bluuebunny/crossref_metadata_2025 huggingface dataset:
The Surplus Transplant Lung Allocation System in Italy: An Evaluation of the Allocation Process via Stochastic Modeling
Authors: Lanera Corrado , Ocagli Honoria , Schiavon Marco , Dell’Amore Andrea , Bottigliengo Daniele , Bartolotta Patrizia , Acar Aslihan Senturk , Lorenzoni Giulia , Berchialla Paola , Baldi Ileana , Rea Federico , Gregori Dario
Published: July 2021

Background: Lung transplantation is a specialized procedure used to treat chronic end-stage respiratory diseases. Due to the scarcity of lung donors, constructing fair and equitable lung transplant allocation methods is an issue that has been addressed with different strategies worldwide. This work aims to describe how Italy’s “national protocol for the management of surplus organs in all transplant programs” functions through an online app to allocate lung transplants. We have developed two probability models to describe the allocation process among the various transplant centers. An online app was then created. The first model considers conditional probabilities based on a protocol flowchart to compute the probability for each area and transplant center to receive each n-th organ in the period considered. The second probability model is based on the generalization of the binomial distribution to correlated binary variables, which is based on Bahadur’s representation, to compute the cumulative probability for each transplant center to receive at least nth organs. Our results show that the impact of the allocation of a surplus organ depends mostly on the region where the organ was donated. The discrepancies shown by our model may be explained by a discrepancy between the northern and southern regions in relation to the number of organs donated.
Climate Change and Its Impacts on Farmer’s Livelihood in Different Physiographic Regions of the Trans-Boundary Koshi River Basin, Central Himalayas
Authors: Paudel Basanta , Wang Zhaofeng , Zhang Yili , Rai Mohan Kumar , Paul Pranesh Kumar
Published: July 2021

The impact of climate change on farmers’ livelihoods has been observed in various forms at the local and regional scales. It is well known that the Himalayas region is affected by climate change, as reflected in the basic knowledge of farmers in the region. A questionnaire-based survey involving a total of 747 households was conducted to gather information on climate change and its impact, where the survey addressed four physiographic regions of the trans-boundary Koshi River Basin (KRB). Moreover, climatic data were used to calculate climatic trends between 1980 and 2018. The Mann–Kendall trend test was performed and the Sen’s slope calculated to analyze the inter-annual climatic trends over time. The survey noted that, for the basin, there was an increase in temperature, climate-induced diseases of crops, an increase in the frequency of pests as well as drought and floods and a decrease in rainfall, all which are strong indicators of climate change. It was perceived that these indicators had adverse impacts on crop production (89.4%), human health (82.5%), livestock (68.7%) and vegetation (52.1%). The observed climatic trends for all the physiographic regions included an increasing temperature trend and a decreasing rainfall trend. The rate of change varied according to each region, hence strongly supporting the farmers’ local knowledge of climate change. The highest increasing trend of temperature noted in the hill region at 0.0975 °C/a (p = 0.0002) and sharpest decreasing trend of rainfall in the mountain region by −10.424 mm/a (p = 0.016) between 1980 and 2018. Formulation of suitable adaptation strategies according to physiographic region can minimize the impact of climate change. New adaptation strategies proposed include the introduction of infrastructure for irrigation systems, the development of crop seeds that are more tolerant to drought, pests and disease tolerance, and the construction of local hospitals for the benefit of farming communities.
A Delphi Study on the Healthcare Needs of Patients with Type 1 Diabetes during the Transition from Adolescence to Adulthood: Consensus among Patients, Primary Caregivers, and Healthcare Providers
Authors: Chiang Yuehtao , Tsay Peikwei , Chen Chiwen , Hsu Chienlung , Yu Hsingyi , Chang Chiwen , Lo Fusung , Moons Philip
Published: July 2021

Patients with type 1 diabetes mellitus at the age of 16–25 face the challenges of the deterioration of disease control and accelerated exacerbation. Providing interventions that meet patient’s healthcare needs can reduce the impact and improve health outcome. The purpose of this study was to identify the healthcare needs of patients with type 1 diabetes during the adolescence to adulthood transition period from the perspectives of patients, parents and healthcare providers. A two-round Delphi study was conducted among 48 participants, and included 17 patients, 16 primary caregivers, and 15 healthcare providers. The central tendency and dispersion were computed to establish a consensus. Seventy-one healthcare needs were identified across five dimensions—technology, external support, internal support, management, and healthcare—and 56 were considered as important healthcare needs and with a moderate to high level of agreement. Meanwhile, patients, primary caregivers, and healthcare providers were found to display significantly different opinions (p < 0.05) for 23 healthcare needs. This study concluded the consensus of the healthcare needs of patients with type 1 diabetes mellitus during the adolescence to adulthood transition period from a systematic investigation. The findings can serve as reference for developing transitional intervention strategies.
Carbamazepine and Diclofenac Removal Double Treatment: Oxidation and Adsorption
Authors: Aldeguer Esquerdo Alejandro , Varo Galvañ Pedro José , Sentana Gadea Irene , Prats Rico Daniel
Published: July 2021

