Miracle-dz/UniFAR-FTUs huggingface dataset:
Case report of hyperacute edema and cavitation following deep brain stimulation lead implantation

Background: Postoperative cerebral edema around a deep brain stimulation (DBS) electrode is an uncommonly reported complication of DBS surgery. The etiology of this remains unknown, and the presentation is highly variable; however, the patients generally report a good outcome. Case Description: Here, we report an unusual presentation of postoperative edema in a 66-year-old female who has bilateral dentatorubrothalamic tract (specifically, the ventral intermediate nucleus) DBS for a mixed type tremor disorder. Initial postoperative computed tomography (CT) was unremarkable and the patient was admitted for observation. She declined later on postoperative day (POD) 1 and became lethargic. Stat head CT scan performed revealed marked left-sided peri-lead edema extending into the centrum semiovale with cystic cavitation, and trace right-sided edema. On POD 2, the patient was alert, but with global aphasia, right-sided neglect, and a plegic right upper extremity. Corticosteroids were started and a complete infectious workup was unremarkable. She was intubated and ultimately required a tracheostomy and percutaneous gastrostomy tube. She returned to the clinic 3 months postoperatively completely recovered and ready for battery implantation. Conclusion: While this is an unusual presentation of cerebral edema following DBS placement, ultimately, the outcome was good similar to other reported cases. Supportive care and corticosteroids remain the treatment of choice for this phenomenon.
The need to shift to a contextualized and collective mental health paradigm: learning from crisis-hit Lebanon

The impact of national and international crises and disasters on population mental health has been well established, with poorer mental health and a higher incidence of mental disorders documented during war and armed conflict, political instability and anti-government protests, natural disasters, economic crises, mass displacement, and linked more broadly to inequality, poverty and social injustices Following the COVID-19 pandemic, mental health has been highlighted as a global priority in response to the fallout of the crisis, with the toll of lockdown, loss of jobs, increase in domestic violence, and lower social support impacting the wellbeing and functioning of affected populations Research has shown that such crises, particularly when chronic, have an impact beyond the experience of the individual, affecting the functioning of families, communities, and wider social structures and socio-political systems Despite major advances in the development and implementation of interventions in responding to crises and emergencies, there remain significant gaps in the evidence-base for public mental health responses that are sufficiently contextualized to this collective suffering (PsycInfo Database Record (c) 2020 APA, all rights reserved)
Superficial temporal artery-superior cerebellar artery bypass and proximal occlusion through anterior petrosal approach for subarachnoid hemorrhage due to basilar artery dissection

Background: Subarachnoid hemorrhage (SAH) due to rupture of basilar artery dissection (BAD) is extremely rare and often has a poor prognosis. Since ruptured BAD has high rate of rebleeding and mortality, treatment to prevent rerupture is mandatory in the acute phase. However, to date, no optimal treatment has been established which satisfies secure prevention of rerupture and ischemia simultaneously. Herein, we report a case of SAH due to BAD treated with proximal occlusion of basilar artery with superficial temporal artery (STA)-superior cerebellar artery (SCA) bypass, preventing rebleeding securely and ensuring adequate blood flow in the upper basilar region. Case Description: A 48-year-old male presenting with headache and altered mental status was found to have SAH and BAD. To prevent rerupture, proximal occlusion of basilar artery with STA-SCA bypass using anterior transpetrosal approach was performed. The postoperative course was relatively good and there is no evidence of recurrent arterial dissection. Conclusion: Proximal occlusion of the basilar artery combined with STA-SCA bypass was successful in preventing rerupture of BAD and ensuring blood flow in the upper basilar region. Although there is controversy regarding acute treatment for ruptured BAD, direct proximal occlusion with sufficient revascularization using bypass skull base technique may be one of the optimal treatments even in this era of endovascular treatment.
Bluetongue in the Desert Bighorn Sheep

