bluuebunny/crossref_metadata_2025 huggingface dataset:
Localization and Classification of Venusian Volcanoes Using Image Detection Algorithms
Authors: Đuranović Daniel , Baressi Šegota Sandi , Lorencin Ivan , Car Zlatan
Published: January 2023

Imaging is one of the main tools of modern astronomy—many images are collected each day, and they must be processed. Processing such a large amount of images can be complex, time-consuming, and may require advanced tools. One of the techniques that may be employed is artificial intelligence (AI)-based image detection and classification. In this paper, the research is focused on developing such a system for the problem of the Magellan dataset, which contains 134 satellite images of Venus’s surface with individual volcanoes marked with circular labels. Volcanoes are classified into four classes depending on their features. In this paper, the authors apply the You-Only-Look-Once (YOLO) algorithm, which is based on a convolutional neural network (CNN). To apply this technique, the original labels are first converted into a suitable YOLO format. Then, due to the relatively small number of images in the dataset, deterministic augmentation techniques are applied. Hyperparameters of the YOLO network are tuned to achieve the best results, which are evaluated as mean average precision (mAP@0.5) for localization accuracy and F1 score for classification accuracy. The experimental results using cross-vallidation indicate that the proposed method achieved 0.835 mAP@0.5 and 0.826 F1 scores, respectively.
Recent Advances of Enzyme-Free Electrochemical Sensors for Flexible Electronics in the Detection of Organophosphorus Compounds: A Review
Authors: Pathiraja Gayani , Bonner Chartanay D. J. , Obare Sherine O.
Published: January 2023

Emerging materials integrated into high performance flexible electronics to detect environmental contaminants have received extensive attention worldwide. The accurate detection of widespread organophosphorus (OP) compounds in the environment is crucial due to their high toxicity even at low concentrations, which leads to acute health concerns. Therefore, developing rapid, highly sensitive, reliable, and facile analytical sensing techniques is necessary to monitor environmental, ecological, and food safety risks. Although enzyme-based sensors have better sensitivity, their practical usage is hindered due to their low specificity and stability. Therefore, among various detection methods of OP compounds, this review article focuses on the progress made in the development of enzyme-free electrochemical sensors as an effective nostrum. Further, the novel materials used in these sensors and their properties, synthesis methodologies, sensing strategies, analytical methods, detection limits, and stability are discussed. Finally, this article summarizes potential avenues for future prospective electrochemical sensors and the current challenges of enhancing the performance, stability, and shelf life.
CMOS Detector Staggered Array Module for Sub-Terahertz Imaging on Conveyor Belt System
Authors: Lee Moon-Jeong , Lee Ha-Neul , Lee Ga-Eun , Han Seong-Tae , Kang Dong-Woo , Yang Jong-Ryul
Published: January 2023

A complementary metal–oxide–semiconductor (CMOS) detector array is proposed to improve the sub-terahertz imaging resolution for objects in the conveyor belt system. The image resolution is limited to the implemented configuration, such as the wide spacing in the detector array, the high conveyor belt speed, and the slow response of the signal conditioning block. The proposed array can improve the image resolution in the direction perpendicular to the movement of the belt, which is determined by the size and interval of the detector pixel, by configuring the array into two replaceable columns located at the misaligned horizontal positions. Replaceable detector unit pixels are individually attached to the motherboard after measuring and evaluating the detection performance to construct the proposed array. The intensities of 32 detector pixels placed under the conveyor belt with a width of 160 mm were initially calibrated in every image, including the beam pattern of 0.2 THz signals generated from the gyrotron. The image resolution of the perpendicular direction obtained from the proposed array was measured to be approximately 5 mm at a conveyor belt speed of 16 mm/s, demonstrating a 200% improvement in resolution compared to the conventional linear array under the same conditions.
ALIEN: Assisted Learning Invasive Encroachment Neutralization for Secured Drone Transportation System
Authors: Ajakwe Simeon Okechukwu , Ihekoronye Vivian Ukamaka , Kim Dong-Seong , Lee Jae-Min
Published: January 2023

