Miracle-dz/UniFAR-FTUs huggingface dataset:
Key differences in photosynthetic characteristics of nine species of intertidal macroalgae are related to their position on the shore

A study of the fluorescence characteristics, net photosynthetic rate, and dark respiration of nine species of intertidal macroalgae, from three divisions and from different positions on a rocky shore on the east coast of Scotland, was carried out to address the following questions: (i) Do algae at different positions along the shore respond differently to the stresses at these positions in terms of photosynthetic efficiency, net photosynthetic rate, and respiration, when fully hydrated? (ii) Do algal species at different positions respond differently, in terms of function, to desiccation, and can these responses be correlated with tolerance or intolerance to desiccation in terms of recovery? The present study demonstrated that algal species that occur higher on the shore have greater photosynthetic rates, respiration rates, maximum fluorescence / initial fluorescence ratios, and photosynthetic efficiencies while fully hydrated than do species that live lower on the shore. These findings point towards an i.
Finger-Knuckle-Print Recognition Using Deep Convolutional Neural Network

Biometric technology has become essential in our daily life. In such a biometric system, personal identification is based on behavioral or biological characteristics. Recently, the trait of the Finger-Knuckle-Print (FKP) is used due to its ease of use and low cost. In order to develop an efficient recognition system based on these images, we propose a deep learning method where we use our own Convolutional Neural Network (CNN) to identify persons. Excellent results were conducted with unimodal and multimodal identification systems.
A Systematic Literature Study on Definition and Modeling of Service-Level Agreements for Cloud Services in IoT

The cloud computing paradigm provides remote computing resources to the cloud service consumers and businesses. When combined with Internet of Things (IoT), both technologies open up a wide range of new possibilities for more agile and flexible applications. However, guaranteed quality of service is essential in provisioning of cloud services, which makes Service Level Agreements (SLAs) a focal point in the cloud computing and IoT ecosystem. The SLA definition and modeling phase is crucial in establishing SLAs between service providers and consumers. This paper identifies that the research on definition and modeling of SLAs for cloud services in IoT is widely dispersed and there is a lack of a systematic and comprehensive literature review. Thus, in this paper we build on top of a previously conducted systematic mapping study on management of SLAs for cloud computing and IoT to perform a comprehensive systematic review and discuss sub-categorization of the definition and modeling aspects of SLAs for cloud services in IoT. Furthermore we analyze the extracted relevant literature, present commonalities in the studies, identify gaps and discuss opportunities for further research in the area.
Real time implementation of fuzzy gain-scheduled PID controller for twin rotor MIMO system (TRMS)

The work has done in this paper concern a strategy of control based on gain adaptive proportional integral derivative (PID) using the fuzzy inference system and their application to the Twin Rotor MIMO System (TRMS), the PID controller with fixed parameters may fail to provide acceptable control performance. To improve the PID control effect, new designs of the fuzzy gain Scheduled PID controller (FGSPID) were presented in this paper. The proposed techniques were applied to the TRMS, where adaptive PID controllers were proposed for control system in the presence of external disturbances. The parameters of PID controller were adjusted by a fuzzy system, used to tune in real-time the controller gain. The obtained simulation and experiment results show that the robustness of TRMS angles (pitch and yaw) driven by proposed controller are guaranteed.
Quadratic Programming Decoder for Binary LDPC Codes via ADMM Technique with Linear Complexity

In this paper, we develop an efficient quadratic programming (QP) decoding algorithm via the alternating direction method of multipliers (ADMM) technique for binary low density parity check (LDPC) codes. Its main content is as follows: first, through transforming the three-variables parity check equation to its equivalent expression, we relax the maximum likelihood decoding problem to a quadratic program. Second, the ADMM technique is exploited to design the solving algorithm of the resulting QP decoding model. Compared with the existing ADMM-based mathematical programming (MP) decoding algorithms, our proposed algorithm eliminates complex Euclidean projection onto the check polytope. Third, we prove that the proposed algorithm satisfies the favorable property of all-zeros assumption. Moreover, by exploiting the inside structure of the QP model, we show that the decoding complexity of our proposed algorithm in each iteration is linear in terms of LDPC code length. Simulation results demonstrate that the proposed QP decoder attains better error-correction performance than the sum-product BP decoder and costs the least amount of decoding time amongst the state-of-the-art ADMM-based MP decoding algorithms.
Identifying Opinion Leaders in Twitter during Social Events: Social Impact Theory from Influencer’s Perspective

