bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing Adherence, Competence and Differentiation in a Stepped-Wedge Randomised Clinical Trial of a Complex Behaviour Change Intervention
Authors: Beck Alison Kate , Baker Amanda L. , Carter Gregory , Wratten Chris , Bauer Judith , Wolfenden Luke , McCarter Kristen , Britton Ben
Published: August 2020

Background: A key challenge in behavioural medicine is developing interventions that can be delivered adequately (i.e., with fidelity) within real-world consultations. Accordingly, clinical trials should (but tend not to) report what is actually delivered (adherence), how well (competence) and the distinction between intervention and comparator conditions (differentiation). Purpose: To address this important clinical and research priority, we apply best practice guidelines to evaluate fidelity within a real-world, stepped-wedge evaluation of “EAT: Eating As Treatment”, a new dietitian delivered health behaviour change intervention designed to reduce malnutrition in head and neck cancer (HNC) patients undergoing radiotherapy. Methods: Dietitians (n = 18) from five Australian hospitals delivered a period of routine care and following a randomly determined order each site received training and began delivering the EAT Intervention. A 20% random stratified sample of audio-recorded consultations (control n = 196; intervention n = 194) was coded by trained, independent, raters using a study specific checklist and the Behaviour Change Counselling Inventory. Intervention adherence and competence were examined relative to apriori benchmarks. Differentiation was examined by comparing control and intervention sessions (adherence, competence, non-specific factors, and dose), via multiple linear regression, logistic regression, or mixed-models. Results: Achievement of adherence benchmarks varied. The majority of sessions attained competence. Post-training consultations were clearly distinct from routine care regarding motivational and behavioural, but not generic, skills. Conclusions: Although what level of fidelity is “good enough” remains an important research question, findings support the real-world feasibility of integrating EAT into dietetic consultations with HNC patients and provide a foundation for interpreting treatment effects.
Neuroinflammation and Neurodegeneration: The Promising Protective Role of the Citrus Flavanone Hesperetin
Authors: Scoditti Egeria
Published: August 2020

Over the past 20 years, there has been a remarkable increase in the scientific interest in polyphenols, bioactive compounds naturally present in plant foods and beverages, due to the recognition of their biological actions, their great abundance in the human diet, and their plausible role in the prevention of various chronic degenerative diseases, such as cancer, cardiovascular and neurodegenerative diseases [...]
Association of Serum Calcium and Phosphate Concentrations with Glucose Metabolism Markers: The Furukawa Nutrition and Health Study
Authors: Akter Shamima , Eguchi Masafumi , Kochi Takeshi , Kabe Isamu , Nanri Akiko , Mizoue Tetsuya
Published: August 2020

Calcium and phosphate may play an important role in cardio-metabolic abnormalities, including type 2 diabetes; however, epidemiological evidence of the association of calcium and phosphate status with glucose metabolism among Asians is limited. In the current study, we performed a cross-sectional analysis of the association of serum calcium, phosphate, and calcium–phosphate product concentrations with glucose metabolism markers among Japanese individuals. Overall, 1701 workers (aged 18–78 years) who participated in a health survey were enrolled in this study. Multivariable linear regression models were used to estimate means of homeostatic model assessment of insulin resistance (HOMA-IR), homeostatic model assessment of β-cell function (HOMA-β), and glycated hemoglobin (HbA1c). Serum calcium concentration was positively associated with HOMA-IR and HbA1c (p for trend < 0.01). Multivariable-adjusted means (95% confidence interval (CI)) of HOMA-IR for the lowest and highest quartiles of serum calcium were 0.78 (0.75–0.82) and 1.01 (0.96–1.07), respectively. The corresponding values for HbA1c were 5.24 (5.22–5.27) and 5.29 (5.26–5.32), respectively. Serum phosphate and calcium–phosphate product concentrations were inversely associated with HOMA-IR (p for trend < 0.01). Multivariable-adjusted means (95% CI) of HOMA-IR for the lowest and highest quartiles of serum phosphate were 1.04 (0.99–1.09) and 0.72 (0.69–0.76), respectively. The corresponding values for calcium–phosphate product were 1.04 (0.99–1.09) and 0.73 (0.69–0.77), respectively. The current findings suggest that higher serum calcium and lower serum phosphate concentrations are associated with IR among apparently healthy adults.
Increased Risk of Acquisition of New Delhi Metallo-Beta-Lactamase-Producing Carbapenem-Resistant Enterobacterales (NDM-CRE) among a Cohort of COVID-19 Patients in a Teaching Hospital in Tuscany, Italy
Authors: Porretta Andrea Davide , Baggiani Angelo , Arzilli Guglielmo , Casigliani Virginia , Mariotti Tommaso , Mariottini Francesco , Scardina Giuditta , Sironi Daniele , Totaro Michele , Barnini Simona , Privitera Gaetano Pierpaolo
Published: August 2020

We describe the epidemiology of New Delhi Metallo-Beta-Lactamase-Producing Carbapenem-Resistant Enterobacterales (NDM-CRE) colonization/infection in a cohort of COVID-19 patients in an Italian teaching hospital. These patients had an increased risk of NDM-CRE acquisition versus the usual patients (75.9 vs. 25.3 cases/10,000 patient days). The co-infection significantly increased the duration of hospital stay (32.9 vs. 15.8 days).
Development of an IPM Strategy for Thrips and Tomato spotted wilt virus in Processing Tomatoes in the Central Valley of California
Authors: Batuman Ozgur , Turini Thomas A. , LeStrange Michelle , Stoddard Scott , Miyao Gene , Aegerter Brenna J. , Chen Li-Fang , McRoberts Neil , Ullman Diane E. , Gilbertson Robert L.
Published: August 2020