In the present research, the effect of two hybrid treatments, ozone followed by powdered activated carbon (PAC) or PAC followed by ozone (O3), was studied for the removal of two drugs present in water: diclofenac and carbamazepine. In the study, two initial concentrations of each of the contaminants, 0.7 mg L−1 and 1.8 mg L−1, were used. Different doses of PAC between 4–20 mg L−1 were studied as variables, as well as different doses of O3 between 0.056–0.280 mg L−1. The evolution of the concentration of each contaminant over time was evaluated. From the results obtained, it was concluded that the combined treatment with ozone followed by PAC reduces between 50% and 75% the time required to achieve 90% removal of diclofenac when compared with the time required when only activated carbon was used. In the case of carbamazepine, the time required was 97% less. For carbamazepine, to achieve reduction percentages of up to 90%, O3 treatment followed by PAC acted faster than PAC followed by O3. In the case of diclofenac, PAC treatment followed by O3 was faster to reach concentrations of up to 90%. However, to reach yields below 80%, O3 treatment followed by PAC was more efficient.
Complementary Feeding Methods—A Review of the Benefits and Risks
Authors: Boswell Nikki
Published: July 2021

Complementary feeding methods have the potential to not only ensure a diet of nutritional adequacy but also promote optimal food-related behaviours and skills. While the complementary feeding practice known as baby-led weaning (BLW) has gained popularity, evidence supporting the potential benefits and/or risks for infant growth, development, and health warrants consideration. A review of 29 studies was conducted with findings indicating that parents who implement BLW typically have higher levels of education, breastfeed for longer, and differ in other personality traits. Fear of choking was an important factor in parents’ decision not to implement BLW; however, this fear was not supported by the literature. Benefits of BLW included lower food fussiness, higher food enjoyment, lower food responsiveness, and higher satiety responsiveness. While this profile of eating behaviours confers a reduced obesity risk, few studies have examined the relationship between BLW and infant growth robustly. BLW does not seem to increase the risk of inadequate zinc or iron intake; however, emphasis needs to be given to ensuring adequate intake of these micronutrients among all infants. A better understanding of the impacts of BLW is needed to inform evidence-based recommendations to support and guide parents in complementary feeding methods.
A Systematic Review of the Current Measures of Theory of Mind in Adults with Schizophrenia
Authors: Yeh Ya-Chin , Lin Chung-Ying , Li Ping-Chia , Hung Chi-Fa , Cheng Chun-Hua , Kuo Ming-Hui , Chen Kuan-Lin
Published: July 2021

Adults with schizophrenia usually have impairments in theory of mind (ToM), which subsequently cause them problems in social interaction. Therefore, it is important for healthcare providers to assess their ToM using adequate measures. This systematic review evaluated current ToM measures (or ToM tasks) for adults with schizophrenia and summarized their specific characteristics, including the concept and construct, administration, and psychometric properties. From a review of 117 articles, 13 types of ToM tasks were identified, and the findings from these articles were qualitatively synthesized. The results showed that ToM tasks are diverse in their presentation modalities, answer modes, strategies of controlling cognitive confounders, and scoring. Most tasks employ cognitive and affective dimensions and target a specific, single ToM concept. The present systematic review found that psychometric evidence supporting the ToM tasks, such as internal consistency, test–retest reliability, unidimensionality, and convergent, criterion, and ecological validities, is insufficient. Based on the results, we propose several principles for selecting appropriate ToM tasks in practice, e.g., selecting a task with multiple ToM concepts, or an exclusive ToM construct containing the cognitive and affective dimensions. Moreover, future studies are needed to provide more psychometric evidence on each type of ToM task applied in people with schizophrenia.
Evaluation of the Bioaerosol Inactivation Ability of Chitosan-Coated Antimicrobial Filters
Authors: Hsu Ying-Fang , Chuang Chi-Yu , Yang Shinhao
Published: July 2021

This work considers the enhancement of indoor bioaerosol removal efficiency by liquid coating of the antimicrobial agent chitosan onto polypropylene fibrous filters (CCFs). Escherichia coli (E. coli) and Bacillus subtilis (B. subtilis) were chosen as the tested bioaerosols. The results revealed that 2.5% (w/w) of CCFs have significantly higher bioaerosol survival capability (23% and 34% of E. coli and B. subtilis, respectively), compared to an untreated filter (65% and 64% for E. coli and B. subtilis, respectively). Increasing face velocity and relative humidity during operating CCFs could reduce the bioaerosol removal capability. The regression analysis of the experimental findings demonstrated that the higher coating concentration of chitosan had the most positive influence on bioaerosol removal, while the face velocity and relative humidity had a negative influence, but a milder effect was observed (R2 = 0.83 and 0.81 for E. coli and B. subtilis bioaerosols, respectively). A CCF-loaded air-cleaning device was tested in a real indoor environment and resulted in 80.1% bioaerosol removal within 3 h of operating, which suggests that the chitosan-coated filter has the potential for further application in improving indoor air quality in the future.
Potential of Telomerase in Age-Related Macular Degeneration—Involvement of Senescence, DNA Damage Response and Autophagy and a Key Role of PGC-1α
Authors: Blasiak Janusz , Szczepanska Joanna , Fila Michal , Pawlowska Elzbieta , Kaarniranta Kai
Published: July 2021

