bluuebunny/crossref_metadata_2025 huggingface dataset:
Antibacterial Properties of PMMA Functionalized with CuFe2O4/Cu2O/CuO Nanoparticles
Authors: Glazkova Elena , Bakina Olga , Rodkevich Nikolay , Mosunov Andrey , Evstigneev Maxim , Evstigneev Vladislav , Klimenko Viktor , Lerner Marat
Published: July 2022

We have prepared a composite thin coating by incorporation of CuFe2O4/Cu2O/CuO nanoparticles in polymethyl methacrylate (PMMA) matrix by using the solution casting method. The electrical explosion of two twisted wires (EETW) was used to obtain multicomponent CuFe2O4/Cu2O/CuO nanoparticles with an average particle size of 20–70 nm. The microscopic studies showed that the nanoparticles in the composite coatings are evenly distributed. However, nanoparticles are strongly agglomerated as the powder concentration in the coating increases to 5 wt.% and 10 wt.%, as the size of particle agglomerates increases to 50 and 100 μm, respectively. Therefore, nanoparticles were pre-treated with ultrasound when introduced into the PMMA matrix. The thermal stability of the composite coating does not change with the introduction of CuFe2O4/Cu2O/CuO nanoparticles in the amount of 5 wt.%. The inclusion of nanoparticles in the PMMA matrix significantly enhances its antibacterial activity. The addition of 5 wt.% nanoparticles inhibited the growth of E. coli by 100% and the growth of MRSA by 99.94% compared to pure PMMA already after 3 h of exposure of bacteria on the surface of the composites. This research provides an easy-to-manufacture and cost-efficient method for producing a CuFe2O4/Cu2O/CuO/PMMA composite coating with a broad application as an antibacterial material.
Effect of 5-Amino-1H-Tetrazole on Combustion Pyrolysis Characteristics and Kinetics of a Combustion Tear Gas Mixture
Authors: Zhai Haolong , Cui Xiaoping , Gan Yuping
Published: July 2022

Taking the combustion tear gas mixture as the research object, the system formula was optimized by adding a different mass fraction of 5-amino-1H-tetrazole(5AT). TG-DSC, a thermocouple, and a laser smoke test system were used to characterize the characteristic combustion parameters such as combustion temperature and velocity, as well as the end-point effects such as smoke concentration and particle size. Starink’s method, the Flynn–Wall–Ozawa method, and the Coats–Redfern method were used to evaluate the pyrolysis kinetic parameters of the samples. The results show that when the mass fraction of 5-amino-1H-tetrazole in the system is 10%, the maximum combustion temperature of the sample decreases by nearly 70 °C and the smoke concentration increases by 12.81%. The kinetic study also found that with a different mass fraction of 5-amino-1H-tetrazole in the system, the main reaction model of the mixed agent in the first, third, and fourth stages of pyrolysis changed significantly, but for the second stage of sample pyrolysis, the main reaction model (the A4 model) showed a high degree of consistency, which can be considered as the thermal diffusion stage of the tear agent capsicum oleoresin (OC) (the temperature range is 220~350 °C), which is highly consistent with the results of the TG-DSC analysis. It was also confirmed that OC’s thermal diffusion is mainly concentrated in this stage. The results of this study show that adding an appropriate amount of the combustible agent 5-amino-1H-tetrazole to the combustion tear gas mixture can improve its combustion performance and smoking performance, which provides an important, new idea for the development of a new generation of safe, efficient, and environmentally friendly tear gas mixtures.
Diagnostic Accuracy of Machine Learning Models on Mammography in Breast Cancer Classification: A Meta-Analysis
Authors: Hanis Tengku Muhammad , Islam Md Asiful , Musa Kamarul Imran
Published: July 2022

In this meta-analysis, we aimed to estimate the diagnostic accuracy of machine learning models on digital mammograms and tomosynthesis in breast cancer classification and to assess the factors affecting its diagnostic accuracy. We searched for related studies in Web of Science, Scopus, PubMed, Google Scholar and Embase. The studies were screened in two stages to exclude the unrelated studies and duplicates. Finally, 36 studies containing 68 machine learning models were included in this meta-analysis. The area under the curve (AUC), hierarchical summary receiver operating characteristics (HSROC) curve, pooled sensitivity and pooled specificity were estimated using a bivariate Reitsma model. Overall AUC, pooled sensitivity and pooled specificity were 0.90 (95% CI: 0.85–0.90), 0.83 (95% CI: 0.78–0.87) and 0.84 (95% CI: 0.81–0.87), respectively. Additionally, the three significant covariates identified in this study were country (p = 0.003), source (p = 0.002) and classifier (p = 0.016). The type of data covariate was not statistically significant (p = 0.121). Additionally, Deeks’ linear regression test indicated that there exists a publication bias in the included studies (p = 0.002). Thus, the results should be interpreted with caution.
Design Framework for ReRAM-Based DNN Accelerators with Accuracy and Hardware Evaluation
Authors: Kao Hsu-Yu , Huang Shih-Hsu , Cheng Wei-Kai
Published: July 2022