Tomato spotted wilt virus (TSWV; species Tomato spotted wilt orthotospovirus; genus Orthotospovirus; family Tospoviridae) is a thrips-transmitted virus that can cause substantial economic losses to many crops, including tomato (Solanum lycopersicum). Since 2005, TSWV emerged as an economically important virus of processing tomatoes in the Central Valley of California, in part due to increased populations of the primary thrips vector, western flower thrips (WFT; Frankliniella occidentalis). To develop an understanding of the epidemiology of TSWV in this region, population densities of WFT and incidence of TSWV were monitored in California’s processing tomato transplant-producing greenhouses and associated open fields from 2007 to 2013. Thrips were monitored with yellow sticky cards and in tomato flowers, whereas TSWV incidence was assessed with indicator plants and field surveys for virus symptoms. All thrips identified from processing tomato fields were WFT, and females were three-fold more abundant on sticky cards than males. Symptoms of TSWV infection were observed in all monitored processing tomato fields. Incidences of TSWV ranged from 1 to 20%, with highest incidence found in late-planted fields. There was no single primary inoculum source, and inoculum sources for thrips/TSWV varied depending on the production region. These results allowed us to develop a model for TSWV infection of processing tomatoes in the Central Valley of California. The model predicts that low levels of primary TSWV inoculum are amplified in early-planted tomatoes and other susceptible crops leading to highest levels of infection in later-planted fields, especially those with high thrips populations. Based upon these findings, an integrated pest management (IPM) strategy for TSWV in processing tomatoes in California was devised. This IPM strategy focuses on strategic field placement (identification of high-risk situations), planting TSWV- and thrips-free transplants, planting resistant varieties, monitoring for TSWV symptoms and thrips, roguing infected plants, thrips management targeting early generations, extensive sanitation after harvest, and strategic cropping to avoid overlap with winter bridge crops.
Polymeric Bioadhesive Patch Based on Ketoprofen-Hydrotalcite Hybrid for Local Treatments
Authors: Pagano Cinzia , Latterini Loredana , Di Michele Alessandro , Luzi Francesca , Puglia Debora , Ricci Maurizio , Antonio Viseras Iborra César , Perioli Luana
Published: August 2020

Ketoprofen (KET) represents one of the most common drugs used in the topical treatment of pain and inflammations. However, its potential is rather limited due to the very low solubility and photochemical instability. The local administration of KET by conventional products, such as gels, emulgels, creams, and foams, does not guarantee an efficacious and safe treatment because of its low absorption (due to low solubility) and its sensitivity to UV rays. The photodegradation of KET makes many photoproducts responsible for different adverse effects. In the present work, KET was intercalated into the lamellar anionic clay ZnAl-hydrotalcite (ZnAl-HTlc), obtaining the hybrid ZnAl-KET with improved stability to UV rays and water solubility in comparison to the crystalline form (not intercalated KET). The hybrid was then formulated in autoadhesive patches for local pain treatment. The patches were prepared by casting method starting from a hydrogel based on the biocompatible and bioadhesive polymer NaCMC (Sodium carboxymethycellulose) and glycerol as a plasticizing agent. The introduction of ZnAl-KET in the patch composition demonstrated the improvement in the mechanical properties of the formulation. Moreover, a sustained and complete KET release was obtained within 8 h. This allowed reducing the frequency of anti-inflammatory administration, compared to the conventional formulations.
Hot-Melt 3D Extrusion for the Fabrication of Customizable Modified-Release Solid Dosage Forms
Authors: Lee Jaemin , Song Chanwoo , Noh Inhwan , Song Sangbyeong , Rhee Yun-Seok
Published: August 2020

In this work, modified-release solid dosage forms were fabricated by adjusting geometrical properties of solid dosage forms through hot-melt 3D extrusion (3D HME). Using a 3D printer with air pressure driving HME system, solid dosage forms containing ibuprofen (IBF), polyvinyl pyrrolidone (PVP), and polyethylene glycol (PEG) were printed by simultaneous HME and 3D deposition. Printed solid dosage forms were evaluated for their physicochemical properties, dissolution rates, and floatable behavior. Results revealed that IBF content in the solid dosage form could be individualized by adjusting the volume of solid dosage form. IBF was dispersed as amorphous state with enhanced solubility and dissolution rate in a polymer solid dosage form matrix. Due to absence of a disintegrant, sustained release of IBF from printed solid dosage forms was observed in phosphate buffer at pH 6.8. The dissolution rate of IBF was dependent on geometric properties of the solid dosage form. The dissolution rate of IBF could be modified by merging two different geometries into one solid dosage form. In this study, the 3D HME process showed high reproducibility and accuracy for preparing dosage forms. API dosage and release profile were found to be customizable by modifying or combining 3D modeling.
A Systematic Review of Randomized Controlled Trials of Telehealth and Digital Technology Use by Community Pharmacists to Improve Public Health
Authors: Crilly Philip , Kayyali Reem
Published: August 2020

