Miracle-dz/UniFAR-FTUs huggingface dataset:
Intraoperative Use of Endoscope, a Valuable, Adjunctive Tool for the Surgical Management of Anterior Communicating Artery Aneurysm Surgery: Our Institutional Experience

Background: Anterior communicating (A-com) artery region is very complex; perforators are not always visualized on the microscope. The neuroendoscope with its higher magnification, better observation, and additional illumination can provide us information that may not be available with the microscope in aneurysm surgery. Objective: The objective was to study the use of endoscope in surgical management of A-com aneurysm surgery and its advantages, whether and how it changes operative management.Materials and Methods: We studied 25 serial cases of A-com aneurysm at Bantane Hospital, Fujita University, Japan, from November 2018 to October 2019. Once aneurysm was exposed, we did preclipping indocyanine green (ICG) study and examination with endoscope. After clipping, we again did ICG and endoscopic assessment. Preclipping and postclipping endoscopic information was used and necessary changes were made in the operative decisions. Whether endoscope gives any additional information over microscope and ICG which led to change in the operative decision was assessed. Results: In six out of 25 A-com aneurysm patients, the use of endoscope has given additional information over microscope, and ICG leading to change in the operative plans such as readjustment of the clip/application of the second clip or release of perforator compromise. Conclusion: Simultaneous endoscopic and microscopic guidance can reveal important information hidden from the microscope. Thus, this method increases the safety and durability of the A-com aneurismal clipping.
In situ analysis of introduced Frankia populations in root nodules of Alnus glutinosa grown under different water availability

The competitive ability for nodulation of Alnus glutinosa (L.) Gaertn. plants by Frankia strains inoculated into soil with indigenous Frankia populations was studied at two matric potentials representing "dry" (-0.016 MPa) and "wet" (-0.001 MPa) conditions. In pots kept at a matric potential of -0.001 MPa, nitrate concentrations decreased within 3 weeks more than 10-fold to an average of approx. 200 µmol·(g soil dry wt.)-1. After 4 months, nitrate concentrations in these pots were 16 ± 16 and 277 ± 328 µmol·(g soil dry wt.)-1 (mean ± SD) for non-inoculated and inoculated soils, respectively. At a matric potential of -0.016 MPa, nitrate concentrations for non-inoculated and inoculated soils were 687 ± 491 and 1796 ± 1746 µmol·(g soil dry wt.)-1, respectively. Inoculated plants always grew better than their non-inoculated counterparts. The largest plants were found on inoculated soil at a matric potential of -0.001 MPa, whereas the smallest plants were found on non-inoculated soil at the same matric potenti.
An Improved Dynamic Pedestrian Grouping Model in Public Transport Space

Dynamic pedestrian grouping is a significant issue in the field of crowd flow control, also a research hotspot in the field of computer vision and artificial intelligence. While it is difficult for a conventional pedestrian grouping model to identify and group the pedestrians with a large span large than 2 m besides depending on the pedestrian velocity modulus and direction. As the pedestrians between large-span possibly have the same movement goals and behavioural motivations, it is necessary to solve the grouping problems. This paper analyses the characteristics of pedestrian velocity vector, large span in a public transport space, then introduces the non-recursive motion similarity clustering (NMSC) algorithm. An improved dynamic pedestrian grouping model is proposed. The connectivity weight graph is designed in the pedestrian clustering. Measurement of differences based on a hierarchical pattern is implemented to analyse the velocity similarity of pedestrians. In addition, five grouping conditions are used to determine the dynamic weight of the pedestrians. Experimental results validate that the improved dynamic grouping model has a good effect on the recognition of regular pedestrian groups and the special groups considering the large-span space.
FuzzyNet: Context Encoding and Spatial Fuzzy Refinement Network in Semantic Segmentation

