bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning-Based Modeling of Air Temperature in the Complex Environment of Yerevan City, Armenia
Authors: Tepanosyan Garegin , Asmaryan Shushanik , Muradyan Vahagn , Avetisyan Rima , Hovsepyan Azatuhi , Khlghatyan Anahit , Ayvazyan Grigor , Dell’Acqua Fabio
Published: May 2023

Machine learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the Partial Least-Squares Regression (PLSR) model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to strongly impact Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000 m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between the predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations, such as precipitation and wind speed, as well as the use of non-parametric ML techniques.
TSML: A New Pig Behavior Recognition Method Based on Two-Stream Mutual Learning Network
Authors: Hao Wangli , Zhang Kai , Zhang Li , Han Meng , Hao Wangbao , Li Fuzhong , Yang Guoqiang
Published: May 2023

Changes in pig behavior are crucial information in the livestock breeding process, and automatic pig behavior recognition is a vital method for improving pig welfare. However, most methods for pig behavior recognition rely on human observation and deep learning. Human observation is often time-consuming and labor-intensive, while deep learning models with a large number of parameters can result in slow training times and low efficiency. To address these issues, this paper proposes a novel deep mutual learning enhanced two-stream pig behavior recognition approach. The proposed model consists of two mutual learning networks, which include the red–green–blue color model (RGB) and flow streams. Additionally, each branch contains two student networks that learn collaboratively to effectively achieve robust and rich appearance or motion features, ultimately leading to improved recognition performance of pig behaviors. Finally, the results of RGB and flow branches are weighted and fused to further improve the performance of pig behavior recognition. Experimental results demonstrate the effectiveness of the proposed model, which achieves state-of-the-art recognition performance with an accuracy of 96.52%, surpassing other models by 2.71%.
Metaformer: A Transformer That Tends to Mine Metaphorical-Level Information
Authors: Peng Bo , Ding Yuanming , Kang Wei
Published: May 2023

Since introducing the Transformer model, it has dramatically influenced various fields of machine learning. The field of time series prediction has also been significantly impacted, where Transformer family models have flourished, and many variants have been differentiated. These Transformer models mainly use attention mechanisms to implement feature extraction and multi-head attention mechanisms to enhance the strength of feature extraction. However, multi-head attention is essentially a simple superposition of the same attention, so they do not guarantee that the model can capture different features. Conversely, multi-head attention mechanisms may lead to much information redundancy and computational resource waste. In order to ensure that the Transformer can capture information from multiple perspectives and increase the diversity of its captured features, this paper proposes a hierarchical attention mechanism, for the first time, to improve the shortcomings of insufficient information diversity captured by the traditional multi-head attention mechanisms and the lack of information interaction among the heads. Additionally, global feature aggregation using graph networks is used to mitigate inductive bias. Finally, we conducted experiments on four benchmark datasets, and the experimental results show that the proposed model can outperform the baseline model in several metrics.
Subgraph Learning for Topological Geolocalization with Graph Neural Networks
Authors: Zha Bing , Yilmaz Alper
Published: May 2023

One of the challenges of spatial cognition, such as self-localization and navigation, is to develop an efficient learning approach capable of mimicking human ability. This paper proposes a novel approach for topological geolocalization on the map using motion trajectory and graph neural networks. Specifically, our learning method learns an embedding of the motion trajectory encoded as a path subgraph where the node and edge represent turning direction and relative distance information by training a graph neural network. We formulate the subgraph learning as a multi-class classification problem in which the output node IDs are interpreted as the object’s location on the map. After training using three map datasets with small, medium, and large sizes, the node localization tests on simulated trajectories generated from the map show 93.61%, 95.33%, and 87.50% accuracy, respectively. We also demonstrate similar accuracy for our approach on actual trajectories generated by visual-inertial odometry. The key benefits of our approach are as follows: (1) we take advantage of the powerful graph-modeling ability of neural graph networks, (2) it only requires a map in the form of a 2D graph, and (3) it only requires an affordable sensor that generates relative motion trajectory.
Acoustic Wave Reflection in Water Affects Underwater Wireless Sensor Networks
Authors: Sathish Kaveripakam , Hamdi Monia , Chinthaginjala Venkata Ravikumar , Alibakhshikenari Mohammad , Ayadi Manel , Pau Giovanni , Abbas Mohamed , Shukla Neeraj Kumar
Published: May 2023

