bluuebunny/crossref_metadata_2025 huggingface dataset:
Replacing Harmful Flame Retardants with Biodegradable Starch-Based Materials in Polyethylene Formulations
Authors: Carvalho Bárbara O. , Gonçalves Luís P. C. , Mendonça Patrícia V. , Pereira João P. , Serra Arménio C. , Coelho Jorge F. J.
Published: October 2023

The addition of toxic flame retardants to commercially available polymers is often required for safety reasons due to the high flammability of these materials. In this work, the preparation and incorporation of efficient biodegradable starch-based flame retardants into a low-density polyethylene (LDPE) matrix was investigated. Thermoplastic starch was first obtained by plasticizing starch with glycerol/water or glycerol/water/choline phytate to obtain TPS-G and TPS-G-CPA, respectively. Various LDPE/TPS blends were prepared by means of melt blending using polyethylene graft maleic anhydride as a compatibilizer and by varying the content of TPS and a halogenated commercial flame retardant. By replacing 38% and 76% of the harmful commercial flame retardant with safe TPS-G-CPA and TPS-G, respectively, blends with promising fire behavior were obtained, while the limiting oxygen index (LOI ≈ 28%) remained the same. The presence of choline phytate improved both the charring ability and fire retardancy of starch and resulted in a 43% reduction in fire growth index compared to the blend with commercial flame retardant only, as confirmed by means of cone calorimetry. Standard UL 94 vertical tests showed that blends containing TPS exhibited dripping behavior (rated V2), while those with commercial flame retardant were rated V0. Overall, this work demonstrates the potential of starch as a natural flame retardant that could reduce the cost and increase the safety of polymer-based materials.
Dielectric and Structural Properties of the Hybrid Material Polyvinylidene Fluoride-Bacterial Nanocellulose-Based Composite
Authors: Janićijević Aleksandra , Filipović Suzana , Sknepnek Aleksandra , Vlahović Branislav , Đorđević Nenad , Kovacević Danijela , Mirković Miljana , Petronijević Ivan , Zivković Predrag , Rogan Jelena , Pavlović Vladimir B.
Published: October 2023

In the search for environmentally friendly materials with a wide range of properties, polymer composites have emerged as a promising alternative due to their multifunctional properties. This study focuses on the synthesis of composite materials consisting of four components: bacterial nanocellulose (BNC) modified with magnetic Fe3O4, and a mixture of BaTiO3 (BT) and polyvinylidene fluoride (PVDF). The BT powder was mechanically activated prior to mixing with PVDF. The influence of BT mechanical activation and BNC with magnetic particles on the PVDF matrix was investigated. The obtained composite films’ structural characteristics, morphology, and dielectric properties are presented. This research provides insights into the relationship between mechanical activation of the filler and structural and dielectric properties in the PVDF/BT/BNC/Fe3O4 system, creating the way for the development of materials with a wide range of diverse properties that support the concept of green technologies.
Structure Characteristics and Removal Behavior of the Deposited Carbon on Ni-Al2O3 Catalyst for CO2 Reforming of CH4
Authors: Mo Wen-Long , Ren Yuan , Ma Yaya , Guo Jing , Feng Zhi-Hui , Zhang Shu-Pei , Yang Xiao-Qin
Published: October 2023

Four catalysts were prepared in our previous work using the solution combustion method, incipient-wetness impregnation method, colloid mill circulating impregnation method, and hydrothermal-precipitation method, respectively, labeled as SCM, IMP, T310, and HTP. And the performance (stability) of the four catalysts for CO2 reforming of CH4 was investigated at 800 °C. In this paper, the composition and structure characteristics of the deposited carbon on the above four catalysts were tested through TEM, Raman, TPH, and TG-DTG technologies. The results showed that filamentous carbon was the primary type of carbon deposition on the catalysts, and a large amount of accumulated carbon would block the catalyst pores, affecting the catalytic performance. The carbon deposited on the catalyst prepared using the hydrothermal-precipitation method calcined at 800 °C exhibited a high degree of graphitization, and the proportion of graphitized carbon was considerable, which is harmful to the stability of the catalyst. The decarburization temperature of the deposited carbon on the four catalysts was mainly in the range of 500–700 °C. Using the Coats–Redfern model, as the reaction order was set as 1, 2, 3, 4, and 5, the decarburization activation energy ranged between 50 and 80 kJ/mol.
Framework for the Strategic Adoption of Industry 4.0: A Focus on Intelligent Systems
Authors: Serey Joel , Alfaro Miguel , Fuertes Guillermo , Vargas Manuel , Ternero Rodrigo , Duran Claudia , Sabattin Jorge , Gutierrez Sebastian
Published: October 2023