In this paper, we explore the characteristics of opinion leaders in social media during a social event – Iran Nuclear Deal, 2018. By extending social impact theory, we conceptualize informational social impact and examine behavioral attributes of opinion leaders. We propose four dimensions of informational social impact: 1) strength of sources, 2) temporal immediacy of sources, 3) social immediacy of sources, and 4) social media commitment. We then use these attributes to predict opinion leaders during the event. Our results provide support for the legitimacy of the last three dimensions. Specifically, we suggest that opinion leaders rely on information seeking, rather than social standing, and use their expertise to exert influence. Also, we claim that opinion leaders occur in the early stages of social events. This work is novel, as we apply social impact theory to modeling informational social impact from influencer’s perspective. Finally, we provide theoretical and practical implications.
Winter presence of prochlorophytes in surface waters of the northwestern Mediterranean Sea

Cells with characteristics similar to the recently discovered oceanic prochlorophytes have been detected with ship-board flow cytometry during winter in the surface waters of the northwestern Mediterranean Sea as well as in the low-salinity dilution zone of the Rhone River. Maximum abundances reached 50,000 cells ml−1 at the surface and were only slightly lower than those observed previously at the bottom of the euphotic zone in the Atlantic and Pacific Oceans. The concentration, light scatter, and pigment fluorescence of these cells correlated tightly with those of Synechococcus spp. cyanobacteria. These results point out that the ecological niches of oceanic prochlorophytes are probably more diverse than initially thought.
New User Grouping Scheme for Better User Pairing in NOMA Systems

Non-orthogonal multiple access (NOMA) guarantees good spectral efficiency and better enhance system radio capacity. However, the deployment of NOMA is not easy due to complexity of successive interference cancellation (SIC). Thus, it is advisable to divide users into orthogonal frequency groups and adopt a Conventional NOMA (C-NOMA) pairing within each group (hybrid NOMA). Knowing that C-NOMA uses the channel gain difference and the power allocation to increase the data rate of the pair, in a dense user environment, this channel gain difference narrows further. To do this, the users are grouped in such a way as to exploit the intergroup gap in order not only to avoid the formation of very small channel gain pairs, but also improved the data throughput of low gain groups. In this paper, we examine the user pairing scheme that we will call D-NOMA (Distributed NOMA), in a uniform deployment scenario, to distribute the data throughput in the NOMA sets, to exploit the intergroup gap and facilitate a controlled data throughput distribution of NOMA weak pairs more precisely. The results show that about 50% of D-NOMA pairs of small channel gain difference benefit from a data rate higher than C-NOMA, and 25% of D-NOMA pairs of small channel gain difference benefit from a higher data rate than conventional Divide-and-Next-Largest-difference based User Pairing Algorithm (D-NLUPA).
Deep Regression for Imaging Solar Magnetograms using Pyramid Generative Adversarial Networks

Monitoring a large active region in the farside of the Sun is important for space weather forecasting. However, direct imaging of the farside is currently not available and usually physicists rely on seismic holography to infer farside magnetograms. On other hand, mapping between holography and magnetic images is non-trivial. In this work, Generative Adversarial Network (GAN) is used; which consists of a pyramid of modified pixel2pixel architectures to capture internal distributions at different scales with higher quality. Generative model is trained and evaluated using frontside of Solar Dynamic Observatory (SDO): Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI) magnetograms. Farside solar magnetograms from Extreme UltraViolet Imager (EUVI) farside data is also generated. The generative model successfully generates frontside solar magnetograms and outperforms state-of-the art method. It also help to monitor the magnetic changes from farside to frontside using generated solar magnetograms.
Evaluation of a Cervical Stabilization Exercise Program for Pain, Disability, and Physical Impairments in University Violinists with Nonspecific Neck Pain