The phenomenon of acoustic wave reflection off fluid–solid surfaces is the focus of this research. This research aims to measure the effect of material physical qualities on oblique incidence acoustic attenuation across a large frequency range. To construct the extensive comparison shown in the supporting documentation, reflection coefficient curves were generated by carefully adjusting the porousness and permeability of the poroelastic solid. The next stage in determining its acoustic response is to determine the pseudo-Brewster angle shift and the reflection coefficient minimum dip for the previously indicated attenuation permutations. This circumstance is made possible by modeling and studying the reflection and absorption of acoustic plane waves encountering half-space and two-layer surfaces. For this purpose, both viscous and thermal losses are taken into account. According to the research findings, the propagation medium has a significant impact on the form of the curve that represents the reflection coefficient, whereas the effects of permeability, porosity, and driving frequency are relatively less significant to the pseudo-Brewster angle and curve minima, respectively. This research additionally found that as permeability and porosity increase, the pseudo-Brewster angle shifts to the left (proportionally to porosity increase) until it reaches a limiting value of 73.4 degrees, and that the reflection coefficient curves for each level of porosity exhibit a greater angular dependence, with an overall decrease in magnitude at all incident angles. These findings are given within the framework of the investigation (in proportion to the increase in porosity). The study concluded that when permeability declined, the angular dependence of frequency-dependent attenuation reduced, resulting in iso-porous curves. The study also discovered that the matrix porosity largely affected the angular dependency of the viscous losses in the range of 1.4 × 10−14 m2 permeability.
Enhancing the Squareness and Bi-Phase Magnetic Switching of Co2FeSi Microwires for Sensing Application
Authors: Salaheldeen Mohamed , Wederni Asma , Ipatov Mihail , Zhukova Valentina , Lopez Anton Ricardo , Zhukov Arcady
Published: May 2023

In the current study we have obtained Co2FeSi glass-coated microwires with different geometrical aspect ratios, ρ = d/Dtot (diameter of metallic nucleus, d and total diameter, Dtot). The structure and magnetic properties are investigated at a wide range of temperatures. XRD analysis illustrates a notable change in the microstructure by increasing the aspect ratio of Co2FeSi-glass-coated microwires. The amorphous structure is detected for the sample with the lowest aspect ratio (ρ = 0.23), whereas a growth of crystalline structure is observed in the other samples (aspect ratio ρ = 0.30 and 0.43). This change in the microstructure properties correlates with dramatic changing in magnetic properties. For the sample with the lowest ρ-ratio, non-perfect square loops are obtained with low normalized remanent magnetization. A notable enhancement in the squareness and coercivity are obtained by increasing ρ-ratio. Changing the internal stresses strongly affects the microstructure, resulting in a complex magnetic reversal process. The thermomagnetic curves show large irreversibility for the Co2FeSi with low ρ-ratio. Meanwhile, if we increase the ρ-ratio, the sample shows perfect ferromagnetic behavior without irreversibility. The current result illustrates the ability to control the microstructure and magnetic properties of Co2FeSi glass-coated microwires by changing only their geometric properties without performing any additional heat treatment. The modification of geometric parameters of Co2FeSi glass-coated microwires allows to obtain microwires that exhibit an unusual magnetization behavior that offers opportunities to understand the phenomena of various types of magnetic domain structures, which is essentially helpful for designing sensing devices based on thermal magnetization switching.
Exploring the Potential of Pulse Transit Time as a Biomarker for Sleep Efficiency through a Comparison Analysis with Heart Rate and Heart Rate Variability
Authors: Bridges Jenna , Shishavan Hossein Hamidi , Salmon Adrian , Metersky Mark , Kim Insoo
Published: May 2023

The relationship between sleep dynamics and blood pressure (BP) changes is well established. Moreover, sleep efficiency and wakefulness during sleep (WASO) events have a significant impact on BP dipping. Despite this knowledge, there is limited research on the measurement of sleep dynamics and continuous blood pressure (CBP). This study aims to explore the relationship between sleep efficiency and cardiovascular function indicators such as pulse transit time (PTT), as a biomarker of CBP, and heart rate variability (HRV), measured using wearable sensors. The results of the study conducted on 20 participants at the UConn Health Sleep Disorders Center suggest a strong linear relationship between sleep efficiency and changes in PTT (r2 = 0.8515) and HRV during sleep (r2 = 5886). The findings of this study contribute to our understanding of the relationship between sleep dynamics, CBP, and cardiovascular health.
Resource Allocation for a Secure SWIPT Network Based on a Quantitative Energy Harvesting Mechanism
Authors: Zhu Long , Xue Liang , Gong Xuan , Wang Chunjie
Published: May 2023

Simultaneous wireless information and power transfer (SWIPT) technology can effectively extend the lifecycle of energy-constrained networks. In order to improve the energy harvesting (EH) efficiency and network performance in secure SWIPT networks, this paper studies the resource allocation problem based on the quantitative EH mechanism in the secure SWIPT network. Based on a quantitative EH mechanism and nonlinear EH model, a quantified power-splitting (QPS) receiver architecture is designed. This architecture is applied in the multiuser multi-input single-output secure SWIPT network. With the goal of maximizing the network throughput, the optimization problem model is established under the conditions of meeting the legal user’s signal-to-interference-plus-noise ratio (SINR), EH requirements, the total transmit power of the base station, and the security SINR threshold constraints. Due to the coupling of variables, the problem is a nonconvex optimization problem. To deal with the nonconvex optimization problem, a hierarchical optimization method is adopted. Firstly, an optimization algorithm based on the optimal received power of EH circuit is proposed, and a power mapping table is constructed through the optimization algorithm, from which the optimal power ratio to meet the user’s EH requirements is obtained; then, the nonconvex problem is transformed into a convex problem by using variable substitution, semidefinite relaxation, dichotomous optimization, etc. The simulation results show that compared with the power splitting receiver architecture, the input power threshold range of the QPS receiver architecture is larger, which can avoid the EH circuit falling into the saturated working area and maintain high network throughput.
Monte Carlo Simulation of Diffuse Optical Spectroscopy for 3D Modeling of Dental Tissues
Authors: Moradi Mousa , Chen Yu
Published: May 2023

