bluuebunny/crossref_metadata_2025 huggingface dataset:
Review of Modeling and Control of Magnetostrictive Actuators
Authors: Apicella Valerio , Clemente Carmine Stefano , Davino Daniele , Leone Damiano , Visone Ciro
Published: May 2019

Magnetostrictive actuators play an important role in the perception of usefulness of smart materials and devices. Their applications are potentially wider than that of piezoelectric actuators because of the higher energy density and intrinsic robustness. However, the non-negligible hysteresis and complexity of their characteristics make the design and control quite difficult and has limited their diffusion in industrial applications. Nevertheless, the scientific literature presents a wide offer of results in design and geometries, modeling and control that may be exploited for applications. This paper gives a reasoned review of the main results achieved in the literature about design, modeling and control of magnetostrictive actuators exploiting the direct effects of magnetostriction (Joule and Wiedemann). Some perspectives and challenges about magnetostrictive actuators development are also gathered.
Design and Investigation of Capsule Networks for Sentence Classification
Authors: Fentaw Haftu Wedajo , Kim Tae-Hyong
Published: May 2019

In recent years, convolutional neural networks (CNNs) have been used as an alternative to recurrent neural networks (RNNs) in text processing with promising results. In this paper, we investigated the newly introduced capsule networks (CapsNets), which are getting a lot of attention due to their great performance gains on image analysis more than CNNs, for sentence classification or sentiment analysis in some cases. The results of our experiment show that the proposed well-tuned CapsNet model can be a good, sometimes better and cheaper, substitute of models based on CNNs and RNNs used in sentence classification. In order to investigate whether CapsNets can learn the sequential order of words or not, we performed a number of experiments by reshuffling the test data. Our CapsNet model shows an overall better classification performance and better resistance to adversarial attacks than CNN and RNN models.
Morphology and Properties of Electrospun PCL and Its Composites for Medical Applications: A Mini Review
Authors: Mochane Mokgaotsa Jonas , Motsoeneng Teboho Simon , Sadiku Emmanuel Rotimi , Mokhena Teboho Clement , Sefadi Jeremia Shale
Published: May 2019

Polycaprolactone (PCL) is one of the most used synthetic polymers for medical applications due to its biocompatibility and slow biodegradation character. Combining the inherent properties of the PCL matrix with the characteristic of nanofibrous particles, result into promising materials that can be suitable for different applications, including the biomedical applications. The advantages of nanofibrous structures include large surface area, a small diameter of pores and a high porosity, which make them of great interest in different applications. Electrospinning, as technique, has been heavily used for the preparation of nano- and micro-sized fibers. This review discusses the different methods for the electrospinning of PCL and its composites for advanced applications. Furthermore, the steady state conditions as well as the effect of the electrospinning parameters on the resultant morphology of the electrospun fiber are also reported.
Atomic Force Spectroscopy on Ionic Liquids
Authors: Rodenbücher Christian , Wippermann Klaus , Korte Carsten
Published: May 2019

Ionic liquids have become of significant relevance in chemistry, as they can serve as environmentally-friendly solvents, electrolytes, and lubricants with bespoke properties. In particular for electrochemical applications, an understanding of the interface structure between the ionic liquid and an electrified interface is needed to model and optimize the reactions taking place on the solid surface. As with ionic liquids, the interplay between electrostatic forces and steric effects leads to an intrinsic heterogeneity, as the structure of the ionic liquid above an electrified interface cannot be described by the classical electrical double layer model. Instead, a layered solvation layer is present with a structure that depends on the material combination of the ionic liquid and substrate. In order to experimentally monitor this structure, atomic force spectroscopy (AFS) has become the method of choice. By measuring the force acting on a sharp microfabricated tip while approaching the surface in an ionic liquid, it has become possible to map the solvation layers with sub-nanometer resolution. In this review, we provide an overview of the AFS studies on ionic liquids published in recent years that illustrate how the interface is formed and how it can be modified by applying electrical potential or by adding impurities and solvents.
Data Analytics and Its Advantages for Addressing the Complexity of Healthcare: A Simulated Zika Case Study Example
Authors: Popova Zhuhadar Lily , Thrasher Evelyn
Published: May 2019

The need to control rising costs in healthcare has led to an increase in the use of data analytics to develop more efficient healthcare business models. This article discusses a simulation that uses data analytics to minimize the number of physicians and nurses needed in healthcare facilities during a crisis situation. Using a hypothetical emergency scenario, the hospital uses a healthcare analytical system to predict the necessary resources to govern the situation. Based on historical data regarding the flow of patients through the facility, a discrete-event simulation estimates resource scheduling and the resulting impact on both wait times and personnel demand. Furthermore, the value of multiple replications for discrete-event simulation models is discussed and defined, along with factors that enable greater control of multiple design points with this simulated experiment. The results of this study demonstrate the value of simulation modeling in effective resource planning. The addition of only a single doctor significantly reduced predicted wait times for patients during the crisis. Further, the findings support the use of data analytics and predictive modeling to mitigate rising healthcare costs in the United States through efficient planning and resource allocation.
SPL Features Quantification and Selection Based on Multiple Multi-Level Objectives
Authors: Khan Fazal Qudus , Musa Shahrulniza , Tsaramirsis Georgios , Buhari Seyed M.
Published: May 2019

