bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison between Capacitive and Microstructured Optical Fiber Soil Moisture Sensors
Authors: Lopez Aldaba Aitor , Lopez-Torres Diego , Campo-Bescós Miguel A. , López José Javier , Yerro David , Elosua César , Arregui Francisco J. , Auguste Jean-Louis , Jamier Raphael , Roy Philippe , López-Amo Manuel
Published: September 2018

Soil moisture content has always been an important parameter to control because it is a deterministic factor for site-specific irrigation, seeding, transplanting, and compaction detection. In this work, a discrete sensor that is based on a SnO2–FP (Fabry-Pérot) cavity is presented and characterized in real soil conditions. As far as authors know, it is the first time that a microstructured optical fiber is used for real soil moisture measurements. Its performance is compared with a commercial capacitive soil moisture sensor in two different soil scenarios for two weeks. The optical sensor shows a great agreement with capacitive sensor’s response and gravimetric measurements, as well as a fast and reversible response; moreover, the interrogation technique allows for several sensors to be potentially multiplexed, which offers the possibility of local measurements instead of volumetric: it constitutes a great tool for real soil moisture monitoring.
Standardization of Fatigue Characteristics of Cement-Treated Aggregate Base Materials under Different Stress States
Authors: Xie Jiawei , Tang Limin , Lv Songtao , Zhang Naitian , Huang Tuo , Liu Hongfu
Published: September 2018

In this study, to decrease the evaluation uncertainty of the fatigue characteristics of cement-treated aggregate base materials under different test conditions, unconfined compressive, indirect tensile, flexural tensile strength tests and fatigue tests of these base materials with different cement content and at different curing times were carried out. The Weibull distribution was employed to analyze fatigue test results. The standardization model of fatigue characteristics for cement-treated aggregate base materials under different stress states was established. Based on the interval analysis theory, the fatigue characteristic model under different stress states was established using interval parameters. Results revealed that the curing time and cement content considerably affect the strength and fatigue characteristics of cement-treated aggregate base materials, and with increasing cement content and curing time, the fatigue resistance of cement-treated aggregate base materials can be improved. Clear differences between the fitting parameters a and b of the S-N fatigue equation of cement-treated aggregate base materials under different stress states were observed, which can be eliminated by using the analysis method based on the Weibull distribution and the standardization model, and a unified expression for the cement-treated aggregate base materials under different test conditions was realized. A Standardization model of fatigue characteristics based on the interval analysis new method could solve several problems such as inadequate sampling representation, low precision, and insufficient stability of test equipment; thus, the errors caused by materials, structures, the environment, and loads can be reduced, making the fatigue life interval more reasonable and scientific compared to the point numerical fatigue life. Regression parameters a and be were in intervals [9.0, 10.6] and [9.9, 11.3], respectively, and parameters a and b were similar, which improve the test accuracy and reduce the data error.
A Lightweight Scheme to Authenticate and Secure the Communication in Smart Grids
Authors: Aziz Israa T. , Jin Hai , Abdulqadder Ihsan H. , Hussien Zaid Alaa , Abduljabbar Zaid Ameen , Flaih Firas M. F.
Published: September 2018

Self-reconfiguration in electrical power grids is a significant tool for their planning and operation during both normal and abnormal conditions. The increasing in employment of Intelligent Electronic Devices (IEDs), as well as the rapid growth of the new communication technologies have increased the application of Feeder Automation (FA) in Distribution Networks (DNs). In a Smart Grid (SG), automation equipment, such as a Smart Breaker (SB), is used. Using either a wired or a wireless network or even a combination of both, communication between the Control Center (CC) and SBs can be made. Nowadays, wireless technology is widely used in the communication of DNs. This may cause several security vulnerabilities in the power system, such as remote attacks, with the goal of cutting off the electrical power provided to significant consumers. Therefore, to preserve the cybersecurity of the system, there is a need for a secure scheme. The available literature investments proposed a heavyweight level in security schemes, while the overhead was not considered. To overcome this drawback, this paper presents an efficient lightweight authentication mechanism with the necessary steps to ensure real-time automatic reconfiguration during a fault. As a first stage, authentication will be made between CC and SB, SB then sends the information about its status. To ensure the integrity of the authentication exchange, a hash function is used, while the symmetric algorithm is used to ensure privacy. The applicability of the suggested scheme has been proved by conducting security performance and analysis. The proposed scheme will be injected on ABB medium voltage breaker with the REF 542plus controller. Therefore, the probable benefit of the suggested scheme is the contribution to provide more flexibility for electrical utilities in terms of reducing the overall computational overhead and withstanding to various types of attacks, while also opening new prospects in FA of SGs.
Efficient Removal of Lead Ions from Water by a Low-Cost Alginate-Melamine Hybrid Sorbent
Authors: Li Keteng , Wu Genhua , Wang Min , Zhou Xiaohong , Wang Zhuqing
Published: September 2018