Age-related macular degeneration (AMD), the main cause of vision loss in the elderly, is associated with oxidation in the retina cells promoting telomere attrition. Activation of telomerase was reported to improve macular functions in AMD patients. The catalytic subunit of human telomerase (hTERT) may directly interact with proteins important for senescence, DNA damage response, and autophagy, which are impaired in AMD. hTERT interaction with mTORC1 (mTOR (mechanistic target of rapamycin) complex 1) and PINK1 (PTEN-induced kinase 1) activates macroautophagy and mitophagy, respectively, and removes cellular debris accumulated over AMD progression. Ectopic expression of telomerase in retinal pigment epithelium (RPE) cells lengthened telomeres, reduced senescence, and extended their lifespan. These effects provide evidence for the potential of telomerase in AMD therapy. Peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) may be involved in AMD pathogenesis through decreasing oxidative stress and senescence, regulation of vascular endothelial growth factor (VEGF), and improving autophagy. PGC-1α and TERT form an inhibitory positive feedback loop. In conclusion, telomerase activation and its ectopic expression in RPE cells, as well as controlled clinical trials on the effects of telomerase activation in AMD patients, are justified and should be assisted by PGC-1α modulators to increase the therapeutic potential of telomerase in AMD.
Effect of Short-Term Cold Treatment on Carbohydrate Metabolism in Potato Leaves
Authors: Orzechowski Sławomir , Sitnicka Dorota , Grabowska Agnieszka , Compart Julia , Fettke Joerg , Zdunek-Zastocka Edyta
Published: July 2021

Plants are often challenged by an array of unfavorable environmental conditions. During cold exposure, many changes occur that include, for example, the stabilization of cell membranes, alterations in gene expression and enzyme activities, as well as the accumulation of metabolites. In the presented study, the carbohydrate metabolism was analyzed in the very early response of plants to a low temperature (2 °C) in the leaves of 5-week-old potato plants of the Russet Burbank cultivar during the first 12 h of cold treatment (2 h dark and 10 h light). First, some plant stress indicators were examined and it was shown that short-term cold exposure did not significantly affect the relative water content and chlorophyll content (only after 12 h), but caused an increase in malondialdehyde concentration and a decrease in the expression of NDA1, a homolog of the NADH dehydrogenase gene. In addition, it was shown that the content of transitory starch increased transiently in the very early phase of the plant response (3–6 h) to cold treatment, and then its decrease was observed after 12 h. In contrast, soluble sugars such as glucose and fructose were significantly increased only at the end of the light period, where a decrease in sucrose content was observed. The availability of the monosaccharides at constitutively high levels, regardless of the temperature, may delay the response to cold, involving amylolytic starch degradation in chloroplasts. The decrease in starch content, observed in leaves after 12 h of cold exposure, was preceded by a dramatic increase in the transcript levels of the key enzymes of starch degradation initiation, the α-glucan, water dikinase (GWD-EC 2.7.9.4) and the phosphoglucan, water dikinase (PWD-EC 2.7.9.5). The gene expression of both dikinases peaked at 9 h of cold exposure, as analyzed by real-time PCR. Moreover, enhanced activities of the acid invertase as well as of both glucan phosphorylases during exposure to a chilling temperature were observed. However, it was also noticed that during the light phase, there was a general increase in glucan phosphorylase activities for both control and cold-stressed plants irrespective of the temperature. In conclusion, a short-term cold treatment alters the carbohydrate metabolism in the leaves of potato, which leads to an increase in the content of soluble sugars.
Isolation and Characterization of Barley (Hordeum vulgare) Extracellular Vesicles to Assess Their Role in RNA Spray-Based Crop Protection
Authors: Schlemmer Timo , Barth Patrick , Weipert Lisa , Preußer Christian , Hardt Martin , Möbus Anna , Busche Tobias , Koch Aline
Published: July 2021

The demonstration that spray-induced gene silencing (SIGS) can confer strong disease resistance, bypassing the laborious and time-consuming transgenic expression of double-stranded (ds)RNA to induce the gene silencing of pathogenic targets, was ground-breaking. However, future field applications will require fundamental mechanistic knowledge of dsRNA uptake, processing, and transfer. There is increasing evidence that extracellular vesicles (EVs) mediate the transfer of transgene-derived small interfering (si)RNAs in host-induced gene silencing (HIGS) applications. In this study, we establish a protocol for barley EV isolation and assess the possibilities for EVs regarding the translocation of sprayed dsRNA from barley (Hordeum vulgare) to its interacting fungal pathogens. We found barley EVs that were 156 nm in size, containing predominantly 21 and 19 nucleotide (nts) siRNAs, starting with a 5′-terminal Adenine. Although a direct comparison of the RNA cargo between HIGS and SIGS EV isolates is improper given their underlying mechanistic differences, we identified sequence-identical siRNAs in both systems. Overall, the number of siRNAs isolated from the EVs of dsRNA-sprayed barley plants with sequence complementarity to the sprayed dsRNA precursor was low. However, whether these few siRNAs are sufficient to induce the SIGS of pathogenic target genes requires further research. Taken together, our results raise the possibility that EVs may not be mandatory for the spray-delivered siRNA uptake and induction of SIGS.
Transmembrane Chloride Intracellular Channel 1 (tmCLIC1) as a Potential Biomarker for Personalized Medicine
Authors: Cianci Francesca , Verduci Ivan
Published: July 2021

