bluuebunny/crossref_metadata_2025 huggingface dataset:
Sacramentality, a Necessary and Permanent Dimension of the Church and Its Implications for Ecumenical Dialogue
Authors: Vázquez Jiménez Rafael
Published: February 2024

On the 60th anniversary of the celebration of the Second Vatican Council, we would like to take up again a statement from the constitution Lumen gentium, which was a source of controversy from the moment it was proposed in the schema De Ecclesia during the Council: «The Church is in Christ, like a sacrament, a sign and instrument of intimate union with God and of the unity of the entire humankind» (Lumen gentium, 1). In this article, we want to take up the concept of the Church as a sacrament, which emerged from the conciliar constitution on the Church, as a first step, although the conception of the Church as a sacrament is found in ecclesiology before the Second Vatican Council. Second, we will focus on the reception of this concept and its development after the Council. We will conclude with a third part devoted to its implications for ecumenical dialogue and the difficulties and possibilities for convergence it offers, with particular reference to the document of the Faith and Order Commission: The Church towards a Common Vision (2013).
Rethinking the Taxonomic Category “Sect/School” (Zong 宗) in the Construction of Modern Buddhism in China—Focusing on Hešeri Rushan’s Eight Schools and Two Practices (“Ba-Zong-Er-Xing 八宗二行”)
Authors: Chen Jidong
Published: February 2024

This paper explores the origin and role of the Buddhist taxonomic category “zong 宗” (“sect” or “school”) in the formation of modern Buddhism in China. It does so by examining a highly significant late-Qing Buddhist text titled Ba-zong-er-xing 八宗二行 (Eight Schools and Two Practices), which the author discovered recently in Japan. Authored by the 19th-century Manchu bannerman official Hešeri Rushan 赫舍裏如山, Eight Schools and Two Practices had a direct influence on the prominent Chinese lay Buddhist Yang Wenhui (1837–1911)’s Shi-zong-lue-shuo 十宗略说 (Brief Outline of the Ten Schools) (1913), which subsequently became the most important narrative model, known as the ten-school model, for describing Chinese Buddhist history in modern times. Historians have long recognized that Yang Wenhui’s Brief Outline of the Ten Schools (1913) was influenced by the medieval Japanese text hasshū kōyō 八宗綱要 (Essentials of the Eight Schools) composed by the 13th-century Japanese monk Gyōnen. Identifying, in detail, Hešeri Rushan’s influence on Yang Wenhui sheds light on how a narrative model for Buddhism in its national form grew out of trans-national intellectual sharing and interactions, and how Chinese Buddhism emerged from the interactive and mutually enabling Sino-Japanese discursive field of the 19th century. Gyōnen, Rushan, and Yang Wenhui all used the category zong, referring to both doctrine and school/sect, to organize narratives of Buddhist history. Their uses were, however, different. Gyōnen’s conception of zong (shū in Japanese) was fixed and exclusive, whereas zong for Rushan and Yang meant more of a mobile, nonexclusive identity. Without knowledge of Japanese Buddhism, Rushan made creative use of zong for describing the history and current condition of Chinese Buddhism, thereby superseding the traditional framework of lineage, doctrine, and precept, or zong 宗, jiao 教, lu 律. Rushan’s zong provided the necessary prerequisite knowledge for Yang Wenhui to understand Gyōnen’s theories, which he studied for constructing his own historical narrative and vision for modern Buddhism.
In Memory of Peter Carr (1958–2022)
Authors: Campolieti Giuseppe , Fahim Arash , Pirjol Dan , Stein Harvey , Wang Tai-Ho , Zhu Lingjiong
Published: February 2024

The editors of this special issue and several of the contributing authors have known Peter for a long time. We thought that the special issue will be enriched by adding a few personal notes and recollections about our interactions with Peter.
Emerging Technologies in Edge Computing and Networking
Authors: Prieto Javier , Durán Barroso Ramón J.
Published: February 2024

The global evolution of the Internet is experiencing a notable and inevitable change towards a convergent scenario known as the Internet of Things (IoT), where a large number of devices with heterogeneous characteristics and requirements have to be interconnected to serve different verticals, such as smart cities, intelligent transportation systems, smart grids, (ITS) or e-health [...]
Multipurpose Modular Wireless Sensor for Remote Monitoring and IoT Applications
Authors: Sámano-Ortega Víctor , Arzate-Rivas Omar , Martínez-Nolasco Juan , Aguilera-Álvarez Juan , Martínez-Nolasco Coral , Santoyo-Mora Mauro
Published: February 2024

