bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Solvent and Catalyst Types on Stability and Properties of Zinc Phthalocyanine in the Organic–Inorganic Hybrid Materials
Authors: Popanda Barbara , Środa Marcin , Cholewa-Kowalska Katarzyna
Published: May 2021

Hybrid materials, i.e., the organically modified silicates (ORMOSIL) based on zincphthalocyanine (ZnPc) and silica glass matrix were synthesized by the sol-gel method using protic solvents (methanol, ethanol, isopropanol, butanol) and aprotic solvent (N,N-dimethylformamide; DMF). The effect of an alkaline environment with NaOH addition (a single-stage process) and acid–alkaline environment with CH3COOH-NH4OH and HCl-NaOH (a two-stage process) was analyzed. UV-Vis spectroscopy was used to study the stability of ZnPc in the sol. The highest stability of zinc phthalocyanine in the glass was obtained for synthesis with isopropanol in the presence of the alkaline catalyst. The lowest stability of ZnPc was observed when the aprotic solvent was used. The structure and optical properties of the gels were studied by SEM, FTIR, and XRD techniques and optically stimulated luminescence (OSL) and thermoluminescence (TL), respectively. The thermal stability of the materials was analyzed by TG-DSC methods.
A New Copper(I) Iodide Based Organic-Inorganic Hybrid Structure with Red Emission
Authors: Lei Bing , Wu Xin , Liu Wei
Published: May 2021

A new organic-inorganic hybrid structure based on copper (I) iodide staircase chain 1D-Cu2I2(5-chloropyrimidine)2 (1) has been synthesized by a slow-diffusion method. It emits red emission peaking at 620 nm. The internal quantum yield (IQY) measured for this compound is 6.5% under 360 nm excitation. This compound exhibits potential as a non-rare-earth light-emitting phosphor alternative.
Citizens’ Eyes on Mnemiopsis: How to Multiply Sightings with a Click!
Authors: Tirelli Valentina , Goruppi Alenka , Riccamboni Rodolfo , Tempesta Milena
Published: May 2021

Monitoring the spreading of marine invasive species represents one of the most relevant challenges for marine scientists in order to understand their impact on the environment. In recent years, citizen science is becoming more and more involved in research programs, especially taking advantage of new digital technologies. Here, we present the results obtained in the first 20 months (from 12 July 2019 to 8 March 2021) since launching avvistAPP. This new app was conceived to track the spreading of the invasive ctenophore Mnemiopsis leidyi in the Adriatic Sea; it was also designed to collect sightings of 18 additional marine taxa (ctenophores, jellyfish, sea turtles, dolphins, salps and noble pen shell). A total of 1224 sightings were recorded, of which 530 referred to Mnemiopsis, followed by the scyphozoan jellyfish Rhizostoma pulmo (22%), Cotylorhiza tuberculata (11%) and Aurelia spp. (8%). avvistAPP produced data confirming the presence of Mnemiopsis (often in abundances > 20 individuals m−2) along almost the entire Italian coast in the summer of 2019 and 2020.
Biodiversity in the Cueva del Viento Lava Tube System (Tenerife, Canary Islands)
Authors: Oromí Pedro , Socorro Sergio
Published: May 2021

Cueva del Viento and Cueva de Felipe Reventón are lava tubes located in Tenerife, Canary Islands, and are considered the volcanic caves with the greatest cave-dwelling diversity in the world. Geological aspects of the island relevant to the formation of these caves are discussed, and their most outstanding internal geomorphological structures are described. An analysis of the environmental parameters relevant to animal communities is made, and an updated list of the cave-adapted species and their way of life into the caves is provided. Some paleontological data and comments on the conservation status of these tubes are included.
Application of the Natural Products NOZEMAT HERB and NOZEMAT HERB PLUS Can Decrease Honey Bee Colonies Losses during the Winter
Authors: Shumkova Rositsa , Balkanska Ralitsa , Koynarski Tsvetoslav , Hristov Peter
Published: May 2021

Honey bees (Apis mellifera L.) are crucial pollinators for many crops and natural ecosystems. However, honey bee colonies have been experiencing heavy overwinter mortality in almost all parts of the world. In the present study we have investigatеd, for the first time, the effects from the application of the herbal supplements NOZEMAT HERB® (NH) and NOZEMAT HERB PLUS® (NHP) on overwintering honey bee colony survival and on total protein and lysozyme content. To achieve this, in early autumn 2019, 45 colonies were selected and treated with these herbal supplements. The total protein and lysozyme content were evaluated after administration of NH and NHP twice the following year (June and September 2020). The obtained results have shown that both supplements have a positive effect on overwintering colony survival. Considerable enhancement in longevity of “winter bees” has been observed after the application of NHP, possibly due to the increased functionality of the immune system and antioxidant detoxification capacity. Although the mechanisms of action of NH and NHP are yet to be completely elucidated, our results suggest a new holistic approach on overwintering honey bee colony survival and welfare.
Data on the Quantification of Aspartate, GABA and Glutamine Levels in the Spinal Cord of Larval Sea Lampreys after a Complete Spinal Cord Injury
Authors: Fernández-López Blanca , Pereiro Natividad , Lafuente Anunciación , Rodicio María Celina , Barreiro-Iglesias Antón
Published: May 2021

