bluuebunny/crossref_metadata_2025 huggingface dataset:
Genetic Dissection of Seasonal Changes in a Greening Plant Based on Time-Series Multispectral Imaging
Authors: Koji Taeko , Iwata Hiroyoshi , Ishimori Motoyuki , Takanashi Hideki , Yamasaki Yuji , Tsujimoto Hisashi
Published: October 2023

Good appearance throughout the year is important for perennial ornamental plants used for rooftop greenery. However, the methods for evaluating appearance throughout the year, such as plant color and growth activity, are not well understood. In this study, evergreen and winter-dormant parents of Phedimus takesimensis and 94 F1 plants were used for multispectral imaging. We took 16 multispectral image measurements from March 2019 to April 2020 and used them to calculate 15 vegetation indices and the area of plant cover. QTL analysis was also performed. Traits such as the area of plant cover and vegetation indices related to biomass were high during spring and summer (growth period), whereas vegetation indices related to anthocyanins were high in winter (dormancy period). According to the PCA, changes in the intensity of light reflected from the plants at different wavelengths over the course of a year were consistent with the changes in plant color and growth activity. Seven QTLs were found to be associated with major seasonal growth changes. This approach, which monitors not only at a single point in time but also over time, can reveal morphological changes during growth, senescence, and dormancy throughout the year.
Transcriptional Regulation of Small Heat Shock Protein 17 (sHSP-17) by Triticum aestivum HSFA2h Transcription Factor Confers Tolerance in Arabidopsis under Heat Stress
Authors: Kumar Ranjeet R. , Dubey Kavita , Goswami Suneha , Rai Gyanendra K. , Rai Pradeep K. , Salgotra Romesh K. , Bakshi Suman , Mishra Dwijesh , Mishra Gyan P. , Chinnusamy Viswanathan
Published: October 2023

Heat shock transcription factors (HSFs) contribute significantly to thermotolerance acclimation. Here, we identified and cloned a putative HSF gene (HSFA2h) of 1218 nucleotide (acc. no. KP257297.1) from wheat cv. HD2985 using a de novo transcriptomic approach and predicted sHSP as its potential target. The expression of HSFA2h and its target gene (HSP17) was observed at the maximum level in leaf tissue under heat stress (HS), as compared to the control. The HSFA2h-pRI101 binary construct was mobilized in Arabidopsis, and further screening of T3 transgenic lines showed improved tolerance at an HS of 38 °C compared with wild type (WT). The expression of HSFA2h was observed to be 2.9- to 3.7-fold higher in different Arabidopsis transgenic lines under HS. HSFA2h and its target gene transcripts (HSP18.2 in the case of Arabidopsis) were observed to be abundant in transgenic Arabidopsis plants under HS. We observed a positive correlation between the expression of HSFA2h and HSP18.2 under HS. Evaluation of transgenic lines using different physio-biochemical traits linked with thermotolerance showed better performance of HS-treated transgenic Arabidopsis plants compared with WT. There is a need to further characterize the gene regulatory network (GRN) of HSFA2h and sHSP in order to modulate the HS tolerance of wheat and other agriculturally important crops.
Flexural Response of Concrete Specimen Retrofitted with PU Grout Material: Experimental and Numerical Modeling
Authors: Haruna Sadi Ibrahim , Ibrahim Yasser E. , Han Zhu , Farouk Abdulwarith Ibrahim Bibi
Published: October 2023

Polyurethane (PU) composite is increasingly used as a repair material for civil engineering infrastructure, including runway, road pavement, and buildings. Evaluation of polyurethane grouting (PUG) material is critical to achieve a desirable maintenance effect. This study aims to evaluate the flexural behavior of normal concrete repaired with polyurethane grout (NC-PUG) under a three-point bending test. A finite element (FE) model was developed to simulate the flexural response of the NC-PUG specimens. The equivalent principle response of the NC-PUG was analyzed through a three-dimensional finite element model (3D FEM). The NC and PUG properties were simulated using stress–strain relations acquired from compressive and tensile tests. The overlaid PUG material was prepared by mixing PU and quartz sand and overlayed on the either top or bottom surface of the concrete beam. Two different overlaid thicknesses were adopted, including 5 mm and 10 mm. The composite NC-PUG specimens were formed by casting a PUG material using different overlaid thicknesses and configurations. The reference specimen showed the highest average ultimate flexural stress of 5.56 MPa ± 2.57% at a 95% confidence interval with a corresponding midspan deflection of 0.49 mm ± 13.60%. However, due to the strengthened effect of the PUG layer, the deflection of the composite specimen was significantly improved. The concrete specimens retrofitted at the top surface demonstrated a typical linear pattern from the initial loading stage until the complete failure of the specimen. Moreover, the concrete specimens retrofitted at the bottom surface exhibit two deformation regions before the complete failure. The FE analysis showed good agreement between the numerical model and the experimental test result. The numerical model accurately predicted the flexural strength of the NC-PUG beam, slightly underestimating Ke by 4% and overestimating the ultimate flexural stress by 3%.
Surface Topography of PLA Implants Defines the Outcome of Foreign Body Reaction: An In Vivo Study
Authors: Ivanova Elena , Fayzullin Alexey , Minaev Nikita , Dolganova Irina , Serejnikova Natalia , Gafarova Elvira , Tokarev Mark , Minaeva Ekaterina , Aleksandrova Polina , Reshetov Igor , Timashev Peter , Shekhter Anatoly
Published: October 2023