Cervical stabilization exercises are frequently used to reduce pain, maximize function, and improve physical impairments for people with nonspecific neck pain. We conducted a single arm study to evaluate the effects of a home-based cervical stabilization exercise program for university violin players with nonspecific neck pain who frequently assume an asymmetrical neck posture and activate their superficial cervical flexors to stabilize the violin. Twenty violin players with nonspecific neck pain from university symphony orchestras participated in this study. All participants received assessments twice before the intervention and once immediately after a 6-week cervical stabilization exercise program. No significant differences were found between the two pretests before the intervention. After the intervention, the Numeric Rating Scale, the Neck Disability Index, the craniocervical flexion test, muscle endurance tests, cervical range of motion (all directions except flexion) tests, and cervicocephalic relocation tests (flexion and left rotation) showed improvements. The forward head posture indicated by the craniovertebral angle also slightly improved. The results of this single arm study suggest that cervical stabilization exercise is feasible and has the potential to improve physical health for collegiate violin players with nonspecific neck pain.
Comparative Study of Classification Algorithms for Cloud IDS using NSL-KDD Dataset in WEKA

Cloud based Intrusion Detection Systems (IDS) presented an effective way to increase the detection rates of malicious activities in the past couple of years. However, the implementation of these systems using traditional techniques is a herculean task due to the exponential growth of network bandwidth with the very limited availability of computational resources. One topic that intuitively stands out as a potential for solving this issue is Artificial Intelligence (AI). In this paper, we present an overview of recent detection proposals in the field of security intrusion detection using the contributions of new trends in AI (Artificial Intelligence) with an experiment carried out to evaluate the performance of different machine learning techniques using the most used dataset in information security research NSL-KDD and its various components in WEKA. Results show that the best classification algorithm is Random Forest with a Precision of 98.6%.
Interventions to support recovery following an episode of delirium: A realist synthesis

Abstract Objectives Persistent delirium is associated with poor outcomes in older adults but little is known about how to support longer-term recovery from delirium. The aim of this review was to identify and synthesise literature to understand mechanisms of recovery from delirium as a basis for designing an intervention that enables more effective recovery. Methods A systematic search of literature relevant to the research question was conducted in two phases. Phase one focused on studies evaluating the efficacy of interventions to support recovery from delirium, and stage two used a wider search strategy to identify other relevant literature including similar patient groups and wider methodologies. Synthesis of the literature followed realist principles. Results Phase one identified four relevant studies and stage two identified a further forty-six studies. Three interdependent recovery domains and four recovery facilitators were identified. Recovery domains were 1) support for physical recovery through structured exercise programmes; 2) support for cognitive recovery through reality orientation and cognitive stimulation; 3) support for emotional recovery through talking with skilled helpers. Recovery facilitators were 1) involvement and support of carers; 2) tailoring intervention to individual needs, preferences and abilities; 3) interpersonal connectivity and continuity in relationships and; 4) facilitating positive expressions of self. Conclusions Multicomponent interventions with elements that address all recovery domains and facilitators may have the most promise. Future research should build on this review and explore patients’, carers’, and professionals’ tacit theories about the persistence of delirium or recovery from delirium in order to inform an effective intervention.
Redescription of two known species, Gastrocirrhus monilifer [Ozaki et Yagiu, 1942] and Gastrocirrhus stentoreus Bullington, 1940, with reconsideration of the genera Gastrocirrhus and Euplotidium