This paper addresses the pertinent object localization problem in deep convolutional neural networks by introducing a spatial fuzzy post-processing function which allows the smooth transition of object edges within individual pixel's neighborhood. We accomplish the task of semantic segmentation by first computing class weights as a means of avoiding class bias or imbalance training. Our proposed FuzzyNet runs a convolutional encoderdecoder network architecture with the following novel features: (i) It incorporates a new Global Context Spatial Module (GCSM) (ii) It exploits the atrous spatial pyramid structure for enriching the semantic encoding (iii) It incorporates the transfer of lower level features connected to higher levels with contextual spatial feature maps (iv) It effectively achieves an attention component with an extensive focus on objects of interest. Thus, the fusion of spatial fuzzy function enables normalization of intensity variation at different object boundaries, avoidance of poor localization and ultimately resulting in quality semantic segmentation. The evaluation of our proposed FuzzyNet model achieves improved performance with respect to the accuracy and object boundary refinement on the PASCAL VOC 2012 and CamVid benchmark datasets.
The Use of Word2vec Model in Sentiment Analysis: A Survey

Sentiment analysis is an area that gains wide interest from research because of its importance and advantages in various fields. Different approaches and techniques are used to classify the sentiment of texts. Word embedding is one of the effective methods that represent aspects of word meaning and help to improve sentiment classification accuracy. Word2vec is well-known and widely used in learning word embedding that includes two models: Skip-Gram (SG) model and Continuous Bag-of-Words model (CBOW). Some of the studies use one of these models and other studies use both of them. In this survey, we highlight the latest studies on using the Word2vec model for sentiment analysis and its role in improving sentiment classification accuracy.
Breakdown of Polyploidy and Isolation of Stable Polysomics in Amphidecaploid Avena sativa L.×A. maroccana Gdgr.

The study involves the chromosomal constitution of advanced progenies of amphidecaploid Avena sativa and A. maroccana. Amphidecaploid 2n 10x 70 was raised from a pentaploid interspecific cross A. sativa A. magna (Syn. A. maroccana). The selfed progenies of the amphiploid were scored for chromosomal constitution in A14, A15 and A16 generations. It was observed that while there was a general breakdown and loss of chromosomes over the generations, but at the same time stable polysomics with 2n 44, 46, 50, 52, 54, 60, 62, 64, 66 and 68 were isolated together with progenies having female parent number of 2n 6x 42 (A. sativa) and original decaploid number of 2n 10x 70. No progenies with less than 2n 6x 42 were recovered, although male parent happens to be a tetraploid with 2n 4x 28. A general reduction in the number of multivalents and consequent increase in bivalent configurations was observed from A0 to A16 generations.
Reproductive consequences of Colletotrichum lindemuthianum (Ascomycota) infection on wild bean plants (Phaseolus vulgaris)

Fungal plant parasites can have strong reproductive consequences on their hosts, but little is known about the amount of parasite-induced fitness loss under natural conditions. We present data from wild populations of Colletotrichum lindemuthianum Sacc. & Magn. , a fungal parasite that attacks the wild common bean (Phaseolus vulgaris L.). We estimated disease prevalence in two populations and fungal virulence by examining naturally infected plants for three fitness-related traits: number of seeds per pod, mass of seed per pod, and germination rate. We observed that C. lindemuthianum infected nearly all plants in the study populations, but that the amount of infected pods per plant varied greatly. In addition, infection was associated with a reduction of host fitness-related traits only in pods with sporulating lesions. These findings suggest that C. lindemuthianum may constitute a significant selection pressure in wild populations, and that impact of spore production on host fitness may influence virulence.
Slippage Estimation of Two Wheeled Mobile Robot Using Recurrent Deep Neural Network