Identification of potential pathological biomarkers has proved to be essential for understanding complex and fatal diseases, such as cancer and neurodegenerative diseases. Ion channels are involved in the maintenance of cellular homeostasis. Moreover, loss of function and aberrant expression of ion channels and transporters have been linked to various cancers, and to neurodegeneration. The Chloride Intracellular Channel 1 (CLIC1), CLIC1 is a metamorphic protein belonging to a partially unexplored protein superfamily, the CLICs. In homeostatic conditions, CLIC1 protein is expressed in cells as a cytosolic monomer. In pathological states, CLIC1 is specifically expressed as transmembrane chloride channel. In the following review, we trace the involvement of CLIC1 protein functions in physiological and in pathological conditions and assess its functionally active isoform as a potential target for future therapeutic strategies.
Cost-Effectiveness Analysis of COVID-19 Case Quarantine Strategies in Two Australian States: New South Wales and Western Australia
Authors: Melia Adrian , Lee Doowon , Mahmoudi Nader , Li Yameng , Paolucci Francesco
Published: July 2021

Two main strategies, home and hotel isolation, have been used to isolate COVID-19 cases in most countries. Both have proven to be somewhat medically effective, but the costs to produce the desired outcome remain unclear. We used a decision tree model to compare alternatives and a simulation model to determine the household structure and provide recommendations for the most cost-effective way to isolate a COVID-19 patient in two Australian States, New South Wales (NSW) and Western Australia (WA). The results show that although the average cost of isolating a confirmed case at home is lower than that of a hotel quarantine, it is demonstrable that the decision depends on household size and the ages of household members. If the household members’ ages are old or the household size is large, the expected mean cost of home quarantine might be higher than hotel quarantine. Our study, therefore, provides the government with a cost-effective insight into making quarantine policies.
Spatially Explicit Assessment of Suitable Conditions for the Sustainable Production of Aviation Fuels in Brazil
Authors: Walter Arnaldo , Seabra Joaquim , Rocha Jansle , Guarenghi Marjorie , Vieira Nathália , Damame Desirèe , Santos João Luís
Published: July 2021

International civil aviation strives to significantly reduce its greenhouse gas (GHG) emissions, and the use of Sustainable Aviation Fuels (SAF) is an alternative for such purpose. However, for an alternative fuel to be considered SAF, some conditions must be met, and production must be certified for sustainability. This paper presents an assessment of the necessary conditions for the sustainable production of these biofuels in Brazil. It is based on a geospatial publicly available database (SAFmaps) that was built with the aim of providing information to stakeholders who would be interested in the production of SAF. The geographic scope corresponds to an area that is about half of the country. The case studies reported in this paper are related to four crop-based feedstocks (eucalyptus, soybean, sugarcane, and corn), which could be used for SAF production, according to three certified routes (FT, HEFA, and ATJ) (Fischer–Tropsch, Hydroprocessed Esters and Fatty Acids and Alcohol to Jet); in total, six potential production sites were assessed. For each crop, the detailed assessment is based on estimates of suitability for biomass production, yields, and costs. The assumptions made allowed us to explicitly analyse the risk of deforestation (production could only occur with displacement of pastures) and the necessary preservation of sensitive biomes and of legally protected areas, in addition to observing the restrictive conditions imposed by CORSIA (Carbon Offsetting and Reduction Scheme for International Aviation). To reduce GHG emissions, transporting biomass over long distances was assumed to be only by rail or pipeline. In addition, we address alternatives to minimise the risks associated with induced land-use change (iLUC) and to reduce impacts on the landscape. The results show that the production of SAF through the ATJ route, using ethanol produced from sugarcane and corn, requires less land. Economic assessment was outside the scope of this paper.
Human Milk Endocannabinoid Levels as a Function of Obesity and Diurnal Rhythm
Authors: Datta Palika , Melkus Michael W. , Rewers-Felkins Kathleen , Patel Dhavalkumar , Bateman Tiffany , Baker Teresa , Hale Thomas W.
Published: July 2021

