bluuebunny/crossref_metadata_2025 huggingface dataset:
Variations in lignin monomer contents and stable hydrogen isotope ratios in methoxy groups during the biodegradation of garden biomass
Authors: Lu Qiangqiang , Jia Lili , Awasthi Mukesh Kumar , Jing Guanghua , Wang Yabo , He Liyan , Zhao Ning , Chen Zhikun , Zhang Zhao , Shi Xinwei
Published: May 2022

Lignin, a highly polymerized organic component of plant cells, is one of the most difficult aromatic substances to degrade. Selective biodegradation under mild conditions is a promising method, but the dynamic variations in lignin monomers during the biodegradation of lignocellulose are not fully understood. In this study, we evaluated the differences in lignin degradation under different microbial inoculation based on the lignin monomer content, monomer ratio, and stable hydrogen isotope ratio of lignin methoxy groups (δ2HLM). The weight loss during degradation and the net loss of lignocellulosic components improved dramatically with fungal inoculation. Syringyl monolignol (S-lignin), which contains two methoxy groups, was more difficult to degrade than guaiacyl (G-lignin), which contains only one methoxy group. The co-culture of Pseudomonas mandelii and Aspergillus fumigatus produced the greatest decrease in the G/S ratio, but δ2HLM values did not differ significantly among the three biodegradation experiments, although the enrichment was done within the fungal inoculation. The fluctuation of δ2HLM values during the initial phase of biodegradation may be related to the loss of pectic polysaccharides (another methoxy donor), which mainly originate from fallen leaves. Overall, the relative δ2HLM signals were preserved despite decreasing G/S ratios in the three degradation systems. Nevertheless, some details of lignin δ2HLM as a biomarker for biogeochemical cycles need to be explored further.
Pyramidal cell axon initial segment in Alzheimer´s disease
Authors: Antón-Fernández Alejandro , León-Espinosa Gonzalo , DeFelipe Javier , Muñoz Alberto
Published: May 2022

The axon initial segment (AIS) is a region of the neuron that is critical for action potential generation as well as for the regulation of neural activity. This specialized structure—characterized by the expression of different types of ion channels as well as adhesion, scaffolding and cytoskeleton proteins—is subjected to morpho-functional plastic changes in length and position upon variations in neural activity or in pathological conditions. In the present study, using immunocytochemistry with the AT8 antibody (phospho-tau S202/T205) and 3D confocal microscopy reconstruction techniques in brain tissue from Alzheimer’s disease patients, we found that around half of the cortical pyramidal neurons with hyperphosphorylated tau showed changes in AIS length and position in comparison with AT8-negative neurons from the same cortical layers. We observed a wide variety of AIS alterations in neurons with hyperphosphorylated tau, although the most common changes were a proximal shift or a lengthening of the AISs. Similar results were found in neocortical tissue from non-demented cases with neurons containing hyperphosphorylated tau. These findings support the notion that the accumulation of phospho-tau is associated with structural alterations of the AIS that are likely to have an impact on normal neuronal activity, which might contribute to neuronal dysfunction in AD.
Effects of the Structured Water Dance Intervention (SWAN) on muscular hypertonia in adults with profound intellectual and multiple disabilities
Authors: Lundqvist Lars-Olov , Frank André , Duberg Anna
Published: May 2022

This study aimed to evaluate the effect of Structured Water Dance Intervention (SWAN) on muscular hypertonia in individuals with profound intellectual and multiple disability (PIMD). Muscular hypertonia has a multitude of negative consequences for people with PIMD because it can lead to contractures, pain, mobility impairment, pressure ulcers that limits functional behavior as well as gross and fine motor function. Thirty-six individuals with PIMD in four Swedish regions were randomized to two groups in a multicenter, crossover design. Two withdrew participation, thus 34 individuals completed the intervention. The intervention was administered for 40 min once a week during a 12-week period. Outcomes related to muscular hypertonia were examined using the Modified Ashworth Scale (MAS), and based on accompanying assistants’ assessments. Hypertonia decreased from baseline to the end of the intervention period, as demonstrated by a decrease in MAS score. Hypertonia also decreased during the sessions, as shown by the assistants’ ratings. In conclusion, this study demonstrates that SWAN holds potential to reduce muscular hypertonia in people with PIMD and points out the importance of customized physical treatment alternatives. The study provides useful information for the design of future non-invasive, non-pharmacological interventions to reduce muscular hypertonia in PIMD.
Stability analysis of rock slope and calculation of rock lateral pressure in foundation pit with structural plane and cave development
Authors: Xu Jin , Wang Yansen , Li Changchun
Published: May 2022