Position, velocity and acceleration information are important for mobile robots. Due to the wheel slippages, encoder data may not be reliable and IMU data also contains a cumulative error. Errors of inertial measurements are accumulated over velocity and position estimates and as time increases, these errors grow higher. Due to robot hardware and the operating surface, ground truth may not be available. In this work recurrent deep neural network is proposed in order to reduce the error in speed and yaw angle estimates coming from encoder and IMU data. Neural networks are commonly used to capture the behavior of linear and nonlinear systems. Since ground-wheel interaction forces are modeled with non-linear models such as the Magic formula and determining parameters of those models require time and test setups, there is a need for simpler methods to model the behavior of simple robots. Neural networks could be used to model non-linear systems. In this work, a recurrent deep neural network is proposed to estimate the speed and yaw angle of a two-wheeled differentially driven mobile robot. Using the information coming from the camera positioned above the test area as ground truth, the network is trained. After that, the output of the network is recorded in the absence of ground truth information in the network. Finally, the performance of the network is evaluated using network output, sensor data calculation, and ground truth.
A Bearing Fault Diagnosis Method using Transfer Learning and Dempster-Shafer Evidence Theory

Rolling element bearings are among the most important components in rotary machines. The reliable operation of rotary machines highly depends on the performance of bearing. Therefore, bearing fault diagnosis is a critical task in the industry. Signal-based fault diagnosis for bearings has applied extensively deep learning algorithms because of their ability to automatically extract features from fault signals measured from rotary machines. However, designing a deep learning model for any fault diagnosis problem is not a trivial task since each deep model has a complex structure and a huge number of hyper-parameters and trainable parameters. Each hyper-parameter of a deep learning model has a profound impact on the performance of that model. The selection of appropriate hyper-parameters is often conducted manually based on the Trial & Error method and experiences of the designer. Transfer learning is a technique that adopts already existing machine learning models into new domains. This technique helps to save the designing and training time of machine learning models, especially deep neural networks. In this paper, transfer learning technique is exploited to the problem of bearing fault diagnosis. A pre-trained deep neural network in the domain of image classification is adopted and modified to extract features from vibration signals measured by multiple sensors. The effectiveness of the proposed method is verified by experiments conducted with actual bearing data set supplied by Case Western Reverse University Bearing Data Center.
PROFIT DECOMPOSITION: ANALYZING THE PATHWAY FROM CARBON DIOXIDE EMISSION REDUCTION TO REVENUES AND COSTS

In 2009, the Indonesian Government has introduced regulation on reducing carbon dioxide (CO2) emission known as “energy management” to follow up its commitment to reduce CO2 emission that was ratified in the Kyoto protocol. The regulation mandates companies in Indonesia which consumed more than 6000-tonne of oil equivalent (TOE). The obligation to reduce CO2 emission has an implication to the companies that the company has to implement strategies incorporating an expensive investment. Through investment, the company’s sales are expected to increase and costs are expected to decrease. Several conceptual frameworks have been developed to identify through which pathway CO2 emission reduction can gain profits for companies but a little study (if any) has been done to analyze the pathways of CO2 emission reduction that can improve companies’ profits. This study aims to examine the pathway of CO2 emission reductions to the profits generated by revenues and costs. The purposive sampling method was used to obtain data from 384 companies that consumed 6000- TOE from 2016-2017 in Indonesia. The multiple regression analysis with ordinary least square was used to analyze their relationship. The results showed that in the case of Indonesia, the CO2 emission reduction can generate profits through both pathways namely revenue improvement and cost-efficiency. The increase in revenues and decrease in cost are due to their ability to meet customers’ interests, the positive responses of stakeholders in compliance with the regulation to reduce CO2 emissions, and the investment of environmentally friendly machinery.
The production of phytoliths in China's grasslands: implications to the biogeochemical sequestration of atmospheric CO2

Among the most promising approaches of long‐term atmospheric CO2 sequestration is terrestrial biogeochemical carbon sequestration. One of the most promising terrestrial biogeochemical carbon sequestration mechanisms is the occlusion of carbon within phytoliths, the silicified features that deposit within plant tissues. Using phytolith content‐biogenic silica content transfer function obtained from our investigation, in combination with published silica content and above‐ground net primary productivity (ANPP) data of China's grasslands, we estimated the production of phytoliths and phytolith‐occluded carbon (PhytOC) in grasslands. The results show that the average above‐ground phytolith production rates of China's grasslands (10.9 106 t yr−1 or 1.45% of world grasslands) are much lower than those of other grasslands (e.g. North American nonwoody grasslands) mainly because of much lower ANPP. Assuming a median content of PhytOC of 1.5%, the average above‐ground PhytOC production rates of China's grasslands and world grasslands are estimated to be 0.6 106 t CO2 yr−1 and 41.4 106 t CO2 yr−1, respectively. The management of grasslands to maximize ANPP has the potential to result in considerable quantities of phytoliths and securely bio‐sequestered carbon.
Portuguese-Chinese Neural Machine Translation

