bluuebunny/crossref_metadata_2025 huggingface dataset:
An Updated Review of the Genus Humulus: A Valuable Source of Bioactive Compounds for Health and Disease Prevention
Authors: Carbone Katya , Gervasi Fabio
Published: December 2022

The medicinal potential of hop (Humulus lupulus L.) is widely cited in ancient literature and is also allowed in several official pharmacopoeias for the treatment of a variety of ailments, mainly related to anxiety states. This is due to the plethora of phytoconstituents (e.g., bitter acids, polyphenols, prenyl flavonoids) present in the female inflorescences, commonly known as cones or strobili, endowed with anti-inflammatory, antioxidant, antimicrobial, and phytoestrogen activities. Hop has recently attracted the interest of the scientific community due to the presence of xanthohumol, whose strong anti-cancer activity against various types of cancer cells has been well documented, and for the presence of 8-prenyl naringenin, the most potent known phytoestrogen. Studies in the literature have also shown that hop compounds can hinder numerous signalling pathways, including ERK1/2 phosphorylation, regulation of AP-1 activity, PI3K-Akt, and nuclear factor NF-κB, which are the main targets of the antiproliferative action of bitter acids and prenylflavonoids. In light of these considerations, the aim of this review was to provide an up-to-date overview of the main biologically active compounds found in hops, as well as their in vitro and in vivo applications for human health and disease prevention. To this end, a quantitative literature analysis approach was used, using VOSviewer software to extract and process Scopus bibliometric data. In addition, data on the pharmacokinetics of bioactive hop compounds and clinical studies in the literature were analysed. To make the information more complete, studies on the beneficial properties of the other two species belonging to the genus Humulus, H. japonicus and H. yunnanensis, were also reviewed for the first time.
Simulated Analysis of Influence of Changes in H+-ATPase Activity and Membrane CO2 Conductance on Parameters of Photosynthetic Assimilation in Leaves
Authors: Sukhova Ekaterina , Ratnitsyna Daria , Sukhov Vladimir
Published: December 2022

Photosynthesis is an important process in plants which influences their development and productivity. Many factors can control the efficiency of photosynthesis, including CO2 conductance of leaf mesophyll, which affects the CO2 availability for Rubisco. It is known that electrical stress signals can decrease this conductance, and the response is probably caused by inactivation of H+-ATPase in the plasma membrane. In the current work, we analyzed the influence of both CO2 conductance in the plasma membrane, and chloroplast envelopes and H+-ATPase activity on photosynthetic CO2 assimilation, using a two-dimensional mathematical model of photosynthesis in leaves. The model included a description of assimilation on the basis of the Farquhar–von Caemmerer–Berry model, ion transport through the plasma membrane, diffusion of CO2 in the apoplast, and transport of CO2 through the plasma membrane and chloroplast envelope. The model showed that the photosynthetic CO2 assimilation rate was mainly dependent on the plasma membrane and chloroplast envelope conductance; direct influence of the H+-ATPase activity (through changes in pH and CO2/HCO3− concentration ratio) on this rate was weak. In contrast, both changes in CO2 conductance of the plasma membrane and chloroplast envelopes and changes in the H+-ATPase activity influenced spatial heterogeneity of the CO2 assimilation on the leaf surface in the simulated two-dimensional system. These effects were also observed under simultaneous changes in the CO2 conductance of the plasma membrane and H+-ATPase activity. Qualitatively similar influence of changes in the CO2 conductance of the plasma membrane and chloroplast envelopes, and changes in the H+-ATPase activity on photosynthesis were shown for two different densities of stomata in the simulated leaf; however, lowering the density of stomata decreased the assimilation rate and increased the heterogeneity of assimilation. The results of the model analysis clarify the potential influence of H+-ATPase inactivation on photosynthesis, and can be the basis for development of new methods for remote sensing of the influence of electrical signals.
Double Puzzle: Morphogenesis of the Bi-Layered Leaf Adaxial Epidermis of Magnolia grandiflora
Authors: Panteris Emmanuel , Adamakis Ioannis-Dimosthenis S.
Published: December 2022

Anticlinal ordinary epidermal cell wall waviness is a widespread feature found in the leaves of a variety of land plant species. However, it has not yet been encountered in leaves with multiple epidermides. Surprisingly, in Magnolia grandiflora leaves, ordinary epidermal cells in both layers of the bi-layered adaxial epidermis exhibit wavy anticlinal contour. During the development of the above cells, cortical microtubules are organized in anticlinally oriented bundles under the anticlinal walls, and radial arrays extending from the bundles at the edges of anticlinal and external periclinal walls, under the external periclinal walls. This microtubule pattern is followed by cell wall reinforcement with local thickenings, the cellulose microfibrils of which are parallel to the underlying microtubules. This specialized microtubule organization and concomitant cell wall reinforcement is initiated in the external epidermal layer, while hypodermis follows. The waviness pattern of each epidermal layer is unrelated to that of the other. The above findings are discussed in terms of morphogenetic mechanism induction and any implications in the functional significance of ordinary epidermal cell waviness.
Echinops spinosissimus Turra Root Methanolic Extract: Characterization of the Bioactive Components and Relative Wound Healing, Antimicrobial and Antioxidant Properties
Authors: Zitouni-Nourine Saida Hanane , Belyagoubi-Benhammou Nabila , El-Houaria Zitouni-Haouar Fatima , Douahi Omar , Chenafi Faouzia , Fetati Habiba , Chabane Sari Siham , Benmahieddine Assia , Zaoui Chahinez , Mekaouche Fatima Zohra Nadjet , Atik Bekkara Fawzia , Kambouche Nadia , Gismondi Angelo , Toumi Houari
Published: December 2022