The endocannabinoid system is involved in the regulation of a variety of physiological and cognitive processes. While the endocannabinoids 2-arachidonoylglycerol (2-AG) and anandamide (N-arachidonoylethanolamine, AEA) have been found in breast milk, their role(s) have yet to be determined. This study determined the normal concentration ranges of endocannabinoids (2-AG and AEA) in breast milk and the influences, if any, of obesity and diurnal rhythms on their levels. Milk samples were collected from 36 breastfeeding mothers at 4–8 weeks postpartum at each feed over a 24-h period, and further stratified into three groups based on body mass index (BMI). The samples were analyzed using liquid chromatography mass spectrometry. AEA was below the limit of detection and 2-AG levels averaged 59.3 ± 18.3 ng/mL (± SD) in women with normal BMI. Wide-ranging 2-AG concentrations in the overweight (65.5 ± 41.9 ng/mL) /obese (66.1 ± 40.6 ng/mL) groups suggest BMI may be a contributing factor influencing its levels. Following a diurnal pattern, there was a significantly higher 2-AG concentration observed during the day, as compared to night time samples. In conclusion, our study clearly suggests that appropriate milk collection and storage conditions are critical. Further, body weight and diurnal rhythm appear to influence levels of 2-AG. Based on these results, future studies are underway to determine what specific roles endocannabinoids may play in human milk and how elevated levels of 2-AG may modulate infant appetite and health.
Maternal Diet, Nutritional Status, and Birth-Related Factors Influencing Offspring’s Bone Mineral Density: A Narrative Review of Observational, Cohort, and Randomized Controlled Trials
Authors: Masztalerz-Kozubek Daria , Zielinska-Pukos Monika A. , Hamulka Jadwiga
Published: July 2021

There is growing evidence that bone health may be programmed in the first years of life. Factors during the prenatal period, especially maternal nutrition, may have an influence on offspring’s skeletal development and thus the risk of osteoporosis in further life, which is an increasing societal, health and economic burden. However, it is still inconclusive which early life factors are the most important and to what extent they may affect bone health. We searched through three databases (PubMed, Google Scholar, Cochrane Library) and after eligibility criteria were met, the results of 49 articles were analyzed. This narrative review is an overall summary of up-to-date studies on maternal diet, nutritional status, and birth-related factors that may affect offspring bone development, particularly bone mineral density (BMD). Maternal vitamin D status and diet in pregnancy, anthropometry and birth weight seem to influence BMD, however other factors such as subsequent growth may mediate these associations. Due to the ambiguity of the results in the analyzed studies, future, well-designed studies are needed to address the limitations of the present study.
Preclinical Studies in Anti-Trypanosomatidae Drug Development
Authors: Perdomo Cintya , Aguilera Elena , Corvo Ileana , Faral-Tello Paula , Serna Elva , Robello Carlos , Wilkinson Shane R. , Yaluff Gloria , Alvarez Guzmán
Published: July 2021

The trypanosomatid parasites Trypanosoma brucei, Trypanosoma cruzi and Leishmania are the causative agents of human African trypanosomiasis, Chagas Disease and Leishmaniasis, respectively. These infections primarily affect poor, rural communities in the developing world, and are responsible for trapping sufferers and their families in a disease/poverty cycle. The development of new chemotherapies is a priority given that existing drug treatments are problematic. In our search for novel anti-trypanosomatid agents, we assess the growth-inhibitory properties of >450 compounds from in-house and/or “Pathogen Box” (PBox) libraries against L. infantum, L. amazonensis, L.braziliensis, T. cruzi and T. brucei and evaluate the toxicities of the most promising agents towards murine macrophages. Screens using the in-house series identified 17 structures with activity against and selective toward Leishmania: Compounds displayed 50% inhibitory concentrations between 0.09 and 25 μM and had selectivity index values >10. For the PBox library, ~20% of chemicals exhibited anti-parasitic properties including five structures whose activity against L. infantum had not been reported before. These five compounds displayed no toxicity towards murine macrophages over the range tested with three being active in an in vivo murine model of the cutaneous disease, with 100% survival of infected animals. Additionally, the oral combination of three of them in the in vivo Chagas disease murine model demonstrated full control of the parasitemia. Interestingly, phenotyping revealed that the reference strain responds differently to the five PBox-derived chemicals relative to parasites isolated from a dog. Together, our data identified one drug candidate that displays activity against Leishmania and other Trypanosomatidae in vitro and in vivo, while exhibiting low toxicity to cultured mammalian cells and low in vivo acute toxicity.
Mixed Noise Estimation Model for Optimized Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction
Authors: Xue Tianru , Wang Yueming , Chen Yuwei , Jia Jianxin , Wen Maoxing , Guo Ran , Wu Tianxiao , Deng Xuan
Published: July 2021

Dimensionality reduction (DR) is of great significance for simplifying and optimizing hyperspectral image (HSI) features. As a widely used DR method, kernel minimum noise fraction (KMNF) transformation preserves the high-order structures of the original data perfectly. However, the conventional KMNF noise estimation (KMNF-NE) uses the local regression residual of neighbourhood pixels, which depends heavily on spatial information. Due to the limited spatial resolution, there are many mixed pixels in HSI, making KMNF-NE unreliable for noise estimation and leading to poor performance in KMNF for classification on HSIs with low spatial resolution. In order to overcome this problem, a mixed noise estimation model (MNEM) is proposed in this paper for optimized KMNF (OP-KMNF). The MNEM adopts the sequential and linear combination of the Gaussian prior denoising model, median filter, and Sobel operator to estimate noise. It retains more details and edge features, making it more suitable for noise estimation in KMNF. Experiments using several HSI datasets with different spatial and spectral resolutions are conducted. The results show that, compared with some other DR methods, the improvement of OP-KMNF in average classification accuracy is up to 4%. To improve the efficiency, the OP-KMNF was implemented on graphics processing units (GPU) and sped up by about 60× compared to the central processing unit (CPU) implementation. The outcome demonstrates the significant performance of OP-KMNF in terms of classification ability and execution efficiency.
Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks
Authors: Qin Huaiyu , Zhao Buhui , Xu Leijun , Bai Xue
Published: July 2021

