bluuebunny/crossref_metadata_2025 huggingface dataset:
The East Asian Mahāyāna Teaching of the One Mind and Its Implications in a Polarized World
Authors: Kang Byongchang
Published: September 2023

This paper addresses the problem of polarization, which is considered one of the most pressing issues facing humanity, from the perspective of Mahāyāna Buddhism, specifically, the East Asian Buddhist teaching of the One Mind. The teaching is outlined in the Treatise on Awakening Mahāyāna Faith (Dasheng qixin lun), a significant text in East Asian Buddhism. The paper suggests that the One Mind teaching can help counteract the deluded and polarized mind, which seems inevitable due to the human condition but gives rise to polarization. We have the potential to move from delusion to awakening, since these two mental states are not separate from each other. By awakening to the One Mind, which is the common foundation of equality and interconnectedness of all sentient beings, we can return to our original still and pure mind that is capable of seeing the reality beyond the discriminating, prioritizing, and repressing mind, which has no intrinsic self-nature. Thus, the solution to the problem of so-called “post-truth”, epistemic bubbles, and echo chambers lies in cultivating mindfulness and awareness of the mind as well as recognizing the fundamental interconnectedness of all beings through the One Mind.
Robust Multiplexing Waveform Design for Integrated OFDM Radar and Communication via Complex Weight Optimization
Authors: Wang Yang , Cao Yunhe , Yeo Tat-Soon , Cheng Yuanhao , Fu Jie
Published: September 2023

Compared to traditional single orthogonal frequency division multiplexing (OFDM) radar and OFDM communication systems, integrated OFDM radar and communication systems have the advantages of improved sharing of scarce spectrum resources, a simple hardware structure and a reduced interference between signals. In this paper, a constraint relaxation-based robust OFDM multiplexing waveform (ROW-CR) design method for integrated radar and communication systems is proposed. Considering the influence of correlated clutter and jamming signals, transmission and reception models of different system platforms are established by allocating subcarrier complex weights of different antenna signals so as to determine radar conditional mutual information and communication channel capacity parameters. Meanwhile, a restricted closed model is introduced through the limit range of the target frequency response. Then, a robust OFDM waveform optimization problem for integrated radar and communication systems is constructed by the minimax criterion, and the closed-form solution is obtained by adopting an improved method based on trace function properties and constraint relaxation, resulting in a better radar and communication performance trade-off. In addition, a parameter hierarchical optimization-based robust OFDM waveform (ROW-PHO) design method is further explored to reduce the computational complexity, and this method can also ensure a low system performance loss. Finally, the numerical simulation results verify the effectiveness of the proposed methods.
Decoupling Effect and Driving Factors of Land-Use Carbon Emissions in the Yellow River Basin Using Remote Sensing Data
Authors: Wang Xiaolei , Zhao Xue , Zhang Shiru , Shi Shouhai , Zhang Xiang
Published: September 2023

Land-use change is a crucial element influencing the patterns of carbon sinks/sources in the Yellow River Basin (YRB). Therefore, studying land-use carbon emissions (LUCE) in the YRB and the decoupling from economic development can help formulate emission reduction strategies. In order to explore the spatiotemporal characteristics of LUCE in the YRB, we estimated the LUCE in 69 cities in the YRB using the downscale energy balance table estimation method and land-use remote sensing data for seven phases from 1990 to 2020. The spatial and temporal features of LUCE were researched from three different spatial scales: the whole spatial scale of the YRB, the sub-basin level, and the city level. Furthermore, the Tapio decoupling model was utilized to research the decoupling state between LUCE and economic development using a multi-scale approach. The Logarithmic Mean Divisia Index (LMDI) model was employed to explore the influencing factors of LUCE in the YRB. These results showed the following: (1) The LUCE in the YRB went through two stages: “stable growth” (1990–2000) and “rapid growth” (2000–2020). The LUCE increased from 165 million tons in 1990 to 1.414 billion tons in 2020, and the average annual growth rate was 25.12%. The spatial pattern of LUCE in the YRB exhibited significant variations, with the LUCE showing a geographic differentiation of midstream > downstream > upstream. (2) Except for the expansive coupling state during 2000–2005 (e: 0.952) and the expansive negative decoupling state during 2015–2020 (e: 2.151), the YRB was in the weak decoupling state for the majority of the time periods. (3) Economic development was the major positive driving factor for the rise of LUCE in this basin, while energy consumption intensity was the primary inhibiting factor. Through a discussion of the features and influencing factors of LUCE, these results can be utilized to provide carbon emission reduction recommendations tailored to the characteristics of cities’ resources and economic development, which will be helpful for achieving low-carbon and sustainable development in the YRB.
Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
Authors: Chen Qingqing , Tang Xiaowen , Li Biao , Tang Zhiya , Miao Fang , Song Guolin , Yang Ling , Wang Hao , Zeng Qiangyu
Published: September 2023