Echinops spinosissimus Turra subsp. bovei (Asteraceae) is a medicinal plant in western Algeria. Traditionally, roots and inflorescences are employed as hypertensive agents and in the treatment of hemorrhoids. The current study evaluates the chemical composition, antioxidant, antimicrobial, and wound-healing properties of the root methanolic extract from E. spinosissimus subsp. bovei. The content of total phenolics, flavonoids, and tannins was determined. In addition, the phenolic profile was typified. The studied plant extract resulted in being primarily composed of Apigenin, Kaempferol, and their derivatives. The total phenolic content was equal to 95.31 ± 2.90 mg GAE/g DW, while the number of flavonoids and condensed tannins was 16.01 ± 0.16 mg CE/g DW and 8.30 ± 0.65 mg CE/g DW, respectively. The methanolic extract was found to exhibit antioxidant activity towards the DPPH radical, with an IC50 of 7.99 ± 0.28 mg/mL and a TAC of 30.30 ± 0.54 mg AAE/g DW, as well as an antibacterial effect, especially against P. aeruginosa. No significant wound-healing property was observed, even though the histopathological observations showed enhanced wound-healing quality. According to our evidence, E. spinosissimus could represent a source of phytochemicals with potential beneficial effects for human health in terms of antioxidant and antibiotic properties, although further investigations on this species are needed.
30-Month Pot Experiment: Biochar Alters Soil Potassium Forms, Soil Properties and Soil Fungal Diversity and Composition in Acidic Soil of Southern China
Authors: Xia Hao , Liu Bo , Riaz Muhammad , Li Yuxuan , Wang Xiangling , Wang Jiyuan , Jiang Cuncang
Published: December 2022

Biochar has a significant impact on improving soil, nutrient supply, and soil microbial amounts. However, the impacts of biochar on soil fungi and the soil environment after 30 months of cultivation experiments are rarely reported. We studied the potential role of peanut shell biochar (0% and 2%) in the soil properties and the soil fungal communities after 30 months of biochar application under different soil potassium (K) levels (100%, 80%, 60%, 0% K fertilizer). We found that biochar had a promoting effect on soil K after 30 months of its application, such as the available K, water-soluble K, exchangeable K, and non-exchangeable K; and increments were 125.78%, 124.39%, 126.01%, and 26.63% under biochar and K fertilizer treatment, respectively, compared to control treatment. Our data revealed that p_Ascomycota and p_Basidiomycota were the dominant populations in the soil, and their sub-levels showed different relationships with the soil properties. The relationships between c_sordariomycetes and its sub-level taxa with soil properties showed a significant positive correlation. However, c_Dothideomycetes and its sub-group demonstrated a negative correlation with soil properties. Moreover, soil enzyme activity, especially related to the soil C cycle, was the most significant indicator that affected the community and structure of fungi through structural equation modeling (SEM) and redundancy analysis (RDA). This work emphasized that biochar plays an important role in improving soil quality, controlling soil nutrients, and regulating fungal diversity and community composition after 30 months of biochar application.
Biochemical Analyses of Bioactive Extracts from Plants Native to Lampedusa, Sicily Minor Island
Authors: Di Lecce Roberta , Mérindol Natacha , Pérez Mayra Galarza , Karimzadegan Vahid , Berthoux Lionel , Boari Angela , Zidorn Christian , Vurro Maurizio , Surico Giuseppe , Desgagné-Penix Isabel , Evidente Antonio
Published: December 2022