Community pharmacists (CPs) continue to have an important role in improving public health, however, advances in telehealth and digital technology mean that the methods by which they support their customers and patients are changing. The primary aim of this study was to identify which telehealth and digital technology tools are used by CPs for public health purposes and determine if these have a positive impact on public health outcomes. A systematic review was carried out using databases including PubMed and ScienceDirect, covering a time period from April 2005 until April 2020. The search criteria were the following: randomized controlled trials, published in English, investigating the delivery of public health services by community pharmacists using a telehealth or digital tool. Thirteen studies were included out of 719 initially identified. Nine studies detailed the use of telephone prompts or calls, one study detailed the use of a mobile health application, two studies detailed the use of a remote monitoring device, and one study detailed the use of photo-aging software. Public health topics that were addressed included vaccination uptake (n = 2), smoking cessation (n = 1), hypertension management (n = 2), and medication adherence and counseling (n = 8). More studies are needed to demonstrate whether or not the use of novel technology by CPs can improve public health.
Actual vs. Perceived Competency Development—How Can Virtual Patients Impact Pharmacist Pre-Registration Training?
Authors: Thompson Jessica , White Simon , Chapman Stephen
Published: August 2020

Virtual patients are an active learning pedagogical tool that simulate clinical scenarios. There is an established disparity in pre-registration examination pass rates depending on whether individuals complete their training in a community or hospital pharmacy. This study aimed to evaluate virtual patient (VP) and non-interactive (NI) case studies, concerning knowledge, skill and confidence development of pre-registration pharmacist trainees. A quasi-experimental evaluation was conducted. Pre-registration pharmacists completed three VP or NI case studies. Each case study was associated with a pre-and post-knowledge quiz. Pre-registration trainees were invited to complete a questionnaire consisting of Likert ranking statements and open-ended questions on the case study features, usability and individual development. Both learning tools significantly improved trainees’ knowledge on the topic areas (except for the NI group in case study 3). Although no significant differences in knowledge improvement were identified between the learning tools, trainees who used the VP reported the development of a wider knowledge base and skill set, an increase in confidence for practice and an opportunity to apply their learning. The sector in which pre-registration pharmacists were completing their training (community or hospital) had a significant impact on knowledge improvement in the three case studies. Future research evaluating VPs with pre-registration and qualified pharmacists should be conducted to explore their benefits and establish their effectiveness as learning tools.
Selection and Validation of Reference Genes for Quantitative Real-Time PCR in White Clover (Trifolium repens L.) Involved in Five Abiotic Stresses
Authors: Pu Qi , Li Zhou , Nie Gang , Zhou Jiqiong , Liu Lin , Peng Yan
Published: August 2020

White clover (Trifolium repens L.) is a widely cultivated cool-season perennial forage legume in temperate grassland systems. Many studies have analyzed the gene expression in this grass species using quantitative real-time reverse transcription PCR (qRT-PCR). The selection of stable reference genes for qRT-PCR is crucial. However, there was no detailed study on reference genes in different tissues of white clover under various abiotic stress conditions. Herein, 14 candidate reference genes (ACT7, ACT101, TUA1109, TUB, CYP, 60SrRNA, UBQ, E3, GAPDH1, GAPDH2, PP2A, BAM3, SAMDC, and ABC) were selected and analyzed by four programs (GeNorm, NormFinder, BestKeeper, and RefFinder). Samples were taken from two tissues (leaves and roots) under five different abiotic stresses (drought, salt, heat, cold, and heavy metal stress). Our results showed that 60SrRNA and ACT101 were the two top-ranked genes for all samples. Under various experimental conditions, the most stable gene was different; however, SAMDC, UBQ, 60SrRNA, and ACT101 were always top ranked. The most suitable reference genes should be selected according to different plant tissues and growth conditions. Validation of these reference genes by expression analysis of Cyt-Cu/Zn SOD and CAT confirmed their reliability. Our study will benefit the subsequent research of gene function in this species.
Efficient Tissue Culture Protocol for Magnolia lucida (Magnoliaceae) and Confirmation of Genetic Stability of the Regenerated Plants
Authors: Kang Lu , Zheng Keyuan , Xie Yuqing , Deng Yanwen , Yu Yina , Zhu Mulan , Xi Ruchun , Deng Xiaomei
Published: August 2020

Magnolia lucida (Magnoliaceae) is classified as an endangered species by the International Union for Conservation of Nature. It has high commercial value owing to its attractive tree shape and flowers. We adopted an excellent genotype of M. lucida as the parent material and established a mini-cut orchard through grafting to provide trunk shoots explants over the long-term. Optimal sterilization was achieved using a combination of 75% ethanol for 30 s, one percent benzalkonium bromide for five minutes, and 0.1% mercuric chloride for five minutes. Modified Murashige and Skoog medium (ML) was the optimal medium for the growth of M. lucida. Addition of one mg/L of 6-benzyl adenine (BA) and 0.05 mg/L of α-naphthaleneacetic acid (NAA) to the medium increased the shoot induction rate to 95.56%, and the ML medium containing 0.4 mg/L BA and 0.04 mg/L NAA achieved the maximum multiplication rate (284.56%). Dark treatment for seven days, followed by continuous light treatment could better resolve the challenge of difficult rooting in M. lucida plants. Using random amplified polymorphic DNA and inter simple sequence repeat markers, we confirmed the genetic uniformity and stability of the regenerated plants. Our protocol should be helpful for the propagation and conservation of this endangered plant.
Doxorubicin–Gelatin/Fe3O4–Alginate Dual-Layer Magnetic Nanoparticles as Targeted Anticancer Drug Delivery Vehicles
Authors: Huang Chiung-Hua , Chuang Ting-Ju , Ke Cherng-Jyh , Yao Chun-Hsu
Published: August 2020