Despite growing interest in smart manufacturing, there is little information on how organizations can approach the alignment of strategic processes with Industry 4.0. This study seeks to fill this knowledge gap by developing a framework for the integration of Industry 4.0 techniques and artificial intelligence systems. This framework will serve as a conceptual guide in the digital transformation processes toward Industry 4.0. This study involved a systematic literature review of the important methodological proposals and identification of thematic axes, research topics, strategic objectives, challenges, drivers, technological trends, models, and design architectures. In total, 160 articles were selected (120 were published between 2017 and 2022). The results provide insights into the prospects for strategic alignment in the adoption of Industry 4.0. The conceptualization of the framework shows that Industry 4.0 needs strategic adjustments mainly in seven objectives (business model, change mindset, skills, human resources, service level, ecosystem, interconnection, and absorption capacity) derived from 10 thematic axes and 28 research topics. Understanding the strategic adoption of Industry 4.0 and artificial intelligence is vital for industrial organizations to stay competitive and relevant in a constantly evolving business landscape.
The Key Role of Intracellular 5-HT2A Receptors: A Turning Point in Psychedelic Research?
Authors: Sapienza Jacopo
Published: October 2023

Psychedelics could have revolutionary potential in psychiatry, although, until recently, the pharmacodynamic properties of such compounds have not seemed to differ much from those of serotonin, whose levels are raised by Serotonin Reuptake Inhibitors (SSRI). The cardinal point is that serotonergic compounds, such as antidepressive drugs, do not have the potential to induce long-lasting neuroplasticity as psychedelics do. Therefore, the biological underpinnings of the peculiar effect of such compounds had not been fully understood until new astonishing molecular findings came out this year to shed new light on them. Specifically, the phenomena of neuroplasticity are triggered by the stimulation of a peculiar type of receptors: the intracellular 5-HT2A receptors. Interestingly, psychedelics can reach this pool of intracellular receptors due to their lipophilic properties, as they can cross the lipophilic neuronal membrane while serotonin cannot. The importance of such a discovery should not be underestimated as the specific mechanisms involved have not yet been elucidated and a better understanding of them could pave the way to the development of new drugs (and/or new tailored therapeutic strategies) able to sustain neuroplasticity while minimizing side effects.
Betwixt and Between: Chaplaincy, Liminality and Elite Sport
Authors: Whitmore William , Parker Andrew
Published: October 2023

It is often assumed that chaplains who serve in elite sports settings inhabit a position which facilitates a significant level of influence. Yet, in reality, sports chaplains routinely experience a sense of marginality within their host organizations, which is commonly perceived (both by themselves and others) as highly restrictive and constraining. Drawing upon the findings of a small-scale, qualitative study of chaplaincy within the US-based National Football League (NFL) and the English Premier League (EPL), this paper explores the contribution that a sports chaplain might make to their host organizations irrespective of the marginal position which they inhabit. Utilizing the anthropological work of Van Gennep (1960) and Turner (1969) on rites of passage, findings illustrate the level of ambiguity which often accompanies the ‘liminal’ role of the sports chaplain and the freedoms which this might afforded in terms of organizational impact. This paper concludes by suggesting that, whilst it is often viewed in a negative light, the sense of marginality which commonly characterizes chaplaincy has the potential to be re-configured as an opportunity to positively impact workplace relationships and organizational cultures.
East Asian Buddhism and Korea’s Transnational Interactions and Influences
Authors: Cawley Kevin N.
Published: October 2023

No one can dispute the significant influence of Sinitic Buddhism in East Asia, but Korean Buddhists were also unquestionably close to the center of the development of different schools of Buddhism in mainland China, particularly in the Jiangnan region, which had historically drawn monks from the peninsula. This article will briefly cover the historical transnational Buddhist interactions between Korea and China, with an emphasis on doctrinal Buddhism, the significance of Ŭisang and Ǔich’ǒn, and the influence of Hangzhou’s Buddhist intellectual advancements. Even though the article’s main focus is on doctrinal contacts, we will also briefly discuss Chan Buddhism in China and how it influenced the texts and techniques of the Korean Sŏn (Zen) monk Chinul (1158–1210), who made an effort to integrate the doctrinal and meditational traditions, as did Ǔich’ǒn. This process of idea-cross-fertilization led to the Tripitaka Koreana, the largest collection of Buddhist texts in East Asia, created by Buddhists during the Koryŏ dynasty (918–1392), which is discussed below. This will aid in our understanding of these transnational exchanges and highlight the fact that Koreans were not only absorbing new ideas as they emerged in China, but they were also influencing them.
Substantial Reduction in Vegetation Photosynthesis Capacity during Compound Droughts in the Three-River Headwaters Region, China
Authors: Miao Jun , An Ru , Zhang Yuqing , Xing Fei
Published: October 2023

