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This paper introduces a novel enzymatic fuel cell design that employs cellulose paper-based quasi-2D microfluidic system to supply biofuel to the enzymatic layer. The state of the art nanoarchitectural design, employing carbon nanotube-based papers for the bioelectrodes, allows a single cell to maintain 400 mV for 16 days of continuous operation in glucose solution and reach 1 mA of current output. Stacks of cells connected in series show successful performance using glucose in Gatorade® resulting in stack-cell potential of 1.8 V, employed to power a digital clock for 36 h, continuously. These designs open the possibility for obtaining enzymatic fuel cells that can run small portable devices on easily available ubiquitous liquids while addressing environmental concerns that are prevalent in traditional fuel cells. Keywords: Enzyme, Bioelectrodes, Enzymatic biofuel cells, Microfluidic system, Capillary driven flow, Bucky paper

Graphical Abstract The prediction model of oral disintegrating tablets formulations with direct compression process by artificial neural network (ANN) and deep neural network (DNN) techniques were established. 145 formulation data were extracted from Web of Science. All datasets were divided into three parts: training set (105 data), validation set (20) and testing set (20) to build prediction model.Image, graphical abstract, Oral disintegrating tablets (ODTs) are a novel dosage form that can be dissolved on the tongue within 3 min or less especially for geriatric and pediatric patients. Current ODT formulation studies usually rely on the personal experience of pharmaceutical experts and trial-and-error in the laboratory, which is inefficient and time-consuming. The aim of current research was to establish the prediction model of ODT formulations with direct compression process by artificial neural network (ANN) and deep neural network (DNN) techniques. 145 formulation data were extracted from Web of Science. All datasets were divided into three parts: training set (105 data), validation set (20) and testing set (20). ANN and DNN were compared for the prediction of the disintegrating time. The accuracy of the ANN model have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testing set respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respectively. Compared with the ANN, DNN showed the better prediction for ODT formulations. It is the first time that deep neural network with the improved dataset selection algorithm is applied to formulation prediction on small data. The proposed predictive approach could evaluate the critical parameters about quality control of formulation, and guide research and process development. The implementation of this prediction model could effectively reduce drug product development timeline and material usage, and proactively facilitate the development of a robust drug product.

Purpose: The purpose of this study was to compare the effects of an 8-week whole-body vibration training program in various frequency and amplitude settings under the same acceleration on the strength and power of the knee extensors. Methods: Sixty-four young participants were randomly assigned to 1 of 4 groups with the same acceleration (4 g): high frequency and low amplitude (n = 16, 32 Hz, 1 mm) group, medium frequency and medium amplitude (n = 16, 18 Hz, 3 mm) group, low frequency and high amplitude (n = 16, 3 Hz, 114 mm) group, and control (n = 16, no vibration) group. All participants underwent 8 weeks of training with body mass dynamic squats, 3 sessions a week. Results: The results showed that the high frequency and low amplitude group increased significantly in isometric contraction strength and 120°/s isokinetic concentric contraction strength; the medium frequency and medium amplitude group increased significantly in 60°/s and 120°/s isokinetic strength of both concentric and eccentric contraction; and the low frequency and high amplitude group increased significantly in 60°/s and 120°/s isokinetic eccentric contraction strength. Conclusion: All frequency and amplitude settings in the 8-week whole-body vibration training increased muscle strength, but different settings resulted in various neuromuscular adaptations despite the same intensity.

The purpose of this short paper is to give a reader acquainted with combinatorial optimization enough background in Constraint Programming to appreciate the other papers in this special issue. We will cover modeling, inference, and search. Whenever appropriate we will make an effort to relate some of the concepts to that of other computational approaches. Those interested to learn more about this computational approach will also find some pointers to the literature on recent advances in the area.

Purpose The purpose of this study was to compare 4 techniques for arrival at a base after sprinting maximally to reach it: sliding head-first, sliding feet-first, running through the base without slowing, and stopping on the base. A secondary purpose of the study was to determine any advantage there may be to diving into first base to arrive sooner than running through the base. Methods Two high-definition video cameras were used to capture 3-dimensional kinematics of sliding techniques of 9 intercollegiate baseball players. Another video camera was used to time runs from first base to second in 4 counterbalanced conditions: running through the base, sliding head-first, sliding feet-first, and running to a stop. Mathematical modeling was used to simulate diving to first base such that the slide would begin when the hand touches the base. Results Based upon overall results, the quickest way to the base is by running through it, followed by head-first, feet-first, and running to a stop. Conclusion There was a non-significant trend toward an advantage for diving into first base over running through it, but more research is needed, and even if the advantage is real, the risks of executing this technique probably outweigh the miniscule gain.