Software Product Lines (SPLs) can aid modern ecosystems by rapidly developing large-scale software applications. SPLs produce new software products by combining existing components that are considered as features. Selection of features is challenging due to the large number of competing candidate features to choose from, with different properties, contributing towards different objectives. It is also a critical part of SPLs as they have a direct impact on the properties of the product. There have been a number of attempts to automate the selection of features. However, they offer limited flexibility in terms of specifying objectives and quantifying datasets based on these objectives, so they can be used by various selection algorithms. In this research we introduce a novel feature selection approach that supports multiple multi-level user defined objectives. A novel feature quantification method using twenty operators, capable of treating text-based and numeric values and three selection algorithms called Falcon, Jaguar, and Snail are introduced. Falcon and Jaguar are based on greedy algorithm while Snail is a variation of exhaustive search algorithm. With an increase in 4% execution time, Jaguar performed 6% and 8% better than Falcon in terms of added value and the number of features selected.
Correction: Xu, S.S.-D.; Huang, H.-C.; Chiu, T.-C.; Lin, S.-K. Biologically-Inspired Learning and Adaptation of Self-Evolving Control for Networked Mobile Robots. Applied Sciences 2019, 9, 1034
Authors: Xu Sendren , Huang Hsu-Chih , Chiu Tai-Chun , Lin Shao-Kang
Published: May 2019

After publication of the research paper [...]
Cow Milk Lactoferrin Possesses Several Catalytic Activities
Authors: Soboleva Svetlana , Sedykh Sergey , Alinovskaya Ludmila , Buneva Valentina , Nevinsky Georgy
Published: May 2019

Lactoferrin (LF) is a Fe3+-binding glycoprotein, that was first recognized in milk and then in other epithelial secretions and barrier body fluids to which many different functions have been attributed to LF including protection from iron-induced lipid peroxidation, immunomodulation, cell growth regulation, DNA and RNA binding, as well as transcriptional activation, etc. The polyfunctional physiological role of LF is still unclear, but it has been suggested to be responsible for primary defense against microbial and viral infections. It was shown previously that human milk LF possesses several enzymatic activities: DNase, RNase, ATPase, phosphatase, and amylase. Analysis of human, cow, horse, buffalo and camel LF showed a highly conserved three-dimensional (3D) structure including only detail differences in the species. Recently, it was shown that similar to human cow LF possesses DNase and RNase activities. Using different methods here we have shown for the first time that LFs from the milk of seven cows of different breeds possess high peroxidase, protease, amylase, protease, and phosphatase activities. Protease activity of cow LFs was activated by Mg2+ and Ca2+ ions. In contrast to human LFs, ATPase activity was revealed only in three of seven cow LF preparations. The discovery that LF possesses these activities may contribute to understanding the multiple physiological functions of this extremely polyfunctional protein including its protective role against microbial and viral infections.
Molecular Characterization of a Long-Term Survivor Double Metastatic Non-Small Cell Lung Cancer and Pancreatic Ductal Adenocarcinoma Treated with Gefitinib in Combination with Gemcitabine Plus Nab-Paclitaxel and mFOLFOX6 as First and Second Line Therapy
Authors: Brunetti Oronzo , Badalamenti Giuseppe , De Summa Simona , Calabrese Angela , Argentiero Antonella , Fucci Livia , Longo Vito , Galetta Domenico , Perrotti Pia Maria Soccorsa , Pinto Rosamaria , Petriella Daniela , Danza Katia , Tommasi Stefania , Leonetti Francesco , Silvestris Nicola
Published: May 2019

The management of multiple primary cancers, an event not so infrequent in oncology practice, is a critical issue due to the lack of literature. In this study, we reported the case of a patient with non-small cell metastatic lung cancer (NSCLC) and pancreatic ductal adenocarcinoma (PDAC) who received gefitinib in combination with gemcitabine plus nab-paclitaxel and with mFOLFOX6 in first and second line, respectively. It achieved a progression-free survival and a28-months overall survival (OS) for NSCLC and PFS-1 and OS of 20 and 13 months, respectively for PDAC. Moreover, the combination of gefitinib and chemotherapy treatmentsshowed a good safety profile. Given the insignificant frequency of this case, we performed a molecular characterization of both neoplasms with the aim to investigate the existence of particular activated pathways and/or similar immunological mutations. It is interesting to note that two neoplasms shared a common mutation ofthe B7-H3 gene, with the consecutive impairment of its expressed protein. In both PDAC and NSCLC, the expression of this protein was associated with a worse survival rate. Since B7-H3 is an anti-apoptotic protein, the reduction of its expression or function should justify a pro-apoptotic activity with a leading justification of the long survival of the patient considered in this report.
PARP Inhibition Increases the Response to Chemotherapy in Uveal Melanoma
Authors: de Koning Leanne , Decaudin Didier , El Botty Rania , Nicolas André , Carita Guillaume , Schuller Mathieu , Ouine Bérengère , Cartier Aurélie , Naguez Adnan , Fleury Justine , Cooke Vesselina , Wylie Andrew , Smith Paul , Marangoni Elisabetta , Gentien David , Meseure Didier , Mariani Pascale , Cassoux Nathalie , Piperno-Neumann Sophie , Roman-Roman Sergio , Némati Fariba
Published: May 2019