Solar-induced chlorophyll fluorescence (SIF) is a reliable proxy for vegetative photosynthesis and is commonly used to characterize responses to drought. However, there is limited research regarding the use of multiple high-resolution SIF datasets to analyze reactions to atmospheric drought and soil drought, especially within mountain grassland ecosystems. In this study, we used three types of high-spatial-resolution SIF datasets (0.05°), coupled with meteorological and soil moisture datasets, to investigate the characteristics of atmospheric, soil, and compound drought types. We centered this investigation on the years spanning 2001–2020 in the Three-River Headwaters Region (TRHR). Our findings indicate that the TRHR experienced a combination of atmospheric drying and soil wetting due to increases in the standardized saturation vapor pressure deficit index (SVPDI) and standardized soil moisture index (SSMI). In the growing season, atmospheric drought was mainly distributed in the southern and eastern parts of the TRHR (reaching 1.7 months/year), while soil drought mainly occurred in the eastern parts of the TRHR (reaching 2 months/year). Compound drought tended to occur in the southern and eastern parts of the TRHR and trended upward during 2001–2020. All three SIF datasets consistently revealed robust photosynthetic activity in the southern and eastern parts of the TRHR, with SIF values generally exceeding 0.2 mW· m−2·nm−1·sr−1. Overall, the rise in SIF between 2001 and 2020 corresponds to enhanced greening of TRHR vegetation. Vegetation photosynthesis was found to be limited in July, attributable to a high vapor pressure deficit and low soil moisture. In the response of CSIF data to a drought event, compound drought (SVPDI ≥ 1 and SSMI ≤ −1) caused a decline of up to 14.52% in SIF across the source region of the Yellow River (eastern TRHR), while individual atmospheric drought and soil drought events caused decreases of only 5.06% and 8.88%, respectively. The additional effect of SIF produced by compound drought outweighed that of atmospheric drought as opposed to soil drought, suggesting that soil moisture predominantly governs vegetation growth in the TRHR. The reduction in vegetation photosynthesis capacity commonly occurring in July, characterized by a simultaneously high vapor pressure deficit and low soil moisture, was more pronounced in Yellow River’s source region as well. Compound drought conditions more significantly reduce SIF compared to singular drought events. Soil drought evidently played a greater role in vegetation growth stress than atmospheric drought in the TRHR via the additional effects of compound drought.
Landslide Susceptibility Mapping and Interpretation in the Upper Minjiang River Basin
Authors: Wang Xin , Bai Shibiao
Published: October 2023

To enable the accurate assessment of landslide susceptibility in the upper reaches of the Minjiang River Basin, this research intends to spatially compare landslide susceptibility maps obtained from unclassified landslides directly and the spatial superposition of different types of landslide susceptibility map, and explore interpretability using cartographic principles of the two methods of map-making. This research using the catalogs of rainfall and seismic landslides selected nine background factors those affect the occurrence of landslides through correlation analysis finally, including lithology, NDVI, elevation, slope, aspect, profile curve, curvature, land use, and distance to faults, to assess rainfall and seismic landslide susceptibility, respectively, by using a WOE-RF coupling model. Then, an evaluation of landslide susceptibility was conducted by merging rainfall and seismic landslides into a dataset that does not distinguish types of landslides; a comparison was also made between the landslide susceptibility maps obtained through the superposition of rainfall and seismic landslide susceptibility maps and unclassified landslides. Finally, confusion matrix and ROC curve were used to verify the accuracy of the model. It was found that the accuracy of the training set, testing set, and the entire data set based on the WOE-RF model for predicting rainfall landslides were 0.9248, 0.8317, and 0.9347, and the AUC area were 1, 0.949, and 0.955; the accuracy of the training set, testing set, and the entire data set for seismic landslides prediction were 0.9498, 0.9067, and 0.8329, and the AUC area were 1, 0.981, and 0.921; the accuracy of the training set, testing set, and the entire data set for unclassified landslides prediction were 0.9446, 0.9080, and 0.8352, and the AUC area were 0.9997, 0.9822, and 0.9207. Both of the confusion matrix and the ROC curve indicated that the accuracy of the coupling model is high. The southeast of the line from Mount Xuebaoding to Lixian County is a high landslide prone area, and through the maps, it was found that the extremely high susceptibility area of seismic landslides is located at a higher elevation than rainfall landslides by extracting the extremely high susceptibility zones of both. It was also found that the results of the two methods of evaluating landslide susceptibility were significantly different. As for a same background factor, the distribution of the areas occupied by the same landslide occurrence class was not the same according to the two methods, which indicates the necessity of conducting relevant research on distinguishing landslide types.
Study on the Potential of Oil Spill Monitoring in a Port Environment Using Optical Reflectance
Authors: Koirala Bikram , Mboga Nicholus , Moelans Robrecht , Knaeps Els , Sels Seppe , Winters Frederik , Samsonova Svetlana , Vanlanduit Steve , Scheunders Paul
Published: October 2023