Summary. The living morphology and infraciliature of two poorly known planktonic ciliates, Gastrocirrhus monilifer (Ozaki et Yagiu, 1942) and G. stentoreus Bullington, 1940, collected from coastal mollusc-farming waters off Qingdao, P. R. China, were described based on observations of specimens in vivo and following protargol impregnation. Since no recent data deriving from silver staining methods on these two species are available, detailed redescriptions were presented. Based on the previous and current studies, improved redefinition for two hitherto confused genera Gastrocirrhus and Euplotidium are supplied. The diagnosis for Gastrocirrhus: marine planktonic Gastrocirrhidae, cells generally cup- or bell-shaped with anterior end truncated; oral field broad and opening anteriorly; adoral zone of membranelles dominant, terminated deeply near cytostome after spiraling around bell-edge in one turn; undulating membrane single-structured; frontoventral cirri arranged in two rows, which are formed by multi-anlagen during morphogenesis; 5 or more well-developed transverse cirri; marginal and caudal cirri absent. Diagnosis for the genus Euplotidium: marine Gastrocirrhidae, body shape cylindrical to dorso-ventrally flattened, buccal apparatus being of Euplotes-pattern; about 10 frontoventral cirri sparsely distributed but not in rows; ca 5 transverse and one or more left marginal cirri; no caudal cirri. Based on the new definition and the data available, 5 nominal species are recognized in the genus Euplotidium: E. itoi Ito, 1958; E. psammophilius (Vacelet, 1961) Borror, 1972 [basonym: Euplotes psammophilius Vacelet, 1961]; E. arenarium Magagnini et Nobili, 1964; E. helgae Hartwig, 1980 and E. prosaltans Tuffrau, 1985, and 5 in Gastrocirrhus: G. intermedius Lepsi, 1928; G. stentoreus Bullington, 1940; G. monilifer (Ozaki et Yagiu, 1942) Curds et Wu, 1983 [junior synonym: G. adhaerens Faure-Fremiet, 1954; G. trichocystus Ito, 1958]; G. agitatus (Noland, 1937) comb. n. [basonym: Euplotidium agitatum Noland, 1937] and G. smalli (Lei Y., Choi J. K. et Xu K., 2002) comb. n. [basonym: Euplotidum smalli Lei Y. , Choi J. K. et Xu K. , 2002].
The modulating impact of cigarette smoking on brain structure in panic disorder: a voxel-based morphometry study

Abstract Cigarette smoking increases the likelihood of developing anxiety disorders, among them panic disorder (PD). While brain structures altered by smoking partly overlap with morphological changes identified in PD, the modulating impact of smoking as a potential confounder on structural alterations in PD has not yet been addressed. In total, 143 PD patients (71 smokers) and 178 healthy controls (62 smokers) participated in a multicenter magnetic resonance imaging (MRI) study. T1-weighted images were used to examine brain structural alterations using voxel-based morphometry in a priori defined regions of the defensive system network. PD was associated with gray matter volume reductions in the amygdala and hippocampus. This difference was driven by non-smokers and absent in smoking subjects. Bilateral amygdala volumes were reduced with increasing health burden (neither PD nor smoking > either PD or smoking > both PD and smoking). As smoking can narrow or diminish commonly observed structural abnormalities in PD, the effect of smoking should be considered in MRI studies focusing on patients with pathological forms of fear and anxiety. Future studies are needed to determine if smoking may increase the risk for subsequent psychopathology via brain functional or structural alterations.
Learning-Based Computation Offloading for Edge Networks with Heterogeneous Resources

Mobile edge computing (MEC) has shown its potential in serving computation intensive tasks via offloading. However, the heterogeneity of MEC systems and the dynamic nature of wireless environment pose a great challenge to the design of offloading policies. In this paper, we investigate this computation offloading problem, where the heterogeneities of computational resource, channel state, task type and input data size are considered. We first propose a greedy algorithm, in which each arrival task is greedily offloaded to the edge server with minimal utility, based on a global information of network states. While this greedy algorithm performs well in terms of system utility, the overhead incurred to collect the global information is large, especially in dense MEC scenarios and time-varying channel scenarios. Inspired by this observation, we then propose a model-free offloading algorithm based on reinforcement learning, which does not rely on such kind of information and can make offloading decisions based on learning experience. By so doing, the communication overhead can be largely reduced. Extensive simulations show that the two proposed algorithms have similar performance in terms of system utility and can decrease the system utility by up to 50% compared with two widely used algorithms. The robustness of the two proposed algorithms is further verified.
HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System