In this study, magnetic nanoparticles composed of a core (doxorubicin–gelatin) and a shell layer (Fe3O4–alginate) were developed to function as targeted anticancer drug delivery vehicles. The anticancer drug doxorubicin (DOX) was selected as a model drug and embedded in the inner gelatin core to obtain high encapsulation efficiency. The advantage of the outer magnetic layer is that it targets the drug to the tumor tissue and provides controlled drug release. The physicochemical properties of doxorubicin–gelatin/Fe3O4–alginate nanoparticles (DG/FA NPs) were characterized using scanning electron microscopy, Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction. The mean diameter of DG/FA NPs, which was determined using a zeta potential analyzer, was 401.8 ± 3.6 nm. The encapsulation rate was 64.6 ± 11.8%. In vitro drug release and accumulation were also studied. It was found that the release of DOX accelerated in an acidic condition. With the manipulation of an external magnetic field, DG/FA NPs efficiently targeted Michigan Cancer Foundation-7 (MCF-7) breast cancer cells and showed in the nucleus after 6 h of incubation. After 12 h of incubation, the relative fluorescence intensity reached 98.4%, and the cell viability of MCF-7 cells decreased to 52.3 ± 4.64%. Dual-layer DG/FA NPs could efficiently encapsulate and deliver DOX into MCF-7 cells to cause the death of cancer cells. The results show that DG/FA NPs have the potential for use in targeted drug delivery and cancer therapy.
The Effect of Recycling on Wood-Fiber Thermoplastic Composites
Authors: Rosenstock Völtz Luísa , Di Guiseppe Irangeli , Geng Shiyu , Oksman Kristiina
Published: August 2020

The aim of this study was to investigate the effect of recycling on polypropylene (PP) and wood-fiber thermoplastic composites (WPCs) using a co-rotating twin-screw extruder. After nine extrusion passes microscopy studies confirmed that the fiber length decreased with the increased number of recycling passes but the increased processing time also resulted in excellent dispersion and interfacial adhesion of the wood fibers in the PP matrix. Thermal, rheological, and mechanical properties were studied. The repeated extrusion passes had minimal effect on thermal behavior and the viscosity decreased with an increased number of passes, indicating slight degradation. The recycling processes had an effect on the tensile strength of WPCs while the effect was minor on the PP. However, even after the nine recycling passes the strength of WPC was considerably better (37 MPa) compared to PP (28 MPa). The good degree of property retention after recycling makes this recycling strategy a viable alternative to discarding the materials. Thus, it has been demonstrated that, by following the most commonly used extrusion process, WPCs can be recycled several times and this methodology can be industrially adapted for the manufacturing of recycled products.
Experimental Investigation of Primary De-NOx Methods Application Effects on NOx and CO Emissions from a Small-Scale Furnace
Authors: Lukáč Ladislav , Rimár Miroslav , Variny Miroslav , Kizek Ján , Lukáč Peter , Jablonský Gustáv , Janošovský Ján , Fedák Marcel
Published: August 2020

Nitrogen oxides (NOx) from combustion contribute significantly to atmospheric pollution. An experimental setup was employed to investigate the application of three primary denitrification methods, i.e., reburning (staged combustion), overfiring air (OFA), and flue-gas recirculation (FGR), individually and in combination, combusting natural gas (NG) and propane–butane gas (PBG). Fuel heat inputs of 16 and 18 kW and air excess coefficients of 1.1 and 1.2, respectively, were tested. The highest individual denitrification efficiency of up to 74% was obtained for FGR, followed by reburning and OFA. A denitrification efficiency between 8.9% (reburning + OFA) and 72% (reburning + OFA + FGR) with NG combustion was observed. Using a 20% FGR rate yielded denitrification efficiency of 74% for NG and 65% for PBG and also led to a significant decrease in carbon monoxide (CO) emissions, so this can be recommended as the most efficient denitrification and de-CO method in small-scale furnaces. Reburning alone led to a sharp, more than 12-fold increase in CO emissions compared to the amount without any other method application. The presented results and the difference between our experimental data and the literature data acquired in some other studies indicate the need for further research.
Upgrading of Biogas to Methane Based on Adsorption
Authors: Liu Jun , Chen Qiang , Qi Peng
Published: August 2020