A low-cost alginate-melamine hybrid sorbent (named as Alg-Mel) was designed and synthesized for the removal of Pb2+ from water. The as-prepared Alg-Mel sorbent exhibited high affinity and selectivity to Pb2+. The selectivity coefficients of the Alg-Mel for Pb2+/Cd2+, Pb2+/Cu2+, Pb2+/Cr3+ and Pb2+/Co2+ were all over 7. It is found that the hybrid sorbent could uptake 95.4% of Pb2+ from Pb2+-containing solutions (0.48 mM), and the maximum adsorption capacity for Pb2+ reaches 1.39 mmol/g (287.7 mg/g), which is much higher than that of most reported lead ion-sorbents. Furthermore, the Alg-Mel can be regenerated by a simple acid-washing process and used repeatedly. The results of adsorption mechanism analysis reveal that the adsorption of Pb2+ by Alg-Mel is mainly ascribed to the chemical coordination and ion exchange effects.
Improved Dynamic Voltage Regulation in a Droop Controlled DC Nanogrid Employing Independently Controlled Battery and Supercapacitor Units
Authors: Malkawi Ahmad M. A. , Lopes Luiz A. C.
Published: September 2018

DC bus voltage signaling (DBS) and droop control are frequently employed in DC nano and microgrids with distributed energy resources (DERs) operating in a decentralized way. This approach is effective in enforcing the desired contributions of power sources and energy storage systems (ESSs) in steady-state conditions. The use of supercapacitors (SCs) along with batteries in a hybrid energy storage system (HESS) can mitigate the impact of high and fast current variations on the losses and lifetime of the battery units. However, by controlling the HESS as a single unit, one forfeits the potential contribution of the SC and its high power capabilities to dynamically improve voltage regulation in a DC nanogrid. This paper discusses an approach where the SC interface is controlled independently from the battery interface, with a small droop factor and a high pass filter (HPF), to produce high and short current pulses and smooth DC bus voltage variations due to sudden power imbalances in the DC nanogrid. Experimental results are presented to show that, unlike in a conventional HESS, the SC unit can be used to improve the dynamic voltage regulation of the DC nanogrid and, indirectly, mitigate the high and fast current variations in the battery.
Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals
Authors: Ren Hao , Qu Jianfeng , Chai Yi , Huang Lei , Tang Qiu
Published: September 2018

This study analyzes bioelectrical signals to achieve automatic epileptic seizure detection. Electroencephalographic (EEG) signals were recorded with electrodes on healthy, epileptic seizure-free, and epileptic seizure patients. The challenges in this field are generally regarded to be the impacts of non-stationarity and nonlinearity in EEG signals. To address these challenges, this study attempts to recognize different brain statuses. The idea originated from a novel hypothesis that considers EEG signals as convolution signals and regards itself as the generation mechanism of EEG signals, to some extent. Based on this hypothesis, the nonlinear problem can be viewed as a deconvolution procedure. As such, the method can be simplified into three parts: eliminating non-stationary is used to catch high-frequency to low-frequency signals, which is followed by a local mean decomposition (LMD) algorithm; these signals are deconvoluted to form ultra-high-dimensional feature sets, which is completely terminated by the mel-frequency cepstrum coefficients (MFCC) algorithm; and several classifiers are combined to achieve highly accurate recognition results and to verify the superiority and reasonableness of this method. The publicly available EEG database from the University of Bonn, Germany is employed to demonstrate the effectiveness and outstanding performance of this method. According to the results, the method has the ability to attain a higher average classification accuracy than other methods in all of the four following cases: healthy (datasets A and B) versus epileptic seizure (dataset E), epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), healthy (datasets A and B) versus epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), and healthy (dataset A) versus healthy (dataset B) versus epileptic seizure-free (dataset C) versus epileptic seizure-free (dataset D) versus epileptic seizure (dataset E).
Movement Noise Cancellation in Second Derivative of Photoplethysmography Signals with Wavelet Transform and Diversity Combining
Authors: Ban Dahee , Shahid Syed Maaz , Kwon Sungoh
Published: September 2018

In this paper, we propose an algorithm to remove movement noise from second derivative of photoplethysmography (SDPPG) signals. SDPPG is widely used in healthcare applications because of its easy and comfortable measurement. However, an SDPPG signal is vulnerable to movement, which degrades the signal. Degradation of SDPPG signal shapes can result in incorrect diagnosis. The proposed algorithm detects movement noise in a measurement signal using wavelet transform, and removes movement noise by selecting the best signal from among multiple signals measured at different locations. Experiment results show that the proposed algorithm outperforms the previous filter-based algorithm, and that movement noise with 30% time duration can be reduced by up to 70.89%.
A Novel Way to Enhance the Spark Plasma-Assisted Ignition for an Aero-Engine Under Low Pressure
Authors: Huang Shengfang , Zhang Zhibo , Song Huimin , Wu Yun , Li Yinghong
Published: September 2018

