bluuebunny/crossref_metadata_2025 huggingface dataset:
Review: Dampak Negatif Indospicine dalam Indigofera sp. pada Ternak
Authors: Yanuartono Yanuartono , Purnamaningsih Hary , Indarjulianto Soedarmanto , Nururrozi Alfarisa , Raharjo Slamet
Published: September 2020

ABSTRAKPakan merupakan aspek penting dari peternakan dan peningkatan produksi daging dapat dicapai melalui nutrisi dan manajemen yang tepat. Indigofera sp adalah salah satu spesies leguminosa hijauan yang potensial untuk ruminansia dan merupakan sumber penting protein, mineral, vitamin, serat serta memiliki palatabilitas  tinggi. Genus Indigofera terdiri atas sekitar 700 spesies yang berbeda, banyak di antaranya adalah tanaman penting yang secara agronomis digunakan sebagai pakan ternak dan suplemen pakan. Namun, beberapa spesies Indigofera mengandung faktor antinutrisi dikenal sebagai indospicine yang merupakan asam amino nonprotein toksik (2,7- diamino-7-iminoheptanoic acid) dan diketahui bersifat  hepatotoksik pada ruminansia. Ternak yang mengonsumsi Indigofera dilaporkan mengalami efek hepatotoksik dengan adanya lesi pada hati, efek teratogenik dan kematian embrio. Oleh karena itu, indospicine harus dipertimbangkan sebagai kemungkinan penyebab rendahnya penampilan ternak, khususnya penurunan berat badan dan gangguan reproduksi pada ruminansia yang diberi pakan indigofera sp dalam jumlah yang berlebihan. Di Indonesia spesies Indigofera zollingeriana merupakan pilihan paling tepat untuk dibudidayakan dan dikembangkan sebagai pakan ternak rumiansia  karena memiliki kandungan indospicine yang rendah untuk menghilangkan kejadian keracunan indospicine.Kata kunci: faktor antinutrisi, hepatotoksik, indigofera, indospicineABSTRACTThe feed is an important aspect of animal production, an increase in meat production can be achieved through proper nutrition and good management. The Indigofera sp is one of the potential forage legume species which are important sources of protein, minerals, vitamins, fiber, and has high palatability that provide essential nutrients for ruminants. The Indigofera genus contains approximately 700 different species, many of which are agronomically important plants that are used as grazing forages and feed supplements. Some Indigofera species, however, contain antinutritional factors (ANF) known as indospicine, a toxic nonprotein amino acid (2,7- diamino-7-iminoheptanoic acid) and is thought to be hepatotoxic in ruminants. Cattle and sheep consuming Indigofera have been reported to experience both hepatotoxic effects with associated liver lesions, and also teratogenic and embryo-lethal effects. Therefore, indospicine should be considered as a possible cause of animal poor performance, particularly reduced weight gain and reproductive disorders in ruminants that are fed with excessive amounts of indigofera sp. in Indonesia Indigofera zollingeriana species are the most appropriate choice to be cultivated and developed as ruminat livestock feed because they have a low indospicine content to eliminate the incidence of indospicine poisoning.Keywords: antinutritional factor, hepatotoxic,  Indigofera sp, indospicine
Hidden variables to Covid 19: Zimbabwe
Authors: Matthew Mare , Chingono Fortunate , Mangezi Simbarashe , Mare Alexander , Mbazangi Spencer
Published: September 2020

This article analyses the contributing variables to the spread of the Novel Corona Virus Disease 2019 (COVID-19), in Zimbabwe. Guided by findings drawn from both primary and secondary sources the study considered the upsurge in local transmissions in Zimbabwe as influenced by social, political, religious, and economic factors. These factors were narrowed down to detailed issues which include porous and weak boarder security, a dysfunctional transport system, corruption at both local and central government level, traditional cultural beliefs, myths, government’s response and lockdown measures, stigmatization, poverty, social media, politics, weak infrastructure, and medical systems, refugee matters and the international or global perspective regarding Covid-19. Following the deep analysis of the various contributing elements the study recommended the drawing lessons from the 1918 Spanish Flue responses.
A Rational Approach to Risk Management of Suicidality vs.
Torsades de Pointes (TdP) in Relation to the Prescribing of
Antipsychotic Medication
Authors: James Megna , Lehmann David , Anugu Vishal , Lynch Maggie
Published: September 2020

Risk-benefit analysis and management is a core responsibility of physicians during the process of formulating and implementing a comprehensive
and effective treatment plan. This assumes particular importance when working with individuals diagnosed with schizophrenia who are
confronted with a 5% lifetime risk of completed suicide, as well as the risk of Torsades de Pointes (TdP), a lethal tachydysrhytmia, as a result of
antipsychotic medication management. However, the actual risk of such an event, based upon recent epidemiologic data, is less than one-sixth
that of completed suicide. Consequently, due consideration must be given to these data while constructing the medication component of a
biopsychosocial treatment plan, as avoidance or under-prescribing of antipsychotics places an individual with schizophrenia at greater risk of
completed suicide than that of TdP.
HUBUNGAN KUALITAS PELAYANAN DENGAN KEPUASAN PASIEN DI PUSKESMAS DURIAN GANTANG KABUPATEN HULU SUNGAI TENGAH
Authors: Anwary Ahmad Zacky
Published: September 2020