The formation of a dense fibrous capsule around the foreign body and its contracture is the most common complication of biomaterial implantation. The aim of our research is to find out how the surface of the implant influences the inflammatory and fibrotic reactions in the surrounding tissues. We made three types of implants with a remote surface topography formed of polylactide granules with different diameters: large (100–200 µm), medium (56–100 µm) and small (1–56 µm). We placed these implants in skin pockets in the ears of six chinchilla rabbits. We explanted the implants on the 7th, 14th, 30th and 60th days and performed optical coherence tomography, and histological, immunohistochemical and morphometric studies. We examined 72 samples and compared the composition of immune cell infiltration, vascularization, the thickness of the peri-implant tissues, the severity of fibrotic processes and α-SMA expression in myofibroblasts. We analyzed the scattering coefficient of tissue layers on OCT scans. We found that implants made from large granules induced a milder inflammatory process and slower formation of a connective tissue capsule around the foreign body. Our results prove the importance of assessing the surface texture in order to avoid the formation of capsular contracture after implantation.
Analysis of the Effect of Network Structure and Disulfide Concentration on Vitrimer Properties
Authors: Azcune Itxaso , Elorza Edurne , Ruiz de Luzuriaga Alaitz , Huegun Arrate , Rekondo Alaitz , Grande Hans-Jürgen
Published: October 2023

A set of five vitrimers with glass transition temperatures in the range of 80–90 °C were designed to assess the effect of the network structure and disulfide concentration on their dynamic and mechanical properties, and to find the best performing system overall compared to the commercial Araldite LY1564/Aradur 3486 commercial thermoset system. Vitrimer networks were prepared by incorporating mono- and bifunctional epoxy reactive diluents and an amine chain extender into the Araldite LY1564/4-aminophenyldisulfide system.
Comprehensive Analysis of Phenolic Compounds, Carotenoids, and Antioxidant Activities in Lactuca sativa var. longifolia Cultivated in a Smart Farm System
Authors: Choi Jungwon , An Joonggeun , Lee Hak-Dong , Kim Woo Jung , Lee Sullim , Lee Sanghyun
Published: October 2023

Smart farming is a promising strategy for future agriculture, and the global market for smart agriculture is growing rapidly. The aim of this study was to compare the quality of agricultural products based on their cultivation method. The components and antioxidant activities of Lactuca sativa (LS) var. longifolia cultivated using conventional agriculture methods (CAMs) and smart farm agriculture methods (SAMs) were compared. ABTS+ and DPPH radical scavenging activity assays were performed to compare the antioxidant activities of LS grown using CAMs and SAMs. High-performance liquid chromatography with a reverse-phase column was employed to analyze the phenolic and carotenoid compounds. In both the SAM and the CAM, β-carotene was found to be the most abundant compound. LS grown using the CAM had high total polyphenol content and ABTS+ radical scavenging activity, while LS grown using the SAM had high individual phenolic content and DPPH radical scavenging activity. This study developed a new method of SAM and confirmed that SAM produces a higher quantity of phenolic compounds and total flavonoid contents than CAM in LS. This study also highlights the SAM’s functioning, gives crop quality inspection guidelines, and standardizes LS. The new SAM is effective for application in undeveloped urban spaces, is more convenient than the CAM, and represents a way to develop and manage LS quality.
Screwed Monolithic Zirconia Crowns for Mono-Implant Posterior Rehabilitation: A Prospective Clinical Study on 41 Patients with a 7-Year Follow-Up
Authors: Barile Giuseppe , Capodiferro Saverio , De Rosa Giovanni , Muci Giovannino , Vanzanelli Alessandro , Corsalini Massimo
Published: October 2023

The usage of monolithic zirconia has been increasing in daily practice in dentistry for the last 20 years. Monolithic zirconia is mainly used for posterior sector rehabilitation, as it lacks optical properties and has good technical properties; it does not chip and reduces antagonist wear compared to metal–ceramic prostheses. However, monolithic zirconia may present some issues, mainly low-temperature degradation (LTD), also known as “aging”, which must be considered and investigated in clinical studies with prolonged follow-up periods. The aim of this study was the aesthetic and functional evaluation of single monolithic zirconia crowns that were screwed onto implants for posterior dental rehabilitation using USPHS parameters and a long follow-up period of 7 years. The results showed that the USPHS scoring reduces year by year, passing from 100% excellence between the first and fourth years of observation, to 88% excellence and 12% success in the seventh year. Screw abutment loosening was the only technical adversity reported; no implant failure, crown fracture, or irreparable damage were additionally registered. Considering the emerging results and comparing them with the data available in the literature, the authors suggest that monolithic zirconia crowns could be considered as a valid alternative to classic metal–ceramic rehabilitations for the rehabilitation of posterior sectors.
The Brooklyn Papyrus Snakebite and Medicinal Treatments’ Magico-Religious Context
Authors: Golding Wendy
Published: October 2023