Finding a new ignition strategy for ignition enhancement in a lean-burn combustor has always been the biggest challenge for high-altitude, long-endurance unmanned aerial vehicles (UAVs). It is of great importance for the development of high-altitude, long-endurance aircraft to improve the secondary ignition ability of the aero-engine at high altitude where the ignition capability of the aero-engine igniter rapidly declines. An innovative ignition mode is therefore urgently needed. A novel plasma-assisted ignition method based on a multichannel discharge jet-enhanced spark (MDJS) was proposed in this study. Compared to the conventional spark igniter (SI), the arc discharge energy of the MDJS was increased by 13.6% at 0.12 bar and by 14.7% at 0.26 bar. Furthermore, the spark plasma penetration depth of the MDJS was increased by 49% and 103% at 0.12 bar and 0.26 bar, respectively. The CH* radicals showed that the MDJS obtained a larger initial spark kernel and reached a higher spark plasma penetration depth, which helped accelerate the burning velocity. Ignition tests in a model swirl combustor showed that the lean ignition limit was extended 24% from 0.034 to 0.026 at 25 m/s with 20 °C kerosene and 17% from 0.075 to 0.062 at 12 m/s with −30 °C kerosene maximally. The MDJS was a unique plasma-assisted ignition method, activated by the custom ignition power supply instead of a special power supply with an extra gas source. The objective of this study was to provide a novel multichannel discharge jet-enhanced spark ignition strategy which would help to increase the arc discharge energy, the spark plasma penetration depth and the activated area without changing the power supply system and to improve the safety and performance of aero-engines.
Edge Machine Learning: Enabling Smart Internet of Things Applications
Authors: Yazici Mahmut , Basurra Shadi , Gaber Mohamed
Published: September 2018

Machine learning has traditionally been solely performed on servers and high-performance machines. However, advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gap between the simple processors embedded in such things and their more complex cousins in personal computers. Thus, with the current advancement in these devices, in terms of processing power, energy storage and memory capacity, the opportunity has arisen to extract great value in having on-device machine learning for Internet of Things (IoT) devices. Implementing machine learning inference on edge devices has huge potential and is still in its early stages. However, it is already more powerful than most realise. In this paper, a step forward has been taken to understand the feasibility of running machine learning algorithms, both training and inference, on a Raspberry Pi, an embedded version of the Android operating system designed for IoT device development. Three different algorithms: Random Forests, Support Vector Machine (SVM) and Multi-Layer Perceptron, respectively, have been tested using ten diverse data sets on the Raspberry Pi to profile their performance in terms of speed (training and inference), accuracy, and power consumption. As a result of the conducted tests, the SVM algorithm proved to be slightly faster in inference and more efficient in power consumption, but the Random Forest algorithm exhibited the highest accuracy. In addition to the performance results, we will discuss their usability scenarios and the idea of implementing more complex and taxing algorithms such as Deep Learning on these small devices in more details.
Distribution and Health Hazards of Polycyclic Aromatic Hydrocarbons in Egyptian Milk and Dairy-Based Products
Authors: Rawash El-Shaimaa , Mohamed Gehad , Souaya Eglal , Khalil Lele , El-Chaghaby Ghadir , El-Gammal Mohamed
Published: September 2018

In a market-basket study conducted in Cairo, Egypt, the most commonly consumed milk products were sampled and the contents of 13 polycyclic aromatic hydrocarbons were analyzed using gas chromatography with a mass spectrometer detector. The obtained data showed that the total amount of 13 PAHs was within the range of 1.3–8.2 µg/g. The results proved that the, highest mean levels of polycyclic aromatic hydrocarbons were detected in powdered milk (8.2 μg/g) followed by ultra-heat treatment milk and milk beverages (6.07 μg/g). The lowest level was detected in unsmoked cheese and yogurt (1.3 μg/g). Estimated daily intake (EDI) was used to estimate the carcinogenic risk. The total mean estimated daily intake for children in different age categories (1–10 years) was calculated with respect to benzo[a]pyrene, which ranged from 0.058 to 0.31 mg/day. The total mean value of EDI for the sum of seven carcinogenic PAHs in terms of benzo[a]pyrene ranged from 0.61 to 1.22 mg/day in all age categories. These results were higher than the critical limit set by the European Food Safety Authority. Therefore, there should be concerns regarding the effects of the consumption of different milk products on the local population.
Novel Synthetic Opioids: The Pathologist’s Point of View
Authors: Frisoni Paolo , Bacchio Erica , Bilel Sabrine , Talarico Anna , Gaudio Rosa Maria , Barbieri Mario , Neri Margherita , Marti Matteo
Published: September 2018

Background: New Psychoactive Substances (NPS) constitute a broad range of hundreds of natural and synthetic drugs, including synthetic opioids, synthetic cannabinoids, synthetic cathinones, and other NPS classes, which were not controlled from 1961 to 1971 by the United Nations drug control conventions. Among these, synthetic opioids represent a major threat to public health. Methods: A literature search was carried out using public databases (such as PubMed, Google Scholar, and Scopus) to survey fentanyl-, fentanyl analogs-, and other synthetic opioid-related deaths. Keywords including “fentanyl”, “fentanyl analogs”, “death”, “overdose”, “intoxication”, “synthetic opioids”, “Novel Psychoactive Substances”, “MT-45”, “AH-7921”, and “U-47700” were used for the inquiry. Results: From our literature examination, we inferred the frequent implication of fentanyls and synthetic opioids in side effects, which primarily affected the central nervous system and the cardiovascular and pulmonary systems. The data showed a great variety of substances and lethal concentrations. Multidrug-related deaths appeared very common, in most reported cases. Conclusions: The investigation of the contribution of novel synthetic opioid intoxication to death should be based on a multidisciplinary approach aimed at framing each case and directing the investigation towards targeted toxicological analyses.
Ethical Concerns in Sport: When the Will to Win Exceed the Spirit of Sport
Authors: Vargas-Mendoza Nancy , Fregoso-Aguilar Tomás , Madrigal-Santillán Eduardo , Morales-González Ángel , Morales-González José
Published: September 2018

