bluuebunny/crossref_metadata_2025 huggingface dataset:
Curcumin: Biological, Pharmaceutical, Nutraceutical, and Analytical Aspects
Authors: Kotha Raghavendhar R. , Luthria Devanand L.
Published: August 2019

Turmeric is a curry spice that originated from India, which has attracted great interest in recent decades because it contains bioactive curcuminoids (curcumin, demethoxycurcumin, and bisdemethoxycurcumin). Curcumin (1,7-bis-(4-hydroxy-3-methoxyphenyl)-hepta-1,6-diene-3,5-dione), a lipophilic polyphenol may work as an anticancer, antibiotic, anti-inflammatory, and anti-aging agent as suggested by several in vitro, in vivo studies and clinical trials. However, poor aqueous solubility, bioavailability, and pharmacokinetic profiles limit curcumin’s therapeutic usage. To address these issues, several curcumin formulations have been developed. However, suboptimal sample preparation and analysis methodologies often hamper the accurate evaluation of bioactivities and their clinical efficacy. This review summarizes recent research on biological, pharmaceutical, and analytical aspects of the curcumin. Various formulation techniques and corresponding clinical trials and in vivo outcomes are discussed. A detailed comparison of different sample preparation (ultrasonic, pressurized liquid extraction, microwave, reflux) and analytical (FT-IR, FT-NIR, FT-Raman, UV, NMR, HPTLC, HPLC, and LC-MS/MS) methodologies used for the extraction and quantification of curcuminoids in different matrices, is presented. Application of optimal sample preparation, chromatographic separation, and detection methodologies will significantly improve the assessment of different formulations and biological activities of curcuminoids.
Comparison of Physicochemical, Microbiological Properties and Bioactive Compounds Content of Grassland Honey and other Floral Origin Honeys
Authors: Scripcă Laura Agripina , Norocel Liliana , Amariei Sonia
Published: August 2019

The aim of this study was to compare the physicochemical, the microbiological, and the antioxidant characteristics of unifloral honey, polyfloral honey, honeydew, and hay meadows honey. Hay meadow is type of semi-natural grassland with a great floral diversity, an important resource for pollinators. Grasslands are the source of the spring nectar honey obtained in May and June. Water content, sugars (fructose, glucose, sucrose, trehalose, melezitose, maltose, erlose, turanose, and raffinose), electrical conductivity, phenolic content (gallic acid, protocatechuic acid, 4-hydrxybenzoic acid, vanilic acid, chlorogenic acid, caffeic acid, p-coumaric acid, rosmarinic acid, myricetin, quercitin, luteolin, kaempferol), color, viscosity, and microbiological characteristics were performed for all samples of honey. The total polyphenols content was significant for grassland honey (21.50 mg/100 g) and honeydew (30.49 mg/100 g) and less significant for acacia (0.08 mg/100 g) and rape honey (0.14 mg/100 g). All samples were microbiologically safe, and standard plate count (SPC) values were <10 cfu/g for all the samples, but the grassland honey had the highest microbiological quality: 33.3% of samples without microorganisms, 50.0% with the presence of yeast under limit, and 16.7% with yeast and mold under limit, a situation that does not meet other types of honey. The results of statistical analysis obtained with principal component analysis (PCA) showed a major difference between the grassland honey and the other types of honey.
Transfer of a Multiclass Method for over 60 Antibiotics in Food from High Resolution to Low Resolution Mass Spectrometry
Authors: Giusepponi Danilo , Paoletti Fabiola , Barola Carolina , Moretti Simone , Saluti Giorgio , Ianni Federica , Sardella Roccaldo , Galarini Roberta
Published: August 2019

A multiclass method has been developed to screen and confirm a wide range of anti-microbial residues in muscle and milk, and validated using liquid-chromatography coupled to (low-resolution, LR) tandem mass spectrometry (LC-QqQ). Over sixty antibiotics, belonging to ten distinct families, were included in the method scope. The development process was rapidly concluded as a result of two previously implemented methods. This consisted of identical sample treatments, followed by liquid chromatography, and coupled with high-resolution (HR) mass spectrometry (LC-Q-Orbitrap). The validation study was performed in the range between 10–1500 μg·kg−1 for muscles and 2–333 μg·kg−1 for milk. The main performance characteristics were estimated and, then, compared to those previously obtained with HR technique. The validity of the method transfer was ascertained also through inter-laboratory studies.
Neuroprotective Effects of Ginseng Phytochemicals: Recent Perspectives
Authors: Huang Xing , Li Ning , Pu Yiqiong , Zhang Tong , Wang Bing
Published: August 2019