This paper investigates the role of magic and religion in the context of the Brooklyn Papyrus (47.218.48 and 47.218.85) snakebite treatments. It examines the extent to which these two factors are involved in the treatments and how they shed light on the importance of treating the mind and body of the patient. Information regarding the methods and ingredients used in snakebite treatments in ancient Egypt is obtained from the author’s doctoral thesis in which the Brooklyn Papyrus (47.218.48 and 47.218.85) is transliterated and then translated into English and offers a commentary on the text. This translation enabled the author to understand that magic and religion form part of the snakebite treatment process. Investigating the relationship between these two factors and medical treatment ingredients and methods enables one to know that magic and religion are inextricably linked in the role of healing. The role played by magic and religion in these treatments resulted in a holistic form of treatment in the process of attempting to heal the ancient Egyptian snakebite victim and patient.
Biosolids-Derived Biochar Improves Biomethane Production in the Anaerobic Digestion of Chicken Manure
Authors: Hassan Soulayma , Ngo Tien , Khudur Leadin S. , Krohn Christian , Dike Charles Chinyere , Hakeem Ibrahim Gbolahan , Shah Kalpit , Surapaneni Aravind , Ball Andrew S.
Published: October 2023

Anaerobic digestion has attracted great interest for use in the management of organic wastes and the production of biomethane. However, this process is facing challenges, such as a high concentration of ammonia nitrogen, which affects the methanogenesis process and, thus, the production of methane. This study investigates the use of biosolid-derived biochar for mitigating ammonia stress and improving methane production during the anaerobic digestion of chicken manure, using both pristine biochar and biochar modified with a potassium hydroxide (KOH) solution. Batch mesophilic anaerobic digestion (37 °C) was carried out over 18 days. When compared to chicken-manure-only controls, a significant increase in methane formation was observed in the digesters amended with biochar and KOH-modified biochar, producing 220 L kg−1 volatile solids (VSs) and 262 L kg−1 VSs of methane, respectively, compared to 139 L kg−1 VSs from the control digesters. The use of biochar and KOH-modified biochar resulted in a significant reduction of 8 days in the lag phase. The total ammonia nitrogen (TAN) concentration was reduced in the digesters with biochar and KOH-modified biochar by 25% and 35.5%, respectively. The quantitative polymerase chain reaction (QPCR) data revealed that the number of 16S rRNA gene copies was around 50,000 and 41,000 times higher in the biochar and KOH-modified biochar digesters, respectively, compared to the control digesters on day 18. The taxonomic profiles indicated that the BC and KOH-BC digesters contained a mixture of methanogenic pathways, including acetoclastic (Methanosaetaceae), hydrogenotrophic (Methanosarcinaceae), and methylation (Methanofastidiosaceae). This mix of pathways suggests a more robust archaeal community and, hence, more efficient methanogenesis. The results show that the addition of biosolids biochar enhances anaerobic digestion, mitigates ammonia stress to methanogens, and significantly increases biogas production.
A Robotic System to Anchor a Patient in a Lateral Position and Reduce Nurses’ Physical Strain
Authors: Hinrichs Pascal , Seibert Kathrin , Arizpe Gómez Pedro , Pfingsthorn Max , Hein Andreas
Published: October 2023

Robotic manipulators can interact with large, heavy objects through whole-arm manipulation. Combined with direct physical interaction between humans and robots, the patient can be anchored in care. However, the complexity of this scenario requires control by a caregiver. We are investigating how such a complex form of manipulation can be controlled by nurses and whether the use of such a system creates physical relief. The use case chosen was washing the back of a patient in the lateral position. The operability of the remote control from the tele-nurse’s point of view, the change in the posture of the nurse on site, the execution times, the evaluation of the cooperation between human and robot, and the evaluation of the system from the nurse’s point of view and from the patient’s point of view were evaluated. The results show that the posture of the worker improved by 11.93% on average, and by a maximum of 26.13%. Ease of use is rated as marginally high. The manipulator is considered helpful. The study shows that remote whole-arm manipulation can anchor bedridden patients in the lateral position and that this system can be operated by nurses and leads to an improvement in working posture.
Hardware-Aware Design of Speed-Up Algorithms for Synthetic Aperture Radar Ship Target Detection Networks
Authors: Zhang Yue , Jiang Shuai , Cao Yue , Xiao Jiarong , Li Chengkun , Zhou Xuan , Yu Zhongjun
Published: October 2023