Power consumption in wireless sensor networks is high, and the lifetime of a battery has become a bottleneck, restricting network performance. Wireless power transfer with a ground mobile charger is vulnerable to interference from the terrain and other factors, and hence it is difficult to deploy in practice. Accordingly, a novel paradigm is adopted where a multi-UAV (unmanned aerial vehicle) with batteries can transfer power and information to SDs (sensor devices) in a large-scale sensor network. However, there are discrete events, continuous process, time delay, and decisions in such a complicated system. From the perspective of a hybrid system, a hybrid colored cyber Petri net system is proposed here to depict and analyze this problem. Furthermore, the energy utilization rate and information collection time delay are conflict with each other; therefore, UAV-aided wireless power and information transfer is formulated as a multi-objective optimization problem. For this reason, the MAC-NSGA II (multiple ant colony-nondominated sorting genetic algorithm II) is proposed in this work. Firstly, the optimal trajectory of multiple UAVs was obtained, and on this basis, the above two objectives were optimized simultaneously. Large-scale simulation results show that the proposed algorithm is superior to NSGA II and MOEA/D in terms of energy efficiency and information collection delay.
Global Sensitivity Analysis for Canopy Reflectance and Vegetation Indices of Mangroves
Authors: Niu Chunyue , Phinn Stuart , Roelfsema Chris
Published: July 2021

Remote sensing has been applied to map the extent and biophysical properties of mangroves. However, the impact of several critical factors, such as the fractional cover and leaf-to-total area ratio of mangroves, on their canopy reflectance have rarely been reported. In this study, a systematic global sensitivity analysis was performed for mangroves based on a one-dimensional canopy reflectance model. Different scenarios such as sparse or dense canopies were set up to evaluate the impact of various biophysical and environmental factors, together with their ranges and probability distributions, on simulated canopy reflectance spectra and selected Sentinel-2A vegetation indices of mangroves. A variance-based method and a density-based method were adopted to compare the computed sensitivity indices. Our results showed that the fractional cover and leaf-to-total area ratio of mangrove crowns were among the most influential factors for all examined scenarios. As for other factors, plant area index and water depth were influential for sparse canopies while leaf biochemical properties and inclination angles were more influential for dense canopies. Therefore, these influential factors may need attention when mapping the biophysical properties of mangroves such as leaf area index. Moreover, a tailored sensitivity analysis is recommended for a specific mapping application as the computed sensitivity indices may be different if a specific input configuration and sensitivity analysis method are adopted.
Increasing the Effectiveness of Active Learning: Introducing Artificial Data Generation in Active Learning for Land Use/Land Cover Classification
Authors: Fonseca Joao , Douzas Georgios , Bacao Fernando
Published: July 2021

In remote sensing, Active Learning (AL) has become an important technique to collect informative ground truth data “on-demand” for supervised classification tasks. Despite its effectiveness, it is still significantly reliant on user interaction, which makes it both expensive and time consuming to implement. Most of the current literature focuses on the optimization of AL by modifying the selection criteria and the classifiers used. Although improvements in these areas will result in more effective data collection, the use of artificial data sources to reduce human–computer interaction remains unexplored. In this paper, we introduce a new component to the typical AL framework, the data generator, a source of artificial data to reduce the amount of user-labeled data required in AL. The implementation of the proposed AL framework is done using Geometric SMOTE as the data generator. We compare the new AL framework to the original one using similar acquisition functions and classifiers over three AL-specific performance metrics in seven benchmark datasets. We show that this modification of the AL framework significantly reduces cost and time requirements for a successful AL implementation in all of the datasets used in the experiment.
Integrating Remote Sensing and a Markov-FLUS Model to Simulate Future Land Use Changes in Hokkaido, Japan
Authors: Chen Zhanzhuo , Huang Min , Zhu Daoye , Altan Orhan
Published: July 2021

As the second largest island in Japan, Hokkaido provides precious land resources for the Japanese people. Meanwhile, as the food base of Japan, the gradual decrease of the agricultural population and more intensive agricultural practices on Hokkaido have led its arable land use to change year by year, which has also caused changes to the whole land use pattern of the entire island of Hokkaido. To realize the sustainable use of land resources in Hokkaido, past and future changes in land use patterns must be investigated, and target-based land use planning suggestions should be given on this basis. This study uses remote sensing and GIS technology to analyze the temporal and spatial changes of land use in Hokkaido during the past two decades. The types of land use include cultivated land, forest, waterbody, construction, grassland, and others, by using the satellite images of the Landsat images in 2000, 2010, and 2019 to achieve this goal to make classification. In addition, this study used the coupled Markov-FLUS model to simulate and analyze the land use changes in three different scenarios in Hokkaido in the next 20 years. Scenario-based situational analysis shows that the cultivated land in Hokkaido will drop by about 25% in 2040 under the natural development scenario (ND), while the cultivated land area in Hokkaido will remain basically unchanged in cultivated land protection scenario (CP). In forest protection scenario (FP), the area of forest in Hokkaido will increase by 1580.8 km2. It is believed that the findings reveal that the forest land in Hokkaido has been well protected in the past and will be protected well in the next 20 years. However, in land use planning for future, Hokkaido government and enterprises should pay more attention to the protection of cultivated land.
Simulating Heat Stress of Coal Gangue Spontaneous Combustion on Vegetation Using Alfalfa Leaf Water Content Spectral Features as Indicators
Authors: Wang Qiyuan , Zhao Yanling , Yang Feifei , Liu Tao , Xiao Wu , Sun Haiyuan
Published: July 2021

