Miracle-dz/UniFAR-FTUs huggingface dataset:
Levels of transforming growth factor β and transforming growth factor β receptors in rat liver during growth, regression by apoptosis and neoplasia

Transforming growth factor β1 (TGF‐β1) has been implicated as inhibitor of cell proliferation and a potent inducer of apoptosis in vitro and in vivo after the administration of high doses. To assess the role of endogenous TGF‐β1, we quantitated the cytokine and its receptors in rat liver during regenerative and hyperplastic growth, regression by apoptosis, and in hepatocellular carcinoma (HCC). This was accomplished by Northern blot analysis and by RNase protection assay of the messenger RNA (mRNA) of TGF‐β1 and TGF‐β receptors (TβR) types I to III and by an activity bioassay of the TGF‐β proteins. Untreated rat livers were found to contain 15.6 ± 4.8 ng TGF‐β1 protein/g tissue; TGF‐β2 protein was not detected. To induce toxic cell death and subsequent regenerative DNA synthesis in the liver, rats were treated with a necrogenic dose of carbon tetrachloride (CCl4). After 24 and 48 hours, there was an upregulation of TGF‐β1 (mRNA, up to tenfold; protein, about twofold) and of TβRs (mRNA: two‐ to fourfold); that indicates an overall enhanced production of and sensitivity to TGF‐β1, which may serve to confine the regenerative response. Hyperplastic liver growth and regression of the hyperplasia were induced by treatment with cyproterone acetate (CPA) or nafenopin (NAF) followed by withdrawal; neither mRNAs of TGF‐β1 and TβR types I to III nor TGF‐β1 protein exhibited significant changes during the growth phase or during regression by apoptosis. We also studied neoplastic growth. HCC, obtained after long‐term treatment with NAF, exhibited high rates of cell replication and apoptosis. The majority of lesions contained mRNA and protein of TGF‐β1 and mRNA of TβR types I to III at concentrations similar to those of the surrounding tissue. In conclusion, during liver regeneration there is a pronounced upregulation of expression of both TGF‐β1 and TβRs I to III, but not during mitogen‐induced liver growth or regression. It appears that apoptosis is induced via altered local concentration of TGF‐β1 in a paracrine and/or autocrine way. By this mechanism the lethal effects of TGF‐β1 may be locally confined, and overshoots of apoptosis in the liver may be prevented.
New Acidic-Rearranged Compounds from Tingenone Derivatives and their Biological Activity

Abstract Quinone-methide triterpenes of the tingenone series are effective cytotoxic and antimicrobial agents. Under an acidic condition, they can rearrange to the phenolic analogues. In the current study, six new compounds (7–12) along with three known products (4–6) were obtained from the acid-rearranged reaction of tingenone derivatives (1–3). Their structures were determined on the basis of spectroscopic evidence. Antiviral and antimicrobial activities of all compounds were evaluated. Data associated to their structures were suggested. The quinone-methide chromophore at rings A/B was necessary for antimicrobial activity. The acid-rearranged phenolic analogues were less toxic but lost antimicrobial potential. However, compounds with the phenolic ring conjugated to two vinyl groups (compounds 4–6) had an inhibitory effect on Herpes simplex virus with a selective index greater than 5.
Measuring Time-Sensitive and Topic-Specific Influence in Social Networks With LSTM and Self-Attention