Priority-based logistics and the polarization of drones in civil aviation will cause an extraordinary disturbance in the ecosystem of future airborne intelligent transportation networks. A dynamic invention needs dynamic sophistication for sustainability and security to prevent abusive use. Trustworthy and dependable designs can provide accurate risk assessment of autonomous aerial vehicles. Using deep neural networks and related technologies, this study proposes an artificial intelligence (AI) collaborative surveillance strategy for identifying, verifying, validating, and responding to malicious use of drones in a drone transportation network. The dataset for simulation consists of 3600 samples of 9 distinct conveyed objects and 7200 samples of the visioDECT dataset obtained from 6 different drone types flown under 3 different climatic circumstances (evening, cloudy, and sunny) at different locations, altitudes, and distance. The ALIEN model clearly demonstrates high rationality across all metrics, with an F1-score of 99.8%, efficiency with the lowest noise/error value of 0.037, throughput of 16.4 Gbps, latency of 0.021, and reliability of 99.9% better than other SOTA models, making it a suitable, proactive, and real-time avionic vehicular technology enabler for sustainable and secured DTS.
LRF-SRNet: Large-Scale Super-Resolution Network for Estimating Aircraft Pose on the Airport Surface
Authors: Yuan Xinyang , Fu Daoyong , Han Songchen
Published: January 2023

The introduction of various deep neural network architectures has greatly advanced aircraft pose estimation using high-resolution images. However, realistic airport surface monitors typically take low-resolution (LR) images, and the results of the aircraft pose estimation are far from being accurate enough to be considered acceptable because of long-range capture. To fill this gap, we propose a brand-new, end-to-end low-resolution aircraft pose estimate network (LRF-SRNet) to address the problem of estimating the pose of poor-quality airport surface surveillance aircraft images. The method successfully combines the pose estimation method with the super-resolution (SR) technique. Specifically, to reconstruct high-resolution aircraft images, a super-resolution network (SRNet) is created. In addition, an essential component termed the large receptive field block (LRF block) helps estimate the aircraft’s pose. By broadening the neural network’s receptive field, it enables the perception of the aircraft’s structure. Experimental results demonstrate that, on the airport surface surveillance dataset, our method performs significantly better than the most widely used baseline methods, with AP exceeding Baseline and HRNet by 3.1% and 4.5%.
The Properties of a Ship’s Compass in the Context of Ship Manoeuvrability
Authors: Felski Andrzej , Jaskólski Krzysztof
Published: January 2023

In evaluating the accuracy of most navigation measuring systems, it is accepted, as a rule, that measurement errors are characterised by a normal distribution. With reference to the compass, the approach of most producers is similar. However, in the case of this measuring device, the dynamics of the ship should also be taken into account. The problem is that any changes in the ship’s heading can be measured exclusively with the use of a compass. Until quite recently, this device was built based on mechanical elements, so it possessed its own dynamic properties. This means the appearance of specific, positive feedback (self-reinforcing feedback) because if the compass did not point to the correct heading, it could lead the ship to stray from the correct heading. On the other hand, it could mean an incorrect compass setup, even though the ship had the correct heading. Any incorrect indications of the compass were then interpreted as a ship’s departure from the correct heading. This problem was not essential in the era of magnetic compasses because the errors in these compasses are relatively constant, unlike the errors in gyrocompasses, which have an oscillatory and random character and, thus, it is not possible to describe them accurately with mathematical relations. This issue was already perceived before WWII, when the Anschutz Company proposed, among other solutions, using the so-called Schuler period in the construction of gyrocompasses. Fibre optic gyrocompasses do not possess mechanical sensors, so the variability of their indications is of a different character. However, computational processes, as well as applied inertial sensors, also cause certain errors of an oscillatory nature. This raises the following questions: what is the spectrum of the error frequency of such compasses, and what is the influence of the ship’s movement on them? The authors attempted to evaluate this phenomenon by performing measurements made on board three hydrographic platforms and comparing them with the headings indicated by other compasses.
An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG
Authors: Abdel-Hamid Lamiaa
Published: January 2023

Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of being easily manipulated by subjects to mask their true emotions. Electroencephalography (EEG) has emerged as a reliable and cost-effective method to detect true human emotions. Recently, huge research effort has been put to develop efficient wearable EEG devices to be used by consumers in out of the lab scenarios. In this work, a subject-dependent emotional valence recognition method is implemented that is intended for utilization in emotion AI applications. Time and frequency features were computed from a single time series derived from the Fp1 and Fp2 channels. Several analyses were performed on the strongest valence emotions to determine the most relevant features, frequency bands, and EEG timeslots using the benchmark DEAP dataset. Binary classification experiments resulted in an accuracy of 97.42% using the alpha band, by that outperforming several approaches from literature by ~3–22%. Multiclass classification gave an accuracy of 95.0%. Feature computation and classification required less than 0.1 s. The proposed method thus has the advantage of reduced computational complexity as, unlike most methods in the literature, only two EEG channels were considered. In addition, minimal features concluded from the thorough analyses conducted in this study were used to achieve state-of-the-art performance. The implemented EEG emotion recognition method thus has the merits of being reliable and easily reproducible, making it well-suited for wearable EEG devices.
Using Gaze for Behavioural Biometrics
Authors: D’Amelio Alessandro , Patania Sabrina , Bursic Sathya , Cuculo Vittorio , Boccignone Giuseppe
Published: January 2023