Major threats to the human lifespan include cancer, infectious diseases, diabetes, mental degenerative conditions and also reduced agricultural productivity due to climate changes, together with new and more devastating plant diseases. From all of this, the need arises to find new biopesticides and new medicines. Plants and microorganisms are the most important sources for isolating new metabolites. Lampedusa Island host a rich contingent of endemic species and subspecies. Seven plant species spontaneously growing in Lampedusa, i.e., Atriplex halimus L. (Ap), Daucus lopadusanus Tineo (Dl), Echinops spinosus Fiori (Es) Glaucium flavum Crantz (Gf) Hypericum aegypticum L: (Ha), Periploca angustifolia Labill (Pa), and Prasium majus L. (Pm) were collected, assessed for their metabolite content, and evaluated for potential applications in agriculture and medicine. The HPLC-MS analysis of n-hexane (HE) and CH2Cl2 (MC) extracts and the residual aqueous phases (WR) showed the presence of several metabolites in both organic extracts. Crude HE and MC extracts from Dl and He significantly inhibited butyrylcholinesterase, as did WR from the extraction of Dl and Pa. HE and MC extracts showed a significant toxicity towards hepatocarcinoma Huh7, while Dl, Ha and Er HE extracts were the most potently cytotoxic to ileocecal colorectal adenocarcinoma HCT-8 cell lines. Most extracts showed antiviral activity. At the lowest concentration tested (1.56 μg/mL), Dl, Gf and Ap MC extracts inhibited betacoronavirus HCoV-OC43 infection by> 2 fold, while the n-hexane extract of Pm was the most potent. In addition, at 1.56 μg/mL, potent inhibition (>10 fold) of dengue virus was detected for Dl, Er, and Pm HE extracts, while Pa and Ap MC extracts dampened infections to undetectable levels. Regarding to phytotoxicity, MC extracts from Er, Ap and Pm were more effective in inhibiting tomato rootlet elongation; the same first two extracts also inhibited seed cress germination while its radicle elongation, due to high sensitivity, was affected by all the extracts. Es and Gf MC extracts also inhibited seed germination of Phelipanche ramosa. Thus, we have uncovered that many of these Lampedusa plants displayed promising biopesticide, antiviral, and biological properties.
Mechanisms of Microbial Plant Protection and Control of Plant Viruses
Authors: Manjunatha Lakshmaiah , Rajashekara Hosahatti , Uppala Leela Saisree , Ambika Dasannanamalige Siddesh , Patil Balanagouda , Shankarappa Kodegandlu Subbanna , Nath Vishnu Sukumari , Kavitha Tiptur Rooplanaik , Mishra Ajay Kumar
Published: December 2022

Plant viral diseases are major constraints causing significant yield losses worldwide in agricultural and horticultural crops. The commonly used methods cannot eliminate viral load in infected plants. Many unconventional methods are presently being employed to prevent viral infection; however, every time, these methods are not found promising. As a result, it is critical to identify the most promising and sustainable management strategies for economically important plant viral diseases. The genetic makeup of 90 percent of viral diseases constitutes a single-stranded RNA; the most promising way for management of any RNA viruses is through use ribonucleases. The scope of involving beneficial microbial organisms in the integrated management of viral diseases is of the utmost importance and is highly imperative. This review highlights the importance of prokaryotic plant growth-promoting rhizobacteria/endophytic bacteria, actinomycetes, and fungal organisms, as well as their possible mechanisms for suppressing viral infection in plants via cross-protection, ISR, and the accumulation of defensive enzymes, phenolic compounds, lipopeptides, protease, and RNase activity against plant virus infection.
Colorful Luminescence of Conjugated Polyelectrolytes Induced by Molecular Weight
Authors: Wang Kunsheng , Shi Yueqin , Li Zhengjun
Published: December 2022

Due to their distinctive intrinsic advantages, the nanoaggregates of conjugated polyelectrolytes (CPEs) are fascinating and attractive for various luminescence applications. Generally, the emission luminescence of CPEs is determined by the conjugated backbone structure, i.e., different conjugated backbones of CPEs produce emission luminescence with different emission wavelength bands. Here, we polymerized the bis(boronic ester) of benzothiadiazole and an alkyl sulfonate sodium-substituted dibromobenzothiatriazole to provide PBTBTz-SO3Na with different molecular weights via controlling the ratio of the monomer and the catalyst. Theoretically, the CPEs with the same molecular structure usually display similar photoelectronic performances. However, the resulting PBTBTz-SO3Na reveal a similar light absorption property, but different luminescence. The higher molecular weight is, the stronger the fluorescence intensity of PBTBTz-SO3Na that occurs. PBTBTz-SO3Na with different molecular weights have different colors of luminescence. It is well known that the molecular aggregates often led to weaker luminescent properties for most of the conjugated polymers. However, PBTBTz-SO3Na exhibits a higher molecular weight with an increasing molecular chain aggregation, i.e., the nanoaggregates of PBTBTz-SO3Na are beneficial to emission luminescence. This work provides a new possible chemical design of CPEs with a controllable, variable luminescence for further optoelectronics and biomedicine applications.
Thermal and Mechanical Degradation of Recycled Polylactic Acid Filaments for Three-Dimensional Printing Applications
Authors: Lee Dongoh , Lee Younghun , Kim Inwhan , Hwang Kyungjun , Kim Namsu
Published: December 2022

The recycling of filaments used in three-dimensional (3D) printing systems not only mitigates the environmental issues associated with conventional 3D printing approaches but also simultaneously reduces manufacturing costs. This study investigates the effects of successive recycling of polylactic acid (PLA) filaments, which were used in the printing process, on the mechanical properties of recycled filaments and printed objects. The mechanical strengths of the printed PLA and the adhesion strengths between 3D-printed beads were evaluated via the tensile testing of the horizontally and vertically fabricated specimens. Gel permeation chromatography analysis revealed a reduction in the molecular weight of the polymer as a result of recycling, leading to a decrease in the mechanical strength of the 3D-printed product. Additionally, scanning electron microscopy images of the cutting plane showed that the fabricated beads were broken in the case of the horizontally fabricated specimen, whereas in the case of the vertically fabricated samples, the adhesion between the beads was weak. These findings indicate that the mechanical strength in the in-plane and out-of-plane directions must be improved by increasing the mechanical strength of the bead itself as well as the adhesion strength of the beads.
Ternary Inclusion Complex of Sinapic Acid with Hydroxypropyl-β-cyclodextrin and Hydrophilic Polymer Prepared by Microwave Technology
Authors: Ahad Abdul , Jardan Yousef A. Bin , Raish Mohammad , Al-Mohizea Abdullah M. , Al-Jenoobi Fahad I.
Published: December 2022