Influence measurement in social networks is vital to various real-world applications, such as online marketing and political campaigns. In this paper, we investigate the problem of measuring time-sensitive and topic-specific influence based on streaming texts and dynamic social networks. A user’s influence can change rapidly in response to a new event and vary on different topics. For example, the political influence of Douglas Jones increased dramatically after winning the Alabama special election, and then rapidly decreased after the election week. During the same period, however, Douglas Jones’ influence on sports remained low. Most existing approaches can only model the influence based on static social network structures and topic distributions. Furthermore, as popular social networking services embody many features to connect their users, multi-typed interactions make it hard to learn the roles that different interactions play when propagating information. To address these challenges, we propose a Time-sensitive and Topic-specific Influence Measurement (TTIM) method, to jointly model the streaming texts and dynamic social networks. We simulate the influence propagation process with a self-attention mechanism to learn the contributions of different interactions and track the influence dynamics with a matrix-adaptive long short-term memory. To the best of our knowledge, this is the first attempt to measure time-sensitive and topic-specific influence. Furthermore, the TTIM model can be easily adapted to supporting online learning which consumes constant training time on newly arrived data for each timestamp. We comprehensively evaluate the proposed TTIM model on five datasets from Twitter and Reddit. The experimental results demonstrate promising performance compared to the state-of-the-art social influence analysis models and the potential of TTIM in visualizing influence dynamics and topic distribution.
Optimization by response surface methodology. Application to rare earth elements recovery

This work focuses on the leaching process of rare earth elements (REEs) from phosphor powder (PP) in order to reach optimal conditions of REEs recovery. Investigated PP was characterized in our previous work, after recovering from waste linear tube fluorescent lamps (FLs). Hydrochloric acid effect on leaching efficiency of REEs from PP is studied. Response surface methodology is used to determine the optimum leaching conditions. Obtained results show that, using 24.S% (v/v) HC1 at 90ºC, 140ml/gL/S ratio and 3h of leaching time, 99% of yttrium is leached out from the studied PP. Due to the highest content of major contaminant (calcium) present in obtained leach liquor, leachant agent (HC1) employed in this study may affect subsequent processes.
Discrimination of Facial Image Generated via GAN (Work-in-Progress)

Generated fake facial images remain a serious problem for corporations, governments, developers and individuals, as the voice of anxiety about the side effects of artificial intelligence grows. However, today the AI is still done mainly as a way to keep up with a real facial image rather than researching how to discriminate the generated image. As the world that is no longer able to distinguish between real and fake facial images is coming, the need for radical AI technology to detect generated images arises. In this paper, we introduce an approach that addresses these issues, describing in feasible detail the discriminative models based on various machine learning algorithms. Specifically, we show that the model with the highest accuracy in supervised learning achieved a 92.5% detection rate at 7.5% false positive rate (FPR), out of 400 images. And we have also achieved positive results in unsupervised learning. Our results demonstrate that the fake facial images generated by the GAN can be discriminated by the machine learning algorithms. Since GAN models tend to improve rapidly, we foresee new neural network discrimination models gaining in importance as part of a generated image detection strategy in coming years.
Dual Band Circularly PolarizedAntenna for Ka Band Applications

In present scenario dual band high gain microstrip patch antennas are very much desirable in view of 5G mobile communication implementation in near future, So, this paper presents a new dual band Circularly Polarized (CP) patch antenna operating in 5G band. A rectangular slot at angle 450is used for the design of this compact dual band CP patch antenna. The proposed antenna can operate at 28 GHz and 38 GHz having bandwidth 900 MHz and 1.3 GHz, respectively, which are useful in mobile 5G communication and multimedia wireless systems. Axial Ratio Band Width (ARBW) is 210 MHz at 28 GHz and 300 MHz at38GHz.The gain of the proposed antenna is 7.5 dBi and 7.7 dBi at 28 GHz and 38 GHz, respectively. The proposed dual band microstrip patch antenna has been simulated, optimized and miniaturized by using EM simulator software HFSS.
Short‐term mortality of Australian bass, Macquaria novemaculeata, after catch‐and‐release angling

