bluuebunny/crossref_metadata_2025 huggingface dataset:
Structural Characterization of Degraded Lycium barbarum L. Leaves’ Polysaccharide Using Ascorbic Acid and Hydrogen Peroxide
Authors: Al-Wraikat Majida , Liu Yun , Wu Limei , Ali Zeshan , Li Jianke
Published: March 2022

Plant-derived polysaccharide’s conformation and chain structure play a key role in their various biological activities. Lycium barbarum L. leaves’ polysaccharide is well renowned for its health functions. However, its functional bioactivities are greatly hindered by its compact globular structure and high molecular weight. To overcome such issue and to improve the functional bioactivities of the polysaccharides, degradation is usually used to modify the polysaccharides conformation. In this study, the ethanol extract containing crude Lycium barbarum L. leaves’ polysaccharide was first extracted, further characterized, and subsequently chemically modified with vitamin C (Ascorbic acid) and hydrogen peroxide (H2O2) to produce degraded Lycium barbarum L. leaves’ polysaccharide. To explore the degradation effect, both polysaccharides were further characterized using inductively coupled plasma mass spectrometry (ICP-MS), gas chromatography–mass spectrometry (GC–MS), Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), high performance gel permeation chromatography (HPGPC), and scanning electron microscope (SEM). Results shown that both polysaccharides were rich in sugar and degradation had no significant major functional group transformation effect on the degraded product composition. However, the molecular weight (Mw) had decreased significantly from 223.5 kDa to 64.3 kDa after degradation, indicating significant changes in the polysaccharides molecular structure caused by degradation.
Encapsulation of β-Carotene in Oil-in-Water Emulsions Containing Nanocellulose: Impact on Emulsion Properties, In Vitro Digestion, and Bioaccessibility
Authors: Fitri Ichlasia Ainul , Mitbumrung Wiphada , Akanitkul Ploypailin , Rungraung Numphung , Kemsawasd Varongsiri , Jain Surangna , Winuprasith Thunnalin
Published: March 2022

The objective of this study was to explore the influence of nanocellulose type (nanocrystalline cellulose (NCC) and nanofibrillated cellulose (NFC)) and concentrations (0.05–0.20%, w/w) on the physicochemical properties, microstructure, and in vitro digestion of β-carotene loaded emulsions and β-carotene bioaccessibility. The optimum conditions for the formation of stable β-carotene loaded emulsions were found when NCC was used as a stabilizer at a concentration of 0.2% w/w. This was due to the rod-shaped structure of NCC, which led to more stable emulsions with smaller droplet size and reduced flocculation. During the in vitro gastrointestinal digestion, NFC emulsions at increased concentrations were found to retard free fatty acid (FFA) release from the emulsions and reduce the bioaccessibility of β-carotene. On the other hand, NCC emulsions at concentrations of 0.2% w/w promoted lipolysis and demonstrated highest β-carotene bioavailability. Hence, these emulsions could be used for the delivery of β-carotene with potential applications in the development of functional foods and nutraceuticals.
Efficacy of Three Commercially Available Desensitizers in Reducing Post-Operative Sensitivity Following Composite Restorations: A Randomized Controlled Clinical Trial
Authors: Sayed Mohammed E. , Dewan Harisha , Kharaf Rawabi , Athlawi Maram , Alfaifi Munira , Mugri Maryam Hassan , Bosly Razan Abu-Alqasem , Fageehi Nada Yousef , Hadi Maryam , Zurbtan Bayan Jebril , Shaabi Fawzia Ibraheem , Alsurayyie Fatimah H. , Bukhary Dalea Mohammed , Alshali Ruwaida Zaki , Chohan Hitesh
Published: March 2022

One of the most widely used esthetic restorations in dentistry is composite. The widespread application of composites can be related to advancements in biomaterials. However, due to various factors, composites are commonly associated with dental sensitivity. Hence, the present study evaluates and compares the effectiveness of three desensitizing agents in reducing post-treatment sensitivity for Class I composite restoration. Eighty subjects with Class I cavities were selected according to the inclusion criteria, and a randomized, double-blind, controlled clinical trial was carried out. Twenty patients were randomly assigned to four groups: Group C (Control group), Group GL (Gluma group), Group SF (Shield Force Plus group), and Group TC (Telio CS group). The desensitizers were applied after Class 1 cavity preparation and acid etching in all the groups, except the Control group, and thereafter, composite restoration was completed in a conventional manner. Questionnaires were provided to all the participants to record the post-operative pain/sensitivity level according to the visual analogue scale (VAS) on intake of cold drinks, intake of hot drinks, and intake of sugar for different periods of time. Significant variation was observed between the three desensitizers for all three stimuli. However, no significant variations were seen with the various age groups and between the maxillary and the mandibular teeth at the different time periods. Group GL performed better than Group SF and Group TC. It can be proposed that the application of the desensitizers reduced the post-restorative sensitivity in the composite restorations and improved acceptance.
Contributions of Women in Recent Research on Biopolymer Science
Authors: Veettil Unnimaya , Olza Sheila , Brugerolle de Fraissinette Nelly , Bascans Elodie , Castejón Natalia , Adrien Amandine , Fernández-Marín Rut , Nardin Corinne , Fernandes Susana
Published: March 2022