Today, maintaining an Internet connection is indispensable; as an example, we can refer to IoT applications that can be found in fields such as environmental monitoring, smart manufacturing, healthcare, smart buildings, smart homes, transportation, energy, and others. The critical elements in IoT applications are both the Wireless Sensor Nodes (WSn) and the Wireless Sensor Networks. It is essential to state that designing an application demands a particular design of a WSn, which represents an important time consumption during the process. In line with this observation, our work describes the development of a modular WSn (MWSn) built with digital processing, wireless communication, and power supply subsystems. Then, we reduce the WSn-implementing process into the design of its modular sensing subsystem. This would allow the development and launching processes of IoT applications across different fields to become faster and easier. Our proposal presents a versatile communication between the sensing modules and the MWSn using one- or two-wired communication protocols, such as I2C. To validate the efficiency and versatility of our proposal, we present two IoT-based remote monitoring applications.
Detection and Validation of Macro-Activities in Human Inertial Signals Using Graph Link Prediction
Authors: Wieland Christoph , Pankratius Victor
Published: February 2024

With the continuous development of new wearable devices, sensor-based human activity recognition is enjoying enormous popularity in research and industry. The signals from inertial sensors allow for the detection, classification, and analysis of human activities such as jogging, cycling, or swimming. However, human activity recognition is often limited to basic activities that occur in short, predetermined periods of time (sliding windows). Complex macro-activities, such as multi-step sports exercises or multi-step cooking recipes, are still only considered to a limited extent, while some works have investigated the classification of macro-activities, the automated understanding of how the underlying micro-activities interact remains an open challenge. This study addresses this gap through the application of graph link prediction, a well-known concept in graph theory and graph neural networks (GNNs). To this end, the presented approach transforms micro-activity sequences into micro-activity graphs that are then processed with a GNN. The evaluation on two derived real-world data sets shows that graph link prediction enables the accurate identification of interactions between micro-activities and the precise validation of composite macro-activities based on learned graph embeddings. Furthermore, this work shows that GNNs can benefit from positional encodings in sequence recognition tasks.
Learn to Bet: Using Reinforcement Learning to Improve Vehicle Bids in Auction-Based Smart Intersections
Authors: Cabri Giacomo , Lugli Matteo , Montangero Manuela , Muzzini Filippo
Published: February 2024

With the advent of IoT, cities will soon be populated by autonomous vehicles and managed by intelligent systems capable of actively interacting with city infrastructures and vehicles. In this work, we propose a model based on reinforcement learning that teaches to autonomous connected vehicles how to save resources while navigating in such an environment. In particular, we focus on budget savings in the context of auction-based intersection management systems. We trained several models with Deep Q-learning by varying traffic conditions to find the most performance-effective variant in terms of the trade-off between saved currency and trip times. Afterward, we compared the performance of our model with previously proposed and random strategies, even under adverse traffic conditions. Our model appears to be robust and manages to save a considerable amount of currency without significantly increasing the waiting time in traffic. For example, the learner bidder saves at least 20% of its budget with heavy traffic conditions and up to 74% in lighter traffic with respect to a standard bidder, and around three times the saving of a random bidder. The results and discussion suggest practical adoption of the proposal in a foreseen future real-life scenario.
The Effect of Water Content on Engine Oil Monitoring Based on Physical and Chemical Indicators
Authors: Zhou Fanhao , Yang Kun , Wang Ling
Published: February 2024

Engine oil oxidation is one of the major reasons for oil aging which can result in variations in the physical and chemical properties of oil. Organic acids generated by oil oxidation can react with water to form inorganic acids and acidic substances (including organic and inorganic acids) that corrode engine parts, resulting in the generation of rust or damage to engine parts. This is one of the important reasons why oil should be regularly changed. One of the most commonly applied methods for judging the aging degree of engine oil is monitoring its acid number (AN). However, generally, the effect of oil water content on acid value measurement is not considered. When oils are used in engines, they are often contaminated by water due to condensation, which accelerates engine oil aging. Therefore, it is crucial to explore the water content effect on AN in the process of engine oil aging. In this research, a water content sensor was applied to characterize moisture content in oxidized oil samples. The sensor could also obtain oil sample electrical conductivity which corresponded to its dielectric constant. Using a mid-infrared spectrometer to measure oil sample AN at this point to obtain the variation in AN with oxidation time, oil sample AN was connected in series with the water content, dielectric constant and electrical conductivity. These parameters were monitored through sensors, and the effect of water content on AN was studied. Experimental results revealed that with the increase in oxidation time, the water content, electrical conductivity, dielectric constant increase and AN of oil were increased. At the same time, since the temperature had a greater effect on electrical conductivity, the application of an air-conditioned constant-temperature environment removed the effect of temperature change on electrical conductivity.
Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose
Authors: Gudiño-Ochoa Alberto , García-Rodríguez Julio Alberto , Ochoa-Ornelas Raquel , Cuevas-Chávez Jorge Ivan , Sánchez-Arias Daniel Alejandro
Published: February 2024

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm’s achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.
Vertical Center-of-Mass Braking and Motor Performance during Gait Initiation in Young Healthy Adults, Elderly Healthy Adults, and Patients with Parkinson’s Disease: A Comparison of Force-Plate and Markerless Motion Capture Systems
Authors: Simonet Arnaud , Delafontaine Arnaud , Fourcade Paul , Yiou Eric
Published: February 2024