As our global population ages, the treatment of neurodegenerative diseases is critical to our society. In recent years, researchers have begun to study the role of biologically active chemicals from plants and herbs to gain new inspiration and develop new therapeutic drugs. Ginseng (Panax ginseng C.A. Mey.) is a famous Chinese herbal medicine with a variety of pharmacological activities. It has been used to treat various diseases since ancient times. Extensive research over the years has shown that ginseng has potential as a neuroprotective drug, and its neuroprotective effects can be used to treat and prevent neurological damage or pathologically related diseases (such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, depression symptoms, and strokes). Moreover, evidence for the medicinal and health benefits of ginsenoside, its main active ingredient, in the prevention of neurodegenerative diseases is increasing, and current clinical results have not reported any serious adverse reactions to ginseng. Therefore, we briefly review the recent research and development on the beneficial effects and mechanisms of ginseng and its main active ingredient, ginsenoside, in the prevention and treatment of neurodegenerative diseases, hoping to provide some ideas for the discovery and identification of ginseng neuroprotection.
Dietary Patterns and Renal Health Outcomes in the General Population: A Review Focusing on Prospective Studies
Authors: Ajjarapu Aparna S. , Hinkle Stefanie N. , Li Mengying , Francis Ellen C. , Zhang Cuilin
Published: August 2019

Healthy dietary patterns may promote kidney health and prevent adverse renal outcomes. Although reviews have summarized the findings from studies on dietary patterns for chronic kidney disease (CKD) management, less is known about dietary patterns for maintaining kidney health prior to CKD development. The current review summarized the results from observational studies from March 2009 to March 2019 investigating associations between dietary patterns and renal outcomes in the general population. The main renal outcome assessed was CKD (estimated glomerular filtration rate < 60 mL/min/1.73 m2). A total of twenty-six research articles met the inclusion criteria. Adherence to the Dietary Approaches to Stop Hypertension (DASH) and Mediterranean diets were significantly associated with a decreased risk of CKD in the majority of the studies. Furthermore, a posteriori “unhealthy” dietary patterns were associated with an increased risk of CKD. In conclusion, the findings from this review suggest that adherence to DASH and Mediterranean dietary patterns may be useful in promoting kidney health and preventing CKD in the general population. More studies, in particular among minorities, are warranted to investigate the role of diet, a potentially modifiable factor, in promoting kidney health.
Incretin Hormones: The Link between Glycemic Index and Cardiometabolic Diseases
Authors: Salvatore Teresa , Nevola Riccardo , Pafundi Pia Clara , Monaco Lucio , Ricozzi Carmen , Imbriani Simona , Rinaldi Luca , Sasso Ferdinando Carlo
Published: August 2019

This review aimed to describe the potential mechanisms by which incretin hormones could mediate the relationship between glycemic index and cardiometabolic diseases. A body of evidence from many studies suggests that low glycemic index (GI) diets reduces the risk for type 2 diabetes and coronary heart disease. In fact, despite the extensive literature on this topic, the mechanisms underlying unfavorable effects of high GI foods on health remain not well defined. The postprandial and hormonal milieu could play a key role in the relationship between GI and cardiovascular risk. Incretin hormones, glucagon-like peptide1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), are important regulators of postprandial homeostasis by amplifying insulin secretory responses. Response of GIP and GLP-1 to GI have been studied more in depth, also by several studies on isomaltulose, which have been taken as an ideal model to investigate the kinetics of incretin secretion in response to foods’ GI. In addition, extrapancreatic effects of these incretin hormones were also recently observed. Emerging from this have been exciting effects on several targets, such as body weight regulation, lipid metabolism, white adipose tissue, cardiovascular system, kidney, and liver, which may importantly affect the health status.
Greater Efficacy of Black Ginseng (CJ EnerG) over Red Ginseng against Lethal Influenza A Virus Infection
Authors: Kim Eun-Ha , Kim Son-Woo , Park Su-Jin , Kim Semi , Yu Kwang-Min , Kim Seong Gyu , Lee Seung Hun , Seo Yong-Ki , Cho Nam-Hoon , Kang Kimoon , Soung Do Y. , Choi Young-Ki
Published: August 2019

Black ginseng (BG, CJ EnerG), prepared via nine repeated cycles of steaming and drying of fresh ginseng, contains more accessible acid polysaccharides and smaller and less polar ginsenosides than red ginseng (RG) processed only once. Because RG exhibits the ability to increase host protection against viral respiratory infections, we investigated the antiviral effects of BG. Mice were orally administered either BG or RG extract at 10 mg/kg bw daily for two weeks. Mice were then infected with a A(H1N1) pdm09 (A/California/04/2009) virus and fed extracts for an additional week. Untreated, infected mice were assigned to either the negative control, without treatments, or the positive control, treated with Tamiflu. Infected mice were monitored for 14 days to determine the survival rate. Lung tissues were evaluated for virus titer and by histological analyses. Cytokine levels were measured in bronchoalveolar lavage fluid. Mice treated with BG displayed a 100% survival rate against infection, while mice treated with RG had a 50% survival rate. Further, mice treated with BG had fewer accumulated inflammatory cells in bronchioles following viral infection than did mice treated with RG. BG also enhanced the levels of GM-CSF and IL-10 during the early and late stages of infection, respectively, compared to RG. Thus, BG may be useful as an alternative antiviral adjuvant to modulate immune responses to influenza A virus.
Metabolic Endotoxemia: A Potential Underlying Mechanism of the Relationship between Dietary Fat Intake and Risk for Cognitive Impairments in Humans?
Authors: André Perrine , Laugerette Fabienne , Féart Catherine
Published: August 2019