Uveal melanoma (UM) remains without effective therapy at the metastatic stage, which is associated with BAP-1 (BRCA1 associated protein) mutations. However, no data on DNA repair capacities in UM are available. Here, we use UM patient-derived xenografts (PDXs) to study the therapeutic activity of the PARP inhibitor olaparib, alone or in combination. First, we show that the expression and the activity of PARP proteins is similar between the PDXs and the corresponding patient’s tumors. In vivo experiments in the PDX models showed that olaparib was not efficient alone, but significantly increased the efficacy of dacarbazine. Finally, using reverse phase protein arrays and immunohistochemistry, we identified proteins involved in DNA repair and apoptosis as potential biomarkers predicting response to the combination of olaparib and dacarbazine. We also observed a high increase of phosphorylated YAP and TAZ proteins after dacarbazine + olaparib treatment. Our results suggest that PARP inhibition in combination with the alkylating agent dacarbazine could be of clinical interest for UM treatment. We also observe an interesting effect of dacarbazine on the Hippo pathway, confirming the importance of this pathway in UM.
Vertical Liquid Crystal Alignment of Comb-Like Alkyl Hydroxybenzoate-Substituted Polystyrene
Authors: Ju Changha , Kang Shinwoo , Kim Taehyung , Park Chanhyuk , Kang Hyo
Published: May 2019

We investigated the liquid crystal (LC) alignment behaviors of butyl hydroxybenzoate-substituted polystyrene (PBHB#; # = 20, 40, 60, 80, and 100, where # indicates the molar fraction of butyl hydroxybenzoate in the side chain), methyl hydroxybenzoate-substituted polystyrene (PMHB100), and ethyl hydroxybenzoate-substituted polystyrene (PEHB100). Generally, LC cells made employing polymer films having longer alkyl groups in the side chain show vertical LC alignment. For instance, a LC cell fabricated with the PMHB100 film showed random planar LC alignment, while the LC cells made from the PEHB100 and PBHB100 films exhibited vertical LC alignment. Moreover, LC cells prepared from a polymer film having a higher molar content of butyl hydroxybenzoate in the side chain exhibited vertical LC alignment. The observed vertical LC alignment behaviors are closely related to the surface energy of these polymer films. For instance, vertical LC alignment was observed when the surface energy of the polymer film was less than ~43.86 mJ/m2, which could result from the nonpolar alkyl groups of the alkyl hydroxybenzoate moiety. The LC cell prepared from PBHB100 as the LC alignment layer showed good electro-optical characteristics such as voltage holding ratio, residual DC voltage, and alignment stability at 200 °C.
Didactics of Historical-Cultural Heritage QR Codes and the TPACK Model: An Analytic Revision of Three Classroom Experiences in Spanish Higher Education Contexts
Authors: Ortega-Sánchez Delfín , Gómez-Trigueros Isabel María
Published: May 2019

This study aims to establish a first reference didactic and methodological framework for the operational and effective integration of Quick Response (QR) codes in the training of Social Sciences teachers in Primary Education, paying special attention to the integrated acquisition of social, civic, and digital competences in the framework TPACK (Technological Pedagogical Content Knowledge). With this purpose, it specifies the didactic potential of these new technological resources in the teaching-learning of the Social Sciences, its eventual integration in the design and implementation of teaching innovation projects for this educational stage, and its capacity for the acquisition of competence digital teaching in Primary Education teachers in initial training. In this sense, the work explores a selection of classroom experiences and research and innovation proposals, pioneers in the field of Social Sciences Didactics in Spain, with the aim of offering an approximation and status of the didactic treatment of History and, particularly, the historical-cultural heritage in the formation of Primary Education teachers.
Assessing the Impact of a Geospatial Data Collection App on Student Engagement in Environmental Education
Authors: Norton Evan , Li Yingkui , Mason Lisa Reyes , Washington-Allen Robert A.
Published: May 2019