To achieve faster design closure, there is a need to provide a design framework for the design of ReRAM-based DNN (deep neural network) accelerator at the early design stage. In this paper, we develop a high-level ReRAM-based DNN accelerator design framework. The proposed design framework has the following three features. First, we consider ReRAM’s non-linear properties, including lognormal distribution, leakage current, IR drop, and sneak path. Thus, model accuracy and circuit performance can be accurately evaluated. Second, we use SystemC with TLM modeling method to build our virtual platform. To our knowledge, the proposed design framework is the first behavior-level ReRAM deep learning accelerator simulator that can simulate real hardware behavior. Third, the proposed design framework can evaluate not only model accuracy but also hardware cost. As a result, the proposed design framework can be used for behavior-level design space exploration. In the experiments, we have deployed different DNN models on the virtual platform. Circuit performance can be easily evaluated on the proposed design framework. Furthermore, experiment results also show that the noise effects are different in different ReRAM array architectures. Based on the proposed design framework, we can easily mitigate noise effects by tuning architecture parameters.
Research on Terminal-Side Computing Force Network Based on Massive Terminals
Authors: Gu Jianhua , Feng Jianhua , Xu Huiyang , Zhou Ting
Published: July 2022

With the explosive growth of the demand for computing power in the era of digital economy and the continuous enhancement of the computing power of terminals, how to provide high bandwidth, low-latency, and low-cost service by leveraging the user devices’ computing, storage, and network resources has become a research hotspot. However, due to differences in magnitude, architecture, and performance, the existing technologies for cloud computing and edge computing need to overcome many challenges such as android-based devices not supporting container technologies. In this paper, a terminal-side computing force network (TSCFN) architecture is proposed, which realizes the unified computing power management of massive user devices by layered and distributed architecture with highly dynamic and domain-federated deployments. At the same time, we propose a cloud-native container resource scheduling scheme based on the Android system to enhance the scalability of TSCFN. Taking the CDN service as a use case, the experiment results show that services provided by TSCFN can reduce latency and improve resource utilization, especially in an unstable network status. Compared with traditional CDN, delay duration of HomeCDN based on TSCFN is decreased by 96% in a bad network environment.
Automatic Rice Disease Detection and Assistance Framework Using Deep Learning and a Chatbot
Authors: Jain Siddhi , Sahni Rahul , Khargonkar Tuneer , Gupta Himanshu , Verma Om Prakash , Sharma Tarun Kumar , Bhardwaj Tushar , Agarwal Saurabh , Kim Hyunsung
Published: July 2022

Agriculture not only supplies food but is also a source of income for a vast population of the world. Paddy plants usually produce a brown-coloured husk on the top and their seed, after de-husking and processing, yields edible rice which is a major cereal food crop and staple food, and therefore, becomes the cornerstone of the food security for half the world’s people. However, with the increase in climate change and global warming, the quality and its production are highly degraded by the common diseases posed in rice plants due to bacteria and fungi (such as sheath rot, leaf blast, leaf smut, brown spot, and bacterial blight). Therefore, to accurately identify these diseases at an early stage, recently, recognition and classification of crop diseases is in burning demand. Hence, the present work proposes an automatic system in the form of a smartphone application (E-crop doctor) to detect diseases from paddy leaves which can also suggest pesticides to farmers. The application also has a chatbot named “docCrop” which provides 24 × 7 support to the farmers. The efficiency of the two most popular object detection algorithms (YOLOv3 tiny and YOLOv4 tiny) for smartphone applications was analysed for the detection of three diseases—brown spot, leaf blast, and hispa. The results reveal that YOLOv4 tiny achieved a mAP of 97.36% which is significantly higher by a margin of 17.59% than YOLOv3 tiny. Hence, YOLOv4 tiny is deployed for the development of the mobile application for use.
Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches
Authors: Wu Yan-Jing , Hwang Wen-Shyang , Shen Chih-Yi , Chen Yu-Yen
Published: July 2022

Massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC) are two key services in fifth-generation (5G) mobile wireless networks. These networks have been developed with extremely high service quality requirements: scalability for mMTC and reliability with low latency for URLLC. Fifth-generation network slicing will play a key role in supporting the distinct requirements of various services. Software-defined networking (SDN), a promising technology for network softwarization, physically separates the network control plane from the data plane by centrally controlling switches with an SDN controller. However, control channel bottleneck and processing delays due to this centralized control may reduce the scalability, reliability, and security of SDN. This paper proposes an SDN framework with programming protocol–independent packet processor (P4) switches (SDNPS), and defines a packet format containing in-band network telemetry data to simultaneously support heavy Internet of Things and URLLC traffic in 5G network slices. The method both satisfies the requirements of mMTC and URLLC and alleviates the load on the SDN controller. P4 is an advanced switch interface technology that provides enhanced stateful forwarding and reveals a persistent state on the SDN data plane. To demonstrate the superiority of SDNPS, simulations are performed on conventional SDNs and SDNPS. SDNPS outperforms the other schemes in terms of average throughput, packet loss ratio, and packet delay for both the mMTC and URLLC network slices.
Blockchain Smart Contract to Prevent Forgery of Degree Certificates: Artificial Intelligence Consensus Algorithm
Authors: Kim Seong-Kyu
Published: July 2022

