based on the socio-critical paradigm was applied. The theoretical bases around the metaverse are presented, and it is linked to the current university educational ecosystem through the metaversity. The main characteristics and scope of the metaversity are identified as well as the gaps present for the creation of the new learning ecosystem based on virtuality. The incipient character of the implementation of the metaversity was evidenced, as well as its main potentialities. The virtualization of all processes associated with higher education, caused by pandemic education, makes the implementation of the metaversity feasible, and it should therefore be part of the future research agenda.
Exploring the Impact of Potassium on Growth, Photosynthetic Performance, and Nutritional Status of Lemon Trees (cv. Adamopoulou) Grafted onto Sour Orange and Volkamer Lemon Rootstocks
Authors: Papadakis Ioannis E. , Ladikou Evangelia-Vasiliki , Oikonomou Alexia , Chatzistathis Theocharis , Chatziperou Georgia
Published: November 2023

(1) Background: This study investigates the effects of potassium (K) and rootstock on the growth, photosynthetic activity, and mineral nutrition of lemon trees; (2) Methods: Lemon trees (cv. Adamopoulou) grafted onto sour orange (Citrus aurantium) (SO) or Volkamer lemon (Citrus volkameriana) (VL) rootstock were cultivated hydroponically under 0.00, 0.75, 1.50, 3.00, and 6.00 mM K. Plant growth and nutrition parameters, as well as leaf photosynthetic rate, stomatal conductance, transpiration rate, intracellular CO2, chlorophyll, and carotenoid concentration were assessed; (3) Results: Under K deficiency (0 mM K), plants exhibited chlorotic and necrotic symptoms, more pronounced in older leaves. Potassium deficiency adversely affected various physiological processes in lemon leaves, including a decrease in photosynthesis rate, stomatal conductance, transpiration rate, intercellular CO2 concentration, water use efficiency, CO2 utilization efficiency, chlorophyll a/b ratio, and carotenoid concentrations (some effects were rootstock-dependent). Low photosynthetic rates under K deficiency were due to both stomatal- and non-stomatal limitations. Elevated K in the nutrient solution consistently reduced the total plant uptake of P, Ca, Mg, B, Mn, and Zn, resulting in nutrient imbalances, as evidenced by the significant decrease in P, Ca, Mg, and Zn concentrations found in scion tissues (especially at the 6 mM K treatment). Rootstock-dependent responses were also observed in scion leaf and stem growth and in mineral nutrient concentrations, uptake, and distribution across plant parts; (4) Conclusions: Our study reveals interesting aspects on how to optimize K fertilization in lemon trees in the context of sustainable agriculture by considering nutrient interactions and rootstock-dependent effects. Understanding these complex interactions and improving K fertilization practices is expected to improve lemon tree performance, yield, and fruit quality.
Testing the Feasibility of Usumacinta River Sediments as a Renewable Resource for Landscaping and Agronomy
Authors: Hussain Mazhar , Levacher Daniel , Leblanc Nathalie , Zmamou Hafida , Djeran-Maigre Irini , Razakamanantsoa Andry
Published: November 2023

Fluvial sediment recycling in agronomy is a relatively recent development, as sediment fertilizing potential for crops is unexplored. Freshwater sediments can act as fertilizer and improve the aeration of soils to increase the yield of crops, support vegetation for landscaping, and provide protective cover against erosion. This study focuses on the investigation of the agronomic potential of Usumacinta River sediments. The pH of the sediments is around 8.5, which is slightly alkaline. The organic matter content is low (5.7%). The sodium absorption ratio is 1.2 and the electrical conductivity is low (0.02 mS/cm). These values indicate that sediments are nonsaline, which is essential for the growth of crops and vegetation. The environmental characteristics of sediments show that the heavy metals, polycyclic aromatic hydrocarbon (PAH), and polychlorinated biphenyl (PCB) pollutants in sediments are below the recommended thresholds. In addition, sediments from the Usumacinta River contain minerals such as potassium and iron oxides that are helpful in improving the biological and nutritional characteristics of the soil. Furthermore, the pH, granulometry, mineralogy, organic matter, and carbonate contents of the Usumacinta River sediments are similar to agronomic soils. The Usumacinta River sediment’s potential for agronomy was practically investigated by sowing ryegrass (Lolium perenne) in a greenhouse by using the local climatic conditions and mixing sediments with potting soil. Three soil compositions were used to evaluate the germination and growth of ryegrass. The soil compositions were 100% potting soil (C1), 50% sediments + 50% potting soil (C2), and 100% sediments (C3). The growth rate of ryegrass was evaluated by monitoring the increase in grass height and production of fresh biomass. The germination of ryegrass was similar in all three compositions. The growth of ryegrass and production of fresh biomass were the most significant with 100% potting soil (0.25 kg/m2), somewhat less with sediment mix (0.18 kg·m2), and were the least significant with 100% sediments (0.05 kg/m2). The mixture of potting soil and sediments shows similar growth to 100% potting soil. The ryegrass seed germination, growth, and production of fresh biomass with the mixture of sediments gave encouraging results, and underlined the potential of sediments for soil amendments for agronomy and protective developments, such as limiting riverbank erosion, gardening, and landscaping.
Short- and Medium-Term Electricity Consumption Forecasting Using Prophet and GRU
Authors: Son Namrye , Shin Yoonjeong
Published: November 2023