Upgrading raw biogas to methane (CH4) is a vital prerequisite for the utilization of biogas as a vehicle fuel or the similar field as well. In this work, biogas yield from the anaerobic fermentation of food waste containing methane (CH4, 60.4%), carbon dioxide (CO2, 29.1%), hydrogen sulfide (H2S, 1.5%), nitrogen (N2, 7.35%) and oxygen (O2, 1.6%) was upgraded by dynamic adsorption. The hydrogen sulfide was removed from the biogas in advance by iron oxide (Fe2O3) because of its corrosion of the equipment. Commercial 13X zeolite and carbon molecular sieve (CMS) were used to remove the other impurity gases from wet or dry biogas. It was found that neither 13X zeolite nor CMS could effectively remove each of the impurities in the wet biogas for the effect of water vapor. However, 13X zeolite could effectively remove CO2 after the biogas was dried with silica and showed a CO2 adsorption capacity of 78 mg/g at the condition of 0.2 MPa and 25 °C. Additionally, 13X zeolite almost did not adsorb nitrogen (N2), so the CH4 was merely boosted to ac. 91% after the desulfurated dry biogas passed through 13X zeolite, nitrogen remaining in the biogas. CMS would exhibit superior N2 adsorption capacity and low CO2 adsorption capacity if some N2 was present in biogas, so CMS was able to remove all the nitrogen and fractional carbon dioxide from the desulfurated dry biogas in a period of time. Finally, when the desulfurated dry biogas passed through CMS and 13X zeolite in turn, the N2 and CO2 were sequentially removed, and then followed the high purity CH4 (≥96%).
A Case of 10-Year Survival after Adrenalectomy for Isolated Adrenal Metastasis of Breast Cancer
Authors: Nakagawa Tsuyoshi , Oda Goshi , Yano Akihiro , Kawachi Hiroshi , Uetake Hiroyuki
Published: August 2020

Isolated adrenal metastasis of breast cancer is very rare, so adrenalectomy for breast cancer metastasis is rarely performed. The case of a breast cancer patient with five-year survival after resection of a left isolated adrenal metastasis is presented. A 70-year-old woman underwent left modified radical mastectomy and axillary lymphadenectomy for invasive ductal carcinoma (T2N1M0) 9 years earlier. At regular follow-up, a left adrenal mass, 4 cm in diameter, was seen on ultrasound examination and computed tomography (CT). Endoscopic adrenalectomy was performed. Pathological examination confirmed isolated adrenal metastasis of breast cancer. After surgery, hormone therapy was given for 5 years. Ten years after adrenalectomy, no metastatic lesions in other organs have been found on CT. Adrenalectomy for a metastatic adrenal tumor of breast cancer may provide survival benefits when combined with systemic hormone therapy and chemotherapy, particularly in patients with disease confined to the adrenal glands.
YOLO-Fine: One-Stage Detector of Small Objects Under Various Backgrounds in Remote Sensing Images
Authors: Pham Minh-Tan , Courtrai Luc , Friguet Chloé , Lefèvre Sébastien , Baussard Alexandre
Published: August 2020

Object detection from aerial and satellite remote sensing images has been an active research topic over the past decade. Thanks to the increase in computational resources and data availability, deep learning-based object detection methods have achieved numerous successes in computer vision, and more recently in remote sensing. However, the ability of current detectors to deal with (very) small objects still remains limited. In particular, the fast detection of small objects from a large observed scene is still an open question. In this work, we address this challenge and introduce an enhanced one-stage deep learning-based detection model, called You Only Look Once (YOLO)-fine, which is based on the structure of YOLOv3. Our detector is designed to be capable of detecting small objects with high accuracy and high speed, allowing further real-time applications within operational contexts. We also investigate its robustness to the appearance of new backgrounds in the validation set, thus tackling the issue of domain adaptation that is critical in remote sensing. Experimental studies that were conducted on both aerial and satellite benchmark datasets show some significant improvement of YOLO-fine as compared to other state-of-the art object detectors.
Using UAV Collected RGB and Multispectral Images to Evaluate Winter Wheat Performance across a Site Characterized by Century-Old Biochar Patches in Belgium
Authors: Heidarian Dehkordi Ramin , Burgeon Victor , Fouche Julien , Placencia Gomez Edmundo , Cornelis Jean-Thomas , Nguyen Frederic , Denis Antoine , Meersmans Jeroen
Published: August 2020

Remote sensing data play a crucial role in monitoring crop dynamics in the context of precision agriculture by characterizing the spatial and temporal variability of crop traits. At present there is special interest in assessing the long-term impacts of biochar in agro-ecosystems. Despite the growing body of literature on monitoring the potential biochar effects on harvested crop yield and aboveground productivity, studies focusing on the detailed crop performance as a consequence of long-term biochar enrichment are still lacking. The primary objective of this research was to evaluate crop performance based on high-resolution unmanned aerial vehicle (UAV) imagery considering both crop growth and health through RGB and multispectral analysis, respectively. More specifically, this approach allowed monitoring of century-old biochar impacts on winter wheat crop performance. Seven Red-Green-Blue (RGB) and six multispectral flights were executed over 11 century-old biochar patches of a cultivated field. UAV-based RGB imagery exhibited a significant positive impact of century-old biochar on the evolution of winter wheat canopy cover (p-value = 0.00007). Multispectral optimized soil adjusted vegetation index indicated a better crop development over the century-old biochar plots at the beginning of the season (p-values < 0.01), while there was no impact towards the end of the season. Plant height, derived from the RGB imagery, was slightly higher for century-old biochar plots. Crop health maps were computed based on principal component analysis and k-means clustering. To our knowledge, this is the first attempt to quantify century-old biochar effects on crop performance during the entire growing period using remotely sensed data. Ground-based measurements illustrated a significant positive impact of century-old biochar on crop growth stages (p-value of 0.01265), whereas the harvested crop yield was not affected. Multispectral simplified canopy chlorophyll content index and normalized difference red edge index were found to be good linear estimators of harvested crop yield (p-value(Kendall) of 0.001 and 0.0008, respectively). The present research highlights that other factors (e.g., inherent pedological variations) are of higher importance than the presence of century-old biochar in determining crop health and yield variability.
Landslide Susceptibility Assessment of Wildfire Burnt Areas through Earth-Observation Techniques and a Machine Learning-Based Approach
Authors: Di Napoli Mariano , Marsiglia Palmira , Di Martire Diego , Ramondini Massimo , Ullo Silvia Liberata , Calcaterra Domenico
Published: August 2020