In this work, we studied the potential of the visible, near-infrared, and shortwave infrared wavelength regions for monitoring oil spill incidents using optical reflectance. First, a simple physical model was designed for accurate oil thickness and volume estimation using optical reflectance. The developed method was made invariant to changes in acquisition and illumination conditions. In the next step, an algorithm based on an artificial neural network was designed to detect spilled oil. The training samples that are required to optimize the parameters of the network were generated by utilizing the proposed physical model. To validate the method, experiments were conducted in laboratory and outdoor scenarios for detection and thickness/volume estimation on four different oil types. In particular, we developed hyperspectral datasets of oil samples with varying thickness between 500 µm and 5000 µm acquired using two different sensors, an Agrispec spectrometer and an Imec snapscan shortwave infrared hyperspectral camera, in strictly controlled experimental settings. To demonstrate the potential of the proposed method in outdoor environments using solely the visible wavelength region, we monitored the evolution of artificially spilled oil in an outdoor scene with an RGB camera mounted on a drone.
Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry
Authors: Zhang Yi , Li Yuanxi , Meng Xingmin , Liu Wangcai , Wang Aijie , Liang Yiwen , Su Xiaojun , Zeng Runqiang , Chen Xu
Published: October 2023

Mapping potential landslides is crucial to mitigating and preventing landslide disasters and understanding mountain landscape evolution. However, the existing methods to map and demonstrate potential landslides in mountainous regions are challenging to use and inefficient. Therefore, herein, we propose a method using hot spot analysis and convolutional neural networks to map potential landslides in mountainous areas at a regional scale based on ground deformation detection using multitemporal interferometry synthetic aperture radar. Ground deformations were detected by processing 76 images acquired from the descending and ascending orbits of the Sentinel-1A satellite. In total, 606 slopes with large ground deformations were automatically detected using hot spot analysis in the study area, and the extraction accuracy rate and the missing rate are 71.02% and 7.89%, respectively. Subsequently, based on the high-deformation areas and potential landslide conditioning factors, we compared the performance of convolutional neural networks with the random forest algorithm and constructed a classification model with the area under the curve (AUC), accuracy, recall, and precision for testing being 0.75, 0.75, 0.82, and 0.75, respectively. Our approach underpins the ability of interferometric synthetic aperture radar (InSAR) to map potential landslides regionally and provide a scientific foundation for landslide risk management. It also enables an accurate and efficient identification of potential landslides within a short period and under extremely hazardous conditions.
Spatial Prediction of Landslide Susceptibility Using Logistic Regression (LR), Functional Trees (FTs), and Random Subspace Functional Trees (RSFTs) for Pengyang County, China
Authors: Shang Hui , Su Lixiang , Chen Wei , Tsangaratos Paraskevas , Ilia Ioanna , Liu Sihang , Cui Shaobo , Duan Zhao
Published: October 2023

Landslides pose significant and serious geological threat disasters worldwide, threatening human lives and property; China is particularly susceptible to these disasters. This paper focuses on Pengyang County, which is situated in the Ningxia Hui Autonomous Region of China, an area prone to landslides. This study investigated the application of machine learning techniques for analyzing landslide susceptibility. To construct and validate the model, we initially compiled a landslide inventory comprising 972 historical landslides and an equivalent number of non-landslide sites (Data sourced from the Pengyang County Department of Natural Resources). To ensure an impartial evaluation, both the landslide and non-landslide datasets were randomly divided into two sets using a 70/30 ratio. Next, we extracted 15 landslide conditioning factors, including the slope angle, elevation, profile curvature, plan curvature, slope aspect, TWI (topographic wetness index), TPI (topographic position index), distance to roads and rivers, NDVI (normalized difference vegetation index), rainfall, land use, lithology, SPI (stream power index), and STI (sediment transport index), from the spatial database. Subsequently, a correlation analysis between the conditioning factors and landslide occurrences was conducted using the certainty factor (CF) method. Three landslide models were established by employing logistic regression (LR), functional trees (FTs), and random subspace functional trees (RSFTs) algorithms. The landslide susceptibility map was categorized into five levels: very low, low, medium, high, and very high susceptibility. Finally, the predictive capability of the three algorithms was assessed using the area under the receiver operating characteristic curve (AUC). The better the prediction, the higher the AUC value. The results indicate that all three models are predictive and practical, with only minor discrepancies in accuracy. The integrated model (RSFT) displayed the highest predictive performance, achieving an AUC value of 0.844 for the training dataset and 0.837 for the validation dataset. This was followed by the LR model (0.811 for the training dataset and 0.814 for the validation dataset) and the FT model (0.776 for the training dataset and 0.760 for the validation dataset). The proposed methods and resulting landslide susceptibility map can assist researchers and local authorities in making informed decisions for future geohazard prevention and mitigation. Furthermore, they will prove valuable and be useful for other regions with similar geological characteristics features.
Untangling the Causal Links between Satellite Vegetation Products and Environmental Drivers on a Global Scale by the Granger Causality Method
Authors: Kovács Dávid D. , Amin Eatidal , Berger Katja , Reyes-Muñoz Pablo , Verrelst Jochem
Published: October 2023

