Miracle-dz/UniFAR-FTUs huggingface dataset:
AlertNet: Deep convolutional-recurrent neural network model for driving alertness detection

Drowsy driving is one of the major problems which has led to many road accidents. Electroencephalography (EEG) is one of the most reliable sources to detect sleep on-set while driving as there is the direct involvement of biological signals. The present work focuses on detecting driver’s alertness using the deep neural network architecture, which is built using ResNets and encoder-decoder based sequence to sequence models with attention decoder. The ResNets with the skip connections allow training the network deeper with a reduced loss function and training error. The model is built to reduce the complex computations required for feature extraction. The ResNets also help in retaining the features from the previous layer and do not require different filters for frequency and time-invariant features. The output of ResNets, the features are input to encoder-decoder based sequence to sequence models, built using Bi-directional long-short memories. Sequence to Sequence model learns the complex features of the signal and analyze the output of past and future states simultaneously for classification of drowsy/sleepstage-1 and alert stages. Also, to overcome the unequal distribution (class-imbalance) data problem present in the datasets, the proposed loss functions help in achieving the identical error for both majority and minority classes during the raining of the network for each sleep stage. The model provides an overall-accuracy of 87.92% and 87.05%, a macro-F1-core of 78.06%, and 79.66% and Cohen's-kappa score of 0.78 and 0.79 for the Sleep-EDF 2013 and 2018 data sets respectively.
Compressor based approximate multiplier architectures for media processing applications

Approximate computing is an attractive technique to gain substantial improvement in the area, speed, and power in applications where exact computation is not required. This paper proposes two improved multiplier designs based on a new 4:2 approximate compressor circuit to simplify the hardware at the partial product reduction stage. The proposed multiplier designs are targeted towards error-tolerant applications. Exhaustive error and hardware analysis has been carried out on the existing and proposed multiplier designs. The results prove that the proposed approximate multiplier architecture performs better than the existing architectures without significant compromise on quality metrics. Experimental results show that die-area and power consumed are reduced upto 28%, and 25.29% respectively in comparison with the existing designs without significant compromise on accuracy.
Design and development of handover simulator model in 5G cellular network

In the modern era of technology, the high speed internet is the most important part of human life. The current available network is reckoned to be slow in speed and not be up to snuff for data transmission regarding business applications. The objective of handover mechanism is to reassign the current session handle by internet gadget. The globe needs the next generation high mobility and throughput performance based internet model. This research paper explains the proposed method of design and development for handover based 5G cellular network. In comparison to the traditional method, we propose to control the handovers between base-stations using a concentric method. The channel simulator is applied over the range of the frequencies from 500 MHz to 150 GHz and radio frequency for the 700 MHz bandwidth. The performance of the simulation system is calculated on the basis of handover preparation and completion time regarding base station as well as number of users. From this experiment we achieve the 7.08 ms handover preparation time and 9.98 ms handover completion time. The author recommended the minimum handover completion time, perform the high speed for 5G cellular networks.
PHIPS-HALO: the airborne Particle Habit Imaging and Polar Scattering probe – Part 3: Single-particle phase discrimination and particle size distribution based on the angular-scattering function

Abstract. A major challenge for in situ observations in mixed-phase clouds remains the phase discrimination and sizing of cloud hydrometeors. In this work, we present a new method for determining the phase of individual cloud hydrometeors based on their angular-light-scattering behavior employed by the PHIPS (Particle Habit Imaging and Polar Scattering) airborne cloud probe. The phase discrimination algorithm is based on the difference of distinct features in the angular-scattering function of spherical and aspherical particles. The algorithm is calibrated and evaluated using a large data set gathered during two in situ aircraft campaigns in the Arctic and Southern Ocean. Comparison of the algorithm with manually classified particles showed that we can confidently discriminate between spherical and aspherical particles with a 98 % accuracy. Furthermore, we present a method for deriving particle size distributions based on single-particle angular-scattering data for particles in a size range from 100 µm ≤ D ≤ 700 µm and 20 µm ≤ D ≤ 700 µm for droplets and ice particles, respectively. The functionality of these methods is demonstrated in three representative case studies.
Association rule hiding using integer linear programming