Australian bass, Macquaria novemaculeata Steindachner, is angled in large numbers from coastal impoundments and rivers throughout south-eastern Australia. Many are released in the belief that most survive with few negative short-term impacts. This assumption was investigated in three experiments involving a range of conventional angling scenarios. A total of 313 Australian bass were angled and then released along with appropriate controls into either land-based tanks or floating cages, where they were monitored for up to 5 days. No controls died. Short-term mortalities of angled fish were low (0-6%) and attributed to the effects of bait type, hook location and fish size (P < 0.05). Specifically, fish that ingested hooks or were caught with natural baits were more likely to die than those that were mouth-hooked or caught on lures. Mortality negatively correlated with fish size. The results supported previous assertions that Australian bass is a resilient, hardy species that should tolerate short-term impacts associated with catch-and-release angling. However, other sublethal physiological impacts that could contribute towards delayed mortalities or reduced reproductive output warrant further investigation, particularly in relation to native populations in coastal rivers that form gauntlet fisheries during their annual spawning season.
A rotation-based capacitor-voltage-balancing method for modular multilevel converters

Modular multilevel converters (MMC) are widely applied in HVDC projects. To ensure that MMC works properly, sub-module (SM) capacitor voltages must be balanced. This paper proposes a voltage balancing method, which alternates the driving signals on the IGBTs of SMs in fundamental period or carrier period, to balance the capacitor voltages. Under normal operating conditions, this proposed method doesn't need the capacitor voltages of SMs and arm currents information, therefore it effectively decreases the hardware cost and software complexity of MMC system, and also improves the system reliability. The proposed method is validated by constructing a 5-level 1kW single-phase experimental platform in the laboratory. The experimental results demonstrate the feasibility and effectiveness of proposed voltage balancing method.
A Top-K Query Scheme With Privacy Preservation for Intelligent Vehicle Network in Mobile IoT

The emergence of mobile Internet of Things (IoT) has brought much more convenience to our daily life than ever before, modern intelligent vehicles are usually equipped with various mobile IoT components or devices. However, when we are enjoying the convenience from the rapid development of mobile IoT services, our privacy can be misused by attackers in an easier way. In this paper, we devise a query scheme with intelligent vehicle privacy guarantees, which enables a vehicle to acquire accurate query service from the service provider (SP) without providing its explicit private information, such as location or query interest privacy. And more than that, we introduce network coding for the first time to make our scheme applicable to a more sophisticated top- $K$ query by multiple vehicles cooperation in mobile IoT. Furthermore, we also consider the unnecessary reveal issue of service data at SP side since the data is its asset. Performance analysis and experiments verify the validity of our scheme and demonstrate a better accuracy and efficiency compared with existing solutions in mobile IoT scenario.
The Impact of Government Support on the Adoption of IaaSBEL by University’s Top Management

Due to globalization and advancement in technological innovations, cloud-based e-learning has become an important paradigm in providing education for all. Numerous Higher Education institutions have realized the significance of adopting innovation that will make learning easier and affordable. Thus, Infrastructure as-a Service-based e-learning has been recognized as an important technology to improve the existing e-learning systems, and thus, is increasingly gaining the attention of practitioners and researchers in both developed and developing countries. The purpose of this study is to identify the factors that influence the intention to adopt Infrastructure as-a Service-based e-learning among ICT directorates top managers in the Nigerian HEIs. The study further deploys the Technology, Organization, Environment, and Diffusion of Innovation theory. Data was collected from 454 ICT directorate’s Top Managers in the Nigerian HEIs using disproportionate stratified random sampling technique. The findings indicate that Relative Advantage, Cost Savings, Service Provider Support, and Government Support are the key predictors of Infrastructure as Service-Based E-learning intention to adopt model in Nigerian HEIs.
Simplified KF-based energy-efficient vehicle positioning for smartphones