Three-dimensional precise models of teeth are critical for a variety of dental procedures, including orthodontics, prosthodontics, and implantology. While X-ray-based imaging devices are commonly used to obtain anatomical information about teeth, optical devices offer a promising alternative for acquiring 3D data of teeth without exposing patients to harmful radiation. Previous research has not examined the optical interactions with all dental tissue compartments nor provided a thorough analysis of detected signals at various boundary conditions for both transmittance and reflectance modes. To address this gap, a GPU-based Monte Carlo (MC) method has been utilized to assess the feasibility of diffuse optical spectroscopy (DOS) systems operating at 633 nm and 1310 nm wavelengths for simulating light-tissue interactions in a 3D tooth model. The results show that the system’s sensitivity to detect pulp signals at both 633 nm and 1310 nm wavelengths is higher in the transmittance compared with that in the reflectance mode. Analyzing the recorded absorbance, reflectance, and transmittance data verified that surface reflection at boundaries can improve the detected signal, especially from the pulp region in both reflectance and transmittance DOS systems. These findings could ultimately lead to more accurate and effective dental diagnosis and treatment.
Yet Another Compact Time Series Data Representation Using CBOR Templates (YACTS)
Authors: Molina Araque Sebastian , Martinez Ivan , Papadopoulos Georgios Z. , Montavont Nicolas , Toutain Laurent
Published: May 2023

The Internet of Things (IoT) technology is growing rapidly, while the IoT devices are being deployed massively. However, interoperability with information systems remains a major challenge for this accelerated device deployment. Furthermore, most of the time, IoT information is presented as Time Series (TS), and while the majority of the studies in the literature focus on the prediction, compression, or processing of TS, no standardized representation format has emerged. Moreover, apart from interoperability, IoT networks contain multiple constrained devices which are designed with limitations, e.g., processing power, memory, or battery life. Therefore, in order to reduce the interoperability challenges and increase the lifetime of IoT devices, this article introduces a new format for TS based on CBOR. The format exploits the compactness of CBOR by leveraging delta values to represent measurements, employing tags to represent variables, and utilizing templates to convert the TS data representation into the appropriate format for the cloud-based application. Moreover, we introduce a new refined and structured metadata to represent additional information for the measurements, then we provide a Concise Data Definition Language (CDDL) code to validate the CBOR structures against our proposal, and finally, we present a detailed performance evaluation to validate the adaptability and the extensibility of our approach. Our performance evaluation results show that the actual data sent by IoT devices can be reduced by between 88% and 94% compared to JavaScript Object Notation (JSON), between 82% and 91% compared to Concise Binary Object Representation (CBOR) and ASN.1, and between 60% and 88% compared to Protocol buffers. At the same time, it can reduce Time-on-Air by between 84% and 94% when a Low Power Wide Area Networks (LPWAN) technology such as LoRaWAN is employed, leading to a 12-fold increase in battery life compared to CBOR format or between a 9-fold and 16-fold increase when compared to Protocol buffers and ASN.1, respectively. In addition, the proposed metadata represent an additional 0.5% of the overall data transmitted in cases where networks such as LPWAN or Wi-Fi are employed. Finally, the proposed template and data format provide a compact representation of TS that can significantly reduce the amount of data transmitted containing the same information, extend the battery life of IoT devices, and improve their lifetime. Moreover, the results show that the proposed approach is effective for different data types and it can be integrated seamlessly into existing IoT systems.
Consensus Control of Linear Parameter-Varying Multi-Agent Systems with Unknown Inputs
Authors: Zhu Fanglai , Tan Chengmin
Published: May 2023

This paper investigates the observer-based consensus control problem for linear parameter-varying (LPV) multi-agent systems (MASs) with unknown inputs. Firstly, an interval observer (IO) is designed to generate the state interval estimation for each agent. Secondly, an algebraic relationship is established between the system state and unknown input (UI). Thirdly, an unknown input observer (UIO) capable of generating estimates of UI and the system state has been developed through the algebraic relations. Finally, a UIO-based distributed control protocol scheme is proposed to realize the consensus of the MASs. In the end, to verify the validity of the proposed method, an example of a numerical simulation is given.
Cross-Viewpoint Semantic Mapping: Integrating Human and Robot Perspectives for Improved 3D Semantic Reconstruction
Authors: Kopácsi László , Baffy Benjámin , Baranyi Gábor , Skaf Joul , Sörös Gábor , Szeier Szilvia , Lőrincz András , Sonntag Daniel
Published: May 2023