Nowadays, biopolymers are playing a fundamental role in our society because of the environmental issues and concerns associated with synthetic polymers. The aim of this Special Issue entitled ‘Women in Polymer Science and Technology: Biopolymers’ is highlighting the work designed and developed by women on biopolymer science and technology. In this context, this short review aims to provide an introduction to this Special Issue by highlighting some recent contributions of women around the world on the particular topic of biopolymer science and technology during the last 20 years. In the first place, it highlights a selection of important works performed on a number of well-studied natural polymers, namely, agar, chitin, chitosan, cellulose, and collagen. Secondly, it gives an insight into the discovery of new polysaccharides and enzymes that have a role in their synthesis and in their degradation. These contributions will be paving the way for the next generation of female and male scientists on this topic.
Geographical Variation in Mental Hospital Discharges in Greece: A Nationwide Study (1999–2012)
Authors: Dadouli Katerina , Hatziefthimiou Apostolia , Mouchtouri Varvara A. , Ntellas Panagiotis , Anagnostopoulos Lemonia , Koureas Michalis , Bonotis Konstantinos , Christodoulou Nikolaos , Hadjichristodoulou Christos
Published: March 2022

Background: The primary goal of this study is to estimate the pattern of hospital discharges throughout Greece due to mental disorders between 1999 and 2012. Methods: Data for discharges were obtained from the Hellenic Statistical Authority. A sex- and age-adjusted proportional hospitalization ratio (PHR) was used to estimate the ratio between the hospitalizations in each prefecture and the overall hospitalizations. Additionally, age-adjusted admission rates and hospitalization days were calculated for each sex. Descriptive and time series analysis were conducted to understand the epidemiological characteristics and to investigate the trend of annual PHR, respectively. Correlation between disorders and sociodemographic characteristics was also tested. Global and local spatial analysis was conducted to assess the spatial homogeneity of disorders and to detect any clusters of similar values. Results: More males (55%) were hospitalized. Schizophrenic and other psychoses were stated as the primary diagnosis of discharges (54.3%) for mental disorders, contributing to the highest annual mean number of hospitalization-days for male (296.9) and female patients (341.0). Most patients were out of the workforce, and most patients with drug dependence (74.5%) and schizophrenia and other psychoses (55.9%) remained unmarried. Higher PHRs were discovered in the north, while schizophrenic and other psychoses (R = 0.492), affective psychoses (R = 0.534), senile and presenile organic psychotic conditions (R = 0.543) were correlated with alcohol consumption (p < 0.001). Conclusions: The study provides evidence of geographical variation of discharges due to mental disorders and a significant association between disorders and alcohol consumption, marriage status and absence of the workforce.
Forthtellers Not Foretellers: The Origins of a Liberal Orthodoxy about the Prophets
Authors: O’Brien Julia M.
Published: March 2022

The insistence that the prophets of the Hebrew Bible were “forthtellers, not foretellers” is ubiquitous in academic, liberal Christian, and even secular circles. It categorically denies that the prophets of ancient Israel predicted the future and characterizes them instead as voices of social critique. This article explains the origins of the phrase, its philosophical and religious underpinnings in Protestant, Enlightenment, Romantic, “scientific” and “modern” thought and traces its rhetorical usage in religious debate.
Residual Biomass: A Comprehensive Review on the Importance, Uses and Potential in a Circular Bioeconomy Approach
Authors: Casau Margarida , Dias Marta Ferreira , Matias João C. O. , Nunes Leonel J. R.
Published: March 2022

The paradigm shift towards sustainable growth is urgent, and biomass, which is the oldest energy source that humans have used since the discovery of fire, might play an important role. Biomass waste from forestry and agriculture is expected to fuel part of the increasing demand for biomass, and its valorization allows for more the efficient use of nutrients and resources. In this study, we carried out an extensive literature review on the valorization of residual agroforestry biomass since the 1970s to understand the leading research focuses on the subject over the last few decades, identify the most recent trends, and establish a possible solution path for the future of biomass. It was observed that most studies focused on biomass as being capable of replacing fossil energy sources. According to the literature, biomass has the most significant potential to meet requirements and ensure fuel supplies in the future. The developments of the last decades have significantly improved the conversion processes, leading to greener solutions, but there is still much to be studied and put into practice. Closing the loop into biomass waste recovery will be essential for a genuinely circular bioeconomy.
Ambiguity Resolution for Long Baseline in a Network with BDS-3 Quad-Frequency Ionosphere-Weighted Model
Authors: Zhang Ruicheng , Gao Chengfa , Wang Zhibin , Zhao Qing , Shang Rui , Peng Zihan , Liu Qi
Published: March 2022