Electricity consumption forecasting plays a crucial role in improving energy efficiency, ensuring stable power supply, reducing energy costs, optimizing facility management, and promoting environmental conservation. Accurate predictions help optimize energy system operations, reduce energy wastage, cut costs, and decrease carbon emissions. Consequently, the research on electricity consumption forecasting algorithms is thriving. However, to overcome challenges like data imbalances, data quality issues, seasonal variations, and event handling, recent forecasting models employ various approaches, including probability and statistics, machine learning, and deep learning. This study proposes a short- and medium-term electricity consumption prediction algorithm by combining the GRU model suitable for long-term forecasting and the Prophet model suitable for seasonality and event handling. (1) The preprocessed data propose the Prophet model in the first step for seasonality and event handling prediction. (2) In the second step, seven multivariate data are experimented with using GRU. Specifically, the seven multivariate data consist of six meteorological data and the residuals between the predicted data from the proposed Prophet model in Step 1 and the observed data. These are utilized to predict electricity consumption at 15 min intervals. (3) Electricity consumption is predicted for short-term (2 days and 7 days) and medium-term (15 days and 30 days) scenarios. The proposed approach outperforms both the Prophet and GRU models, reducing prediction errors and offering valuable insights into electricity consumption patterns.
Numerical Simulation of 3D Flow Structure and Turbulence Characteristics near Permeable Spur Dike in Channels with Varying Sinuosities
Authors: Xie Peng , Li Chunguang , Lv Suiju , Zhang Fengzhu , Jing Hefang , Li Xiaogang , Liu Dandan
Published: November 2023

Owing to the different degrees of bending in rivers in nature, it is difficult to conduct experiments in situ. In this study, the renormalization group (RNG) k-ε turbulence model in ANSYS Fluent was used to analyze the three-dimensional flow structure and turbulence characteristics near a spur dike and to evaluate the variation trend of flow in rivers with different degrees of bending. The results show that in channels with different curvatures, the vortex appears between the spur dikes and is disturbed by the permeable hole, and the backflow area moves downstream. The strength of secondary flow (SSF) fluctuates greatly in the vicinity of the spur dike and the downstream region, and the peak value appears 3.22 m (21.5 times L) away from the inlet of the bend. The SSF increases as the bend curvature increases. The SSF displays similar variation trends in the three kinds of bends. The peak value of normalized turbulent kinetic energy (NTKE) appears 3.14 m away from the entrance of the bend, the NTKE is the largest in the 45° bend and the smallest in the 180° bend, and it decreases only at distances of 3.25–4.19 m away from the entrance of the bend as the bend curvature increases.
The Influence of Corporate Social Responsibility Aspects on Business Model Innovation, Competitive Advantage, and Company Performance: A Study on Small- and Medium-Sized Enterprises in Iran
Authors: Omidvar Mohammadsadegh , Palazzo Maria
Published: November 2023

This study explores how the dimensions of corporate social responsibility (CSR) as defined by Carroll, along with environmental responsibility, impact business model innovation (BMI), competitive advantage, and firm performance in small- and medium-sized enterprises in Iran. This paper proposes a theoretical framework, based on past studies. Afterward, structural equation modeling was used to test the conceptual model. The data of this research were collected face-to-face, and 483 valid questionnaires were collected from small- and medium-sized businesses in Iran. The results show that all dimensions of CSR (except philanthropic) directly and significantly affect Business Model Innovation. Furthermore, the competitive advantage is significantly impacted by the economic, legal, and ethical aspects of CSR. Additionally, the findings demonstrate that both BMI and competitive advantage play a direct and substantial role in influencing a company’s performance. This study represents one of the initial investigations to specifically analyze how each facet of corporate social responsibility influences Business Model Innovation and competitive advantage. It is worth noting that a new dimension, environmental responsibility, was incorporated into Carroll’s original model due to the growing significance of environmental concerns. This paper gives managers a better insight into CSR and its effects on company performance. In addition, it shows managers which aspects of CSR can have an impact on BMI and competitive advantage.