A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The relevant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field.
Single-Point and Surface Quality Assessment Algorithm in Continuous Production with the Use of 3D Laser Doppler Scanning Vibrometry System
Authors: Scislo Lukasz
Published: January 2023

In the current economic situation of many companies, the need to reduce production time is a critical element. However, this cannot usually be carried out with a decrease in the quality of the final product. This article presents a possible solution for reducing the time needed for quality management. With the use of modern solutions such as optical measurement systems, quality control can be performed without additional stoppage time. In the case of single-point measurement with the Laser Doppler Vibrometer, the measurement can be performed quickly in a matter of milliseconds for each product. This article presents an example of such quality assurance measurements, with the use of fully non-contact methods, together with a proposed evaluation criterion for quality assessment. The proposed quality assurance algorithm allows the comparison of each of the products’ modal responses with the ideal template and stores this information in the cloud, e.g., in the company’s supervisory system. This makes the presented 3D Laser Vibrometry System an advanced instrumentation and data acquisition system which is the perfect application in the case of a factory quality management system based on the Industry 4.0 concept.
Simulating the Effect of Gut Microbiome on Cancer Cell Growth Using a Microfluidic Device
Authors: Mittal Ekansh , Cupp Grace , Kang Youngbok (Abraham)
Published: January 2023

The imbalance in the gut microbiome plays a vital role in the progression of many diseases, including cancer, due to increased inflammation in the body. Since gut microbiome-induced inflammation can serve as a novel therapeutic strategy, there is an increasing need to identify novel approaches to investigate the effect of inflammation instigated by gut microbiome on cancer cells. However, there are limited biomimetic co-culture systems that allow testing of the causal relationship of the microbiome on cancer cells. Here we developed a microfluidic chip that can simulate the interaction of the gut microbiome and cancer cells to investigate the effects of bacteria and inflammatory stress on cancer cells in vitro. To test the microfluidic chip, we used colorectal cancer cells, as an increased microbiome abundance has been associated with poor outcomes in colorectal cancer. We cultured colorectal cancer cells with Bacillus bacteria or lipopolysaccharide (LPS), a purified bacterial membrane that induces a significant inflammatory response, in the microfluidic device. Our results showed that both LPS and Bacillus significantly accelerated the growth of colorectal cancer cells, therefore supporting that the increased presence of certain bacteria promotes cancer cell growth. The microfluidic device included in this study may have significant implications in identifying new treatments for various cancer types in the future.
An Electrochemical Sensor for Sulfadiazine Determination Based on a Copper Nanoparticles/Molecularly Imprinted Overoxidized Polypyrrole Composite
Authors: Elamin Manahil Babiker , Ali Shazalia Mahmoud Ahmed , Essousi Houda , Chrouda Amani , Alhaidari Laila M. , Jaffrezic-Renault Nicole , Barhoumi Houcine
Published: January 2023

To protect consumers from risks related to overexposure to sulfadiazine, total residues of this antibacterial agent in animal-origin foodstuffs not exceed international regulations. To this end, a new electrochemical sensor based on a molecularly imprinted polymer nanocomposite using overoxidized polypyrrole and copper nanoparticles for the detection of sulfadiazine is elaborated. After optimization of the preparation of the electrochemical sensors, their differential pulse voltammetric signal exhibits an excellent stability and reproducibility at 1.05 V, with a large linear range between 10−9 and 10−5 mol L−1 and a low detection limit of 3.1 × 10−10 mol L−1. The produced sulfadiazine sensor was successfully tested in real milk samples. The combination of the properties of the electrical conduction of copper nanoparticles with the properties of the preconcentration of the molecularly imprinted overoxidized polypyrrole allows for the highly sensitive detection of sulfadiazine, even in real milk samples. This strategy is new and leads to the lowest detection limit yet achieved, compared to those of the previously published sulfadiazine electrochemical sensors.
A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control
Authors: Dai Yicheng , Deng Zhihao , Wang Xin , Yuan Han
Published: January 2023

Due to the outstanding characteristics of the large structural flexibility and strong dexterity of soft robots, they have attracted great attention. However, the dynamic modeling and precise control of soft robots face huge challenges. Traditional model-based and model-free control methods find it difficult to obtain a balance between complexity and accuracy. In this paper, a dynamic model of a three-chamber continuous pneumatic manipulator is established based on the modal method. Moreover, a hybrid controller integrating model predictive control (MPC) and iterative learning control (ILC) is proposed, which can simultaneously perform model parameter learning and trajectory tracking control. Experimental results show that the proposed control method can optimize the parameters of the dynamic model in real time with less iterations than the traditional model-free method and have good control performance in trajectory tracking experiments. In the future, the proposed dynamic model and the hybrid controller should be verified on a multi-section manipulator.
Towards a Safe Human–Robot Collaboration Using Information on Human Worker Activity
Authors: Orsag Luka , Stipancic Tomislav , Koren Leon
Published: January 2023