Large-area soil moisture (SM) data with high resolution and precision are the foundation for the research and application of hydrological and meteorological models, water resource evaluation, agricultural management, and warning of geological disasters. It is still challenging to downscale SM products in complex terrains that require fine spatial details. In this study, SM data from the Soil Moisture Active and Passive (SMAP) satellite were downscaled from 36 to 1 km in the summer and autumn of 2017 in Sichuan Province, China. Genetic-algorithm-optimized backpropagation (GABP) neural network, random forest, and convolutional neural network were applied. A fusion model between SM and longitude, latitude, elevation, slope, aspect, land-cover type, land surface temperature, normalized difference vegetation index, enhanced vegetation index, evapotranspiration, day sequence, and AM/PM was established. After downscaling, the in situ information was fused through a geographical analysis combined with a spatial interpolation to improve the quality of the downscaled SM. The comparative results show that in complex terrains, the GABP neural network better captures the soil moisture variations in both time and space domains. The GDA_Kriging method is able to merge in situ information in the downscaled SM while simultaneously maintaining the dynamic range and spatial details.
PolSAR Image Classification by Introducing POA and HA Variances
Authors: Lan Zeying , Liu Yang , He Jianhua , Hu Xin
Published: September 2023

A polarimetric synthetic aperture radar (PolSAR) has great potential in ground target classification. However, current methods experience difficulties in separating forests and buildings, especially oriented buildings. To address this issue, inspired by the three-component decomposition method, multiple new scattering models were proposed to describe the difference between forest scattering and building scattering. However, this problem cannot effectively be solved with scattering power alone since HV polarization records significant scattering powers from building areas that are similar to vegetation. Therefore, in this study, two new parameters, the polarization orientation angle (POA) variance and helix angle (HA) variance, were defined to describe the distributions of buildings and forests. By combining scattering power with POA and HA variances, the random forest algorithm was used to conduct the land cover classification, focusing on distinguishing between forests and oriented buildings. Finally, the C- and L-band polarimetric SAR data acquired by the GF-3, ALOS1 PALSAR, and SAOCOM systems were selected to test the proposed method. The results indicate that it is feasible to improve PolSAR classification accuracy by introducing polarimetric parameters. Quantitatively, the classification accuracies increased by 23.78%, 10.80%, and 12.97% for the ALOS1 PALSAR, GF-3, and SAOCOM data, respectively.
Leaf Area Index Inversion of Spartina alterniflora Using UAV Hyperspectral Data Based on Multiple Optimized Machine Learning Algorithms
Authors: Fang Hua , Man Weidong , Liu Mingyue , Zhang Yongbin , Chen Xingtong , Li Xiang , He Jiannan , Tian Di
Published: September 2023

The leaf area index (LAI) is an essential biophysical parameter for describing the vegetation canopy structure and predicting its growth and productivity. Using unmanned aerial vehicle (UAV) hyperspectral imagery to accurately estimate the LAI is of great significance for Spartina alterniflora (S. alterniflora) growth status monitoring. In this study, UAV hyperspectral imagery and the LAI of S. alterniflora during the flourishing growth period were acquired. The hyperspectral data were preprocessed with Savitzky–Golay (SG) smoothing, and the first derivative (FD) and the second derivative (SD) spectral transformations of the data were then carried out. Then, using the band combination index (BCI) method, the characteristic bands related to the LAI were extracted from the hyperspectral image data obtained with the UAV, and spectral indices (SIs) were constructed through the characteristic bands. Finally, three machine learning (ML) regression methods—optimized support vector regression (OSVR), optimized random forest regression (ORFR), and optimized extreme gradient boosting regression (OXGBoostR)—were used to establish LAI estimation models. The results showed the following: (1) the three ML methods accurately predicted the LAI, and the optimal model was provided by the ORFR method, with an R2 of 0.85, an RMSE of 0.19, and an RPD of 4.33; (2) the combination of FD SIs improved the model accuracy, with the R2 value improving by 41.7%; (3) the band combinations screened using the BCI method were mainly concentrated in the red and near-infrared bands; (4) the higher LAI was distributed on the seaward side of the study area, while the lower LAI was located at the junction between the S. alterniflora and the tidal flat. This study serves as both theoretical and technological support for research on the LAI of S. alterniflora and as a solid foundation for the use of UAV remote sensing technologies in the supervisory control of S. alterniflora.
Photonic Integrated Circuit Based Temperature Sensor for
Out-of-Autoclave Composite Parts Production Monitoring
Authors: Syriopoulos Georgios , Poulopoulos Ioannis , Zervos Charalampos , Kyriazi Evrydiki , Poulimenos Aggelos , Szaj Michal , Missinne Jeroen , van Steenberge Geert , Avramopoulos Hercules
Published: September 2023