Privacy preserving data mining has become the focus of attention of government statistical agencies and database security research community who are concerned with preventing privacy disclosure during data mining. Repositories of large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one of the powerful techniques for hiding sensitive knowledge that exists in data before it is published. In this paper, we present a constraint-based optimization approach for hiding a set of sensitive association rules, using a well-structured integer linear program formulation. The proposed approach reduces the database sanitization problem to an instance of the integer linear programming problem. The solution of the integer linear program determines the transactions that need to be sanitized in order to conceal the sensitive rules while minimizing the impact of sanitization on the non-sensitive rules. We also present a heuristic sanitization algorithm that performs hiding by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database.
Wind energy development in Morocco: Evolution and impacts

Over the past ten years, Morocco has been focusing on developing renewable energy, especially wind power. This new energy policy has enabled it to become, in 2017, the leading country in the Middle East and North Africa region and the second one in Africa in terms of installed wind power capacity. In 2019, Morocco moved for the first time from the status of electricity importer to that of electricity exporter, better yet green electricity. This paper provides a quantitative study of the demand, production and installed power capacity of electrical energy in Morocco over the past two decades. It mainly focuses on the evolution of installed wind power capacity and its share in the global energy mix during this period, as well as its future prospects by year 2030. This article presents a synthesis work based on an updated assessment of the carried out wind projects and aims to assess the realization of Morocco’s national energy strategy which sets out to achieve 42% of renewable energy by 2020, and more specifically 14% of the overall energy mix being wind energy. It also aims to show the impact of wind energy integration in terms of energy autonomy, industrial integration and CO2 emissions reduction.
Prediction of nodes mobility in 3-D space

Recently, mobility prediction researches attracted increasing interests, especially for mobile networks where nodes are free to move in the three-dimensional space. Accurate mobility prediction leads to an efficient data delivery for real time applications and enables the network to plan for future tasks such as route planning and data transmission in an adequate time and a suitable space. In this paper, we proposed, tested and validated an algorithm that predicts the future mobility of mobile networks in three-dimensional space. The prediction technique uses polynomial regression to model the spatial relation of a set of points along the mobile node’s path and then provides a time-space mapping for each of the three components of the node’s location coordinates along the trajectory of the node. The proposed algorithm was tested and validated in MATLAB simulation platform using real and computer generated location data. The algorithm achieved an accurate mobility prediction with minimal error and provides promising results for many applications.
Text classification model for methamphetamine-related tweets in Southeast Asia using dual data preprocessing techniques

Methamphetamine addiction is a prominent problem in Southeast Asia. Drug addicts often discuss illegal activities on popular social networking services. These individuals spread messages on social media as a means of both buying and selling drugs online. This paper proposes a model, the “text classification model of methamphetamine tweets in Southeast Asia” (TMTA), to identify whether a tweet from Southeast Asia is related to methamphetamine abuse. The research addresses the weakness of bag of words (BoW) by introducing BoW and Word2Vec feature selection (BWF) techniques. A domain-based feature selection method was performed using the BoW dataset and Word2Vec. The BWF dataset provided a smaller number of features than the BoW and TF–IDF dataset. We experimented with three candidate classifiers: Support vector machine (SVM), decision tree (J48) and naive bayes (NB). We found that the J48 classifier with the BWF dataset provided the best performance for the TMTA in terms of accuracy (0.815), F-measure (0.818), Kappa (0.528), Matthews correlation coefficient (0.529) and high area under the ROC Curve (0.763). Moreover, TMTA provided the lowest runtime (3.480 seconds) using the J48 with the BWF dataset.
Bimodal mesoporous titanium nitride/carbon microfibers as efficient and stable electrocatalysts for Li-O2 batteries