Recently, synthetic aperture radar (SAR) target detection algorithms based on Convolutional Neural Networks (CNN) have received increasing attention. However, the large amount of computation required burdens the real-time detection of SAR ship targets on resource-limited and power-constrained satellite-based platforms. In this paper, we propose a hardware-aware model speed-up method for single-stage SAR ship targets detection tasks, oriented towards the most widely used hardware for neural network computing—Graphic Processing Unit (GPU). We first analyze the process by which the task of detection is executed on GPUs and propose two strategies according to this process. Firstly, in order to speed up the execution of the model on a GPU, we propose SAR-aware model quantification to allow the original model to be stored and computed in a low-precision format. Next, to ensure the loss of accuracy is negligible after the acceleration and compression process, precision-aware scheduling is used to filter out layers that are not suitable for quantification and store and execute them in a high-precision mode. Trained on the dataset HRSID, the effectiveness of this model speed-up algorithm was demonstrated by compressing four different sizes of models (yolov5n, yolov5s, yolov5m, yolov5l). The experimental results show that the detection speeds of yolov5n, yolov5s, yolov5m, and yolov5l can reach 234.7785 fps, 212.8341 fps, 165.6523 fps, and 139.8758 fps on the NVIDIA AGX Xavier development board with negligible loss of accuracy, which is 1.230 times, 1.469 times, 1.955 times, and 2.448 times faster than the original before the use of this method, respectively.
Mine Subsidence Monitoring Integrating DS-InSAR with UAV Photogrammetry Products: Case Studies on Hebei and Inner Mongolia
Authors: Zhao Jinqi , Yang Xuhai , Zhang Zhaojiang , Niu Yufen , Zhao Zheng
Published: October 2023

Frequent mining activities create a series of geological and environmental problems resulting in an immeasurable loss of life and property. Adopting effective technologies that monitor the surface subsidence of mining areas reliably and accurately is necessary. Targeting problems associated with conventional distributed scatterers interferometric synthetic aperture radar (DS-InSAR) technology, we propose a DS-InSAR technology integrating unmanned aerial vehicle (UAV) photogrammetry products divided into two key technical contents: generating an external reference digital elevation model (DEM) fused with UAV DEM and refining distributed scatterers candidates (DSCs) fused with an UAV digital orthophoto map (DOM). We selected two mining areas, one in Wu’an, Hebei, and the other in Inner Mongolia, with different surface cover types, mining depths, and topographies as the research area. We used Sentinel-1A SAR images covering a mine in Wu’an from 4 November 2018 to 4 March 2019 and a mining area in Inner Mongolia from 11 June 2018 to 21 October 2018 to compare and analyze the subsidence results. We also combined these results with data from their respective field observation stations to assess accuracy. We could apply DS-InSAR technology integrated with UAV photogrammetry products to the subsidence monitoring of two mining areas with different landforms and mining characteristics. Comparing with the leveling and total station, the experimental results show that the RMSE was reduced by about 2 mm in both mining areas, and accuracy for the Wu’an region improved to a higher degree than Inner Mongolia did. Furthermore, the refinement method of DS eliminated 965 and 2948 lower-quality DSCs in the two mining areas. These demonstrate that our proposed method can effectively improve the accuracy and reliability of subsidence results from two mining areas.
Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC)
Authors: Sheu Ruey-Kai , Lin Yuan-Cheng , Pardeshi Mayuresh Sunil , Huang Chin-Yin , Pai Kai-Chih , Chen Lun-Chi , Huang Chien-Chung
Published: October 2023

The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient’s remote healthcare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient’s affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient’s data/report while saving the travel and waiting time for the hospital services. The patient’s data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network.
Self-Adjustment Energy Efficient Redeployment Protocol for Underwater Sensor Networks
Authors: Mahfoudh Saoucene
Published: October 2023

The diversity of applications supported by Underwater Sensor Networks (UWSNs) explains the success of this type of network and the increasing interest in exploiting and monitoring seas and oceans. One of the most important research fields is network deployment, since this deployment will affect all other research aspects in the UWSNs. Moreover, the initial random deployment resulting from scattering underwater sensor nodes on the network area’s surface does not ensure this area’s coverage and network connectivity. In this research, we propose a self-adjustment redeployment protocol that enhances network coverage and connectivity while reducing the energy consumed during network deployment. This protocol takes into account the peculiar dynamism of the underwater environment due to the water currents. First, we study the impact of these water currents on network deployment. Then, we exploit these water currents to adjust the nodes’ positions to achieve total area coverage and reduce the energy consumed during the deployment by reducing the total distance traveled by the underwater sensor nodes. Simulation results show that the proposed protocol achieves a very high coverage rate (97%) and reduces the distance traveled by nodes during the deployment by 41%.
Data Anomaly Detection for Structural Health Monitoring Based on a Convolutional Neural Network
Authors: Kim Soon-Young , Mukhiddinov Mukhriddin
Published: October 2023