Latar Belakang: Kepuasan pasien akan kualitas pelayanan dapat ditentukan dengan membandingkan persepsi antara harapan dan kenyataan pelayanan yang diterima. Jika harapan terlampaui, pelayanan tersebut dirasakan sebagai kualitas yang memuaskan, begitu juga sebaliknya. Standar kepuasan pasien pada pelayanan kesehatan ditetapkan secara nasional, menurut Peraturan Menteri Kesehatan Republik Indonesia Tahun 2016 tentang Standar Pelayanan Minimal untuk kepuasan pasien yaitu diatas 95%.Tujuan:Penelitian ini bertujuan untuk mengetahui hubungan antara Dimensi Kualitas Pelayanan (Keandalan, Ketanggapan, Kepastian, Empati, dan Bukti Fisik) dengan Kepuasan Pasien.Metode: penelitian survei dengan instrumen pengumpulan data berupa kuesioner. Populasi target adalah seluruh pasien dengan jumlah kunjungan pada bulan Januari - Maret 2019 sebanyak 2.620 orang, adapun jumlah sampel ditetapkan sebanyak 96 responden.Hasil:Berdasarkan analisis data menggunakan uji Chi-Square dengan tingkat kemaknaan ρ α (0,05) diketahui bahwa ada hubungan antara Keandalan dengan Kepuasan Pasien (ρ = 0,000), ada hubungan antara Ketanggapan dengan Kepuasan Pasien (ρ = 0,000), terdapat hubungan antara Kepastian dengan Kepuasan Pasien (ρ = 0,005), terdapat hubungan antara Empati dengan Kepuasan Pasien (ρ = 0,006), dan terdapat hubungan antara Bukti Fisik dengan Kepuasan Pasien (ρ = 0,023).Simpulan : terdapat hubungan yang signifikan antara kualitas pelayanan dengan kepuasan pasien yang dapat dilihat dari  variabel Keandalan dengan Kepuasan Pasien dengan nilai probabilitas 0,000, variabel Ketanggapan dengan Kepuasan Pasien dengan nilai probabilitas 0,000, variabel Kepastian dengan Kepuasan Pasien dengan nilai probabilitas 0,005. Ada hubungan yang signifikan antara variabel Empati dengan Kepuasan Pasien, p-value = 0,006. Juga terdapat hubungan yang signifikan antara Bukti Fisik dengan Kepuasan Pasien, p-value = 0,023.Kata kunci: Keandalan, Ketanggapan, Kepastian, Empati, Bukti Fisik, dan Kepuasan.
3D Printing of Metal/Metal Oxide Incorporated Thermoplastic Nanocomposites With Antimicrobial Properties
Authors: Abdullah Turdimuhammad , Qurban Rayyan O. , Bolarinwa Sherifdeen O. , Mirza Ahmed A. , Pasovic Mirza , Memic Adnan
Published: September 2020

Three-dimensional (3D) printing has experienced a steady increase in popularity for direct manufacturing, where complex geometric items can be produced without the aid of templating tools, and manufacturing waste can be remarkably reduced. While customized medical devices and daily life items can be made by 3D printing of thermoplastics, microbial contamination has been a serious obstacle during their usage. A very clever approaches to overcome this challenge is to incorporate antimicrobial metal or metal oxide (M/MO) nanoparticles within the thermoplastics during or prior to 3D printing. Many M/MO nanoparticles can prevent contamination from a wide range of microorganism, including antibiotic-resistant bacteria via various antimicrobial mechanisms. Additionally, they can be easily printed with thermoplastic without losing their integrity and functionality. In this mini review, we summarize recent advancements and discuss future trends related to the development of 3D printed antimicrobial thermoplastic nanocomposites by addition of M/MO nanoparticles.
On the Variability of Stratification in the Freshwater-Influenced Laptev Sea Region
Authors: Janout Markus A. , Hölemann Jens , Laukert Georgi , Smirnov Alexander , Krumpen Thomas , Bauch Dorothea , Timokhov Leonid
Published: September 2020

In this paper, we investigate the seasonal and spatial variability of stratification on the Siberian shelves with a case study from the Laptev Sea based on shipboard hydrographic measurements, year-round oceanographic mooring records from 2013 to 2014 and chemical tracer-based water mass analyses. In summer 2013, weak onshore-directed winds caused spreading of riverine waters throughout much of the eastern and central shelf. In contrast, strong southerly winds in summer 2014 diverted much of the freshwater to the northeast, which resulted in 50% less river water and significantly weaker stratification on the central shelf compared with the previous year. Our year-long records additionally emphasize the regional differences in water column structure and stratification, where the northwest location was well-mixed for 6 months and the central and northeast locations remained stratified into spring due to the lower initial surface salinities of the river-influenced water. A 26 year record of ocean reanalysis highlights the region’s interannual variability of stratification and its dependence on winds and sea ice. Prior the mid-2000s, river runoff to the perennially ice-covered central Laptev Sea shelf experienced little surface forcing and river water was maintained on the shelf. The transition toward less summer sea ice after the mid-2000s increased the ROFI’s (region of freshwater influence) exposure to summer winds. This greatly enhanced the variability in mixed layer depth, resulting in several years with well-mixed water columns as opposed to the often year-round shallow mixed layers before. The extent of the Lena River plume is critical for the region since it modulates nutrient fluxes and primary production, and further controls intermediate heat storage induced by lateral density gradients, which has implications for autumnal freeze-up and the eastern Arctic sea ice volume.