The use of composite materials has seen widespread adoption in modern aerospace industry. This has been facilitated due to their favourable mechanical characteristics, namely, low weight and high stiffness and strength. For broader implementation of those materials though, the out-of-autoclave production processes have to be optimized, to allow for higher reliability of the parts produced as well as cost reduction and improved production speed. This optimization can be achieved by monitoring and controlling resin filling and curing cycles. Photonic Integrated Circuits (PICs), and, in particular, Silicon Photonics, owing to their fast response, small size, ability to operate at higher temperatures, immunity to electromagnetic interference, and compatibility with CMOS fabrication techniques, can offer sensing solutions fulfilling the requirements for composite material production using carbon fibres. In this paper, we demonstrate a passive optical temperature sensor, based on a 220 nm height Silicon-on-Insulator platform, embedded in a composite tool used for producing RTM-6 composite parts of high quality (for use in the aerospace industry). The design methodology of the photonic circuit as well as the experimental results and comparison with the industry standard thermocouples during a thermal cycling of the tool are presented. The optical sensor exhibits high sensitivity (85 pm/°C), high linearity (R2 = 0.944), and is compatible with the RTM-6 production process, operating up to 180 °C.
A Sustainability-Based Expert System for Additive Manufacturing and CNC Machining
Authors: Perera Josage Chathura , Gopalakrishnan Bhaskaran , Bisht Prakash Singh , Chaudhari Subodh , Sundaramoorthy Senthil
Published: September 2023

The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid®V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology.
The Suspended Sediment Flux in Winter in the South of Chengshantou, between the North and South Yellow Sea
Authors: Li Bowen , Xiong Xuejun , Duan Baichuan , Wang Daolong , Yu Long
Published: September 2023

Due to the regional differences between the North and South Yellow Sea, and under the influence of winter winds, the relative changes in the coastal current and the Yellow Sea warm current will lead to the instability of the front, which will lead to the cross-front transport of sediment. Therefore, the study of sediment exchange between the North and South Yellow Sea has become an indispensable part of the study of the Yellow Sea environment. In this study, the current field and sediment concentration in the southern part of Chengshantou, a representative area of the Yellow Sea, were observed in winter in order to analyze the sediment exchange process between the North Yellow Sea and the South Yellow Sea in winter. The observation results show that in the southern sea area of Chengshantou, in winter, the current velocity does not change with the water depth when it exceeds 15 m, and the tides are regular semi-diurnal tides. When the water depth is less than 15 m, the current direction changes clockwise with the increase in the water depth. The turbidity increases rapidly when the wind direction is offshore and the bottom residual current is onshore, which may cause the sediment transported offshore under the action of wind and ocean current to settle under the obstruction of the Yellow Sea warm current, resulting in the rise of bottom turbidity. This also indicates that the change in residual current direction at different water depths may also lead to an increase in suspended sediment concentration. Based on this, in the estuarine area, the relative change in the current direction between the wind current and the coastal current may also be the cause of the change in the maximum turbidity zone.
PSBF: p-adic Integer Scalable Bloom Filter
Authors: Yi Wenlong , Wang Chuang , Xie Qiliang , Zhao Yingding , Jia Jing
Published: September 2023

Given the challenges associated with the dynamic expansion of the conventional bloom filter’s capacity, the prevalence of false positives, and the subpar access performance, this study employs the algebraic and topological characteristics of p-adic integers to introduce an innovative approach for dynamically expanding the p-adic Integer Scalable Bloom Filter (PSBF). The proposed method involves converting the target element into an integer using a string hash function, followed by the conversion of said integer into a p-adic integer through algebraic properties. This process automatically establishes the topological tree access structure of the PSBF. The experiment involved a comparison of access performance among the standard bloom filter, dynamic bloom filter, and scalable bloom filter. The findings indicate that the PSBF offers advantages such as avoidance of a linear storage structure, enhanced efficiency in element insertion and query, improved storage space utilization, and reduced likelihood of false positives. Consequently, the PSBF presents a novel approach to the dynamic extensibility of bloom filters.
Modeling and Optimization of LoRa Networks under Multiple Constraints
Authors: Zhang Hui , Song Yuxin , Yang Maoheng , Jia Qiming
Published: September 2023