We used high-performance liquid chromatography (HPLC) methods to quantify aspartate, GABA, and glutamine levels in the spinal cord of larval sea lampreys following a complete spinal cord injury. Mature larval sea lampreys recover spontaneously from a complete spinal cord transection and the changes in neurotransmitter systems after spinal cord injury might be related to their amazing regenerative capabilities. The data presented here show the concentration of the aminoacidergic neurotransmitters GABA (and its precursor glutamine) and aspartate in the spinal cord of control (non-injured) and 2-, 4-, and 10-week post-lesion animals. Statistical analyses showed that GABA and aspartate levels significantly increase in the spinal cord four weeks after a complete spinal cord injury and that glutamine levels decrease 10 weeks after injury as compared to controls. These data might be of interest to those studying the role of neurotransmitters and neuromodulators in recovery from spinal cord injury in vertebrates.
Vitamin D and Folate as Predictors of MMSE in Alzheimer’s Disease: A Machine Learning Analysis
Authors: Murdaca Giuseppe , Banchero Sara , Tonacci Alessandro , Nencioni Alessio , Monacelli Fiammetta , Gangemi Sebastiano
Published: May 2021

Vitamin D (VD) and micronutrients, including folic acid, are able to modulate both the innate and the adaptive immune responses. Low VD and folic acid levels appear to promote cognitive decline as in Alzheimer’s disease (AD). A machine learning approach was applied to analyze the impact of various compounds, drawn from the blood of AD patients, including VD and folic acid levels, on the Mini-Mental State Exam (MMSE) in a cohort of 108 patients with AD. The first analysis was aimed at predicting the MMSE at recruitment, whereas a second investigation sought to predict the MMSE after a 4 year follow-up. The simultaneous presence of low levels of VD and folic acid allow to predict MMSE, suggestive of poorer cognitive function. Such results suggest that the low levels of VD and folic acid could be associated with more severe cases of cognitive impairment in AD. It could be hypothesized that simultaneous supplementation of VD and folic acid could slow down the progression of cerebral degeneration at least in a subset of AD individuals.
Influence of Ectopic Beats on Heart Rate Variability Analysis
Authors: Zhao Lina , Li Peng , Li Jianqing , Liu Chengyu
Published: May 2021

The analysis of heart rate variability (HRV) plays a dominant role in the study of physiological signal variability. HRV reflects the information of the adjustment of sympathetic and parasympathetic nerves on the cardiovascular system and, thus, is widely used to evaluate the functional status of the cardiovascular system. Ectopic beats may affect the analysis of HRV. However, the quantitative relationship between the burden of ectopic beats and HRV indices, including entropy measures, has not yet been investigated in depth. In this work, we analyzed the effects of different numbers of ectopic beats on several widely accepted HRV parameters in time-domain (SDNN), frequency-domain (LF/HF), as well as non-linear features (SampEn and Pt-SampEn (physical threshold-based SampEn)). The results showed that all four indices were influenced by ectopic beats, and the degree of influence was roughly increased with the increase of the number of ectopic beats. Ectopic beats had the greatest impact on the frequency domain index LF/HF, whereas the Pt-SampEn was minimally accepted by ectopic beats. These results also indicated that, compared with the other three indices, Pt-SampEn had better robustness for ectopic beats.
Part-Aware Mask-Guided Attention for Thorax Disease Classification
Authors: Zhang Ruihua , Yang Fan , Luo Yan , Liu Jianyi , Li Jinbin , Wang Cong
Published: May 2021

Thorax disease classification is a challenging task due to complex pathologies and subtle texture changes, etc. It has been extensively studied for years largely because of its wide application in computer-aided diagnosis. Most existing methods directly learn global feature representations from whole Chest X-ray (CXR) images, without considering in depth the richer visual cues lying around informative local regions. Thus, these methods often produce sub-optimal thorax disease classification performance because they ignore the very informative pathological changes around organs. In this paper, we propose a novel Part-Aware Mask-Guided Attention Network (PMGAN) that learns complementary global and local feature representations from all-organ region and multiple single-organ regions simultaneously for thorax disease classification. Specifically, multiple innovative soft attention modules are designed to progressively guide feature learning toward the global informative regions of whole CXR image. A mask-guided attention module is designed to further search for informative regions and visual cues within the all-organ or single-organ images, where attention is elegantly regularized by automatically generated organ masks and without introducing computation during the inference stage. In addition, a multi-task learning strategy is designed, which effectively maximizes the learning of complementary local and global representations. The proposed PMGAN has been evaluated on the ChestX-ray14 dataset and the experimental results demonstrate its superior thorax disease classification performance against the state-of-the-art methods.
Selection of the Optimal Smart Meter to Act as a Data Concentrator with the Use of Graph Theory
Authors: Kiedrowski Piotr
Published: May 2021