In this study, numerical simulations were carried out to analyze the influence of caves in different positions and shapes, in combination with structural planes, on the stability of the slope and the failure characteristics of a rock slope in a deep foundation pit with high inclination structural planes and cave development. The schemes for substituting a single karst cave for karst caves were constructed. Based on the penetration failure characteristics of karst caves between parallel structural planes, calculation methods for the safety factor of the rock foundation pits and the upper bound of the lateral pressure of supporting structures under the combined influence of the caves and structural planes have been developed, which can be used to assess the safety factor of a rock mass and to calculate the lateral pressure under complex geological conditions. The example of Xuzhou Metro Line 3 Sanhuan South Road Station is taken as an example to verify. The displacement calculation results show that the proposed rock and soil pressure calculation method is close to the actual measured value.
Expression of ferroptosis-related gene correlates with immune microenvironment and predicts prognosis in gastric cancer
Authors: Song Siyuan , Shu Peng
Published: May 2022

The study is to explore the role of ferroptosis-related genes (FRGs) in the occurrence and development of gastric cancer (GC), and to construct a new prognosis signature to predict the prognosis in GC. Clinical information and corresponding RNA data of GC patients were downloaded from TCGA and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, CIBERSORT, McpCounter and TIMER algorithm were used to analyze the infiltration of immune cells in two molecular subgroups. LASSO algorithm and multivariate Cox analysis were used to construct a prognostic risk signature. Functional analysis was conducted to elucidate the underlying mechanisms. Finally, the FRPGs were verified by Quantitative Real-Time PCR. We obtained 16 FRGs and divided GC patients into two subgroups by consistent clustering. Cluster C1 with a higher abundance of immune cell infiltration but lower probability in response to immunotherapy, it was reasonable to speculate that Cluster C1 was in accordance with the immune rejection type. Functional analysis showed that the biological process of DEGs in training cohort mainly included immune globulin, and human immune response mediated by circulating immune globulin. GSEA analysis showed that compared with Cluster C2, Cluster C1 showed lower expression in lipid metabolism. The nomogram combined with risk signature and clinical features can accurately predict the prognosis of GC patients. We identified two molecular subtypes, Clusters C1 and C2. In Cluster C1, patients with poor prognosis present with a hyperimmune status and low lipid metabolism, and we speculate that Cluster C1 was in accordance with the immune rejection type. The risk model based on FRPGs can accurately predict the prognosis of GC. These results indicated that ferroptosis is associated with TIME, and deserved considerable attention in determining immunotherapy treatment strategy for GC patients.
Prognosis and diastolic dysfunction predictors in patients with heart failure and recovered ejection fraction
Authors: Takada Takuma , Matsuura Katsuhisa , Minami Yuichiro , Abe Takuro , Yoshida Ayano , Kishihara Makoto , Watanabe Shonosuke , Shirotani Shota , Jujo Kentaro , Hagiwara Nobuhisa
Published: May 2022

There is limited data on whether diastolic dysfunction in patients with heart failure (HF) and recovered ejection fraction (HFrecEF) is associated with worse prognosis. We retrospectively assessed 96 patients diagnosed with HFrecEF and created ROC curve of their diastolic function at the 1-year follow-up for the composite endpoint of cardiovascular death and HF readmission after the follow-up. Eligible patients were divided into two groups according to the cutoff value of E/e′ ratio (12.1) with the highest AUC (0.70). Kaplan–Meier analysis showed that HFrecEF with high E/e′ group had a significantly poorer prognosis than the low E/e′ group (log-rank, p = 0.01). Multivariate Cox regression analysis revealed that the high E/e′ group was significantly related to the composite endpoint (hazard ratio 5.45, 95% confidence interval [CI] 1.23–24.1). The independent predictors at discharge for high E/e′ ratio at the 1-year follow-up were older age and female sex after adjustment for covariates (odds ratio [OR] 1.07, 95% CI 1.01–1.13 and OR 4.70, 95% CI 1.08–20.5). In conclusion, HFrecEF with high E/e′ ratio might be associated with a poor prognosis. Older age and female sex were independent predictors for a sustained high E/e′ ratio in patients with HFrecEF.
Crystal structure of ultra-humanized anti-pTau Fab reveals how germline substitutions humanize CDRs without loss of binding’
Authors: Brinth Alette R. , Svenson Kristine , Mosyak Lidia , Cunningham Orla , Hickling Timothy , Lambert Matthew
Published: May 2022

Administration of therapeutic antibodies can elicit adverse immune responses in patients through the generation of anti-drug antibodies that, in turn, reduce the efficacy of the therapeutic. Removal of foreign amino acid content by humanization can lower the immunogenic risk of the therapeutic mAb. We previously developed the ultra-humanization technology “Augmented Binary Substitution” (ABS) which enables single-step CDR germlining of antibodies. The application of ABS to a chicken anti-pTau antibody generated an ultra-humanized variant, anti-pTau C21-ABS, with increased human amino acid content in the CDRs and reduced in-silico predicted immunogenicity risk. Here, we report the high-resolution crystal structure of anti-pTau C21-ABS Fab in complex with the pTau peptide (7KQK). This study examines how ultra-humanization, via CDR germlining, is facilitated while maintaining near-identical antigen affinity (within 1.6-fold). The co-complex structure reveals that the ABS molecule targets the same antigenic epitope, accommodated by structurally-similar changes in the paratope. These findings confirm that ABS enables the germlining of amino acids within CDRs by exploiting CDR plasticity, to reduce non-human amino acid CDR content, with few alterations to the overall mechanism of binding.
FXR expression in rats of hilar cholangiocarcinoma
Authors: Zhang Meng-yu , Luo Ming , Wang Jie-ping
Published: May 2022