With the access of massive terminals of the Internet of Things (IoT), the low-power wide-area networks (LPWAN) applications represented by Long Range Radio (LoRa) will grow extensively in the future. The specific Long Range Wide Area Network (LoRaWAN) protocol within the LoRa network considers both low power consumption and long-range communication. It can optimize data transmission to achieve low communication latency, ensuring a responsive system and a favorable user experience. However, due to the limited resources in LoRa networks, if certain terminals have heavy traffic loads, it may result in unfair impacts on other terminals, leading to increased data transmission latency and disrupted operations for other terminals. Therefore, effectively optimizing resource allocation in LoRa networks has become a key issue in enhancing LoRa transmission performance. In this paper, a Mixed Integer Linear Programming (MILP) model is proposed to minimize network energy consumption under the maximization of user fairness as the optimization goal, which considers the constraints in the system to achieve adaptive resource allocation for spreading factor and transmission power. In addition, an efficient algorithm is proposed to solve this optimization problem by combining the Gurobi mathematical solver and heuristic genetic algorithm. The numerical results show that the proposed algorithm can significantly reduce the number of packet collisions, effectively minimize network energy consumption, as well as offering favorable fairness among terminals.
Paper-Based Aptasensors: Working Principles, Detection Modes, and Applications
Authors: Economou Anastasios , Kokkinos Christos , Bousiakou Leda , Hianik Tibor
Published: September 2023

Aptamers are short oligonucleotides designed to possess high binding affinity towards specific target compounds (ions, molecules, or cells). Due to their function and unique advantages, aptamers are considered viable alternatives to antibodies as biorecognition elements in bioassays and biosensors. On the other hand, paper-based devices (PADs) have emerged as a promising and powerful technology for the fabrication of low-cost analytical tools, mainly intended for on-site and point-of-care applications. The present work aims to provide a comprehensive overview of paper-based aptasensors. The review describes the fabrication methods and working principles of paper-based devices, the properties of aptamers as bioreceptors, the different modes of detection used in conjunction with aptasensing PADs, and representative applications for the detection of ions, small molecules, proteins, and cells. The future challenges and prospects of these devices are also discussed.
Saliency-Driven Hand Gesture Recognition Incorporating Histogram of Oriented Gradients (HOG) and Deep Learning
Authors: Jafari Farzaneh , Basu Anup
Published: September 2023

Hand gesture recognition is a vital means of communication to convey information between humans and machines. We propose a novel model for hand gesture recognition based on computer vision methods and compare results based on images with complex scenes. While extracting skin color information is an efficient method to determine hand regions, complicated image backgrounds adversely affect recognizing the exact area of the hand shape. Some valuable features like saliency maps, histogram of oriented gradients (HOG), Canny edge detection, and skin color help us maximize the accuracy of hand shape recognition. Considering these features, we proposed an efficient hand posture detection model that improves the test accuracy results to over 99% on the NUS Hand Posture Dataset II and more than 97% on the hand gesture dataset with different challenging backgrounds. In addition, we added noise to around 60% of our datasets. Replicating our experiment, we achieved more than 98% and nearly 97% accuracy on NUS and hand gesture datasets, respectively. Experiments illustrate that the saliency method with HOG has stable performance for a wide range of images with complex backgrounds having varied hand colors and sizes.
High-Precision Inertial Sensor Charge Management Based on Ultraviolet Discharge: A Comprehensive Review
Authors: Yu Tao , Wang Yuhua , Liu Yang , Wang Zhi
Published: September 2023

The charge accumulation caused by cosmic rays and solar energetic particles poses a significant challenge as a source of noise for inertial sensors used in space gravitational wave detection. To address this issue, the implementation of charge management systems based on ultraviolet discharge becomes crucial. This paper focuses on elucidating the principles and methods of using ultraviolet discharge for charge management in high-precision inertial sensors. Furthermore, it presents the design and implementation of relevant payloads. Through an analysis of the charge accumulation effect and its impact on noise, key considerations regarding coatings, light sources, and optical paths are explored, and some current and valuable insights into the future development of charge management systems are also summarized. The conclusions drawn from this research also provide guidance for the advancement of higher precision ultraviolet discharge technology and the design of charge management systems.
SGAN-IDS: Self-Attention-Based Generative Adversarial Network against Intrusion Detection Systems
Authors: Aldhaheri Sahar , Alhuzali Abeer
Published: September 2023

In cybersecurity, a network intrusion detection system (NIDS) is a critical component in networks. It monitors network traffic and flags suspicious activities. To effectively detect malicious traffic, several detection techniques, including machine learning-based NIDSs (ML-NIDSs), have been proposed and implemented. However, in much of the existing ML-NIDS research, the experimental settings do not accurately reflect real-world scenarios where new attacks are constantly emerging. Thus, the robustness of intrusion detection systems against zero-day and adversarial attacks is a crucial area that requires further investigation. In this paper, we introduce and develop a framework named SGAN-IDS. This framework constructs adversarial attack flows designed to evade detection by five BlackBox ML-based IDSs. SGAN-IDS employs generative adversarial networks and self-attention mechanisms to generate synthetic adversarial attack flows that are resilient to detection. Our evaluation results demonstrate that SGAN-IDS has successfully constructed adversarial flows for various attack types, reducing the detection rate of all five IDSs by an average of 15.93%. These findings underscore the robustness and broad applicability of the proposed model.
Fuzzy-Based Efficient Healthcare Data Collection and Analysis Mechanism Using Edge Nodes in the IoMT
Authors: Khan Muhammad Nafees Ulfat , Tang Zhiling , Cao Weiping , Abid Yawar Abbas , Pan Wanghua , Ullah Ata
Published: September 2023

