bluuebunny/crossref_metadata_2025 huggingface dataset:
Beverage Intake and Drinking Patterns—Clues to Support Older People Living in Long-Term Care to Drink Well: DRIE and FISE Studies
Authors: Jimoh Oluseyi F. , Brown Tracey J. , Bunn Diane , Hooper Lee
Published: February 2019

Low-intake dehydration, due to insufficient beverage intake, is common in older people and associated with increased mortality and morbidity. We aimed to document the drinking patterns of older adults living in long-term care and compared patterns in those drinking well with those not drinking enough. One-hundred-and-eighty-eight people aged ≥ 65 years living in 56 UK long-term care homes were interviewed and hydration status was assessed in the Dehydration Recognition In our Elders (DRIE) study. In 22 DRIE residents, the Fluid Intake Study in our Elders (FISE) directly observed, weighed and recorded all drinks intake over 24 h. Twenty percent of DRIE participants and 18% of FISE participants had low-intake dehydration (serum osmolality > 300 mOsm/kg). Mean total drinks intake was 1787 mL/day (SD 693) in FISE participants (2033 ± 842 mL/day in men; 1748 ± 684 mL/day in women). Most drinks intake was between meals (59%, including 10% with medications). Twelve (55%) FISE participants achieved European Food Safety Authority drinks goals (3/6 men drank ≥ 2.0 L/day, 9/16 women drank ≥ 1.6 L/day). Those drinking well were offered beverages more frequently and drank more with medications and before breakfast (beverage variety did not differ). Promising strategies to support healthy drinking include offering drinks more frequently, particularly before and during breakfast and with medication.
Iodine Biofortification of Four Brassica Genotypes is Effective Already at Low Rates of Potassium Iodate
Authors: Gonnella Maria , Renna Massimiliano , D’Imperio Massimiliano , Santamaria Pietro , Serio Francesco
Published: February 2019

The use of iodine-biofortified vegetables may be a health alternative instead of iodine-biofortified salt for preventing iodine (I) deficiency and related human disorders. In this study, four Brassica genotypes (broccoli raab, curly kale, mizuna, red mustard) were hydroponically grown with three I-IO3− rates (0, 0.75 and 1.5 mg/L) to produce iodine-biofortified vegetables. Crop performances and quality traits were analyzed; iodine content was measured on raw, boiled, and steamed vegetables. The highest I rate generally increased I content in all Brassica genotypes, without plants toxicity effects in terms of reduced growth or morphological symptoms. After 21 day-iodine biofortification, the highest I content (49.5 µg/100 g Fresh Weight (FW)) was reached in broccoli raab shoots, while after 43 day-iodine biofortification, genotype differences were flattened and the highest I content (66 µg/100 g FW, on average) was obtained using 1.5 mg I-IO3/L. Nitrate content (ranging from 1800 to 4575 mg/kg FW) was generally higher with 0.75 mg I-IO3/L, although it depended on genotypes. Generally, boiling reduced iodine content, while steaming increased or left it unchanged, depending on genotypes. Applying low levels of I proved to be suitable, since it could contribute to the partial intake of the recommended dose of 150 µg/day: A serving size of 100 g may supply on average 24% of the recommended dose. Cooking method should be chosen in order to preserve and/or enhance the final I amount.
Sugar-Sweetened Beverage Consumption and Associated Factors in School-Going Adolescents of New Caledonia
Authors: Wattelez Guillaume , Frayon Stéphane , Cavaloc Yolande , Cherrier Sophie , Lerrant Yannick , Galy Olivier
Published: February 2019

This cross-sectional study assessed sugar-sweetened beverage (SSB) consumption and its associations with the sociodemographic and physical characteristics, behavior and knowledge of New Caledonian adolescents. The survey data of 447 adolescents from ages 11 to 16 years were collected in five secondary public schools of New Caledonia between July 2015 and April 2016. These data included measured height and weight, SSB consumption, sociodemographic characteristics, body weight perception, physical activity, and knowledge (sugar quantity/SSB unit; energy expenditure required to eliminate a unit) and opinions about the SSB‒weight gain relationship. Ninety percent of these adolescents declared regularly drinking SSBs. Quantities were associated with living environment (1.94 L·week−1 in urban environment vs. 4.49 L·week−1 in rural environment, p = 0.001), ethnic community (4.77 L·week−1 in Melanesians vs. 2.46 L·week−1 in Caucasians, p < 0.001) and knowledge about energy expenditure (6.22 L·week−1 in unknowledgeable adolescents vs. 4.26 L·week−1 in adolescents who underestimated, 3.73 L·week−1 in adolescents who overestimated, and 3.64 L·week−1 in adolescents who correctly responded on the energy expenditure required to eliminate an SSB unit, p = 0.033). To conclude, community-based health promotion strategies should (1) focus on the physical effort needed to negate SSB consumption rather than the nutritional energy from SSB units and (2) highlight how to achieve sustainable lifestyles and provide tools for greater understanding and positive action.
Magnesium: A Magic Bullet for Cardiovascular Disease in Chronic Kidney Disease?
Authors: Leenders Nicoline H. J. , Vervloet Marc G.
Published: February 2019