The study objective was to detect the expression of farnesoid X receptor (FXR) in a rat model of hilar cholangiocarcinoma to provide a new therapeutic target for gene therapy in hilar cholangiocarcinoma. Sixty male Wistar rats (weighing 190 ± 8 g) were randomly divided into three groups (experimental group, control group and sham operation group, 20 rats in each group). The three groups were fed a standard diet. The QBC939 cell suspension of cholangiocarcinoma was injected into the hilar bile duct in the experimental group with a microsyringe. The control group was injected with normal saline, and the sham operation group was not injected with any drugs. A modified tail suspension test (TST) was used to evaluate the mental state and physical activity of rats every day. At 5 weeks, one rat in the experimental group was euthanized, and the changes in the hilar bile duct were recorded. The procedure was repeated at one and half months. After one and half months, hilar cholangiocarcinoma only occurred in the experimental group. Pathological examination confirmed the formation of tumours, and hilar bile duct tissues were taken from the three groups. FXR expression in the hilar bile duct was detected by real-time polymerase chain reaction (RT–PCR) and immunohistochemistry. After two weeks, the rats in the experimental group ate less, and their weight was significantly reduced. One and half months later, hilar cholangiocarcinoma was detected in 16 rats in the experimental group. The levels of alanine aminotransferase and aspartate transaminase in the experimental group were higher than those in the other two groups. The ratio of FXR/GAPDH mRNA was significantly different among the hilar cholangiocarcinoma, control and sham operation groups. Under the light microscope, FXR protein reacted with anti-FXR antibody and showed granular expression. Every pathological section included 4800 cells. A total of 1856 positive cells were in the experimental group, 3279 positive cells were in the control group, and 3371 positive cells were in the sham operation group. FXR expression in the hilar cholangiocarcinoma of rats was significantly lower than that in normal hilar bile duct tissues, suggesting that drugs targeting FXR may be a new strategy for the treatment of hilar cholangiocarcinoma.
Carbon fiber coated by quinoa cellulose nanosheet with outstanding scaled salt self-cleaning performance and purification of organic and antibiotic contaminated water
Authors: Yang Jie , Suo Xidong , Zhao Jingjing , Wang Jing , Zhou Runye , Zhang Yu , Zhang Yifei , Qiao Hongtao , Luo Xiaohang
Published: May 2022

To date, various solar driven evaporation technologies have been developed for treatment of seawater and wastewater but with the threat from salt polluted and single treatment of seawater. Herein, we develop a multifunctional evaporator constructed by carbon fiber coated by quinoa cellulose nanosheet (CFQC) with outstanding self-cleaning performance and good purification property for treatment of organic and antibiotic polluted water. The resulting Zn-CFQC exhibits good light to thermal performance which can absorb about 86.95% lights in the range of UV–Vis–NIR (200–2500 nm); therefore, the wet and dry surface temperatures of Zn-CFQC are held at 62.1 and 124.3 °C respectively, and keep a speed of 3.2 kg m−2 h−1for water evaporating under 1000 W m−2illumination. Such good light-to-thermal capabilities can be mainly imputed to the unique surface microstructures of the carbon fiber which decorated by two-dimension cellulose and activated by ZnCl2. Additionally, Zn-CFQC shows good salt automatic-cleaning capability at night and corresponding mechanism has been simply elucidated according to the chemical potential theory. The method of treatment of carbon fiber opens a new way for commercial carbon fiber utilization of solar assisted water purification.
Bacterial repair and recovery after UV LED disinfection: implications for water reuse
Authors: Randall Tara E. , Linden Yarrow S. , Gamboa Jasmine , Real Breanna , Friedler Eran , Linden Karl G.
Published: May 2022

UV disinfection inactivates viable bacteria but repair or recovery is possible. Treatment with UV LED lamps was evaluated in different water matrices and over a range of UV doses and recovery/repair assessed.
Correction: Glycyrrhizic acid promotes neural repair by directly driving functional remyelination
Authors: Tian Jing , Li Xing , Zhao Li , Shen Peixin , Wang Zhezhi , Zhu Lin , Li Cuiqin , Su Chun , Zhang Yuan
Published: May 2022

Correction for ‘Glycyrrhizic acid promotes neural repair by directly driving functional remyelination’ by Jing Tian et al., Food Funct., 2020, 11, 992–1005, https://doi.org/10.1039/C9FO01459D.
Insight into understanding magnetic transition quite sensitive to nonmagnetic impurity in a one-dimensional S = ½ regular linear chain system
Authors: Shao Dong-Sheng , Yao Wan-Wan , Zhang Jin , Qian Yin , Ren Xiao-Ming
Published: May 2022