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products’ (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused areas over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T’s causality is evenly distributed amongst all biomes with fractional covers between ∼10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet’s ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (S5P) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers.
Validation of Inertial Measurement Units for Analyzing Golf Swing Rotational Biomechanics
Authors: Kim Sung Eun , Burket Koltsov Jayme Carolynn , Richards Alexander Wilder , Zhou Joanne , Schadl Kornel , Ladd Amy L. , Rose Jessica
Published: October 2023

Training devices to enhance golf swing technique are increasingly in demand. Golf swing biomechanics are typically assessed in a laboratory setting and not readily accessible. Inertial measurement units (IMUs) offer improved access as they are wearable, cost-effective, and user-friendly. This study investigates the accuracy of IMU-based golf swing kinematics of upper torso and pelvic rotation compared to lab-based 3D motion capture. Thirty-six male and female professional and amateur golfers participated in the study, nine in each sub-group. Golf swing rotational kinematics, including upper torso and pelvic rotation, pelvic rotational velocity, S-factor (shoulder obliquity), O-factor (pelvic obliquity), and X-factor were compared. Strong positive correlations between IMU and 3D motion capture were found for all parameters; Intraclass Correlations ranged from 0.91 (95% confidence interval [CI]: 0.89, 0.93) for O-factor to 1.00 (95% CI: 1.00, 1.00) for upper torso rotation; Pearson coefficients ranged from 0.92 (95% CI: 0.92, 0.93) for O-factor to 1.00 (95% CI: 1.00, 1.00) for upper torso rotation (p < 0.001 for all). Bland–Altman analysis demonstrated good agreement between the two methods; absolute mean differences ranged from 0.61 to 1.67 degrees. Results suggest that IMUs provide a practical and viable alternative for golf swing analysis, offering golfers accessible and wearable biomechanical feedback to enhance performance. Furthermore, integrating IMUs into golf coaching can advance swing analysis and personalized training protocols. In conclusion, IMUs show significant promise as cost-effective and practical devices for golf swing analysis, benefiting golfers across all skill levels and providing benchmarks for training.
Mechatronic Pole System for Monitoring the Correctness of Nordic Walking
Authors: Wudarczyk Sławomir , Woźniewski Marek , Szpala Agnieszka , Winiarski Sławomir , Bałchanowski Jacek
Published: October 2023

Marching with Nordic walking (NW) poles is a common form of physical activity. It is recommended in the treatment and rehabilitation of many diseases. NW’s wide range of applications in rehabilitation and its effectiveness are limited by the need for experienced physiotherapists to supervise patients during the training. A prerequisite for good rehabilitation results is correctly using the poles during walking. Essential parameters of NW include the angle of inclination of the pole, the force of the pole on the ground, and proper coordination of performed movements. The purpose of this paper is to present the design and operating principle of a mechatronic NW pole system for measuring and recording the gait parameters. The subject of the work was the assessment of the usefulness of the mechatronic NW pole system for phases identified during marching. The study was conducted in field conditions. The study’s main objective was to compare the obtained results from the developed system with those of a commercial system for measuring foot pressure distributions on the ground. The paper also presents sample results measuring walkers’ gait with NW poles in the field and the resulting gait phase analysis.
A Systematic Literature Review of LoRaWAN: Sensors and Applications
Authors: Bonilla Vicky , Campoverde Brandon , Yoo Sang Guun
Published: October 2023

LoRaWAN is a communication protocol designed especially for Internet of Things (IoT) applications that offers benefits such as long-distance connection and low power consumption. Due to the characteristics of LoRaWAN, this technology has gained great popularity in various IoT applications, such as environmental monitoring, smart agriculture, and applications in the areas of health and mobility, among others. Given this situation, the objective of this work is to provide an in-depth overview of LoRaWAN technology in terms of its applications, as well as the devices that have been used for the development of such applications. Additionally, this work reviews what other areas of LoRaWAN have been covered in different scientific articles, i.e., performance improvement and security. Among the main results of this study though analyzing previous works, we can say that most of them have been developed in the area of environmental monitoring and have used low-cost devices such as Arduinos, Raspberry Pis, and relatively low-cost commercial products such as those of the Semtech and STMicroelectronics brands. The analysis of the present work shows objectively and formally that LoRaWAN technology can be applied in various applications and that there are many studies that try to optimize its performance and security. This paper seeks to identify and describe the most relevant applications of LoRaWAN in different sectors, such as agriculture, health, and environmental monitoring, among others, and the challenges and solutions found in each area. This literature review will provide a valuable reference to understand the potential and opportunities offered by LoRaWAN technology.
A UAV Intelligent System for Greek Power Lines Monitoring
Authors: Tsellou Aikaterini , Livanos George , Ramnalis Dimitris , Polychronos Vassilis , Plokamakis Georgios , Zervakis Michalis , Moirogiorgou Konstantia
Published: October 2023