Certificates are often falsified, such as fake diplomas and forged transcripts. As such, many schools and educational institutions have begun to issue diplomas online. Although diplomas can be issued conveniently anytime, anywhere, there are many cases wherein diplomas are forged through hacking and forgery. This paper deals with the required Blockchain diploma. In addition, we use an automatic translation system, which incorporates natural language processing, to perform verification work that does not require an existing public certificate. The hash algorithm is used to authenticate security. This paper also proposes the use of these security protocols to provide more secure data protection. In addition, each transaction history, whether a diploma is true or not, may be different in length if it is presented in text, but converting it into a hash function means that it is always more than a certain length of SHA-512 or higher. It is then verified using the time stamp values. These chaining codes are designed. This paper also provides the necessary experimental environment. At least 10 nodes are constructed. Blockchain platform development applies and references Blockchain standardization, and a platform test, measurement test, and performance measurement test are conducted to assess the smart contract development and performance measurement. A total of 500 nodes were obtained by averaging 200 times, and a Blockchain-based diploma file was agreed upon at the same time. It shows performance information of about 4100 TPS. In addition, the analysis of artificial intelligence distribution diagram was conducted using a four-point method, and the distribution chart was evenly distributed, confirming the diploma with the highest similarity. The verified values were then analyzed. This paper proposes these natural language processing-based Blockchain algorithms.
An Intelligent Breast Ultrasound System for Diagnosis and 3D Visualization
Authors: Lu Yuanyuan , Chen Yunqing , Chen Cheng , Li Junlai , He Kunlun , Xiao Ruoxiu
Published: July 2022

Background: Ultrasonography is the main examination method for breast diseases. Ultrasound imaging is currently relied upon by doctors to form statements of characteristics and locations of lesions, which severely limits the completeness and effectiveness of ultrasound image information. Moreover, analyzing ultrasonography requires experienced ultrasound doctors, which are not common in hospitals. Thus, this work proposes a 3D-based breast ultrasound system, which can automatically diagnose ultrasound images of the breasts and generate a representative 3D breast lesion model through typical ultrasonography. Methods: In this system, we use a weighted ensemble method to combine three different neural networks and explore different combinations of the neural networks. On this basis, a breast locator was designed to measure and transform the spatial position of lesions. The breast ultrasound software generates a 3D visualization report through the selection and geometric transformation of the nodular model. Results: The ensemble neural network improved in all metrics compared with the classical neural network (DenseNet, AlexNet, GoogLeNet, etc.). It proved that the ensemble neural network proposed in this work can be used for intelligent diagnosis of breast ultrasound images. For 3D visualization, magnetic resonance imaging (MRI) scans were performed to achieve their 3D reconstructions. By comparing two types of visualized results (MRI and our 3D model), we determined that models generated by the 3D-based breast ultrasound system have similar nodule characteristics and spatial relationships with the MRI. Conclusions: In summary, this system implements automatic diagnosis of ultrasound images and presents lesions through 3D models, which can obtain complete and accurate ultrasound image information. Thus, it has clinical potential.
Model Checking-Based Performance Prediction for P4
Authors: Lukács Dániel , Pongrácz Gergely , Tejfel Máté
Published: July 2022

Next-generation networks focus on scale and scope at the price of increasing complexity, leading to difficulties in network design and planning. As a result, anticipating all hardware- and software-related factors of network performance requires time-consuming and expensive benchmarking. This work presents a framework and software tool for automatically inferring the performance of P4 programmable network switches based on the P4 source code and probabilistic models of the execution environment with the hope of eliminating the requirement of the costly set-up of networked hardware and conducting benchmarks. We designed the framework using a top-down approach. First, we transform high-level P4 programs into a representation that can be refined incrementally by adding probabilistic environment models of increasing levels of complexity in order to improve the estimation precision. Then, we use the PRISM probabilistic model checker to perform the heavy weight calculations involved in static performance prediction. We present a formalization of the performance estimation problem, detail our solution, and illustrate its usage and validation through a case study conducted using a small P4 program and the P4C-BM reference switch. We show that the framework is already capable of performing estimation, and it can be extended with more concrete information to yield better estimates.
Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process
Authors: Dahmane Sofiane , Yagoubi Mohamed Bachir , Brik Bouziane , Kerrache Chaker Abdelaziz , Calafate Carlos Tavares , Lorenz Pascal
Published: July 2022

Unmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Federated Learning (FL), and Blockchain technology have ushered in a new paradigm of Secure Multi-Access Edge Computing (S-MEC). Indeed, FL enables UAV devices to leverage their sensed data to build local DL models. The latter are then sent to a central node, e.g., S-MEC node, for aggregation, in order to generate a global DL model. Therefore, FL enables UAV devices to collaborate during several FL rounds in generating a learning model, while avoiding to share their local data, and thus ensuring UAVs’ privacy. However, UAV devices are usually limited in terms of resources such as battery, memory, and CPU. Some of the UAV devices may not be able to build a local learning models due to their resources capacity. Hence, there is a great need to select the adequate UAVs at each FL round, that are able to build a local DL model based on their resource capacities. In this paper, we design a novel and S-MEC-enabled framework that optimizes the selection of UAV participants at each FL training round, named FedSel. FedSel considers the available UAVs along with their resource capacities, in terms of energy, CPU, and memory, to determine which UAV device is able to participant in the FL process. Thus, we formulate the UAV selection problem as an Integer Linear Program, which considers the aforementioned constraints. We also prove that this problem is NP-hard, and suggest a Tabu Search (TS) metaheuristic-based approach to resolve it. Moreover, FedSel is built on top of blockchain technology, in order to ensure a secure selection of UAV participants, and hence building reliable FL-based models. Simulation results validate the efficiency of our FedSel scheme in balancing computational load among available UAVs and optimizing the UAV selection process.
Mesoporous VCN Nanobelts for High-Performance Flexible Zn-Ion Batteries
Authors: Zhou Zeyan , Zeng Taotao , Zhang Haoran , Chen Ding
Published: July 2022

Vanadium nitride (VN) with a wide working window has been identified as a promising electrode material candidate for batteries due to the high specific capacitance and the excellent electrical conductivity. Here, we have successfully prepared VCN nanobelts, which display mesoporous structure with high specific surface area (54.4 m2 g−1) and the total pore volume was 0.266 cm3 g−1. Furthermore, the prepared flexible Zn-ion battery (FZIB) with VCN-5 not only exhibited high specific capacitance (81 μAh cm−2), excellent rate capability, and long cyclic durability (77% after 1000 cycles at 0.6 mA cm−2) but also had the characteristics of flexibility. This FZIB is important to reduce the difficulty in thermal management and can be used in a series of applications, including wearable electric devices.
Entrapment of Airborne Particles via Simulated Highway Noise-Induced Piezoelectricity in PMMA and EPDM
Authors: Lyu Mengyao , Thomas Som V. , Wei Heng , Wang Julian , Reponen Tiina A. , Ryan Patrick H. , Shi Donglu
Published: July 2022

The US highway system features a huge flux of energy transportation in terms of weight, speed, volume, flow density, and noise levels, with accompanying environmental effects. The adverse effects of high-volume traffic cause health concerns for nearby residential areas. Both chronic and acute exposure to PM 2.5 have detrimental effects on respiratory and cardiovascular health, and motor vehicles contribute 25–35% of direct PM 2.5 emissions. In addition to traffic-related pollutants, residing near major roadways is also associated with exposure to increased noise, and both affect the health and quality of life of residents. While regulatory and policy actions may reduce some exposures, engineering means may offer novel and significant methods to address these critical health and environmental issues. The goal of this study was to harvest highway-noise energy to induce surface charge via a piezoelectric material to entrap airborne particles, including PM 2.5. In this study, we experimentally investigated the piezoelectric effect of a polymethyl methacrylate (PMMA) sheet and ethylene propylene diene monomer (EPDM) rubber foam on the entrapment of copper (II)-2,4 pentanedione powder (Cu II powder). Appreciable voltages were induced on the surfaces of the PMMA via mechanical vibrations, leading to the effective entrapment of the Cu II powder. The EPDM rubber foam was found to attract a large amount of Cu II powder under simulated highway noise in a wide range, of 30–70 dB, and at frequencies of 700–1300 Hz, generated by using a loudspeaker. The amount of Cu II powder entrapped on the EPDM rubber-foam surfaces was found to scale with the SPL, but was independent of frequency. The experimental findings from this research provide a valuable base for the design of a robust piezoelectric system that is self-powered by harvesting the wasted sound energy from highway noise and reduces the amount of airborne particles over highways for effective environmental control.
Pathways to Overcoming Natural Gas Dependency on Russia—The German Case
Authors: Halser Christoph , Paraschiv Florentina
Published: July 2022

The war in Ukraine has sensitized German policy makers towards the negative economic impact of a curtailment of natural gas flows from Russia. Given its large import dependency, Germany has implemented regulatory measures for mitigating a possible gas shortage and is seeking to diversify from pipeline imports of liquefied natural gas (LNG). In this context, we provide a comprehensive review of the natural gas crisis in Europe and place it in the context of the peculiar role of natural gas in Germany. We critically discuss the economic impact of an embargo, and assess demand and supply factors capable of mitigating a supply shortage. We derive a short-term import substitution potential of 13 bcm, assuming timely installation of Floating Storage and Regasification Units (FSRUs). We discuss the potential for demand reductions in the power sector, in industry consumption, and in households, and estimate a combined maximum of 24.1 bcm. Under decreased industrial demand, the most optimistic scenario indicates an import gap of about 9 bcm for a one-year perspective. Given our findings, we advocate for the delayed phasing out of coal and nuclear power, the accelerated deployment of renewable energy, and caution in the initial execution of storage quotas and restrictions to industrial consumers.
Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm
Authors: Yan Li , Zhu Zhengyu , Kang Xiaopeng , Qu Boyang , Qiao Baihao , Huan Jiajia , Chai Xuzhao
Published: July 2022