Magnesium is essential for many physiological functions in the human body. Its homeostasis involves dietary intake, absorption, uptake and release from bone, swifts between the intra- and extracellular compartment, and renal excretion. Renal excretion is mainly responsible for regulation of magnesium balance. In chronic kidney disease (CKD), for a long time the general policy has been limiting magnesium intake. However, this may not be appropriate for many patients. The reference ranges for magnesium are not necessarily optimal concentrations, and risks for insufficient magnesium intake exist in patients with CKD. In recent years, many observational studies have shown that higher (in the high range of “normal” or slightly above) magnesium concentrations are associated with better survival in CKD cohorts. This review gives an overview of epidemiological associations between magnesium and overall and cardiovascular survival in patients with CKD. In addition, potential mechanisms explaining the protective role of magnesium in clinical cardiovascular outcomes are described by reviewing evidence from in vitro studies, animal studies, and human intervention studies with non-clinical endpoints. This includes the role of magnesium in cardiac arrhythmia, heart failure, arterial calcification, and endothelial dysfunction. Possible future implications will be addressed, which will need prospective clinical trials with relevant clinical endpoints before these can be adopted in clinical practice.
Nutrition and Chronic Conditions
Authors: Ojo Omorogieva
Published: February 2019

This editorial discusses and analyses the role of dietary interventions in the management of chronic conditions in recognition of the global increase of these diseases, the rise in the ageing population, and the significant cost to health services around the world. Evidence has shown that low-glycaemic index (GI) and low-carbohydrate diets are effective in the management of type 2 diabetes, and the role of unsaturated fatty acids, vitamins, and bioactive compounds in chronic disease management have been the subject of intense research. However, although multidimensional approaches are important in the management of these chronic conditions, nutritional interventions are critical and central to these strategies.
Changes in Micronutrient Intake and Status, Diet Quality and Glucose Tolerance from Preconception to the Second Trimester of Pregnancy
Authors: Looman Moniek , Geelen Anouk , Samlal Rahul A. K. , Heijligenberg Rik , Klein Gunnewiek Jacqueline M. T. , Balvers Michiel G. J. , Wijnberger Lia D. E. , Brouwer-Brolsma Elske M. , Feskens Edith J. M.
Published: February 2019

Data on changes in dietary intake and related blood parameters throughout pregnancy are scarce; moreover, few studies have examined their association with glucose homeostasis. Therefore, we monitored intake of folate, vitamin B6, vitamin B12, vitamin D and iron, their status markers, and diet quality from preconception to the second trimester of pregnancy, and we examined whether these dietary factors were associated with glucose homeostasis during pregnancy. We included 105 women aged 18–40 years with a desire to get pregnancy or who were already <24 weeks pregnant. Women at increased gestational diabetes (GDM) risk were oversampled. Measurements were scheduled at preconception (n = 67), and 12 (n =53) and 24 weeks of pregnancy (n =66), including a fasting venipuncture, 75-grams oral glucose tolerance test, and completion of a validated food frequency questionnaire. Changes in micronutrient intake and status, and associations between dietary factors and glucose homeostasis, were examined using adjusted repeated measures mixed models. Micronutrient intake of folate, vitamin B6 and vitamin D and related status markers significantly changed throughout pregnancy, which was predominantly due to changes in the intake of supplements. Micronutrient intake or status levels were not associated with glucose homeostasis, except for iron intake (FE µg/day) with fasting glucose (β = −0.069 mmol/L, p = 0.013) and HbA1c (β = −0.4843 mmol, p = 0.002). Diet quality was inversely associated with fasting glucose (β = −0.006 mmol/L for each DHD15-index point, p = 0.017). It was shown that micronutrient intakes and their status markers significantly changed during pregnancy. Only iron intake and diet quality were inversely associated with glucose homeostasis.
Interesterified Fats Induce Deleterious Effects on Adipose Tissue and Liver in LDLr-KO Mice
Authors: Lavrador Maria Silvia Ferrari , Afonso Milessa Silva , Cintra Dennys Esper , Koike Marcia , Nunes Valeria Sutti , Demasi Marina , Lin Chin Jia , Beda Lis Mie Masuzawa , Gioielli Luiz Antonio , Bombo Renata de Paula Assis , Machado Roberta Marcondes , Catanozi Sergio , Nakandakare Edna Regina , Lottenberg Ana Maria
Published: February 2019