A critical component of environmental education is to ensure student understanding and use of available technologies to better experience and analyze spatially distributed features of the environment. Combining mobile technologies with geographic information systems in field data collection may provide a unique opportunity for students to feel engaged in what they are learning and take ownership of their learning process. We customized an open access data collection application using Collector for ArcGIS and investigated its impacts on student engagement and perception of the incorporation of technology within an environmental science curriculum. Analyses of pre- and post-surveys indicate that the inclusion of geospatial technologies as a part of environmental curricula allows students to take the lead on their own research, view field data interactively as opposed to looking at a database in hindsight and analyze multiscale data as it is presented during field data collection. The findings of this study are consistent with previous studies, suggesting a strong association between the inclusion of geospatial technologies as a part of curricula and student engagement.
Automatic Configuration of OPC UA for Industrial Internet of Things Environments
Authors: Gutierrez-Guerrero Jose Miguel , Holgado-Terriza Juan Antonio
Published: May 2019

Software technologies play an increasingly significant role in industrial environments, especially for the adoption of Industrial Internet of Things (IIoT). In this context, the application of mechanisms for the auto-configuration of industrial systems may be relevant for reducing human errors and costs in terms of time and money, improving the maintenance and the quality control. OPC UA (OLE for Process Control Unified Architecture) systems are usually integrated into an industrial system to provide a standard way for setting a secure and reliable data exchange between industrial devices of multiple vendors and software systems. In this paper, a novel mechanism for the auto-configuration of OPC UA systems is proposed from an initial setup of industrial devices interconnected to a basic Ethernet network. The auto-configuration of the OPC UA is self-managed over the TCP/IP protocol. This mechanism allows automating the configuration process of the OPC UA server automatically from the programmable logic controller (PLC) devices connected to a basic Ethernet network. Once the PLC devices are identified, they exchange information directly with OPC using a Modbus protocol over the same Ethernet network. To test the feasibility of this approach, a case study is prepared and evaluated.
Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm
Authors: Chao Kuei-Hsiang , Hsieh Cheng-Chieh
Published: May 2019

In this study, the output characteristics of partial modules in a photovoltaic module array when subject to shading were first explored. Then, an improved particle swarm optimization (PSO) algorithm was applied to track the global maximum power point (MPP), with a multi-peak characteristic curve. The improved particle swarm optimization algorithm proposed, combined with the artificial bee colony (ABC) algorithm, was used to adjust the weighting, cognition learning factor, and social learning factor, and change the number of iterations to enhance the tracking performance of the MPP tracker. Finally, MATLAB software was used to carry out a simulation and prove the improved that the PSO algorithm successfully tracked the MPP in the photovoltaic array output curve with multiple peaks. Its tracking performance is far superior to the existing PSO algorithm.
Enforcing Optimal ACL Policies Using K-Partite Graph in Hybrid SDN
Authors: Amin Rashid , Shah Nadir , Mehmood Waqar
Published: May 2019

Software Defined Networking (SDN) as an innovative network paradigm that separates the management and control planes from the data plane of forwarding devices by implementing both the management and control planes at a logically centralized entity, called controller. Therefore, it ensures simple network management and control. However, due to several reasons (e.g., deployment cost, fear of downtime) organizations are very reluctant to adopt SDN in practice. Therefore, a viable solution is to replace the legacy devices by SDN devices incrementally. This results in a new network architecture called hybrid SDN. In hybrid SDN, both SDN and legacy devices operate in such a way to achieve the maximum benefit of SDN. The legacy devices are running a traditional protocol and SDN devices are operating using Open-flow protocols. Network policies play an essential role to secure the entire network from several types of attacks like unauthorized access and port/protocol control. In a hybrid SDN, policy implementation is a tedious task that requires extreme care and attention due to the hybrid nature of network traffic. Network policies may be implemented at various positions in hybrid SDN, e.g., near the destination or source node, and at the egress or ingress ports of a router. Each of these schemes has some trade-offs. For example, if policies are implemented near the source nodes then each packet generated from the source must pass through the filter and, thus, requires more processing power, time, resources, etc. Similarly, if policies are installed near the destination nodes, then a lot of unwanted traffic generated causing network congestion. This is an NP-hard problem. To address these challenges, we propose a systematic design approach to implement network policies optimally by using decision tree and K-partite graph. By traversing all the policies, we built up the decision tree that identifies which source nodes can communicate with which destination. Then, we traverse the decision tree and constructs K-partite graph to find possible places (interfaces of the routers) where ACL policies are to be implemented based on the different criteria (i.e., the minimum number of ACL rules and the minimum number of transmissions for unwanted traffic). The edge weight represents the cost per criteria. Then, we traverse the K-partite graph to find the optimal place for ACL rules implementation according to the given criteria. The simulation results indicate that the proposed technique outperforms existing approaches in terms of computation time, traffic optimization and successful packet delivery, etc. The results also indicate that the proposed method improves network performance and efficiency by decreasing network congestion and providing ease of policy implementation.
Picking Robot Visual Servo Control Based on Modified Fuzzy Neural Network Sliding Mode Algorithms
Authors: Chen Wei , Xu Tongqing , Liu Junjie , Wang Mo , Zhao Dean
Published: May 2019