Dynamic economic emission dispatch (DEED) in combination with renewable energy has recently attracted much attention. However, when wind power is considered in DEED, due to its generation uncertainty, some additional costs will be introduced and the stability of the dispatch system will be affected. To address this problem, in this paper, the energy-storage characteristic of electric vehicles (EVs) is utilized to smooth the uncertainty of wind power and reduce its impact on the system. As a result, an interaction model between wind power and EV (IWEv) is proposed to effectively reduce the impact of wind power uncertainty. Further, a DEED model based on the IWEv system (DEEDIWEv) is proposed. For solving the complex model, a self-adaptive multiple-learning multi-objective harmony-search algorithm is proposed. Both elite-learning and experience-learning operators are introduced into the algorithm to enhance its learning ability. Meanwhile, a self-adaptive parameter adjustment mechanism is proposed to adaptively select the two operators to improve search efficiency. Experimental results demonstrate the effectiveness of the proposed model and the superiority of the proposed method in solving the DEEDIWEv model.
Exergetic Effects of Cooled Cooling Air Technology on the Turbofan Engine during a Typical Mission
Authors: Zhuang Laihe , Xu Guoqiang , Dong Bensi , Liu Qihang , Li Mengchen , Wen Jie
Published: July 2022

The cooled cooling air technology (CCA technology) shows expected performance in solving the growing thermal challenge for advanced aero engines by reducing the temperature of cooling air. The effect of CCA technology on the overall propelling performance with or without adjusting cycle parameters is controversial. Based on this, both the energy and exergy methods have been adopted to elaborate the specific mechanisms of the above energy utilization discrepancy. As a result, the scheme of CCA technology without optimizing cycle parameters has lower propelling work and efficiency with the total exergy destruction increasing 0.5~2%. Oppositely, as for the scheme of CCA with meliorated cycle parameters, the propelling efficiency improved by around 2~4% with total exergy destruction reduced by 1~3.5%. By analyzing the distribution of exergy destruction, the avoidable and unavoidable exergy destruction caused by the combustion chamber, compressors, and turbines accounts for the largest proportion, which indicates that more attention needs to be paid in the future. During the whole flight mission, the percentage of exergy destruction is much higher in supersonic, subsonic cruise, combat, and escape conditions. In conclusion, the improvement of cycle parameters to reduce the exergy destruction should be considered when introducing CCA technology.
Experimental Study on Renewable Porous Carbon Dioxide Adsorbent Materials for Space Shuttles
Authors: Zhang Chun , Wang Yu , Liu Tao , Ke Hanbing
Published: July 2022

Porous adsorbent material is promising to be used to regeneratively remove CO2 from space shuttles. In this work, the amount and isosteric heat of CO2 adsorption in solid amine are experimentally studied at pressures ranging from 0 to 6 bar and temperatures ranging from 20 °C to 60 °C. The amount and isosteric heat of water adsorption in the solid amine is tested at different humidities (relative humidity 30–80%). The effective thermal conductivity of the solid amine at different atmospheres (air, N2, CO2 and water), pressures and temperatures is also investigated. The results show that the best temperature for CO2 adsorption in the solid amine is 45 °C under dry conditions. The amount of water adsorption increases with enhanced humidity, while the isosteric heat of water adsorption remains a constant value. The effective thermal conductivity of the solid amine increases with an increase in pressure. The adsorbed phase (CO2 and water) in the solid amine makes a contribution to improving the effective thermal conductivity of solid amine particles. The above findings can help design a better adsorption system in space.
Novel Trends in Proton Exchange Membrane Fuel Cells
Authors: Olabi Abdul Ghani , Wilberforce Tabbi , Alanazi Abdulrahman , Vichare Parag , Sayed Enas Taha , Maghrabie Hussein M. , Elsaid Khaled , Abdelkareem Mohammad Ali
Published: July 2022

Fuel cells (FCs) have received huge attention for development from lab and pilot scales to full commercial scale. This is mainly due to their inherent advantage of direct conversion of chemical energy to electrical energy as a high-quality energy supply and, hence, higher conversion efficiency. Additionally, FCs have been produced at a wide range of capacities with high flexibility due to modularity characteristics. Using the right materials and efficient manufacturing processes is directly proportional to the total production cost. This work explored the different components of proton exchange membrane fuel cells (PEMFCs) and their manufacturing processes. The challenges associated with these manufacturing processes were critically analyzed, and possible mitigation strategies were proposed. The PEMFC is a relatively new and developing technology so there is a need for a thorough analysis to comprehend the current state of fuel cell operational characteristics and discover new areas for development. It is hoped that the view discussed in this paper will be a means for improved fuel cell development.
Ecological Dynamics and Regeneration Expansion of Treeline Ecotones in Response to Climate Change in Northern Bhutan Himalayas
Authors: Khandu Yeshey , Polthanee Anan , Isarangkool Na Ayutthaya Supat
Published: July 2022