For long baseline in a network, the traditional combined ionosphere-free (IF) + wide-lane (WL) strategy is commonly used, but the residual tropospheric delays and larger noise hamper the basic ambiguity resolution (AR). With the completion of the BeiDou global navigation satellite system (BDS-3) and the quad-frequency signals provided by BDS-3 satellites, we can construct more combinations that are conducive to ambiguity resolution. Compared with ionosphere-free linear combinations, we estimated ionospheric delay using three independent WL observations, and formed an ionosphere-weighted model using uncombined code and phase observations, which proved to be quite effective. Based on the real quad-frequency BDS-3 observations of two CORS (Continuously Operating Reference Stations) and two user stations, we processed eight days of data to study the formal and empirical ambiguity success rates and user positioning errors. The rounding success rate of WL ambiguity was significantly improved with ionospheric correction. The success rate of the basic ambiguity increased from 94.4 and 96.1% to 98.0% using the quad-frequency ionosphere-weighted (QFIW) model compared with the double-frequency ionosphere-free (DFIF) model and the triple-frequency geometry-based (TFGB) model. Furthermore, the user E/N/U positioning accuracy improved by 20.6/31.5/13.1% and 6.3/22.9/5.8%, respectively.
HD Camera-Equipped UAV Trajectory Planning for Gantry Crane Inspection
Authors: Tang Gang , Gu Jiaxu , Zhu Weidong , Claramunt Christophe , Zhou Peipei
Published: March 2022

While Unmanned Aerial Vehicles (UAVs) can be a valuable solution for the damage inspection of port machinery infrastructures, their trajectories are still prone to collision risks, trajectory non-smoothness, and large deviations. This research introduces a trajectory optimization method for inspecting vulnerable parts of a gantry crane by a UAV fitted with a high-definition (HD) camera. We first analyze the vulnerable parts of a gantry crane, then use the A* algorithm to plan a path for the UAV. The trajectory optimization process is divided into two steps, the first is a trajectory correction method and the second is an objective function that applies a minimum snap method while taking into consideration flight corridor constraints. The experimental simulation results show that, compared with previous methods, our approach can not only generate a collision-free and smooth trajectory but also shorten the trajectory length significantly while substantially reducing the maximum deviation average deviation distances. The simulation results show that this modelling approach provides a valuable solution for UAV trajectory planning for gantry crane inspection.
Woody Plant Encroachment: Evaluating Methodologies for Semiarid Woody Species Classification from Drone Images
Authors: Olariu Horia G. , Malambo Lonesome , Popescu Sorin C. , Virgil Clifton , Wilcox Bradford P.
Published: March 2022

Globally, native semiarid grasslands and savannas have experienced a densification of woody plant species—leading to a multitude of environmental, economic, and cultural changes. These encroached areas are unique in that the diversity of tree species is small, but at the same time the individual species possess diverse phenological responses. The overall goal of this study was to evaluate the ability of very high resolution drone imagery to accurately map species of woody plants encroaching on semiarid grasslands. For a site in the Edwards Plateau ecoregion of central Texas, we used affordable, very high resolution drone imagery to which we applied maximum likelihood (ML), support vector machine (SVM), random forest (RF), and VGG-19 convolutional neural network (CNN) algorithms in combination with pixel-based (with and without post-processing) and object-based (small and large) classification methods. Based on test sample data (n = 1000) the VGG-19 CNN model achieved the highest overall accuracy (96.9%). SVM came in second with an average classification accuracy of 91.2% across all methods, followed by RF (89.7%) and ML (86.8%). Overall, our findings show that RGB drone sensors are indeed capable of providing highly accurate classifications of woody plant species in semiarid landscapes—comparable to and even greater in some regards to those achieved by aerial and drone imagery using hyperspectral sensors in more diverse landscapes.
Coded-GFDM for Reliable Communication in Underwater Acoustic Channels
Authors: Murad Mohsin , Tasadduq Imran A. , Otero Pablo
Published: March 2022

The performance of the coded generalized frequency division multiplexing (GFDM) transceiver has been evaluated in a shallow underwater acoustic channel (UAC). Acoustic transmission is the scheme of choice for communication in UAC since radio waves suffer from absorption and light waves scatter. Although orthogonal frequency division multiplexing (OFDM) has found its ground for multicarrier acoustic underwater communication, it suffers from high peak to average power ratio (PAPR) and out of band (OOB) emissions. We propose a coded-GFDM based multicarrier system since GFDM has a higher spectral efficiency compared to a traditional OFDM system. In doing so, we assess two block codes, namely Bose, Chaudari, and Hocquenghem (BCH) codes, Reed-Solomon (RS) codes, and several convolutional codes. We present the error performances of these codes when used with GFDM. Furthermore, we evaluate the performance of the proposed system using two equalizers: Matched Filter (MF) and Zero-Forcing (ZF). Simulation results show that among the various block coding schemes that we tested, BCH (31,6) and RS (15,3) give the best error performance. Among the convolutional codes that we tested, rate 1/4 convolutional codes give the best performance. However, the performance of BCH and RS codes is much better than the convolutional codes. Moreover, the performance of the ZF equalizer is marginally better than the MF equalizer. In conclusion, using the channel coding schemes with GFDM improves error performance manifolds thereby increasing the reliability of the GFDM system despite slightly higher complexity.
Assessing Nordihydroguaiaretic Acid Therapeutic Effect for Glioblastoma Multiforme
Authors: Manciu Felicia S. , Guerrero Jose , Bennet Kevin E. , Chang Su-Youne , Rahman Masum , Martinez Lopez Lizbeth V. , Chantigian Siobhan , Castellanos Mariana , Manciu Marian
Published: March 2022