Most industrial workplaces involving robots and other apparatus operate behind the fences to remove defects, hazards, or casualties. Recent advancements in machine learning can enable robots to co-operate with human co-workers while retaining safety, flexibility, and robustness. This article focuses on the computation model, which provides a collaborative environment through intuitive and adaptive human–robot interaction (HRI). In essence, one layer of the model can be expressed as a set of useful information utilized by an intelligent agent. Within this construction, a vision-sensing modality can be broken down into multiple layers. The authors propose a human-skeleton-based trainable model for the recognition of spatiotemporal human worker activity using LSTM networks, which can achieve a training accuracy of 91.365%, based on the InHARD dataset. Together with the training results, results related to aspects of the simulation environment and future improvements of the system are discussed. By combining human worker upper body positions with actions, the perceptual potential of the system is increased, and human–robot collaboration becomes context-aware. Based on the acquired information, the intelligent agent gains the ability to adapt its behavior according to its dynamic and stochastic surroundings.
How I Met Your V2X Sensor Data: Analysis of Projection-Based Light Field Visualization for Vehicle-to-Everything Communication Protocols and Use Cases
Authors: Kara Peter A. , Wippelhauser Andras , Balogh Tibor , Bokor Laszlo
Published: January 2023

The practical usage of V2X communication protocols started emerging in recent years. Data built on sensor information are displayed via onboard units and smart devices. However, perceptually obtaining such data may be counterproductive in terms of visual attention, particularly in the case of safety-related applications. Using the windshield as a display may solve this issue, but switching between 2D information and the 3D reality of traffic may introduce issues of its own. To overcome such difficulties, automotive light field visualization is introduced. In this paper, we investigate the visualization of V2X communication protocols and use cases via projection-based light field technology. Our work is motivated by the abundance of V2X sensor data, the low latency of V2X data transfer, the availability of automotive light field prototypes, the prevalent dominance of non-autonomous and non-remote driving, and the lack of V2X-based light field solutions. As our primary contributions, we provide a comprehensive technological review of light field and V2X communication, a set of recommendations for design and implementation, an extensive discussion and implication analysis, the exploration of utilization based on standardized protocols, and use-case-specific considerations.
Enhancing the Generalization for Text Classification through Fusion of Backward Features
Authors: Seng Dewen , Wu Xin
Published: January 2023

Generalization has always been a keyword in deep learning. Pretrained models and domain adaptation technology have received widespread attention in solving the problem of generalization. They are all focused on finding features in data to improve the generalization ability and to prevent overfitting. Although they have achieved good results in various tasks, those models are unstable when classifying a sentence whose label is positive but still contains negative phrases. In this article, we analyzed the attention heat map of the benchmarks and found that previous models pay more attention to the phrase rather than to the semantic information of the whole sentence. Moreover, we proposed a method to scatter the attention away from opposite sentiment words to avoid a one-sided judgment. We designed a two-stream network and stacked the gradient reversal layer and feature projection layer within the auxiliary network. The gradient reversal layer can reverse the gradient of features in the training stage so that the parameters are optimized following the reversed gradient in the backpropagation stage. We utilized an auxiliary network to extract the backward features and then fed them into the main network to merge them with normal features extracted by the main network. We applied this method to the three baselines of TextCNN, BERT, and RoBERTa using sentiment analysis and sarcasm detection datasets. The results show that our method can improve the sentiment analysis datasets by 0.5% and the sarcasm detection datasets by 2.1%.
System and Method for Driver Drowsiness Detection Using Behavioral and Sensor-Based Physiological Measures
Authors: Bajaj Jaspreet Singh , Kumar Naveen , Kaushal Rajesh Kumar , Gururaj H. L. , Flammini Francesco , Natarajan Rajesh
Published: January 2023

The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver’s body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection methods using only single measures that failed to produce good outcomes. Furthermore, these measures were only functional in certain conditions. This paper proposes a model that combines the two approaches, non-intrusive and intrusive, to detect driver drowsiness. Behavioral measures as a non-intrusive approach and sensor-based physiological measures as an intrusive approach are combined to detect driver drowsiness. The proposed hybrid model uses AI-based Multi-Task Cascaded Convolutional Neural Networks (MTCNN) as a behavioral measure to recognize the driver’s facial features, and the Galvanic Skin Response (GSR) sensor as a physiological measure to collect the skin conductance of the driver that helps to increase the overall accuracy. Furthermore, the model’s efficacy has been computed in a simulated environment. The outcome shows that the proposed hybrid model is capable of identifying the transition from awake to a drowsy state in the driver in all conditions with the efficacy of 91%.
An Assessment of Economic Sustainability and Efficiency in Small-Scale Broiler Farms in Limpopo Province: A Review
Authors: Ramukhithi Tumelo Francinah , Nephawe Khathutshelo Agree , Mpofu Takalani Judas , Raphulu Thomas , Munhuweyi Karen , Ramukhithi Fhulufhelo Vincent , Mtileni Bohani
Published: January 2023