Structural health monitoring (SHM) has been extensively utilized in civil infrastructures for several decades. The status of civil constructions is monitored in real time using a wide variety of sensors; however, determining the true state of a structure can be difficult due to the presence of abnormalities in the acquired data. Extreme weather, faulty sensors, and structural damage are common causes of these abnormalities. For civil structure monitoring to be successful, abnormalities must be detected quickly. In addition, one form of abnormality generally predominates the SHM data, which might be a problem for civil infrastructure data. The current state of anomaly detection is severely hampered by this imbalance. Even cutting-edge damage diagnostic methods are useless without proper data-cleansing processes. In order to solve this problem, this study suggests a hyper-parameter-tuned convolutional neural network (CNN) for multiclass unbalanced anomaly detection. A multiclass time series of anomaly data from a real-world cable-stayed bridge is used to test the 1D CNN model, and the dataset is balanced by supplementing the data as necessary. An overall accuracy of 97.6% was achieved by balancing the database using data augmentation to enlarge the dataset, as shown in the research.
Characterisation of Cognitive Load Using Machine Learning Classifiers of Electroencephalogram Data
Authors: Wang Qi , Smythe Daniel , Cao Jun , Hu Zhilin , Proctor Karl J. , Owens Andrew P. , Zhao Yifan
Published: October 2023

A high cognitive load can overload a person, potentially resulting in catastrophic accidents. It is therefore important to ensure the level of cognitive load associated with safety-critical tasks (such as driving a vehicle) remains manageable for drivers, enabling them to respond appropriately to changes in the driving environment. Although electroencephalography (EEG) has attracted significant interest in cognitive load research, few studies have used EEG to investigate cognitive load in the context of driving. This paper presents a feasibility study on the simulation of various levels of cognitive load through designing and implementing four driving tasks. We employ machine learning-based classification techniques using EEG recordings to differentiate driving conditions. An EEG dataset containing these four driving tasks from a group of 20 participants was collected to investigate whether EEG can be used as an indicator of changes in cognitive load. The collected dataset was used to train four Deep Neural Networks and four Support Vector Machine classification models. The results showed that the best model achieved a classification accuracy of 90.37%, utilising statistical features from multiple frequency bands in 24 EEG channels. Furthermore, the Gamma and Beta bands achieved higher classification accuracy than the Alpha and Theta bands during the analysis. The outcomes of this study have the potential to enhance the Human–Machine Interface of vehicles, contributing to improved safety.
Enhancing Comprehensive Sexuality Education for Students with Disabilities: Insights from Ontario’s Educational Framework
Authors: Davies Adam , Brass Justin , Martins Mendonca Victoria , O’Leary Samantha , Bryan Malissa , Neustifter Ruth
Published: October 2023

Comprehensive sexuality education (CSE) is an important framework utilized worldwide to provide students and young people with accurate, affirming, and socially conscious sexuality education. However, there is still a lack of CSE curricula in school contexts that is relevant for students with various disabilities. This article takes the Ontario, Canada, context as an example of where and how CSE can improve to be more inclusive for students with disabilities. This article reviews the current context of CSE in Ontario, Canada, including its controversies, while providing recommendations for meeting the needs of students with various disabilities, including psychological, intellectual, and physical disabilities. This article aims to provide practical recommendations, such as pertinent curricular content and pedagogical recommendations for scholars, researchers, and policymakers for improving CSE for students with disabilities in schooling.
Cognitive Soil Digital Twin for Monitoring the Soil Ecosystem: A Conceptual Framework
Authors: Tsakiridis Nikolaos L. , Samarinas Nikiforos , Kalopesa Eleni , Zalidis George C.
Published: October 2023

The digital twin concept has found widespread application across diverse industries. Herein, we present a comprehensive conceptual framework for the cognitive soil digital twin, which embodies the intricate physical reality of the soil ecosystem, aiding in its holistic monitoring and comprehension. The digital twin can seamlessly integrate a multitude of sensor data sources, including field Internet of Things sensors, remote sensing data, field measurements, digital cartography, surveys, and other Earth observation datasets. By virtue of its duality, this digital counterpart facilitates data organisation and rigorous analytical exploration, unravelling the intricacies of physical, chemical, and biological soil constituents while discerning their intricate interrelationships and their impact on ecosystem services. Its potential extends beyond mere data representation, acting as a versatile tool for scenario analysis and enabling the visualisation of diverse environmental impacts, including the effects of climate change and transformations in land use or management practices. Beyond academic circles, the digital twin’s utility extends to a broad spectrum of stakeholders across the entire quadruple helix, encompassing farmers and agronomists, soil researchers, the agro-industry, and policy-makers. By fostering collaboration among these stakeholders, the digital twin catalyses informed decision-making, underpinned by data-driven insights. Moreover, it acts as a testbed for the development of innovative sensors and monitoring frameworks, in addition to providing a platform that can educate users and the broader public using immersive and innovative visualisation tools, such as augmented reality. This innovative framework underscores the imperative of a holistic approach to soil ecosystem monitoring and management, propelling the soil science discipline into an era of unprecedented data integration and predictive modelling, by harnessing the effects of climate change towards the development of efficient decision-making.
The Effects of Strong Earthquakes on Built Heritage: A Preliminary Case Study of Rector’s Palace in Dubrovnik’s Old City
Authors: Penava Davorin , Valinčić Marin , Vrban Ante , Abrahamczyk Lars , Guljaš Ivica , Kraus Ivan
Published: October 2023