The alpine treeline ecotones are an early indicator of vegetation’s response to changes in climate, and the advancement of diffuse treeline ecotones has been associated with mean annual warming temperatures. However, the knowledge of how tree demographic size, age and population distribution, and regeneration decrease with increasing elevation and mean annual temperature remain fragmentary in Bhutan. There was no explanation of how treelines migrate in response to the climate. Therefore, the objectives of this study were to investigate tree demographic size and age and population distribution, as well as the regeneration expansion of treeline ecotones of Abies densa trees in response to climate change. Demographic data from thirty transect bands from treeline ecotones and reconstructed mean annual temperatures from tree-rings were used. Regression analysis was used to establish a relationship between elevation/temperature and demographic tree size and age, as well as to determine recruitment frequency distributions and whether these could be driven by climate change. The tree demography indicated that the treeline ecotone in our sampling site is temperature limited. Hence, cooler temperatures at higher elevations should drive decreases in basal diameter, age and recruitment frequencies. From the dendroecological analysis, the diffuse treeline ecotones appear to be climbing on average 1.00 m per year in Northern Bhutan. We also found that the recruitment frequency has increased over recent years (1850–2017), as temperatures continue to rise. The thermal treeline ecotones will be likely to serve as a line of bioclimatic reference against which other zones of bioclimate can be defined. With documented responses of treeline ecotones toward mean annual temperatures, the expectation is that additional warming will continue to influence regeneration expansion in the future. This dynamic response of treeline ecotones towards the climate acts as an indicator of climate change. Information about climbing treelines and altered ecotones should be a vital part of the material for decision makers to consider, to assess impacts and threats to Himalayan alpine biota.
Innovative Teaching and Learning Formats for the Implementation of Agroforestry Systems—An Impact Analysis after Five Years of Experience with the Real-World Laboratory “Ackerbaum”
Authors: Lorenz Tommy , Gerster Lea , Elias Wodzinowski Dustin , Wartenberg Ariani , Martetschläger Lea , Molitor Heike , Cremer Tobias , Bloch Ralf
Published: July 2022

Since 2017, the Eberswalde University for Sustainable Development (HNEE) offers transformative learning modules in their curricula, which are called ITL—Innovative Teaching and Learning formats. Student tutors change into the role of teachers and organize lectures, excursions, and assignments at the Real-World Laboratory “Ackerbaum”—an agroforestry system in the federal state of Brandenburg, Germany. Students can learn about agroforestry systems, participate in research, and take practical action. The examination of the module is a scientific report linked to the experimental area. In this study, an attempt was made to verify the quality and impact of teaching formats in the ITL via the analysis of 53 reports created by 170 students as well as surveys among participants. For this purpose, indicators were formulated that capture the quality of scientific methods and the contribution to higher education for sustainable development. Students and tutors appreciate the open working atmosphere and the possibility to actively participate in the course; many leave the module motivated. Some even move toward transformation in agriculture professionally as, e.g., consultants in the field of agroforestry. As a transformative institution, HNEE offers with ITL a rare opportunity for practical application, scientific methods, and transdisciplinary collaboration with different stakeholders to work on future models to change today’s agriculture.
Biocontrol of Phytophthora xcambivora on Castanea sativa: Selection of Local Trichoderma spp. Isolates for the Management of Ink Disease
Authors: Frascella Arcangela , Sarrocco Sabrina , Mello Antonietta , Venice Francesco , Salvatici Cristina , Danti Roberto , Emiliani Giovanni , Barberini Sara , Della Rocca Gianni
Published: July 2022

Ink disease is a devastating disease of chestnut (Castanea sativa) worldwide, caused by Phytophthora species. The only management measures of this disease are chemical and agronomic interventions. This work focuses on the evaluation of the in vitro antagonistic capacity of 20 isolates of Trichoderma spp. selected in a diseased chestnut orchard in Tuscan Apennines (San Godenzo, Italy) for the biocontrol of Phytophthora xcambivora. Each Trichoderma isolate was tested to investigate pathogen inhibition capability by antagonism in dual cultures and antibiosis by secondary metabolites production (diffusible and Volatile Organic Compounds). The six most performing isolates of Trichoderma spp. were further assessed for their aptitude to synthesize chitinase, glucanase and cellulase, and to act as mycoparasite. All six selected isolates displayed the capability to control the pathogen in vitro by synergistically coupling antibiosis and mycoparasitism at different levels regardless of the species they belong to, but rather, in relation to specific features of the single genotypes. In particular, T. hamatum SG18 and T. koningiopsis SG6 displayed the most promising results in pathogen inhibition, thus further investigations are needed to confirm their in vivo efficacy.
Effect of Sourdough and Whey Protein Addition on the Technological and Nutritive Characteristics of Sponge Cake
Authors: Maravić Nikola , Škrobot Dubravka , Dapčević-Hadnađev Tamara , Pajin Biljana , Tomić Jelena , Hadnađev Miroslav
Published: July 2022