Interesterified fats are being widely used by the food industry in an attempt to replace trans fatty acids. The effect of interesterified fats containing palmitic or stearic acids on lipid metabolism and inflammatory signaling pathways in adipose and hepatic tissues was evaluated. Male LDLr-KO mice were fed a high-fat diet containing polyunsaturated (PUFA), palmitic (PALM), palmitic interesterified (PALM INTER), stearic (STEAR), or stearic interesterified (STEAR INTER) fats for 16 weeks. The expression of genes and protein levels involved in lipid metabolism and inflammatory processes in liver and white adipose tissue was determined by quantitative RT-PCR and by Western blot, respectively. The infiltration of inflammatory cells in hepatic and adipose tissues was determined by eosin and hematoxylin, while liver collagen content was determined by Sirius Red staining. Both interesterified fats increased liver collagen content and JNK phosphorylation. Additionally, the STEAR INTER group developed nonalcoholic steatohepatitis (NASH) associated with higher neutrophil infiltration. PALM INTER induced adipose tissue expansion and enlargement of adipocytes. Furthermore, PALM INTER triggered increased IKK phosphorylation and TNFα protein content, conditions associated with the upstream activation of the NFkB signaling pathway. STEAR INTER induced NASH, while PALM INTER triggered hepatic fibrosis and adipocyte hypertrophy with inflammatory response in LDLr-KO mice.
The Process of Evolution, Human Enhancement Technology, and Cyborgs
Authors: Barfield Woodrow
Published: February 2019

The human body is a remarkable example of the process of evolution which ultimately created a sentient being with cognitive, motor, and information-processing abilities. The body can also be thought of as an amazing feat of engineering, and specifically as an example of molecular nanotechnology, positioning trillions of cells throughout the body, and creating the billions of unique individuals that have existed since the beginning of humanity. On the other hand, from an engineering perspective, there are numerous limitations associated with the human body and the process of evolution to effect changes in the body is exceedingly slow. For example, our skeletal structure is only so strong, our body is subject to disease, and we are programmed by our DNA to age. Further, it took millions of years for Homo sapiens to evolve and hundreds of thousands of years for hominids to invent the most basic technology. To allow humans to go beyond the capabilities that evolution provided Homo sapiens, current research is leading to technologies that could significantly enhance the cognitive and motor abilities of humans and eventually create the conditions in which humans and technology could merge to form a cybernetic being. Much of this technology is being developed from three fronts: due to medical necessity, an interest within the military to create a cyborg soldier, and the desire among some people to self-enhance their body with technology. This article discusses the processes of biological evolution which led to the current anatomical, physiological, and cognitive capabilities of humans and concludes with a discussion of emerging technologies which are directed primarily at enhancing the cognitive functions performed by the brain. This article also discusses a timeframe in which the body will become increasingly equipped with technology directly controlled by the brain, then as a major paradigm shift in human evolution, humans will merge with the technology itself.
S-Nitrosoglutathione Reductase—The Master Regulator of Protein S-Nitrosation in Plant NO Signaling
Authors: Jahnová Jana , Luhová Lenka , Petřivalský Marek
Published: February 2019

S-nitrosation has been recognized as an important mechanism of protein posttranslational regulations, based on the attachment of a nitroso group to cysteine thiols. Reversible S-nitrosation, similarly to other redox-base modifications of protein thiols, has a profound effect on protein structure and activity and is considered as a convergence of signaling pathways of reactive nitrogen and oxygen species. In plant, S-nitrosation is involved in a wide array of cellular processes during normal development and stress responses. This review summarizes current knowledge on S-nitrosoglutathione reductase (GSNOR), a key enzyme which regulates intracellular levels of S-nitrosoglutathione (GSNO) and indirectly also of protein S-nitrosothiols. GSNOR functions are mediated by its enzymatic activity, which catalyzes irreversible GSNO conversion to oxidized glutathione within the cellular catabolism of nitric oxide. GSNOR is involved in the maintenance of balanced levels of reactive nitrogen species and in the control of cellular redox state. Multiple functions of GSNOR in plant development via NO-dependent and -independent signaling mechanisms and in plant defense responses to abiotic and biotic stress conditions have been uncovered. Extensive studies of plants with down- and upregulated GSNOR, together with application of transcriptomics and proteomics approaches, seem promising for new insights into plant S-nitrosothiol metabolism and its regulation.
Revisiting the Foraging Ecology and Extinction History of Two Endemic Vertebrates from Tenerife, Canary Islands
Authors: Crowley Brooke Erin , Yanes Yurena , Mosher Stella Grace , Rando Juan Carlos
Published: February 2019