Changing the construction of mart Meter (SM) devices, more specifically equipping them with more than one communication module, facilitates the elimination of a Transformer Station Data Concentrator (TSC) module, moving its function to one of the SMs. The opportunity to equip a chosen device in an additional communication module makes it possible to use it as an acquisition node. The introduction of this solution creates a problem with the optimum selection of the above-mentioned node out of all the nodes of the analyzed network. This paper suggests the criterion of its location and, as per the criterion, the way of conduct using the elements of the graph theory. The discussion is illustrated with the examples of the possibility to use the method for the optimization of the architecture of the network. The described method makes it possible to choose the location of a backup acquisition node as well as locate intermediary nodes (signal repeaters) in case of a failure (removal) of some SM devices. In the era of the common introduction of dispersed telemetric systems requiring an adequate level of performance and reliability of information transmission, the offered method can be used for the optimization of the structures of Smart Grids.
Do Operating Profits Induce a Wage Premium Equally Shared among Employees Earning High or Low Incomes?
Authors: Aarstad Jarle , Kvitastein Olav A.
Published: May 2021

Panel data show that between 2001 and 2014 Norwegian industries’ increasing aggregated operating profits per employee increased average wages and wage inequality. The data imply that increasing profits, perhaps unsurprisingly, induce a wage premium. The data further imply that employees earning high incomes at the outset had the highest wage increase percentage-wise. Decreasing operating profits per employee had opposite but less robust effects on average wages and wage inequality. Panel data Granger causality tests finally showed that average wages, but not wage inequality, reversely and positively affect operating profits per employee.
Predictors of Parental Contentment with the Amount of Encouraging Digital Feedback from Teachers in Finnish Schools
Authors: Kuusimäki Anne-Mari , Uusitalo Lotta , Tirri Kirsi
Published: May 2021

The Finnish National curriculum obligates teachers to give parents encouraging feedback about their children’s learning and development, the aim being to build a constructive relationship between homes and schools and to encourage close collaboration among all parties. Teachers in Finland nowadays use digital platforms that allow effective online communication. The frequency and quality of such communication vary a great deal. In particular, there seems to be a lack of clarity concerning the amount of encouraging feedback delivered in this way. The focus in this paper is on the extent to which Finnish parents (N = 1117) in both urban and rural areas are content with the amount of such feedback. We carried out a logistic regression analysis to predict parental contentment with the amount of encouraging messaging, with the pupil’s grade level, parental attitudes to digital communication, as well as parental educational level and gender as independent variables. In sum, parents who were less highly educated, with a neutral-to-positive attitude to digital communication and with a child in lower secondary school were most likely to be content with the amount of communication. These results have both research and practical implications in terms of enhancing the understanding of how best to deliver encouraging digital feedback between homes and schools. Furthermore, it seems that teacher education should focus on communicative competence early on. The current study completes our three-part series of studies on digital home–school communication in Finland.
What Can We Learn about Science Teachers’ Technology Use during the COVID-19 Pandemic?
Authors: Adov Liina , Mäeots Mario
Published: May 2021

The purpose of this qualitative research was to describe teachers’ experiences in a technology-mediated teaching context during the COVID-19 pandemic. We mainly focused on teachers’ experiences with technology use (change and variety of the use) and their willingness to use technology in teaching. We designed an interview-based study. The participants were Estonian science teachers who voluntarily agreed to share their experiences about teaching in new and—for most of them—unexpected, distanced learning conditions. Based on teachers’ reflections on technology use we could distinguish between three groups in which teachers described different levels of willingness to use technology, change in technology use from pre-COVID to distanced learning, and variety in the use of technology. Our results revealed that the higher teachers’ perceived willingness to use technology, the easier it was for them to overcome potential obstacles and cope with the unexpected distanced learning. The main obstacles there were grouped as external (e.g., issues with internet connection, lack of students’ digital skills) and internal (e.g., teachers’ beliefs about technology use for teaching). I was observed that some obstacles were shared by all teachers (such as limitations on students’ digital skills) whereas others where more prevalent in separate groups. This highlights the importance of understanding and considering the variability in the possible obstacles that emerge in using technology in education for teachers with different levels of experience.
Impact of Prosocial Motivation on Organizational Citizenship Behavior and Organizational Commitment: The Mediating Role of Managerial Support
Authors: Arshad Muhammad , Abid Ghulam , Contreras Francoise , Elahi Natasha Saman , Athar Muhammad Ahsan
Published: May 2021