Allocentric semantic 3D maps are highly useful for a variety of human–machine interaction related tasks since egocentric viewpoints can be derived by the machine for the human partner. Class labels and map interpretations, however, may differ or could be missing for the participants due to the different perspectives. Particularly, when considering the viewpoint of a small robot, which significantly differs from the viewpoint of a human. In order to overcome this issue, and to establish common ground, we extend an existing real-time 3D semantic reconstruction pipeline with semantic matching across human and robot viewpoints. We use deep recognition networks, which usually perform well from higher (i.e., human) viewpoints but are inferior from lower viewpoints, such as that of a small robot. We propose several approaches for acquiring semantic labels for images taken from unusual perspectives. We start with a partial 3D semantic reconstruction from the human perspective that we transfer and adapt to the small robot’s perspective using superpixel segmentation and the geometry of the surroundings. The quality of the reconstruction is evaluated in the Habitat simulator and a real environment using a robot car with an RGBD camera. We show that the proposed approach provides high-quality semantic segmentation from the robot’s perspective, with accuracy comparable to the original one. In addition, we exploit the gained information and improve the recognition performance of the deep network for the lower viewpoints and show that the small robot alone is capable of generating high-quality semantic maps for the human partner. The computations are close to real-time, so the approach enables interactive applications.
Laser Linewidth Analysis and Filtering/Fitting Algorithms for Improved TDLAS-Based Optical Gas Sensor
Authors: Tong Chen , Sima Chaotan , Chen Muqi , Zhang Xiaohang , Li Tailin , Ai Yan , Lu Ping
Published: May 2023

Tunable Diode Laser Absorption Spectroscopy (TDLAS) has been widely applied in in situ and real-time monitoring of trace gas concentrations. In this paper, an advanced TDLAS-based optical gas sensing system with laser linewidth analysis and filtering/fitting algorithms is proposed and experimentally demonstrated. The linewidth of the laser pulse spectrum is innovatively considered and analyzed in the harmonic detection of the TDLAS model. The adaptive Variational Mode Decomposition-Savitzky Golay (VMD-SG) filtering algorithm is developed to process the raw data and could significantly eliminate the background noise variance by about 31% and signal jitters by about 12.5%. Furthermore, the Radial Basis Function (RBF) neural network is also incorporated and applied to improve the fitting accuracy of the gas sensor. Compared with traditional linear fitting or least squares method (LSM), the RBF neural network brings along the enhanced fitting accuracy within a large dynamic range, achieving an absolute error of below 50 ppmv (about 0.6%) for the maximum 8000 ppmv methane. The proposed technique in this paper is universal and compatible with TDLAS-based gas sensors without hardware modification, allowing direct improvement and optimization for current optical gas sensors.
A Simulation Study on Peak Carbon Emission of Public Buildings—In the Case of Henan Province, China
Authors: Li Hui , Zheng Yanan , Gong Guan , Guo Hongtao
Published: May 2023

With the continuous development of the social economy, carbon emissions from various buildings are increasing. As the most important category of building carbon emissions, the rapid peaking of public buildings is an important part of achieving carbon peak and carbon neutrality. This paper is based on the industrial background of the energy consumption structure of Henan Province, a central province in the developing country of China. Firstly, the energy consumption intensity of buildings and public buildings in Henan Province from 2010 to 2020 was calculated according to the energy balance sheet. The Kaya–LMDI decomposition method was also used to analyse the carbon emissions of public buildings, determining the impact of each influencing parameter on public buildings. Secondly, the scenario prediction model Monte Carlo was run 100,000 times to set the stochastic parameters of the variables in the model to predict the time of carbon peak and carbon emissions. The analysis results indicated that: ① Carbon emissions in Henan Province have exhibited a steady growth trend, increasing from 1533 t in 2010 to 6561 t in 2020; ② The primary factors influencing carbon emissions of public buildings in Henan Province were urbanization rate, public floor area per capita, and energy intensity per unit of public floor area; and ③ Carbon emissions of public buildings in Henan Province followed an inverted U-shaped distribution and are expected to peak at approximately 7423 t by the year 2035. The research method in this paper can guide the simulation study of peak carbon emission prediction in Henan Province based on the influencing parameters of carbon emission from different types of buildings. Moreover, the results of this paper can provide a reference for a more precise study of building carbon reduction in similar regions of developing countries.
The Importance of Architectural Icons of the City of Szczecin for the Transformation of Landscape Identity and Promotion of the City’s Image
Authors: Bal Wojciech , Czalczynska-Podolska Magdalena , Nieścior Maja
Published: May 2023

The practice of promoting an image of the city using architectural objects, and the need to identify or create architectural icons in urban spaces, outlined in this article, occurs in many cities worldwide. The city of Szczecin in Poland, the subject of this research, is no exception. The turbulent history of the city, which for years remained within the borders of Germany, makes it particularly worth analyzing in these terms because it shows the shifting popularity of architectural objects in the context of changing political, cultural, and economic conditions. The research presented here aims to identify iconic objects commonly exploited to build an image of Szczecin, assess their prominent features, and analyze changes over time in their popularity and use for these purposes. The historical and interpretive research was based on a detailed analysis of old and contemporary architectural objects of Szczecin depicted in postcards from the last hundred years, from the period spanning 1920 to 2023. The analysis covered several digital databases containing archival and contemporary postcards. This analysis enables the identification of the most popular objects, suggests the key characteristics of iconic buildings, and identifies four basic types of buildings, which are distinct in terms of purpose, message, and the specific image of the city to which each contributes. The duration of each type as a popular object, the strength of its impact on the city’s image, and its importance for the urban landscape are also identified. The research revealed that: architecturally iconic buildings are created concerning political, economic, and social conditions and the need to promote a specific image of the city, certain features contribute to a building’s potential as an architectural icon, and the strength of the building’s impact, its importance for the city landscape, and the possibility of creating a specific image depending on its iconic type.
Adaptive Stability Control Based on Sliding Model Control for BEVs Driven by In-Wheel Motors
Authors: Ge Pingshu , Guo Lie , Feng Jindun , Zhou Xiaoyue
Published: May 2023