In this study, we demonstrate that Raman microscopy combined with computational analysis is a useful approach to discriminating accurately between brain tumor bio-specimens and to identifying structural changes in glioblastoma (GBM) bio-signatures after nordihydroguaiaretic acid (NDGA) administration. NDGA phenolic lignan was selected as a potential therapeutic agent because of its reported beneficial effects in alleviating and inhibiting the formation of multi-organ malignant tumors. The current analysis of NDGA’s impact on GBM human cells demonstrates a reduction in the quantity of altered protein content and of reactive oxygen species (ROS)-damaged phenylalanine; results that correlate with the ROS scavenger and anti-oxidant properties of NDGA. A novel outcome presented here is the use of phenylalanine as a biomarker for differentiating between samples and assessing drug efficacy. Treatment with a low NDGA dose shows a decline in abnormal lipid-protein metabolism, which is inferred by the formation of lipid droplets and a decrease in altered protein content. A very high dose results in cell structural and membrane damage that favors transformed protein overexpression. The information gained through this work is of substantial value for understanding NDGA’s beneficial as well as detrimental bio-effects as a potential therapeutic drug for brain cancer.
Slope with Predetermined Shear Plane Stability Predictions under Cyclic Loading with Innovative Time Series Analysis by Mechanical Learning Approach
Authors: Wu Tingyao , Yu Hongan , Jiang Nan , Zhou Chuanbo , Luo Xuedong
Published: March 2022

We propose a mechanical learning method that can be used to predict stability coefficients for slopes where slopes with predetermined shear planes are subjected to cyclic seismic loads under undrained conditions. Firstly, shear tests with cyclic loading of different parameters were simulated on designated slip zone soil specimens, in which the strain softening process leading to landslide occurrence was closely observed. At the same time, based on the limit equilibrium analysis of the Sarma method, the variation of slope stability coefficients under different cyclic loads was investigated. Finally, a Box–Jenkins’ modeling approach is used to predict the data from the time series of slope stability coefficients using a mechanical learning approach. The simulation results show that (1) reduction in coordination number can be an accurate indicator of the level of strain softening and evolutionary processes; (2) the gradual reduction of shear stress facilitates the soil strain softening process, while different cyclic loading stress amplitudes will result in rapid penetration or non-penetration of the fracture zone by means of particulate flow. Although the confining pressure of the slip zone soil can inhibit the increase of fractures, it has a limited inhibitory effect on strain softening; (3) based on field observations of the slope stability factor and stress field, two possible landslide triggering mechanisms are described. (4) Mechanical learning of time series can accurately predict the changing pattern of stability coefficients of slopes without loading. This study establishes a potential bridge between the geological investigation of landslides and the theoretical background of landslide stability coefficient prediction.
Data Gathering Techniques in WSN: A Cross-Layer View
Authors: Gurewitz Omer , Shifrin Mark , Dvir Efi
Published: March 2022

Wireless sensor networks (WSNs) have taken a giant leap in scale, expanding their applicability to a large variety of technological domains and applications, ranging from the Internet of things (IoT) for smart cities and smart homes to wearable technology healthcare applications, underwater, agricultural and environmental monitoring and many more. This expansion is rapidly growing every passing day in terms of the variety, heterogeneity and the number of devices which such applications support. Data collection is commonly the core application in WSN and IoT networks, which are typically composed of a large variety of devices, some constrained by their resources (e.g., processing, storage, energy) and some by highly diverse demands. Many challenges span all the conceptual communication layers, from the Physical to the Applicational. Many novel solutions devised in the past do not scale well with the exponential growth in the population of the devices and need to be adapted, revised, or new innovative solutions are required to comply with this massive growth. Furthermore, recent technological advances present new opportunities which can be leveraged in this context. This paper provides a cross-layer perspective and review of data gathering in WSN and IoT networks. We provide some background and essential milestones that have laid the foundation of many subsequent solutions suggested over the years. We mainly concentrate on recent state-of-the-art research, which facilitates the scalable, energy-efficient, cost-effective, and human-friendly functionality of WSNs and the novel applications in the years to come.
0.3-Volt Rail-to-Rail DDTA and Its Application in a Universal Filter and Quadrature Oscillator
Authors: Khateb Fabian , Kumngern Montree , Kulej Tomasz , Biolek Dalibor
Published: March 2022