Through an analysis of the kinematics and dynamics relations between the target positioning of manipulator joint angles of an apple-picking robot, the sliding-mode control (SMC) method is introduced into robot servo control according to the characteristics of servo control. However, the biggest problem of the sliding-mode variable structure control is chattering, and the speed, inertia, acceleration, switching surface, and other factors are also considered when approaching the sliding die surface. Meanwhile, neural network has the characteristics of approaching non-linear function and not depending on the mechanism model of the system. Therefore, the fuzzy neural network control algorithm can effectively solve the chattering problem caused by the variable structure of the sliding mode and improve the dynamic and static performances of the control system. The comparison experiment is carried out through the application of the PID algorithm, the sliding mode control algorithm, and the improved fuzzy neural network sliding mode control algorithm on the picking robot system in the laboratory environment. The result verified that the intelligent algorithm can reduce the complexity of parameter adjustments and improve the control accuracy to a certain extent.
Black-Box Behavioral Modeling of Voltage and Frequency Response Characteristic for Islanded Microgrid
Authors: Shi Yong , Xu Dong , Su Jianhui , Liu Ning , Yu Hongru , Xu Huadian
Published: May 2019

The voltage and frequency response model of microgrid is significant for its application in the design of secondary voltage frequency controller and system stability analysis. However, most models developed for this aspect are complex in structure due to the difficult mechanism modeling process and are only suitable for offline identification. To solve these problems, this paper proposes a black-box modeling method to identify the voltage and frequency response model of microgrid online. Firstly, the microgrid system is set as a two-input, two-output black-box system and can be modeled only by data sampled at the input and output ports. Therefore, the simplicity of modeling steps can be guaranteed. Meanwhile, the recursive damped least squares method is used to realize the online model identification of the microgrid system, so that the model parameters can be adjusted with the change of the microgrid operating structure, which makes the model more adaptable. The paper analyzes the black-box modeling process of the microgrid system in detail, and the microgrid platform, including 100 kW rated power inverters, is employed to validate the analysis and experimental results.
Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey
Authors: Mancini Francesco , Lo Basso Gianluigi , De Santoli Livio
Published: May 2019

This work shows the outcomes of a research activity aimed at the energy characterization of residential users. Specifically, by data analysis related to the real energy consumption of sample buildings, the flexible loads amount has been identified so as to investigate on the opportunity to implement a demand/response (DR) program. The most meaningful input data have been collected by an on-line questionnaire created within an Excel spreadsheet allowing one to simulate and compare the calculations with the actual dwellings’ consumption; 412 questionnaires have been used as statistical sample and simulations have been performed based on single-zone dynamic model. Additionally, once the energy consumptions have been sorted by the different services, reference key performance indicators (KPIs) have been also calculated normalising those ones by people and house floor surface. From data analysis, it emerges how the Italian residential users are not very electrified. Furthermore, the flexible loads are low and, implementing minor maintenance interventions, the potential of flexibility can decrease up to 20%. For that reason, the current research can be further developed by investigating on suitable flexibility extensions as well as on the automation system requirements which is needed managing the flexible loads.
A Physical Calibrator for Partial Discharge Meters
Authors: Krbal Michal , Pelikan Ludek , Stepanek Jaroslav , Orsagova Jaroslava , Kolcunova Iraida
Published: May 2019

This article offers an alternative method of calibrating partial discharge meters for research and teaching purposes. Most current modern calibrators are implemented as precise voltage pulse sources with a coupling capacitor. However, our calibrator is based on the physical principles of dielectric materials distributed in a plane or space. Calibrator design is unique and there is an attempt to get closer to the behavior of the measured real objects. The calibration impulses are created by energy from a high voltage power supply at the specific or nominal value of the applied voltage. At the same time, it is possible to simulate the value and quantity of the discharges and their position in the object relative to the input electrodes. The calibrator creates conditions as a real measured object with adjustable parameters. This paper describes a design of this type of calibrator, its implementation, numerical simulation of discharge values and laboratory measurements with functional verification using the Tettex 9520 calibrator and galvanic measured system DDX 7000/8003 and DDX 9121b. All measurements are carried out using the CVVOZEPowerLab Research Infrastructure equipment.
Integrated Energy and Thermal Management for Electrified Powertrains
Authors: Wei Caiyang , Hofman Theo , Ilhan Caarls Esin , van Iperen Rokus
Published: May 2019

This study presents an integrated energy and thermal management system to identify the fuel-saving potential caused by cold-starting an electrified powertrain. In addition, it quantifies the benefit of adopting waste heat recovery (WHR) technologies on the ultimate fuel savings. A cold-start implies a low engine temperature, which increases the frictional power dissipation in the engine, leading to excess fuel usage. A dual-source WHR (DSWHR) system is employed to recuperate waste heat from exhaust gases. The energy harvested is stored in a battery and can be retrieved when needed. Moreover, the system recovers waste heat from an electric machine, including power electronics and a continuous variable transmission, to boost the heating performance of a heat pump for cabin heating. This results in a decrease in the load on the battery. The integrated energy and thermal management system aims at maximizing the fuel efficiency for a pre-defined drive cycle. Simulation results show that cold-start conditions affect the fuel-saving potential significantly, up to 7.1% on the New European Driving Cycle (NEDC), yet have a small impact on the optimal controller. The DSWHR system improves the fuel economy remarkably, up to 13.1% on the NEDC, from which the design of WHR technologies and dimensioning of powertrain components can be derived. As the optimal solution is obtained offline, a complete energy consumption minimization strategy framework, considering both energy and thermal aspects, is proposed to enable online implementation.
Optimization of Safety Stock under Controllable Production Rate and Energy Consumption in an Automated Smart Production Management
Authors: Sarkar Mitali , Sarkar Biswajit
Published: May 2019