High-speed and complex road conditions make it easy for vehicles to reach limit conditions, increasing the risk of instability. Consequently, there is an urgent need to solve the problem of vehicle stability and safety. In this paper, adaptive stability control is studied in BEVs driven by in-wheel motors. Based on the sliding model algorithm, a joint weighting control of the yaw rate and sideslip angle is carried out, and a weight coefficient is designed using a fuzzy algorithm to realize adaptive direct yaw moment control. Next, optimal torque distribution is designed with the minimum sum of four tire load rates as the optimization objective. Then, combined with the road adhesion coefficient and the maximum motor torque constraint, the torque distribution problem is transformed into a functionally optimal solution problem with constraints. The simulation results show that the direct yaw moment controller based on the adaptive sliding mode algorithm has a good control effect on the yaw rate and sideslip angle, and it can effectively improve vehicle adaptive stability control. In the optimal torque distributor based on road surface recognition, the estimated error of road adhesion is within 10%, and has a greater margin to deal with vehicle instability, which can effectively improve vehicle adaptive stability control.
Development of Kampung Susun Akuarium Based on Sustainable Housing Principles
Authors: Pane Annisa , Gunawan Budhi , Withaningsih Susanti
Published: May 2023

Adequate housing is one of the rights of Indonesian citizens. Nevertheless, forced eviction is something familiar in Indonesia. One of the areas that experienced forced evictions was Kampung Akuarium. In April, the Provincial Government of DKI Jakarta evicted Kampung Akuarium settlement area residents. As a form of demonstration, they survived on the rubble from the evictions and built tents to carry out their activities in the Kampung Akuarium area. The DKI Jakarta Provincial Government began to rebuild the site into an adequate residential area called Kampung Susun Akuarium (KSA) as a solution for the residents. Based on the government’s solution, the study aims to determine whether the KSA meets the principles of sustainable housing. Sustainable housing promotes environmental preservation, social equality, and economic development to improve the residents’ quality of life. This study conducts a preliminary study to compile sustainable housing variables and indicators. The variables used in this research are community development, environment, social, and economy. Data analysis using Structural Equation Modeling was performed with SmartPLS software, based on the data collected, with an intensive study by distributing questionnaires to 102 residents. The findings indicate that KSA residents have performed most of the sustainable housing and community development indicators well and can still be improved by considering solutions related to poorly implemented indicators. This study’s results also emphasize that community development is a significant variable in building sustainable housing to be used in subsequent studies.
Determination of Heavy Metals Immobilization by Chemical Fractions in Contaminated Soil Amended with Biochar
Authors: Barčauskaitė Karolina , Anne Olga , Mockevičienė Ieva , Repšienė Regina , Šiaudinis Gintaras , Karčauskienė Danutė
Published: May 2023

Biochar is a promising tool to immobilize heavy metals (HMs) in the soil. Biochar’s effect on HMs immobilization into acidic soil (pH < 5) and the interaction of plants have been investigated. Three types of feedstocks were used for biochar development via pyrolysis at two temperatures and then applied as soil amendments. A vegetative experiment has been carried out with buckwheat and white mustard to determine the effect of biochar as an HMs immobilizing agent in the presence of sewage sludge. The results show promising biochar properties to immobilize heavy metals and reduce their availability for plants. Biochar incorporation increased soil pH and reduced heavy metal forms available to plants. A sequential extraction procedure was applied to investigate five different forms of six heavy metals (Cd, Cr, Cu, Ni, Pb, Zn) and evaluate their distribution after plants’ cultivation. The proportion of the residual fraction (RES) of HMs varied widely and differed from metal to metal and from plant species. RES in the soil after treatment with biochar and buckwheat harvest varied between 68.14 and 96.40% for Zn, 42.39 and 59.48% (Cu), 75.89 and 93.11% (Cr), 81.85 and 92.83% (Ni), and 98.94 and 99.20% (Pb). In comparison, a slightly opposite trend was found in the soil after white mustard cultivation. The proportion of RES was: 0.82–53.44% for Zn, 0.99–52.93% (Cu), 48.87–76.41% (Cr), 10.22–72.63% (Ni), and 98.31–99.32% (Pb). HMs immobilization efficiency in the soil after biochar treatment followed the order Ni > Cr > Pb > Cu > Zn and Ni > Pb > Zn > Cr > Cu after buckwheat and white mustard cultivation, respectively.
Artificial Intelligence Assisted Social Failure Mode and Effect Analysis (FMEA) for Sustainable Product Design
Authors: Spreafico Christian , Sutrisno Agung
Published: May 2023