A young desert bighorn ram (Ovis canadensis) approximately 8 months old was found ill in its natural habitat. Inoculation of a suspension of its lung tissue into domestic sheep resulted in lesions typical of bluetongue disease. Serological studies confirmed the diagnosis. The possibility is presented that this disease may have been partially responsible for the disappearance of the desert bighorn from the Trans-Pecos area of Texas. Bluetongue is a viral disease of sheep primarily, although cattle and goats may be affected (Howell 1963). It has been hypothesized that wild ruminants are reservoirs of infection. Experimental transmission to the blesbuck (Damaliscus albifrons) (Neitz 1933), an African antelope, has been achieved. No clinical signs were reported. This disease is widely distributed in Africa and the Middle East but was not reported in North America until fairly recently. An entity, first called "soremuzzle" and later identified as bluetongue, was reported in Texas by Hardy and Price (1952), and it is believed that the disease was present in Texas several years earlier than this report (Hardy 1966, Personal communication). Dr. D. H. Bennett described the lesions and epidemiology of "soremuzzle" in Texas as early as 1923 in a communication with Dr. H. Schmidt. Transmission of the virus isolated in Texas was accomplished experimentally in sheep by the use of insects of the genus Culicoides (Price and Hardy 1954). The incubation time in sheep varied from 1 to 2 weeks and the severity of the disease was variable and unpredictable. The clinical signs observed in domestic sheep usually include an increase in temperature, hyperemia of the mucous membranes, and swelling of the face and lips (Howell 1963). Buccal erosions may occur and coronitis is often observed in animals with light colored hooves. A clear mucopurulent nasal exudate usually is present, and terminal pneumonia commonly occurs in fatal cases. The disease must be reproduced in susceptible sheep to establish a positive diagnosis. The occurrence of this disease in wild ruminants such as deer, bighorn, and pronghorn antelope has not been reported, although it has been reported in domestic sheep (Howell 1963) throughout the southwestern United States.
Analysis of urban green spaces and surface temperature using quantitative remote sensing technology in Bangkok

Bangkok Metropolitan Administration (BMA) is the capital city of Thailand. In recent years, city surface has been changed greatly by rapid urbanization. Many green spaces in the urban area are destroyed and replaced with the commercial area. It can have effect on the climate and weather that could lead to the Urban Heat Island (UHI). This research analyzes the correlation between Urban Green Spaces (UGS) and Land Surface Temperature (LST) using quantitative remote sensing technology in BMA. More than five years of Landsat-5 TM and Landsat-8 images are used to study urban temperature. The LST, UGS and their correlation of whole area of BMA are analyzed. The LST is retrieved from mono-window algorithm, which used only one thermal band. Several indices of UGS, including the Normalized Difference Vegetation Index (NDVI), and measurement of vegetation cover percentage are also used to study on UGS. The result showed that from 2008 to 2018 LST is increased while UGS is decreased. In addition, Pearson product-moment correlation coefficient is used to analyze the linear correlation between UGS and LST. The correlation between LST and NDVI indicates the negative correlation. The average correlation coefficient is -0.44. That can imply that the higher vegetated area, the LST was lower. To get more detail relationship between LST and UGS, three typical regional areas are selected to study. LST contour is used to analyze LST and HERCULES system is used to classify land cover. The result showed that firstly, the density of tree affect LST. Secondly, the location of the public park is important. Thirdly, water bodies help to decrease LST. The results of this study have key implications for BMA sustainable urban planning and development; to mitigate UHI effects it is important to not only increase canopy cover or the size of UGS, but also to optimize their spatial configuration.
Control Method Based on Sensor Data for Stable Walking of a Biped Robot under Port Environment

ABSTRACT Jing, C.L. , You, X.F. , and Xia, T. , 2020. Control method based on sensor data for stable walking of a biped robot under port environment. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 271–275. Coconut Creek (Florida), ISSN 0749-0208. In spite of great improvement of walking ability after the merging of various walking techniques, there still exists a large gap between a biped robot and a person. In particular, under some complex environments such as port, the development and perfection of the control theory based on the detected sensor data are of great significance to achieving ideal walking ability for the robot. In this study, the detected sensor data are merged into the established dynamic model, and then, both the force and the moment of the connecting rod are accurately calculated for programming actual ZMP trajectory. Further, the data are sent to the slave computer for robot control, thereby enhancing the walking stability. The present simulation results prove that the biped robot can successfully realize stable walking.
Influence of Water Flow Velocity on the Formation of Algae-induced Odorous Black Water Agglomerate

ABSTRACT Wang, G.F. ; Fang, Y. ; Li, X.N. , and Xue, J.P., 2020. Influence of water flow velocity on the formation of algae-induced odorous black water agglomerate. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 478–485. Coconut Creek (Florida), ISSN 0749-0208. Flow velocity is the most basic and intuitive factor to characterize hydrodynamic conditions. In this study, the formation characteristics of algae-induced odorous black water agglomerate under different water flow velocities were observed and analyzed, by simulating the dynamic process of water flow in Taihu lake. The experiment lasted for 20 days. The results revealed that the increase of flow velocity had different effects on the stinking and blackening of water. With the increase of the flow velocity, the concentration of Dimethyl trisulfide, an odorous matter, decreased sharply, and the concentration of β-ionone increased slightly. In terms of water blackening, compared with the flow velocity of 5∼6cm/s, the water flow velocity of 1∼2cm/s did not influence the anaerobic reduction environment of water, but promoted the formation of sulfide and the dissolution of Fe2+, accelerated the formation of blackening causing substances, and aggravated the water blackening. Low water velocity would be a key factor to promote the occurrence of the algae-induced odorous black water agglomerate and could be used as a warning factor.
A DVERSARIAL AND N ATURAL P ERTURBATIONS FOR G ENERAL R OBUSTNESS