Background. This study tested the agreement between a markerless motion capture system and force-plate system (“gold standard”) to quantify stability control and motor performance during gait initiation. Methods. Healthy adults (young and elderly) and patients with Parkinson’s disease performed gait initiation series at spontaneous and maximal velocity on a system of two force-plates placed in series while being filmed by a markerless motion capture system. Signals from both systems were used to compute the peak of forward center-of-mass velocity (indicator of motor performance) and the braking index (indicator of stability control). Results. Descriptive statistics indicated that both systems detected between-group differences and velocity effects similarly, while a Bland–Altman plot analysis showed that mean biases of both biomechanical indicators were virtually zero in all groups and conditions. Bayes factor 01 indicated strong (braking index) and moderate (motor performance) evidence that both systems provided equivalent values. However, a trial-by-trial analysis of Bland–Altman plots revealed the possibility of differences >10% between the two systems. Conclusion. Although non-negligible differences do occur, a markerless motion capture system appears to be as efficient as a force-plate system in detecting Parkinson’s disease and velocity condition effects on the braking index and motor performance.
Collaborative Intelligent Traffic Planning in the Internet of Vehicles
Authors: Zhu Yan
Published: February 2024

With the increasing number of urban vehicles, as well as the current situation of non-intelligent traffic control systems, spatiotemporal non-uniform traffic resource occupation, and limited traffic planning and design, existing urban traffic planning methods cannot effectively solve problems such as frequent traffic congestion and uncontrollable commuting time for residents. In order to solve the above problems, this paper first constructs a multi-queue, multi-server queuing model based on the server vacation and a multi-hop cascaded queuing model from the perspective of local intersections and global commuting paths. We analyze the theoretical changes in passage delay costs at local intersections and on global commuting paths as a function of traffic flow and the random duration of traffic signals. On this basis, this article proposes a collaborative intelligent traffic planning algorithm based on artificial intelligence, which utilizes traffic sensors to dynamically perceive traffic congestion status and collaboratively plans the optimal duration of traffic signals and the optimal driving path of vehicles from both local and global perspectives, thereby maximizing the on-time arrival ratio of vehicles while ensuring the required commuting delay. The simulation results show that the proposed method can increase the on-time arrival ratio of vehicles by at least 20% compared to contrast methods while meeting the requirements relating to commuting delays. This verifies that our method can provide support for the improvement in efficiency in future Internet of vehicles.
Characteristics of Micro-Seismic Events Induced by Ground Collapse—A Case Study in the Rongxing Gypsum Mine in Hubei Province, China
Authors: Wang Hongzhu , Chen Taiyi , Xu Guangli
Published: February 2024

Mining activities can damage rock masses and easily induce ground collapse, which seriously threatens safe production in mining areas. Micro-seismic systems can monitor rock mass deformation signals in real time and provide more accurate data for rock mass deformation analysis. Therefore, in this study, the waveform characteristics of micro-seismic events induced by ground collapse in the Rongxing gypsum mine were analyzed; the occurrence of these events was introduced on the basis of Fast Fourier Transform, an established Frequency–Time–Amplitude model, in order to put forward the index of energy proportion of the main band. The results showed the following. (1) The seismic sequence type of ground collapse was foreshock–mainshock–aftershocks. The interval between the foreshock and mainshock was longer than that between the mainshock and aftershocks. (2) The deformation corresponding to the foreshock micro-seismic events was mainly that of a small-scale crack. The deformation corresponding to the micro-seismic events during the mainshock was characterized by the gradual development of small-scale cracks, and the development of large-scale cracks accelerated, accompanied by slight rock collapse. The deformation corresponding to the micro-seismic events during the aftershocks showed that almost no small-scale cracks developed, and the large-scale crack development was intense, and accompanied by numerous rock and soil mass collapses. (3) The observed decreasing frequency distribution and energy dispersion can be used as possible precursors of ground collapse.
Mechanical Behavior Monitoring and Load Inversion Analysis of Large-Diameter Underwater Shield Tunnel during Construction
Authors: Ma Si-Yuan , Ye Xiao-Wei , Liu Zhi-Xiong , Ding Yang , Zhang Di , Sun Feng
Published: February 2024