This paper presents the extremely low-voltage supply of the CMOS structure of a differential difference transconductance amplifier (DDTA). With a 0.3-volt supply voltage, the circuit offers rail-to-rail operational capability. The circuit is designed for low-frequency biomedical and sensor applications, and it consumes 357.4 nW of power. Based on two DDTAs and two grounded capacitors, a voltage-mode universal filter and quadrature oscillator are presented as applications. The universal filter possesses high-input impedance and electronic tuning ability of the natural frequency in the range of tens up to hundreds of Hz. The total harmonic distortion (THD) for the band-pass filter was 0.5% for 100 mVpp @ 84.47 Hz input voltage. The slight modification of the filter yields a quadrature oscillator. The condition and the frequency of oscillation are orthogonally controllable. The frequency of oscillation can also be controlled electronically. The THD for a 67 Hz oscillation frequency was around 1.2%. The circuit is designed and simulated in a Cadence environment using 130 nm CMOS technology from United Microelectronics Corporation (UMC). The simulation results confirm the performance of the designed circuits.
Context-Aware Automatic Sign Language Video Transcription in Psychiatric Interviews
Authors: Pikoulis Erion-Vasilis , Bifis Aristeidis , Trigka Maria , Constantinopoulos Constantinos , Kosmopoulos Dimitrios
Published: March 2022

Sign language (SL) translation constitutes an extremely challenging task when undertaken in a general unconstrained setup, especially in the absence of vast training datasets that enable the use of end-to-end solutions employing deep architectures. In such cases, the ability to incorporate prior information can yield a significant improvement in the translation results by greatly restricting the search space of the potential solutions. In this work, we treat the translation problem in the limited confines of psychiatric interviews involving doctor-patient diagnostic sessions for deaf and hard of hearing patients with mental health problems.To overcome the lack of extensive training data and be able to improve the obtained translation performance, we follow a domain-specific approach combining data-driven feature extraction with the incorporation of prior information drawn from the available domain knowledge. This knowledge enables us to model the context of the interviews by using an appropriately defined hierarchical ontology for the contained dialogue, allowing for the classification of the current state of the interview, based on the doctor’s question. Utilizing this information, video transcription is treated as a sentence retrieval problem. The goal is predicting the patient’s sentence that has been signed in the SL video based on the available pool of possible responses, given the context of the current exchange. Our experimental evaluation using simulated scenarios of psychiatric interviews demonstrate the significant gains of incorporating context awareness in the system’s decisions.
A Novel Framework for Open-Set Authentication of Internet of Things Using Limited Devices
Authors: Huang Keju , Yang Junan , Hu Pengjiang , Liu Hui
Published: March 2022

The Internet of Things (IoT) is promising to transform a wide range of fields. However, the open nature of IoT makes it exposed to cybersecurity threats, among which identity spoofing is a typical example. Physical layer authentication, which identifies IoT devices based on the physical layer characteristics of signals, serves as an effective way to counteract identity spoofing. In this paper, we propose a deep learning-based framework for the open-set authentication of IoT devices. Specifically, additive angular margin softmax (AAMSoftmax) was utilized to enhance the discriminability of learned features and a modified OpenMAX classifier was employed to adaptively identify authorized devices and distinguish unauthorized ones. The experimental results for both simulated data and real ADS–B (Automatic Dependent Surveillance–Broadcast) data indicate that our framework achieved superior performance compared to current approaches, especially when the number of devices used for training is limited.
Indoor Positioning of Low-Cost Narrowband IoT Nodes: Evaluation of a TDoA Approach in a Retail Environment
Authors: Neunteufel Daniel , Grebien Stefan , Arthaber Holger
Published: March 2022

The localization of internet of things (IoT) nodes in indoor scenarios with strong multipath channel components is challenging. All methods using radio signals, such as received signal strength (RSS) or angle of arrival (AoA), are inherently prone to multipath fading. Especially for time of flight (ToF) measurements, the low available transmit bandwidth of the used transceiver hardware is problematic. In our previous work on this topic we showed that wideband signal generation on narrowband low-power transceiver chips is feasible without any changes to existing hardware. Together with a fixed wideband receiving anchor infrastructure, this facilitates time difference of arrival (TDoA) and AoA measurements and allows for localization of the fully asynchronously transmitting nodes. In this paper, we present a measurement campaign using a receiver infrastructure based on software-defined radio (SDR) platforms. This proves the actual usability of the proposed method within the limitations of the bandwidth available in the ISM band at 2.4 GHz. We use the results to analyze the effects of possible anchor placement schemes and scenario geometries. We further demonstrate how this node-to-infrastructure-based localization scheme can be supported by additional node-to-node RSS measurements using a simple clustering approach. In the considered scenario, an overall positioning root-mean-square error (RMSE) of 2.19 m is achieved.
Denoising for 3D Point Cloud Based on Regularization of a Statistical Low-Dimensional Manifold
Authors: Liu Youyu , Zou Baozhu , Xu Jiao , Yang Siyang , Li Yi
Published: March 2022