Recently, smart mobile devices, such as smartphone and tablet PC, have become so prevalent. Most of them are equipped with a set of sensors including a global positioning system (GPS) receiver, a digital magnetic compass, a gyroscope, and an ac-celerometer. Unlike traditional vehicle-fixed sensors, smartphone-embedded sensors can be utilized as a user-friendly and portable measurement probe for vehicle positioning systems, owing to their flexibility and mobility. However, GPS modules and inertial navigation system (INS) sensors, such as an accelerometer and a gyroscope, on smartphones consume a lot of battery power. Continued use of the battery for a long time may cause the battery to discharge immediately. Therefore, one of the main concerns for smartphone-based GPS/INS positioning algorithms is energy efficiency. Furthermore, low-cost INS sensors on smartphones may result in large localization errors due to sensor drift and bias. Unlike smartphone-based GPS/INS positioning algorithms, we use only the GPS receiver and digital compass without INS sensors. This makes it possible to offer more accurate positioning results and to save more energy. However, GPS receivers and digital compasses on smart-phones may continue to experience positional errors due to multi-path fading and disturbances in GPS signals and magnetic sources. Therefore, we propose an enhanced vehicle positioning method that provides more reliable localization results by fusing measurements from GPS receiver and digital compass based on a Bayesian filter, called a simplified Kalman filter (SKF). Compared to existing Bayes filters, such as Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), while the SKF is simple and intuitive to be implemented, it can achieve competitive positioning accuracy with less computational cost. Experimental results through various road configurations using the smartphone and test vehicle in real environments show that the SKF-based vehicle localization scheme can achieve about 92% higher energy-efficiency and about 31 % higher positioning accuracy than GPS/INS localization methods based on the KF, UKF, and PF.
A Quantile-Based Approach for Transmission Expansion Planning

Transmission expansion planning is an integral part of power system planning and consists of generating and selecting transmission proposals for maintaining sufficient transmission capacity to satisfy the electric load. Specifically, the desire to increase the use of renewable energy has exposed the limitations of transmission networks and has elevated the importance of transmission expansion planning. However, considering the random nature of renewable sources in conjunction with the power outages makes the planning process very challenging. We present a new procedure for selecting the best transmission enhancement proposal from a set of finite proposals under uncertainty. The selection is based on the quantile value of the cost of each proposal. The procedure uses a combination of simulation and optimization and considers randomness of uncertain parameters of the network. Wind energy and network contingencies are among the considered random parameters. The procedure is suitable for evaluating investor-initiated enhancement proposals by the planner and statistically guarantees satisfaction of the planner’s prespecified probability of correct selection since simulation is involved. Two IEEE test networks are used for demonstrating the implementation of the new procedure. For these two test networks, solutions obtained using quantiles are compared with those when the expected value or a weighted combination of the expected value and the conditional-value-at-risk are used as selection criteria. The comparison shows that similar to the use of conditional-value-at-risk, the selection is sensitive to the choice of the quantile.
A Novel Adaptive Control Method for Performance Enhancement of Grid-Connected Variable-Speed Wind Generators

Huge penetration of grid-tied wind generators into the existing electricity networks increased various challenges in modern power grids. Tremendous attempts are accomplished to properly enhance the behavior of the wind generation systems. This article exhibits a new self-tuned control approach for enhancing the performance of a permanent-magnet synchronous generator-based wind turbine, which is interlinked to the electricity network. The self-tuned technique relies on an improved multiband-structured subband adaptive filter (IMSAF) algorithm, which achieves less computational intricacy over the least-mean-square approach. The IMSAF algorithm-based self-tuned proportional-integral (PI) controller is employed to adjust the interface voltage source converters through a cascaded control structure. The IMSAF algorithm updates the multiple PI controllers’ gains on-line without the necessity to optimize or fine-tune. To achieve realistic responses, practical wind speed data measured in Zaafarana wind farm, Egypt, are implemented in this study. The efficacy of self-tuned control approach is compared with that realized using an optimized PI control approach by the water cycle and the genetic algorithms, considering symmetrical and unsymmetrical faults, as the network disturbances. The validity of self-tuned control approach is widely confirmed by performing simulation analyses using MATLAB/Simulink software, and satisfactory responses are achieved. Notably, the IMSAF-based self-tuned control approach is realized to be an accurate means for improving the characteristic of grid-tied wind generators.
A Secure Link Adaptation in an Indoor Heterogeneous VLC/RF Network