Climate change has increased the likelihood of the occurrence of disasters like wildfires, floods, storms, and landslides worldwide in the last years. Weather conditions change continuously and rapidly, and wildfires are occurring repeatedly and diffusing with higher intensity. The burnt catchments are known, in many parts of the world, as one of the main sensitive areas to debris flows characterized by different trigger mechanisms (runoff-initiated and debris slide-initiated debris flow). The large number of studies produced in recent decades has shown how the response of a watershed to precipitation can be extremely variable, depending on several on-site conditions, as well as the characteristics of precipitation duration and intensity. Moreover, the availability of satellite data has significantly improved the ability to identify the areas affected by wildfires, and, even more importantly, to carry out post-fire assessment of burnt areas. Many difficulties have to be faced in attempting to assess landslide risk in burnt areas, which present a higher likelihood of occurrence; in densely populated neighbourhoods, human activities can be the cause of the origin of the fires. The latter is, in fact, one of the main operations used by man to remove vegetation along slopes in an attempt to claim new land for pastures or construction purposes. Regarding the study area, the Camaldoli and Agnano hill (Naples, Italy) fires seem to act as a predisposing factor, while the triggering factor is usually represented by precipitation. Eleven predisposing factors were chosen and estimated according to previous knowledge of the territory and a database consisting of 400 landslides was adopted. The present work aimed to expand the knowledge of the relationship existing between the triggering of landslides and burnt areas through the following phases: (1) Processing of the thematic maps of the burnt areas through band compositions of satellite images; and (2) landslide susceptibility assessment through the application of a new statistical approach (machine learning techniques). The analysis has the scope to support decision makers and local agencies in urban planning and safety monitoring of the environment.
Integrated Satellite–Terrestrial Connectivity for Autonomous Ships: Survey and Future Research Directions
Authors: Höyhtyä Marko , Martio Jussi
Published: August 2020

An autonomous vessel uses multiple different radio technologies such as satellites, mobile networks and dedicated narrowband systems, to connect to other ships, services, and the remote operations center (ROC). In-ship communication is mainly implemented with wired technologies but also wireless links can be used. In this survey paper, we provide a short overview of autonomous and remote-controlled systems. This paper reviews 5G-related standardization in the maritime domain, covering main use cases and both the role of autonomous ships and that of people onboard. We discuss the concept of a connectivity manager, an intelligent entity that manages complex set of technologies, integrating satellite and terrestrial technologies together, ensuring robust in-ship connections and ship-to-outside connections in any environment. This survey paper describes the architecture and functionalities of connectivity management required for an autonomous ship to be able to operate globally. As a specific case example, we have implemented a research environment consisting of ship simulators with connectivity components. Our simulation results on the effects of delays to collision avoidance confirm the role of reliable connectivity for safety. Finally, we outline future research directions for autonomous ship connectivity research, providing ideas for further work.
Use of Remote Sensing in Comprehending the Influence of Urban Landscape’s Composition and Configuration on Land Surface Temperature at Neighbourhood Scale
Authors: Ejiagha Ifeanyi R. , Ahmed M. Razu , Hassan Quazi K. , Dewan Ashraf , Gupta Anil , Rangelova Elena
Published: August 2020

The spatial composition and configuration of land use land cover (LULC) in the urban landscape impact the land surface temperature (LST). In this study, we assessed such impacts at the neighbourhood level of the City of Edmonton. In doing so, we employed Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) satellite images to derive LULC and LST maps, respectively. We used three classification methods, such as ISODATA, random forest, and indices-based, for mapping LULC classes including built-up, water, and green. We obtained the highest overall accuracy of 98.53 and 97.90% with a kappa value of 0.96 and 0.92 in the indices-based method for the 2018 and 2015 LULC maps, respectively. Besides, we estimated the LST map from the brightness temperature using a single-channel algorithm. Our analysis showed that the highest contributors to LST were the industrial (303.51 K in 2018 and 295.99 K in 2015) and residential (303.47 K in 2018 and 296.56 K in 2015) neighbourhoods, and the lowest contributor was the riverine/creek (298.77 K in 2018 and 292.89 K in 2015) during the 2018 late summer and 2015 early spring seasons. We also found that the residential neighbourhoods exhibited higher LST in comparison with the industrial with the same LULC composition. The result was also supported by our surface albedo analysis, where industrial and residential neighbourhoods were giving higher and lower albedo values, respectively. This indicated that the rooftop materials played further role in impacting the LST. In addition, our spatial autocorrelation (local Moran’s I) and proximity (near distance) analyses revealed that the structural configurations would additionally play an important role in contributing to the LST in the neighbourhoods. For example, the cluster pattern with a small gap of minimum 2.4 m between structures in the residential neighbourhoods were showing higher LST in compared with the sparse pattern, with large gaps between structures in the industrial areas. The wide passages for wind flow through the large gaps would be responsible for cooling the LST in the industrial neighbourhoods. The outcomes of this study would help planners in planning and designing urban neighbourhoods, and policymakers and stakeholders in developing strategies to balance surface energy and mitigate local warming.
Deep Learning Based Multi-Modal Fusion Architectures for Maritime Vessel Detection
Authors: Farahnakian Fahimeh , Heikkonen Jukka
Published: August 2020