Heart disease, one of the major causes of mortality worldwide, can be mitigated by early heart disease diagnosis. A clinical decision support system (CDSS) can be used to diagnose the subjects’ heart disease status earlier. This study proposes an effective heart disease prediction model (HDPM) for a CDSS which consists of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and eliminate the outliers, a hybrid Synthetic Minority Over-sampling Technique-Edited Nearest Neighbor (SMOTE-ENN) to balance the training data distribution and XGBoost to predict heart disease. Two publicly available datasets (Statlog and Cleveland) were used to build the model and compare the results with those of other models (naive bayes (NB), logistic regression (LR), multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), and random forest (RF)) and of previous study results. The results revealed that the proposed model outperformed other models and previous study results by achieving accuracies of 95.90% and 98.40% for Statlog and Cleveland datasets, respectively. In addition, we designed and developed the prototype of the Heart Disease CDSS (HDCDSS) to help doctors/clinicians diagnose the patients’/subjects’ heart disease status based on their current condition. Therefore, early treatment could be conducted to prevent the deaths caused by late heart disease diagnosis.
Repeat Head CT for Neurologically Stable Patients With Mild Traumatic Subarachnoid Hemorrhage During Interfacility Transfer and Follow-Up Does Not Alter Patient Care

Purpose: To evaluate the impact of repeat head computed tomography (CT) during (1) interfacility transfer and (2) inpatient and/or outpatient follow-up on management, cost-effectiveness, and radiation dose in neurologically stable patients with mild traumatic subarachnoid hemorrhage (tSAH). Material and Methods: This is a single-center retrospective study evaluating patients with mild tSAH presenting between January 2017 and July 2019. A total of 101 and 140 patients met the eligibility criteria for the first and second subgroups, respectively. Common inclusion criteria were isolated mild tSAH, Glasgow Coma Scale between 13 and 15, and neurological stability. Additional inclusion criteria for the first subgroup were availability of brain imaging at the outside institution prior to transfer and the second subgroup was the availability of follow-up imaging. Results: In the first subgroup, 76.20% of patients had stable SAH, 18.80% had reduced SAH, while 5% had an interval increase in SAH. None required any surgical intervention. Additional per-patient mean radiation exposure was 1.77 ± 0.26 mSv. In the second subgroup, all 140 patients had complete resolution of tSAH. One patient had a new tiny subdural hemorrhage, which subsequently resolved on follow-up. The additional mean radiation exposure was 2.47 ± 1.29 mSv. A total of 256 avoidable CT scans were performed resulting in excess health care costs of about US$531 696. Conclusion: In neurologically stable isolated tSAH patients, repeat brain imaging during interfacility transfer and inpatient and/or outpatient follow-up do not alter patient management despite increased health care costs and radiation burden.
Improving the Mass Rearing Possibilities of Serangium montazerii Fürsch (Coleoptera: Coccinellidae) on Different Host Plants of Bemisia tabaci (Genn.) (Homoptera: Aleyrodidae)

The objectives of this study were to identify suitable plant species for mass rearing and to determine some biological characteristics of the predator Serangium montazerii Fursch (Coleoptera: Coccinellidae) used in the biological control of Dialeurodes citri (Ashmead) (Homoptera: Aleyrodidae). The development time, mortality rate, preoviposition, oviposition and postoviposition periods and fecundity of S. montazerii were studied on various host plants using Bemisia tabaci (Genn.) (Homoptera: Aleyrodidae). Two cotton (Cukurova-1518 and Deltapine) and two eggplant (Pala and Cukurova topagi) cultivars were used at 25 oC and 65 ± 5% RH in growth chambers. Additionally, life tables were constructed from the data obtained to compare the effects of different diets. The development time of S. montazerii was shortest on Cukurova-1518 (22.9 days) and longest on Cukurova topagi (28.0 days). The total mortality of the immature stages was lowest on Cukurova-1518 (20.8%) and highest on Pala (48.9%). The highest Ro and T levels were found on Deltapine 90. According to the results, cotton varieties constituted more suitable plant species for mass rearing due to the short development time, low mortality rate and high fecundity of S. montazerii.