Background: The need to advance and achieve success is deeply ingrained in human evolution. As a species, humans developed instincts that allowed them to survive and transmit their genes along generations. The will to win is an instinct that has been maintained in the species for millions of years. Sport is an activity as old as humans themselves and is subject to rules; Objective: The proposal of this work is to explore some of the most recurrent practices to achieve the athletes’ goals, and the origins and historical use of methods or substances to improve performance and its regulation, as well as to review the impact of new technologies on achieving better results and to make a proposal of what actions should be takenin order to prevent bad practices; Methods: A narrative literature review of ethical sports issues and decision-making was performed in the English language; Results: Practically all behavior with regards to the theme of sports is regulated by ethical codes that must be followed by sportspersons, as well as by everyone involved in the athlete’s healthcare and in the athlete’s administrative, marketing, and business aspects. Notwithstanding this, winning and reaping glory implies a reward far greater than fame and fortune, which can lead to poor ethical practices in athletes, as well as in interested parties who detract from the intrinsic value of the spirit of sports. The will to win could exceed the limits of what is permitted in fair-play, like the use of prohibited methods or substances; Conclusions: In this work, we review some of the bioethical aspects ofsports. Additionally, recommendations are offered for good practices and to prevent falling into poor ethical behavior.
Measurement of Mood States Following Light Alcohol Consumption: Evidence from the Implicit Association Test
Authors: Ito Motohiro , Matsuzaki Naoyuki , Kawahara Jun
Published: September 2018

As the problems of mood measurements during alcohol consumption of alcoholic beverages do not necessarily evoke interpretable physiological responses, explicit reports may be contaminated by various cognitive biases or expectations. The present study examined whether emotional responses induced by the consumption of beverages containing low concentrations of alcohol can be measured using the Implicit Association Test (IAT). The IAT can detect the estimates of internal proximity between bipolar target concepts (e.g., cheerfulness and fatigue). Participants (N = 30) received three IAT sessions, followed by drinking a beverage containing 0% (control), 1%, or 3% alcohol by volume, and three IATs (at 0, 30, and 60 min after the time of consumption). We also recorded the explicit responses regarding the extent of drunkenness. The analyses of variance with alcohol concentration and time reveal dissociation between implicit and explicit measures. The IAT scores under the alcohol conditions reflect a more cheerful mood state relative to the baseline test. This effect of enhanced cheerfulness was not observed under the non-alcohol control condition. These results demonstrate that the impact of the consumption of low-alcohol beverages on mood can be measured using the IAT.
Rare Genetic Diseases with Defects in DNA Repair: Opportunities and Challenges in Orphan Drug Development for Targeted Cancer Therapy
Authors: Bhattacharjee Sonali , Nandi Saikat
Published: September 2018

A better understanding of mechanistic insights into genes and enzymes implicated in rare diseases provide a unique opportunity for orphan drug development. Advances made in identification of synthetic lethal relationships between rare disorder genes with oncogenes and tumor suppressor genes have brought in new anticancer therapeutic opportunities. Additionally, the rapid development of small molecule inhibitors against enzymes that participate in DNA damage response and repair has been a successful strategy for targeted cancer therapeutics. Here, we discuss the recent advances in our understanding of how many rare disease genes participate in promoting genome stability. We also summarize the latest developments in exploiting rare diseases to uncover new biological mechanisms and identify new synthetic lethal interactions for anticancer drug discovery that are in various stages of preclinical and clinical studies.
Metabolic Syndrome and the Risk of Breast Cancer and Subtypes by Race, Menopause and BMI
Authors: Dibaba Daniel T. , Braithwaite Dejana , Akinyemiju Tomi
Published: September 2018