An important factor in determining the success of a small-scale broiler farm is its economic viability and efficiency. During times of trouble for the industry, the idea receives more attention. The conceptual considerations of economic sustainability and efficiency are frequently quite constrained, according to the difficulties raised in this study and by other authors. There is a lack of information about South Africa’s small-scale broiler production’s economic viability and effectiveness. Furthermore, it is clear that small-scale broiler producers have the ability to increase their economic efficiency. By reducing the mortality rate, feed conversion rate, and production duration, both their technical and financial efficiency could be improved. Profitability in the production of broilers will be considerably increased by lowering the cost of these variable inputs, particularly feed and day-old chicks. Additionally, raising the education level, capacity utilization ratio, and broiler production would all contribute to raising the farms’ efficiency levels. To ensure effective resource use and to maximize practicable profit, small-scale broiler producers who are not operating close to the profit frontier must make efforts to reduce both technical and allocation inefficiencies. Collectively, all these measures would ensure the economic sustainability of small-scale farmers in South Africa would be met. Moreover, the sustainability of small-scale broiler producers can be achieved if strategies that build local capacity and that empower them to sustain high levels of productivity are provided. In addition, the efficient use of resources will ensure that productivity is enhanced, and might increase profitability. It is therefore important to ensure that small-scale broiler producers achieve maximum profit for a given set of inputs. Approaches in assessing the farm-level profitability such as cost-benefit and gross margin analyses can be used.
Upcycled Animal Feed: Sustainable Solution to Orange Peels Waste
Authors: Andrianou Christina , Passadis Konstantinos , Malamis Dimitris , Moustakas Konstantinos , Mai Sofia , Barampouti Elli Maria
Published: January 2023

Currently, in an effort to increase their sustainability and reduce their carbon footprint, industries look for ways to valorise their waste instead of simply treating it. At the same time, food insecurity is increasing with alarming rates and thus solutions are sought. To this end, the main objective of this paper was to optimise an innovative valorisation strategy to turn orange juice industry by-products into high-value secondary feedstuff for animals. In this context, a valorisation strategy was designed where a saccharification step of the orange peels and an aerobic fermentation step of the liquid residue were included. Both processes were optimised via factorial deign. The saccharification process was optimised in terms of pectinolytic and cellulolytic enzymes and solid loading, whereas the aerobic fermentation method was optimised in terms of nutrients addition, the yeast to glucose ratio, and pH control. According to the optimised conditions, the final animal feedstuff should be formulated by mixing the solid residue of orange peels after the saccharification process under the optimum conditions (50 °C, 24 h, 7.5% solids loading, Pectinex 25 μL/g TS, CellicCTec3 25 μL/g TS), with the harvested yeast cultivated aerobically on orange peels hydrolysate (30 °C, 24 h, orange peels hydrolysate as sugar source, nutrients addition, yeast to glucose ratio equal to 0.02). Finally, the formulated feedstock should be dried in order to stabilise the product in terms of shelf life and feed safety. The final feedstuff presented 23.11% higher in vitro organic matter digestibility and threefold protein content.
Sustainable Renewal of Spontaneous Spatial Characteristics of a Historical–Cultural District: A Case Study of Tanhualin, Wuhan, China
Authors: Shang Wei , He Cong , Lv Sang
Published: January 2023

Based on a fixed-point survey of spontaneous space in the Tanhualin area of Wuhan, China, spontaneous space samples were obtained. The spatial typology method was used to summarize and compare the morphological and functional characteristics of the samples, and the space was divided into 10 types and seven functions. Through quantitative statistical analysis of the relationship between different spontaneous space forms and their spatial functions, it was found that residents’ behavior in daily life has a significant effect on spontaneous space renewal and district renewal. Based on the study of the spontaneous space of the Tanhualin historical–cultural district, this study determined the status quo of the space renewal and development of the district and put forward corresponding protection and renewal strategies. Finally, it provides a theoretical basis for the sustainable renewal and coordinated development of historical–cultural districts.
Geotechnical Investigation of Gelatin Biopolymer on Cohesive Soils
Authors: Vishweshwaran Muralidaran , Sujatha Evangelin Ramani
Published: January 2023