Object detection is a fundamental computer vision task for many real-world applications. In the maritime environment, this task is challenging due to varying light, view distances, weather conditions, and sea waves. In addition, light reflection, camera motion and illumination changes may cause to false detections. To address this challenge, we present three fusion architectures to fuse two imaging modalities: visible and infrared. These architectures can provide complementary information from two modalities in different levels: pixel-level, feature-level, and decision-level. They employed deep learning for performing fusion and detection. We investigate the performance of the proposed architectures conducting a real marine image dataset, which is captured by color and infrared cameras on-board a vessel in the Finnish archipelago. The cameras are employed for developing autonomous ships, and collect data in a range of operation and climatic conditions. Experiments show that feature-level fusion architecture outperforms the state-of-the-art other fusion level architectures.
Evaluation of Assimilating FY-3C MWHS-2 Radiances Using the GSI Global Analysis System
Authors: Jiang Lipeng , Shi Chunxiang , Zhang Tao , Guo Yang , Yao Shuang
Published: August 2020

The MicroWave Humidity Sounder 2 (MWHS-2) onboard the FY-3C satellite provides an extra important data source for atmospheric water vapor monitoring besides the Microwave Humidity Sounder (MHS) and the Advanced Technology Microwave Sounder (ATMS). This paper introduces MWHS-2 radiance data into the community Gridpoint Statistical Interpolation (GSI) global analysis system. More than one-year cycling assimilation experiments with and without MWHS-2 data are performed. Results show that MWHS-2 has similar data quality to MHS and ATMS. The biases of MWHS-2 are stable except some sudden jumps that can be removed nicely by the variational bias correction scheme within GSI. Assimilating MWHS-2 makes the 6-h forecasts fit more closely to radiosonde observations, with a reduction of 0.55–1% for the observation-minus-simulation standard deviation of specific humidity. The 500 hPa geopotential height anomaly correlation scores are increased by around 0.006 for the 144-h forecast, indicating that assimilating MWHS-2 may also help to improve 3–8-day forecasts.
Measuring Functional Urban Shrinkage with Multi-Source Geospatial Big Data: A Case Study of the Beijing-Tianjin-Hebei Megaregion
Authors: Ma Qiwei , Gong Zhaoya , Kang Jing , Tao Ran , Dang Anrong
Published: August 2020

Most of the shrinking cities experience an unbalanced deurbanization across different urban areas in cities. However, traditional ways of measuring urban shrinkage are focused on tracking population loss at the city level and are unable to capture the spatially heterogeneous shrinking patterns inside a city. Consequently, the spatial mechanism and patterns of urban shrinkage inside a city remain less understood, which is unhelpful for developing accommodation strategies for shrinkage. The smart city initiatives and practices have provided a rich pool of geospatial big data resources and technologies to tackle the complexity of urban systems. Given this context, we propose a new measure for the delineation of shrinking areas within cities by introducing a new concept of functional urban shrinkage, which aims to capture the mismatch between urban built-up areas and the areas where significantly intensive human activities take place. Taking advantage of a data fusion approach to integrating multi-source geospatial big data and survey data, a general analytical framework is developed to construct functional shrinkage measures. Specifically, Landsat-8 remote sensing images were used for extracting urban built-up areas by supervised neural network classifications and Geographic Information System tools, while cellular signaling data from China Unicom Inc. was used to depict human activity areas generated by spatial clustering methods. Combining geospatial big data with urban land-use functions obtained from land surveys and Points-Of-Interests data, the framework further enables the comparison between cities from dimensions characterized by indices of spatial and urban functional characteristics and the landscape fragmentation; thus, it has the capacity to facilitate an in-depth investigation of fundamental causes and internal mechanisms of urban shrinkage. With a case study of the Beijing-Tianjin-Hebei megaregion using data from various sources collected for the year of 2018, we demonstrate the validity of this approach and its potential generalizability for other spatial contexts in facilitating timely and better-informed planning decision support.
Landscape Dynamics in an Iconic Watershed of Northwestern Mexico: Vegetation Condition Insights Using Landsat and PlanetScope Data
Authors: Cornejo-Denman Lara , Romo-Leon Jose Raul , Hartfield Kyle , van Leeuwen Willem J. D. , Ponce-Campos Guillermo E. , Castellanos-Villegas Alejandro
Published: August 2020