In this paper we aim to explore the general robustness of neural network classifiers by utilizing adversarial as well as natural perturbations. Different from previous works which mainly focus on studying the robustness of neural networks against adversarial perturbations, we also evaluate their robustness on natural perturbations before and after robustification. After standardizing the comparison between adversarial and natural perturbations, we demonstrate that although adversarial training improves the performance of the networks against adversarial perturbations, it leads to drop in the performance for naturally perturbed samples besides clean samples. In contrast, natural perturbations like elastic deformations, occlusions and wave does not only improve the performance against natural perturbations, but also lead to improvement in the performance for the adversarial perturbations. Additionally they do not drop the accuracy on the clean images.
Predictors of Female Sexual Aggression Among a U.S. MTurk Sample: The Protective Role of Sexual Assertiveness

We studied predictors of female sexual aggression (FSA) among a U.S. Mechanical Turk sample (from Amazon’s online crowdsourcing platform) of 634 adult women (median age = 32). A logistic regression analysis revealed that five measures contributed significantly to a model predicting past use of a tactic of post-refusal sexual persistence (PRSP), accounting for 19% of variance. Women’s use of a PRSP tactic was associated with lower scores on two sexual assertiveness measures (the ability to refuse sex and the ability to ask questions about a partner’s sexual health history) and higher scores on measures of acceptance of heterosexual male rape myths, early courtship rehearsal (number of others called, texted, tweeted, sexted due to attraction before the age of 18), and sexual sensation seeking. Based on our results, we suggest that sexual assertiveness training may be a useful addition to anti-sexual assault programming. For better prediction and potential prevention of FSA, we recommend continued research on the variables in this study and additional factors related to maladaptive personality (e.g., psychopathy, narcissism, and antisocial values) and the use of alcohol and drugs.
Predicting Emission Costs for Urban Transportation in Smart Cities using Machine Learning Models

Singapore ratified the Paris Agreement on September 21, 2016, and the unconditional target by 2030 is the reduction of emission by 36%. Furthermore, the cities are the major source of emission, and transportation accounts for about 14% of the total emissions in Singapore. Transportation is one of the necessary infrastructures for cities and over 80% of the population in Singapore depends on public transportation systems. To significantly cut emissions, smart cities are expected to play an essential role, therefore the conventional transportation systems are transforming to be intelligent. The emission of vehicles depends on various factors like the average speed of the vehicle, total distance covered by the vehicle, urban driving conditions, and type of vehicle. In this research, COPERT model is used for calculation of emission cost after extracting features from real-world GPS datasets, and machine learning models are built to predict the total emission cost of the buses in an area of Singapore. In this study, the performance of emission cost estimation using three modeling techniques, namely MLP, RF, and Light GBM are compared. Results prove that the prediction accuracy of the proposed model is 97% when Light GBM is implemented.
Multi-scale Characteristics of Precipitation and Its Relationship with ENSO and AO

ABSTRACT Wang, J.J.; Shi, B. ; Pan, X.Y. , and Liu, L.F. , 2020. Multi-scale characteristics of precipitation and its relationship with ENSO and AO. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 23–28. Coconut Creek (Florida), ISSN 0749-0208. This study aims to reveal the multi-scale characteristics of precipitation and its response relationship with El Niño Southern Oscillation (ENSO) and Arctic Oscillation (AO) in Shandong province of China. Historical total monthly precipitation data of 21 meteorological stations in the research region from 1961 to 2017 were preprocessed firstly by wavelet power spectrum for analyzing the intensity of oscillations in different time-frequency scales. The results of wavelet power spectrum reveal that the 12-month periodicity was significant of precipitation. Then, the ensemble empirical mode decomposition (EEMD) was used to analyse the periodicity of precipitation, the precipitation time series was decomposed into 8 IMFs (Intrinsic Mode Function, IMF) and 1 residual, the significance test of IMFs shows that the annual periodicity oscillation is stronger than the interdecadal periodicity. The results reveal that the period of IMF2 was 12-month through the fast Fourier transform analysis, and the periodicity passed the significance test of 99%. Finally, the cross wavelet analysis and wavelet coherence were used to analyse the resonance periodicity and coherence of precipitation with ENSO and AO in different time-frequency scales. The results show that ENSO and precipitation were mainly in negative phase, and the resonance periodicity of 12-month and 32-64 months were strong. Simultaneously, the phase relationship between AO and precipitation is complicated, the resonance periodicity of 12 months was strong too. This research preliminarily explores of the multi-scale characteristics of precipitation in Shandong province of China, which can provide some references for precipitation prediction and water resources utilization in this region.
Exploring Architectures for Cryptographic Access Control Enforcement in the Cloud for Fun and Optimization