Power line inspection is one important task performed by electricity distribution network operators worldwide. It is part of the equipment maintenance for such companies and forms a crucial procedure since it can provide diagnostics and prognostics about the condition of the power line network. Furthermore, it helps with effective decision making in the case of fault detection. Nowadays, the inspection of power lines is performed either using human operators that scan the network on foot and search for obvious faults, or using unmanned aerial vehicles (UAVs) and/or helicopters equipped with camera sensors capable of recording videos of the power line network equipment, which are then inspected by human operators offline. In this study, we propose an autonomous, intelligent inspection system for power lines, which is equipped with camera sensors operating in the visual (Red–Green–Blue (RGB) imaging) and infrared (thermal imaging) spectrums, capable of providing real-time alerts about the condition of power lines. The very first step in power line monitoring is identifying and segmenting them from the background, which constitutes the principal goal of the presented study. The identification of power lines is accomplished through an innovative hybrid approach that combines RGB and thermal data-processing methods under a custom-made drone platform, providing an automated tool for in situ analyses not only in offline mode. In this direction, the human operator role is limited to the flight-planning and control operations of the UAV. The benefits of using such an intelligent UAV system are many, mostly related to the timely and accurate detection of possible faults, along with the side benefits of personnel safety and reduced operational costs.
DLTN-LOSP: A Novel Deep-Linear-Transition-Network-Based Resource Allocation Model with the Logic Overhead Security Protocol for Cloud Systems
Authors: Ramachandran Divya , Naqi Syed , Perumal Ganeshkumar , Abbas Qaisar
Published: October 2023

Cloud organizations now face a challenge in managing the enormous volume of data and various resources in the cloud due to the rapid growth of the virtualized environment with many service users, ranging from small business owners to large corporations. The performance of cloud computing may suffer from ineffective resource management. As a result, resources must be distributed fairly among various stakeholders without sacrificing the organization’s profitability or the satisfaction of its customers. A customer’s request cannot be put on hold indefinitely just because the necessary resources are not available on the board. Therefore, a novel cloud resource allocation model incorporating security management is developed in this paper. Here, the Deep Linear Transition Network (DLTN) mechanism is developed for effectively allocating resources to cloud systems. Then, an Adaptive Mongoose Optimization Algorithm (AMOA) is deployed to compute the beamforming solution for reward prediction, which supports the process of resource allocation. Moreover, the Logic Overhead Security Protocol (LOSP) is implemented to ensure secured resource management in the cloud system, where Burrows–Abadi–Needham (BAN) logic is used to predict the agreement logic. During the results analysis, the performance of the proposed DLTN-LOSP model is validated and compared using different metrics such as makespan, processing time, and utilization rate. For system validation and testing, 100 to 500 resources are used in this study, and the results achieved a make-up of 2.3% and a utilization rate of 13 percent. Moreover, the obtained results confirm the superiority of the proposed framework, with better performance outcomes.
Segment-Based Unsupervised Learning Method in Sensor-Based Human Activity Recognition
Authors: Takenaka Koki , Kondo Kei , Hasegawa Tatsuhito
Published: October 2023

Sensor-based human activity recognition (HAR) is a task to recognize human activities, and HAR has an important role in analyzing human behavior such as in the healthcare field. HAR is typically implemented using traditional machine learning methods. In contrast to traditional machine learning methods, deep learning models can be trained end-to-end with automatic feature extraction from raw sensor data. Therefore, deep learning models can adapt to various situations. However, deep learning models require substantial amounts of training data, and annotating activity labels to construct a training dataset is cost-intensive due to the need for human labor. In this study, we focused on the continuity of activities and propose a segment-based unsupervised deep learning method for HAR using accelerometer sensor data. We define segment data as sensor data measured at one time, and this includes only a single activity. To collect the segment data, we propose a measurement method where the users only need to annotate the starting, changing, and ending points of their activity rather than the activity label. We developed a new segment-based SimCLR, which uses pairs of segment data, and propose a method that combines segment-based SimCLR with SDFD. We investigated the effectiveness of feature representations obtained by training the linear layer with fixed weights obtained by unsupervised learning methods. As a result, we demonstrated that the proposed combined method acquires generalized feature representations. The results of transfer learning on different datasets suggest that the proposed method is robust to the sampling frequency of the sensor data, although it requires more training data than other methods.
Remote Sensing Monitoring and Assessment of Global Vegetation Status and Changes during 2016–2020
Authors: Li Li , Xin Xiaozhou , Zhao Jing , Yang Aixia , Wu Shanlong , Zhang Hailong , Yu Shanshan
Published: October 2023