The construction of large-diameter shield tunnels underwater involves complex variations in water and earth load outside the tunnel segment, as well as intricate mechanical responses. This study analyzes the variation laws of external loads, axial forces, and bending moments acting on the segment ring during the shield assembly and removal from the shield tail. It accomplishes this through the establishment of an on-site monitoring system based on the Internet of Things (IoT) and proposes a Bayesian-genetic algorithm model to estimate the water and earth pressure. The fluctuation section exhibits a peak load twice as high as that in the stable section. These variations are influenced by Jack thrust, shield shell force, and grouting pressure. The peak load observed in the fluctuation section is twice as high as the load observed in the stable section. During the shield tail removal process, the internal forces undergo significant fluctuations due to changes in both load and boundary conditions, and the peak value of the axial force during the fluctuation section is eight times higher than that during the stable section, while the peak value of the bending moment during the fluctuation section is five times higher than that during the stable section. The earth and water pressure calculated using the inversion analysis method, which relies on the measured internal forces, closely matches the actual measured values. The results demonstrate that the accuracy of the water and earth pressure obtained through inversion analysis is twice as high as that obtained using the full coverage pressure method. These results can serve as a valuable reference for similar projects.
Electronic Nose Drift Suppression Based on Smooth Conditional Domain Adversarial Networks
Authors: Zhu Huichao , Wu Yu , Yang Ge , Song Ruijie , Yu Jun , Zhang Jianwei
Published: February 2024

Anti-drift is a new and serious challenge in the field related to gas sensors. Gas sensor drift causes the probability distribution of the measured data to be inconsistent with the probability distribution of the calibrated data, which leads to the failure of the original classification algorithm. In order to make the probability distributions of the drifted data and the regular data consistent, we introduce the Conditional Adversarial Domain Adaptation Network (CDAN)+ Sharpness Aware Minimization (SAM) optimizer—a state-of-the-art deep transfer learning method.The core approach involves the construction of feature extractors and domain discriminators designed to extract shared features from both drift and clean data. These extracted features are subsequently input into a classifier, thereby amplifying the overall model’s generalization capabilities. The method boasts three key advantages: (1) Implementation of semi-supervised learning, thereby negating the necessity for labels on drift data. (2) Unlike conventional deep transfer learning methods such as the Domain-adversarial Neural Network (DANN) and Wasserstein Domain-adversarial Neural Network (WDANN), it accommodates inter-class correlations. (3) It exhibits enhanced ease of training and convergence compared to traditional deep transfer learning networks. Through rigorous experimentation on two publicly available datasets, we substantiate the efficiency and effectiveness of our proposed anti-drift methodology when juxtaposed with state-of-the-art techniques.
From Local Issues to Global Impacts: Evidence of Air Pollution for Romania and Turkey
Authors: Pekdogan Tugce , Udriștioiu Mihaela Tinca , Yildizhan Hasan , Ameen Arman
Published: February 2024

Air pollution significantly threatens human health and natural ecosystems and requires urgent attention from decision makers. The fight against air pollution begins with the rigorous monitoring of its levels, followed by intelligent statistical analysis and the application of advanced machine learning algorithms. To effectively reduce air pollution, decision makers must focus on reducing primary sources such as industrial plants and obsolete vehicles, as well as policies that encourage the adoption of clean energy sources. In this study, data analysis was performed for the first time to evaluate air pollution based on the SPSS program. Correlation coefficients between meteorological parameters and particulate matter concentrations (PM1, PM2.5, PM10) were calculated in two urban regions of Romania (Craiova and Drobeta-Turnu Severin) and Turkey (Adana). This study establishes strong relationships between PM concentrations and meteorological parameters with correlation coefficients ranging from −0.617 (between temperature and relative humidity) to 0.998 (between PMs). It shows negative correlations between temperature and particulate matter (−0.241 in Romania and −0.173 in Turkey) and the effects of humidity ranging from moderately positive correlations with PMs (up to 0.360 in Turkey), highlighting the valuable insights offered by independent PM sensor networks in assessing and improving air quality.
Monitoring the Velocity of Domain Wall Motion in Magnetic Microwires
Authors: Chizhik Alexander , Corte-Leon Paula , Zhukova Valentina , Blanco Juan Mari , Zhukov Arcady
Published: February 2024

An approach was proposed to control the displacement of domain walls in magnetic microwires, which are employed in magnetic sensors. The velocity of the domain wall can be altered by the interaction of two magnetic microwires of distinct types. Thorough investigations were conducted utilizing fluxmetric, Sixtus–Tonks, and magneto-optical techniques. The magneto-optical examinations revealed transformation in the surface structure of the domain wall and facilitated the determination of the mechanism of external influence on the movement of domain walls in magnetic microwires.
A Review and Comparative Analysis of Relevant Approaches of Zero Trust Network Model
Authors: Dhiman Poonam , Saini Neha , Gulzar Yonis , Turaev Sherzod , Kaur Amandeep , Nisa Khair Ul , Hamid Yasir
Published: February 2024