To manage the risk of insurance companies, a reinsurance transaction is among the myriad risk management mechanisms the top ranked choice. In this paper, we study the design of optimal reinsurance contracts within a risk measure minimization framework and subject to the Vajda condition. The Vajda condition requires the reinsurer to take an increasing proportion of the loss when it increases and therefore imposes constraints on the indemnity function. The distortion-risk-measure-based objective function is very generic, and allows for, for example, an objective to minimize the risk-adjusted value of the insurer’s liability, and for heterogeneous beliefs regarding the loss distribution by the insurer and reinsurer. Under a mild condition, we propose a backward-forward optimization method that is based on a marginal indemnity function formulation. To show the applicability and simplicity of our strategy, we provide three concrete examples with the Value-at-Risk: one with the risk-adjusted value of the insurer’s liability, one with an objective function that follows from imposing Pareto optimality, and one with heterogeneous beliefs. We conclude this paper with an empirical application with Danish fire insurance losses and the Value-at-Risk and the Tail Value-at-Risk.

In order to improve the effect of intelligent sports prediction and analysis, this paper analyzes the traditional high-speed model and proposes a reliable sports action recognition algorithm on this basis. With this algorithm as the core algorithm, sports action recognition can be performed. The paper mainly constructs a standard database structure, inputs a reliable standardized movement model into the database structure, and compares the identified sports movement model with the standard movement model to judge the accuracy of the sports movement. Moreover, this paper uses statistical analysis to study sports training and sports competitions, and obtains prediction results through multi-factor analysis and comparison. In addition, this paper uses statistical methods to process experimental data to draw graphs and tables that can visually observe the results of sports predictive analysis. From the experimental analysis, it can be seen that the algorithm model constructed in this paper has a significant effect.

For multi-attribute decision-making problems with interval numbers whose attribute weights are completely unknown, it is difficult to compare the value of interval numbers. So this paper proposes a method based on TOPSIS and the weighted parameter to deal with it. Firstly, this paper transforms the interval number matrix into two exact number matrices which reduces sorting complexity. Secondly, a parameter is given when determining the weights with entropy weight in order to reflect all the information of the interval numbers. Then TOPSIS is used to determine the order of each scheme. In addition, the average value of the ranking number is used to reflect the actual situation better. Finally, the analysis of the car purchase case shows the proposed method is feasible and practical. And the comparative analysis with the other method based on practical application dataset demonstrates the proposed method is stable and effective.

Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin’s theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997. This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years. The methodology used to search for relevant DE papers and an overview of the original DE are firstly explained. Recent advances in the modifications proposed to enhance the effectiveness and efficiency of the original DE are reviewed by analysing the strengths and weaknesses of each published work, followed by the potential applications of these DE variants in solving different real-world engineering problems. In contrast to most existing DE review papers, additional analyses are performed in this survey by investigating the impacts of various parameter settings on given DE variants to identify their optimal values required for solving certain problem classes. The qualities of modifications incorporated into selected DE variants are also evaluated by measuring the performance gains achieved in terms of search accuracy and/or efficiency against the original DE. The additional surveys conducted in this study are anticipated to provide more insightful perspectives for both beginners and experts of DE research, enabling their better understanding about current research trends and new motivations to outline appropriate strategic planning for future development works.

The paper deals with the performance checking of an optimally designed LCL-filter connected along the grid-side converter (GSC) in a doubly fed induction generator (DFIG) based wind energy conversion system (WECS) in attenuating harmonics. A repetitive approach is required for designing the parameters of the LCL-filter. The reasons being the explicitness of design parameters and design requirements of the proposed filter like IEEE-519 Std. for harmonic current attenuation, maximum voltage difference allowed in between two sides of the filter to limit switching losses, and limit of reactive power compensation. The proposed filter is compared with the conventional LCL-filter. Based on the proposed method, a minimal inductance and optimal capacitance-based filter is obtained in this paper which is validated using a 2.2 kW DFIG based WECS. Comparative stability analysis of DFIG system with and without proposed filter is performed using small signal stability analysis. The system under study is simulated using MATLAB/Simulink and OPAL-RT 4510, simultaneously. Simulation results validates the effective use of proposed filter design method into DFIG system in terms of harmonic attenuation, total harmonic distortion and reactive power compensation.

With the continuous use of various new radar systems and complex radar systems, the electromagnetic environment is extremely deteriorated. The traditional emitter recognition methods have been difficult to meet the requirements of recognition performance in the rapidly changing battlefield environment. To solve this problem, a radar electronic signal recognition algorithm based on wavelet transform and deep learning is proposed in this paper. Starting from the radar reconnaissance system, the causes of signal preprocessing are analyzed, and the methods of signal denoising, signal normalization, signal intra pulse modulation recognition, multipath signal detection and suppression are deeply studied. In particular, the denoising algorithm based on threshold wavelet transform is proposed, which significantly improves the reliability of the algorithm. Aiming at the individual feature extraction of emitter signal, the extraction methods of emitter signal time domain feature, frequency domain feature, fuzzy function slice feature and cyclic spectrum feature based on wavelet transform are studied and analyzed, which provides stable and reliable classification features for emitter signal recognition. According to the characteristics of radar emitter signal, an optimized convolution neural network is designed, and the feature fusion processing is carried out at the decision-making level, which greatly improves the recognition effect and enhances the robustness of the recognition system. Experiments on radar data show that the fusion recognition rate is higher than any single feature and has strong robustness. In addition, compared with the traditional SVM and elm networks, the CNN network proposed in this paper can extract detailed features more effectively and improve the recognition rate of electronic radar.