We used carbon (δ13C) and nitrogen (δ15N) isotopes to examine the foraging ecology of Tenerife giant rats (Canariomys bravoi) and lizards (Gallotia goliath) in northwestern Tenerife, which until recently, were the island’s largest terrestrial vertebrates. We combined new isotope data for 28 C. bravoi and 14 G. goliath with published regional data for both species and then compared these with data for co-occurring extant taxa and modern C3 plants. Isotope data suggest both extinct species relied primarily on C3 resources and were trophic omnivores. However, the two species appear to have partitioned their resources when living in sympatry. Isotopic overlap between C. bravoi and Rattus spp., and between G. goliath, extant Gallotia galloti, and introduced rabbits (Oryctolagus cuniculus) suggests reliance on similar foods. We radiocarbon dated four C. bravoi and two G. goliath with the most extreme isotope values. These new dates do not settle the question of what triggered the demise of either species. Nevertheless, the data are most consistent with anthropogenically-induced extinction. Temporal isotopic trends contradict expectations if regional climate were responsible, and confidence intervals for radiocarbon dates suggest it is highly likely that both species were present when humans first settled the island.
An Indexation Mechanism for Retirement Age: Analysis of the Gender Gap
Authors: Coppola Mariarosaria , Russolillo Maria , Simone Rosaria
Published: February 2019

The management of National Social Security Systems is being challenged more and more by the rapid ageing of the population, especially in the industrialized countries. In order to chase the Pension System sustainability, several countries in Europe are setting up pension reforms linking the retirement age and/or benefits to life expectancy. In this context, the accurate modelling and projection of mortality rates and life expectancy play a central role and represent issues of great interest in recent literature. Our study refers to the Italian mortality experience and considers an indexing mechanism based on the expected residual life to adjust the retirement age and keep costs at an expected budgeted level, in the spirit of sharing the longevity risk between Social Security Systems and retirees. In order to combine fitting and projections performances of selected stochastic mortality models, a model assembling technique is applied to face uncertainty in model selection, while accounting for uncertainty of estimation as well. The resulting proposal is an averaged model that is suitable to discuss about the gender gap in longevity risk and its alleged narrowing over time.
Shrinkage of Nepal’s Second Largest Lake (Phewa Tal) Due to Watershed Degradation and Increased Sediment Influx
Authors: Watson C. Scott , Kargel Jeffrey S. , Regmi Dhananjay , Rupper Summer , Maurer Joshua M. , Karki Alina
Published: February 2019

Phewa Lake is an environmental and socio-economic asset to Nepal and the city of Pokhara. However, the lake area has decreased in recent decades due to sediment influx. The rate of this decline and the areal evolution of Phewa Lake due to artificial damming and sedimentation is disputed in the literature due to the lack of a historical time series. In this paper, we present an analysis of the lake’s evolution from 1926 to 2018 and model the 50-year trajectory of shrinkage. The area of Phewa Lake expanded from 2.44 ± 1.02 km2 in 1926 to a maximum of 4.61 ± 0.07 km2 in 1961. However, the lake area change was poorly constrained prior to a 1957–1958 map. The contemporary lake area was 4.02 ± 0.07 km2 in April 2018, and expands seasonally by ~0.18 km2 due to the summer monsoon. We found no evidence to support a lake area of 10 km2 in 1956–1957, despite frequent reporting of this value in the literature. Based on the rate of areal decline and sediment influx, we estimate the lake will lose 80% of its storage capacity in the next 110–347 years, which will affect recreational use, agricultural irrigation, fishing, and a one-megawatt hydroelectric power facility. Mitigation of lake shrinkage will require addressing landslide activity and sediment transport in the watershed, as well as urban expansion along the shores.
Research on Resource Allocation Method of Space Information Networks Based on Deep Reinforcement Learning
Authors: Meng Xiangli , Wu Lingda , Yu Shaobo
Published: February 2019

The space information networks (SIN) have a series of characteristics, such as strong heterogeneity, multiple types of resources, and difficulty in management. Aiming at the problem of resource allocation in SIN, this paper firstly establishes a hierarchical and domain-controlled SIN architecture based on software-defined networking (SDN). On this basis, the transmission, caching, and computing resources of the whole network are managed uniformly. The Asynchronous Advantage Actor-Critic (A3C) algorithm in deep reinforcement learning is introduced to model the process of resource allocation. The simulation results show that the proposed scheme can effectively improve the expected benefits of unit resources and improve the resource utilization efficiency of the SIN.
Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter
Authors: Tong Shengwu , Liu Xiuguo , Chen Qihao , Zhang Zhengjia , Xie Guangqi
Published: February 2019