This study, based on the conservation of resources (COR) theory, explores the impact of contextual variables, such as prosocial motivation, on employee discretionary behavior and organizational commitment. The mediating mechanism of managerial support at work defines the nature of the proposed relationships. Data from 303 administrative, instructional, and supervisory staff—predominantly male (95%) and with an average age of 30 years—working on Technical and Vocational Education and Training (TVET) for Pakistan’s public sector were collected and analyzed by employing SPSS version 24. Confirmatory factor analyses suggested a good fit model, while a correlation matrix provided a significant and positive effect of prosocial motivation on employee citizenship behaviour and organizational commitment. Managerial support mediated the relationship between prosocial motivation and the employees’ organizational commitment and citizenship behaviour. The theoretical and practical implications discussed in this study seek to guide the management area to promote managerial support for better outcomes. These outcomes have considerable tactical, statistical, and real-world inferences for the stakeholders of the TVET sector.
Multi-Objective GRASP for Maximizing Diversity
Authors: Casas-Martínez Pedro , Casado-Ceballos Alejandra , Sánchez-Oro Jesús , Pardo Eduardo G.
Published: May 2021

This work presents a novel greedy randomized adaptive search procedure approach for dealing with the maximum diversity problem from a multi-objective perspective. In particular, five of the most extended diversity metrics were considered, with the aim of maximizing all of them simultaneously. The metrics considered have been proven to be in conflict, i.e., it is not possible to optimize one metric without deteriorating another one. Therefore, this results in a multi-objective optimization problem where a set of efficient solutions that are diverse with respect to all the metrics at the same time must be obtained. A novel adaptation of the well-known greedy randomized adaptive search procedure, which has been traditionally used for single-objective optimization, was proposed. Two new constructive procedures are presented to generate a set of efficient solutions. Then, the improvement phase of the proposed algorithm consists of a new efficient local search procedure based on an exchange neighborhood structure that follows a first improvement approach. An effective exploration of the exchange neighborhood structure is also presented, to firstly explore the most promising ones. This feature allowed the local search proposed to limit the size of the neighborhood explored, resulting in an efficient exploration of the solution space. The computational experiments showed the merit of the proposed algorithm, when comparing the obtained results with the best previous method in the literature. Additionally, new multi-objective evolutionary algorithms derived from the state-of-the-art were also included in the comparison, to prove the quality of the proposal. Furthermore, the differences found were supported by non-parametric statistical tests.
smartPlastic: Innovative Touch-Based Human-Vehicle Interface Sensors for the Automotive Industry
Authors: Alves Cristiano , Custódio Tiago , Silva Pedro , Silva Jorge , Rodrigues Carlos , Lourenço Rui , Pessoa Rui , Moreira Fernando , Marques Ricardo , Tomé Gonçalo , Falcao Gabriel
Published: May 2021

Environmental concern regularly leads to the study and improvement of manufacturing processes and the development of new industrial products. The purpose of this work is to optimize the amount of injected plastic and reduce the number of parts used in the production of entrance panels to control features inside the car cabin. It focuses on a particular case study, namely the control of opening and closing windows and rotation of the rear-view mirrors of a car, maintaining all of the functionality and introducing a futuristic and appealing design inline with new autonomous driving vehicles. For this purpose, distinct low-cost touch sensor technologies were evaluated and the performance of several types of sensors that were integrated with plastic polymers of distinct thickness was analyzed. Discrete sensors coupled to the plastic part were tested and integrated in the injected plastic procedure. In the former, sensitivity tests were performed for finding the maximum plastic thickness detectable by the different sensors. For the latter, experiments were carried out on the sensors subject to very high pressure and temperature inside the molds—the two most relevant characteristics of industrial plastic injection in this context—and functional results were observed later. We conclude that, by changing the way the user interacts with the car cabin, the replacement of conventional mechanical buttons—composed of dozens of parts—by a component consisting of a single plastic part that is associated with conventional low-cost electronics allows the control of a more diversified set of features, including many that are not yet usual in the interior of automobiles today, but that will eventually be required in the near future of autonomous driving, in which the user will interact less with driving and more with other people or services around her/him, namely of the multimedia type. Additionally, the economic factor was considered, namely regarding the cost of the new technology as well as its manufacturing, replacement, and subsequent recycling processes.
A Lightweight Dense Connected Approach with Attention on Single Image Super-Resolution
Authors: Zha Lei , Yang Yu , Lai Zicheng , Zhang Ziwei , Wen Juan
Published: May 2021