Nonprecious transition metal nitrides have attracted considerable attention as an alternative catalyst to noble metals for electrochemical reactions. Titanium nitride (TiN), in particular, is an interesting material that exhibits excellent thermal, chemical, and mechanical stability, electrical conductivity, and in particular electrocatalytic activity. TiN is often prepared by the nitridation of titanium oxide using gaseous ammonia or hydrazine as a nitrogen source. This high temperature synthesis, however, gives a material with low surface area, which is unfavorable for its application in heterogeneous electrocatalysis. The shape and size of the assembly, and thus the resulting g-CN materials, can be tailored simply by using different precipitation temperatures, solvents, and comonomers. Furthermore, the assembly develops unique nanostructures during the polycondensation without losing the pristine macrostructures.
The use of plant material for enrichment of fresh juices by biologically active substances

The fresh apple and orange juice with the use of aqueous extracts of non-traditional plant material such as peppermint, garden sage and lemongrass was developed for restaurants. It was chosen the simplest method of extraction such as maceration for obtaining extracts as a source of biologically active substances due to its ease of implementation at restaurants. The optimal process parameters (temperature, hydromodulus, time) which allow to obtain the extracts out of peppermint, garden sage and lemongrass with a pronounced and harmonious flavour were investigated. The food basis was chosen due to the popularity of fresh juices among the consumers of restaurants as well as availability and nutritional value of plant materials. The fresh apple and orange juice with the extracts of peppermint, garden sage and lemongrass had the original taste and aromatic profile. The biological value of the extracts of peppermint, garden sage and lemongrass was evaluated by the content of such antioxidant as vitamin C and the total antioxidant activity. The increasing of biological activity of the fresh juice with the aqueous extracts of plant materials was detected. It allows positioning the juice like a health drink.
Earth System Models Are Not Capturing Present‐Day Tropical Forest Carbon Dynamics

Tropical forests play a key role in absorbing carbon from the atmosphere into the land surface. Recent analyses of long‐term (1985–2014) forest inventory plots across the tropics show that structurally intact tropical forest are a large carbon sink, but that this sink has saturated and is projected to be in long‐term decline. Here we compare these results with estimates from the two latest generations of Earth System Models, Climate Modelling Intercomparison Project 5 (CMIP5) (19 models) and CMIP6 (17 models). While CMIP5 and CMIP6 are of similar skill, they do not reproduce the observed 1985–2014 carbon dynamics. The “natural” pan‐tropical carbon sink from inventory data is 0.99 Pg C yr−1 (95% CI 0.7–1.3, n = 614) between 2000 and 2010, the best sampled decade, double the CMIP6 multimodel‐mean of 0.45 Pg C yr−1 (95% CI 0.35–0.55). The observed saturating and declining sink is not captured by the models, which show modest increases in sink strength. The future (2015–2040) “natural” pan‐tropical sink from a statistical model driven by extrapolating past trends of its putative environmental drivers decreases by 0.23 Pg C per decade (95% CI 0.09–0.39) until the 2030s, while the CMIP6 multimodel‐mean under the climate change scenario closest to the statistical model project an increasing carbon sink (0.54 Pg C per decade; 95% CI 0.25–0.67). CMIP multimodel‐means reproduce the response of carbon gains from tree growth to environmental drivers, but the modeling of carbon losses from tree mortality does not correspond well to the inventory data. The model‐observation differences primarily result from the treatment of mortality in models.
Decoupling salinity and carbonate chemistry: low calcium ion concentration rather than salinity limits calcification in Baltic Sea mussels