A point cloud obtained by stereo matching algorithm or three-dimensional (3D) scanner generally contains much complex noise, which will affect the accuracy of subsequent surface reconstruction or visualization processing. To eliminate the complex noise, a new regularization algorithm for denoising was proposed. In view of the fact that 3D point clouds have low-dimensional structures, a statistical low-dimensional manifold (SLDM) model was established. By regularizing its dimensions, the denoising problem of the point cloud was expressed as an optimization problem based on the geometric constraints of the regularization term of the manifold. A low-dimensional smooth manifold model was constructed by discrete sampling, and solved by means of a statistical method and an alternating iterative method. The performance of the denoising algorithm was quantitatively evaluated from three aspects, i.e., the signal-to-noise ratio (SNR), mean square error (MSE) and structural similarity (SSIM). Analysis and comparison of performance showed that compared with the algebraic point-set surface (APSS), non-local denoising (NLD) and feature graph learning (FGL) algorithms, the mean SNR of the point cloud denoised using the proposed method increased by 1.22 DB, 1.81 DB and 1.20 DB, respectively, its mean MSE decreased by 0.096, 0.086 and 0.076, respectively, and its mean SSIM decreased by 0.023, 0.022 and 0.020, respectively, which shows that the proposed method is more effective in eliminating Gaussian noise and Laplace noise in common point clouds. The application cases showed that the proposed algorithm can retain the geometric feature information of point clouds while eliminating complex noise.
Implementing Data-Driven Smart City Applications for Future Cities
Authors: Kaluarachchi Yamuna
Published: March 2022

Cities are investing in data-driven smart technologies to improve performance and efficiency and to generate a vast amount of data. Finding the opportunities to innovatively use this data help governments and authorities to forecast, respond, and plan for future scenarios. Access to real-time data and information can provide effective services that improve productivity, resulting in environmental, social, and economic benefits. It also assists in the decision-making process and provides opportunities for community engagement and participation by improving digital literacy and culture. This paper aims to review and analyze current practices of data-driven smart applications that contribute to the smooth functioning of urban city systems and the problems they face. The research methodology is qualitative: a systematic and extensive literature review carried out by PRISMA method. Data and information from different case studies carried out globally assisted in the inductive approach. Content analysis identified smart city indicators and related criteria in the case study examples. The study concluded that smart people, smart living, and smart governance methods that have come into practice at a later stage are as important as smart mobility, smart environments, and smart economy measures that were implemented early on, and cities are opening up to new, transparent participatory governance approaches where citizens play a key role. It also illustrates that the current new wave of smart cities with real time data are promoting citizen participation focusing on human, social capital as an essential component in future cities.
The Relevance of Recyclability for the Life Cycle Assessment of Packaging Based on Design for Life Cycle
Authors: Keller Jonas , Scagnetti Carla , Albrecht Stefan
Published: March 2022

The awareness for more environmentally sustainable packaging solutions is steadily growing. With both consumers and manufacturers looking to minimize their impacts on the environment, the need for easy-to-implement and standardized measures strengthening a circular economy rises. In the research, the goal was to determine whether the carbon footprint and circularity of non-food plastic packaging can be improved by simple design changes. The results should then lead to design recommendations, providing a Design for Life Cycle approach. The methodology of the study was to conceptually design a single-use plastic packaging with attributes having positive and negative effects on recyclability. Herein, only design characteristics from products obtainable on the market were regarded. Moreover, a comparison over existing recyclability assessment methods is given. The recyclability was then determined with the selected approach by Cyclos HTP, and a reference calculation was conducted. Life Cycle Assessments were implemented for 14 packaging designs using the GaBi software and the Environmental Footprint method. The results showed that dark color, material compounds, insoluble adhesives, and large labels result in lower recyclability of the single-use packaging. The impacts on climate change range from 0.13 kg CO2-equivalent emissions (100% recyclability) to 0.21 kg CO2-equivalent emissions (0% recyclability) per packaging, showing that lower recyclability leads to a larger carbon footprint in all assessed scenarios. Concluding, the research demonstrated that by applying Design for Life Cycle measures, impacts on climate change can be reduced. Lastly, design recommendations for decision makers are outlined.
Integrated Off-Site Construction Design Process including DfMA Considerations
Authors: Hyun Hosang , Kim Hyung-Geun , Kim Jin-Sung
Published: March 2022