MAIN POINTS

1.

CTD surveys and moorings highlight the regional and temporal variations in water column stratification on the Laptev Sea shelf.

2.

Summer winds increasingly control the extent of the region of freshwater influence under decreasing sea ice.

3.

Further reductions in sea ice increases surface warming, heat storage, and the interannual variability in mixed layer depth.
Optimal Force Allocation and Position Control of Hybrid Pneumatic–Electric Linear Actuators
Authors: Rouzbeh Behrad , Bone Gary M.
Published: September 2020

Hybrid pneumatic–electric actuators (HPEAs) are redundant actuators that combine the large force, low bandwidth characteristics of pneumatic actuators with the large bandwidth, small force characteristics of electric actuators. It has been shown that HPEAs can provide both accurate position control and high inherent safety, due to their low mechanical impedance, making them a suitable choice for driving the joints of assistive, collaborative, and service robots. If these characteristics are mathematically modeled, input allocation techniques can improve the HPEA’s performance by distributing the required input (force or torque) between the redundant actuators in accordance with each actuator’s advantages and limitations. In this paper, after developing a model for a HPEA-driven system, three novel model-predictive control (MPC) approaches are designed that solve the position tracking and input allocation problem using convex optimization. MPC is utilized since the input allocation can be embedded within the motion controller design as a single optimization problem. A fourth approach based on conventional linear controllers is included as a comparison benchmark. The first MPC approach uses a model that includes the dynamics of the payload and pneumatics; and performs the motion control using a single loop. The latter methods simplify the MPC law by separating the position and pressure controllers. Although the linear controller was the most computationally efficient, it was inferior to the MPC-based controllers in position tracking and force allocation performance. The third MPC-based controller design demonstrated the best position tracking with RMSE of 46%, 20%, and 55% smaller than the other three approaches. It also demonstrated sufficient speed for real-time operation.
S-Genotyping and Seed Paternity Testing of the Pear Cultivar ‘Celina’
Authors: Gasi Fuad , Frøynes Oddmund , Kalamujić Stroil Belma , Lasić Lejla , Pojskić Naris , Fotirić Akšić Milica , Meland Mekjell
Published: September 2020

The diploid Celina/QTee® (‘Colorée de Juillet’ × ‘Williams’), one of the most promising pear cultivars developed by the Norwegian breeding program Graminor, was launched in 2010. In Norway, the flowering is medium to late, while the fruits ripen in the beginning of September. The fruits are attractive with an intense red blush (50%) on a green background. Although, ‘Celina’ is cultivated in the most climatically suitable regions for fruit cultivation, present in Norway, unfavorable environmental conditions for pear pollination can have a very negative effect on fruit set and consequent yield. The aim of this study was to determine the S-alleles of ‘Celina’, as well as its frequently used pollinizers, and, through paternity testing of ‘Celina’ seeds, give a recommendation regarding the most important pollinizers of this pear cultivar. In order to accomplish this, ‘Celina’ and its potential pollinizers were all S-genotyped. After harvest, seeds collected from ‘Celina’ fruit in 2017 and 2018 were genotyped using eleven microsatellite markers. Genomic DNA was also extracted from leaf material collected from ‘Celina’, as well as from five pear cultivars used as pollinizers in the three examined orchards, and analyzed using the same marker set. Subsequently a simple sequence repeat (SSR) database was constructed and used for gene assignment analyses with the aim of quantifying pollen donor contribution from individual pollinizers. The obtained results indicate that ‘Anna’, the only examined pollinizer that was fully cross-compatible with ‘Celina’, together with ‘Fritjof’, the genotype which had the highest flowering overlap with ‘Celina’, proved to be the most successful pollinizers across all seasons and orchards. Although both cultivars were ubiquitous in the examined orchards, either as planted trees or as branches introduced during the flowering period, they were the most abundant pollinizers in only one orchard each. It is therefore possible to conclude that pollinizer abundance has a secondary significance in pollinizer success within investigated ‘Celina’ orchards.
Potassium Positively Affects Skin Characteristics of Sweet Potato Storage Roots
Authors: Klipcan Liron , van Oss Ruth , Keren-Kieserman Alexandra , Yermiyahu Uri , Ginzberg Idit
Published: September 2020

Sweet potato (Ipomoea batatas) growth faces two critical problems: variability in storage root (SR) number and size among individual plants, and skinning injuries that render the SR susceptible to pathogen infections during storage. We hypothesized that application of potassium (K) fertilizer, an essential mineral for sweet potato, would contribute to increased yield, uniformity, and skin quality of SRs. Sweet potatoes were grown in sandy soil, which is poor in K, and in loess soil. The fertilizers potassium chloride (KCl) and polyhalite were applied before planting. Polyhalite is a hydrated sulfate of K, calcium, and magnesium that has been shown to improve potato skin appearance. Soil type was the major factor affecting SR yield—higher in sandy vs. loess soil. The K fertilizers did not affect yield in either soil type, or improve SR uniformity. However, the skin of the SRs from loess soil had more phellem layers and larger phellem cells following fertilization, mainly with KCl. Accordingly, the expression of suberin marker genes was significantly higher in mature vs. immature skin of SRs fertilized with KCl. Overall, soil type was the major factor affecting sweet potato yield, and addition of K positively affected skin morphology and related gene expression.
Effects of Fertilization Management under WSPI on Soil Nitrogen Distribution and Nitrogen Absorption in Apple Orchard in Loess Plateau
Authors: Cheng Qiyun , Ma Juanjuan , Ren Rong , Zheng Lijian , Guo Xianghong , Sun Xihuan
Published: September 2020