Abstract. The Baltic Sea has a salinity gradient decreasing from fully marine (> 25) in the west to below 7 in the central Baltic Proper. Habitat-forming and ecologically dominant mytilid mussels exhibit decreasing growth when salinity < 11; however, the mechanisms underlying reduced calcification rates in dilute seawater are not fully understood. Both [HCO3-] and [Ca2+] also decrease with salinity, challenging calcifying organisms through CaCO3 undersaturation (Ω≤1) and unfavourable ratios of calcification substrates ([Ca2+] and [HCO3-]) to the inhibitor (H+), expressed as the extended substrate–inhibitor ratio (ESIR). This study combined in situ monitoring of three southwest Baltic mussel reefs with two laboratory experiments to assess how various environmental conditions and isolated abiotic factors (salinity, [Ca2+], [HCO3-] and pH) impact calcification in mytilid mussels along the Baltic salinity gradient. Laboratory experiments rearing juvenile Baltic Mytilus at a range of salinities (6, 11 and 16), HCO3- concentrations (300–2100 µmol kg−1) and Ca2+ concentrations (0.5–4 mmol kg−1) reveal that as individual factors, low [HCO3-], pH and salinity cannot explain low calcification rates in the Baltic Sea. Calcification rates are impeded when Ωaragonite ≤ 1 or ESIR ≤ 0.7 primarily due to [Ca2+] limitation which becomes relevant at a salinity of ca. 11 in the Baltic Sea. Field monitoring of carbonate chemistry and calcification rates suggest increased food availability may be able to mask the negative impacts of periodic sub-optimal carbonate chemistry, but not when seawater conditions are permanently adverse, as observed in two Baltic reefs at salinities < 11. Regional climate models predict a rapid desalination of the southwest and central Baltic over the next century and potentially a reduction in [Ca2+] which may shift the distribution of marine calcifiers westward. It is therefore vital to understand the mechanisms by which the ionic composition of seawater impacts bivalve calcification for better predicting the future of benthic Baltic ecosystems.
A Stochastic Distribution System Market Clearing and Settlement Model With Distributed Renewable Energy Utilization Constraints

The increasing integration of distributed generation (DG) has changed the way modern power systems are planned and operated. However, new market mechanisms at the retail level are still needed to effectively integrate energy resources and emerging market participants in the distribution grid. In this article, a stochastic market clearing and settlement model with distributed renewable energy (DRE) utilization constraints is presented. The proposed model considers the uncertainties associated with the DRE productions in the distribution grid and ensures that a certain portion of the total available DRE at a specified time will be utilized with a high probability. The problem is formulated as a chance-constrained two-stage stochastic programming model that minimizes the expected distribution system operation cost and is solved through a sample average approximation algorithm. Case studies are conducted to verify the effectiveness of the proposed model for different DRE utilization requirements.
Correlation of Clinicopathological Features and IL6 Expression in Tumor Budding of Colorectal Adenocarcinoma

Background: Interleukin-6 (IL6) is one of the main cytokines produced by cancer-associated fibroblasts (CAFs). IL6 is linked with cancer progression and poor prognosis by activating cancer cells and modifying the cancer microenvironment. However, little is known about the expression of IL6 in tumor budding (TB) and its association with TB in colorectal adenocarcinoma. Methods: The clinicopathological and prognostic significance of IL6 in TB was examined using a tissue microarray consisting of 36 patient samples of TB in CA. IL6 mRNA was detected by RNAscope kit. Patients were stratified into negative and positive IL6 expression groups. Results: IL6 expression was overwhelmingly observed in CAFs but was negligible in cancer cells. In the IL6-positive group in CAFs, TB grade was higher than in the IL6-negative group (P=0.0161). There was a significant difference in overall survival (OS) between CA cases in the IL6-positive group and the IL6-negative group (log rank test, P=0.0367). Cox proportional hazard regression model revealed that the IL6-negative group (OR = 0.25; 95% CI: 0.05–0.96; P=0.0440) had better OS for CA than the IL6-positive group. Conclusions: TB may be affected by IL6 expression, and IL6 expression in CAFs at TB may make IL6 an important prognostic marker.
Eye blink detection using CNN to detect drowsiness level in drivers for road safety

Blinking is a regular bodily function and it is the semiautomatic fast closing of the eyelid. A specific blink is examined by dynamic folding of the eyelid. It is a vital function of the eye which helps in spread of tears across and eliminates irritants from the shallow of cornea. In this research work we made use of convolution neural network, the deep learning concepts and image processing to detect drowsiness level in drivers. To train the blink detection model the mobilenet V2 is used as base. The loss function used for training was RMSprop and the optimizer is binary cross entropy. The dlib facial landmark was exploited to perceive and pre-process the detected faces. The dataset used for the training model is selected from the “Xiaoyang Tan” of nanjing university of aeronautics and astronautics. Based on the experimental outcome the projected method achieves an accuracy of 97%. The prototype developed serves as a base for further development of this process to achieve better road safety.
Constraining Jumps in Density and Elastic Properties at the 660 km Discontinuity Using Normal Mode Data via the Backus‐Gilbert Method