Sinapic acid (SA) is a poorly water-soluble substance which could result in poor bioavailability. The aim of this study was to determine the “hydroxypropyl β-cyclodextrin (HPβCD)” solubilization of SA in the presence of the auxiliary substance hydroxypropyl methylcellulose (HPMC) and to evaluate the ternary inclusion complex prepared by microwave technology. Phase-solubility profiles showed that HPβCD exhibited the greatest solubilizing effect on SA in the presence of HPMC. The enhanced rate of SA dissolution was exhibited by a ternary complex. Outcomes of analyses such as “DSC, FTIR, NMR, and SEM” confirmed the embedding of SA into the cavity of the HPβCD and the formation of a ternary inclusion complex. The outcomes of antioxidant activity (ABTS and nitric oxide scavenging activity) demonstrated that SA ternary inclusion complex (TIC) presented strong antioxidant activity, which might be a result of the enhanced solubility of SA in the TIC prepared by microwave technology. Hence, SA-TIC formulation could be a better dosage form which may protect the body from free radical damage and oxidative stress. Microwave technology greatly boosted the interaction of SA with HPβCD and HPMC, and such findings are expected to contribute to raising the solubility of SA, thereby improving the bioavailability of SA.
Vehicle Rescheduling with Delivery Delay Considering Perceived Waiting Cost of Heterogeneous Customers
Authors: Wu Lirong , Zhang Hang
Published: December 2022

The original schedule may not be optimal or feasible due to delivery delay caused by disruption. To solve the vehicle rescheduling problem with delivery delay based on loss aversion in prospect theory and customer heterogeneity, a mathematical model is established to minimize the sum of distance cost and penalty cost. Next, an improved compressed annealing algorithm with heterogeneous pressure is proposed to solve the model. Finally, numerical experiments are executed on the basis of 30 classic Solomon benchmarks to test the performance of the proposed solution approach. Sensitivity tests are carried out for the customer waiting sensitivity parameter, the length of delay time, and the time when the delivery delay occurs. The computational results show that, compared to the traditional rescheduling method, the higher the degree of customer heterogeneity, the longer the length of delay time, and, the earlier the distribution delay occurs, the stronger the validity and practicability of the model and algorithm proposed in this paper.
Ecological Environmental Effects and Their Driving Factors of Land Use/Cover Change: The Case Study of Baiyangdian Basin, China
Authors: Xia Boyu , Zheng Linchang
Published: December 2022

Due to the combined effects of the natural environment, climate change and human activities, profound changes have occurred in terms of the eco-environmental effects of land use/cover change (LUCC) in the Baiyangdian basin. Therefore, based on land remote sensing monitoring data from 2000 to 2020, the Eco-environmental Quality Index (EQI) was introduced in this study to measure the eco-environmental effects of land use change in the Baiyangdian basin. Subsequently, the GeoDetector model was applied to detect the formation mechanism of the eco-environmental effects in the Baiyangdian basin from 2000 to 2020. The results of the study showed that cropland, woodland and grassland were the most widely distributed land use types in the Baiyangdian basin. The area of cropland declined the most and was mostly converted to construction land. The EQI increased slightly during the study period. The eco-environment of the mountainous areas in the western part of the basin and in Baiyangdian Lake was better than that of other areas. Land use intensity had a significantly stronger influence on the quality of the eco-environment than other factors. The interaction between the influencing factors was mainly a non-linear enhancement and a two-factor enhancement, with non-linear enhancement dominating.
Technological Energy Efficiency Improvements in Glass-Production Industries and Their Future Perspectives in Italy
Authors: Cantini Alessandra , Leoni Leonardo , Ferraro Saverio , De Carlo Filippo , Martini Chiara , Martini Fabrizio , Salvio Marcello
Published: December 2022

The glass industry is highly energy-intensive, consuming approximately 500–700 million GJ each year. Replacing inefficient equipment with better-performing equipment is a good strategy to reduce the energy consumption of a glass plant. Since there are many alternative solutions, the choice of which technological improvement to implement is usually difficult. Therefore, a review of solutions to reduce energy consumption in a glass plant is pivotal. The literature offers similar studies, but they are not sufficiently up-to-date and do not represent the actual state of the art, which should be updated. Thus, this paper aims to provide an updated list of alternative solutions, clustering them into different categories (e.g., the process stage). Moreover, this paper investigates the current applicability of energy-saving solutions in Italy. Specifically, a sample of 103 Italian companies is considered and the type of interventions that the companies recently implemented or that they intend to adopt is analyzed. Quantitative statistical and economic analyses were conducted to highlight the most popular solutions and determine their cost-effectiveness. The results show that most interventions consist of replacing machinery with more efficient ones, mainly in auxiliary systems (132 out of 426). The outcome of this paper could represent a guide to select energy-saving solutions.
The Effects of Index Futures Trading Volume on Spot Market Volatility in a Frontier Market: Evidence from Ho Chi Minh Stock Exchange
Authors: Truong Loc Dong , Friday H. Swint , Nguyen Anh Thi Kim
Published: December 2022