The Zero Trust safety architecture emerged as an intriguing approach for overcoming the shortcomings of standard network security solutions. This extensive survey study provides a meticulous explanation of the underlying principles of Zero Trust, as well as an assessment of the many strategies and possibilities for effective implementation. The survey begins by examining the role of authentication and access control within Zero Trust Architectures, and subsequently investigates innovative authentication, as well as access control solutions across different scenarios. It more deeply explores traditional techniques for encryption, micro-segmentation, and security automation, emphasizing their importance in achieving a secure Zero Trust environment. Zero Trust Architecture is explained in brief, along with the Taxonomy of Zero Trust Network Features. This review article provides useful insights into the Zero Trust paradigm, its approaches, problems, and future research objectives for scholars, practitioners, and policymakers. This survey contributes to the growth and implementation of secure network architectures in critical infrastructures by developing a deeper knowledge of Zero Trust.
Lightweight and Real-Time Infrared Image Processor Based on FPGA
Authors: Wang Xiaoqing , He Xiang , Zhu Xiangyu , Zheng Fu , Zhang Jingqi
Published: February 2024

This paper presents an FPGA-based lightweight and real-time infrared image processor based on a series of hardware-oriented lightweight algorithms. The two-point correction algorithm based on blackbody radiation is introduced to calibrate the non-uniformity of the sensor. With precomputed gain and offset matrices, the design can achieve real-time non-uniformity correction with a resolution of 640×480. The blind pixel detection algorithm employs the first-level approximation to simplify multiple iterative computations. The blind pixel compensation algorithm in our design is constructed on the side-window-filtering method. The results of eight convolution kernels for side windows are computed simultaneously to improve the processing speed. Due to the proposed side-window-filtering-based blind pixel compensation algorithm, blind pixels can be effectively compensated while details in the image are preserved. Before image output, we also incorporated lightweight histogram equalization to make the processed image more easily observable to the human eyes. The proposed lightweight infrared image processor is implemented on Xilinx XC7A100T-2. Our proposed lightweight infrared image processor costs 10,894 LUTs, 9367 FFs, 4 BRAMs, and 5 DSP48. Under a 50 MHz clock, the processor achieves a speed of 30 frames per second at the cost of 1800 mW. The maximum operating frequency of our proposed processor can reach 186 MHz. Compared with existing similar works, our proposed infrared image processor incurs minimal resource overhead and has lower power consumption.
From CySkin to ProxySKIN: Design, Implementation and Testing of a Multi-Modal Robotic Skin for Human–Robot Interaction
Authors: Giovinazzo Francesco , Grella Francesco , Sartore Marco , Adami Manuela , Galletti Riccardo , Cannata Giorgio
Published: February 2024

The Industry 5.0 paradigm has a human-centered vision of the industrial scenario and foresees a close collaboration between humans and robots. Industrial manufacturing environments must be easily adaptable to different task requirements, possibly taking into account the ergonomics and production line flexibility. Therefore, external sensing infrastructures such as cameras and motion capture systems may not be sufficient or suitable as they limit the shop floor reconfigurability and increase setup costs. In this paper, we present the technological advancements leading to the realization of ProxySKIN, a skin-like sensory system based on networks of distributed proximity sensors and tactile sensors. This technology is designed to cover large areas of the robot body and to provide a comprehensive perception of the surrounding space. ProxySKIN architecture is built on top of CySkin, a flexible artificial skin conceived to provide robots with the sense of touch, and arrays of Time-of-Flight (ToF) sensors. We provide a characterization of the arrays of proximity sensors and we motivate the design choices that lead to ProxySKIN, analyzing the effects of light interference on a ToF, due to the activity of other sensing devices. The obtained results show that a large number of proximity sensors can be embedded in our distributed sensing architecture and incorporated onto the body of a robotic platform, opening new scenarios for complex applications.
VELIE: A Vehicle-Based Efficient Low-Light Image Enhancement Method for Intelligent Vehicles
Authors: Ye Linwei , Wang Dong , Yang Dongyi , Ma Zhiyuan , Zhang Quan
Published: February 2024

In Advanced Driving Assistance Systems (ADAS), Automated Driving Systems (ADS), and Driver Assistance Systems (DAS), RGB camera sensors are extensively utilized for object detection, semantic segmentation, and object tracking. Despite their popularity due to low costs, RGB cameras exhibit weak robustness in complex environments, particularly underperforming in low-light conditions, which raises a significant concern. To address these challenges, multi-sensor fusion systems or specialized low-light cameras have been proposed, but their high costs render them unsuitable for widespread deployment. On the other hand, improvements in post-processing algorithms offer a more economical and effective solution. However, current research in low-light image enhancement still shows substantial gaps in detail enhancement on nighttime driving datasets and is characterized by high deployment costs, failing to achieve real-time inference and edge deployment. Therefore, this paper leverages the Swin Vision Transformer combined with a gamma transformation integrated U-Net for the decoupled enhancement of initial low-light inputs, proposing a deep learning enhancement network named Vehicle-based Efficient Low-light Image Enhancement (VELIE). VELIE achieves state-of-the-art performance on various driving datasets with a processing time of only 0.19 s, significantly enhancing high-dimensional environmental perception tasks in low-light conditions.
Protective Effect of Panicum dichotomiflorum in a Rodent Model of Testosterone-Induced Benign Prostatic Hyperplasia
Authors: Baek Eun Bok , Hong Eun-Ju , Kim Jung-Hee , Kim Min , Ahn Jongmin , Kwun Hyo-Jung
Published: February 2024