The Old City of Dubrovnik’s historical urban heritage architecture, consisting of poorly to well-built irregular stone masonry construction, is at high risk of earthquakes. It was enlisted as a UNESCO World Heritage after the severely damaging 1979 Mw = 7.1 Montenegro earthquake. Retrofitting strategies to a certain degree of earthquake protection have been made to the monument heritage architecture after repeating destructive earthquakes for several centuries. The originally 13th-century Rector’s Palace underwent several major modifications throughout history after disastrous events: fire in 1435, a gunpowder explosion in 1463, and earthquakes in 1520, 1667, and 1979. The design and construction information were collected from historical records and studies performed by various researchers, including field measurements and laboratory tests. Based on the data gathered, the building’s resistance to destructive earthquakes in compliance with contemporary building codes was determined using simulations on a calibrated spatial structural model. The study revealed that the building’s critical parts are most susceptible to a certain degree of damage or even collapse. The presented case study is the basis for decision-making and implementing the building’s earthquake risk reduction measures. Additionally, it will serve as a guide for earthquake risk evaluation on similar buildings, even though they may differ in degree or detail.
Simulation-Based Study of the Resilience of Flexible Manufacturing Layouts Subject to Uncertain Demands of Product Variants
Authors: Li Simon , Eshragh Bahareh , Ebufegha Akposeiyifa Joseph
Published: October 2023

Due to market competition, manufacturers typically produce their products with different customized features, leading to the production of product variants (or a product family). Since the market trend can change swiftly, the demands of individual product variants can be difficult to predict. Two flexible manufacturing layouts are commonly considered: functional and cellular layouts. While the functional layout is more resilient to demand changes due to better resource pooling, the cellular layout can be more productive on some occasions due to better routing efficiency. In this context, the purpose of this paper is to quantify and study the criticality of product variants. The criticality score of a product variant can estimate and rank which product variants can sensitively cause bottlenecks in the functional and cellular layouts. The proposed criticality analysis starts with the estimation of bottleneck machines. Through the dependency information of machines and parts, we can estimate the criticality of product variants. The criticality analysis is demonstrated and examined through a simulation study with a study case involving the production of five furniture products with 16 unique parts using 11 machines. The simulation results show that the productions with more critical product variants tend to deteriorate the completion time of the cellular layout more severely. In practice, manufacturers can use the proposed criticality analysis to evaluate the criticality of product variants and support their facility layout decision. For example, if more demand for critical products is expected, the layout should support more resource pooling (e.g., functional layouts).
Sustainable Silt Management in the Lower Kosi River, North Bihar, India: Demand Assessment, Investment Model and Socio-Economic Development
Authors: Sinha Rajiv , Mishra Kanchan , Salunke Priyesh , Sounderajan Vidya
Published: October 2023

The Kosi River, draining through Nepal and north Bihar, India, has been known for excessive sediment (commonly called silt) deposition—a primary cause of several hazards. However, there are still no good estimates of the volume of silt accumulated in the Kosi River channel, which makes removal and utilization of silt a major challenge, both technically as well as economically. In this work, we first present a novel method to estimate sediment volume on a reach scale using hydrological and channel planform data mapped from satellite images. We then identify various commercial uses of the Kosi River sediments such as embankment construction, backfilling, land reclamation, landscaping, agricultural applications, industrial applications, and geotextile silt walls. In consultation with various stakeholders, backfilling and embankment construction were identified as potentially the best solutions shortlisted for the development of a business case and investment model. Therefore, we prepared an investment model based on economic viability, cost-to-benefit ratio, and stakeholder consultations for two districts. We performed a SWOT analysis by breaking down the opportunities and risks into political, economic, social, technological, environmental, and legal (PEST-EL) factors to identify the pros and cons within the sector and of the ecosystem in which the stakeholders operate.
Research on Water Stability and Moisture Damage Mechanism of a Steel Slag Porous Asphalt Mixture
Authors: Chen Xiaobing , Zhang Miao , Yao Jianming , Zhang Xiaofei , Wen Wei , Yin Jinhai , Liang Zhongshan
Published: October 2023