The objective of this study was to investigate the association of metabolic syndrome (MetS) with the risk of invasive breast cancer and molecular subtypes across race, menopause, and body mass index (BMI) groups. We examined the association of metabolic syndrome and its components with risk of invasive breast cancer among 94,555 female participants of the National Institute of Health-American Association of Retired Persons (NIH-AARP) Diet and Health Study, accounting for ductal carcinoma in situ as a competing risk. Cox proportional hazard regression with the Fine and Gray method was used to generate hazard ratios (HR) and 95% confidence intervals (CI) adjusting for baseline sociodemographic, behavioral, and clinical covariates. During a mean follow-up of 14 years, 5380 (5.7%) women developed breast cancer. Overall, MetS at baseline was associated with a 13% increased risk of breast cancer compared to women without MetS (HR: 1.13, 95% CI: 1.00, 1.27); similar estimates were obtained among postmenopausal women (HR: 1.14, 95% CI: 1.01, 1.29). MetS was associated with a slight but non-significantly increased risk of breast cancer among those with both normal weight and overweight/obesity, and those with estrogen receptor positive breast cancer subtype. In the NIH-AARP cohort, MetS was associated with an increased risk of breast cancer. Further studies are needed to definitively evaluate the association of MetS with triple negative breast cancer subtypes across all levels of BMI.
Statistical Design, a Powerful Tool for Optimizing Biosurfactant Production: A Review
Authors: Bertrand Brandt , Martínez-Morales Fernando , Rosas-Galván Nashbly Sarela , Morales-Guzmán Daniel , Trejo-Hernández María R.
Published: September 2018

Biosurfactants (Bs) have been studied for decades and applied in different industrial sectors because of their competitive biochemical characteristics, and the fact that they are environmentally friendly. Current scientific investigations mainly involve the search for novel Bs producing organisms with attractive characteristics. Bs are expected to replace synthetic surfactants in the near future, but low production yields and inefficient downstream processes have prevented their widespread use. Although there are numerous reports on Bs optimization, to date there has been no critical compilation or revision of the statistical designs and strategies employed for improved production. The purpose of this mini review is to briefly discuss the factors that affect Bs production and the importance of statistical design as an essential tool for increasing production.
The Use of Orientation Templates and Free-Hand Implant Insertion in Artificial Mandibles—An Experimental Laboratory Examination in Fifth-Year Dental Students
Authors: Schulz Matthias C. , Rittmann Lena , Range Ursula , Lauer Günter , Haim Dominik
Published: September 2018

Implant dentistry is a growing field in the education of undergraduate dental students. The present laboratory study evaluates factors which may potentially influence the accuracy of free-hand implant insertion and the use of an orientation template. After three-dimensional planning using coDiagnostiXTM, orientation templates, including sleeves for the pilot-drill in regions 41 and 45, were manufactured by thermoforming. Sixty-one fifth year dental students inserted one implant using the orientation template and another implant free-hand in an artificial mandible. Information regarding age, sex, handedness, education, and the time required for implant insertion were recorded. Subsequently, the mandibles were scanned using cone-beam-computed tomography and the accuracy of the implant position was assessed, while statistical analysis followed. The free-hand implant insertion resulted in a distal deviation of −1.34 ± 5.15° and a mesial mismatch of 0.06 ± 0.79 mm at the artificial bone level compared to the sleeves. When using the orientation templates, the deviation decreased to −0.67 ± 3.48° and a distal mismatch of −0.22 ± 0.62 mm was achieved. The difference was statistically significant for the mismatch (p < 0.049). Regarding the limitations of our study, it could be said that the accuracy level achieved by dental undergraduates using implant placement with orientation templates is comparable to that in other studies.
What Kind of Economic Citizen? A Comment
Authors: Cameron Michael P.
Published: September 2018

Crowley and Swan (2018) proposed a categorisation of economic citizenship, in which they extend earlier work to include a new type, the discerning economic citizen. I argue that the discerning economic citizen is not a distinct type from the other three (the personally responsible economic citizen, the participatory economic citizen, and the justice oriented economic citizen). The underlying idea of whether an economic citizen is discerning or not instead represents a different dimension across which the goals of economics education could be conceptualised.
Design of Multilayer Ring Emitter Based on Metamaterial for Thermophotovoltaic Applications
Authors: Maremi Fekadu Tolessa , Lee Namkyu , Choi Geehong , Kim Taehwan , Cho Hyung Hee
Published: September 2018

The objective of this study is to design a broadband and wide-angle emitter based on metamaterials with a cut-off wavelength of 2.1 µm to improve the spectral efficiency of thermophotovoltaic emitters. To obtain broadband emission, we conducted the geometric parameter optimization of the number of stacked layers, the inner and outer radii of the nano-rings, and the thickness of the nano-rings. The numerical simulation results showed that the proposed emitter had an average emissivity of 0.97 within the targeted wavelength, which ranged from 0.2 µm to 2.1 µm. In addition, the presented multilayer nano-ring emitter obtained 79.6% spectral efficiency with an InGaAs band gap of 0.6 eV at 1400 K.
Smart Power Meters in Augmented Reality Environment for Electricity Consumption Awareness
Authors: Angrisani Leopoldo , Bonavolontà Francesco , Liccardo Annalisa , Schiano Lo Moriello Rosario , Serino Francesco
Published: September 2018