In recent years, neural networks for single image super-resolution (SISR) have applied more profound and deeper network structures to extract extra image details, which brings difficulties in model training. To deal with deep model training problems, researchers utilize dense skip connections to promote the model’s feature representation ability by reusing deep features of different receptive fields. Benefiting from the dense connection block, SRDensenet has achieved excellent performance in SISR. Despite the fact that the dense connected structure can provide rich information, it will also introduce redundant and useless information. To tackle this problem, in this paper, we propose a Lightweight Dense Connected Approach with Attention for Single Image Super-Resolution (LDCASR), which employs the attention mechanism to extract useful information in channel dimension. Particularly, we propose the recursive dense group (RDG), consisting of Dense Attention Blocks (DABs), which can obtain more significant representations by extracting deep features with the aid of both dense connections and the attention module, making our whole network attach importance to learning more advanced feature information. Additionally, we introduce the group convolution in DABs, which can reduce the number of parameters to 0.6 M. Extensive experiments on benchmark datasets demonstrate the superiority of our proposed method over five chosen SISR methods.
Zero-Keep Filter Pruning for Energy/Power Efficient Deep Neural Networks
Authors: Woo Yunhee , Kim Dongyoung , Jeong Jaemin , Ko Young-Woong , Lee Jeong-Gun
Published: May 2021

Recent deep learning models succeed in achieving high accuracy and fast inference time, but they require high-performance computing resources because they have a large number of parameters. However, not all systems have high-performance hardware. Sometimes, a deep learning model needs to be run on edge devices such as IoT devices or smartphones. On edge devices, however, limited computing resources are available and the amount of computation must be reduced to launch the deep learning models. Pruning is one of the well-known approaches for deriving light-weight models by eliminating weights, channels or filters. In this work, we propose “zero-keep filter pruning” for energy-efficient deep neural networks. The proposed method maximizes the number of zero elements in filters by replacing small values with zero and pruning the filter that has the lowest number of zeros. In the conventional approach, the filters that have the highest number of zeros are generally pruned. As a result, through this zero-keep filter pruning, we can have the filters that have many zeros in a model. We compared the results of the proposed method with the random filter pruning and proved that our method shows better performance with many fewer non-zero elements with a marginal drop in accuracy. Finally, we discuss a possible multiplier architecture, zero-skip multiplier circuit, which skips the multiplications with zero to accelerate and reduce energy consumption.
A Secure Protocol against Selfish and Pollution Attacker Misbehavior in Clustered WSNs
Authors: Rhim Hana , Sauveron Damien , Abassi Ryma , Tamine Karim , Guemara Sihem
Published: May 2021

Wireless sensor networks (WSNs) have been widely used for applications in numerous fields. One of the main challenges is the limited energy resources when designing secure routing in such networks. Hierarchical organization of nodes in the network can make efficient use of their resources. In this case, a subset of nodes, the cluster heads (CHs), is entrusted with transmitting messages from cluster nodes to the base station (BS). However, the existence of selfish or pollution attacker nodes in the network causes data transmission failure and damages the network availability and integrity. Mainly, when critical nodes like CH nodes misbehave by refusing to forward data to the BS, by modifying data in transit or by injecting polluted data, the whole network becomes defective. This paper presents a secure protocol against selfish and pollution attacker misbehavior in clustered WSNs, known as (SSP). It aims to thwart both selfish and pollution attacker misbehaviors, the former being a form of a Denial of Service (DoS) attack. In addition, it maintains a level of confidentiality against eavesdroppers. Based on a random linear network coding (NC) technique, the protocol uses pre-loaded matrices within sensor nodes to conceive a larger number of new packets from a set of initial data packets, thus creating data redundancy. Then, it transmits them through separate paths to the BS. Furthermore, it detects misbehaving nodes among CHs and executes a punishment mechanism using a control counter. The security analysis and simulation results demonstrate that the proposed solution is not only capable of preventing and detecting DoS attacks as well as pollution attacks, but can also maintain scalable and stable routing for large networks. The protocol means 100% of messages are successfully recovered and received at the BS when the percentage of lost packets is around 20%. Moreover, when the number of misbehaving nodes executing pollution attacks reaches a certain threshold, SSP scores a reception rate of correctly reconstructed messages equal to 100%. If the SSP protocol is not applied, the rate of reception of correctly reconstructed messages is reduced by 90% at the same case.
Deep Learning Control for Digital Feedback Systems: Improved Performance with Robustness against Parameter Change
Authors: Alwan Nuha A. S. , Hussain Zahir M.
Published: May 2021

Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, such as sensor networks. The promising results of robustness against parameter changes are calling for future research in the direction of robust DL control.
Concurrent Validation of 3D Joint Angles during Gymnastics Techniques Using Inertial Measurement Units
Authors: Barreto Joana , Peixoto César , Cabral Sílvia , Williams Andrew Mark , Casanova Filipe , Pedro Bruno , Veloso António P.
Published: May 2021