This work reports on a study addressing Neural Machine Translation for the language pair Portuguese ↔ Chinese where the development of a state-of-the-art Machine Translation system for this pair was undertaken by using only freely available resources. There are two main challenges to this work: (i) the gathering of corpora with good enough quality and quantity, which for this under-resourced pair of languages is complicated issue; and (ii) the adoption of a suitable architecture that is able to make the most of these corpora. Three approaches are experimented with, one of them outperforming a baseline consisting of the Google Translate service for this language pair, in both translation directions. All the implemented systems make use of deep learning, namely by resorting to the Transformer architecture. An online translation service was also developed, showcasing the two translation directions, and is freely available online. 1 Part of the work presented in this dissertation was peer reviewed and published in EPIA 2019 (Santos et al., 2019).
Invasive Lactococcus lactis producing mycobacterial Hsp65 ameliorates intestinal inflammation in acute TNBS‐induced colitis in mice by increasing the levels of the cytokine IL‐10 and secretory IgA

To investigate the anti‐inflammatory activity of an invasive and Hp65‐producing strain Lactococcus lactis NCDO2118 FnBPA+ (pXYCYT:Hsp65) in acute 2,4,6‐trinitrobenzene sulphonic acid (TNBS)‐induced colitis in mice as an innovative therapeutic strategy against Crohn's disease (CD).
Managing Burns During COVID-19 Outbreak

Abstract The coronavirus disease pandemic has affected our practice as healthcare professionals. As burn surgeons, we are obliged to provide the best possible care to our patients. However, due to the risk of viral transmission, the goal should be to provide safe care to our patients as well as ensure the safety of the whole team providing burn care. The burn patients are usually debilitated and require a prolonged hospital stay and multiple operative procedures which put them and everyone involved in their care at increased risk of coronavirus infections and transmission. This warrants special caution to the burn team while managing such patients. In this review, we aim to highlight the key considerations for burn care teams while dealing with burn patients during the COVID-19 pandemic.
Rationale of a loading dose initiation for hydroxychloroquine treatment in COVID-19 infection in the DisCoVeRy trial

Abstract Around the world, several dose regimens of hydroxychloroquine have been used for COVID-19 infection treatment, with the objective of identifying a short-term course. Hydroxychloroquine was found to decrease the viral replication in a concentration-dependent manner in vitro and to be more active when added prior to the viral challenge. A loading dose is used to rapidly attain a target drug concentration, which is usually considered as approximately the steady-state concentration. With a loading dose, the minimum effective concentration is reached much more rapidly than when using only the maintenance dose from the start. Thus, we propose a hydroxychloroquine sulphate dose regimen of 400 mg twice daily at Day 1 then 400 mg once daily from Day 2 to Day 10. We aim to evaluate this in the C-20-15 DisCoVeRy trial.
EFFECT OF SOAKING THE LOCAL ORANGE GRAFTS WITH THE GROWTH REGULATOR BENZYL-ADENINE AND ON THREE ROOTSTOCKS OF CITRUS