Water storage pit irrigation (WSPI) has been proven effective in improving the water use efficiency of fruit trees in Loess Plateau, but so far there are still no matching efficient fertilization management methods. A two-year experiment was conducted to explore the management strategy of fertilization under the consideration of apple production and environmental sustainability. N isotope tracer technique was used to study the distribution of labelled nitrogen in soil, leaf, root and fruit. Moreover, the yield in different fertilizer managements were observed to evaluate the apple production. The results showed that increasing the amount of fertilizer could increase the accumulation of fertilizer nitrogen in soil, but also increased the risk of nitrogen leaching. Under the same amount of fertilizer, split fertilization can effectively increase of fertilizer nitrogen in soil by a mean of 4.7 times. Further, N300 application with split fertilization effectively increased apple yield. The yield of N300II treatment was higher than other treatment by maximum 68.5%. In addition, the root system mainly absorbed the fertilizer nitrogen applied in the current year, and the fruit mainly absorbed the fertilizer nitrogen applied in the previous year, but there was no significant difference in the leaves.
Root Trait Variability in Coffea canephora Genotypes and Its Relation to Plant Height and Crop Yield
Authors: Silva Larícia Olária Emerick , Schmidt Raquel , Valani Gustavo Pereira , Ferreira Adésio , Ribeiro-Barros Ana I. , Partelli Fábio Luiz
Published: September 2020

Coffee breeding based on root traits is important to identify productive genotypes under adverse environmental conditions. This study assessed the diversity of root traits in Coffea canephora and its correlation with plant height and crop yield. Undisturbed soil samples were collected down to 60 cm from 43 coffee genotypes, in which one of them was propagated by seed and all others by stem cutting. The roots were washed, scanned, and processed to quantify root length density, root volume, root superficial area, and root diameter. Additionally, plant height and crop yield were also assessed. Root length density ranged from 40 to 1411 mm cm−3, root volume from 6 to 443 mm3 cm−3, root superficial area from 61 to 1880 mm2 cm−3, and root diameter from 0.6 to 1.1 mm. Roots were concentrated in the topsoil (0–20 cm) for most genotypes. In deeper depths (30–60 cm), root length density, root volume, and root superficial area were higher in genotypes 14, 25, 31, and 32. Positive correlations were found between root traits and both plant height and crop yield. The results of this work may contribute to the overall cultivation of C. canephora, specially for crop breeding in adverse environmental conditions.
Antibiogram, Prevalence of OXA Carbapenemase Encoding Genes, and RAPD-Genotyping of Multidrug-Resistant Acinetobacter baumannii Incriminated in Hidden Community-Acquired Infections
Authors: El-Kazzaz Waleed , Metwally Lobna , Yahia Reham , Al-Harbi Najwa , El-Taher Ayat , Hetta Helal F.
Published: September 2020

Acinetobacter spp. has gained fame from their ability to resist difficult conditions and their constant development of antimicrobial resistance. This study aimed to investigate the prevalence, susceptibility testing, OXA carbapenemase-encoding genes, and RAPD-genotyping of multidrug resistant Acinetobacter baumannii incriminated in hidden community-acquired infections in Egypt. The antimicrobial susceptibility testing was assessed phenotypically using Kirby–Bauer disk diffusion method. Also, Modified-Hodge test (MHT) was carried out to detect the carbapenemases production. Multiplex-PCR was used to detect the carbapenemase-encoding genes. Furthermore, the genetic relationship among the isolated strains was investigated using RAPD fingerprinting. The bacteriological examination revealed that, out of 200 Gram-negative non-fermentative isolates, 44 (22%) were identified phenotypically and biochemically as Acinetobacter spp. and 23 (11.5%) were molecularly confirmed as A.baumannii. The retrieved A.baumannii strains were isolated from urine (69%), sputum (22%), and cerebrospinal fluid (csf) (9%). The isolated A. baumannii strains exhibited multidrug resistance and the production rates of carbapenemases were 56.5, 60.9, and 78.3% with meropenem, imipenem, and ertapenem disks, respectively. The blaOXA-24-like genes were the most predominant among the tested strains (65.2%), followed by blaOXA-23 (30.4%) and blaOXA-58 (17.4%), in addition, the examined strains are harbored IMP, VIM, and NDM genes with prevalence of 60.9, 43.5, and 13%, respectively, while KPC and GES genes were not detected. RAPD-PCR revealed that the examined strains are clustered into 11 different genotypes at ≥90% similarity. Briefly, to the best of our knowledge, this study is the first report concerning community-associated A. baumannii infections in Egypt. The high prevalence of hidden multidrug-resistant (MDR) and extensively drug-resistant (XDR) A.baumannii strains associated with non-hospitalized patients raises an alarm for healthcare authorities to set strict standards to control the spread of such pathogens with high rates of morbidity and mortality.
Evaluation of Multiple Impacts of Furfural Acetone on Nematodes In Vitro and Control Efficiency against Root-Knot Nematodes in Pots and Fields
Authors: Cheng Wanli , Yang Xue , Zeng Li , Huang Dian , Cai Minmin , Zheng Longyu , Yu Ziniu , Zhang Jibin
Published: September 2020