The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors generate huge amounts of data, it is better for the data to be aggregated before being transmitting the data further. These sensors generate redundant data frequently and transmit the same values again and again unless there is no variation in the data. The base scheme has no mechanism to comprehend duplicate data. This problem has a negative effect on the performance of heterogeneous networks.It increases energy consumption; and requires high control overhead, and additional transmission slots are required to send data. To address the above-mentioned challenges posed by duplicate data in the IoT-based health sector, this paper presents a fuzzy data aggregation system (FDAS) that aggregates data proficiently and reduces the same range of normal data sizes to increase network performance and decrease energy consumption. The appropriate parent node is selected by implementing fuzzy logic, considering important input parameters that are crucial from the parent node selection perspective and share Boolean digit 0 for the redundant values to store in a repository for future use. This increases the network lifespan by reducing the energy consumption of sensors in heterogeneous environments. Therefore, when the complexity of the environment surges, the efficiency of FDAS remains stable. The performance of the proposed scheme has been validated using the network simulator and compared with base schemes. According to the findings, the proposed technique (FDAS) dominates in terms of reducing energy consumption in both phases, achieves better aggregation, reduces control overhead, and requires the fewest transmission slots.
Improved Frequency Domain Turbo Equalization with Expectation Propagation Interference Cancellation in Underwater Acoustic Communications
Authors: Jiang Bin , Tang Yue , Zhao Yinan , Bao Jianrong , Liu Chao , Tang Xianghong
Published: September 2023

This paper proposes an improved frequency domain turbo equalization (IFDTE) with iterative channel estimation and feedback to achieve both a good performance and low complexity in underwater acoustic communications (UWACs). A selective zero-attracting (SZA) improved proportionate normal least mean square (SZA-IPNLMS) algorithm is adopted by utilizing the sparsity of the UWAC channel to estimate it using a training sequence. Simultaneously, a set-membership (SM) SZA differential IPNLMS (SM SZA-DIPNLMS) with variable step size is adopted to estimate the channel status information (CSI) in the iterative channel estimation with soft feedback. In this way, the computational complexity for iterative channel estimation is reduced effectively with minimal performance loss. Different from traditional schemes in UWACs, an IFDTE with expectation propagation (EP) interference cancellation is adopted to estimate the a posteriori probability of transmitted symbols iteratively. A bidirectional IFDTE with the EP interference cancellation is proposed to further accelerate the convergence. THe simulation results show that the proposed channel estimation obtains 1.9 and 0.5 dB performance gains, when compared with those of the IPNLMS and the l0-IPNLMS at a bit error rate (BER) of 10−3. The proposed channel estimation also effectively reduces the unnecessary updating of the coefficients of the UWAC channel. Compared with traditional time-domain turbo equalization and FDTE in UWACs, the IFDTE obtains 0.5 and 1 dB gains in the environment of SPACE’08 and it obtains 0.5 and 0.4 dB gains in the environment of MACE’04 at a BER of 10−3. Therefore, the proposed scheme obtains a good BER performance and low complexity and it is suitable for efficient use in UWACs.
Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction
Authors: Tseng Yu-Hsuan , Wen Chih-Yu
Published: September 2023

This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing a real-time HAR system architecture with a wearable inertial measurement unit (IMU) sensor, which aims to achieve networked sensing and data sampling of human activity, data pre-processing and feature analysis, data generation and correction, and activity classification using hybrid learning models. Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost) model and the Convolutional Variational Autoencoder (CVAE) model as the classifier and generator, respectively, with 96.03% classification accuracy.
Multi-Sensors System and Deep Learning Models for Object Tracking
Authors: El Natour Ghina , Bresson Guillaume , Trichet Remi
Published: September 2023

Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement arises to accurately track and predict these objects’ trajectories. Three deep recurrent network architectures were defined to achieve this, fine-tuning their weights to optimize the tracking process. The effectiveness of this proposed pipeline has been assessed, with diverse tracking scenarios demonstrated in both sub-urban and highway environments. The evaluations have yielded promising results, affirming the potential of this approach in enhancing autonomous navigation capabilities.
Environmental Impact Assessment of Different Manufacturing Technologies Oriented to Architectonic Recovery and Conservation of Cultural Heritage
Authors: Altadonna Alessio , Cucinotta Filippo , Raffaele Marcello , Salmeri Fabio , Sfravara Felice
Published: September 2023