Gelatin, a biopolymer derived from animal proteins, has been selected to stabilize three fine-grained soils by determining select index and engineering properties. Specimens for California Bearing Ratio (CBR) were tested using three different curing methods, i.e., thermally cured at 60 °C, unsoaked, and 7 days air-cured submerged specimens. The amount of gelatin added to the soil ranged from 0.5% to 2% by soil weight. The sequence of the interaction between gelatin and the clays is as follows: (A) The biopolymer solution is adsorbed and agglomerated onto the surface of the clay. (B) The presence of Al3+, Si4+, and K+ ions on the clay promotes the blending of connective linkages with negatively charged gelatin. (C) The connection reinforcements harden with the curing period and subsequent drying of the stabilized soils. (D) Drying of the gelatin–clay complex also establishes alternative bonding modes such as van der Waals interactions and ligand exchange. The biopolymer formed dry, rigid films after 72 h which were responsible for coating and reinforcing the soil particles. Thermal curing by 1% addition of gelatin yielded the maximum CBR of 91.42%, 141.1%, and 122.3% for high compressible clay, low compressible clay, and low compressible silt, respectively, and a maximum Unconfined Compressive Strength (UCS) of 3968 kN/m2 for the low compressible clay. The UCS results revealed that brittle failure was predominant for the gelatin-amended soils after 28 days of curing while shear failure was observed for the treated soils tested 2 h after sample preparation. Tests on pH revealed that the gelatin-stabilized soils displayed marginal variations after 28 days. Spectroscopic analysis revealed the various types of bonds between gelatin and the clays. A reduction in mass of 9% was observed for the alternate wetting and drying of the high compressible clay after a period of 12 cycles. The adsorption of the clay–gelatin complex was indicated by variation in average particle diameter and specific surface. Savings in 450 m3 and 93.75 m3 of coarse aggregates and dense bituminous macadam, respectively, were observed for a 1 km pavement for the stabilized low compressible clay.
State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles Based on Regional Capacity and LGBM
Authors: Zhang Zhaosheng , Wang Shuo , Lin Ni , Wang Zhenpo , Liu Peng
Published: January 2023

Battery state of health (SOH) estimation is a prerequisite for battery health management and is vital for second-life utilization. Existing techniques implemented in well-controlled experimental conditions fail to reflect complex working conditions during actual vehicular operation. In this article, a novel SOH estimation method for battery systems in real-world electric vehicles (EVs) is presented by combing results of regional capacity calculation and a light gradient boosting machine (LGBM) model. The LGBM model is used to capture the relationship between battery degeneration and influential factors based on datasets from real-world EVs. The regional capacity, which is calculated through incremental capacity analysis with a Gaussian smoothing filter, is utilized to reflect the battery degradation level while ensuring high flexibility and applicability. Accumulated mileage, average charging current, average charging temperature, and start and end of SOC values are chosen as influential factors for model establishment. The effectiveness, complexity, superiority, and robustness of the proposed method are verified using data from real-world EVs. Results indicate accurate SOH estimation can be achieved with an average absolute error of only 0.89 Ah, where the MAPE and RMSE of the test vehicles are 2.049% and 1.153%, respectively.
The Mediating Effect of Green Human Resources Management on the Relationship between Organizational Sustainability and Innovative Behavior: An Application in Turkey
Authors: Kara Ergün , Akbaba Melda , Yakut Emre , Çetinel Makbule Hürmet , Pasli Mehmet Mert
Published: January 2023

With globalization, it is required to support the innovative behaviors of employees as a driving force in order for organizations to increase their market share, provide competitive advantage and ensure organizational sustainability. The purpose of this study is to establish the intermediary role of green human resources management in the relationship between employees’ innovative behavior and organizational sustainability in the tourism sector. Data collection was achieved by conducting a survey of 615 hotel employees who work in accommodation establishments which are located in the city of Muğla (Turkey). In the scope of this study, validity and reliability analysis of the measurement scales were conducted and SmartPLS software was applied to implement a variance-based structural equation model. Within this framework, the research primarily discussed the variables among the relationship of organizational sustainability, IB, and green human resources management, and hypotheses were formed. A mediation test was conducted by selecting the bootstrap method in order to test the indirect effect that employees have on the perception of innovative behavior. The result of the study shows organizational sustainability has a significantly positive effect on innovative behavior and green human resources management, and also it emerged that green human resources management has a partial mediating effect on the success of organizational Sustainability on innovative behavior. Findings of the research state that whilst it is anticipated that the study can contribute to the literature theoretically and practically, a variety of proposals have been put forward for the benefit of future studies and operators to use.
Digital Transformation and Competitive Advantage in the Service Sector: A Moderated-Mediation Model
Authors: Shehadeh Maha , Almohtaseb Ahmad , Aldehayyat Jehad , Abu-AlSondos Ibrahim A.
Published: January 2023