Nowadays, the social dimension of product sustainability is increasingly in demand, however, industrial designers struggle to pursue it much more than the environmental or economic one due to their unfamiliarity in correlating design choices with social impacts. In addition, this gap is not filled even by the supporting methods that have been conceived to only support specific areas of application. To fill this gap, this study proposed a method to support social failure mode and effect analysis (SFMEA), though the automatic failure determination, based on the use of a chatbot (i.e., an artificial intelligence (AI)-based chat). The method consists of 84 specific questions to ask the chatbot, resulting from the combination of known failures and social failures, elements from design theories, and syntactic structures. The starting hypothesis to be verified is that a GPT Chat (i.e., a common AI-based chat), properly queried, can provide all the main elements for the automatic compilation of a SFMEA (i.e., to determine the social failures). To do this, the proposed questions were tested in three case studies to extract all the failures and elements that express predefined SFMEA scenarios: a coffee cup provoking gender discrimination, a COVID mask denying a human right, and a thermometer undermining the cultural heritage of a community. The obtained results confirmed the starting hypothesis by showing the strengths and weaknesses of the obtained answers in relation to the following factors: the number and type of inputs (i.e., the failures) provided in the questions; the lexicon used in the question, favoring the use of technical terms derived from design theories and social sustainability taxonomies; the type of the problem. Through this test, the proposed method proved its ability to support the social sustainable design of different products and in different ways. However, a dutiful recommendation instead concerns the tool (i.e., the chatbot) due to its filters that limit some answers in which the designer tries to voluntarily hypothesize failures to explore their social consequences.
Synergetic Benefits for a Pig Farm and Local Bioeconomy Development from Extended Green Biorefinery Value Chains
Authors: Gaffey James , O’Donovan Cathal , Murphy Declan , O’Connor Tracey , Walsh David , Vergara Luis Alejandro , Donkor Kwame , Gottumukkala Lalitha , Koopmans Sybrandus , Buckley Enda , O’Connor Kevin , Sanders Johan P. M.
Published: May 2023

As the global population rises, agriculture and industry are under increasing pressure to become more sustainable in meeting this growing demand, while minimizing impacts on global emissions, land use change, and biodiversity. The development of efficient and symbiotic local bioeconomies can help to respond to this challenge by using land, resources, and side streams in efficient ways tailored to the needs of different regions. Green biorefineries offer a unique opportunity for regions with abundant grasslands to use this primary resource more sustainably, providing feed for cows, while also generating feed for monogastric animals, along with the co-production of biomaterials and energy. The current study investigates the impact of a green biorefinery co-product, leaf protein concentrate (LPC), for input to a pig farm, assessing its impact on pig diets, and the extended impact on the bioenergy performance of the pig farm. The study found that LPC replaced soya bean meal at a 50% displacement rate, with pigs showing positive performance in intake and weight gain. Based on laboratory analysis, the resulting pig slurry demonstrated a higher biogas content and 26% higher biomethane potential compared with the control slurry. The findings demonstrate some of the local synergies between agricultural sectors that can be achieved through extended green biorefinery development, and the benefits for local bioeconomy actors.
Opportunities of IoT in Fog Computing for High Fault Tolerance and Sustainable Energy Optimization
Authors: Reyana A. , Kautish Sandeep , Alnowibet Khalid Abdulaziz , Zawbaa Hossam M. , Wagdy Mohamed Ali
Published: May 2023

Today, the importance of enhanced quality of service and energy optimization has promoted research into sensor applications such as pervasive health monitoring, distributed computing, etc. In general, the resulting sensor data are stored on the cloud server for future processing. For this purpose, recently, the use of fog computing from a real-world perspective has emerged, utilizing end-user nodes and neighboring edge devices to perform computation and communication. This paper aims to develop a quality-of-service-based energy optimization (QoS-EO) scheme for the wireless sensor environments deployed in fog computing. The fog nodes deployed in specific geographical areas cover the sensor activity performed in those areas. The logical situation of the entire system is informed by the fog nodes, as portrayed. The implemented techniques enable services in a fog-collaborated WSN environment. Thus, the proposed scheme performs quality-of-service placement and optimizes the network energy. The results show a maximum turnaround time of 8 ms, a minimum turnaround time of 1 ms, and an average turnaround time of 3 ms. The costs that were calculated indicate that as the number of iterations increases, the path cost value decreases, demonstrating the efficacy of the proposed technique. The CPU execution delay was reduced to a minimum of 0.06 s. In comparison, the proposed QoS-EO scheme has a lower network usage of 611,643.3 and a lower execution cost of 83,142.2. Thus, the results show the best cost estimation, reliability, and performance of data transfer in a short time, showing a high level of network availability, throughput, and performance guarantee.
Machine Learning-Based Imputation Approach with Dynamic Feature Extraction for Wireless RAN Performance Data Preprocessing
Authors: Dahj Jean Nestor M. , Ogudo Kingsley A.
Published: May 2023