Vegetation heat-stress assessment in the reclamation areas of coal gangue dumps is of great significance in controlling spontaneous combustion; through a temperature gradient experiment, we collected leaf spectra and water content data on alfalfa. We then obtained the optimal spectral features of appropriate leaf water content indicators through time series analysis, correlation analysis, and Lasso regression analysis. A spectral feature-based long short-term memory (SF-LSTM) model is proposed to estimate alfalfa’s heat stress level; the live fuel moisture content (LFMC) varies significantly with time and has high regularity. Correlation analysis of the raw spectrum, first-derivative spectrum, spectral reflectance indices, and leaf water content data shows that LFMC and spectral data were the most strongly correlated. Combined with Lasso regression analysis, the optimal spectral features were the first-derivative spectral value at 1661 nm (abbreviated as FDS (1661)), RVI (1525,1771), DVI (1412,740), and NDVI (1447,1803). When the classification strategies were divided into three categories and the time sequence length of the spectral features was set to five consecutive monitoring dates, the SF-LSTM model had the highest accuracy in estimating the heat stress level in alfalfa; the results provide an important theoretical basis and technical support for vegetation heat-stress assessment in coal gangue dump reclamation areas.
Optimization of X-ray Investigations in Dentistry Using Optical Coherence Tomography
Authors: Erdelyi Ralph-Alexandru , Duma Virgil-Florin , Sinescu Cosmin , Dobre George Mihai , Bradu Adrian , Podoleanu Adrian
Published: July 2021

The most common imaging technique for dental diagnoses and treatment monitoring is X-ray imaging, which evolved from the first intraoral radiographs to high-quality three-dimensional (3D) Cone Beam Computed Tomography (CBCT). Other imaging techniques have shown potential, such as Optical Coherence Tomography (OCT). We have recently reported on the boundaries of these two types of techniques, regarding. the dental fields where each one is more appropriate or where they should be both used. The aim of the present study is to explore the unique capabilities of the OCT technique to optimize X-ray units imaging (i.e., in terms of image resolution, radiation dose, or contrast). Two types of commercially available and widely used X-ray units are considered. To adjust their parameters, a protocol is developed to employ OCT images of dental conditions that are documented on high (i.e., less than 10 μm) resolution OCT images (both B-scans/cross sections and 3D reconstructions) but are hardly identified on the 200 to 75 μm resolution panoramic or CBCT radiographs. The optimized calibration of the X-ray unit includes choosing appropriate values for the anode voltage and current intensity of the X-ray tube, as well as the patient’s positioning, in order to reach the highest possible X-rays resolution at a radiation dose that is safe for the patient. The optimization protocol is developed in vitro on OCT images of extracted teeth and is further applied in vivo for each type of dental investigation. Optimized radiographic results are compared with un-optimized previously performed radiographs. Also, we show that OCT can permit a rigorous comparison between two (types of) X-ray units. In conclusion, high-quality dental images are possible using low radiation doses if an optimized protocol, developed using OCT, is applied for each type of dental investigation. Also, there are situations when the X-ray technology has drawbacks for dental diagnosis or treatment assessment. In such situations, OCT proves capable to provide qualitative images.
Optimization of AUTOSAR Communication Stack in the Context of Advanced Driver Assistance Systems
Authors: Bogdan Răzvan , Crișan-Vida Mihaela , Barmayoun Darius , Staicu Loredana Lavinia , Puiu Robert Valentin , Lup Mădălina , Marcu Marius
Published: July 2021

New trends in the automotive industry such as autonomous driving and Car2X require a large amount of data to be exchanged between different devices. Radar sensors are key components in developing vehicles of the future, therefore these devices are used in a large spectrum of applications, where data traffic is of paramount importance. As a result, communication traffic volumes have become more complex, leading to the research of optimization approaches to be applied at the AUTOSAR level. Our paper offers such an optimization solution at the AUTOSAR communication level. The radar sensor is accessed in a remote manner, and the experiments aimed at performance measurements revealed that our solution is superior to the Full AUTOSAR implementation in terms of memory usage and runtime measurements.
Estimating Vehicle and Pedestrian Activity from Town and City Traffic Cameras
Authors: Chen Li , Grimstead Ian , Bell Daniel , Karanka Joni , Dimond Laura , James Philip , Smith Luke , Edwardes Alistair
Published: July 2021