Reducing or optimizing electrical power consumption is one of the most fundamental goals within the current frameworks of smart energy management. This approach would be spread from the industrial plants, characterized by high consumptions often well distributed throughout the day, down to the domestic utilities, typically discontinuous and with limited consumption at the single user level. More specifically, it is desirable for the latter case to be able to control in a simple and effective way the power consumption of typical household appliances by means of technologies that are already used and spread (such as tablets and smartphones) to become aware of their actual impact, both economic and environmental. To this aim, the authors present the proof-of-principle of user-friendly monitoring system for power consumption awareness based on the recent technologies of Internet of Things (IoT) and Augmented Reality (AR). In particular, common devices such as smartphones associated along with appropriate measurement nodes and a suitable app, developed to the purpose, allow consumers to view in AR environment electrical consumption of their domestic electrical loads to consciously decide whether to switch them off. Performance of both sensor nodes and AR environment were preliminarily assessed in either laboratory experiments or actual household context, highlighting the promising effectiveness of the proposed approach.
Constructing Accurate Equivalent Electrical Circuit Models of Lithium Iron Phosphate and Lead–Acid Battery Cells for Solar Home System Applications
Authors: Yu Yunhe , Narayan Nishant , Vega-Garita Victor , Popovic-Gerber Jelena , Qin Zian , Wagemaker Marnix , Bauer Pavol , Zeman Miro
Published: September 2018

The past few years have seen strong growth of solar-based off-grid energy solutions such as Solar Home Systems (SHS) as a means to ameliorate the grave problem of energy poverty. Battery storage is an essential component of SHS. An accurate battery model can play a vital role in SHS design. Knowing the dynamic behaviour of the battery is important for the battery sizing and estimating the battery behaviour for the chosen application at the system design stage. In this paper, an accurate cell level dynamic battery model based on the electrical equivalent circuit is constructed for two battery technologies: the valve regulated lead–acid (VRLA) battery and the LiFePO 4 (LFP) battery. Series of experiments were performed to obtain the relevant model parameters. This model is built for low C-rate applications (lower than 0.5 C-rate) as expected in SHS. The model considers the non-linear relation between the state of charge ( S O C ) and open circuit voltage ( V OC ) for both technologies. Additionally, the equivalent electrical circuit model for the VRLA battery was improved by including a 2nd order RC pair. The simulated model differs from the experimentally obtained result by less than 2%. This cell level battery model can be potentially scaled to battery pack level with flexible capacity, making the dynamic battery model a useful tool in SHS design.
Investigating the Dehumidification Characteristics of Turbine Stator Cascades with Parallel Channels
Authors: Han Zhonghe , Zeng Wei , Han Xu , Xiang Peng
Published: September 2018

Steam turbines play a vital role in the power industry. The efficiency of the steam turbine can be improved by reforming the flow structure. For the White cascade, a parallel channel which is an additional steam passage was designed to connect adjacent steam flow channels. In view of the steepening and sensitive characteristics of the steam condensation parameters in the White cascade, the two-fluid model was applied to study the condensation and aerodynamic characteristics of the prototype cascade and the modified cascade. Wetness losses were regarded as the evaluation standard, and the optimum design of the parallel channel was obtained. The influence of channel width was also studied. The location of the suction surface (D point) has great influence on the dehumidification performance of the cascade. On the contrary, the location of the pressure surface (C point) has no significant effect on dehumidification performance. Moreover, it is necessary to select the width of the parallel channel in combination with the cascade performance. It is suggested that the parallel channel width should be between 3 and 5 mm. The research results can provide a reference for the optimal design of a turbine stator cascade.
Improvement of Wastewater Treatment Performance and Power Generation in Microbial Fuel Cells by Enhancing Hydrolysis and Acidogenesis, and by Reducing Internal Losses
Authors: López Zavala Miguel Ángel , Torres Delenne Pamela Renée , González Peña Omar Israel
Published: September 2018

In this study, biodegradation performance and power generation in MFCs were improved. Domestic wastewater was biodegraded in a dual-chamber MFC system equipped with a DupontTM Nafion® 117 proton exchange membrane, graphite electrodes (8.0 cm × 2.5 cm × 0.2 cm) in both chambers and an external electric circuit with a 100 Ω resistor. Experiments were conducted using an anaerobic inoculum that was prepared onsite by acclimating mixed liquor from municipal wastewater. Aqueous hydrochloric acid (0.1 M HCl, pH 1.82) was used as the electrolyte in the cathode chamber. Free-oxygen conditions were promoted in both chambers by means of a vacuum (77.3 kPa). Low pH (< 5) and mixing conditions were maintained in the anode chamber and all the tests were carried out at 25 ± 1 °C. These conditions enhanced the hydrolysis and acidogenesis, inhibited the methanogenesis and reduced the internal losses. All of them together contributed to improve the treatment performance and power generation of the MFCs. Results of batch tests show COD reductions of up to 95%, voltages peaks of 0.954 V, maximum power densities on the order of 2.1 W·m−2 and 36.9 W·m−3, and energy generation peaks of 99.4 J·mg−1 COD removed. These values are greater than those reported in the MFCs’ literature for municipal wastewater (26 mW·m−2–146 mW·m−2), industrial wastewater (419 mW·m−2) and culture medium solutions (1.17 W·m−2), and similar to those of glucose (3.6 W·m−2). Thus, these results can contribute to further enhancing the energy generated in MFCs and moving forward to make the MFCs more ready for practical applications of bioenergy production.
Theoretical and Experimental Contributions on the Use of Smart Composite Materials in the Construction of Civil Buildings with Low Energy Consumption
Authors: Bendic Vasile , Dobrotă Dan
Published: September 2018