Herein, we demonstrate that the antiferromagnetic couplings in a [Ni(mnt)2]− stack are realized mainly through spatial dipole–dipole type interactions.
Dynamic observer‐based H∞ robust control for a ducted coaxial‐rotor UAV
Authors: Xu Chang , Su Chengzhi
Published: May 2022

This paper is concerned with the problem of aerodynamic modelling and robust attitude control for a novel ducted coaxial‐rotor UAV (DCUAV) with time‐varying model uncertainties and external disturbances. In order to achieve robust attitude stabilization and tracking control for the highly non‐linear and coupled system of the DCUAV, a united dynamic observer (DO) based control design is proposed, which can deal with the uncertainty and disturbance simultaneously without estimating and compensating them during the design problem. Through the combination of the theory and the DO technique, the developed control method can achieve a better performance and more accurate tracking control compared with the existing statice observer‐based control design approaches. In addition, the parameterization of the proposed controller is derived from the solution of new LMI formulations based on the transformation of the algebraic constraint. By combining the designed DO‐based controller with the linearized DCUAV system that includes model uncertainties and disturbances, better robustness and tracking performance has been achieved. Simulation results are carried out to demonstrate the efficiency and good performance of the proposed control design.
The prediction of the inside temperature and relative humidity of a greenhouse using ANN method with limited environmental and meteorological data
Authors: Meryem El Alaoui , Mohammed Rougui , Allal Senhaji
Published: May 2022

In this paper, the prediction of the internal temperature (Tin) and relative humidity (Rhin) of a greenhouse located near Agadir, Morocco using artificial neural net-work (ANN) as machine learning method. First, an analyze of correlations be-tween inputs and outputs is studied in order to select the adequate input parameters. External temperature, relative humidity and solar radiations were the parameters that have the highest correlation coefficient with the outputs. They are thus selected as the only input parameters. The prediction of Tin and Rhin with the previously cited inputs gives a perfect coefficient of correlation (R=0.996). The aim of this study is to use only one measured input parameter (external temperature) and eliminate the two environmental parameters (relative humidity and solar radiation), by introducing the factor of time as input of the ANN model. Results were very satisfying and 20 neurons was sufficient to reach a correlation of about 0.98.
Harmonic pollution filtering: case study of a university campus
Authors: Boharb Ali , Moufid Ismail , Soukaina Naciri , Hassane El Markhi , Hassan El Moussaoui , Tijani Lamhamdi
Published: May 2022

This paper provides a study of the benefits and improvements in electrical power quality obtained by filtering harmonic pollution. A continuous 7-day measurement by a network analyzer at the secondary level of a higher education institution's transformer proved the existence of important harmonic pollution. We proposed to attenuate these harmonics by installing passive filters at the general circuit breaker. The simulations were carried out with Matlab/Simulink software, based on a set of data measured by the network analyzer. This harmonic pollution-filtering project will improve the quality of power and make significant energy savings.
Experimental Implementation of a Wireless Communication System for Electric Vehicle WPT Charger
Authors: Oulcaid M. , El Fadil H. , Njili S. , Zytoune O. , Bajit A.
Published: May 2022

Electric Vehicle (EV) market is growing rapidly and the demand for new charging methods is up surging. In order to meet this growing demand, wireless power transmission is a good choice for providing energy because it has many advantages such as convenience, user-friendliness and safety. Such charging method requires a wireless communication between EVs and electric vehicle supply equipment (EVSE) to ensure that power is transferred in a pre-cisely controlled way. This paper proposes a wireless communication system based on Raspberry Pi 4 and TMS320F28335 development kit from Texas In-struments. It consists in acquisition and wireless transmission of analogue data from a client to a server. The system is experimentally verified and the obtained result shows that the system provides a good performance for a range of fre-quencies. Thus, the proposed solution presents a good basis for the next step of controlling wireless power transmission.
A Distributed Fault Tolerant Algorithm for Load Balancing in Cloud Computing Environments
Authors: Semmoud Abderraziq , Hakem Mourad , Benmammar Badr
Published: May 2022

Cloud computing is a promising paradigm that provides users with higher computing benefits in terms of cost, availability and flexibility. Nevertheless, with potentially thousands of connected machines, faults become more frequent and may have an adverse effect on the application. Consequently, fault-tolerant load balancing becomes necessary in order to optimize resource utilization while ensuring the reliability of the system. Different approaches have been proposed in the literature for fault tolerance in cloud computing. However, they suffer from several shortcomings: some fault tolerance techniques use task replication which reduce the cloud’s efficiency in terms of resource utilization. While other models rely on checkpoint recovery to tolerate failures, resulting in an increase in the mean response time. To address these shortcomings, an efficient and adaptive fault tolerant algorithm for load balancing is proposed. Based on the CloudSim simulator, some series of test-bed scenarios are considered to assess the behavior of the proposed algorithm.
Bulk-driven current conveyor optimization using simulation-based method
Authors: Lberni Abdelaziz , Marktani Malika Alami , Ahaitouf Abdelaziz , Ahaitouf Ali
Published: May 2022