Benign prostatic hyperplasia (BPH) is a common disease in aging men. Panicum dichotomiflorum (PD) is an annual grass species of Poaceae that is distributed worldwide. The present study examined whether PD has a protective effect against BPH. BPH was generated in rats by daily subcutaneous administration of testosterone for four weeks. During this period, the rats were also given daily oral gavages of an extract of PD (150 mg/kg). After the final treatment, all animals were euthanized and their prostates were collected and weighed. In BPH model rats, the prostate weight and levels of dihydrotestosterone (DHT) and 5α-reductase expression were inhibited following treatment with PD extract. Testosterone-induced increases in prostate gland epithelial thickness and expression of cyclin D1 and proliferating cell nuclear antigen (PCNA) were markedly suppressed in PD-treated rats, whereas cleaved caspase-3 levels were increased. PD administration also decreased the expression of transforming growth factor (TGF)-β and vascular endothelial growth factor (VEGF), the phosphorylation of Akt, and inflammatory cytokines levels. Taken together, these results show that PD extract protects against testosterone-induced BPH progression by alleviating prostate cell growth and reducing levels of growth factors and inflammatory cytokines, indicating that PD extract may have potential in protecting against BPH.
Integration of Land Use Potential in Energy System Optimization Models at Regional Scale: The Pantelleria Island Case Study
Authors: Mosso Daniele , Rajteri Luca , Savoldi Laura
Published: February 2024

In the context of the energy transition, the integration of land use considerations into energy planning can provide significant improvements. In energy system optimization models (ESOMs), land use aspects can be integrated at the cost of a finer spatial resolution and a more detailed characterization of land, tailored to regional constraints and specificities. Additionally, an assessment of trade-offs with alternative land uses is necessary. Nevertheless, they are commonly neglected. This study addresses the challenge of incorporating land use aspects into ESOMs, with a focus on the unique context of Pantelleria Island. It aims to bridge the gap in methodologies for renewable energy potential assessment and model integration, considering the critical role of land pricing and availability. It combines geospatial data aggregation with model adaptation to include detailed land use aspects. The findings highlight the substantial impact of land costs on renewable energy planning, with land pricing significantly altering model outcomes. This research offers key insights for sustainable energy planning and underscores the importance of considering land use in energy transition strategies.
Rewilding as a Multifaceted Concept and Emerging Approach: The Romanian Experience
Authors: Gorghiu Gabriel , Bîzoi Mihai , Gorghiu Laura Monica , Buruleanu Claudia Lavinia , Suduc Ana-Maria
Published: February 2024

We conduct a review of the multifaceted rewilding concept and rewilding initiatives worldwide, focusing on specific features and outcomes of European rewilding issues and Romanian rewilding projects. In this review of the literature and case studies, we undertake the following: (a) identify the definitions and thorough meanings of the term rewilding worldwide, including its guiding principles aiming at rebuilding the natural ecosystems; (b) map the international and European treaties and policies referring to ecosystems’ restoration, biodiversity, environment, sustainability, and rewilding-related issues; (c) explore the benefits and criticisms associated with the rewilding activities, including from a social perspective; (d) structure an overview on the tools used for rewilding assessment; (e) identify the projects and initiatives developed and on-going in Romania on rewilding. Our findings reveal a great diversity of topics addressed within the rewilding umbrella and also a significant growth in this area, despite the numerous challenges faced by experts and locals (e.g., understanding the context of rewilding and identifying the most suitable modalities of operationalizing it) and questions (such as the following: is rewilding a real need? will it bring benefits to nature and people?) that remained to be answered. In Romania, a series of efforts are being undertaken both by the government and NGOs to restore self-sustaining ecosystems and to raise awareness of this topic among different stakeholders.
Can Regional Integration Policies Enhance the Win–Win Situation of Economic Growth and Environmental Protection? New Evidence for Achieving Carbon Neutrality Goals
Authors: Lv Xianwang , Zhu Yingming , Du Jiazhen
Published: February 2024

In the pursuit of carbon neutrality goals, local governments usually face the dilemma of whether to focus on economic development or environmental protection. Regional integration is a beneficial policy solution to address the challenges inherent to this dilemma. This research considers the expansion of the Urban Economic Coordination Committee in the Yangtze River Delta as a quasi-natural experiment in regional integration. It employs the difference-in-differences model to assess its potential for fostering a win–win situation in terms of economic growth and environmental protection, with the results showing that regional integration has a significantly positive impact on attaining this win–win situation. Inhibiting pollution transfer and promoting green transformation are crucial mechanisms by which regional integration can help strike a balance between economic growth and environmental protection. However, the effects of both regional integration and win–win mechanisms are heterogeneous across cities. The conclusion suggests that the government should accelerate the implementation of regional integration policies across a wider range, recognize and maximize the important role of intermediate mechanisms, and encourage cities to adopt different strategies according to their heterogeneous characteristics, forming a high-level collaborative development pattern.
Reusing Thermal Insulation Materials: Reuse Potential and Durability Assessment of Stone Wool Insulation in Flat Roofs
Authors: Acar Gentiel , Steeman Marijke , Van Den Bossche Nathan
Published: February 2024