Our cultural society has made remarkable advancements in creating digital models that depict the built environment, landscape, and reality. The advent of technologies such as terrestrial laser scanning and drone-based photogrammetry, coupled with sophisticated software capable of processing hundreds of photographs to generate point clouds, has elevated the significance of three-dimensional surveying in documentation and restoration. Point cloud processing and modeling software enable the creation of precise digital replicas of the investigated architecture, which can be scaled down and transformed into physically identical models. Through the export of STL files and the utilization of both subtractive and additive 3D printing technologies, tactile models resembling traditional manually crafted plastics can be obtained. An exemplary study focuses on the Gothic church of Santa Maria Alemanna in Messina, Italy, where laser scanner surveys and 3D prints using various technologies were applied to different parts of the building. The models were produced using a CNC milling machine and a 3D printer for fused deposition modeling. The sustainability of these production technologies was assessed through a Life Cycle Assessment, demonstrating the environmental advantages of additive manufacturing, including the use of materials with high recyclability and lower energy consumption. Additionally, the additive approach helps reduce processing waste.
Technologies Designed to Assist Individuals with Cognitive Impairments
Authors: Szabó Patrícia , Ara Jinat , Halmosi Bence , Sik-Lanyi Cecilia , Guzsvinecz Tibor
Published: September 2023

Information Technology (IT) plays a vital role in promoting sustainability and enabling independent living. People with cognitive disabilities face numerous challenges in their daily lives, such as social interactions, learning new things, and improving specific abilities. A variety of therapies and treatments have been introduced to help address these challenges. Recently, computer-assisted therapeutic procedures, treatment procedures, and assistive systems have emerged as beneficial tools to improve the lives of people with cognitive disabilities. Advances in technology have made it possible to develop effective applications specifically designed for this group of individuals. The objective of this paper is to identify potential applications of these developed solutions for people with cognitive disabilities, evaluate their effectiveness, strengths, and limitations, and understand their contribution in addressing various difficulties due to cognitive impairments. To achieve this goal, we reviewed 23 studies that demonstrate several applications developed for people with cognitive disabilities to address their unique issues. Our investigation indicates that the developed applications hold promise, although a few issues with cost-effectiveness, device transparency, and specific disability dependency may limit their effectiveness. Hence, this paper aims to shed light on these innovative applications, their implications, and their role in aiding users in tackling their specific challenges.
A Complex Circular-Economy Quality Indicator for Assessing Production Systems at the Micro Level
Authors: Kowalski Zygmunt , Kulczycka Joanna , Banach Marcin , Makara Agnieszka
Published: September 2023

Measuring circular-economy progress requires indicators to examine the implementation of management systems. A complex quality indicator for assessing production systems at the circular-economy micro level was proposed. One innovative aspect of this evaluation of quality indicators is the classification of a set of sixty selected options divided into four core groups (technical, environmental, economic, and social). The second one is the use of a trimmed-mean method to summarize the individual options score into core groups and complex circular-economy indicators of analyzed production systems. The individual options were weighed according to indices defined by the professional team. Here is presented a case study of the use of developed complex quality indicators, including a comparison of two sodium tripolyphosphate production processes. The calculated indicator of the new method was higher, at 204.8%, than the indicator of the old method. This confirms the significant advantage of the new technology.
Optimizing Hybrid Photovoltaic/Battery/Diesel Microgrids in Distribution Networks Considering Uncertainty and Reliability
Authors: Memon Zulfiqar Ali , Akbari Mohammad Amin
Published: September 2023

Due to the importance of the allocation of energy microgrids in the power distribution networks, the effect of the uncertainties of their power generation sources and the inherent uncertainty of the network load on the problem of their optimization and the effect on the network performance should be evaluated. The optimal design and allocation of a hybrid microgrid system consisting of photovoltaic resources, battery storage, and a backup diesel generator are discussed in this paper. The objective of the problem is minimizing the costs of power losses, energy resources generation, diesel generation as backup resource, battery energy storage as well as load shedding with optimal determination of the components energy microgrid system include its installation location in the 33-bus distribution network and size of the PVs, batteries, and Diesel generators. Additionally, the effect of uncertainties in photovoltaic radiation and network demand are evaluated on the energy microgrid design and allocation. A Monte Carlo simulation is used to explore the full range of possibilities and determine the optimal decision based on the variability of the inputs. For an accurate assessment of the system’s reliability, a forced outage rate (FOR) analysis is performed to calculate potential photovoltaic losses that could affect the operational probability of the system. The cloud leopard optimization (CLO) algorithm is proposed to optimize this optimization problem. The effectiveness of the proposed algorithm in terms of accuracy and convergence speed is verified compared to other state-of-the-art optimization methods. To further improve the performance of the proposed algorithm, the reliability and uncertainties of photovoltaic resource production and load demand are investigated.
Back to the New Normal in Engineering Education towards Student-Centered Learning: Remote? In Person? Hybrid?
Authors: AlMunifi Abdullatif A. , Alfawzan Mohammed S.
Published: September 2023