A smart production system is essential to produce complex products under the consumption of efficient energy. The main ramification of controllable production rate, amount of production size, and safety stocks is simultaneously optimized under proper utilization of energy within a smart production system with a random breakdown of spare parts. Due to the random breakdown, a greater amount of energy may be used. For this purpose, this study is concerned about the optimum safety stock level under the exact amount of energy utilization. For random breakdown, there are three cases as production inventory meets the demand without utilization of the safety stock, with using of the safety stock, and consumed the total safety stock amount and facing shortages. After the random breakdown time, the smart production system may move to an out-of-control state and may produce defective items, where the production rate of defective items is a random variable, which follows an exponential distribution. The total cost is highly nonlinear and cannot be solved by any classical optimization technique. A mathematical optimization tool is utilized to test the model. Numerical study proves that the effect of energy plays an important role for the smart manufacturing system even though random breakdowns are there. it is found that the controllable production rate under the effect of the optimum energy consumption really effects significantly in the minimization cost. It saves cost regarding the corrective and preventive maintenance cost. The amount of safety stock can have more support under the effect of optimum energy utilization. The energy can be replaced by the solar energy.
Grain and Extent Considerations Are Integral for Monitoring Landscape-Scale Desired Conditions in Fire-Adapted Forests
Authors: Wasserman Tzeidle N. , Sánchez Meador Andrew J. , Waltz Amy E. M.
Published: May 2019

Remotely-sensed data are commonly used to evaluate forest metrics, such as canopy cover, to assess change detection, and to inform land management planning. Often, canopy cover is measured only at the scale of the spatial data product used in the analysis, and there is a mismatch between the management question and the scale of the data. We compared four readily available remotely sensed landscape data products— Light detection and ranging (LiDAR), Landsat-8, Sentinel-2, and National Agriculture Imagery Program (NAIP) imagery —at different spatial grains and multiple extents to assess their consistency and efficacy for quantifying key landscape characteristics of forest canopy patches and sensitivity to change. We examined landscape-scale patterns of forest canopy cover across three landscapes in northern Arizona and assessed their performance using six landscape metrics. Changes in grain and extent affect canopy cover patch metrics and the inferences that can be made from each data product. Overall data products performed differently across landscape metrics. When performing analyses and choosing data layers, it is essential to match the scale of the data product to the management question and understand the limitations inherent in using canopy cover as a stand-alone metric.
Wild Edible Fruits: A Systematic Review of an Under-Researched Multifunctional NTFP (Non-Timber Forest Product)
Authors: Sardeshpande Mallika , Shackleton Charlie
Published: May 2019

Wild edible fruits (WEFs) are among the most widely used non-timber forest products (NTFPs), and important sources of nutrition, medicine, and income for their users. In addition to their use as food, WEF species may also yield fiber, fuel, and a range of processed products. Besides forests, WEF species also thrive in diverse environments, such as agroforestry and urban landscapes, deserts, fallows, natural lands, and plantations. Given the multifunctional, ubiquitous nature of WEFs, we conducted a systematic review on the literature specific to WEFs and highlighted links between different domains of the wider knowledge on NTFPs. We found that literature specific to WEFs was limited, and a majority of it reported ethnobotanical and taxonomic descriptions, with relatively few studies on landscape ecology, economics, and conservation of WEFs. Our review identifies priorities and emerging avenues for research and policymaking to promote sustainable WEF management and use, and subsequent biodiversity and habitat conservation. In particular, we recommend that ecosystem services, economic incentives, market innovations, and stakeholder synergies are incorporated into WEF conservation strategies.
We’re Not Doing Enough Prescribed Fire in the Western United States to Mitigate Wildfire Risk
Authors: Kolden Crystal
Published: May 2019

Prescribed fire is one of the most widely advocated management practices for reducing wildfire hazard and has a long and rich tradition rooted in indigenous and local ecological knowledge. The scientific literature has repeatedly reported that prescribed fire is often the most effective means of achieving such goals by reducing fuels and wildfire hazard and restoring ecological function to fire-adapted ecosystems in the United States (US) following a century of fire exclusion. This has translated into calls from scientists and policy experts for more prescribed fire, particularly in the Western US, where fire activity has escalated in recent decades. The annual extent of prescribed burning in the Western US remained stable or decreased from 1998 to 2018, while 70% of all prescribed fire was completed primarily by non-federal entities in the Southeastern US. The Bureau of Indian Affairs (BIA) was the only federal agency to substantially increase prescribed fire use, potentially associated with increased tribal self-governance. This suggests that the best available science is not being adopted into management practices, thereby further compounding the fire deficit in the Western US and the potential for more wildfire disasters.
The Truncated Lindley Distribution with Applications in Astrophysics
Authors: Zaninetti Lorenzo
Published: May 2019