Synthetic aperture radar (SAR) is an important means to detect ocean oil spills which cause serious damage to the marine ecosystem. However, the look-alikes, which have a similar behavior to oil slicks in SAR images, will reduce the oil spill detection accuracy. Therefore, a novel oil spill detection method based on multiple features of polarimetric SAR data is proposed to improve the detection accuracy in this paper. In this method, the self-similarity parameter, which is sensitive to the randomness of the scattering target, is introduced to enhance the discrimination ability between oil slicks and look-alikes. The proposed method uses the Random Forest classification combing self-similarity parameter with seven well-known features to improve oil spill detection accuracy. Evaluations and comparisons were conducted with Radarsat-2 and UAVSAR polarimetric SAR datasets, which shows that: (1) the oil spill detection accuracy of the proposed method reaches 92.99% and 82.25% in two datasets, respectively, which is higher than three well-known methods. (2) Compared with other seven polarimetric features, self-similarity parameter has the better oil spill detection capability in the scene with lower wind speed close to 2–3 m/s, while, when the wind speed is close to 9–12 m/s, it is more suitable for oil spill detection in the downwind scene where the microwave incident direction is similar to the sea surface wind direction and performs well in the scene with incidence angle range from 29.7° to 43.5°.
Multi-GNSS Relative Positioning with Fixed Inter-System Ambiguity
Authors: Chen Hua , Jiang Weiping , Li Jiancheng
Published: February 2019

In multi-GNSS cases, two types of Double Difference (DD) ambiguity could be formed including an intra-system ambiguity and an inter-system ambiguity, which are identified as the DD ambiguity between satellites from the same and from different GNSS systems, respectively. We studied the relative positioning methods using intra-system DD observations and using Un-Difference (UD) observations, and developed a frequency-free approach for fixing inter-system ambiguity based on UD observations for multi-GNSS positioning, where the inter-system phase bias is calculated with the help of a fixed Single-Difference (SD) ambiguity. The consistency between the receiver-end uncalibrated phase delays (RUPD) and the SD ambiguity were investigated and the positioning performance of this new approach was assessed. The results show that RUPD could be modeled as a constant if the receiver were tracking satellites continuously. Furthermore, compared to the method using DD observations with only an intra-system DD ambiguity fixed, the new ambiguity fixing approach has a better performance, especially in hard environments with a large cut-off angle or serve signal obstructions.
Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)
Authors: Pasqualotto Nieves , Delegido Jesús , Van Wittenberghe Shari , Rinaldi Michele , Moreno José
Published: February 2019

The spatial quantification of green leaf area index (LAIgreen), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2) a spatial resolution of up to 10 m, and (3) narrow bands in the red and red-edge region, a highly promising mission for agricultural monitoring. The aim of this work is to define an easy implementable LAIgreen index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAIgreen measurements were used. Commonly used LAIgreen indices applied on the Sentinel-2 10 m × 10 m pixel resulted in a validation R2 lower than 0.6. By calculating all Sentinel-2 band combinations to identify high correlation and physical basis with LAIgreen, the new Sentinel-2 LAIgreen Index (SeLI) was defined. SeLI is a normalized index that uses the 705 nm and 865 nm centered bands, exploiting the red-edge region for low-saturating absorption sensitivity to photosynthetic vegetation. A R2 of 0.708 (root mean squared error (RMSE) = 0.67) and a R2 of 0.732 (RMSE = 0.69) were obtained with a linear fitting for the calibration and validation datasets, respectively, outperforming established indices. Sentinel-2 LAIgreen maps are presented.
Recent Advances in Electrochemical Sensors for Detecting Toxic Gases: NO2, SO2 and H2S
Authors: Khan Md Ashfaque Hossain , Rao Mulpuri V. , Li Qiliang
Published: February 2019

Toxic gases, such as NOx, SOx, H2S and other S-containing gases, cause numerous harmful effects on human health even at very low gas concentrations. Reliable detection of various gases in low concentration is mandatory in the fields such as industrial plants, environmental monitoring, air quality assurance, automotive technologies and so on. In this paper, the recent advances in electrochemical sensors for toxic gas detections were reviewed and summarized with a focus on NO2, SO2 and H2S gas sensors. The recent progress of the detection of each of these toxic gases was categorized by the highly explored sensing materials over the past few decades. The important sensing performance parameters like sensitivity/response, response and recovery times at certain gas concentration and operating temperature for different sensor materials and structures have been summarized and tabulated to provide a thorough performance comparison. A novel metric, sensitivity per ppm/response time ratio has been calculated for each sensor in order to compare the overall sensing performance on the same reference. It is found that hybrid materials-based sensors exhibit the highest average ratio for NO2 gas sensing, whereas GaN and metal-oxide based sensors possess the highest ratio for SO2 and H2S gas sensing, respectively. Recently, significant research efforts have been made exploring new sensor materials, such as graphene and its derivatives, transition metal dichalcogenides (TMDs), GaN, metal-metal oxide nanostructures, solid electrolytes and organic materials to detect the above-mentioned toxic gases. In addition, the contemporary progress in SO2 gas sensors based on zeolite and paper and H2S gas sensors based on colorimetric and metal-organic framework (MOF) structures have also been reviewed. Finally, this work reviewed the recent first principle studies on the interaction between gas molecules and novel promising materials like arsenene, borophene, blue phosphorene, GeSe monolayer and germanene. The goal is to understand the surface interaction mechanism.
Uplink Non-Orthogonal Multiple Access with Channel Estimation Errors for Internet of Things Applications
Authors: Rim Minjoong , Kang Chung G.
Published: February 2019