Root-knot nematodes (RKNs) seriously endanger agricultural development and cause great economic losses worldwide. Natural product furfural acetone (FAc) is a promising nematicide with strong attractant and nematicidal activities, but baseline information about the impact of FAc on the reproduction, egg hatching, feeding, and growth of nematodes and its pest control efficiency in field are lacking. Here, the inhibition effects of FAc on nematodes in vitro and its RKN control efficiency in pot and field were investigated. FAc inhibited the egg hatching of Meloidogyne incognita by 91.7% at 200 mg/L after 2 days and suppressed the reproduction, feeding, and growth of Caenorhabditis elegans in vitro. In pot experiments, FAc in various dosages reduced the disease index of plant root significantly. In field experiments, FAc exhibited control effect on RKNs equivalent to commercial nematicides avermectin and metam sodium, with a reduction in disease index by 36.9% at a dose of 50 mg/plant. FAc also reduced the population density of RKNs in soil, with a reduction rate of 75.3% at the dose of 750 mg/m2. No adverse effect was detected on plant growth after FAc application. These results provide compelling evidence for development of FAc as an appropriate alternative for current nematicides.
Macrocycles and Supramolecules as Antioxidants: Excellent Scaffolds for Development of Potential Therapeutic Agents
Authors: Lee Jung-Seop , Song In-ho , Shinde Pramod B. , Nimse Satish Balasaheb
Published: September 2020

Oxidative stress due to the high levels of reactive oxygen species (ROS) that damage biomolecules (lipids, proteins, DNA) results in acute inflammation. However, without proper intervention, acute inflammation progresses to chronic inflammation and then to several chronic diseases, including cancer, myocardial infarction, cardiovascular diseases, chronic inflammation, atherosclerosis, and more. There has been extensive research on the antioxidants of natural origin. However, there are myriad possibilities for the development of synthetic antioxidants for pharmacological applications. There is an increasing interest in the identification of novel synthetic antioxidants for the modulation of biochemical processes related to ROS. In this regard, derivatives of supramolecules, such as calix[n]arene, resorcinarene, calixtyrosol, calixpyrrole, cucurbit[n]uril, porphyrin etc. are gaining attention for their abilities to scavenge the free radicals. Supramolecular chemistry offers excellent scaffolds for the development of novel antioxidants that can be used to modulate free radical reactions and to improve the disorders related to oxidative stress. This review focuses on the interdisciplinary approach for the design and development of novel synthetic antioxidants based on supramolecular scaffolds, with potentially protective effects against oxidative stress.
Learning Optimal Time Series Combination and Pre-Processing by Smart Joins
Authors: Gil Amaia , Quartulli Marco , Olaizola Igor G. , Sierra Basilio
Published: September 2020

In industrial applications of data science and machine learning, most of the steps of a typical pipeline focus on optimizing measures of model fitness to the available data. Data preprocessing, instead, is often ad-hoc, and not based on the optimization of quantitative measures. This paper proposes the use of optimization in the preprocessing step, specifically studying a time series joining methodology, and introduces an error function to measure the adequateness of the joining. Experiments show how the method allows monitoring preprocessing errors for different time slices, indicating when a retraining of the preprocessing may be needed. Thus, this contribution helps quantifying the implications of data preprocessing on the result of data analysis and machine learning methods. The methodology is applied to two case studies: synthetic simulation data with controlled distortions, and a real scenario of an industrial process.
A Low-Cost Constant-Moisture Automatic Irrigation System Using Dynamic Irrigation Interval Adjustment
Authors: Huang Shih-Chang , Lin Ye-Ze
Published: September 2020

This paper developed a Soil Moisture Forecasting (SMF) model and a Constant-moisture Automatic Irrigation System (CAIS). The SMF model used the soil moisture data at different depths in an experimental plot inside a greenhouse to infer the soil moisture level after a specific interval. CAIS integrated the SMF data with dynamic watering interval adaption to maintain soil moisture at a constant level. Most intelligent irrigation products incur high installation costs that farmers cannot afford. CAIS used a low-cost component to achieve the same functionality that is found in intelligent irrigation products. Most low-cost irrigation systems water the plants from a single point that may lead to variable soil moisture if the terrain or the soil density is uneven. CAIS divided the experimental plot into multiple virtual planting areas (VPAs) and dynamically adapted the watering interval of each VPA to balance the soil moisture of the whole experimental plot. Results showed that the forecasting error of the SMF model was less than 12 moisture levels over a scale of 1024 levels. Furthermore, CAIS maintained the soil moisture of the whole experimental plot at a constant level within ±5 error points with multiple watering points.
Written Documents Analyzed as Nature-Inspired Processes: Persistence, Anti-Persistence, and Random Walks—We Remember, as Along Came Writing—T. Holopainen
Authors: López-Ortega Omar , Pérez-Cortés Obed , Castillejos-Fernández Heydy , Castro-Espinoza Félix-Agustín , González-Mendoza Miguel
Published: September 2020