(1) Background: Nutrition is a major lifestyle factor that can prevent the risk of cognitive impairment and dementia. Diet-induced metabolic endotoxemia has been proposed as a major root cause of inflammation and these pathways emerge as detrimental factors of healthy ageing. The aim of this paper was to update research focusing on the relationship between a fat-rich diet and endotoxemia, and to discuss the potential role of endotoxemia in cognitive performances. (2) Methods: We conducted a non-systematic literature review based on the PubMed database related to fat-rich meals, metabolic endotoxemia and cognitive disorders including dementia in humans. A total of 40 articles out of 942 in the first screening met the inclusion criteria. (3) Results: Evidence suggested that a fat-rich diet, depending on its quality, quantity and concomitant healthy food components, could influence metabolic endotoxemia. Since only heterogeneous cross-sectional studies are available, it remains unclear to what extent endotoxemia could be associated or not with cognitive disorders and dementia. (4) Conclusions: A fat-rich diet has the capability to provide significant increases in circulating endotoxins, which highlights nutritional strategies as a promising area for future research on inflammatory-associated diseases. The role of endotoxemia in cognitive disorders and dementia remains unclear and deserves further investigation.
Oligomeric Enteral Nutrition in Undernutrition, due to Oncology Treatment-Related Diarrhea. Systematic Review and Proposal of An Algorithm of Action
Authors: Sanz-Paris Alejandro , Martinez-García María , Martinez-Trufero Javier , Lambea-Sorrosal Julio , Calvo-Gracia Fernando , López-Alaminos María Elena
Published: August 2019

Oncology treatment-related diarrhea and malnutrition appear together in oncological patients because of the disease itself, or the treatments that are administered for it. Therefore it is essential to carry out a nutritional treatment. Enteral nutrition formulas, containing peptides and medium chain triglycerides, can facilitate absorption in cases of malabsorption. There are few references to the use of enteral nutrition in the clinical society guidelines of patient management with oncology treatment-related diarrhea (OTRD). A bibliographic review of the studies with oligomeric enteral nutrition in OTRD found only nine studies with chemotherapy (all with the same oligomeric formula in which oral mucositis improves, while the rest of the outcomes show different results), and eight studies with radiotherapy (with different products and very heterogeneous results). We hereby present our action algorithm to supplement the diet of OTRD patients with an oligomeric enteral nutrition formula. The first step is the nutritional assessment, followed by the assessment of the functional capacity of the patient’s intestine. With these two aspects evaluated, the therapeutic possibilities available vary in degrees of complexity: These will range from the usual dietary recommendations, to supplementation with oral oligomeric enteral nutrition, along with complete enteral nutrition with oligomeric formula, and up to potentially total parenteral nutrition.
RETRACTED: Retinal Pigment Epithelial Cell Line with Fast Differentiation and Improved Barrier Properties
Authors: Hellinen Laura , Pirskanen Lea , Tengvall-Unadike Unni , Urtti Arto , Reinisalo Mika
Published: August 2019

Retinal pigment epithelium (RPE) acts as an outer blood–retinal barrier that limits the access of circulating xenobiotics to the eye. In addition, the RPE limits posterior elimination of intravitreally injected drugs to circulation. Thus, permeation in the RPE has a significant effect on ocular pharmacokinetics. The RPE is also a potentially important drug target in age-related macular degeneration. Therefore, the cell models of the RPE are important tools in ocular drug development, but poor availability and problems in reproducibility limit the use of primary RPE cell cultures. Furthermore, the best and widely used human cell line ARPE19 requires specialized culture conditions and a long time for cellular differentiation. In this paper, we describe a cell population arisen from the ARPE19 culture, with fast differentiation and improved barrier properties. This cell line, LEPI, forms clear microvilli and rapidly displays RPE-like cobblestone morphology after subculture in simple culture conditions. The LEPI cells show RPE-specific functions and expression of RPE65, ezrin, and BEST1 proteins. On filter, the LEPI cells develop tighter barrier than the ex vivo bovine RPE-choroid: permeability coefficients of beta-blockers (atenolol, nadolol, timolol, pindolol, metoprolol, betaxolol) ranged from 0.4 × 10−6 cm/sec to 2.3 × 10−6 cm/sec depending on the drug lipophilicity. This rapidly differentiating cell line will be an asset in ocular studies since it is easily maintained, it grows and differentiates quickly and does not require specialized culture conditions for differentiation. Thus, this cell line is suitable for both small scale assays and high throughput screening in drug discovery and development.
Preparation and Comparison of Properties of Three Phase Change Energy Storage Materials with Hollow Fiber Membrane as the Supporting Carrier
Authors: Xiang Li , Luo Dajun , Yang Jingkui , Sun Xin , Qi Yating , Qin Shuhao
Published: August 2019