Whey protein and sourdough ferment were used in different combinations to prepare functional sponge cakes, and their mutual influence on batter rheological behaviour as well as product physico–chemical, textural, colour and sensory properties were evaluated. All samples containing whey protein concentrate could bear the nutrition claim ‘a source of protein’. The substitution of wheat flour with whey protein significantly influenced batter viscoelastic behaviour, lowered cake-specific volume, increased product hardness, chewiness, gumminess, and browning index and modified its sensory characteristics. The incorporation of sourdough in protein-enriched sponge cakes improved product-specific volume and appearance compared to a protein-containing sample without sourdough. Although sourdough addition has less of a deteriorating effect on sponge cake rheological and textural properties, when combined with whey protein, it led to a significant reduction in batter elasticity and an increase in product hardness. It was also shown that spontaneously fermented sourdough cannot act as the only leavening agent in sponge cake production. In general, the results of this study have shown that sourdough addition can contribute to improvement in protein-enriched sponge cake quality and that further investigations are necessary in terms of different sourdough and flour type incorporation to minimize the negative effects of protein addition.
Squalene-Rich Amaranth Oil Pickering Emulsions Stabilized by Native α-Lactalbumin Nanoparticles
Authors: Cuevas-Gómez Andrea P. , González-Magallanes Berenice , Arroyo-Maya Izlia J. , Gutiérrez-López Gustavo F. , Cornejo-Mazón Maribel , Hernández-Sánchez Humberto
Published: July 2022

The stabilization of Pickering emulsions by nanoparticles has drawn great interest in the field of food science and technology. In this study, α-Lactalbumin nanoparticles prepared by the desolvation and cross-linking method from protein solutions with initial pH values of 9 and 11 were used to stabilize squalene-rich amaranth oil Pickering o/w emulsions. The effect of different concentrations of nanoparticles on the size, size distribution, ζ potential, and emulsion stability was evaluated using dynamic light scattering, electron microscopy, and light backscattering. Dependence of the emulsions’ droplet size on the nanoparticle concentration was observed, and the critical coverage ratio was reached when 5–10% nanoparticles concentration was used. Our findings suggest that α-LA nanoparticles at a 10% concentration can be used as novel stabilizers for Pickering emulsions to provide protection for beneficial lipophilic bioactive compounds. This is the first time that native α-LA nanoparticles have been used as stabilizers of Pickering emulsions.
Spatial Characteristics Analysis for Coupling Strength among Air Pollutants during a Severe Haze Period in Zhengzhou, China
Authors: Sun Linan , Wang Antao , Wang Jiayao
Published: July 2022

This paper investigates the multifractal characteristics of six air pollutants using the coupling detrended fluctuation analysis method. The results show that coupling correlations exist among the air pollutants and have multifractal characteristics. The sources of multifractality are identified using the chi square test. The coupling strengths between different pollutants are quantified. In addition, the coupling contribution of a series in the haze system is calculated, and SO2, as the main pollutant, plays a key role in the pollution system. Moreover, the Kriging interpolation method is used to analyze the spatial characteristic on coupling contribution of SO2. The spatial analysis of coupling strength for air pollutants will provide an effective approach for pollution control.
First Report on the Co-Occurrence and Clustering Profiles of Cardiovascular Lifestyle Risk Factors among Adults in Burkina Faso
Authors: Cissé Kadari , Samadoulougou Sékou , Coppieters Yves , Bonnechère Bruno , Zabsonré Patrice , Kirakoya-Samadoulougou Fati , Kouanda Seni
Published: July 2022

The co-occurrence of cardiovascular risk factors is usually associated with a higher risk of cardiovascular disease (CVD) or cancer. This study aimed to determine the prevalence of the co-occurrence and its determinants and to identify the clustering profiles of lifestyle risk factors among the adult population in Burkina Faso. Among 4692 participants, 4377 adults from the first STEPS survey conducted in Burkina Faso were considered in this analysis. Four lifestyle risk factors (smoking, alcohol consumption, inadequate fruit and vegetable intake and low physical activity) were analysed. The clustering was evaluated using the observed/expected (O/E) ratio approach. To identify the determinants of co-occurrence, we performed a modified Poisson regression. The prevalence of the co-occurrence of two or more cardiovascular lifestyle risk factors was 46.4% (95% CI: 43.1–49.7). The main determinants of the co-occurrence were being male (adjusted prevalence ratio (aPR): 1.27 (95% CI: 1.16–1.38)), advanced age (55–64 years old: aPR: 1.45 (95% CI: 1.31–1.60)) and a high level of education (aPR: 1.29 (95% CI: 1.09–1.52)). The clustering profile for lifestyle risk factors was tobacco consumption combined with alcohol consumption (O/E: 2.77 (95% CI: 2.12–3.56)), and concurrent involvement in all four lifestyle risk factors (O/E = 1.51 (95% CI: 1.19–1.89)). This first population-based report on the co-occurrence of lifestyle risk factors calls for action to tailor health-promoting interventions to increase healthy lifestyle behaviors. The identified CVD-risk clustering should be considered as an important step in this strategy development in Burkina Faso.
Knowledge, Attitudes, and Stigma: The Perceptions of Tuberculosis in Equatorial Guinea
Authors: Vericat-Ferrer Marta , Ayala Alba , Ncogo Policarpo , Eyene-Acuresila Juan , García Belén , Benito Agustín , Romay-Barja María
Published: July 2022