Natural vegetation in arid and semi-arid environments of Northwestern Mexico has been subject to transformation due to extensive and intensive human occupation related mostly to primary activities. Keystone habitats such as riparian ecosystems are extremely sensitive to land use changes that occur in their surrounding landscape. In this study, we developed remote sensing-based land cover classifications and post-classification fragmentation analysis, by using data from Landsat’s moderate resolution sensors Thematic Mapper and Operational Land Imager (TM and OLI) to assess land use changes and the shift in landscape configuration in a riparian corridor of a dynamic watershed in central Sonora during the last 30 years. In addition, we derived a high spatial resolution classification (using PlanetScope-PS2 imagery) to assess the “recent state” of the riparian corridor. According to our results, riparian vegetation has increased by 40%, although only 9% of this coverage corresponds to obligate riparian species. Scrub area shows a declining trend, with a loss of more than 17,000 ha due to the expansion of mesquite and buffelgrass-dominated areas. The use of moderate resolution Landsat data was essential to register changes in vegetation cover through time, however, higher resolution PlanetScope data were fundamental for the detection of limited aerial extent classes such as obligate riparian vegetation. The unregulated development of anthropogenic activities is suggested to be the main driver of land cover change processes for arid ecosystems in this region. These results highlight the urgent need for alternative management and restoration projects in an area where there is almost a total lack of protection regulations or conservation efforts.
Chimney Detection Based on Faster R-CNN and Spatial Analysis Methods in High Resolution Remote Sensing Images
Authors: Han Chunming , Li Guangfu , Ding Yixing , Yan Fuli , Bai Linyan
Published: August 2020

Spatially location and working status of pollution sources are very important pieces of information for environment protection. Waste gas produced by fossil fuel consumption in the industry is mainly discharged to the atmosphere through a chimney. Therefore, detecting the distribution of chimneys and their working status is of great significance to urban environment monitoring and environmental governance. In this paper, we use an open access dataset BUAA-FFPP60 and the faster regions with convolutional neural network (Faster R-CNN) algorithm to train the preliminarily detection model. Then, the trained model is used to detect the chimneys in three high-resolution remote sensing images of Google Maps, which is located in Tangshan city. The results show that a large number of false positive targets are detected. For working chimney detection, the recall rate is 77.27%, but the precision is only 40.47%. Therefore, two spatial analysis methods, the digital terrain model (DTM) filtering, and main direction test are introduced to remove the false chimneys. The DTM is generated by ZiYuan-3 satellite images and then registered to the high-resolution image. We set an elevation threshold to filter the false positive targets. After DTM filtering, we use principle component analysis (PCA) to calculate the main direction of each target image slice, and then use the main direction to remove false positive targets further. The results show that by using the combination of DTM filtering and main direction test, more than 95% false chimneys can be removed and, therefore, the detection precision is significantly increased.
Absolute Positioning Accuracy Improvement in an Industrial Robot
Authors: Jiang Yizhou , Yu Liandong , Jia Huakun , Zhao Huining , Xia Haojie
Published: August 2020

The absolute positioning accuracy of a robot is an important specification that determines its performance, but it is affected by several error sources. Typical calibration methods only consider kinematic errors and neglect complex non-kinematic errors, thus limiting the absolute positioning accuracy. To further improve the absolute positioning accuracy, we propose an artificial neural network optimized by the differential evolution algorithm. Specifically, the structure and parameters of the network are iteratively updated by differential evolution to improve both accuracy and efficiency. Then, the absolute positioning deviation caused by kinematic and non-kinematic errors is compensated using the trained network. To verify the performance of the proposed network, the simulations and experiments are conducted using a six-degree-of-freedom robot and a laser tracker. The robot average positioning accuracy improved from 0.8497 mm before calibration to 0.0490 mm. The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed network on an industrial robot.
Sensors 2020 Best Paper Awards
Authors:   Sensors Editorial Office
Published: August 2020

Sensors is instituting the Best Paper Award to recognize outstanding papers published in the journal [...]
Prediction of Soil Organic Carbon in a New Target Area by Near-Infrared Spectroscopy: Comparison of the Effects of Spiking in Different Scale Soil Spectral Libraries
Authors: Li Hongyang , Jia Shengyao , Le Zichun
Published: August 2020

Near-infrared (NIR) spectroscopy is widely used to predict soil organic carbon (SOC) because it is rapid and accurate under proper calibration. However, the prediction accuracy of the calibration model may be greatly reduced if the soil characteristics of some new target areas are different from the existing soil spectral library (SSL), which greatly limits the application potential of the technology. We attempted to solve the problem by building a large-scale SSL or using the spiking method. A total of 983 soil samples were collected from Zhejiang Province, and three SSLs were built according to geographic scope, representing the provincial, municipal, and district scales. The partial least squares (PLS) algorithm was applied to establish the calibration models based on the three SSLs, and the models were used to predict the SOC of two target areas in Zhejiang Province. The results show that the prediction accuracy of each model was relatively poor regardless of the scale of the SSL (residual predictive deviation (RPD) < 2.5). Then, the Kennard-Stone (KS) algorithm was applied to select 5 or 10 spiking samples from each target area. According to different SSLs and numbers of spiking samples, different spiked models were established by the PLS. The results show that the predictive ability of each model was improved by the spiking method, and the improvement effect was inversely proportional to the scale of the SSL. The spiked models built by combining the district scale SSL and a few spiking samples achieved good prediction of the SOC of two target areas (RPD = 2.72 and 3.13). Therefore, it is possible to accurately measure the SOC of new target areas by building a small-scale SSL with a few spiking samples.