This analysis is the first to investigate the influence of index futures trading volume on spot market volatility for the Ho Chi Minh Stock Exchange (HOSE). The data utilized in this study are the daily VN30-Index futures contract trading volume starting at the inception date for the VN30-Index futures contract, 10 August 2017 and going through 10 August 2022. Using an autoregressive distributed lag (ARDL) bounds testing approach, the empirical findings reveal a positive relation between VN30-Index futures trading volume and the volatility of the spot market for the HOSE in the short-run. In addition, the results of the ARDL tests confirm in for the long-run, trading volume of futures contracts has a significant positive influence on spot market volatility. Moreover, the results derived from the error correction model (ECM) indicate that only 5.54% of the disequilibria from the previous trading day are converged and corrected back to the long-run equilibrium from the current day. Based on the findings, we recommend that Vietnamese policymakers establish relevant intervention polices on speculation of individual investors in order to provide stabilization safeguards for the underlying stock market.
A Driving-Adapt Strategy for the Electric Vehicle with Magneto-Rheological Fluid Transmission Considering the Powertrain Characteristics
Authors: Liao Peng , Ning Donghong , Wang Tao , Du Haiping
Published: December 2022

The additional energy consumption caused by the incompatibility between existing electric vehicle (EV) powertrain characteristics and driving conditions inevitably curbs the promotion and development of EVs. Hence, there is an urgent demand for the driving-adapt strategy, which aims to minimize EV energy consumption due to both powertrain characteristics and driving conditions. In order to fully explore the EV driving-adapt potential, this paper equips the EV with a magneto-rheological fluid transmission (MRFT). First, an EV dynamics analysis of the driving conditions, the powertrain model considering the energy transmission process, and the driving-adapt transmission model considering magneto-rheological fluid (MRF) is conducted to clarify the quantitative relation between the driving conditions and the powertrain. Second, a driving-adapt optimization strategy in the specific driving condition is proposed. Finally, the results and discussions are executed to study (i) the determination of the MRFT fixed speed ratio and variable speed ratio range, (ii) the application potential analysis of the proposed strategy, and (iii) the feasibility analysis of the proposed strategy. The results indicate that (i) the urban driving condition has higher requirements for the MRFT, (ii) EVs equipped with MRFT achieve the expected driving performance at the most states of charge (SOCs) and environmental temperatures, except for the SOC lower than 10%, and (iii) the driving time with efficiency greater than 80% can be increased by the MRFT from 10.1% to 58.7% and from 66.8% to 88.8% in the urban and suburban driving conditions, respectively. Thus, the proposed driving-adapt strategy for the EV equipped with the MRFT has the potential to alleviate or eliminate the traffic problems caused by the incompatibility of the EV powertrain characteristics and the driving conditions.
Short-Term Photovoltaic Power Forecasting Based on Historical Information and Deep Learning Methods
Authors: Guo Xianchao , Mo Yuchang , Yan Ke
Published: December 2022

The accurate prediction of photovoltaic (PV) power is essential for planning power systems and constructing intelligent grids. However, this has become difficult due to the intermittency and instability of PV power data. This paper introduces a deep learning framework based on 7.5 min-ahead and 15 min-ahead approaches to predict short-term PV power. Specifically, we propose a hybrid model based on singular spectrum analysis (SSA) and bidirectional long short-term memory (BiLSTM) networks with the Bayesian optimization (BO) algorithm. To begin, the SSA decomposes the PV power series into several sub-signals. Then, the BO algorithm automatically adjusts hyperparameters for the deep neural network architecture. Following that, parallel BiLSTM networks predict the value of each component. Finally, the prediction of the sub-signals is summed to generate the final prediction results. The performance of the proposed model is investigated using two datasets collected from real-world rooftop stations in eastern China. The 7.5 min-ahead predictions generated by the proposed model can reduce up to 380.51% error, and the 15 min-ahead predictions decrease by up to 296.01% error. The experimental results demonstrate the superiority of the proposed model in comparison to other forecasting methods.
Design and Implementation of an Open-Source SCADA System for a Community Solar-Powered Reverse Osmosis System
Authors: Uddin Sheikh Usman , Baig Mirza Jabbar Aziz , Iqbal Mohammad Tariq
Published: December 2022