Digital transformation is important not only in the manufacturing sector but can also help service-based organizations to achieve competitive advantage. Therefore, this study has examined the impact of digital transformation on competitive advantage and the mediating role of entrepreneurial orientation. Innovation capability is considered a moderator between digital transformation and competitive advantage. The data for the research were gathered from service companies in Jordan, and AMOS was utilized for the analysis. The results revealed that digital transformation not only influences competitive advantage but also affects entrepreneurial orientation. Entrepreneurial orientation significantly mediates the relationship between digital transformation and competitive advantage. In addition, innovation capabilities moderate the relationship between digital transformation, competitive advantage, and entrepreneurial orientation. Innovation, production, and operational managers as well as owners of service companies can use the results of this research as a guideline for policy-making in order to develop a competitive advantage.
Fighting the Consequences of the COVID-19 Pandemic: Mindfulness, Exercise, and Nutrition Practices to Reduce Eating Disorders and Promote Sustainability
Authors: Baldassano Sara , Alioto Anna , Amato Alessandra , Rossi Carlo , Messina Giulia , Bruno Maria Roberta , Stallone Roberta , Proia Patrizia
Published: January 2023

Over the past two years, the world’s population has been tested by the COVID-19 health emergency. This has changed population habits worldwide by encouraging a sedentary lifestyle and overnutrition. Isolation and reduction of social life, for most of the population, was mandatory but it quickly became a new lifestyle. Nowadays, we are encountering the consequences with an increase in nutritional associated disorders and conditions that cause illnesses in the general population. These disorders include diet excesses that lead to obesity and diet deficiencies and malnutrition which could rapidly lead to death. These eating disorders are very complex to manage because they become mental disorders which can negatively impact physical or mental health. This work will disucss the benefits associated with the mindfulness–exercise–nutrition (MEN) technique. From a nutritional point of view it will focus on the nutritional effect of a plant-based diet, such as the Mediterranean diet (MD) which has a high tryptophan content which can increase serotonin (the “feel good” hormone) levels. The MEN technique takes a multidisciplinary approach and aims to integrate healthy behaviors into clinical practice using healthy eating, active living, and mindfulness. This method includes controlled physical movements, stretching techniques such as yoga, and aerobic exercise to achieve optimal mental and physical health. This literature review, carried out using the PubMed, ScienceDirect, Scopus, and Google Scholar databases, aims to investigate the latest research on this topic. This study may be useful for healthcare professionals and clinicians and may help patients to be more self-aware, encouraging them to lead a healthier lifestyle, make thoughtful choices, and ameliorate their mental health. The final aim of this study is to promote physiological homeostasis and well-being.
Digital Twin Applications in 3D Concrete Printing
Authors: Wang Yuxin , Aslani Farhad , Dyskin Arcady , Pasternak Elena
Published: January 2023

The benefits of 3D concrete printing (3DCP) include reducing construction time and costs, providing design freedom, and being environmentally friendly. This technology is expected to be effective in addressing the global house shortage. This review highlights the main 3DCP applications and four critical challenges. It is proposed to combine 3D concrete printing with Digital Twin (DT) technology to meet the challenges the 3DCP faces and improve quality and sustainability. This paper provides a critical review of research into the application of DT technology in 3DCP, categorize the applications and directions proposed according to different lifecycles, and explore the possibility of incorporating them into existing 3DCP systems. A comprehensive roadmap was proposed to detail how DT can be used at different lifecycle stages to optimize and address the four main challenges of 3DCP, providing directions and ideas for further research.
New Estimation of Error in the Hadamard Inequality Pertaining to Coordinated Convex Functions in Quantum Calculus
Authors: Raees Muhammad , Anwar Matloob
Published: January 2023

Convex bodies are symmetric in nature. Between the two variables of symmetry and convexity, a correlation connection is also perceptible. Due to the interchangeable analogous properties, the application on either of them has been practicable in these modern years. The current analysis sheds insight on a general new identity involving a number of parameters for a twice partial quantum differentiable function. We find several unique quantum integral inequalities by using the new identity and a twice partial quantum differentiable function whose absolute value is coordinated convex. In addition, we present several novel and interesting error estimation-like results related to the well-known quantum Hermite–Hadamard inequality. Some examples are provided at the end to support and demonstrate the effectiveness of the new outcomes.
Sequencing the Entangled DNA of Fractional Quantum Hall Fluids
Authors: Cruise Joseph R. , Seidel Alexander
Published: January 2023