Herein, we have used a hollow fiber membrane as a support layer material to encapsulate paraffin in order to prepare a phase change energy storage material. The phase change energy storage materials with three different support layers were successfully prepared and various properties were systematically characterized. There are also few reports on the use of hollow fiber membranes as the support carrier for the preparation of phase change energy storage materials. The significance of this work is in exploring the use of hollow fiber membranes as a support layer to prepare phase change energy storage materials. In addition, the choice of support carriers for phase change energy storage materials was enriched. Both the hollow fiber membrane columnar hollow portion and the pore structure of the membrane wall could be used to encapsulate paraffin, which makes it more advantageous than the other support materials. The SEM characterization confirmed that paraffin was successfully encapsulated on the membrane wall and columnar hollow part of the membranes. The TGA results indicate that the introduction of the membrane as an encapsulation carrier delayed the decomposition of the composite phase change energy storage materials. The activation energy during the decomposition in the three different phase change energy storage materials was calculated by the decomposition kinetics. Among the three fiber membranes explored in this work, the polypropylene (PP) encapsulation membrane performed better than the other two encapsulation membranes in terms of encapsulation content of paraffin, porosity, latent heats, thermal stability, and activation energy.
Thermal Conductivity Enhancement Derived from Poly(Methyl Methacrylate)-Grafted Carbon Nanotubes in Poly(Methyl Methacrylate)/Polystyrene Blends
Authors: Wie Jaehyun , Kim Jooheon
Published: August 2019

This paper presents a method to enhance thermal conductivity using poly(methyl methacrylate)(PMMA), polystyrene(PS) blends, and incorporation of multiwalled carbon nanotubes (MWCNTs). MWCNTs are selectively localized in PMMA phase to improve conductive properties. In addition, Surface of MWCNTs was treated with PMMA to enhance affinity between matrix and filler. PMMA grafting helps filler localization on matrix phase. Composites using two polymers enhanced thermal conductivity by ~11% compared with composites using only PS or PMMA. Also, PMMA grafting on the surface of MWCNTs enhanced thermal conductivity by ~13% compared with samples without PMMA grafting.
In-mold and Machine Sensing and Feature Extraction for Optimized IC-tray Manufacturing
Authors: Nian Shih-Chih , Fang Yung-Chih , Huang Ming-Shyan
Published: August 2019

Injection molding is a mature technology that has been used for decades; factors including processed raw materials, molds and machines, and the processing parameters can cause significant changes in product quality. Traditionally, researchers have attempted to improve injection molding quality by controlling screw position, injection and packing pressures, and mold and barrel temperatures. However, even when high precision control is applied, the geometry of the molded part tends to vary between different shots. Therefore, further research is needed to properly understand the factors affecting the melt in each cycle so that more effective control strategies can be implemented. In the past, injection molding was a “black box”, so when based on statistical experimental methods, computer-aided simulations or operator experience, the setting of ideal process parameters was often time consuming and limited. Using advanced sensing technology, the understanding of the injection molding process is transformed into a “grey box” that reveals the physical information about the flow behavior of the molten resin in the cavity. Using the process parameter setting data provided by the machine, this study developed a scientific method for optimal parameter adjustment, analyzing and interpreting the injection speed, injection pressure, cavity pressure, and the profile of the injection screw position. In addition, the main parameters for each phase are determined separately, including injection speed/pressure during the mold filling phase, velocity-to-pressure switching point, packing pressure and time. In this study, the IC tray was taken as an example. The experimental results show that the method can effectively reduce the warpage of the IC-tray from 0.67 mm to 0.20 mm. In addition, the parameters profiles obtained by parameter optimization can be applied for continuous mass production and process monitoring.
ADDTID: An Alternative Tool for Studying Earthquake/Tsunami Signatures in the Ionosphere. Case of the 2011 Tohoku Earthquake
Authors: Yang Heng , Monte Moreno Enrique , Hernández-Pajares Manuel
Published: August 2019

In this work, we characterized the ionospheric disturbances generated during the Japan Tohoku earthquake of 11 March 2011, by means of the Atomic Decomposition Detector of Traveling Ionospheric Disturbances (ADDTID) algorithm. This algorithm automatically detects and characterizes Traveling Ionospheric Disturbances (TIDs) from Global Navigation Satellite System (GNSS) measurements. Applying the high-precision estimates of ADDTID, the propagation parameters would make it easier to distinguish TIDs from different origins, in particular the characteristics conforming the acoustic gravity waves driven by the earthquake/tsunami. This method does not assume that disturbances follow a circular pattern of propagation, and can estimate the location by the propagation pattern of tsunami wavefronts and related TIDs. In this work, we present in a single framework a description of phenomena observed by different researchers. By means of the ADDTID algorithm, we detect: (a) simultaneous TIDs of different characteristics, where the detection was robust against the curvature of the wave fronts of the perturbations and the accuracy of the estimated parameters. The results were double-checked by visual inspection from detrended Vertical Total Electron Content (VTEC) maps and keogram plots, and the parameters of the slow-speed TIDs were consistent with the tsunami waveform measurements; (b) different wavefronts between the west and east TIDs around the epicenter, consistent in time and space with the post-earthquake tsunami; (c) complete evolution of the circular TIDs driven by the tsunami during the GNSS observable area; (d) fast and short circular TIDs related to the acoustic waves of earthquake; (e) the pre-seismic activity consisting of a set of fast westward TIDs, and the comparison with neighboring days; (f) the location estimation of the tsunami wavefront along the coast and the possible use as early warning. Finally, we report disturbances that had not been previously published with a possible application to local and real-time detection of tsunamis.
Integrated On-Chip Transformers: Recent Progress in the Design, Layout, Modeling and Fabrication
Authors: Bajwa Rayan , Yapici Murat Kaya
Published: August 2019