In the building renovation industry, a growing volume of discarded insulation materials, such as stone wool insulation, prematurely finds its way to landfills or incinerators after building demolitions. However, these materials often did not reach their complete service life potential, and the reuse of insulation materials is usually not considered in current building practices. This study provides a comprehensive overview of the potential challenges associated with repurposing stone wool insulation from existing flat roofs. By means of detailed assessments via dismantling and performance evaluations of collected stone wool insulation boards up to 28 years old, this research reveals the unavoidable damages that occur upon dismantling yet emphasizes that this does not impede reuse. While density and thermal performance remain stable over time, water absorption and mechanical stability are affected. In total, 48% of all short-term tests revealed an increase in water absorption, possibly due to hydrophobic substance degradation. Mechanical performances of aged SW insulation from flat roofs depend on various factors, with 43% and 33% of compression and puncture resistance tests, respectively, not meeting current standards. Beyond a durability assessment, this study advocates for a multidisciplinary approach, uniting materials science, construction engineering, and sustainability insights, to creatively repurpose used insulation materials into future projects.
Effects of Biochar-Coated Nitrogen Fertilizer on the Yield and Quality of Bok Choy and on Soil Nutrients
Authors: Bi Haiwen , Xu Jiafeng , Li Kaixuan , Li Kaiang , Cao Huanling , Zhao Chao
Published: February 2024

This study was aimed at problems associated with low fertilizer using efficiency, excessive nitrate content of vegetables, and soil degradation in greenhouse vegetable production. A pot experiment was conducted to assess the effects of applying biochar-coated nitrogen fertilizer (BCNF) on the yield, quality, and nitrate content of bok choy (Brassica rapa subsp. Chinensis) as well as on soil nutrients in greenhouses. Four treatments were set up as follows: no nitrogen fertilizer application (BA), chemical nitrogen fertilizer application (CK), biochar-coated nitrogen fertilizer application (BCNF, the amount of nitrogen was equal to that of chemical fertilizer), and reduced biochar-coated nitrogen fertilizer application (D-BCNF, the amount of fertilizer was 80% of BCNF). Compared with the other treatments, BCNF treatment increased chlorophyll content, plant height, maximum leaf length, maximum leaf width, and other biological characters of bok choy. Compared with CK treatment, BCNF treatment increased the fresh weight of bok choy by 14.02%, while reducing the root–shoot ratio and nitrate content by 19.1% and 46%, respectively. It was further found that the application of BCNF could effectively increase the content of soil organic matter; reduce the leaching loss of nitrate nitrogen, exchangeable calcium and magnesium; and effectively improve nitrogen use efficiency. Therefore, the application of BCNF can not only reduce the loss of fertilizer nutrients, promote plant growth, and improve fertilizer utilization, but it can also improve soil nutrients, fix carbon, and reduce emissions. It is a new type of environmental protection fertilizer with application prospects.
Addressing Hydrogen Sulfide Corrosion in Oil and Gas Industries: A Sustainable Perspective
Authors: Vakili Mohammadtaghi , Koutník Petr , Kohout Jan
Published: February 2024

In the oil and gas industry, the corrosion attributed to hydrogen sulfide (H2S) is one of the most significant challenges. This review paper systematically investigates the diverse facets of H2S corrosion, including its sources, corrosion locations, mechanisms, and resultant corrosion products. Understanding different forms of H2S corrosion, such as stress-oriented hydrogen-induced cracking (SO-HIC), sulfide stress cracking (SSC), and hydrogen-induced cracking (HIC), provides a thorough comprehension of these phenomena. The paper discusses critical factors influencing H2S corrosion, such as temperature, flow rate, pH, and H2S concentration, highlighting their implications for sustainable practices in the oil and gas sector. The review emphasizes the significance of monitoring and mitigation strategies, covering continuous monitoring, applying corrosion inhibitors, selecting materials, and conducting thorough data analysis and reporting. Furthermore, the role of training in fostering a sustainable approach to H2S corrosion management is highlighted. This exploration advances the overarching goal of sustainable development in the oil and gas industries by providing insights into understanding, monitoring, and mitigating H2S corrosion. The findings presented here offer a foundation for developing environmentally conscious strategies and practices to guarantee the long-term viability and flexibility of refinery operations.
Indoor Thermal Comfort Sector: A Review of Detection and Control Methods for Thermal Environment in Livestock Buildings
Authors: Cheng Qiongyi , Wang Hui , Xu Xin , He Tengfei , Chen Zhaohui
Published: February 2024