In order to build a smart campus teaching system with good teaching efficiency, this paper improves the traditional ZigBee algorithm and builds a smart campus teaching system based on ZigBee wireless sensors. Moreover, this paper improves the estimation algorithm of students' specific location and direction on campus, and analyzes the network structure of the smart teaching system based on the actual needs of smart teaching, and builds a three-tier teaching structure system. In addition, on the basis of demand analysis, this paper sets the system function modules, analyzes the data processing effect of the network nodes of this smart campus through experimental research, and evaluates the system teaching effect. Finally, this paper uses simulation technology to analyze 100 sets of data to evaluate the data processing effect of ZigBee network. Through the experimental research results, we can see that the smart campus teaching system constructed in this paper has good results.

With the development of science and technology, the power source of the automobile industry has gradually changed from non-renewable energy sources such as petroleum to electric energy, a clean and renewable energy source. By the end of 2020, scientists have found that the global demand for batteries has greatly increased. The demand for power batteries has reached 50% of all battery demand, occupying half of the market. In addition, scientists also boldly predict that by 2025, China's consumption of batteries in the automobile industry will reach more than 100,000 tons per year. Faced with so much battery consumption, how to dispose of and recycle LiFePO4 used batteries has become the key to restricting the development of the industry. Failure to dispose of used batteries will not only hinder the development of the industry, but also cause serious pollution to the environment. The application of sensors today has been extended to various fields, and the emergence of satellite navigation and positioning technology is one of the driving forces to promote its development. However, the sensor cannot be directly applied to various environments. When in a complex environment, the obstruction of various objects in the environment will seriously affect the stability of the sensor. In order to ensure the normal operation of the sensor, positioning technology began to be applied to the sensor. The industrial production process of solid-phase regeneration and electrochemical performance of waste lithium iron phosphate materials discussed in this article is based on this perspective, through the use of computer vision positioning to reduce the impact of environmental factors on the accuracy of the sensor, thereby improving the quality of the battery. In order to match the image point of the waste lithium iron phosphate material with the space point, this paper creatively uses the image point and the point in the corresponding space field. In addition, the author also studied the camera's rear rendezvous method and proposed a new rear rendezvous method. In addition, in order to strengthen the detection of waste lithium iron phosphate materials, this paper strengthened the construction of the sensor hardware system and software system, and completed the upgrade of the waste lithium iron phosphate material detection system.

In this paper, a new approach to time series classification is proposed. It transforms the scalar time series into a two-dimensional space of amplitude (time series values) and a change of amplitude (increment). Subsequently, it uses this representation to plot the data. One figure is produced for each time series. In consequence, the time series classification problem is converted into the visual pattern recognition problem. This transformation allows applying a wide range of algorithms for standard pattern recognition – in this domain, there are more options to choose from than in the domain of time series classification. In this paper, we demonstrated the high effectiveness of the new method in a series of experiments on publicly available time series. We compare our results with several state-of-the-art approaches dedicated to time series classification. The new method is robust and stable. It works for time series of differing lengths and is easy to extend and alter. Even with a baseline variant presented in an empirical study in this paper, it achieves a satisfying classification accuracy. Furthermore, the proposed conversion of raw time series into images that are subjected to feature extraction opens the possibility to apply standard clustering algorithms.

A faulty situation occurred in Service Oriented Architecture (SOA) based systems can affect its functionality. It is challenging to guarantee the reliability of service utilization in a distributed, concurrent and dynamic composition of web services. This paper aims to present a fault detection approach of SOA using dependency analysis. Data and control dependencies are identified and a fault diagnosis approach based on these dependencies is proposed in this paper. The presented approach extends discrete state space techniques to simulate the concurrent and distributed behavior through Colored Petri Nets (CPNs) for the SOA-based systems in case of the faulty situation. Concrete translation from SOA based system to a CPN model is presented. Model-based technique is used and some heuristics are presented for dependency analysis and fault detection purpose. The experimental performance analysis shows the feasibility of our proposed approach.

Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution; as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is provided. Some advantages and disadvantages found through literature reviews are summarized for ease of reference. Finally, future recommendations and overall conclusions drawn from the results of researchers are included. This current review focuses on papers from high-impact factor publications based on 10 years starting from (2009 to 2020).