We introduce and prove the “root theorem”, which establishes a condition for families of operators to annihilate all root states associated with zero modes of a given positive semi-definite k-body Hamiltonian chosen from a large class. This class is motivated by fractional quantum Hall and related problems, and features generally long-ranged, one-dimensional, dipole-conserving terms. Our theorem streamlines analysis of zero-modes in contexts where “generalized” or “entangled” Pauli principles apply. One major application of the theorem is to parent Hamiltonians for mixed Landau-level wave functions, such as unprojected composite fermion or parton-like states that were recently discussed in the literature, where it is difficult to rigorously establish a complete set of zero modes with traditional polynomial techniques. As a simple application, we show that a modified V1 pseudo-potential, obtained via retention of only half the terms, stabilizes the ν=1/2 Tao–Thouless state as the unique densest ground state.
Multi-Objective Reliability-Based Partial Topology Optimization of a Composite Aircraft Wing
Authors: Sleesongsom Suwin , Kumar Sumit , Bureerat Sujin
Published: January 2023

Reliability-based partial topology optimization (RBPTO) is a new approach for aircraft structural design; however, it brings computational complexity and makes aeroelasticity analysis quite challenging. Therefore, the present study proposed the multi-objective reliability-based partial topology optimization of a composite aircraft wing using a fuzzy-based metaheuristic (MRBPTOFBMH) approach. The objective is to obtain an optimal layout including partial topology and sizing of the aircraft wing structure. Here, an optimal aeroelastic structure is designed by taking into account the uncertain nature of material properties and the permitted transverse displacement. To increase computational efficiency in the design process, a non-probabilistic approach called a possibilistic safety index-based design optimization (PSIBDO) with fuzzy uncertainties is proposed to quantify the uncertainties due to aeroelastic and structural constraints. Various optimum partial topological shapes and sizing of aircraft wing structures with various PSI values in the range of [0.001, 1.0] have been obtained in a single optimization run. These outcomes, including deterministic and reliable optimal aircraft wing structures, demonstrate the high effectiveness of the proposed MRBPTOFBMH technique to alleviate the complexity of unconventional aircraft wing structure design. The findings also reveal the ease in cooperation of the suggested technique with a high-performance multi-objective evolutionary algorithm (MOEA) and its application in real-world multi-objective design optimization (MODO) problems with the least computational requirements against the traditional method’s multiple runs. Furthermore, the proposed methodology can generate potential aircraft wing structures in a range of m = [89.38–127.84] kg, and flutter speed = [285.61–632.78] m/s, that adhere to all the constraints requirements.
An Improved Model Combining Outlook Attention and Graph Embedding for Traffic Forecasting
Authors: Zhang Jin , Liu Yuanyuan , Gui Yan , Ruan Chang
Published: January 2023

Due to the highly non-linear nature of traffic data and the complex structure of road networks, traffic forecasting faces significant challenges. In this paper, we propose an improved model that combines outlook attention and graph embedding (MOAGE) for traffic forecasting, focusing on the construction of reasonable and effective spatio-temporal dependencies. Inspired by the idea of symmetry, MOAGE adopts a symmetrical encoder and decoder structure. Outlook attention blocks are important components of the encoder and decoder, consisting of spatial outlook attention and temporal outlook attention, used to model spatio-temporal dependencies in the road network. Cross attention are added to the model to reduce propagation errors. In addition, we learned the vertex representation of the graph via the node2vec algorithm and integrated the graph information into our model for a better prediction performance. Extensive experiments on two real datasets further demonstrate that the RMSE errors of the MOAGE on PEMS_BAY and METR_LA are reduced by approximately 14.6% and 12.2% for 60 min compared with the latest baseline models. Finally, the methodology used in this study will provide guidance to relevant ministries to better allocate transport resources and improve the efficiency and safety of traffic operations.
Symmetric Matrices on Inverse Soft Expert Sets and Their Applications
Authors: Sathiyaseelan Nandhagopal , Vijayabalaji Srinivasan , Alcantud José Carlos R.
Published: January 2023

The role of experts in decision-making is highly significant. The soft expert set (SES) is yet another milestone, as it plays a vital role in generalizing soft set theory. Soft expert set is creative and original because it involves more than one expert. This served as a driving force behind the creation of the current work. The present paper generalizes the framework of soft experts set to the inverse soft setting, namely the inverse soft expert set (ISES). Some captivating characteristic features of the soft expert set (SES) are established. On the inverse soft expert sets, numerous striking characteristics of symmetric matrices are presented. The ground-breaking idea of symmetric matrices on inverse soft expert sets (SMISES) is introduced. A MCDM problem for SMISES is described with an algorithm and interesting comparative analysis.