Off-site construction (OSC) offers a promising means to improve the efficiency of construction projects. However, the lack of experience and knowledge regarding its use results in errors in design owing to conflicts and omissions of considerations for OSC projects. To mitigate these problems, the design for manufacturing and assembly (DfMA) is widely used to include the considerations in the OSC design process. Several studies concerning the DfMA application in OSC have been performed, but the comprehensive design process is not suggested for mitigating the aforementioned problems. This study proposes an OSC design process by integrating the fragmented DfMA considerations reported in previous studies. The considerations are identified through a systematic literature review and classified into structural and architectural types. To validate the proposed process, an OSC project design has been undertaken as a case study, wherein a significant portion of the building structure has been modified to comprise precast concrete (PC), instead of its reinforced counterpart, with a demonstrated reduction in the PC element design duration. The proposed process would guide and support the design process for reduction in the duration and errors incurred in the process. Moreover, the process can be considered a design guideline for the execution of future projects.
Typical JUNCAO Overwintering Performance and Optimized Cultivation Conditions of Pennisetum sp. in Guizhou, Southwest China
Authors: Zhu Senlin , Zhang Qinyu , Yang Rende , Chen Bo , Zhang Bangxi , Yang Zhen , Chen Xu , Wang Xiaomin , Du Muyun , Tang Ling
Published: March 2022

JUNCAO technology plays a critical role in managing soil ecology and alleviating contradiction between mushroom and forest, as JUNCAO can partially replace the wood chip as mushroom culture medium. At present, few reports focus on exploring the effects of seeding density, nitrogen fertilizers on JUNCAO growth and their overwintering performance. To close the above-mentioned research gaps, five typical types of JUNCAO were evaluated by investigating their grass yield, overwintering germination rates and nutrient adsorption condition. The results indicated that Pennisetum sp. showed the best overwintering performance. In addition, the optimized planting conditions for Pennisetum sp. include cultivation density (60 cm × 50 cm), oblique seeding using stem with double nodes, and 800 kg·ha−1 nitrogen fertilizer. This study gave good insights into low-temperature resisting performance and their overwintering characteristics of diverse JUNCAO species that favor for promoting the safe and efficient productions of the JUNCAO industry in subtropical areas.
Problems, Needs, and Challenges of a Sustainability-Based Production Planning
Authors: Zarte Maximilian , Pechmann Agnes , Nunes Isabel L.
Published: March 2022

Climate change, air and sea pollution, and social inequality are examples of current challenges for global sustainability strategies. Manufacturing enterprises supplying the globalized demand for products significantly contribute to these problems since they are responsible for several environmental and social issues (e.g., greenhouse gases, waste, and poor working conditions). Researchers have widely addressed these issues and warned politicians and society about the risk of the collapse of ecosystems. Despite these warnings, enterprises still experience difficulties in improving the sustainability of their production processes. Therefore, new technologies are required to support them, so as to help them determine their production processes’ sustainability status considering multiple aspects (economic, environmental, and social). The research presents a fuzzy decision support system and a case study for sustainability-based production planning. For this approach, systematic literature reviews were performed, analysing concepts methods for sustainability-based production management and planning. The results show, among other things, that current methods for sustainability-production planning are focused on single aspects of sustainability (e.g., energy or waste planning). Therefore, a fuzzy decision support system was developed, simultaneously evaluating social, environmental, and economic aspects. The decision support system’s model identifies the most significant opportunities in improving the production program’s sustainability and provides recommendations on how to change it. The decision support system was tested and validated in a case study in the production-planning laboratory at the University of Applied Sciences Emden. The case study results elucidate problems, needs, and challenges affecting sustainability-based production planning. Moreover, opportunities for future research were identified based on the limitations of the case study.
Measuring Online Public Opinion for Decision Making: Application of Deep Learning on Political Context
Authors: Kim Daesik , Chung Chung Joo , Eom Kihong
Published: March 2022

Thoughts travel faster and farther through cyberspace where people interact with one another regardless of limitations in language, space, and time. Is a poll sufficient to measure people’s opinions in this era of hyperconnectivity? This study introduces a deep learning method to measure online public opinion. By analyzing Korean texts from Twitter, this study generates time-series data on online sentiment toward the South Korean president, comparing it to traditional presidential approval to demonstrate the independence of the masses’ online discourse. The study tests different algorithms and deploys the model with high accuracy and advancement. The analysis suggests that online public opinion represents a unique population as opposed to offline surveys. The study model examines Korean texts generated by online users and automatically predicts their sentiments, which translate into group attitudes by aggregation. The research method can extend to other studies, including those on environmental and cultural issues, which have greater online presence. This provides opportunities to examine the influences of social phenomenon, benefiting individuals seeking to understand people in an online context. Moreover, it helps scholars in analyzing those public opinions—online or offline—that are more important in their decision making to assess the practicality of the methods.
Influence of Organic Amendments and Moisture Regime on Soil CO2-C Efflux and Polycyclic Aromatic Hydrocarbons (PAHs) Degradation
Authors: EL-Saeid Mohamed Hamza , Usman Adel R. A.
Published: March 2022