This paper presents the optimization of a novel low-voltage (LV) and low-power (LP) bulk driven current conveyor (CCII). For the bulk-driven circuits, the transistors are biased at the subthreshold regime for LV and LP operation. In contrast, the input transistors of the differential stage are controlled from the bulk terminals to achieve a rail-to-rail input voltage range. A simulation-based technique is adapted for the optimal design of LV-LP bulk driven CCII, using the single-objective particle swarm optimization (PSO) algorithm. It is designed using CMOS 0.18 μm technology to operate at a voltage of 0.3 V and have a power consumption of 37 nW. Optimization results and also process/temperature corner results confirm the correct operation of the designed circuit.
Low frequency-based energy disaggregation using sliding windows and deep learning
Authors: Laouali Inoussa Habou , Bot Karol , Ruano Antonio , Ruano Maria da Graça , Bennani Saad Dosse , El Fadili Hakim
Published: May 2022

The issue of controlling energy use is becoming extremely important. People’s behavior is one of the most important elements influencing electric energy usage in the residential sector, one of the most significant energy consumers globally. The building’s energy usage could be reduced by using feedback programs. Non-Intrusive Load Monitoring (NILM) approaches have emerged as one of the most viable options for energy disaggregation. This paper presents a deep learning algorithm using Long Short-Term Memory (LSTM) models for energy disaggregation. It employs low-frequency sampling power data collected in a private house. The aggregated active and reactive powers are used as inputs in a sliding window. The obtained results show that the proposed approach gives high performances in term of recognizing the devices' operating states and predicting the energy consumed by each device.
An enhanced EEG prediction system for motor cortex-imagery tasks using SVM
Authors: Abenna Said , Nahid Mohammed , Bouyghf Hamid
Published: May 2022

After the emergence of many new technologies, it is possible to search on the development of new devices that can be predicting what is happening in human thought based on EEG signals, such as the method used this paper contains a novel classification of the EEG signals acquired for multiple motor cortex-imagery tasks, where this method was based on the use of the Extra Tree algorithm to well select the best channels that were used for the acquisition of EEG signals, then the use of support vector machine (SVM) algorithm for data classification, moreover this work uses grey wolf optimizer (GWO) algorithm to improve all SVM parameters quickly and to converge the accuracy of the system towards the highest possible values. As a result, this work shows that the accuracy of prediction of motor cortex-imagery based EEG signals can be increased more than 99%. Also, this paper contains a comparison with other methods of the literature.
Classification of Fetal State using Machine Learning Models
Authors: Rafie Abderrazzak , Chenouni Salma , Alami Nabil , El Mallahi Mostafa , Blali Aziz , Tahiri Ahmed
Published: May 2022

In gynecology, the problem of fetus during pregnancy in pregnant women have more interests. In the literature, several means are used to follow the pregnancy such as cardiotocography to measure heart rate, accelerations, fetal movements, and uterine contractions. In this proposed study, we use some algorithms to classify some diseases, and confusion matrix to specify the normal, and suspicious pathology using Random Forest, Support Vector Machine, and Artificial Neural Network. To validate this experimentation, the dataset of UCI has suggested to classify the fetus into three classes: normal, suspicious, and pathological the best performing model for detecting the fetal state is the ANN model which gave better accuracy values for 99.19% for training accuracy and 99.09% for test accuracy.
Using Deep Learning to detect Facial Expression from front camera: Towards students’ interactions analyze
Authors: El Bahri N. , Itahriouan Z. , Belhaouari S. Brahim , Abtoy A.
Published: May 2022

The recent advancement of Artificial Intelligence (AI) affords ambition to exploit this revolution in multiple fields. Computer-assisted teaching and learning creates a very important area of AI application. Consequently, this last will be able to revolutionize this field. In research conducted by our laboratory, we are interested to explore AI trends to teaching and learning technologies. As part of this, we aim to study learner’s behaviors in education and learning environment, thus we aim to analyze the student through the front camera, as a first step we intend to develop a model that classify face’s images based on deep learning and Convolutional Neural Networks (CNN) in particular. Model development of images classification can be realized based in several technologies, we have chosen for this study to use IBM solutions, which are provided on the cloud. This paper describes the training experiment and the model development based on two alternatives proposed by IBM where the goal is to generate the most precise model. It presents a comparative study between the two approaches and ends with result discussing and the choice of the accurate solution for deployment in our teaching and learning system.
Ontology-based Requirements Specification Process
Authors: Bencharqui Hatime , Haidrar Saida , Anwar Adil
Published: May 2022