There are advantages in using inertial measurement unit systems (IMUS) for biomechanical analysis when compared to 2D/3D video-based analysis. The main advantage is the ability to analyze movement in the natural performance environment, preserving the ecological validity of the task. Coaches can access accurate and detailed data in real time and use it to optimize feedback and performance. Efforts are needed to validate the accuracy of IMUS. We assess the accuracy of the IMUS Xsens MVN Link system using an optoelectronic system (OS) as a reference when measuring 3D joint angles during the gymnastics round-off back handspring technique. We collected movement kinematics from 10 participants. The coefficient of multiple correlation (CMC) results showed very good and excellent values for the majority of the joint angles, except for neck flexion/extension (F/E). Root mean square errors (RMSE) were below/near 10°, with slightly higher values for shoulder (12.571°), ankle (11.068°), thorax-thigh F/E (21.416°), and thorax–thigh internal/external rotation (I/E) (16.312°). Significant SPM-1D {t} differences for thorax–thigh abduction/adduction (A/A), neck, thorax–thigh, knee, shoulder and ankle F/E were demonstrated during small temporal periods. Our findings suggest that the Xsens MVN Link system provides valid data that can be used to provide feedback in training.
Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling
Authors: Alibabaei Khadijeh , Gaspar Pedro D. , Lima Tânia M.
Published: May 2021

Deep learning has already been successfully used in the development of decision support systems in various domains. Therefore, there is an incentive to apply it in other important domains such as agriculture. Fertilizers, electricity, chemicals, human labor, and water are the components of total energy consumption in agriculture. Yield estimates are critical for food security, crop management, irrigation scheduling, and estimating labor requirements for harvesting and storage. Therefore, estimating product yield can reduce energy consumption. Two deep learning models, Long Short-Term Memory and Gated Recurrent Units, have been developed for the analysis of time-series data such as agricultural datasets. In this paper, the capabilities of these models and their extensions, called Bidirectional Long Short-Term Memory and Bidirectional Gated Recurrent Units, to predict end-of-season yields are investigated. The models use historical data, including climate data, irrigation scheduling, and soil water content, to estimate end-of-season yield. The application of this technique was tested for tomato and potato yields at a site in Portugal. The Bidirectional Long Short-Term memory outperformed the Gated Recurrent Units network, the Long Short-Term Memory, and the Bidirectional Gated Recurrent Units network on the validation dataset. The model was able to capture the nonlinear relationship between irrigation amount, climate data, and soil water content and predict yield with an MSE of 0.017 to 0.039. The performance of the Bidirectional Long Short-Term Memory in the test was compared with the most commonly used deep learning method, the Convolutional Neural Network, and machine learning methods including a Multi-Layer Perceptrons model and Random Forest Regression. The Bidirectional Long Short-Term Memory outperformed the other models with an R2 score between 0.97 and 0.99. The results show that analyzing agricultural data with the Long Short-Term Memory model improves the performance of the model in terms of accuracy. The Convolutional Neural Network model achieved the second-best performance. Therefore, the deep learning model has a remarkable ability to predict the yield at the end of the season.
Effect of Mechanical Ventilation on Accidental Hydrogen Releases—Large-Scale Experiments
Authors: Lach Agnieszka W. , Gaathaug André V.
Published: May 2021

This paper presents a series of experiments on the effectiveness of existing mechanical ventilation systems during accidental hydrogen releases in confined spaces, such as underground garages. The purpose was to find the mass flow rate limit, hence the TPRD diameter limit, that will not require a change in the ventilation system. The experiments were performed in a 40 ft ISO container in Norway, and hydrogen gas was used in all experiments. The forced ventilation system was installed with a standard 315 mm diameter outlet. The ventilation parameters during the investigation were British Standard with 10 ACH and British Standard with 6 ACH. The hydrogen releases were obtained through 0.5 mm and 1 mm nozzles from different hydrogen reservoir pressures. Both types of mass flow, constant and blowdown, were included in the experimental matrix. The analysis of the hydrogen concentration of the created hydrogen cloud in the container shows the influence of the forced ventilation on hydrogen releases, together with TPRD diameter and reservoir pressure. The generated experimental data will be used to validate a CFD model in the next step.
Compensated Single Input Multiple Output Flyback Converter
Authors: Tahan Mohammad , Bamgboje David O. , Hu Tingshu
Published: May 2021

A new single-input multiple-output (SIMO) converter is proposed in this work by incorporating flyback and buck converters in a master–slave configuration. The objective of this work is to address the cross regulation problem, achieve tight voltage regulation, improve the circuit form factor and attain a fast transient response for a SIMO flyback converter. The flyback converter maintains the output channels within 10% of their rated voltages and the SIMO buck converter is placed in series with the flyback converter such that it compensates for the output voltage deviation. Moreover, a time multiplexing switching scheme decouples output channel to eliminate the cross-regulation problem and remove the need for an additional winding transformer per each output channel. A type II compensator with a peak current mode controller was designed to achieve faster transient response which is critical for the proposed configuration. A thorough steady-state analysis was carried out on a triple output channel topology to obtain the design criteria and component values. MATLAB/Simscape modelling and simulation was used to validate the effectiveness of the proposed converter with the result yielding satisfactory transience even with load disturbance. Additionally, the result of the proposed converter is compared with previously published works.
Flexibility Reserve of Self-Consumption Optimized Energy Systems in the Household Sector
Authors: Dörre Elias , Pfaffel Sebastian , Dreher Alexander , Girón Pedro , Heising Svenja , Wiedemann Kay
Published: May 2021