In this study, a 30-day incubation experiment was performed to investigate the interactive effects of soil moisture content and two types of organic manure (animal manure: M and wheat straw: WS) on organic C mineralization and the degradation of PAH compounds. Specifically, washed sandy soil sample free from PAHs was treated with combined standard solution containing six different PAHs; pyrene (Pyr), fluoranthene (Flt), benzo[a]pyrene (BaP), benzo[g,h,i]perylene (BghiP), benzo[k]fluoranthene (BkF), and indeno[123-cd]pyrene (IP). The soil samples treated with PAHs were amended with M or WS and then, the soil samples were incubated and subjected to two levels of moisture content (50% and 100% field capacity, FC). The results indicate that CO2–C rates were the highest at day 1, but they tended to be decreased sharply when incubation time increased. The results showed that the higher rate of CO2-C efflux rate and cumulative were observed in M and WS treatments at 100% FC. Applying organic amendments at 50% FC increased the total cumulative CO2-C from 21.6 mg kg−1 to 228 mg kg−1 for M and to 216 mg kg−1 for WS. Meanwhile, applying organic amendments at 50% FC increased the total cumulative CO2-C from 30 mg kg−1 to 381 mg kg−1 for M and to 492 mg kg−1 for WS. The highest increases at 100% FC could be explained by the optimum water content at field capacity. PAHs concentrations decreased significantly in the presence of organic amendments in relation to enhance CO2-C efflux (soil respiration) and to decrease soil pH. It could be concluded that applying organic amendments might be a useful technique to remediate soil PAHs through mineralization.
Protecting the Planet or Destroying the Universe? Understanding Reactions to Space Mining
Authors: Hornsey Matthew J. , Fielding Kelly S. , Harris Emily A. , Bain Paul G. , Grice Tim , Chapman Cassandra M.
Published: March 2022

There is currently a surge in interest from both private and government sectors in developing technology for mining asteroids and the moon (“space mining”). One of the key benefits highlighted by advocates of space mining is that it minimizes the usual problems associated with mining on earth in terms of pollution, environmental degradation, and encroachment on human habitats. Two studies—one conducted on a 27-nation sample (N = 4819), the other conducted in the U.S. (N = 607)—provide the first test of the assumed (but never studied) notion that space mining is more palatable to the public than terrestrial mining. Both studies indicate broad support for asteroid mining: levels of support were reliably above the mid-point, and much greater than for other forms of frontier mining such as mining the ocean floor, mining Antarctica, mining the Alaskan tundra, and lunar mining. Unlike terrestrial mining, community attitudes toward mining asteroids were largely non-ideological; support was not correlated with perceptions of ecological fragility, political ideology, or individualistic/hierarchical worldviews. In summary, the current studies suggest that mining companies have a “social license to operate” for mining asteroids, but less so for lunar mining.
ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania
Authors: Raudonis Vidas , Paulauskaite-Taraseviciene Agne , Eidimtas Linas
Published: March 2022

Waste management is currently a fast-growing environmental business and one of solutions to manage the huge amount of waste being generated on landfills is to use the disposed waste as an energy source. There is a major focus on energy forecasting, highlighting the importance of having reliable data on the volume and composition of municipal solid waste in landfills. However, the lack of historical data is forcing the development of machine-learning based models. This study contributes to this field by proposing a hybrid ANN-based model to forecast the total amount of landfill waste, different waste fraction and the potential for energy recovery. The proposed model includes an adaptive number of inputs adjusted to the relevant waste fraction and to the specific landfill. The obtained results substantiated that the proposed model allows for stable and accurate forecasting of recovered energy potential in cases where there is insufficient historical data. The experiments showed that the model with 12 inputs (meaning the forecast of the future value takes into account the last 12 months of data) was the most accurate in the energy forecasting task, with the lowest forecasting error in terms of mean absolute error −8.9878 gigawatt hours per year.
A Method for Identifying Urban Functional Zones Based on Landscape Types and Human Activities
Authors: Jing Yongcai , Sun Ranhao , Chen Liding
Published: March 2022

The effects of land use and socioeconomic changes on urban landscape patterns and functional zones have been increasingly investigated around the world; however, our knowledge on these effects is still inadequate for sustainably managing urban ecosystems. The urban functional zone (UFZ) refers to a kind of regional space that provides specific functions for human activities and reflects the land use type in a city. They are important for urban planning and exploring urban texture dynamics. UFZs improve understanding of sustainable development for urban ecosystems with extreme environments and unique social backgrounds. However, the identification methods for UFZs are incomplete because of a lack of socioeconomic attributes, as well as their hierarchical relations. Here, we present a hierarchical weighted clustering model to identify UFZs based on the entropy weight method. The data included points of interest (POIs), land use type data, road network data, socioeconomic data, and population density. We found that the adjusted cosine metric and the average criterion were the optimal distance metric and linkage strategy, respectively, to cluster urban zone data. The performance with weighted data was better than that with raw data, and the level of the POI classification scheme and landscape pattern affected the accuracy of identification UFZs. The research indicated that the hierarchical weighted clustering model was a useful method to classify UFZs in order to improve urban planning and environmental management schemes.