Written communication is pivotal for societies to develop. However, lexicon and depth of information vary greatly among texts according to their purpose. Scientific texts, diffusion of science reports, general and area-specific news are all written differently. Thus, we explore the characterization of different text categories through a nature-inspired feature known as the Hurst parameter. We contend that the Hurst exponent is useful to unveil the rhetorical structure within written documents. We collected and processed texts in five categories: scientific articles, diffusion of science reports, business news, entertainment news, and random texts. Each category contains 350 documents. We found that the median for scientific texts has the highest value of the Hurst parameter (0.575), followed by business news (0.54); the median for randomly-generated texts is 0.48, which lies in the region associated with random walks. The median value for diffusion texts is 0.49, and for entertainment texts is 0.53. However, these two categories present high dispersion. We conclude that the Hurst parameter is a measure that quantifies the structure of communication in the selected categories of texts. Application of our finding in the field of e-research is discussed.
Tool Wear and Material Removal Predictions in Micro-EDM Drilling: Advantages of Data-Driven Approaches
Authors: Bellotti Mattia , Wu Ming , Qian Jun , Reynaerts Dominiek
Published: September 2020

In micro electrical discharge drilling, regression models are commonly used for predicting the material removal rate (MRR) and tool wear rate (TWR) from the applied processing parameters. However, these models can be inaccurate since the processing parameters might not always be representative of the actual machining conditions, which depend on several other factors such as the tool length or gap flushing efficiency. In order to increase the prediction accuracy, the present work investigates the capability of data-driven regression models for tool wear and material removal prediction. The errors in predicting the MRR and TWR are shown to decrease of about 65% and 85% respectively when using data collected through process monitoring as input of the regression models. Data-driven approaches for in-process tool wear prediction have also been implemented in drilling experiments, demonstrating that a more accurate control of the hole depth (50% average reduction of the depth error) can be achieved by using data-driven predictive models.
Preventing the Interaction between Coronaviruses Spike Protein and Angiotensin I Converting Enzyme 2: An In Silico Mechanistic Case Study on Emodin as a Potential Model Compound
Authors: Dellafiora Luca , Dorne Jean Lou C M , Galaverna Gianni , Dall’Asta Chiara
Published: September 2020

Emodin, a widespread natural anthraquinone, has many biological activities including health-protective and adverse effects. Amongst beneficial effects, potential antiviral activity against coronavirus responsible for the severe acute respiratory syndrome outbreak in 2002–2003 has been described associated with the inhibition of the host cells target receptors recognition by the viral Spike protein. However, the inhibition mechanisms have not been fully characterized, hindering the rational use of emodin as a model compound to develop more effective analogues. This work investigates emodin interaction with the Spike protein to provide a mechanistic explanation of such inhibition. A 3D molecular modeling approach consisting of docking simulations, pharmacophoric analysis and molecular dynamics was used. The plausible mechanism is described as an interaction of emodin at the protein–protein interface which destabilizes the viral protein-target receptor complex. This analysis has been extended to the Spike protein of the coronavirus responsible for the current pandemic hypothesizing emodin’s functional conservation. This solid knowledge-based foothold provides a possible mechanistic rationale of the antiviral activity of emodin as a future basis for the potential development of efficient antiviral cognate compounds. Data gaps and future work on emodin-related adverse effects in parallel to its antiviral pharmacology are explored.
Insight into the Influence of Grinding on the Extraction Efficiency of Selected Bioactive Compounds from Various Plant Leaves
Authors: Alsaud Noor , Farid Mohammed
Published: September 2020

The impact of particle size on bioactive compounds recovery was investigated for two valuable plant matters. This project was to assess the effect of grinding on bioactives extraction of New Zealand Manuka leaves of the Leptospermum scoparium and Stevia rebaudiana plants. Non-grounded and grounded Manuka with particle sizes ranging from 68 μm to 1400 μm were processed using n-hexane extraction for 0–60 min. Moreover, the extraction of two sweeteners from Stevia powder (d ≤ 200 µm) was carried out using hot water extraction and Ultrasound as a non-conventional method. As particle size was reduced, the extraction of bioactives increased, which is expected. However, it was observed that for the small particle size, which was obtained by sever grinding, most of the extraction was achieved as soon as the powder was exposed to the solvent (at zero time) with no further extraction at an extended period. This indicates that short-time exposure appears to be sufficient to recover most of the bioactive compounds, since most of the release of these compounds happened during grinding to fine powder, an issue that has not been addressed in the literature.
Reconstruction of Pressureless Sintered Micron Silver Joints and Simulation Analysis of Elasticity Degradation in Deep Space Environment
Authors: Guo Wendi , Fu Guicui , Wan Bo , Zhu Ming
Published: September 2020

With excellent economy and properties, pressureless sintered micron silver has been regarded as an environmentally friendly interconnection material. In order to promote its reliable application in deep space exploration considering the porous microstructural evolution and its effect on macroscopic performance, simulation analysis based on the reconstruction of pressureless sintered micron silver joints was carried out. In this paper, the deep space environment was achieved by a test of 250 extreme thermal shocks of −170 °C~125 °C, and the microstructural evolution was observed by using SEM. Taking advantage of the morphology autocorrelation function, three-dimensional models of the random-distribution medium consistent with SEM images were reconstructed, and utilized in further Finite Element Analysis (FEA) of material effective elastic modulus through a transfer procedure. Compared with test results and two analytical models, the good consistency of the prediction results proves that the proposed method is reliable. Through analyzing the change in autocorrelation functions, the microstructural evolution with increasing shocks was quantitively characterized. Mechanical response characteristics in FEA were discussed. Moreover, the elasticity degradation was noticed and the mechanism in this special environment was clarified.
Table-Top Water-Window Microscope Using a Capillary Discharge Plasma Source with Spatial Resolution 75 nm
Authors: Parkman Tomáš , Nevrkla Michal , Jančárek Alexandr , Turňová Jana , Pánek Dalibor , Vrbová Miroslava
Published: September 2020