Energy generation and consumption in the power grid must be balanced at every single moment. Within the synchronous area of continental Europe, flexible generators and loads can provide Frequency Containment Reserve and Frequency Restoration Reserve marketed through the balancing markets. The Transmission System Operators use these flexibilities to maintain or restore the grid frequency when there are deviations. This paper shows the future flexibility potential of Germany’s household sector, in particular for single-family and twin homes in 2025 and 2030 with the assumption that households primarily optimize their self-consumption. The primary focus is directed to the flexibility potential of Electric Vehicles, Heat Pumps, Photovoltaics and Battery Storage Systems. A total of 10 different household system configurations were considered and combined in a weighted average based on the scenario framework of the German Grid Development Plan. The household generation, consumption and storage units were simulated in a mixed-integer linear programming model to create the time series for the self-consumption optimized households. This solved the unit commitment problem for each of the decentralized households in their individual configurations. Finally, the individual household flexibilities were evaluated and then aggregated to a Germany-wide flexibility profile for single-family and twin homes. The results indicate that the household sector can contribute significantly to system stabilization with an average potential of 30 GW negative and 3 GW positive flexibility in 2025. In 2030, the corresponding flexibilities potentially increase to 90 GW and 30 GW, respectively. This underlines that considerable flexibility reserves could be provided by single-family and twin homes in the future.
Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks
Authors: Tran Minh-Quan , Zamzam Ahmed S. , Nguyen Phuong H. , Pemen Guus
Published: May 2021

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.
Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network
Authors: Zhou Yichen , Yang Xiaohui , Tao Lingyu , Yang Li
Published: May 2021

Dissolved gas analysis (DGA) based in insulating oil has become a more mature method in the field of transformer fault diagnosis. However, due to the complexity and diversity of fault types, the traditional modeling method based on oil sample analysis is struggling to meet the industrial demand for diagnostic accuracy. In order to solve this problem, this paper proposes a probabilistic neural network (PNN)-based fault diagnosis model for power transformers and optimizes the smoothing factor of the pattern layer of PNN by the improved gray wolf optimizer (IGWO) to improve the classification accuracy and robustness of PNN. The standard GWO easily falls into the local optimum because the update mechanism is too single. The update strategy proposed in this paper enhances the convergence ability and exploration ability of the algorithm, which greatly alleviates the dilemma that GWO is prone to fall into local optimum when dealing with complex data. In this paper, a reliability analysis of thirteen diagnostic methods is conducted using 555 transformer fault samples collected from Jiangxi Power Supply Company, China. The results show that the diagnostic accuracy of the IGWO-PNN model reaches 99.71%, which is much higher than that of the traditional IEC (International Electrotechnical Commission) three-ratio method. Compared with other neural network models, IGWO-PNN also has higher diagnostic accuracy and stability, and is more applicable to the field of transformer fault diagnosis.
Looking for Sustainability Scoring in Apparel: A Review on Environmental Footprint, Social Impacts and Transparency
Authors: Gonçalves Anabela , Silva Carla
Published: May 2021