Design and implementation of an open-source-based supervisory control and data acquisition (SCADA) system for a community solar-powered reverse osmosis are presented in this paper. A typical SCADA system available on the market is proprietary and has a high initial and maintenance cost. Aside from that, there is no SCADA system with an alert system available to give users updates and status information concerning the system. The objective of this study is to develop a comprehensive SCADA design that takes advantage of open-source technology to address the world’s most pressing problem, access to clean water. The designed reverse Osmosis system also uses renewable energy-based power sources. In this system, all data is stored and analyzed locally, which ensures the data is secure and allows the user to make data-driven decisions based on the collected data. Among the main components of this system are the field instrument devices (FIDs), the remote terminal unit (RTU), the main terminal units (MTUs), the web-based programming software, and the data analytics software. The Node-Red programming and dashboard tool, Grafana for data analytics, and InfluxDB for database management run on the main terminal unit having Debian operating system. Data is transmitted from the FIDs to the RTU, which then redirects it to the MTU via serial communication. Node-Red displays the data processed by the MTU on its dashboard as well, as the data is stored locally on the MTU and is displayed by means of Grafana, which is also installed on the same MTU. Through the Node-Red dashboard, the system is controlled, and notifications are sent to the community.
Performance Comparison of NB-Fi, Sigfox, and LoRaWAN
Authors: Levchenko Polina , Bankov Dmitry , Khorov Evgeny , Lyakhov Andrey
Published: December 2022

LPWANs are a promising solution for wireless sensor networks. To compete with such widespread technologies as LoRaWAN and Sigfox, recently a new LPWAN technology called NB-Fi has been developed. In a short time, many NB-Fi networks have been deployed in various countries. Although NB-Fi, Sigfox, and LoRaWAN have been designed for similar applications, they implement different approaches. However, no detailed comparisons of them are present in academic literature. This paper aims to fill this gap by analyzing and comparing NB-Fi, Sigfox, and LoRaWAN focusing on performance evaluation results rather than just nominal parameters declared by the developers. Specifically, the paper evaluates the packet loss rate, packet error rate, and average delay in these networks in different scenarios. The results are used to provide guidelines to decide which technology to use under which conditions. Specifically, Sigfox performs best in scenarios when devices transmit small pieces of data without repetitions and acknowledgments, and LoRaWAN is the most reliable for transmitting bigger pieces of data, while NB-Fi is best suited for acknowledged transmissions of small pieces of data.
Comprehensive Eco-Environment Quality Index Model with Spatiotemporal Characteristics
Authors: Li Ning , Wang Jiayao
Published: December 2022

Ecological environment assessment, which forms the basis for the survival and development of human society, is a crucial measure for the sustainable development of society and economy. However, current quantitative assessment models such as EI, EQI, RSEI et al. are insufficient to meet the requirements of dynamic research in large areas, long time series and dense time intervals. Therefore, in this paper, we constructed the comprehensive ecological environment quality index model SCEQI that can meet these needs by applying the remote sensing big data as the data source. The SCEQI aggregated the ecological indices NDVI, NDBSI, Lst and Wet by full-sequence dynamic dimensionless, automated principal component analysis and multi-temporal average method. In order to verify the spatial and temporal accuracy of the model, we took Henan Province as an example to compare the result of SCEQI with that of EI, EQI, RSEI and proved that SCEQI performed better in both time and space. Also, we verified the superiority of time, space, and precision of SCEQI with profiles, samples, and cluster analysis.
RealPrice: Blockchain-Powered Real-Time Pricing for Software-Defined Enabled Edge Network
Authors: Oktian Yustus Eko , Le Thi-Thu-Huong , Jo Uk , Kim Howon
Published: December 2022

With the limited Internet bandwidth in a given area, unlimited data plans can create congestion because there is no retribution for transmitting many packets. The real-time pricing mechanism can inform users of their Internet consumption to limit congestion during peak hours. However, implementing real-time pricing is opex-heavy from the network provider side and requires high-integrity operations to gain consumer trust. This paper aims to leverage the software-defined network to solve the opex issues and blockchain technology to solve trust issues. First, the network congestion level in a given area is analyzed. Then, the price is adjusted accordingly. Devices that send a lot of traffic during congestion will be charged more expensive bills than if transmitting traffic during an off-peak period. To prevent over-charging, the consumers can pre-configure a customized Internet profile stating how many data bytes they are willing to send during congestion. The software-defined controller also authenticates consumers and checks whether they have enough token deposits in the blockchain as Internet usage fees. We implement our work using Ethereum and POX controllers. The experiment results show that the proposed real-time pricing can be performed seamlessly, and the network provider can reap up to 72.91% more profits than existing approaches, such as usage-based pricing or time-dependent pricing. The fairness and trustability of real-time pricing is also guaranteed through the proof-of-usage mechanism and the transparency of the blockchain.
Predicting Vertical Ground Reaction Forces in Running from the Sound of Footsteps
Authors: Oliveira Anderson Souza , Pirscoveanu Cristina-Ioana , Rasmussen John
Published: December 2022