The paper presents the theoretical and experimental studies undertaken for the realization of an intelligent composite material with phase shift that has optimal characteristics in the thermal energy storage process and an experimental method for integrating the material with phase change in a possible efficient system to be used in the construction of a dwelling. It analyzes the main factors in designing such systems (the temperature limits between which the system must operate, the melting/solidification temperature of the Phase Change Material (PCM), the latent heat of the PCM, the degree of thermal loading, the bed configuration of PCM capsules and a PCM-RB01 material is set. A micro-encapsulation method was chosen and a “solar wall” is made where the incident solar radiation is absorbed by the PCM embedded in the wall, so the stored heat is used for heating and ventilation of a home. Experimental research has shown that developed PCM allows a maximum room temperature reduction of about 4 °C during the day and can reduce the night-time heating load. Also, despite the lower thermal energy absorption capacity, the developed PCM-RB01 material provides a superior physical stability compared to the classical types of integration.
Characterization and Prediction of Complex Natural Fractures in the Tight Conglomerate Reservoirs: A Fractal Method
Authors: Gong Lei , Fu Xiaofei , Gao Shuai , Zhao Peiqiang , Luo Qingyong , Zeng Lianbo , Yue Wenting , Zhang Benjian , Liu Bo
Published: September 2018

Using the conventional fracture parameters is difficult to characterize and predict the complex natural fractures in the tight conglomerate reservoirs. In order to quantify the fracture behaviors, a fractal method was presented in this work. Firstly, the characteristics of fractures were depicted, then the fracture fractal dimensions were calculated using the box-counting method, and finally the geological significance of the fractal method was discussed. Three types of fractures were identified, including intra-gravel fractures, gravel edge fractures and trans-gravel fractures. The calculations show that the fracture fractal dimensions distribute between 1.20 and 1.50 with correlation coefficients being above 0.98. The fracture fractal dimension has exponential correlation with the fracture areal density, porosity and permeability and can therefore be used to quantify the fracture intensity. The apertures of micro-fractures are distributed between 10 μm and 100 μm, while the apertures of macro-fractures are distributed between 50 μm and 200 μm. The areal densities of fractures are distributed between 20.0 m·m−2 and 50.0 m·m−2, with an average of 31.42 m·m−2. The cumulative frequency distribution of both fracture apertures and areal densities follow power law distribution. The fracture parameters at different scales can be predicted by extrapolating these power law distributions.
Mechanism Analysis of Roadway Rockbursts Induced by Dynamic Mining Loading and Its Application
Authors: Wang Zheng-yi , Dou Lin-ming , Wang Gui-feng
Published: September 2018

Roadway rockbursts seriously restrict the safety production of coal mines; however, the interaction between dynamic loads and roadway surrounding rocks has not been fully considered in existing studies. A dynamic failure analysis model of anchoring supporting structures was established to analyze dynamic effects of stress waves. Taking a rockburst in LW402103 of the Hujiahe coal mine as the case, the theoretical model was well applied and verified. The static cumulative resistance Qs (210.5 kN) which was incurred by deformation of rocks provided the basis (81.93% in the overall real-time resistance Q) of dynamic failures. However, the additional impact resistance Qd (46.43 kN) brought about by the energy release in the failure process of elastic zones triggered impact failures. As a result, under conditions that the overall real-time resistance Q (256.93 kN) exceeded the ultimate resistance [Q] (250.8 kN), the dynamic failure of supports occurred. The in situ application was implemented by taking pressure-relief measures and parameter optimizations of roadway supports, which achieved an effective prevention of rockbursts.
Planning of Electric Vehicle Charging Infrastructure for Urban Areas with Tight Land Supply
Authors: Guo Chunlin , Yang Jingjing , Yang Lin
Published: September 2018

Motivated by the development of electric vehicles (EVs), this paper addresses the planning of EV charging infrastructures (EVCIs). Considering that the optimal locations for EVCIs in most cities with a tight land supply are difficult to obtain, it is significant to study the planning of EVCIs in such cities. This paper proposes a planning method and model of EVCIs under the condition that alternative locations of centralized charging infrastructures (CCIs) are known. Firstly, based on the principle of energy equivalence, the sales volume of the fuel of gas stations in the planned area is converted into the equivalent electric quantity (EEQ) and the EEQ is divided according to the demands of different EVs for charging. Then, an equal product of the load and distance (EPLD) criterion is used to distribute the EEQ to CCIs and distributed charging infrastructures (DCIs) located in areas for load forecasting. The final plan is given out after checking the constraints and planning rationality. In addition, the net present value (NPV), the average charging distance, and the total harmonic distortion (THD) rate are used to evaluate the planning scheme. Finally, the feasibility and practicability of the proposed method are verified by a case study in Beijing.
Function Value-Based Multi-Objective Optimisation of Reheating Furnace Operations Using Hooke-Jeeves Algorithm
Authors: Gao Bo , Wang Chunsheng , Hu Yukun , Tan C. K. , Roach Paul Alun , Varga Liz
Published: September 2018