Requirements specification activity in a human-readable and formal representation is a crucial task while developing a complex system. If the meaning of requirements description and their fragments can be handled, Stakeholders can produce more effective requirement specifications of a higher quality. To that end, an ontology is developed to control capturing requirements in a formal way. The proposed ontology is built using Protege tool. This paper focuses on developing a generic ontology that can be used to define different concepts of requirements description. The suggested ontology can be used in many ways in analyzing requirements specification in any requirement engineering process. It would help system engineers to design requirement models susceptible to detect incomplete and conflicted requirements and a variety of other problems. It could also ensure consistency between many requirements specifications and increase communication between involved protagonists due to the use of the same terminology during all system specification process.
A Blind Digital Signature Protocol over NTRU
Authors: Laaji El Hassane , Azizi Abdelmalek
Published: May 2022

Actually, the NIST post-quantum cryptosystem standardization competition reached its third round with seven finalist candidates. And NIST invites the cryptographic community to analyzing the selected candidates. In this context, we contribute by creating a new blind digital signature protocol over our release of NTRU post-quantum cryptosystem. Our protocol can be a variant of FALCON digital signature scheme, which is among of those finalist candidates. Because of our NTRU release is additively homomorphic, we successfully blind and unblind the digital signature by adding a random message. We obtained good results; the speed performance of our protocol outperforms FALCON by a factor up to 27, with a stronger security level, and perfect correctness.
Electricity consumption forecasting using neural networks for low-carbon power systems planning
Authors: Perez-Moscote Daniel , Tyagunov Mikhail
Published: May 2022

Power systems require the continuous balance of energy supply and demand for their appropriate functioning, which makes electricity forecast a necessary process for the successful planning of operation and expansion of modern power systems, especially with the increase of renewable energy resources to be accommodated in order to realize low-carbon power systems. The task of predicting electricity consumption is complex because electricity demand patterns are intricate and involve various factors such as weather conditions. Recurring Neural Networks (RNN), such as Long Short-Term Memory (LSTM) networks, can learn long sequence patterns and make multi-step forecasts at once considering several variables, which can be especially useful for time series forecasts such as electricity consumption. This paper presents the application and assessment of a multivariate multi-step times series forecasting model based on LSTM neural networks for short-term prediction of electricity consumption using a dataset that encompasses data on energy load and meteorological elements from Belgorod Oblast in Russia as a case study.
Fetal organ dose assessment during pelvic CT examination using Monte Carlo/GATE simulation and pregnancy voxelized phantom Katja
Authors: Benameur Y. , Tahiri M. , Mkimel M. , El Baydaoui R. , Mesradi M.R. , El hariri B.
Published: May 2022

In certain situations where there is a risk of fetal-pelvis disproportion (risk that the baby will not be able to pass through the pelvis), pelvic scan examination may be prescribed to the mother at the end of pregnancy. This examination assesses the dimensions of the pelvis by measuring various bone diameters. It also provides details information about pelvis morphology.

Patients are routinely worried about the risk of unwanted effects on the fetus from radiation exposure. Accepting the possibility of adverse effects from fetal exposure and the inability to measure directly in vivo [1]. Medical physicists have developed several techniques to determine the amount of radiation that reaches the fetus. Physicians need to understand these methods and how the numbers they produce relate to possible biological effects on the fetus.

In this study A 16 helical multi-slice CT scanner was simulated. Full technical specifications were obtained from the constructor and simulated in GATE to produce single x-ray beams. Katja voxelized patient phantom, pregnant in 24 weeks, was scanned with pelvis protocol.

The code was used to estimate the absorbed and effective doses in the important organs of the fetus as a consequence of the scanning parameters.

The purpose of this study was to use patient voxelized phantom Katja to assessed fetal organ dose, effective dose and evaluate the risk related to radiation due to pelvis CT examination using Monte Carlo/Gate platform.

Fetal effective doses were 4.8, 6.3 and 8.2 mSv for 80, 110 and 130 kV respectively. Fetal dose in heart, eye lens, brain was 1.61, 1.6 and 0.8 mGy for 80 kV.

Since the dose to the fetus from a pelvic scan is lower than 50 mGy, when exposed to a single scan teratogenic effects on the fetus are not significant concern [2].
Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems
Authors: Belhabib Abdelfettah , Amadid Jamal , Khabba Asma , Zeroual Abdelouhab
Published: May 2022

the pilot contamination (PC) issue still faces and limits the promising massive MIMO (mMIMO) technology, to unleash the high benefits of mMIMO, we provide here a new decontaminating strategy, which exploits the large-scale fading coefficients' characteristics to construct a search space for the Ant colony-based optimization (ACO) algorithm. This algorithm is employed to find the best pattern in which each UE is linked to its most concurrent UEs of the adjacent cells; specifically, each UEs has an enemy UEs that if they are allocated with the same pilot, the severity of the PC upon the two UEs become subversive for the quality-of-service of the two UEs. Hence, the ACO algorithm finds for each UE its enemy UE, which leads to construct a Hamiltonian graph. This graph is exploited during the assignment of the pilot sequences to the overall UEs; specifically, the linked UEs are successively allocated with the available OPSs, which leads to address the PC problem within multi-cell mMIMO systems.
Design of agile monopole antenna for WiFi and WiMAX applications
Authors: Attalhaoui Abdellah , Bendali Abdelhak , Elwardi Reda Abid , Habibi Sanae , El Kobbi Mouad , Zarrik Samia , Habibi Mohamed
Published: May 2022