On-chip transformers are considered to be the primary components in many RF wireless applications. This paper provides an in-depth review of on-chip transformers, starting with a presentation on the various equivalent circuit models to represent transformer behavior and characterize their performance. Next, a comparative study on the different design and layout strategies is provided, and the fabrication techniques for on-chip implementation of transformers are discussed. The critical performance parameters to characterize on-chip transformers, such as the Q-factor, coupling factor (k), resonance frequency (fSR), and others, are discussed with reference to trade-offs in silicon chip real-estate. The performance parameters and area requirements for different types of on-chip transformers are summarized in tabular form and compared. Several techniques for performance enhancement of on-chip transformers, including the different types of micromachining and integration approaches stemming from MEMS (microelectromechanical systems) technologies are also analyzed. Lastly, the different uses and applications of on-chip transformers are discussed to highlight the evolution of on-chip transformer technology over the recent years and provide directions for future work in this field.
A Robust DOA Estimator Based on Compressive Sensing for Coprime Array in the Presence of Miscalibrated Sensors
Authors: Kou Jiaxun , Li Ming , Jiang Chunlan
Published: August 2019

Coprime array with M + N sensors can achieve an increased degrees-of-freedom (DOF) of O ( M N ) for direction-of-arrival (DOA) estimation. Utilizing the compressive sensing (CS)-based DOA estimation methods, the increased DOF offered by the coprime array can be fully exploited. However, when some sensors in the array are miscalibrated, these DOA estimation methods suffer from degraded performance or even failed operation. Besides, the key to the success of CS-based DOA estimation is that every target falls on the predefined grid. Thus, a coarse grid may cause the mismatch problem, whereas a fine grid requires great computational cost. In this paper, a robust CS-based DOA estimation algorithm is proposed for coprime array with miscalibrated sensors. In the proposed algorithm, signals received by the miscalibrated sensors are viewed as outliers, and correntropy is introduced as the similarity measurement to distinguish these outliers. Incorporated with maximum correntropy criterion (MCC), an iterative sparse reconstruction-based algorithm is then developed to give the DOA estimation while mitigating the influence of the outliers. A multiresolution grid refinement strategy is also incorporated to reconcile the contradiction between computational cost and the mismatch problem. The numerical simulation results verify the effectiveness and robustness of the proposed method.
Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System
Authors: Xie Hao , Zhang Yujun , He Ying , You Kun , Fan Boqiang , Yu Dongqi , Li Mengqi
Published: August 2019

Optical remote sensing systems (RSSs) for monitoring vehicle emissions can be installed on any road and provide non-contact on-road measurements, that allow law enforcement departments to monitor emissions of a large number of on-road vehicles. Although many studies in different research fields have been performed using RSSs, there has been little research on the automatic recognition of on-road high-emitting vehicles. In general, high-emitting vehicles and low-emitting vehicles are classified by fixed emission concentration cut-points, that lack a strict scientific basis, and the actual cut-points are sensitive to environmental factors, such as wind speed and direction, outdoor temperature, relative humidity, atmospheric pressure, and so on. Besides this issue, single instantaneous monitoring results from RSSs are easily affected by systematic and random errors, leading to unreliable results. This paper proposes a method to solve the above problems. The automatic and fast-recognition method for on-road high-emitting vehicles (AFR-OHV) is the first application of machine learning, combined with big data analysis for remote sensing monitoring of on-road high-emitting vehicles. The method constructs adaptively updates a clustering database using real-time collections of emission datasets from an RSS. Then, new vehicles, that pass through the RSS, are recognized rapidly by the nearest neighbor classifier, which is guided by a real-time updated clustering database. Experimental results, based on real data, including the Davies-Bouldin Index (DBI) and Dunn Validity Index (DVI), show that AFR-OHV provides faster convergence speed and better performance. Furthermore, it is not easily disturbed by outliers. Our classifier obtains high scores for Precision (PRE), Recall (REC), the Receiver Operator Characteristic (ROC), and the Area Under the Curve (AUC). The rates of different classifications of excessive emissions and self-adaptive cut-points are calculated automatically in order to provide references for law enforcement departments to establish evaluation criterion for on-road high-emitting vehicles, detected by the RSS.
A Real-Time Channel Prediction Model Based on Neural Networks for Dedicated Short-Range Communications
Authors: Zhang Tianhong , Liu Sheng , Xiang Weidong , Xu Limei , Qin Kaiyu , Yan Xiao
Published: August 2019