Machine learning (ML) in wireless mobile communication is becoming more and more customary, with application trends leaning toward performance improvement and network automation. The radio access network (RAN), critical for service access, frequently generates performance data that mobile network operators (MNOs) and researchers leverage for planning, self-optimization, and intelligent network operations. However, missing values in the RAN performance data, as in any valuable data, impact analysis. Poor handling of such missing data in the RAN can distort the relationships between different metrics, leading to inaccurate and unreliable conclusions and predictions. Therefore, there is a need for imputation methods that preserve the overall structure of the RAN data to an optimal level. In this study, we present an imputation approach for handling RAN performance missing data based on machine learning algorithms. The method customizes the feature-extraction mechanism by using dynamic correlation analysis. We apply the method to actual RAN performance indicator data to evaluate its performance. We finally compare and evaluate the proposed approach with statistical imputation techniques such as the mean, median, and mode. The results show that machine learning-based imputation, as approached in this experimental study, preserves some relationships between KPIs compared to non-ML techniques. Random Forest regressor gave the best performance in imputing the data.
Publisher’s Note: Targets—A New Open Access Journal
Authors: Craciun Ioana
Published: May 2023

Targets (ISSN 2813-3137) is a new open-access journal launched under the leadership of Prof [...]
Validation of Aerobic Capacity (VO2max) and Lactate Threshold in Wearable Technology for Athletic Populations
Authors: Carrier Bryson , Helm Macy M. , Cruz Kyle , Barrios Brenna , Navalta James W.
Published: May 2023

As wearable technology (WT) has evolved, devices have developed the ability to track a range of physiological variables. These include maximal aerobic capacity (VO2max) and lactate threshold (LT). With WT quickly growing in popularity, independent evaluation of these devices is important to determine the appropriate use-cases for the devices. Therefore, the purpose of this study was to determine the validity of WT in producing estimates of VO2max and LT in athletic populations. METHODS: 21 participants completed laboratory LT and VO2max testing, as well as an outdoor testing session guided by the WT being tested (Garmin fēnix 6® watch and accompanying heart rate monitor). Statistical analysis was completed, using hypothesis testing (ANOVA, t-test), correlation analysis (Pearson’s r, Lin’s Concordance Correlation [CCC]), error analysis (mean absolute percentage error [MAPE]), equivalence testing (TOST test), and bias assessment (Bland–Altman analysis). RESULTS: The Garmin watch was found to have acceptable agreement for VO2max when compared to the 1 min averaged values (MAPE = 6.85%, CCC = 0.7) and for LT and the onset of blood lactate accumulation (OBLA), (MAPE = 7.52%, CCC = 0.79; MAPE = 8.20%, CCC = 0.74, respectively). Therefore, the Garmin fēnix 6® produces accurate measurements of VO2max and LT in athletic populations and can be used to make training decisions among athletes.
Urinary Levels of 14 Metal Elements in General Population: A Region-Based Exploratory Study in China
Authors: Zhang Zining , Guo Sai , Hua Liting , Wang Beibei , Chen Qiusheng , Liu Lu , Xiang Li , Sun Hongwen , Zhao Hongzhi
Published: May 2023

Metal pollution may lead to a variety of diseases; for this reason, it has become a matter of public concern worldwide. However, it is necessary to use biomonitoring approaches to assess the risks posed to human health by metals. In this study, the concentrations of 14 metal elements in 181 urine samples obtained from the general population of Gansu Province, China, were analyzed using inductively coupled plasma mass spectrometry. Eleven out of fourteen target elements had detection frequencies above 85%, namely, Cr, Ni, As, Se, Cd, Al, Fe, Cu and Rb. The concentrations of most metal elements in the urine of our subjects corresponded to the medium levels of subjects in other regional studies. Gender exerted a significant influence (p < 0.05) on the concentrations of Tl, Rb and Zn. The concentrations of Ni, As, Pb, Sr, Tl, Zn, Cu and Se showed significant differences among different age groups and the age-related concentration trends varied among these elements. There were significant differences in the urine concentrations of Zn and Sr between those subjects in the group who were frequently exposed to soil (exposed soil > 20 min/day) and those in the group who were not, indicating that people in regular contact with soil may be more exposed to metals. This study provides useful information for evaluating the levels of metal exposure among general populations.
ZIKV Strains Elicit Different Inflammatory and Anti-Viral Responses in Microglia Cells
Authors: Oliveira Fernanda Bellaniza Caminha de , Freire Vanessa Paola Alves Sampaio de Sá , Coelho Sharton Vinicius Antunes , Meuren Lana Monteiro , Palmeira Julys da Fonseca , Cardoso Ana Luísa , Neves Francisco de Assis Rocha , Ribeiro Bergmann Morais , Argañaraz Gustavo Adolfo , Arruda Luciana Barros de , Argañaraz Enrique Roberto
Published: May 2023