Vegetation plays a fundamental role within terrestrial ecosystems, serving as a cornerstone of their functionality. Presently, these crucial ecosystems face a myriad of threats, including deforestation, overgrazing, wildfires, and the impact of climate change. The implementation of remote sensing for monitoring the status and dynamics of vegetation ecosystems has emerged as an indispensable tool for advancing ecological research and effective resource management. This study takes a comprehensive approach by integrating ecosystem monitoring indicators and aligning them with the objectives of SDG15. We conducted a thorough analysis by leveraging global 500 m resolution products for vegetation Leaf Area Index (LAI) and land cover classification spanning the period from 2016 to 2020. This encompassed the calculation of annual average LAI, identification of anomalies, and evaluation of change rates, thereby enabling a comprehensive assessment of the global status and transformations occurring within major vegetation ecosystems. In 2020, a discernible rise in the annual Average LAI of major vegetation ecosystems on a global scale became evident when compared to data from 2016. Notably, the ecosystems demonstrating a slight increase in area constituted the largest proportion (34.23%), while those exhibiting a significant decrease were the least prevalent (6.09%). Within various regions, such as Eastern Europe, Central Africa, and South Asia, substantial increases in both forest ecosystem area and annual Average LAI were observed. Furthermore, Eastern Europe and Central America recorded significant expansions in both grassland ecosystem area and annual average LAI. Similarly, regions experiencing notable growth in both cropland ecosystem areas and annual average LAI encompassed Southern Africa, Northern Europe, and Eastern Africa.
Learning about What? Non-Confessional Religious Education after the Dissolution of the Binary Categories ‘Religion’ and ‘Secular’
Authors: Thalén Peder
Published: October 2023

The binary division between ‘religion’ and ‘secular’ as an analytical tool has long been criticised within the research field of ‘critical religion’ in religious studies. There has also been a parallel critique in the academic discussion about post-secularity. Recently, sociologists have picked up and deepened this criticism, as expressed in Mitsutoshi Horii’s book ‘Religion’ and ‘Secular’ Categories in Sociology: Decolonizing the Modern Myth (2021). Based on a critical processing of Horii’s application to sociology, the aim of this article is to discuss the challenges for non-confessional religious education (RE) that the ongoing dismantling of this binary division entails. In particular, it looks at how a non-confessional RE could be designed that transcends the binary division and how powerful knowledge could be understood in a non-binary context.
Evaluation of the Performance of Polycrystalline and Monocrystalline PV Technologies in a Hot and Arid Region: An Experimental Analysis
Authors: Benghanem Mohamed , Haddad Sofiane , Alzahrani Ahmed , Mellit Adel , Almohamadi Hamad , Khushaim Muna , Aida Mohamed Salah
Published: October 2023

In arid regions, the behavior of solar panels changes significantly compared to the datasheets provided by the manufacturer. Therefore, the objective of this study is to determine the performance of both polycrystalline and monocrystalline solar modules in an arid region characterized by a large potential for solar irradiation and high temperatures. The influence of environmental parameters, such as temperature and dust, on the output power of solar modules with different technologies (monocrystalline and polycrystalline) has been investigated. The Artificial Hummingbirds Algorithm (AHA) has been used to extract parameters for PV modules. As a result, it has been demonstrated that for high solar irradiation, the polycrystalline PV module experiences a smaller decrease in output power than the monocrystalline PV module as the module temperature increases. The percentage drop in output power is approximately 14% for the polycrystalline PV module and nearly 16% for the monocrystalline PV module. However, for low solar irradiation, it is advisable to use monocrystalline modules, as a 21% decrease in power was observed for polycrystalline modules compared to a 9% decrease for monocrystalline modules. Additionally, the monocrystalline PV module was more affected by dust than the polycrystalline PV module under high solar irradiation conditions, while under low incident solar radiation, the polycrystalline PV module was more affected by dust than the monocrystalline PV module. The power drop of the monocrystalline PV module was greater than that of the polycrystalline PV module for high solar radiation (>500 W/m2). Therefore, the advantage of this proposed work is to recommend the use of polycrystalline solar panels in regions characterized by high solar irradiation and high temperatures instead of monocrystalline solar panels, which are more efficient in regions worldwide characterized by low solar irradiation and low temperatures.
Impact of Climate Risk on the Financial Performance and Financial Policies of Enterprises
Authors: Zhang Xin , Zhang Mateng , Fang Zhong
Published: October 2023