A steel slag porous asphalt (SSPA) mixture, as the surfacing layer of permeable asphalt pavements, not only ensures the pavement surface drainage and noise reduction functions, but also improves the comprehensive utilization of steel slag resources and the inherent protection of the ecological environment. However, compared with ordinary asphalt mixtures, SSPA is more susceptible to water damage, such as scouring and frost swelling caused by external rainwater intrusion, resulting in the deterioration of the pavement performance. Therefore, it is of good practical imperative to study the water stability and moisture damage mechanism of SSPAs. In this study, the water stability of SSPA, that was subjected to a series of time–temperature H2O-immersion schemes, was investigated using the pull-out and H2O-immersion Marshall tests, whilst the microscopic mechanism of moisture damage was studied using the scanning electron microscopy (SEM), Fourier infrared spectroscopy (FTIR), and X-ray diffraction (XRD) tests. The corresponding results showed that: (a) with the increase in the H2O immersion time, the water stability of SSPA first increased and then decreased; and (b) the water stability of SSPA was strong under medium-temperature H2O-immersion or short-term high-temperature H2O-immersion. SEM, on the other hand, showed that the transition zone spacing was closely related to the chemical adhesion mechanism between the asphalt and steel slag aggregate. Additionally, the FTIR analysis further showed that the steel slag asphalt mastic spectra had new absorption peaks at 3200~3750 cm−1, inherently indicating the existence of chemical bonding between the asphalt and steel slag, with the XRD results showing that CaSO4·2H2O had a beneficial effect on the water stability of SSPA.
Personally Meaningful Design: Sound Making to Foster Engineering Practices with Artifacts from Home
Authors: Hurtado Santiago , Leinonen Teemu , Keune Anna
Published: October 2023

Early engineering experiences can provide young people with experiences that can contribute to developing longer-term interest in the field and addressing dropout issues faced in engineering internationally. One way to engage young people with engineering is through the creation of personally meaningful sound-making projects with everyday materials and electronic kits. Sound making can make it possible for people to connect to their personal experiences and to represent these personal experiences through artistic means while also performing engineering practices, like asking questions, defining and delimiting problems, and developing and optimizing solutions with physical materials that produce sounds. Such design processes are referred to as engaging in the design of personally meaningful projects. However, it remains underspecified what personally meaningful means and, therefore, what aspects to integrate into engineering educational activity and technology designs to foster personally meaningful design opportunities. Building on constructionist perspectives on learning, this qualitative research investigated engineering practices as middle-school-aged youth used electronic construction kits and personal tangible material objects to create sounds. Iterative and thematic analysis of engineering practices of semi-structured interviews and video-recorded youth workshops showed that sound making with personal objects and electronic construction kits is a context for engineering design practices. This study also showed that integrating personal tangible projects that materialize personal histories can foster engineering practices. The findings contribute to our understanding of the theoretical idea of personally meaningful design in constructionism by presenting the importance of integrating personal histories through the design of personal projects with tangible material objects of a person’s life.
The Biosynthesis of Nickel Oxide Nanoparticles: An Eco-Friendly Approach for Azo Dye Decolorization and Industrial Wastewater Treatment
Authors: Mustafa Sadia , Mahmood Faisal , Shafqat Usman , Hussain Sabir , Shahid Muhammad , Batool Fatima , Elnour Rehab O. , Hashem Mohamed , Asseri Tahani A. Y. , Shahzad Tanvir
Published: October 2023

Wastewater is one of the major concerns for agriculture, and the composition of wastewater depends on its origin. Generally, industrial wastewater consists of azo dyes and heavy metals that contaminate the food chain. In this study, nickel oxide nanoparticles (NiO-NPs) were biosynthesized from Shewnella spp. and characterized by UV–visible spectroscopy (UV–vis), Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FTIR). Azo dye decolorization indicated that NiO-NPs decolorize methylene blue (MB) (82.36%), Congo red (CR) (93.57%), malachite green (MCG) (91.05%), reactive black 5(RB5) (55.17%), reactive red-II(RR-II) (55.45%), and direct blue-I(DB-I) (59.94%) at a dye concentration of 25 mg L−1 after 4 h of sunlight exposure. Additionally, the rate of decolorization was also examined for a 50 mg L−1 concentration of dye. In order to investigate the photocatalytic potential of NiO-NPs, different dyes were also subjected to static and shaking conditions for dye decolorization. The treatment of industrial wastewater with NiO-NPs showed a significant reduction in pH from 8.5 to 6.1, EC (48.38%), chemical oxygen demand (49.24%), total dissolved solids (67.05%), sulfates (52.5%), and phosphates (49.49%). The results of this study indicated that biosynthesized NiO-NPs are an attractive choice for azo dye degradation and industrial wastewater treatment, and they can help save the depleted natural resources of water for agricultural purposes.
How Important Is Stakeholder Collaboration in the MICE Industry: Antecedents and Outcomes of Supply Chain Integration
Authors: Yoon Tae-Hwan , Wang Saerom
Published: October 2023