One of the key requirements for next generation wireless or cellular communication systems is to efficiently support a large number of connections for Internet of Things (IoT) applications, and uplink non-orthogonal multiple access (NOMA) schemes can be used for this purpose. In uplink NOMA systems, pilot symbols, as well as data symbols can be superimposed onto shared resources. The error rate performance can be severely degraded due to channel estimation errors, especially when the number of superimposed packets is large. In this paper, we discuss uplink NOMA schemes with channel estimation errors, assuming that quadrature phase shift keying (QPSK) modulation is used. When pilot signals are superimposed onto the shared resources and a large number of devices perform random accesses concurrently to a single resource of the base station, the channels might not be accurately estimated even in high SNR environments. In this paper, we propose an uplink NOMA scheme, which can alleviate the performance degradation due to channel estimation errors.
Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose
Authors: Cao Wen , Liu Chunmei , Jia Pengfei
Published: February 2019

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.
Fingerprints and Floor Plans Construction for Indoor Localisation Based on Crowdsourcing
Authors: Santos Ricardo , Barandas Marília , Leonardo Ricardo , Gamboa Hugo
Published: February 2019

The demand for easily deployable indoor localisation solutions has been growing. Although several systems have been proposed, their limitations regarding the high implementation costs hinder most of them to be widely used. Fingerprinting-based IPS (Indoor Positioning Systems) depend on characteristics pervasively available in buildings. However, such systems require indoor floor plans, which might not be available, as well as environmental fingerprints, that need to be collected through human resources intensive processes. To overcome these limitations, this paper proposes an algorithm for the automatic construction of indoor maps and fingerprints, solely depending on non-annotated crowdsourced data from smartphones. Our system relies on multiple gait-model based filtering techniques for accurate movement quantification in combination with opportunistic sensing observations. After the reconstruction of users’ movement with PDR (Pedestrian Dead Reckoning) techniques, Wi-Fi measurements are clustered to partition the trajectories into segments. Similar segments, which belong to the same cluster, are identified using an adaptive approach based on a geomagnetic field distance. Finally, the floor plans are obtained through a data fusion process. Merging the acquired environmental data using the obtained floor plan, fingerprints are aligned to physical locations. Experimental results show that the proposed solution achieved comparable floor plans and fingerprints to those acquired manually, allowing the conclusion that is possible to automate the setup process of infrastructure-free IPS.
A Polydimethylsiloxane (PDMS) Waveguide Sensor that Mimics a Neuromast to Measure Fluid Flow Velocity
Authors: Wiesmayr Bianca , Höglinger Markus , Krieger Michael , Lindner Philip , Baumgartner Werner , Stadler Anna T.
Published: February 2019

Accurate flow measurement is a ubiquitous task in fields such as industry, medical technology, or chemistry; it remains however challenging due to small measurement ranges or erosive flows. Inspiration for possible measurement methods can come from nature, for example from the lateral line organ of fish, which is comprised of hair cells embedded in a gelatinous cupula. When the cupula is deflected by water movement, the hair cells generate neural signals from which the fish gains an accurate representation of its environment. We built a flow sensor mimicking a hair cell, but coupled it with an optical detection method. Light is coupled into a PDMS waveguide that consists of a core and a cladding with a low refractive index contrast to ensure high bending sensitivity. Fluid flow bends the waveguide; this leads to a measurable light loss. The design of our sensory system allows flow measurement in opaque and corrosive fluids while keeping production costs low. To prove the measurement concept, we evaluated the light loss while (a) reproducibly bending the fiber with masses, and (b) exposing the fiber to air flow. The results demonstrate the applicability of an optical fiber as a flow sensor.
Fault Identification of Chemical Processes Based on k-NN Variable Contribution and CNN Data Reconstruction Methods
Authors: Wang Guo-Zhu , Li Jing , Hu Yong-Tao , Li Yuan , Du Zhi-Yong
Published: February 2019

Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, and multi-operating mode. To cope with these issues, the k-NN (k-Nearest Neighbor) fault detection method and extensions have been developed in recent years. Nevertheless, these methods are primarily used for fault detection, and few papers can be found that examine fault identification. In this paper, in order to extract effective fault information, the relationship between various faults and abnormal variables is studied, and an accurate “fault–symptom” table is presented. Then, a novel fault identification method based on k-NN variable contribution and CNN data reconstruction theories is proposed. When there is an abnormality, a variable contribution plot method based on k-NN is used to calculate the contribution index of each variable, and the feasibility of this method is verified by contribution decomposition theory, which includes a feasibility analysis of a single abnormal variable and multiple abnormal variables. Furthermore, to identify all the faulty variables, a CNN (Center-based Nearest Neighbor) data reconstruction method is proposed; the variables that have the larger contribution indices can be reconstructed using the CNN reconstruction method in turn. The proposed search strategy can guarantee that all faulty variables are found in each sample. The reliability and validity of the proposed method are verified by a numerical example and the Continuous Stirred Tank Reactor system.
Optimal Shadowing Filter for a Positioning and Tracking Methodology with Limited Information
Authors: Zaitouny Ayham , Stemler Thomas , Algar Shannon Dee
Published: February 2019