Based on a multiple layer perceptron neural networks, this paper presents a real-time channel prediction model, which could predict channel parameters such as path loss (PL) and packet drop (PD), for dedicated short-range communications (DSRC). The dataset used for training, validating, and testing was extracted from experiments under several different road scenarios including highways, local areas, residential areas, state parks, and rural areas. The study shows that the proposed PL prediction model outperforms conventional empirical models. Meanwhile, the proposed PD prediction model achieves higher prediction accuracy than the statistical one. Moreover, the prediction model can operate in real-time, through updating its training set, to predict channel parameters. Such a model can be easily extended to the applications of autonomous driving, the Internet of Things (IoT), 5th generation cellular network technology (5G) and many others.
Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations
Authors: Lygouras Eleftherios , Santavas Nicholas , Taitzoglou Anastasios , Tarchanidis Konstantinos , Mitropoulos Athanasios , Gasteratos Antonios
Published: August 2019

Unmanned aerial vehicles (UAVs) play a primary role in a plethora of technical and scientific fields owing to their wide range of applications. In particular, the provision of emergency services during the occurrence of a crisis event is a vital application domain where such aerial robots can contribute, sending out valuable assistance to both distressed humans and rescue teams. Bearing in mind that time constraints constitute a crucial parameter in search and rescue (SAR) missions, the punctual and precise detection of humans in peril is of paramount importance. The paper in hand deals with real-time human detection onboard a fully autonomous rescue UAV. Using deep learning techniques, the implemented embedded system was capable of detecting open water swimmers. This allowed the UAV to provide assistance accurately in a fully unsupervised manner, thus enhancing first responder operational capabilities. The novelty of the proposed system is the combination of global navigation satellite system (GNSS) techniques and computer vision algorithms for both precise human detection and rescue apparatus release. Details about hardware configuration as well as the system’s performance evaluation are fully discussed.
An Integrated In Silico and In Vitro Assays of Dipeptidyl Peptidase-4 and α-Glucosidase Inhibition by Stellasterol from Ganoderma australe
Authors: Budipramana Krisyanti , Junaidin Junaidin , Ruslan Wirasutisna Komar , Budi Pramana Yanatra , Sukrasno Sukrasno
Published: August 2019

Background: Ganoderma fungus is rich in terpenoids. These compounds are known for their anti-hyperglycemic activities. However, the study of terpenoids as the secondary metabolite from Ganoderma as a dipeptidyl peptidase-4 (DPP-4) inhibitor remains unexplored. In addition, we examined the α-glucosidase inhibition activity. Objective: This study aimed to isolate the major terpenoid from non-laccate Ganoderma and examined its inhibitor activity on DPP-4 and α-glucosidase enzymes, and its interaction. Methods: The compound was isolated using column chromatography from Ganoderma australe. The structure of the isolated compound was confirmed by 1H and 13C nuclear magnetic resonance spectroscopy, while the inhibitory activity was evaluated using an enzymatic assay. The interaction of the isolated compound with DPP-4 and α-glucosidase enzymes was investigated using an in silico study. Results: The isolated compound was identified as stellasterol; IC50 values for DPP-4 and α-glucosidase inhibitor were 427.39 µM and 314.54 µM, respectively. This study revealed that the inhibitory effect of stellasterol on DPP-4 enzyme is through hydrophobic interaction, while the α-glucosidase enzyme is due to the interaction with six amino acids of the enzyme. Conclusion: Stellasterol is the major component of the steroid from G. australe. Enzyme inhibitory assay and in silico study suggest that stellasterol may contribute antidiabetic activity with a mechanism closer to acarbose rather than to sitagliptin.
A Customized Decision Support System for Renewable Energy Application by Housing Association
Authors: Besser Aleksandra , Kazak Jan K. , Świąder Małgorzata , Szewrański Szymon
Published: August 2019

One of the major problems in socio-environmental systems is the growing depletion of non-renewable resources and environmental degradation, resulting from inadequate environmental management and planning. Deepening environmental problems have forced countries to create management instruments that will help repair damage and support environmental protection efforts. The aim of this research is to develop a customized decision support system for the management of renewable energy based on the existing Geographic Information Systems (GIS). The proposed tool enables assessing the potential of solar energy production at the local scale, analyzing each rooftop. Due to the scale of the analyzed area and the details of the assessment, the tool is customized to the needs of housing associations. The system combines an existing GIS tool for calculating the solar radiation potential of rooftops (SOLIS) together with Tableau software that was used to aggregate and analyze data. In order to present the applicability of the developed tool, visualizations were prepared based on housing buildings managed by the “Biskupin” Housing Association in Wrocław (Poland) which is responsible for the management of 3415 residential premises. The created system based on spatial and environmental data will help to decide how to manage the available resources and the environment at the local scale while reducing the pressure on the environment. The tool allows for the aggregation, filtering and presentation of spatial data for the entire area of a housing association, as well as for a single building.
Investment Sustained by Consumption: A Linear and Nonlinear Time Series Analysis
Authors: Perez-Montiel Jose , Manera Erbina Carles
Published: August 2019