We present a design of a compact transmission water-window microscope based on the Z-pinching capillary discharge nitrogen plasma source. The microscope operates at wavelength of 2.88 nm (430 eV), and with its table-top dimensions provides an alternative to large-scale soft X-ray (SXR) microscope systems based on synchrotrons and free-electron lasers. The emitted soft X-ray radiation is filtered by a titanium foil and focused by an ellipsoidal condenser mirror into the sample plane. A Fresnel zone plate was used to create a transmission image of the sample onto a charge-coupled device (CCD) camera. To assess the resolution of the microscope, we imaged a standard sample-copper mesh. The spatial resolution of the microscope is 75 nm at half-pitch, calculated via a 10–90% intensity knife-edge test. The applicability of the microscope is demonstrated by the imaging of green algae-Desmodesmus communis. This paper describes the principle of capillary discharge source, design of the microscope, and experimental imaging results of Cu mesh and biological sample.
Design and Optimization of Lattice Structures: A Review
Authors: Pan Chen , Han Yafeng , Lu Jiping
Published: September 2020

Cellular structures consist of foams, honeycombs, and lattices. Lattices have many outstanding properties over foams and honeycombs, such as lightweight, high strength, absorbing energy, and reducing vibration, which has been extensively studied and concerned. Because of excellent properties, lattice structures have been widely used in aviation, bio-engineering, automation, and other industrial fields. In particular, the application of additive manufacturing (AM) technology used for fabricating lattice structures has pushed the development of designing lattice structures to a new stage and made a breakthrough progress. By searching a large number of research literature, the primary work of this paper reviews the lattice structures. First, based on the introductions about lattices of literature, the definition and classification of lattice structures are concluded. Lattice structures are divided into two general categories in this paper: uniform and non-uniform. Second, the performance and application of lattice structures are introduced in detail. In addition, the fabricating methods of lattice structures, i.e., traditional processing and additive manufacturing, are evaluated. Third, for uniform lattice structures, the main concern during design is to develop highly functional unit cells, which in this paper is summarized as three different methods, i.e., geometric unit cell based, mathematical algorithm generated, and topology optimization. Forth, non-uniform lattice structures are reviewed from two aspects of gradient and topology optimization. These methods include Voronoi-tessellation, size gradient method (SGM), size matching and scaling (SMS), and homogenization, optimization, and construction (HOC). Finally, the future development of lattice structures is prospected from different aspects.
Structural Design and Kinematics Simulation of Hydraulic Biped Robot
Authors: Zhang Jianrui , Yuan Zhaohui , Dong Sheng , Sadiq Muhammad Tariq , Zhang Fuli , Li Jingchao
Published: September 2020

This paper presents a novel framework of hydraulic-driven biped robot. The target biped robot is a humanoid robot NWPUBR-1 with 12 degrees of freedom (DOF), and its dimensions are close to that of an average male. The joint axis adopts the modular structure design of sensor with angle measurement, and the force sensor is deployed on the side of hydraulic actuator to facilitate force and position control of the robot. Meanwhile, the finite element analysis of critical components is conducted to meet the requirements of mechanical strength in motion. Based on the screw theory, the forward kinematics and Jacobian matrix models are established, and the inverse kinematics of robot is solved by using the analytical geometric method. To achieve real-time control of the robot gait in 3D space, a three-dimensional linear inverted pendulum model (3D-LIPM) is built, and a 3D gait model is generated by combining the zero-moment point (ZMP) theory. In the virtual environment of MATLAB software, the results of programming simulation show that the biped robot can walk stably in the virtual environment, which proves the correctness of 3D-LIPM.
Accurate Bearing Fault Diagnosis under Variable Shaft Speed using Convolutional Neural Networks and Vibration Spectrogram
Authors: Pham Minh Tuan , Kim Jong-Myon , Kim Cheol Hong
Published: September 2020

Predicting bearing faults is an essential task in machine health monitoring because bearings are vital components of rotary machines, especially heavy motor machines. Moreover, indicating the degradation level of bearings will help factories plan maintenance schedules. With advancements in the extraction of useful information from vibration signals, diagnosis of motor failures by maintenance engineers can be gradually replaced by an automatic detection process. Especially, state-of-the-art methods using deep learning have contributed significantly to automatic fault diagnosis. This paper proposes a novel method for diagnosing bearing faults and their degradation level under variable shaft speed. In the proposed method, vibration signals are represented by spectrograms to apply deep learning methods through preprocessing using Short-Time Fourier Transform (STFT). Then, feature extraction and health status classification are performed by a convolutional neural network (CNN), VGG16. According to our various experiments, our proposed method can achieve very high accuracy and robustness for bearing fault diagnosis even under noisy environments.
Model Predictive Control of Grid Forming Converters with Enhanced Power Quality
Authors: Alhasheem Mohammed , Abdelhakim Ahmed , Blaabjerg Frede , Mattavelli Paolo , Davari Pooya
Published: September 2020