The study was conducted in the Lath house of the Department of Horticulture and Landscape Gardening, College of Agriculture, University of Baghdad for the period from March to November 2013. To study the effect of soaking local orange grafts in the BA growth regulator on three rootstocks of citrus, the study included two factors: The first factor is citrus rootstocks (Sour orange, Volkameriana lemon, Cleopatra mandarin). The second factor is to soaking into the growth regulator of BenzylAdenine at three levels of (0, 150, 250 mg.L -1 ). The experiment was designed according to Randomized Complete Blocks Design (RCBD) as a factorial experiment with three replicates. The results were summarized as follows: The cultivar had a significant effect, so Volkameriana was excelled in success rate of grafts, plant height, number of leaves and branches, leaves content of chlorophyll, phosphorus and potassium. While the Cleopatra mandarin rootstock excelled by giving the highest content of carbohydrates in the leaves reached of 3.48%, while the sour orange rootstock excelled in the leaves content of nitrogen. As for the effect of soaking grafts in (BA). It was showed that the spraying treatment of with BenzylAdenine at a concentration of (250 mg.L -1 ) excelled in all studied traits (success rate of grafts, plant height, number of leaves and branches, leaves content of chlorophyll, phosphorus and potassium). As for the interaction between the rootstock and the soaking levels in the BenzylAdenine, while the interaction treatment between the volkameriana and soaking of the grafts in BenzylAdenine with a concentration of 250 mg.L -1 was given the highest rate of all studied traits.
The life cycle of the Upland Goose Chloëphaga picta in the Falkland Islands

Upland Geese Chloephaga picta were studied between 1977 and 1980, primarily around Darwin, East Falkland, in order to describe their breeding biology, moulting and adult survival. The population of breeding birds in a valley reached a peak from mid-September to late November when nesting took place. The average territory length was 240 m in five valleys. Breeding adults generally returned to breed in the same territory each year and with the same mate. Nests were on the ground, usually amongst whitegrass Cortaderiapilosa. The mean clutch size was 6-1, brood size was 5-1 and fledged family 3–9. Incubation took 30 days and the fledgling period was about 70 days. Most broods were raised in the nesting territory. Growth of goslings is described. The breeding success between laying and fledging was 0–34 (in 1977) and 0–29(in 1978), giving an annual production of 21 and 1–8 young per breeding pair. Fledglings remained in family parties through the autumn and winter and were evicted by their parents in early spring. Some siblings stayed together for short periods and then joined other non-breeders. Females started pairing at ten months of age and most were paired at 17–18 months. Some bred for the first time at 23 months. Males started pairing at about 20 months of age. Flightless moult (shedding) took place at ponds or in sheltered inlets of the sea, in flocks of up to several hundred birds. Flightless birds were found between 14 November and 11 February, though 50% were flightless between 26 November and 2 January. Individuals were flightless for 36 days. First-year birds were more synchronized in shedding than adults. The percentage of first-year males (in the male component) varied from 16-5 to 45-9% in shedding flocks, and significantly more males were present in some flocks. The flocks were composed of first-year and second-year birds too young to breed and failed breeders. The percentage of a shedding population which returned to the same site in successive years was 25-3 and 15-1% at two localities. The moult of other feather tracts is described. The annual survival rate of breeding adults was 82%. A model of the population dynamics is presented. The current level of culling to control the goose population is less than the number which must die each year to maintain a stable population.
Workplace Leadership and Job Satisfaction: Imperative for Sustainable Productivity in Nigeria

: Leadership is critical in every organization and strategic for sustainable performance and development of any economy. On the other hand sustainable productivity will not only require good leadership but the human element as well. The objective of the paper is to explore the relationship between workplace leadership and employee job satisfaction; and how it affects organizational productivity in particular and the performance of the economy as a whole. To achieve this objective, the author reviewed some empirical studies on leadership and employee satisfaction in the workplace; and highlighted its implication on sustainable productivity. The paper further articulated ways by which employee job satisfaction can be enhanced through leadership. Existing empirical studies found that leadership is positively and significantly related to employee job satisfaction. Further finding reviewed that many factors are responsible for employee satisfaction and as a result leadership has the task of identifying the right motivating instrument in order to achieve the desired outcome and thereby improve performance. The paper concludes that for meaningful and sustainable development of the Nigerian economy, the leadership of the Nigerian private sector organizations and government agencies should work on improving the working conditions of the employees generally, while paying more attention to intrinsic motivators than extrinsic motivators. This will help in improving employee job satisfaction and thereby reducing the high migration rate of human capital being experienced in Nigeria.