This paper reviews the Lindley distribution and then introduces the scale and the double truncation. The unknown parameters of the truncated Lindley distribution are evaluated with the maximum likelihood estimators. An application of the Lindley distribution with scale is done to the initial mass function for stars. The magnitude version of the Lindley distribution with scale is applied to the luminosity function for the Sloan Digital Sky Survey (SDSS) galaxies and to the photometric maximum of the 2MASS Redshift Survey (2MRS) galaxies. The truncated Lindley luminosity function allows to model the Malquist bias of the 2MRS galaxies.
Effects of Dehydration and Rehydration on Cognitive Performance and Mood among Male College Students in Cangzhou, China: A Self-Controlled Trial
Authors: Zhang Na , Du Song M. , Zhang Jian F. , Ma Guan S.
Published: May 2019

Water accounts for 75% of brain mass. Associations may exist between hydration and cognitive performance. The objective of this study was to investigate the effects of dehydration and rehydration on cognitive performance and mood. In this self-control trial, 12 men were recruited from a medical college in Cangzhou, China. After 12 h of overnight fasting, the participants took baseline tests at 8:00 AM on day 2. First morning urine and blood osmolality were analyzed to determine hydration state. Height, weight, and blood pressure were measured following standardized procedures. A visual analog scale for the subjective sensation of thirst was applied, and a profile of mood states questionnaire was applied. Tests were conducted for cognitive performance, including a test of digit span forward and backward, digit-symbol substitutions, dose-work, and stroop effects. Participants were required not to drink water for 36 h but were given three meals on day 3. On day 4, the same indexes were tested as a baseline test. At 8:30 AM, participants drank 1500 mL of purified water over 15 min. After a 1 h interval, the same measurements were performed. Compared with baseline test results, during the dehydration test, participants had lower scores of vigor (11.9 vs. 8.8, %, p = 0.007) and esteem-related affect (8.2 vs. 5.7, %, p = 0.006), lower total scores of digit span (14.3 vs. 13.3, %, p = 0.004), and higher error rates for dose-work (0.01 vs. 0.16, %, p = 0.005). Compared with the dehydration test scores, rehydration test scores showed that fatigue (4.3 vs. 2.1, %, p = 0.005) and total mood disturbance (TMD) (99.0 vs. 90.2, %, p = 0.008) improved, and scores of forward, backward, and total digit span increased (7.7 vs. 8.6, p = 0.014; 5.7 vs. 1.2, p = 0.019; 13.3 vs. 15.4, p = 0.001). Increases were also noted in correct number of digit symbol substitutions, reading speed, and mental work ability (70.8 vs. 75.4, p < 0.001; 339.3 vs. 486.4, n/min, p < 0.001; 356.1 vs. 450.2, p < 0.001), and reaction time decreased (30.2 vs. 28.7, s, p = 0.002). As a conclusion, dehydration had negative effects on vigor, esteem-related affect, short-term memory, and attention. Rehydration after water supplementation alleviated fatigue and improved TMD, short-term memory, attention, and reaction.
Predictors of In-Hospital Mortality in Aboriginal Children Admitted to a Tertiary Paediatric Hospital
Authors: Singer Rebecca , Zwi Karen , Menzies Robert
Published: May 2019

Background: Aboriginal Australian children have higher rates of mortality at younger ages than non-Aboriginal Australian children. We aimed to (i) calculate the case fatality rate (CFR) for Aboriginal and non-Aboriginal children admitted to children’s hospitals in New South Wales (NSW) and (ii) identify predictors of CFR. Methods: We used a retrospective cross-sectional analysis of data from electronic medical records for in-patient admissions to the Sydney Children’s Hospitals Network (SCHN) over five years (2011–2015). Logistic regression analysis was used to identify predictors of mortality and excess deaths in Aboriginal children were calculated. Results: There were 241,823 presentations over the 5-year period. The CFR for Aboriginal children was double that of non-Aboriginal children (0.4% vs. 0.2%, p = 0.002), with Aboriginal children under 2 years and from remote and regional Australia at highest risk of excess mortality. Predictors of death for all children in order of significance were: Circulatory disorders (Odds Ratio (OR) 17.16, p < 0.001), neoplasm/blood/immune disorders (OR 2.77, p < 0.001), emergency admissions (OR 1.94, p < 0.001), aboriginality (OR 1.73, p = 0.005) and longer length of stay (OR 1.012; p < 0.001). Conclusions: Our data show that Aboriginal children are almost twice as likely to die than non-Aboriginal children. In particular, excess deaths in Aboriginal children are most commonly from outer regional and remote areas and children aged under 2 years with perinatal or circulatory conditions.
Study on the In Vitro Activity of Five Disinfectants against Nosocomial Bacteria
Authors: Montagna Maria Teresa , Triggiano Francesco , Barbuti Giovanna , Bartolomeo Nicola , De Giglio Osvalda , Diella Giusy , Lopuzzo Marco , Rutigliano Serafina , Serio Gabriella , Caggiano Giuseppina
Published: May 2019