Positioning and tracking a moving target from limited positional information is a frequently-encountered problem. For given noisy observations of the target’s position, one wants to estimate the true trajectory and reconstruct the full phase space including velocity and acceleration. The shadowing filter offers a robust methodology to achieve such an estimation and reconstruction. Here, we highlight and validate important merits of this methodology for real-life applications. In particular, we explore the filter’s performance when dealing with correlated or uncorrelated noise, irregular sampling in time and how it can be optimised even when the true dynamics of the system are not known.
Big Data-Driven Cellular Information Detection and Coverage Identification
Authors: Wang Hai , Xie Su , Li Ke , Ahmad M. Omair
Published: February 2019

As one of the core data assets of telecom operators, base station almanac (BSA) plays an important role in the operation and maintenance of mobile networks. It is also an important source of data for the location-based service (LBS) providers. However, it is always less timely updated, nor it is accurate enough. Besides, it is not open to third parties. Conventional methods detect only the location of the base station (BS) which cannot satisfy the needs of network optimization and maintenance. Because of these drawbacks, in this paper, a big-data driven method of BSA information detection and cellular coverage identification is proposed. With the help of network-related data crowd sensed from the massive number of smartphone users in the live network, the algorithm can estimate more parameters of BSA with higher accuracy than conventional methods. The coverage capability of each cell was also identified in a granularity of small geographical grids. Computational results validate the proposed algorithm with higher performance and detection ability over the existing ones. The new method can be expected to improve the scope, accuracy, and timeliness of BSA, serving for wireless network optimization and maintenance as well as LBS service.
An Improvement in the Identification of the Centres of Checkerboard Targets in Point Clouds Using Terrestrial Laser Scanning
Authors: Fryskowska Anna
Published: February 2019

Measurement using terrestrial laser scanning is performed at several stations to measure an entire object. In order to obtain a complete and uniform point cloud, it is necessary to register each and every scan in one local or global coordinate system. One registration method is based on reference points—in this case, checkerboard targets. The aim of this research was to analyse the accuracy of checkerboard target identification and propose an algorithm to improve the accuracy of target centre identification, particularly for low-resolution and low-quality point clouds. The proposed solution is based on the geometric determination of the target centre. This work presents an outline of a new approach, designed by the author, to discuss the influence of the point cloud parameters on the process of checkerboard centre identification and to propose an improvement in target centre identification. The validation of the proposed solutions reveals that the difference between the typical automatic target identification and the proposed method amounts to a maximum of 6 mm for scans of different qualities. The proposed method may serve as an alternative to, or supplement for, checkerboard identification, particularly when the quality of these scans is not sufficient for automatic algorithms.
3D LiDAR-Based Precision Vehicle Localization with Movable Region Constraints
Authors: Hsu Chih-Ming , Shiu Chung-Wei
Published: February 2019

This paper discusses a high-performance similarity measurement method based on known map information named the cross mean absolute difference (CMAD) method. Applying the conventional normalized cross-correlation (NCC) feature registration method requires sufficient numbers of feature points, which must also exhibit near-normal distribution. However, Light Detection and Ranging (LiDAR) ranging point cloud data scanned and collected on-site are scarce and do not fulfill near-normal distribution. Consequently, considerable localization errors occur when NCC features are registered with map features. Thus, the CMAD method was proposed to effectively improve the NCC algorithm and localization accuracy. Because uncertainties in localization sensors cause deviations in the localization processes, drivable moving regions (DMRs) were established to restrict the range of location searches, filter out unreasonable trajectories, and improve localization speed and performance. An error comparison was conducted between the localization results of the window-based, DMR–CMAD, and DMR–NCC methods, as well as those of the simultaneous localization and mapping methods. The DMR–CMAD method did not differ considerably from the window-based method in its accuracy: the root mean square error in the indoor experiment was no higher than 10 cm, and that of the outdoor experiment was 10–30 cm. Additionally, the DMR–CMAD method was the least time-consuming of the three methods, and the DMR–NCC generated more localization errors and required more localization time than the other two methods. Finally, the DMR–CMAD algorithm was employed for the successful on-site instant localization of a car.
Variation in Access to Safe Drinking Water across Different Countries: An Explanatory Framework
Authors: Ryu Shin Kue
Published: February 2019