We apply the Backus‐Gilbert approach to normal mode center frequency data, to constrain jumps in P, S, bulk‐sound speed and density at the “660” discontinuity in the earth’s mantle (∼650–670 km depth). Different 1‐D models are considered to compute sensitivity kernels. When using model PREM (Dziewonski & Anderson, 1981, Physics of the Earth and Planetary Interiors, 25, 297–356. doi:10.1016/0031‐9201(81)90046‐7) as reference, with a “660” at 670 km depth, the best‐fitting jumps in density, P‐ and S‐wave speeds range from (5.1–8.2)%, (5.3–8.0)%, (5.0–7.0)%, respectively, so the PREM values lie outside the ranges of acceptable density and P wave speed jumps. When shifting the depth of “660” to 660 km, the density and S wave speed jumps increase, while the P‐wave speed jump decreases. Normal mode data do not support a global transition at 650 km depth. The density jumps are closer to those of pyrolite than PREM, while our bulk‐sound wave speed jumps suggest a larger garnet proportion at “660. ”
The Imbricated Foreshock and Aftershock Activities of the Balsorano (Italy) Mw 4.4 Normal Fault Earthquake and Implications for Earthquake Initiation

Foreshocks in the form of microseismicity are among the most powerful tools to study the physical processes that occur before main earthquakes. However, their detection and precise characterization is still sparse, especially for small-to-moderate-size earthquakes (Mw<6). We present here a detailed foreshock analysis for the 7 November 2019, Balsorano, Italy, normal fault earthquake (Mw 4.4). To improve the detection of the microseismicity before and after the mainshock, we use six three-component broadband receivers at distances of less than 75 km from the targeted seismicity, through template matching. To improve the understanding of the physical mechanism(s) behind the earthquake initiation process, as well as other accompanying phenomena, we also detail the spatiotemporal evolution of the sequence associated with this medium-sized earthquake, using waveform clustering and hypocenter relocation. Clear differences between foreshocks and aftershocks are revealed by this analysis. Moreover, five distinct spatiotemporal patterns associated with the different seismic activities are revealed. The observed spatiotemporal behavior shown by the foreshocks highlights a complex initiation process, which apparently starts on an adjacent unmapped antithetic fault. Finally, the aftershock activity comprises four different clusters with distinct spatiotemporal patterns, which suggests that the different clusters in this sequence have distinct triggering mechanisms.
Naphtho[1,2-b:5,6-b′]dithiophene-Based Donor–Acceptor Copolymer Semiconductors for High-Mobility Field-Effect Transistors and Efficient Polymer Solar Cells

Two donor–acceptor (D–A) copolymers, PzNDTDTBT and PzNDTDTBO, using 4,9-bis(2-ethylhexyloxy)naphtho[1,2-b:5,6-b′]dithiophene as an electron-rich unit and benzodiathiazole (BT) or benzoxadiazole(BO) as an electron-deficient one, were designed and synthesized via a Pd-catalyzed Stille-coupling method. The acceptor units were varied from BT to BO for adjusting the energy levels and optimizing the structures of polymers. Both copolymers possess good solubility, high thermal stability, broad absorption, and low bandgap and exhibit not only high field-effect mobilities but also high photovoltaic properties. The hole mobilities reach 0.43 and 0.34 cm2 V–1 s–1 for PzNDTDTBT and PzNDTDTBO film, respectively. Bulk heterojunction solar cells fabricated by using PzNDTDTBT or PzNDTDTBO as electron donor and PC71BM as acceptor show a power conversion efficiency of 3.22% and 5.07%, respectively, under illumination of AM1.5G, 100 mW cm–2. Both the hole mobilities and PCEs are among the highest values in the current repor.