Traffic cameras are a widely available source of open data that offer tremendous value to public authorities by providing real-time statistics to understand and monitor the activity levels of local populations and their responses to policy interventions such as those seen during the COrona VIrus Disease 2019 (COVID-19) pandemic. This paper presents an end-to-end solution based on the Google Cloud Platform with scalable processing capability to deal with large volumes of traffic camera data across the UK in a cost-efficient manner. It describes a deep learning pipeline to detect pedestrians and vehicles and to generate mobility statistics from these. It includes novel methods for data cleaning and post-processing using a Structure SImilarity Measure (SSIM)-based static mask that improves reliability and accuracy in classifying people and vehicles from traffic camera images. The solution resulted in statistics describing trends in the ‘busyness’ of various towns and cities in the UK. We validated time series against Automatic Number Plate Recognition (ANPR) cameras across North East England, showing a close correlation between our statistical output and the ANPR source. Trends were also favorably compared against traffic flow statistics from the UK’s Department of Transport. The results of this work have been adopted as an experimental faster indicator of the impact of COVID-19 on the UK economy and society by the Office for National Statistics (ONS).
Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization/Maximization
Authors: Dehghani Mohammad , Trojovský Pavel
Published: July 2021

Population-based optimization algorithms are one of the most widely used and popular methods in solving optimization problems. In this paper, a new population-based optimization algorithm called the Teamwork Optimization Algorithm (TOA) is presented to solve various optimization problems. The main idea in designing the TOA is to simulate the teamwork behaviors of the members of a team in order to achieve their desired goal. The TOA is mathematically modeled for usability in solving optimization problems. The capability of the TOA in solving optimization problems is evaluated on a set of twenty-three standard objective functions. Additionally, the performance of the proposed TOA is compared with eight well-known optimization algorithms in providing a suitable quasi-optimal solution. The results of optimization of objective functions indicate the ability of the TOA to solve various optimization problems. Analysis and comparison of the simulation results of the optimization algorithms show that the proposed TOA is superior and far more competitive than the eight compared algorithms.
Dual Cancelled Channel STAP for Target Detection and DOA Estimation in Passive Radar
Authors: Blasone Giovanni Paolo , Colone Fabiola , Lombardo Pierfrancesco , Wojaczek Philipp , Cristallini Diego
Published: July 2021

This paper deals with the problem of detection and direction of arrival (DOA) estimation of slowly moving targets against clutter in multichannel mobile passive radar. A dual cancelled channel space-time adaptive processing (STAP) scheme is proposed, aiming at reducing the system computational complexity, as well as the amount of required training data, compared to a conventional full array solution. The proposed scheme is shown to yield comparable target detection capability and DOA estimation accuracy with respect to the corresponding full array solution, despite the lower computational cost required. Moreover, it offers increased robustness against adaptivity losses, operating effectively even in the presence of a limited set of training data, as often available in the highly non-homogeneous clutter scenarios experienced in bistatic passive radar. The effectiveness of the proposed scheme and its suitability for passive GMTI are demonstrated against both simulated and experimental data collected by a DVB-T-based multichannel mobile passive radar.
Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition
Authors: Arnia Fitri , Oktiana Maulisa , Saddami Khairun , Munadi Khairul , Roslidar Roslidar , Pradhan Biswajeet
Published: July 2021

Facial recognition has a significant application for security, especially in surveillance technologies. In surveillance systems, recognizing faces captured far away from the camera under various lighting conditions, such as in the daytime and nighttime, is a challenging task. A system capable of recognizing face images in both daytime and nighttime and at various distances is called Cross-Spectral Cross Distance (CSCD) face recognition. In this paper, we proposed a phase-based CSCD face recognition approach. We employed Homomorphic filtering as photometric normalization and Band Limited Phase Only Correlation (BLPOC) for image matching. Different from the state-of-the-art methods, we directly utilized the phase component from an image, without the need for a feature extraction process. The experiment was conducted using the Long-Distance Heterogeneous Face Database (LDHF-DB). The proposed method was evaluated in three scenarios: (i) cross-spectral face verification at 1m, (ii) cross-spectral face verification at 60m, and (iii) cross-spectral face verification where the probe images (near-infrared (NIR) face images) were captured at 1m and the gallery data (face images) was captured at 60 m. The proposed CSCD method resulted in the best recognition performance among the CSCD baseline approaches, with an Equal Error Rate (EER) of 5.34% and a Genuine Acceptance Rate (GAR) of 93%.
An Overview of CMOS Photodetectors Utilizing Current-Assistance for Swift and Efficient Photo-Carrier Detection
Authors: Jegannathan Gobinath , Seliuchenko Volodymyr , Van den Dries Thomas , Lapauw Thomas , Boulanger Sven , Ingelberts Hans , Kuijk Maarten
Published: July 2021

This review paper presents an assortment of research on a family of photodetectors which use the same base mechanism, current assistance, for the operation. Current assistance is used to create a drift field in the semiconductor, more specifically silicon, in order to improve the bandwidth and the quantum efficiency. Based on the detector and application, the drift field can be static or modulated. Applications include 3D imaging (both direct and indirect time-of-flight), optical receivers and fluorescence lifetime imaging. This work discusses the current-assistance principle, the various photodetectors using this principle and a comparison is made with other state-of-the-art photodetectors used for the same application.