From the point of view of measurement, footstep sounds represent a simple, wearable and inexpensive sensing opportunity to assess running biomechanical parameters. Therefore, the aim of this study was to investigate whether the sounds of footsteps can be used to predict the vertical ground reaction force profiles during running. Thirty-seven recreational runners performed overground running, and their sounds of footsteps were recorded from four microphones, while the vertical ground reaction force was recorded using a force plate. We generated nine different combinations of microphone data, ranging from individual recordings up to all four microphones combined. We trained machine learning models using these microphone combinations and predicted the ground reaction force profiles by a leave-one-out approach on the subject level. There were no significant differences in the prediction accuracy between the different microphone combinations (p < 0.05). Moreover, the machine learning model was able to predict the ground reaction force profiles with a mean Pearson correlation coefficient of 0.99 (range 0.79–0.999), mean relative root-mean-square error of 9.96% (range 2–23%) and mean accuracy to define rearfoot or forefoot strike of 77%. Our results demonstrate the feasibility of using the sounds of footsteps in combination with machine learning algorithms based on Fourier transforms to predict the ground reaction force curves. The results are encouraging in terms of the opportunity to create wearable technology to assess the ground reaction force profiles for runners in the interests of injury prevention and performance optimization.
Automated Camera Exposure Control for Accuracy-Enhanced Stereo-Digital Image Correlation Measurement
Authors: Zhang Xiaoying , Tang Xiaojun , Yu Liping , Pan Bing
Published: December 2022

An automated camera exposure control method, which allows a two-camera stereo-digital image correlation (stereo-DIC) system to capture high-quality speckle image pairs, is presented for accuracy-enhanced stereo-DIC measurement. By using this method, the two synchronized cameras can automatically determine the optimal camera exposure and ideal average grayscale for capturing the optimal reference image pair in the reference state. Furthermore, high-quality deformed image pairs can be recorded during the test by adaptively adjusting the camera exposure in case of serious ambient light variations. Validation tests, including varying illumination tests and translation tests, were performed to verify the effectiveness and robustness of this method. Experimental results indicate that the proposed method overperforms the existing stereo-DIC technique with empirically determined fixed camera exposure time. The practicality of the proposed automated camera exposure control method was verified using real high-temperature experiments.
Towards Outdoor Electromagnetic Field Exposure Mapping Generation Using Conditional GANs
Authors: Mallik Mohammed , Tesfay Angesom Ataklity , Allaert Benjamin , Kassi Redha , Egea-Lopez Esteban , Molina-Garcia-Pardo Jose-Maria , Wiart Joe , Gaillot Davy P. , Clavier Laurent
Published: December 2022

With the ongoing fifth-generation cellular network (5G) deployment, electromagnetic field exposure has become a critical concern. However, measurements are scarce, and accurate electromagnetic field reconstruction in a geographic region remains challenging. This work proposes a conditional generative adversarial network to address this issue. The main objective is to reconstruct the electromagnetic field exposure map accurately according to the environment’s topology from a few sensors located in an outdoor urban environment. The model is trained to learn and estimate the propagation characteristics of the electromagnetic field according to the topology of a given environment. In addition, the conditional generative adversarial network-based electromagnetic field mapping is compared with simple kriging. Results show that the proposed method produces accurate estimates and is a promising solution for exposure map reconstruction.
Automatic Gender and Age Classification from Offline Handwriting with Bilinear ResNet
Authors: Rabaev Irina , Alkoran Izadeen , Wattad Odai , Litvak Marina
Published: December 2022

This work focuses on automatic gender and age prediction tasks from handwritten documents. This problem is of interest in a variety of fields, such as historical document analysis and forensic investigations. The challenge for automatic gender and age classification can be demonstrated by the relatively low performances of the existing methods. In addition, despite the success of CNN for gender classification, deep neural networks were never applied for age classification. The published works in this area mostly concentrate on English and Arabic languages. In addition to Arabic and English, this work also considers Hebrew, which was much less studied. Following the success of bilinear Convolutional Neural Network (B-CNN) for fine-grained classification, we propose a novel implementation of a B-CNN with ResNet blocks. To our knowledge, this is the first time the bilinear CNN is applied for writer demographics classification. In particular, this is the first attempt to apply a deep neural network for the age classification. We perform experiments on documents from three benchmark datasets written in three different languages and provide a thorough comparison with the results reported in the literature. B-ResNet was top-ranked in all tasks. In particular, B-ResNet outperformed other models on KHATT and QUWI datasets on gender classification.
Intra Prediction Method for Depth Video Coding by Block Clustering through Deep Learning
Authors: Lee Dong-seok , Kwon Soon-kak
Published: December 2022

In this paper, we propose an intra-picture prediction method for depth video by a block clustering through a neural network. The proposed method solves a problem that the block that has two or more clusters drops the prediction performance of the intra prediction for depth video. The proposed neural network consists of both a spatial feature prediction network and a clustering network. The spatial feature prediction network utilizes spatial features in vertical and horizontal directions. The network contains a 1D CNN layer and a fully connected layer. The 1D CNN layer extracts the spatial features for a vertical direction and a horizontal direction from a top block and a left block of the reference pixels, respectively. 1D CNN is designed to handle time-series data, but it can also be applied to find the spatial features by regarding a pixel order in a certain direction as a timestamp. The fully connected layer predicts the spatial features of the block to be coded through the extracted features. The clustering network finds clusters from the spatial features which are the outputs of the spatial feature prediction network. The network consists of 4 CNN layers. The first 3 CNN layers combine two spatial features in the vertical and horizontal directions. The last layer outputs the probabilities that pixels belong to the clusters. The pixels of the block are predicted by the representative values of the clusters that are the average of the reference pixels belonging to the clusters. For the intra prediction for various block sizes, the block is scaled to the size of the network input. The prediction result through the proposed network is scaled back to the original size. In network training, the mean square error is used as a loss function between the original block and the predicted block. A penalty for output values far from both ends is introduced to the loss function for clear network clustering. In the simulation results, the bit rate is saved by up to 12.45% under the same distortion condition compared with the latest video coding standard.
Using the Capability Approach to Review the National Legislative Frameworks for Support Services for Persons with Disabilities in Four Countries in Asia
Authors: Gupta Shivani , Meershoek Agnes , Witte Luc P. de
Published: December 2022