Academic institutions are back to normal, but what about students? What were the cultural and lifestyle changes and impacts due to the remote mode of learning? Classes in Saudi universities in the pre-pandemic time were predominantly 100% face to face. During the COVID-19 pandemic, the education sector moved to 100% online and then went through hybrid mode (a mixture of face to face and remote) until the return to the old normal, i.e., 100% in person. Students in the post-pandemic period are wondering “Why are we coming to campus? What is the value in this teaching and learning experience where we have to commute daily?” This research addresses students’ concerns and questions, and it addresses the following questions: to what extent do they prefer learning remotely? In person? Hybrid? Observations and discussions preceded the design of the research instruments. A questionnaire survey that consists of closed-ended and open-ended questions was distributed and received responses from 74 engineering students, at the Qassim University, who were in the last three semesters of their studies and who went through the three modes of learning. To complement the quantitative research and to achieve a better understanding of matters that were revealed through the survey, the results of the analysis were shared through three focus group sessions with students and faculty members. The findings from this mixed methods approach revealed that the hybrid mode of learning is gaining significant preference among students and partial support from the teaching staff. On the other hand, unfavorable perceptions were expressed towards both going back to the old normal or the transition to a completely remote mode of learning.
Assessing Soil Dynamics and Improving Long-Standing Irrigation Management with Treated Wastewater: A Case Study on Citrus Trees in Palestine
Authors: Dragonetti Giovanna , Khadra Roula
Published: September 2023

Irrigation with Treated Wastewater (TWW) is a well-known agricultural practice in Palestine. The long-term use of irrigation with TWW, a source of water and nutrients, can affect plant development, soil, and groundwater quality. Consequently, the frequency and the intervals of irrigation events should be adequately scheduled, especially when nutrients (TWW-N) cannot be separated from the water. Achieving good water quality implies its immediate reuse in irrigated agriculture. In contrast, long-term soil and groundwater quality conservation is marked by the complex mechanisms that correlate the soil, water, plant, and atmosphere. Therefore, monitoring and modeling (MMA) are combined to retrieve the soil water and nitrate fluxes and identify a proper irrigation management plan in a case study in Beit Dajan-Palestine, where a schedule adapted to conventional water was applied to a 6-year-old citrus orchard continuously irrigated with TWW. Soil nitrogen concentration and water content data were collected from March to August 2021 to calibrate the Hydrus-1D model under the (1) farmer demand (F) scenario, where irrigation volumes are delivered according to the farmer experience, and to define an optimal irrigation management strategy with TWW according to the (2) model demand (M) scenario, based on the irrigation frequency. The latter respects the allowable thresholds of soil solution electrical conductivity, σe, assuming an average soil salinity profile and estimated leaf nitrogen concentrations tolerance as reference; 2021 was taken as a calibration year to retrieve water and nitrate fluxes for 2019 and 2020. In 2021, the measured soil electrical conductivity, σe, showed no salinity risk with an average value of 1.07 dS m−1 (low salinity < 2 dS m−1) but with a leaf nitrogen deficit. Although an acceptable level of available soil nitrogen was observed (ranging between 10 and 35 mg kg−1, whereas the standard value is 10–40 mg kg−1), critical concentrations were observed in the leaves (below 1%) in scenario (F) compared to scenario (M) (ranging between 1.7 and 1.9%). The latter also showed a decrease in nitrate leaching by 33% compared to the former. Overall, the comparison between the simulated and measured soil variables shows that the 1D-Hydrus model could follow the temporal variation in the monitored data, with some overestimation of the measured data during the simulation period. The simulations demonstrate that by modulating the salt tolerance threshold, the M scenario achieved better results in terms of root water and N uptake despite the stress inevitably experienced by citrus with long-term TWW irrigation. Moreover, the optimum threshold values used to assess the soil quality and citrus response under conventional water irrigation were inadequate for TWW practices. Therefore, MMA could be an alternative strategy to schedule proper TWW irrigation.
Research on the Spatial Pattern of Carbon Emissions and Differentiated Peak Paths at the County Level in Shandong Province, China
Authors: Han Xinyu , Qu Peng , Wu Jiaqi , Su Beile , Qiu Ning , Zhang Lili
Published: September 2023