This paper studies the dynamic relationship between consumption and investment in the United States between 1947 and 2018. Our findings support the postulates of Keynesian economics—while they are contrary to the theoretic background on which the numerous empirical studies on the saving-investment nexus are based. We find a long-run nexus between consumption and investment, and positive linear Granger-causality running unidirectionally from consumption to investment. Therefore, investment is sustained by consumption. Further, we find that the variables have nonlinear structures and, thus, we apply nonlinear causality tests. We provide evidence of nonlinear causality running unidirectionally from consumption to investment. Nevertheless, after controlling for Government Expenditure, this nonlinear causal relationship disappears, indicating that Government Expenditure drives the nonlinear causal relationship between private consumption and investment. We argue that this finding is consistent with the notion that investment decisions are guided by permanent aggregate demand, because public expenditure allows private consumption to have a sufficiently permanent trajectory to be considered as a guide for investment decisions. Our results do not support the austerity and deflation measures implemented in the last years (especially in the European Union). On the other hand, our findings call for the incentive of final public expenditure, since it favours the long-run link between the private decisions to consume and invest.
Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms
Authors: Thai Pham Binh , Shirzadi Ataollah , Shahabi Himan , Omidvar Ebrahim , Singh Sushant K. , Sahana Mehebub , Talebpour Asl Dawood , Bin Ahmad Baharin , Kim Quoc Nguyen , Lee Saro
Published: August 2019

Landslides have multidimensional effects on the socioeconomic as well as environmental conditions of the impacted areas. The aim of this study is the spatial prediction of landslide using hybrid machine learning models including bagging (BA), random subspace (RS) and rotation forest (RF) with alternating decision tree (ADTree) as base classifier in the northern part of the Pithoragarh district, Uttarakhand, Himalaya, India. To construct the database, ten conditioning factors and a total of 103 landslide locations with a ratio of 70/30 were used. The significant factors were determined by chi-square attribute evaluation (CSEA) technique. The validity of the hybrid models was assessed by true positive rate (TP Rate), false positive rate (FP Rate), recall (sensitivity), precision, F-measure and area under the receiver operatic characteristic curve (AUC). Results concluded that land cover was the most important factor while curvature had no effect on landslide occurrence in the study area and it was removed from the modelling process. Additionally, results indicated that although all ensemble models enhanced the power prediction of the ADTree classifier (AUCtraining = 0.859; AUCvalidation = 0.813); however, the RS ensemble model (AUCtraining = 0.883; AUCvalidation = 0.842) outperformed and outclassed the RF (AUCtraining = 0.871; AUCvalidation = 0.840), and the BA (AUCtraining = 0.865; AUCvalidation = 0.836) ensemble model. The obtained results would be helpful for recognizing the landslide prone areas in future to better manage and decrease the damage and negative impacts on the environment.
Optimizing Urban Distribution Routes for Perishable Foods Considering Carbon Emission Reduction
Authors: Lin Diansheng , Zhang Zhiyong , Wang Jiaxin , Yang Liu , Shi Yongqiang , Soar Jeffrey
Published: August 2019

The increasing demand for urban distribution increases the number of transportation vehicles which intensifies the congestion of urban traffic and leads to a lot of carbon emissions. This paper focuses on carbon emission reduction in urban distribution, taking perishable foods as the object. It carries out optimization analysis of urban distribution routes to explore the impact of low carbon policy on urban distribution routes planning. On the basis of analysis of the cost components and corresponding constraints of urban distribution, two optimization models of urban distribution routes with and without carbon emissions cost are constructed. Fuel quantity related to cost and carbon emissions in the model is calculated based on traffic speed, vehicle fuel quantity and passable time period of distribution. Then an improved algorithm which combines genetic algorithm and tabu search algorithm is designed to solve models. Moreover, an analysis of the influence of carbon tax price is also carried out. It is concluded that in the process of urban distribution based on the actual network information, path optimization considering the low carbon factor can effectively reduce the distribution process of CO2, and reduce the total cost of the enterprise and society, thus achieving greater social benefits at a lower cost. In addition, the government can encourage low-carbon distribution by rationally adjusting the price of carbon tax to achieve a higher social benefit.
Bibliometric Review of Research on Knowledge Management and Sustainability, 1994–2018
Authors: Sanguankaew Pattarin , Vathanophas Ractham Vichita
Published: August 2019