In this paper, we consider an indoor environment where both visible light communication (VLC) and radio frequency (RF) networks are co-deployed. A source transmitter can use either a VLC-link or a RF-link to transmit the information to the legitimate user and we assume that an eavesdropper is also present to intercept the legitimate information transmission. Here, VLC network is considered as a primary network and RF network is used as a backup. Therefore, a source transmitter utilizes the RF-link only when VLC-link offers negative secrecy-rate for legitimate information transmission. In particular, we propose a secrecy-rate based secure link adaptation mechanism to select the RF-link. We derive the exact closed-form expressions for secrecy outage probability and system secrecy outage probability, and further, observe the effect of various physical parameters on security performance analysis.
Standard Plane Identification in Fetal Brain Ultrasound Scans Using a Differential Convolutional Neural Network

Ultrasound scanning has become a highly recommended examination in prenatal diagnosis in many countries. The accurate identification of fetal brain ultrasound scans is crucial to accurate head measurement and brain lesion detection, such as the measurement of the biparietal diameter and the detection of hydrocephalus. In recent years, deep learning has made great progress in the field of image processing. However, there are two difficulties in the identification of fetal brain ultrasound standard planes (FBSPs). First, since the fetal brain tissue is not mature, the fetal brain tissue features are not easy to be detected. Second, because of the expensive collection costs, the amount of labeled image data is limited, which can cause over-fitting and decrease the identification precision. In this study, we proposed a differential convolutional neural network (differential-CNN) to automatically identify six fetal brain standard planes (FBSPs) from the non-standard planes. In this differential-CNN framework, the additional differential feature maps were derived from the feature maps in the original CNN using differential operators. The derivation process did not increase the number of convolution layers and parameters. Moreover, the differential convolution maps have the large advantage of analyzing the directional pattern of pixels and their neighborhoods using additional variation calculations. Therefore, the differential convolution maps would result in good identification performance and cost no extra computational burden. To test the performance of these algorithms, we constructed a dataset consisting of 30,000 2D ultrasound images from 155 fetal subjects ranging from 16 to 34 weeks. The experimental results showed that this method achieved an accuracy of 92.93%. Our work shows that the differential-CNN can be used to facilitate the implementation of the automated identification of FBSPs.
Stiffness Analysis of a Pneumatic Soft Manipulator Based on Bending Shape Prediction

Soft manipulators can perform continuous operations due to their inherent compliance and dexterity, thus enabling safe interactions and smooth movements in confined environments. However, high compliance usually means low load capacity. It is important for a soft manipulator to possess proper flexibility while maintaining an acceptable stiffness to widen its applications. This paper has hence devoted efforts to a kind of variable stiffness mechanism for a soft manipulator actuated by pneumatic artificial muscles (PAMs). Due to the combination of contractile and extensor PAMs, the manipulator is able to vary its stiffness independently from the configuration. The stiffness characteristics of the soft manipulator are quantitatively analyzed by bending shape prediction under different loading and inflation conditions, and the prediction is built upon a nonlinear statics model coupled with PAM nonlinearity and the Cosserat theory. In addition, experimental measurements are conducted to further validate the expected performance of the manipulator design. The experimental and verified theoretical analysis results indicate that the manipulator shape and stiffness are greatly affected by the pressure variation of PAMs, realizing a large bending space with a high output force. The variable stiffness design obviously increases the manipulator’s ability to resist additional interference at the same position.
Analyzing Moroccan Stock Market using Machine Learning and Sentiment Analysis

Behavioral Finance studies demonstrated over the last decade that stock market can be driven by emotions for market participants. On the other hand, online sentiment tracking over social media network and news showed promising results in predicting financial markets. Hence, measuring investor sentiment has become a key research issue in financial predictions. In this paper, we present our methodology for collecting, analyzing and inferring sentiments from several information sources regarding Casablanca Stock Exchange Market. With this data we apply sentiment analysis and machine learning algorithms to infer the relationship between the general public view regarding a stock and its evolution within the stock market.