In recent years, the Zika Virus (ZIKV) has caused pandemic outbreaks associated with a high rate of congenital ZIKV syndrome (CZS). Although all strains associated with worldwide outbreaks derive from the Asian lineage, the reasons for their enhanced spread and severity are not fully understood. In this study, we conducted a comparative analysis of miRNAs (miRNA-155/146a/124) and their cellular targets (SOCS1/3, SHP1, TRAF6, IRAK1), as well as pro- and anti-inflammatory and anti-viral cytokines (IL-6, TNF-α, IFN-γ, IL-10, and IFN-β) and peroxisome proliferator-activated receptor γ (PPAR-γ) expression in BV2 microglia cells infected with ZIKV strains derived from African and Asian lineages (ZIKVMR766 and ZIKVPE243). BV2 cells were susceptible to both ZIKV strains, and showed discrete levels of viral replication, with delayed release of viral particles without inducing significant cytopathogenic effects. However, the ZIKVMR766 strain showed higher infectivity and replicative capacity, inducing a higher expression of microglial activation markers than the ZIKVPE243 strain. Moreover, infection with the ZIKVMR766 strain promoted both a higher inflammatory response and a lower expression of anti-viral factors compared to the ZIKVPE243 strain. Remarkably, the ZIKKPE243 strain induced significantly higher levels of the anti-inflammatory nuclear receptor—PPAR-γ. These findings improve our understanding of ZIKV-mediated modulation of inflammatory and anti-viral innate immune responses and open a new avenue to explore underlining mechanisms involved in the pathogenesis of ZIKV-associated diseases.
Neutralizing and Enhancing Epitopes of the SARS-CoV-2 Receptor-Binding Domain (RBD) Identified by Nanobodies
Authors: Kaewchim Kanasap , Glab-ampai Kittirat , Mahasongkram Kodchakorn , Saenlom Thanatsaran , Thepsawat Watayagorn , Chulanetra Monrat , Choowongkomon Kiattawee , Sookrung Nitat , Chaicumpa Wanpen
Published: May 2023

Engineered nanobodies (VHs) to the SARS-CoV-2 receptor-binding domain (RBD) were generated using phage display technology. A recombinant Wuhan RBD served as bait in phage panning to fish out nanobody-displaying phages from a VH/VHH phage display library. Sixteen phage-infected E. coli clones produced nanobodies with 81.79–98.96% framework similarity to human antibodies; thus, they may be regarded as human nanobodies. Nanobodies of E. coli clones 114 and 278 neutralized SARS-CoV-2 infectivity in a dose-dependent manner; nanobodies of clones 103 and 105 enhanced the virus’s infectivity by increasing the cytopathic effect (CPE) in an infected Vero E6 monolayer. These four nanobodies also bound to recombinant Delta and Omicron RBDs and native SARS-CoV-2 spike proteins. The neutralizing VH114 epitope contains the previously reported VYAWN motif (Wuhan RBD residues 350–354). The linear epitope of neutralizing VH278 at Wuhan RBD 319RVQPTESIVRFPNITN334 is novel. In this study, for the first time, we report SARS-CoV-2 RBD-enhancing epitopes, i.e., a linear VH103 epitope at RBD residues 359NCVADVSVLYNSAPFFTFKCYG380, and the VH105 epitope, most likely conformational and formed by residues in three RBD regions that are spatially juxtaposed upon the protein folding. Data obtained in this way are useful for the rational design of subunit SARS-CoV-2 vaccines that should be devoid of enhancing epitopes. VH114 and VH278 should be tested further for clinical use against COVID-19.
The Longitudinal Analysis of Convergent Antibody VDJ Regions in SARS-CoV-2-Positive Patients Using RNA-Seq
Authors: Liu Kate J. , Zelazowska Monika A. , McBride Kevin M.
Published: May 2023

Severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) is an ongoing pandemic that continues to evolve and reinfect individuals. To understand the convergent antibody responses that evolved over the course of the pandemic, we evaluated the immunoglobulin repertoire of individuals infected by different SARS-CoV-2 variants for similarity between patients. We utilized four public RNA-seq data sets collected between March 2020 and March 2022 from the Gene Expression Omnibus (GEO) in our longitudinal analysis. This covered individuals infected with Alpha and Omicron variants. In total, from 269 SARS-CoV-2-positive patients and 26 negative patients, 629,133 immunoglobulin heavy-chain variable region V(D)J sequences were reconstructed from sequencing data. We grouped samples based on the SARS-CoV-2 variant type and/or the time they were collected from patients. Our comparison of patients within each SARS-CoV-2-positive group found 1011 common V(D)Js (same V gene, J gene and CDR3 amino acid sequence) shared by more than one patient and no common V(D)Js in the noninfected group. Taking convergence into account, we clustered based on similar CDR3 sequence and identified 129 convergent clusters from the SARS-CoV-2-positive groups. Within the top 15 clusters, 4 contain known anti-SARS-CoV-2 immunoglobulin sequences with 1 cluster confirmed to cross-neutralize variants from Alpha to Omicron. In our analysis of longitudinal groups that include Alpha and Omicron variants, we find that 2.7% of the common CDR3s found within groups were also present in more than one group. Our analysis reveals common and convergent antibodies, which include anti-SARS-CoV-2 antibodies, in patient groups over various stages of the pandemic.