Sustainability has been recognized as a major concern globally since the Brudtland Report, in 1987, and further reinforced in 2015 by the United Nations Sustainable Development Goals (UNSDG) 2030. This paper reviews the methodologies and criteria of sustainability applied to fashion products, regarding products’ environmental footprint (environmental life cycle assessment/analysis; e-LCA), the social issues (including the social life cycle assessment/analysis; s-LCA) and the transparency in reporting sustainability. In our review we seek KPIs (key performance indicators) that allow classification of a pair of shoes or a piece of cloth on a scale from A to E, i.e., products can be compared with a benchmark and classified accordingly with a simple labelling scheme, which is easily understandable by the consumers. This approach is similar to those used to classify electrical appliances, housing energy consumption for thermal comfort, food Nutri-Scores, CO2 levels of road vehicles, and tire performance. In this review we aim to identify the initiatives and measures being put into practice by the top global fashion brands. We found that, despite the existence of GRI (global sustainability reporting initiative) standard reporting, most companies follow their own methods or others created within the industry rather than those created in the scientific community. Examples include the Higg index, the Transparency Index, and the Social Codes of Conduct (CoC). In this study, we conducted an extensive review of certification schemes and labels already applied to fashion products, and identified a multitude of labels and lack of harmonization in communicating sustainability. As result, we compiled a summary table of all criteria, methodologies, and possible KPIs that can be considered the basis for a benchmark and score of a fashion product. This topic is crucial to avoid “green washing” and a lack of transparency for the buyer’s community, i.e., business to consumer (B2C), and for the business community, i.e., business to business (B2B) relationships, which comprise a complex multi-layer supply chain of suppliers and sub-suppliers. The UNSDG 2030 “Responsible Consumption and Production” frames these efforts to facilitate standardization of KPIs in terms of structure, criteria, and their measurement. The most common KPI is environmental global warming impact (expressed as CO2eq) based on life cycle assessment/analysis (LCA) principles (established in 2000), which provide an appropriate base to monitor and benchmark products. However, in our innovative review of t-shirt e-LCA, we identified a wide range of e-LCA assumptions, relating to different boundaries, allocations, functional units, and impact categories, which represent a major challenge in benchmarking.
Upgrading the Organic Fraction of Municipal Solid Waste by Low Temperature Hydrothermal Processes
Authors: Sailer Gregor , Knappe Victoria , Poetsch Jens , Paczkowski Sebastian , Pelz Stefan , Oechsner Hans , Bosilj Monika , Ouardi Siham , Müller Joachim
Published: May 2021

In comparison to lignocellulosic biomass, which is suitable for thermo-chemical valorization, the organic fraction of municipal solid waste (OFMSW) is mainly treated via composting or anaerobic digestion (AD). An efficient utilization of OFMSW is difficult due to variations in its composition. Based on the characteristics of OFMSW, hydrothermal treatment (HTT) experiments at temperatures < 200 °C as an alternative OFMSW-processing were evaluated in this study. The raw OFMSW was characterized with a dry matter (DM)-based organic dry matter (oDM) content of 77.88 ± 1.37 %DM and a higher heating value (HHV) of 15,417 ± 1258 J/gDM. Through HTT at 150, 170 and 185 °C, the oDM contents as well as H/C and O/C ratios were lowered while the HHV increased up to 16,716 ± 257 J/gDM. HTT led to improved fuel properties concerning ash melting, corrosion stress and emission behavior. Negative consequences of the HTT process were higher contents of ash in the biochar as well as accumulated heavy metals. In the sense of a bioeconomy, it could be beneficial to first convert raw OFMSW into CH4 through AD followed by HTT of the AD-digestate for the generation of solid fuels and liquid products. This could increase the overall utilization efficiency of OFMSW.
Modeling Oil Recovery in Brazilian Carbonate Rock by Engineered Water Injection Using Numerical Simulation
Authors: Bordeaux-Rego Fabio , Ferreira Jose Adriano , Tejerina Claudio Alberto Salinas , Sepehrnoori Kamy
Published: May 2021

Waterflooding remains the most commonly used method to improve oil recovery. Although the injected brine type is mainly dependent on its availability, few of its characteristics can be controlled during project design. Published laboratory work indicates that the adjustment of injected brine composition can cause an increase in oil production by wettability alteration. This research objective is to propose a novel four-step framework for modeling improved oil recovery by Engineered Water Injection from laboratory to numerical simulation for carbonate formations. We use a geochemical-based model that estimates contact angles to predict wettability alteration. The steps are (1) screening criteria, (2) geochemical evaluation, (3) wettability alteration modeling, and (4) coreflood history-match. We validate our framework by conducting history-match simulations of Brazilian Pre-Salt corefloods. Incremental oil recovery factors are between 5 to 11%, consistent with those reported during experiments. The reduction in residual oil saturation varied from 3 to 5%. This work is a new systematic procedure to model oil recovery using a comprehensive approach that is fundamental to understanding the underlying wettability alteration mechanisms by Engineered Water Injection.
Influence of the Precoat Layer on the Filtration Properties and Regeneration Quality of Backwashing Filters
Authors: Bächle Volker , Morsch Patrick , Gleiß Marco , Nirschl Hermann
Published: May 2021

For solid–liquid separation, filter meshes are still used across large areas today, as they offer a cost-effective alternative, for example, compared to membranes. However, particle interaction leads to a continuous blocking of the pores, which lowers the flow rate of the mesh and reduces its lifetime. This can be remedied by filter aids. In precoat filtration, these provide an already fully formed filter cake on the fabric, which acts as a surface and depth filter. This prevents interaction of the particles to be separated with the mesh and thus increases the service life of the mesh. In this work, the influence of a precoat layer with different fibre lengths of cellulose on the filtration behavior is investigated. A satin with a pore size of 11 µm is used as the filter medium. The effects of the precoat layer on the filter media resistance, the filter cake resistance, the turbidity impact, and the regenerability of the fabrics are investigated. This study shows an overview of the suitability of various cellulose fibres based on different aspects as filter aids for particles in ultrafine filtration.