In the pursuit of China’s carbon peak and carbon neutrality objectives, county-level areas assume a pivotal role in orchestrating diverse initiatives for low-carbon development. However, empirical evidence is limited. This paper aims to fill this gap by exploring the driving factors of carbon peak and carbon peak path at the county level, using Shandong Province as a case study. Employing data related to economic development, industrial structure, land utilization, energy consumption, and emission characteristics, a principal component analysis (PCA) was utilized to extract the following five driving factors of carbon peak: green transformation, urbanization, industrial construction, energy consumption, and environmental constraints. Subsequently, K-means clustering identified five cluster areas: (1) agricultural transformation pending area, (2) low-carbon lagging area, (3) industrial transformation area, (4) low-carbon potential areas, and (5) low-carbon demonstration area. Based on these areas, this study further elucidates spatial combination models of carbon peak within the urban system, spanning central cities, coastal cities, resource-based cities, and agricultural cities. The paper enhances comprehension of the integral role county-level areas play in achieving China’s carbon reduction objectives. By providing nuanced insights into diverse developmental trajectories and spatial interactions, the study contributes to effective low-carbon strategy formulation. The findings underscore the importance of considering specific county attributes in urban areas to devise precise optimization strategies and trajectories, ultimately facilitating the realization of carbon peak goals.
Management Soil Zones, Irrigation, and Fertigation Effects on Yield and Oil Content of Coriandrum sativum L. Using Precision Agriculture with Fuzzy k-Means Clustering
Authors: Filintas Agathos , Gougoulias Nikolaos , Kourgialas Nektarios , Hatzichristou Eleni
Published: September 2023

Precision agriculture (PA), management zone (MZ) strategies at the field level, soil analyses, deficit irrigation (DI), and fertilizer Variable Rate Application (VRA) are management strategies that help farmers improve crop production, fertilizer use efficiency, and irrigation water use efficiency (IWUE). In order to further investigate these management strategies, the effects of four soil MZ treatments, which were delineated using PA with fuzzy k-means clustering, two irrigation levels [IR1:FI = full drip irrigation (>90% of θfc), IR2:VDI = variable deficit drip irrigation (60–75% of θfc)], and four VRA fertilizations were studied on coriander yield and essential oil content in a two-year research project in Greece. A daily soil-water-crop-atmosphere (SWCA) balance model and a daily depletion model were developed using sensor measurements (climatic parameter sensors as well as soil moisture sensors). Unbalanced one-way ANOVA (p = 0.05) statistical analysis results revealed that correct delineation of MZs by PA with fuzzy k-means clustering, if applied under deficit irrigation and VRA fertilization, leads to increased essential oil content of coriander with statistically significant differences (SSD) and lower fruit yields; however, without SSD differences among management zones, when appropriate VRA fertilization is applied to leverage soil nutrient levels through the different fuzzy clustered MZs for farming sustainability. Moreover, VDI compared to full irrigation in different MZs yields 22.85% to 29.44% in water savings, thus raising IWUE (up to 64.112 kg m−3), nitrogen efficiency (up to 5.623), and N-P-K fertilizer productivity (up to 5.329).
Perceived Behavior Analysis to Boost Physical Fitness and Lifestyle Wellness for Sustainability among Gen Z Filipinos
Authors: Kurata Yoshiki B. , Ong Ardvin Kester S. , Cunanan Alyssa Laraine M. , Lumbres Alwin G. , Palomares Kyle Gericho M. , Vargas Christine Denise A. , Badillo Abiel M.
Published: September 2023

The recommended moderate-intensity physical activity per week is 150–300 min for adults and 60 min of moderate-to-vigorous physical activity for adolescents. However, 81% of adolescents and 23% of adults globally do not meet the recommendations for physical activity. With the increasing business in the fitness industry on the rise, sedentary activities were also seen to be prevalent—especially due to the COVID-19 pandemic lockdown. This study aimed to identify and evaluate factors affecting the perceived behavior of Filipinos to boost physical fitness and lifestyle wellness by incorporating the Theory of Effort Minimization (TEMPA) and Protection Motivation Theory (PMT). About 565 Filipinos answered a self-administered questionnaire with 54 adapted questions (45 indicators and 9 latent variables). With the use of a convenience sampling approach, valid responses were evaluated using Structural Equation Modeling (SEM). The results showed that self-efficacy, response efficacy, automatic precursors, and controlled precursors significantly and indirectly affect perceived behavior. It was explained how self-efficacy, or one’s ability to perform a specific behavior, connects with response-efficacy, which covers an individual’s perception of the effectiveness of a task, in this case, health-promoting practices. The influence of others on the individual was presented as conscientiousness, neuroticism, and agreeableness. The effect on other people influences an individual’s positive relationship with adaptable behavior for physical activities. Similarly, the effects of other people may influence individuals to perform healthier lifestyles. These precursors had a direct significance with the intention to adapt, which subsequently led to the perceived behavior of Filipinos in boosting physical fitness and lifestyle wellness. The results of this study could be utilized by the fitness industry, government, local government units (LGUs), and private and public institutions. Further implications were discussed both from a theoretical and practical standpoint, which can help to create successful fitness and wellness programs. Finally, the SEM constructs can be modified and extended to evaluate factors affecting perceived behavior to boost physical fitness and lifestyle wellness among other nationalities.