The thermal environment is crucial for livestock production. Accurately detecting thermal environmental conditions enables the implementation of appropriate methods to control the thermal environment in livestock buildings. This study reviewed a comprehensive survey of detection and control methods for thermal environments in livestock buildings. The results demonstrated that temperature, humidity, velocity, and radiation are major elements affecting the thermal comfort of animals. For single thermal environmental parameters, the commonly employed detection methods include field experiments, scale models in wind tunnels, computational fluid dynamics (CFD) simulation, and machine learning. Given that thermal comfort for livestock is influenced by multiple environmental parameters, the Effective Temperature (ET) index, which considers varying proportions of different environmental parameters on the thermal comfort of livestock, is a feasible detection method. Environmental control methods include inlet and outlet configuration, water-cooled floors, installation of a deflector and perforated air ducting (PAD) system, sprinkling, etc. Reasonable inlet configuration increased airflow uniformity by more than 10% and decreased ET by more than 9 °C. Proper outlet configuration improved airflow uniformity by 25%. Sprinkling decreased the temperature by 1.1 °C. This study aims to build a comprehensive dataset for the identification of detection and control methods in research of the thermal environment of livestock buildings.
RETRACTED: Alyousef et al. Durability Enhancement of Sustainable Concrete Composites Comprising Waste Metalized Film Food Packaging Fibers and Palm Oil Fuel Ash. Sustainability 2022, 14, 5253
Authors: Alyousef Rayed , Mohammadhosseini Hossein , Ebid Ahmed Abdel Khalek , Alabduljabbar Hisham , Ngian Shek Poi , Mohamed Abdeliazim Mustafa
Published: February 2024

The Journal retracts the article “Durability Enhancement of Sustainable Concrete Composites Comprising Waste Metalized Film Food Packaging Fibers and Palm Oil Fuel Ash” [...]
Energy, Exergy, Exergoeconomic Analysis, and Optimization in a Natural Gas Decompression Station with a Vortex Tube and Geothermal Preheating
Authors: Villalón-López Luis F. , Ambriz-Díaz Víctor M. , Rubio-Maya Carlos , Chávez Oscar , Rosas Israel Y.
Published: February 2024

Natural gas stations require a preheating stage to prevent the formation of hydrates inside of them provoked by a sudden decompression process of the natural gas. The preheating process has been investigated to improve efficiency and to reduce costs as well. This work studies the behavior of a natural gas decompression station with a first-stage preheating process using a vortex tube and a geothermal heat exchanger, followed by a second stage involving a water bath heater (heating vat). An energetic, exergetic, and exergoeconomic study has been carried out based on a mathematical model and the theory of exergetic cost, obtaining key thermodynamic and thermoeconomic variables, including exergy flows and equipment costs. A heat flow of 26.41 kW was obtained in the geothermal preheating stage; meanwhile, a 60.43 kW heat flow was obtained in the heating vat. The results showed a saving in station fuel using only 2.046% of the natural gas in the system at the second preheating stage. Also, the system was optimized, obtaining a 15.73% reduction in the decompressed natural gas cost. These findings show the possibility of implementing these systems in zones with many geothermal resources to reach a constant, profitable natural gas supply in areas where a pipeline network does not exist.
Unveiling Urban Regeneration Risks in China: A Social Perspective
Authors: Xie Hong , Zheng Saina , Zhai Yue , Yuan Jingfeng , Li Qiming
Published: February 2024

China’s urbanization process is currently in a transition phase from rapid growth to slow growth, necessitating the implementation of sustainable measures in urban planning. Urban regeneration (UR), being one of the fundamental mechanisms for achieving sustainable urban development, has received considerable attention. UR promotes sustainable development by reusing abandoned land and buildings, improving energy efficiency, and enhancing the ecological environment. However, UR involves numerous stakeholders who may have conflicting interests due to factors such as the environment, technology, and economy, thereby giving rise to social risks (SRs). These SRs pose a threat to the success of UR projects and can also lead to social instability, as well as hindering sustainable urban development. Identifying risk sources forms the foundation of and key to risk management. Therefore, this research employs an integrated qualitative and quantitative method to explore the SR factors (SRFs) related to UR for China. On the basis of the grounded theory method, case study examination was used for data collection, resulting in the identification of 22 specific categories and five main categories. Through quantitative analysis, the identified SRFs and five main classifications of UR in China were verified, namely the negative effects of demolition and relocation, the negative environmental effect, the negative effect of technology, the organization-related negative effect, and the negative effect of policy. Among them, the unfair compensation for demolition and relocation has the greatest impact on the SRs in UR. Based on further analysis of the quantitative results, this study proposes three measures to alleviate the UR-related SRs for China on the macro, meso, and micro levels, which include improving policy and the legal system, enhancing collaborative governance capacity, and strengthening public participation. This research also has reference value in the context of promoting UR for other developing countries.