Tuberculosis remains one of the major causes of morbidity and mortality in Equatorial Guinea, with an estimated incidence of 280 per 100,000 inhabitants, an estimated mortality rate of 96 per 100,000 inhabitants, and a treatment non-adherence rate of 21.4%. This study aimed to identify the factors associated to TB-related knowledge, attitudes, and stigma in order to design community intervention strategies that could improve TB diagnostic and treatment adherence in Equatorial Guinea. A nationwide cross-sectional survey of 770 household caregivers was conducted in Equatorial Guinea about TB knowledge, attitudes, and practices. Knowledge, attitude, and stigma scores were calculated through correct answers and the median was used as cut-off. Associated factors were analyzed calculating prevalence ratio (PR) and a 95% confidence interval (95% CI) through Poisson regression with robust variance. The percentage of women was 53.0% and median age was 46 years (IQR: 33–60). The percentage of caregivers with high TB related knowledge was 34.9%, with a bad attitude (52.5%) and low stigma (40.4%). A greater probability of having good knowledge was observed in those 45 years old or less (PR: 1.3, 95% CI: 1.1–1.6), those with higher education level (PR: 1.4, 95% CI: 1.1–1.8) and higher wealth (PR: 1.4, 95% CI: 1.0–2.0), while sex (PR = 0.8, 95% CI: 0.6–0.9), religion (PR = 1.4, 95% CI: 1.0–1.8), and good knowledge (PR = 1.4, 95% CI: 1.2–1.7) were associated with good attitudes. Wage employment (PR = 95% CI: 1.2–1.4), feeling well informed (PR = 0.7, 95% CI: 0.6–0.8), having good TB knowledge (PR = 1.3, 95% CI: 1.1–1.7), and some sources of information were associated with having lower TB-related stigma. This study found that a high percentage of caregivers in Equatorial Guinea lack important knowledge about TB disease and have bad attitudes and high TB-related stigma. Given the epidemiological situation of TB in the country, it is urgent to improve TB knowledge and awareness among Equatorial Guinea’s general population.
Study on the Regularity of Ammonia-Related Refrigeration Accidents in China from 2010 to 2020
Authors: Luo Cong , Zhao Yunsheng , Xu Ke
Published: July 2022

The frequent occurrence of ammonia-related refrigeration accidents (ArRAs) restricts the safety and sustainable development of cold storage. As an essential tool for safety management, accident statistical analysis can provide a crucial decision-making basis for accident prevention and control. The present study combined descriptive statistics and comparative analysis methods to explore the characteristics and regularities of 82 ArRAs in China from 2010 to 2020. The results showed that the annual evolution of ArRAs presents a bimodal “M” mode in which 2013 and 2016 were the peaking years of accidents. The monthly distribution has an agglomeration effect, and the period from June to September had a high incidence period of accidents. The ArRAs mainly occurred in East China and Central China in the spatial dimension. Zhejiang, Shandong, Hubei, and Sichuan are the pivotal provinces for preventing and controlling ArRAs. Human factors and equipment failure are the leading causes of ArRAs. Accident numbers and casualties have inconsistent trends due to the uncertainty and variability of ArRAs’ consequences. The safety situation of ammonia-related refrigeration enterprises has improved but still needs to strive to prevent and control major accidents. This study draws valuable references for safety decision-making by ammonia-related refrigeration enterprises and safety regulators.
Are Iron Tailings Suitable for Constructing the Soil Profile Configuration of Reclaimed Farmland? A Soil Quality Evaluation Based on Chronosequences
Authors: Jin Wenjuan , Wu Han , Wei Zhongyi , Han Chunlan , Bian Zhenxing , Zhang Xufeng
Published: July 2022

Iron tailings used as soil substitute materials to construct reclaimed farmland soil can effectively realize the large-scale resource utilization of iron tailings and reduce environmental risks. It is vital to understand the mechanisms affecting reclaimed soil quality and determine the appropriate pattern for reclamation with iron tailings. Thus, a soil quality index (SQI) was developed to evaluate the soil quality of reclaimed farmland with iron tailings in a semi-arid region. Soil samples were collected from two reclamation measures (20 cm subsoil + 20 cm iron tailings + 30 cm topsoil and 20 cm subsoil + 20 cm iron tailings + 50 cm topsoil) with reclamation years of 3 (R3), 5 (R5), and 10 (R10) at three soil depths (0–10, 10–20, and 20–30 cm) to measure 13 soil physicochemical properties in western Liaoning, China. Adjacent normal farmland (NF) acted as a reference. Results indicated that iron tailings were suitable for constructing the soil profile configuration of reclaimed farmland. SQI of reclaimed soil increased with the reclamation year, but it has not reached the NF level after 3 years, while it was better than NF after 5 years. The nutrient content of reclaimed soil increased with the reclamation year, but it still did not reach the NF level after 10 years. SQI of R10 (with 50 cm topsoil) was also better than NF but slightly lower than R5 (with 30 cm topsoil). For the semi-arid region with sticky soil texture, the topsoil thickness of reclamation was not the thicker the better, and 30 cm topsoil covered on iron tailings in western Liaoning could achieve a better reclamation effect than 50 cm.