Nosocomial infections cause significant morbidity and mortality worldwide, and the pathogenic organisms responsible for such infections can develop resistance to antimicrobial agents. Understanding the activity of disinfectants against clinical and environmental bacterial isolates is therefore crucial. We analysed the in vitro activity of five antimicrobial products (phenolic compounds, didecyldimethylammonium chloride (DDAC), sodium hypochlorite, isopropanol + ammonium compounds (IACs), hydrogen peroxide) against 187 bacterial strains comprising clinical isolates, as well as 30 environmental isolates of Pseudomonas aeruginosa from hospital water samples. Disk diffusion assays were employed to assess antimicrobial activity. Hydrogen peroxide was significantly more active (p < 0.0001) than the other disinfectants against all P. aeruginosa, Klebsiella pneumoniae, Enterococcus faecalis and Staphylococcus aureus strains. It was also the only disinfectant with activity against both clinical and environmental strains of P. aeruginosa. DDAC and IAC-based disinfectants were ineffective against Gram-negative strains, but showed significant activity (particularly IACs, p < 0.0001) against the Gram-positive strains. Compared with IACs, DDAC was significantly more active on E. faecalis and less active on S. aureus (p < 0.0001). Sodium hypochlorite and phenol compounds, by contrast, were inactive against all bacterial strains. The development of disinfection procedures that are effective against all microorganisms is essential for limiting the spread of nosocomial infections.
Alcohol Use and Misuse Among School-Going Adolescents in Thailand: Results of a National Survey in 2015
Authors: Pengpid Supa , Peltzer Karl
Published: May 2019

The aim of this study was to assess the prevalence of alcohol use and misuse, and to identify its associated factors among in-school adolescents in the 2015 Thailand Global School-based Student Health Survey (GSHS). The sample included 5994 school-going adolescents (mean age 14.5 years, SD = 1.7) from Thailand that responded to the 2015 GSHS. Overall, 22.2% were current alcohol users, 24.3% had ever been drunk, 12.1% had drunk two or more alcoholic drinks in a day in the past 30 days and 10.8% had gotten into trouble because of drinking alcohol. In adjusted Poisson regression analysis, older age, psychological distress, current tobacco use, the consumption of one or more soft drinks a day, school truancy, having been in a physical fight in the past 12 months, and having been seriously injured in the past 12 months were associated with current alcohol use. Older age, psychological distress, current tobacco use and injury also increased the odds for lifetime drunkenness, having two or more drinks in a day and trouble resulting from drinking. Soft drink consumption and having been in a physical fight also increased the odds for lifetime drunkenness and having two or more drinks in a day and school truancy also increased the odds for lifetime drunkenness and trouble resulting from drinking. In addition. Parental tobacco use was associated with lifetime drunkenness and trouble resulting from drinking, cannabis use with trouble resulting from drinking, and parental support was protective from trouble resulting from drinking. There were no significant sex differences regarding any of the four alcohol use indicators. More than one in five school-going adolescents in Thailand use and misuse alcohol, and strategies to prevent alcohol misuse, including a cluster of risk behaviours, are needed.
Variables Selection for Aboveground Biomass Estimations Using Satellite Data: A Comparison between Relative Importance Approach and Stepwise Akaike’s Information Criterion
Authors: Adame-Campos Rita Libertad , Ghilardi Adrian , Gao Yan , Paneque-Gálvez Jaime , Mas Jean-François
Published: May 2019

It is still a major challenge to select appropriate variables from remote sensing sensors, which implicates finding reliable selection methods that can maximize the performance of chosen variables in regression models. In this study, we compare the performance of stepwise variable selection based on Akaike information criterion and an approach that integrates relative importance techniques and the decomposition criteria of R 2 using two different remote sensing data: SPOT-5 and RapidEye images, with the purpose of selecting suitable variables in multiple linear regression models to estimate aboveground biomass. The obtained accuracy of the regression models was evaluated by triple cross-validation. We carried out this study in a mixed pine–oak forest of central Mexico where intensive wood extraction occurs and therefore different levels of degradation are found. We estimated aboveground biomass from field inventory data at the plot level (n = 52) and used well-established allometric equations. The results showed that a better fit was obtained with the explanatory variables selected from the RapidEye image ( R 2 = 0.437 with stepwise variable selection based on the Akaike information criterion approach and R 2 = 0.420 with relative importance techniques) and the approach that integrates the relative importance can generate better regression models to estimate forest biomass with a reduced number of variables and less error in the estimates.