Improved thermal efficiency in energy-intensive metal-reheating furnaces has attracted much attention recently in efforts to reduce both fuel consumption, and CO2 emissions. Thermal efficiency of these furnaces has improved in recent years (through the installation of regenerative or recuperative burners), and improved refractory insulation. However, further improvements can still be achieved through setting up reference values for the optimal set-point temperatures of the furnaces. Having a reasonable expression of objective function is of particular importance in such optimisation. This paper presents a function value-based multi-objective optimisation where the objective functions, which address such concerns as discharge temperature, temperature uniformity, and specific fuel consumption, are dependent on each other. Hooke-Jeeves direct search algorithm (HJDSA) was used to minimise the objective functions under a series of production rates. The optimised set-point temperatures were further used to construct an artificial neural network (ANN) of set-point temperature in each control zone. The constructed artificial neural networks have the potential to be incorporated into a more advanced control solution to update the set-point temperatures when the reheating furnace encounters a production rate change. The results suggest that the optimised set-point temperatures can highly improve heating accuracy, which is less than 1 °C from the desired discharge temperature.
Anaerobic Co-Digestion of Sludge and Organic Food Waste—Performance, Inhibition, and Impact on the Microbial Community
Authors: Keucken Alexander , Habagil Moshe , Batstone Damien , Jeppsson Ulf , Arnell Magnus
Published: September 2018

Anaerobic co-digestion allows for under-utilised digesters to increase biomethane production. The organic fraction of municipal solid waste (OFMSW), i.e., food waste, is an abundant substrate with high degradability and gas potential. This paper investigates the co-digestion of mixed sludge from wastewater treatment plants and OFMSW, through batch and continuous lab-scale experiments, modelling, and microbial population analysis. The results show a rapid adaptation of the process, and an increase of the biomethane production by 20% to 40%, when co-digesting mixed sludge with OFMSW at a ratio of 1:1, based on the volatile solids (VS) content. The introduction of OFMSW also has an impact on the microbial community. With 50% co-substrate and constant loading conditions (1 kg VS/m3/d) the methanogenic activity increases and adapts towards acetate degradation, while the community in the reference reactor, without a co-substrate, remains unaffected. An elevated load (2 kg VS/m3/d) increases the methanogenic activity in both reactors, but the composition of the methanogenic population remains constant for the reference reactor. The modelling shows that ammonium inhibition increases at elevated organic loads, and that intermittent feeding causes fluctuations in the digester performance, due to varying inhibition. The paper demonstrates how modelling can be used for designing feed strategies and experimental set-ups for anaerobic co-digestion.
Willingness to Pay for Nature Conservation Policies in State-Owned Forests: An Austrian Case Study
Authors: Getzner Michael , Meyerhoff Jürgen , Schläpfer Felix
Published: September 2018

The Austrian Federal Forests (ÖBf), the Republic of Austria’s state-owned company, manages 15% of the Austrian forests; about 50% of the land is devoted to nature conservation. This paper presents the results of a representative survey of Austrian households ascertaining the acceptance of, preferences regarding, and willingness to pay for three different management scenarios. One program would increase commercial forestry, while two other programs would significantly enhance biodiversity conservation. The majority of respondents considers it an important task of state-owned forests to enhance biodiversity conservation. The study reveals that the preferences of the respondents are very heterogeneous. For instance, in addition to socio-economic characteristics, the willingness to pay for nature conservation depends on personal experiences and perceptions (e.g., whether respondents feel anxious in forests), political views (e.g., the acceptance of strict legal protection of natural resources), and opinions on forest policy issues (e.g., preferences regarding privatization of public land). The study places special emphasis on the thorough description and presentation of the scenarios to the respondents and is one of the first European studies to elicit opinions on forest policies regarding public land in an environmental valuation framework.
Recent Health and Safety Incident Trends Related to the Storage of Woody Biomass: A Need for Improved Monitoring Strategies
Authors: Krigstin Sally , Wetzel Suzanne , Jayabala Nivatha , Helmeste Christopher , Madrali Sebnem , Agnew Joy , Volpe Sylvain
Published: September 2018

Self-heating fires, dust explosions and off-gassing during biomass storage are serious hazards which can have devastating consequences, resulting in worker fatalities and health impacts, as well as bioenergy plant destruction and complete loss of production. A compilation of incident reports involving biomass storage from 2000–2018 has revealed that these potential hazards continue to be a major concern in the bioenergy sector. Higher occurrence rates were found for incidents categorized as self-heating fires and fires of uncertain causes in recent years through our study of online reports. This paper highlights a critical need for improved safety protocols for bioenergy plant workers, detailed incident documentation and enhanced biomass monitoring strategies to drastically reduce the occurrence of threats associated with the storage of woody biomass. In order to manage the high risks associated with self-heating, a system for real-time monitoring of internal pile temperature was investigated. A monitoring system supplied by Braingrid Corporation was verified using embedded Tinytag thermologgers indicating that this methodology shows potential for preventing spontaneous combustion events by providing real time temperature data for superior pile management.