Even though Knowledge Management (KM) is already widely used in business and public sector organisations, it also has potentially important implications when applied to the concept of sustainability. This research review aimed to examine the topography of research on KM and Sustainability. A total of 3025 articles from 1994 to 2018 were selected and analysed using bibliometric analysis to identify the growth trajectory of this literature, identify influential researchers and documents, explore the intellectual structure of the knowledge base and identify topical trends. The review found a knowledge of moderate but rapidly growing size. Key authors and documents were identified who can serve as guiding references for scholars entering this field of sustainability studies. Author co-citation analysis yielded a network map visualising the intellectual structure of this knowledge base which consisted of four Schools of Thought: Knowledge Management for Sustainability, Socio-Ecological System, Sustainability Science, KM for Sustainability Application. Keyword analysis highlighted climate change, learning, communities of practice and socio-economic management as topical trends emerging in the research front of this knowledge base. As the first bibliometric review of the KM and Sustainability literature, the findings from this paper establish a baseline for scholarship in this field which can be as a benchmark as the field continues to evolve in the future.
The Relationship between Birth Order, Sex, Home Scholarly Culture and Youths’ Reading Practices in Promoting Lifelong Learning for Sustainable Development in Vietnam
Authors: Tran Trung , Le Thi-Thu-Hien , Nguyen Thu-Trang , Pham Anh-Giang , Vu Thi-Hanh , Nguyen Minh-Hoang , Vuong Ha-My , Vuong Thu-Trang , Hoang Phuong-Hanh , Ho Manh-Toan , Vuong Quan-Hoang
Published: August 2019

Book reading is an important factor contributing to children’s cognitive development and education for sustainable development. However, in a developing country like Vietnam, statistics have reported a low figure in book reading: only 1.2 books a year. This research study used a dataset of 1676 observations of junior high school students from Northern Vietnam to explore students’ reading behavior and its association with demographic factors, and the family’s reading culture. Data analysis suggests the older the student gets, the less inclined they are to read, and being female and having hobbies of low sensory stimulation are linked to higher preference for reading. Regarding scholarly culture at home, students who read more varied types of books and spend more time on books are correlated with higher reading interest. Reading habits are also positively reinforced by the capacity to access books and parental book reading.
Development of a Cost Forecasting Model for Air Cargo Service Delay Due to Low Visibility
Authors: Kim Hee Kyung , Lee Chang Won
Published: August 2019

The air cargo market is growing due to the spread of information technology (IT) products, the expansion of e-commerce, and high value-added products. Weather deterioration is one factor with a substantial impact on cargo transportation. If the arrival of cargo is delayed, supply chain delays occur. Because delays are directly linked to costs, companies need precise predictions of cargo transportation. This study develops a forecasting model to predict delay times and costs caused by the delayed arrival of cargo due to severe weather in the air cargo service environment. A seasonal autoregressive integrated moving average (SARIMA) model is developed and analyzed to address delay reductions in cargo transportation. Necessary data are identified and collected using time series data provided by Incheon International Airport Corporation, with an emphasis on monthly data on cargo throughput at Incheon International Airport from January 2009 to December 2016. The model makes forecasts for further analysis. The model stands to provide decision makers with strategic and sustainable insights for cargo transportation planning and other similar applications.
Fertilizer Use in China: The Role of Agricultural Support Policies
Authors: Wu Yinhao , Wang Enru , Miao Changhong
Published: August 2019

Using a decomposition method, this paper proposes an analytical framework to investigate the mechanisms by which agricultural support policies affect farmers’ use of fertilizers in agriculture in China. The mechanisms are decomposed into “three effects” (structural, scale, and technological effects). It is found that China’s agricultural support polices have significantly contributed to the increased use of agricultural fertilizers through encouraging farmers to bring more land under cultivation (the scale effect). Meanwhile, some policies have also helped reduce fertilizer consumption when farmers were motivated to increase the area of grains crops (the structural effect). The role of technological progress in affecting fertilizer consumption (the technological effect) appears to be minimal and uncertain. Compared to direct subsidies, indirect subsidies play a much greater role in affecting farmers’ production decision making and are more environmentally consequential. This paper argues that some of China’s agricultural support policies are not well aligned with one key objective of the country’s rural policies—improving environmental sustainability. It is recommended that the government takes measures to reform agricultural support policies and to reconcile agricultural and rural policies in order to achieve sustainable rural development.
Corporate Social Responsibility and Sustainable Development in China: Current Status and Future Perspectives
Authors: Zhang Dongyong , Morse Stephen , Ma Qiaoyun
Published: August 2019

With a long history, large population, rapid economic growth, and major social transformation in recent years and the launch of the Belt and Road Initiative, China has increasingly become an important global player. However, the negative social and environmental consequences of such a fast and extensive economic expansion are becoming significant. A series of measures have been taken to tackle the current problems faced by the country, including the issuing of new laws and regulations, and the most recent is China’s ban on plastic waste imports. However, there is a significant gap between Chinese laws and their implementation. Therefore, more people are putting their hope in a combination of legislation and Corporate Social Responsibility (CSR) to help address the current social and environmental problems faced by the country. This paper discusses the drivers of CSR in China and compares them to the drivers of CSR in the West. The paper also explores the extent to which CSR can make a contribution to solving the sustainable development challenges faced by China and discusses possible solutions if the current CSR pattern fails. Finally, the paper makes suggestions for future research on CSR in China.