Building upon extensive field interviews with past and current public officials from domestic agencies and international organizations who are involved in safe drinking water policy in Indonesia, Vietnam, Malaysia, Singapore, and South Korea, the research identifies and clarifies the causal mechanisms behind the different rates of expansion in access to safe drinking water among these five countries. It then examines how well theories in public administration, particularly with emphasis on theoretical frameworks in implementation scholarship, help capture and explain these mechanisms. Both strengths and weaknesses of public administration theory are examined where efforts to improve existing frameworks are suggested with their merger with theories from comparative politics. This opens the discussion on how public administration scholars should be involved in addressing and offering insights and advice to tackle the outstanding global challenge of 663 million people still living without access to safe drinking water.
Family Functioning, Self-Concept and Cybervictimization: An Analysis Based on Gender
Authors: Romero-Abrio Ana , León-Moreno Celeste , Musitu-Ferrer Daniel , Villarreal-González María Elena
Published: February 2019

The aim of this study was to analyze the relationships between cybervictimization, family functioning, and self-concept in adolescents, while taking the gender perspective into account. A study was conducted with a sample of 8115 adolescents, aged between 11 and 16 years (M = 13.34; SD = 1.04) from the State of Nuevo Leon, Mexico. A MANOVA 3 × 2 was performed to analyze the data. The results showed that family functioning, family self-concept, and academic self-concept were higher when cybervictimization was low. It was also observed that, in situations of cybervictimization, the girls had lower family self-concept, lower academic self-concept, and lower family functioning than the boys. The results that were obtained and their implications are discussed in the final section.
Shared Identity, Family Influence, and the Transgenerational Intentions in Family Firms
Authors: Mahto Raj V. , Chen Jiun-Shiu , McDowell William C. , Ahluwalia Saurabh
Published: February 2019

A family’s transgenerational intention (TI) to pass ownership of the firm to the next generation of family members is the defining characteristic of a family. TI reflects a family’s intention to engage in succession planning, which is the primary predictor for succession success. In this study, we draw on psychological ownership theory to develop and test a model of a family’s TI. In the model, we argue that family influence impacts TI through shared identity. We also argue that a family firm CEO’s relationship to the family (by blood vs. marriage vs. hire) moderates the relationship between shared identity and TI. We tested our hypotheses and the model on a sample of North American family firms and found support for most hypotheses.
Assessing Freshwater Provision and Consumption in the Alpine Space Applying the Ecosystem Service Concept
Authors: Meisch Claude , Schirpke Uta , Huber Lisa , Rüdisser Johannes , Tappeiner Ulrike
Published: February 2019

A key challenge in the sustainable management of freshwater is related to non-stationary processes and transboundary requirements. The assessment of freshwater is often hampered due to small-scale analyses, lacking data and with the focus on only its provision. Based on the ecosystem service (ES) concept, this study aims at quantitatively comparing potential water supply with the demand for freshwater in the European Alps and their surrounding lowlands. We propose an easy-to-use combination of different mapping approaches, including a large-scale hydrologic model to estimate water supply and the downscaling of regional data to the local scale to map demand. Our results demonstrate spatial mismatches between supply and demand and a high dependency of the densely populated lowlands from water providing mountain areas. Under expected climate variations and future demographic changes, our results suggest increasing pressures on freshwater in the south of the Alps. Hence, sustainable water management strategies need to assure the supply of freshwater under changing environmental conditions to meet the increasing water demand of urbanized areas in the lowlands. Moreover, national water management strategies need to be optimally concerted at the international level, as transboundary policies and frameworks can strengthen future water provision.
Research on the Learning Effect of the Positive Emotions of "Ship Fuel-Saving Project" APP for Engineering Students
Authors: Chung Chih-Chao , Cheng Yuh-Ming , Shih Ru-Chu , Lou Shi-Jer
Published: February 2019

The purpose of this study is to explore the impacts of “positive emotions” through the App Inventor 2 Ship Fuel-Saving Project on the “positive emotions literacy” and “ship fuel-saving literacy” of university students of the Engineering Department. In this study, 115 students were selected as the research object, experimental teaching was conducted in a group cooperative learning mode, and the positive emotional elements were integrated into the AI 2 Ship Fuel-Saving Project. The research conclusions were as follows. (1) The Blended teaching method and the mixed learning program of the “Positive AI 2 Fuel-Saving Project” provided students with diversified learning channels; (2) the Project presented significant positive affirmation for the improvement of students’ positive emotional literacy; (3) the Project resulted in significantly greater improvement of students’ fuel-saving literacy in the experimental group than in the control group; (4) students in the experimental group showed the best positive emotion with confidence in the learning process of the project; and (5) the “ship fuel-saving literacy” and “ship fuel-saving works achievement” of students in the experimental group showed a significantly high positive correlation. Finally, the “Positive AI 2 Fuel-Saving Project Teaching Mode” was constructed, which can be a reference for promoting the practical teaching of positive emotions and ship fuel-saving literacy in the future.