Implementation of the United Nations Convention on the rights of persons with disabilities (UN CRPD) requires countries to harmonise their legislative frameworks with it. This paper investigates the national legislative frameworks of four Asian countries to see the extent to which they provide support services in accordance with Article 19 of the UN CRPD. The UN CRPD requires persons with disabilities to have access to and choice and control over support services. To analyse the policy alignment with the UN CRPD, an analytical framework based on the Capability Approach (CA) was developed. The results show that most countries address support services, including assistive devices, only from the perspective of a social security measure for persons with disabilities living in poverty, failing to uphold the rights of those not meeting those eligibility criteria. However, while support services are inseparably linked to social security, they also are a right for persons with disabilities. Therefore, a paradigm shift is required in the approach of support services and the distributive systems of countries, from one that addresses persons with disabilities as those requiring care considered a burden, to one that considers them rights holders with equal opportunities, for which, support services are a pre-requisite.
Migrant Organizations and Social Protection in Germany: The Functions of MOs for Their Target Groups’ Social Protection Practices
Authors: Bonfert Lisa , Günzel Eva , Kellmer Ariana
Published: December 2022

This article engages with the functions assumed by migrant organizations (MOs) in Germany in the context of the social protection of people with migration biographies. Based on document analyses and qualitative interviews with three groups of actors, we identify four functions through which MOs contribute to their target groups’ social protection practices, and show how diverging perceptions toward these functions shape the current role of MOs in a changing welfare system. In addition to providing social services themselves (service function), they mediate with the welfare system (hinging function) and advocate for the interests of people with migration biographies in public and political discourse (advocacy function). Moreover, we demonstrate that these functions are shaped and complemented by a “homemaking” function, a form of informal protection based on mutual support, trust and understanding. In this article, the discussion of the specific ways in which these functions play a role for the social protection of people with migration biographies is based on joint analysis of three data sets. Thus, we juxtapose the viewpoints of MO representatives, their target groups and people associated with welfare state institutions and political administrations. In this way, we show how MOs use these various functions to actively engage with a changing welfare landscape, whereas welfare institutions and political administrations often perceive of the work undertaken by MOs rather as an ‘integration’-oriented prerequisite for their own social service provision. As a result, contrasting and sometimes competing perspectives challenge the role of MOs within the German welfare system, even though these organizations already fulfill key functions for their target groups’ social protection.
Does Environmental Regulation Drive Economic Growth through Technological Innovation: Application of Nonlinear and Spatial Spillover Effect
Authors: Chen Jing , Hu Liyuan
Published: December 2022

Based on provincial dynamic panel data from 2010 to 2019, this paper constructs a panel threshold model and explores the nonlinear relationship between environmental regulation and economic growth through channels of technological innovation. In addition, a spatial panel Durbin model is constructed to test the spatial spillover effects of environmental regulation and technological innovation on economic growth. The results show that environmental regulation has a significant positive impact on economic growth through channels of technological innovation. The interaction between environmental regulation and technological innovation has a single threshold effect on economic growth. When the intensity of environmental regulation exceeds the critical value, environmental regulation will change from promoting economic growth to inhibiting economic growth through channels of technological innovation. Environmental regulation has negative spatial spillover effect on economic growth of neighboring regions, while technological innovation has positive spatial spillover effect on economic growth of neighboring regions.
Research on Enterprise Financial Risk Conduction Mechanism Based on System Dynamics
Authors: Zhang Zhi
Published: December 2022

Affected by the COVID-19 pandemic and the economic situation, many enterprises have fallen into financial crisis. In order to explore the causes of enterprise financial risk and the conduction path of risk sources, this paper starts from the theory, characteristics, and path of financial risk conduction, combines Hall three-dimensional structure and system dynamics models, establishes the path of enterprise financial risk conduction (causality graph), and combines the value-at-risk VaR model to measure the risk. Based on this methodology, a three-dimensional multiple risk interaction and dynamic–static combination of an enterprise financial risk conduction model is established, aiming at identifying the sources of financial risk in different periods and providing timely risk control countermeasures to avoid financial crises. This paper does not refine some of the indicators and takes into account the probability of different scenarios and/or the number of trigger strategies to avoid or reduce risk. In the future, refining the indicators to include considerations such as production technology will enable a more robust model of corporate financial risk conduction.