To facilitate the adoption of cloud by organizations, Cryptographic Access Control (CAC) is the obvious solution to control data sharing among users while preventing partially trusted Cloud Service Providers (CSP) from accessing sensitive data. Indeed, several CAC schemes have been proposed in the literature. Despite their differences, available solutions are based on a common set of entities---e.g., a data storage service or a proxy mediating the access of users to encrypted data---that operate in different (security) domains---e.g., on-premise or the CSP. However, the majority of the CAC schemes assume a fixed assignment of entities to domains; this has security and usability implications that are not made explicit and can make inappropriate the use of a CAC scheme in certain scenarios with specific requirements. For instance, assuming that the proxy runs at the premises of the organization avoids the vendor lock-in effect but may substantially undermine scalability. To the best of our knowledge, no previous work considers how to select the best possible architecture (i.e., the assignment of entities to domains) to deploy a CAC scheme for the requirements of a given scenario. In this paper, we propose a methodology to assist administrators in exploring different architectures of CAC schemes for a given scenario. We do this by identifying the possible architectures underlying the CAC schemes available in the literature and formalizing them in simple set theory. This allows us to reduce the problem of selecting the most suitable architecture satisfying a heterogeneous set of requirements arising from the considered scenario to a Multi-Objective Optimization Problem (MOOP) for which state-of-the-art solvers can be invoked. Finally, we show how the capability of solving the MOOP can be used to build a prototype tool assisting administrators to preliminary perform a "What-if'' analysis to explore the trade-offs among the various architectures and then use available standards and tools (such as TOSCA and Cloudify) for automated deployment in multiple CSPs.
Pharmacological postconditioning: a molecular aspect in ischemic injury

Ischaemia/reperfusion (I/R) injury is defined as the damage to the tissue which is caused when blood supply returns to tissue after ischaemia. To protect the ischaemic tissue from irreversible injury, various protective agents have been studied but the benefits have not been clinically applicable due to monotargeting, low potency, late delivery or poor tolerability.
Performance improvements of a sweet pepper harvesting robot in protected cropping environments

Using robots to harvest sweet peppers in protected cropping environments has remained unsolved despite considerable effort by the research community over several decades. In this paper, we present the robotic harvester, Harvey, designed for sweet peppers in protected cropping environments that achieved a 76.5% success rate on 68 fruit (within a modified scenario) which improves upon our prior work which achieved 58% on 24 fruit and related sweet pepper harvesting work which achieved 33% on 39 fruit (for their best tool in a modified scenario). This improvement was primarily achieved through the introduction of a novel peduncle segmentation system using an efficient deep convolutional neural network, in conjunction with three‐dimensional postfiltering to detect the critical cutting location. We benchmark the peduncle segmentation against prior art demonstrating an improvement in performance with a F1 score of 0.564 compared to 0.302. The robotic harvester uses a perception pipeline to detect a target sweet pepper and an appropriate grasp and cutting pose used to determine the trajectory of a multimodal harvesting tool to grasp the sweet pepper and cut it from the plant. A novel decoupling mechanism enables the gripping and cutting operations to be performed independently. We perform an in‐depth analysis of the full robotic harvesting system to highlight bottlenecks and failure points that future work could address.
Robust Learning of Mislabeled Training Samples for Remote Sensing Image Scene Classification

Label information plays an important role in supervised high-resolution remote sensing (HRRS) image scene classification. However, the labels of a dataset are probably unreliable and may contain “noisy” labels. Focusing on uncertain labels problem, a covariance matrix representation-based noisy label model (CMR-NLD) is designed for HRRS image scene classification. The main steps are as follows. First, a pretrained convolutional neural network model is employed to extract the scene images deep features and a principal component analysis based dimensionality reduction method is applied to the first fully connected layer to reduce the computational complexity. Then, the noisy training set is constructed by randomly selecting samples into a specific class from other classes samples. We use this set to simulate the actual situation of tag noise to simulate the actual situation of label noise. Second, the covariance between noisy training samples is calculated to obtain the corresponding covariance matrix, and the average value of the obtained covariance matrix is calculated by rows. As a feature of the matrix form, it can both enlarge the subtle differences between different classes and reduce the visual differences of scene images from the same semantic classes. Then, a decision threshold is set to realize the detection and removal of noisy labels. Finally, the improved training sample set will be evaluated by a support vector machine classifier to demonstrate the proposed detector's effectiveness. Experimental results indicate that the proposed method indeed shows great improvement in noisy label detection of HRRS image scene classification.