A monopole antenna with frequency diversity has been designed. Depending on the state of one PIN diode, this antenna has the ability to alter between two frequencies. The first one is 2.45 GHz which corresponds to the WiFi applications. The second is 3.45 GHz which corresponds to the WiMAX applications. This monopole antenna has been excited by a 50Ω microstrip line. The radiating element and a partial ground plane, that was used to achieve better gain and better bandwidth, have been printed respectively at each side of an FR4 substrate having total dimensions of 30mm × 40mm × 1.6mm. The parameters related to this antenna are analyzed using the HFSS software. For the two cases of operation of the antenna, the reflection coefficient and the gain have been simulated and showed good results. Also, for the two resonance frequencies, the VSWR is strictly lower than the value 1.2.
A compact microstrip coupler using T-shape and open stubs for fifth generation applications
Authors: Abouhssous Khadija , Zugari Asmaa , Zakriti Alia
Published: May 2022

The paper presents a new design of a compact branch-line coupler (BLC) operating at 3.5 GHz, that is suitable for 5G applications. In order to miniaturize the proposed coupler, two miniaturization methods are combined where a T-shaped structure is used instead of quarter wavelengths parallels, as well as open stubs are introduced into the series lines resulting in a size reduction of 52.56% with 25.05% fractional bandwidth (FBW). The frequency responses indicate that the presented coupler is able to operate at 3.04 GHz to 3.91 GHz with a low return loss and high isolation that is improved to -28.9 and -31.1 dB in 3.5 GHz, insertion loss and coupling of -3.6 dB and -3.5 dB, respectively, and a phase difference of about 90°±1°. The strong agreement of measurement and simulation results indicated the suitability of this proposed design for 5G microwave integrated circuits (ICs) that require a small size.
Evolutionary Machine Learning-Based Energy Consumption Prediction for the industry
Authors: Bahij Mouad , Labbadi Moussa , Chatri Chakib , Cheikh Ahmed Cheikhne , Cherkaoui Mohamed
Published: May 2022

In the digitalization of industry and the industry 4.0 environment, it is important to master the accurate forecasting of energy demand in order to guarantee the continuity of production service as well as to improve the reliability of the electrical system while promoting energy efficiency strategies in the industrial sector. This paper proposes machine learning models to predict the energy consumption demand in an industrial plant, which takes into account the at-tributes that directly the consumption. The proposed models in this work include Multiple Linear Regression (MLR), Decision Tree (DT), Recurrent Neural Networks (RNN) and Gated Recurrent United (GRU), which are compared according to their performances criteria which help to find the best forecasting models. Basing on simulation results, it is proven that the MLR approach is the best forecasting method.
Science of evolution: between the historical science and functionalist science. Which approach adopted in the secondary education in Morocco for improving the environment of science study?
Authors: Seghir Hakima , El alaoui Mustafa , Idrissi Rachid Janati
Published: May 2022

Science of evolution is the basic of life and earth science (LES). It searches for mobilize the process and reasoning of tow scientific poles: functionalist and historical. Because it try together to reconstruct the history of living beings from their origins and to explain the working biological mechanisms in this process. Therefore, studies of evolution must respect this double polarization.

In this article, we analyzed the programs of studied subjects in life and earth sciences for secondary school in Morocco to know how this polarization is taken into accent.

We show that the programs of life and earth sciences are heaving biased toward the scientific pole functionalist. So, the historical sciences remains poor and implicit.

This tendency, patent in the programs of life and earth science, might reveal probably more the difficult of evolution studies in Morocco.
Assessment of the organic carbon stock in the green oak forest in the Morrocan Oulmes Central Plateau
Authors: Ikraoun Hicham , Nassiri Laila , El Mderssa Mohamed , Ibijbijen Jamal
Published: May 2022

the present study was conducted in the Morrocan Oulmes Central Plateau to assess the organic carbon stock (OCS) in the soil and biomass of the green oak ecosystem. Soil samples taken from green oak plots at the upper organomineral layer (<30 cm depth) were analyzed by conventional assay for the determination of carbon stock in each sample. Also, other soil physicochemical parameters namely pH, total nitrogen, total limestone, organic carbon, bulk density, texture, and stability of soil aggregates were studied. Aliquots of ten green oak trees were used to assess the carbon stock in the different compartments of the tree. The studied soil is slightly acidic to neutral with a pH ranging from 5.49 to 7.19. The texture of the studied area is loam. The soil organic carbon stock ranges from 16.03 t/ha to 97.33 t/ha with an average value of 40.07 t/ha. The total above-ground biomass of the green oak stands in the studied plots has an average value of 55.28 t/ha, and the average value of the total carbon mass is 31.06 t/ha.
Development and Testing Requirements for an Integrated Maternal and Child Health Information System in Iran: A Design Thinking Case Study
Authors: Meidani Zahra , Moravveji Alireza , Gohari Shirin , Ghaffarian Hamideh , Zare Sahar , Vaseghi Fatemeh , Moosavi Gholam Abbas , Nickfarjam Ali mohammad , Holl Felix
Published: May 2022

Background Management of child health care can be negatively affected by incomplete recording, low data quality, and lack of data integration of health management information systems to support decision making and public health program needs. Given the importance of identifying key determinants of child health via capturing and integrating accurate and high-quality information, we aim to address this gap through the development and testing requirements for an integrated child health information system.