This paper proposes an enhanced finite control set model predictive control (FCS-MPC) strategy for voltage source converter (VSC) with a LC output filter. The proposed control scheme is based on tracking the voltage reference trajectory by using only a single-step prediction within the controller horizon. Besides, the suitability of different frequency control schemes with the proposed scheme to prevent from inherent variable switching behaviour of conventional FCS-MPC is investigated. Based on that, the proposed method targets two major factors influencing power quality in grid forming applications by enhancing the output voltage harmonic distortion and also preventing variable switching behaviour of FCS-MPC. Although compared to multi-step prediction approaches, only a single-step multi-objective cost function to improve computation efficiency is utilized, the introduced control schemes are able to deliver higher power quality compared to its counterpart methods as well. Furthermore, the effect of different applied cost functions on the transient response of the system is studied and investigated for the future use of the VSC in microgrids (MGs). The effectiveness of the proposed scheme was assessed by simulation using MATLAB/SIMULINK and experiment using a 5.5 kVA VSC module and the results were in good agreement.
Effects of Air Pollution on the Risk of Low Birth Weight in a Cold Climate
Authors: Balogun Hamudat A. , Rantala Aino K. , Antikainen Harri , Siddika Nazeeba , Amegah A. Kofi , Ryti Niilo R. I. , Kukkonen Jaakko , Sofiev Mikhail , Jaakkola Maritta S. , Jaakkola Jouni J. K.
Published: September 2020

There is accumulating evidence that prenatal exposure to air pollution disturbs fetal growth and development, but little is known about these effects in cold climates or their season-specific or joint effects. Our objective was to assess independent and joint effects of prenatal exposure to specific air pollutants on the risk of low birth weight (LBW). We utilized the 2568 children of the Espoo Cohort Study, born between 1984 and 1990 and living in the City of Espoo. We conducted stratified analyses for births during warm and cold seasons separately. We analyzed the effect estimates using multi-pollutant Poisson regression models with risk ratio (RR) as the measure of effect. The risk of LBW was related to exposure to CO (adjusted RR 1.44, 95% confidence interval [CI]: 1.04–2.00) and exposure to O3 in the spring–summer season (1.82, 1.11–2.96). There was also evidence of synergistic effects between CO and O3 (relative risk due to interaction (RERI), all year 1.08, 95% CI: 0.27–4.94, spring–summer 3.97, 2.17–25.85) and between PM2.5 and O3 (all year 0.72, −0.07–3.60, spring–summer 2.80, 1.36–19.88). We present new evidence of both independent and joint effects of prenatal exposure to low levels of air pollution in a cold climate on the risk of LBW.
The BrIdge voLcanic LIdar—BILLI: A Review of Data Collection and Processing Techniques in the Italian Most Hazardous Volcanic Areas
Authors: Parracino Stefano , Santoro Simone , Fiorani Luca , Nuvoli Marcello , Maio Giovanni , Aiuppa Alessandro
Published: September 2020

Volcanologists have demonstrated that carbon dioxide (CO2) fluxes are precursors of volcanic eruptions. Controlling volcanic gases and, in particular, the CO2 flux, is technically challenging, but we can retrieve useful information from magmatic/geological process studies for the mitigation of volcanic hazards including air traffic security. Existing techniques used to probe volcanic gas fluxes have severe limitations such as the requirement of near-vent in situ measurements, which is unsafe for operators and deleterious for equipment. In order to overcome these limitations, a novel range-resolved DIAL-Lidar (Differential Absorption Light Detection and Ranging) has been developed as part of the ERC (European Research Council) Project “BRIDGE”, for sensitive, remote, and safe real-time CO2 observations. Here, we report on data collection, processing techniques, and the most significant findings of the experimental campaigns carried out at the most hazardous volcanic areas in Italy: Pozzuoli Solfatara (Phlegraen Fields), Stromboli, and Mt. Etna. The BrIdge voLcanic LIdar—BILLI has successfully obtained accurate measurements of in-plume CO2 concentration and flux. In addition, wind velocity has also been retrieved. It has been shown that the measurements of CO2 concentration performed by BILLI are comparable to those carried out by volcanologists with other standard techniques, heralding a new era in the observation of long-term volcanic gases.
DC-MMD-GAN: A New Maximum Mean Discrepancy Generative Adversarial Network Using Divide and Conquer
Authors: Zhou Zhaokun , Zhong Yuanhong , Liu Xiaoming , Li Qiang , Han Shu
Published: September 2020

Generative adversarial networks (GANs) have a revolutionary influence on sample generation. Maximum mean discrepancy GANs (MMD-GANs) own competitive performance when compared with other GANs. However, the loss function of MMD-GANs is an empirical estimate of maximum mean discrepancy (MMD) and not precise in measuring the distance between sample distributions, which inhibits MMD-GANs training. We propose an efficient divide-and-conquer model, called DC-MMD-GANs, which constrains the loss function of MMD to tight bound on the deviation between empirical estimate and expected value of MMD and accelerates the training process. DC-MMD-GANs contain a division step and conquer step. In the division step, we learn the embedding of training images based on auto-encoder, and partition the training images into adaptive subsets through k-means clustering based on the embedding. In the conquer step, sub-models are fed with subsets separately and trained synchronously. The loss function values of all sub-models are integrated to compute a new weight-sum loss function. The new loss function with tight deviation bound provides more precise gradients for improving performance. Experimental results show that with a fixed number of iterations, DC-MMD-GANs can converge faster, and achieve better performance compared with the standard MMD-GANs on celebA and CIFAR-10 datasets.