The MICE (meetings, incentives, conferences, and exhibitions) industry consists of various stakeholders and their collaboration is essential in achieving the success of the entities involved. Yet, limited attention has been paid in the literature to examining cooperation among them. Thus, this research intends to understand the impact of social capital on supply chain integration in the MICE industry and the influence of supply chain integration on corporate performance and MICE destination competitiveness. Based on purposive sampling to recruit respondents working in the MICE industry in Busan, Korea, surveys were distributed online and offline. A total of 158 valid samples were utilized for data analysis, in which partial least squares (PLS)-SEM was conducted. According to the results of this study, relational and cognitive social capital affects supply chain integration and enhanced supply chain integration leads to higher corporate performance and destination competitiveness. The findings unearth novel understanding regarding the importance and function of collaboration among stakeholders in the MICE industry, from the perspective of social capital and supply chain integration, that offers valuable theoretical and practical implications.
Design of Energy-Management Strategy for Solar-Powered UAV
Authors: Gao Yuanjin , Qiao Zheng , Pei Xinbiao , Wu Guangxin , Bai Yue
Published: October 2023

Energy management plays a crucial role in achieving extended endurance for solar-powered Unmanned Aerial Vehicles (UAVs). Current studies in energy management primarily focus on natural energy harvesting and task-oriented path planning. This paper aims to optimize energy consumption during the climb and glide stages by exploring variable climb speeds and glide powers. To achieve this, fitness functions are established for both the climb and glide stages, taking into account the maximum climb speed and glide power limits of the aircraft. The particle swarm optimization (PSO) algorithm is employed to solve the problem, resulting in significant energy savings of over 68% in the climb stage and 4.8% in the glide stage. Based on an analysis of the optimization trends, this study proposes an energy-management strategy to fulfill the demand for long-endurance flights. The findings of this study can serve as a valuable reference for high-altitude missions that require extended flight times.
A Novel Comprehensive Classification for Non-Prosthetic Peri-Implant Fractures
Authors: Lucenti Ludovico , de Cristo Claudia , Costarella Luciano , Caldaci Alessia , Sapienza Marco , Testa Gianluca , Pavone Vito
Published: October 2023

Non-prosthetic peri-implant fractures (NPPIFs) are often reported mixed with periprosthetic fractures (PPFs), but they are different entities. Due to the increase in the age of the world’s population and to the intensification of surgeries for fractures, nowadays, peri-implant fractures are a very frequent entity in clinical practice, with an increasing trend expected in the future. A clear exclusive classification of NPPIFs is not reported in the literature. The aim of this study is to provide a valid comprehensive classification for all the NPPIFs. X-rays of all the peri-implant cases treated in our unit in a 3-year period were retrospectively collected. Five orthopedic surgeons reviewed 30 X-rays of NPPIFs, providing a code according to the classification proposed. After a 3-month interval, they reviewed the same X-rays. Eighteen femoral, eight humeral, and four forearm peri-implant fractures were collected and showed to the raters. Inter- and intra-observer reliability was calculated using a k-statistic, showing a moderate agreement between observers (κ = 0.73) and a substantial agreement between the observations of the same viewer (κ = 0.82). The literature lacks a comprehensive classification for peri-implant fractures that considers all the bones and all the types of implants. The proposed classification is meant to be an instrument for orthopedic surgeons to categorize these types of fractures and seems to be simple, easy to comprehend, and reproducible. This new classification can provide the orthopedic surgeon a reliable method to clearly catalogue different fractures according to the site and the implants; the physicians can use it, through a code, in clinical practice to describe an NPPIF without the need of images. Further studies may be necessary to confirm the validity and eventually to improve the suggested classification.
Thermal Characteristics and Safety Aspects of Lithium-Ion Batteries: An In-Depth Review
Authors: Madani Seyed Saeed , Ziebert Carlos , Marzband Mousa
Published: October 2023

This paper provides an overview of the significance of precise thermal analysis in the context of lithium-ion battery systems. It underscores the requirement for additional research to create efficient methodologies for modeling and controlling thermal properties, with the ultimate goal of enhancing both the safety and performance of Li-ion batteries. The interaction between temperature regulation and lithium-ion batteries is pivotal due to the intrinsic heat generation within these energy storage systems. A profound understanding of the thermal behaviors exhibited by lithium-ion batteries, along with the implementation of advanced temperature control strategies for battery packs, remains a critical pursuit. Utilizing tailored models to dissect the thermal dynamics of lithium-ion batteries significantly enhances our comprehension of their thermal management across a wide range of operational scenarios. This comprehensive review systematically explores diverse research endeavors that employ simulations and models to unravel intricate thermal characteristics, behavioral nuances, and potential runaway incidents associated with lithium-ion batteries. The primary objective of this review is to underscore the effectiveness of employed characterization methodologies and emphasize the pivotal roles that key parameters—specifically, current rate and temperature—play in shaping thermal dynamics. Notably, the enhancement of thermal design systems is often more feasible than direct alterations to the lithium-ion battery designs themselves. As a result, this thermal review primarily focuses on the realm of thermal systems. The synthesized insights offer a panoramic overview of research findings, with a deeper understanding requiring consultation of specific published studies and their corresponding modeling endeavors.