Subjects and Methods A five-phase design thinking approach including empathizing, defining, ideation, prototyping, and testing was applied. We employed observations and interviews with the health workers at the primary health care network to identify end-users' challenges and needs using tools in human-centered design and focus group discussion. Then, a potential solution to the identified problems was developed as an integrated maternal and child health information system (IMCHIS) prototype and tested using Software Quality Requirements and Evaluation Model (SQuaRE) ISO/IEC 25000.

Results IMCHIS was developed as a web-based system with 74 data elements and seven maternal and child health care requirements. The requirements of “child disease” with weight (0.26), “child nutrition” with weight (0.20), and “prenatal care” with weight (0.16) acquired the maximum weight coefficient. In the testing phase, the highest score with the weight coefficient of 0.48 and 0.73 was attributed to efficiency and functionality characteristics, focusing on software capability to fulfill the tasks that meet users' needs.

Conclusion Implementing a successful child health care system integrates both maternal and child health care information systems to track the effect of maternal conditions on child health and support managing performance and optimizing service delivery. The highest quality score of IMCHIS in efficiency and functionality characteristics confirms that it owns the capability to identify key determinants of child health.
Research Challenges in Homoeopathy
Authors: Taneja Divya , Michael James , Nayak Chaturbhuj
Published: May 2022

Through this article, we attempt to identify and understand some of the major challenges in homoeopathic research and find solutions to overcome them. We have classified and compiled the challenges broadly under the headings of (1) conceptual challenges—difficulties in research arising due to conceptual differences between homoeopathy and conventional medicine, in the understanding of disease and its treatment; (2) methodological challenges—such as weak study designs, use of incorrect outcome measures, lack of knowledge of statistics, poor adherence to homoeopathic tenets leading to poor remedy selection etc.; (3) difficulties arising due to logistic challenges—lack of well-trained homoeopathic physicians in principles of homoeopathy as well as research, lack of proper infrastructure leading to hurdles such as limited access to research literature and other necessary resources; (4) research culture—these pertain to the difficulties in homoeopathic research stemming from the reluctance of the homoeopathic society to assimilate research into its culture. Some steps have been suggested to overcome these challenges, namely improving research education among homoeopathic fraternity, ensuring precise application of principles of homoeopathy as well as utilising correct research methodology, promoting interdisciplinary communication and encouraging interaction between academicians, researchers and practitioners and taking steps to improve the quality of documentation.
Imaging of Nerve Disorders in the Elbow
Authors: Gunio Drew , Babaa Ahmad , Bencardino Jenny T.
Published: May 2022

Neuropathies of the elbow represent a spectrum of disorders that involve more frequently the ulnar, radial, and median nerves. Reported multiple pathogenic factors include mechanical compression, trauma, inflammatory conditions, infections, as well as tumor-like and neoplastic processes. A thorough understanding of the anatomy of these peripheral nerves is crucial because clinical symptoms and imaging findings depend on which components of the affected nerve are involved. Correlating clinical history with the imaging manifestations of these disorders requires familiarity across all diagnostic modalities. This understanding allows for a targeted imaging work-up that can lead to a prompt and accurate diagnosis.
Strategic decision making to achieve a path to net zero
Authors: Corso Ashley , Coopersmith Ellen , Cejka Kris
Published: May 2022

With continued changes to global policy and implementation of greenhouse gas emissions reductions, organisations are increasingly creating goals for net zero emissions. Not knowing where carbon policy is heading and what incentives and penalties will be makes it difficult to make decisions on projects while trying to achieve a path to net zero. Many avenues are currently being investigated from carbon capture and storage (CCS) to operational equipment replacement and leak reduction. There are no standard business models, and this presents significant challenges which need to be understood. Though new, they are not novel, as the industry knows how to face challenges which include evolving policies and tax implications. This paper showcases how energy companies can structure, evaluate and communicate CCS decision making using the Decision Quality process. The fundamentals of Decision Quality are applied to describe a case study which focuses on the challenge for organisations with greenhouse gas emissions, considering which facilities to prioritise, which technologies to cost-effectively capture, how to transport and ultimately how to reuse or store the emissions. The Decision Quality – Scenario Thinking process will be instrumental in understanding the uncertain scenarios in the global carbon arena. We will define the solution space of viable alternative strategies and provide unique insight into the impact of the key risks and uncertainties on the alternatives in play – answering the question, under what terms and conditions might each alternative be viable? Using structured decision techniques, insights into CCS strategies and what it will take to achieve will be gained.