In this study, The Global Climate Risk Index, a global climate risk score, along with firm-level data are used to investigate how climate risk affects corporate financial performance and financial policy in various nations. To evaluate the impact of climate risk on various parts of a firm, this study uses the capabilities of SPSS (version: 28.0.1.1(15)) to perform correlation and regression analysis between climate risk indicators and the firm’s operational data. We obtained financial information from the Bloomberg database for companies in 37 different countries from 2017 to 2021, including return on assets, cash from operations, sales growth, short-term debt, long-term debt and short- and long-term debt. This is combined with a climate risk index to analyze the impact of climate risk on companies in different countries. The financial performance of a firm is found to be adversely, but not significantly, associated with climate risk in this study using correlation and regression analysis, whereas the long-term debt of the company is found to be favorably related to climate risk. From this investigation, the following findings can be derived. A lower returns on assets is the indicator of the comparatively poor financial performance of businesses in nations with increased climate risk. Businesses in nations with greater climate risk typically retain more long-term debt. Companies in nations with lower climate risk, on the other hand, typically retain less long-term debt. Lastly, this study contributes to the understanding of how climate risk affects different countries and how climate risk affects corporate financing strategies.
The Impact of Highway Infrastructure on Population Mobility: Evidence from a Sample of 800 Counties and Districts (2000–2019) in China
Authors: Ji Zhiying , Huang Yuting
Published: October 2023

Modern transport infrastructure plays an important role in shaping urban areas, yet the impact on population distribution and mobility remains uncertain. The aim of this study is to investigate the impacts of road infrastructure on population mobility through a sample of 800 counties and districts in China covering the period from 2000 to 2019 using panel fixed-effects regression models. We find that the improvement in highway infrastructure density can significantly increase the inflow of the population, which is robust to the different measures of the intensity of population mobility and highway infrastructure and to the estimation of 2SLS. This impact has regional and administrative hierarchy heterogeneity. We also investigate the moderating effects of distance from high-order centers, human capital, and digital economic development on the nexus of highway infrastructure and population migration. Our results reveal policy implications for road planning and new rural construction.
Sustainable Development in Peri-Urban Regions: A Triangulation Analysis
Authors: Ticau Iulia Ruxandra , Dan Mihaela Cornelia , Hadad Shahrazad , Nistoreanu Puiu
Published: October 2023

The recent tendency towards overpopulated cities has created an increased demand for the expansion of borders towards rural areas. This has modified the city–countryside relationship, giving rise to the concept of peri-urbanity. Based on these assumptions, this research paper aims to highlight the role and importance of sustainable development for the sustained progress of peri-urban regions, particularly by looking at international standards proposed by the United Nations. In other words, by encompassing the topics of sustainable and inclusive economic growth, decent work for all, and sustainable tourism, Goal 8 of the 17 Sustainable Goals proposed by United Nations represents a framework for the analysis of a peri-urban region located in Romania which is subject to massive development changes. This is realized by conducting extended qualitative research via the triangulation method, intended to compare and contrast three relevant perspectives: those of locals, those of professionals directly involved in the development of the area, and those presented in publicly available reports and statistics. The obtained results indicate factors such as air quality, water quality, space efficiency, and waste collection as being topics directly related to sustainability, while job availability and touristic development perspectives received lower interest. We stress that, despite the growth of urban areas and the migration of people from urban to peri-urban areas, more attention needs to be directed towards the protection of the environment. This is aimed at maintaining the authenticity of the region, specifically in terms of agricultural lands that can support the surrounding major cities. Our study will be relevant both to scholars and to actors in the domains of sustainability, sustainable economic growth, and tourism.
Concept and Practices of Preventive Social Policy in Germany and Some Lessons for China
Authors: Ni Xiaoshan , Klammer Ute
Published: October 2023

Since the 1990s, discussion on changes in the welfare state models and social policies shifts has intensified globally, primarily driven by the new technological revolution and aging populations. In parallel, Germany has placed increasing importance on preventive social policies, aligning with the evolving concept of social investment theory in Europe. This article examines preventive social policy in Germany through the analysis of policy documents, expert reports, and academic papers. We elucidate its conceptual framework, guiding principles, and action strategies. Furthermore, we showcase representative preventive social policies at both the federal and state levels, highlighting their accomplishments and challenges. Lastly, we endeavor to bridge the theoretical and practical aspects of German preventive social policy with China’s social policy reform goals. We suggest that China can draw lessons from Germany’s innovative concept of preventive social policy, emphasizing a life-cycle perspective in institutional design, policy implementation, and outcome evaluation, as well as fostering diverse networking approaches at the governance level to address the challenges inherent in social policy development.