In data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier detection techniques depend on the properties of a particular type of data and can not deal with a large volume of data well, which mean that there is a necessity for improved methodologies and techniques to be applied to a large amount of data with different types in other application areas. In this paper, a parallel outlier detection technique is developed to detect the outliers in the sequential data. Although there are many types of outliers, this paper concentrates on the contextual anomalies. The proposed technique uses a graph approach to detect the outliers. It is very flexible, fast, and no labeled data is needed comparing to many previous approaches. The experimental results show the detected contextual outliers in the sequential data, as well as the efficient scaling up to handle the massive data by increasing the number of processors. The results prove that the parallelism of the proposed technique is very valuable.

Color medical images hold noteworthy impact in clinical conclusion and treatment. Medical image segmentation reduces the uncertainty by providing detailed information about the shape, size, or location characteristics. However, traditional methods suffer from low accuracy, high complexity, and are less robust. To overcome these drawbacks, this paper proposes an efficient metaheuristic algorithm, exchange market algorithm (EMA) for multilevel thresholding (MLT) of distinct medical images. Optimal threshold is effectively obtained through the most promising objective functions such as Kapur, Otsu and minimum cross entropy (MCE) aided with EMA. The EMA involves exchange of shares among the investors in stable and unstable market situations to achieve profit. Exploration and exploitation are achieved by second and third groups of stable and unstable modes of EMA. Moreover, the execution time is reduced by the highly competent shareholders retaining their top rank without any changes in their shares. The efficacy of the proposed paper is evaluated on three distinct medical images at 4, 5, 6 and 7th threshold levels and compared with the recent algorithms such as Krill herd (KHA), Teaching-learning based optimization (TLBO) and Cuckoo search algorithm (CSA). Quantitative and qualitative validation by metrics such as computational time, Peak signal to noise ratio (PSNR), Structural similarity index (SSIM) and Wilcoxon rank sum test affirm that the EMA is superior to other algorithms. On the other hand, Otsu based EMA method is found to be more accurate and robust for improved clinical decision making and diagnosis.

In sample surveys, when we need information regarding rare sensitive issues which people often do not prefer to share with others. In such situations this is also awkward for interviewers to ask the direct questions related to confidential and private matters of interviewees. An approach towards the open queries about sensitive issues generally results in the high non-response rates or misleading answers. The aim of this paper is to develop an effective randomized response model to overcome with these types of challenges arising due to sensitive nature of characteristic under study. In this paper we have proposed three-stage randomized response model for estimating mean number of individuals who possessed rare sensitive attribute which makes use of Poisson distribution. The properties of the proposed estimation procedures have been deeply examined when the parameter of a rare unrelated attribute is known as well as unknown. Privacy protection of respondents is also an equally important matter of concern. So measure of privacy protection for the proposed randomized response model has also been examined. Empirical studies are performed to support the theoretical results, which show the dominance of the proposed estimators over well-known contemporary estimators. From the findings of this study we may conclude that proposed randomized response model is rewarding in terms of percent relative efficiencies and privacy protection and may be recommended to survey practitioners for real life applications.

Nowadays, different types of dispatchable and non-dispatchable distributed energy resource (DER) units, combined heat and power (CHP) plants as well as heat only units are integrated into a micro-grid (MG) platform. These facilities can help MG operators to satisfy the required electricity and heat demands of controllable and non-controllable loads even with no power exchange between the external main grid and the MG. This paper introduces a network constrained optimal operation model based on an integrated active-reactive and heat powers scheduling in MG for both grid-connected and isolated modes. The DERs like fuel cell (FC) units, diesel generators (DGs) and micro turbines (MTs) are motivated to take part in providing reactive power to the MG based on a reactive power payment function. Meanwhile, the dependency of produced heat and active power by CHP units through feasible operating region as well as the dependency of produced active and reactive powers by DER units through capability curves are taken into account in the optimal economic dispatch of the MGs. The main goal of this paper is to find the optimal economic dispatch of active and reactive powers as well as heat energy for DER and CHP based micro-grids in which network constraints such as AC power flow limitations are taken into consideration. The proposed model is examined on a 33-bus test MG for grid-connected and isolated modes and the simulation results are presented to investigate and analyze the different features of the proposed method.

Oil price forecasting is one of the most challenging issues since it is noisy, non-stationary, and chaotic. In this paper, we design a Bayesian Nonlinear Quantile method consisting of a Threshold Improved model and an Adaptive MCMC model to improve predicting performance. Specifically, the threshold improve model is introduced to solve the problems caused by the completely asymmetric distribution, and the Adaptive MCMC model is used to get the optimal threshold. Besides, the two-stage framework is applied to improve traditional methods' performance, including the Indirect GARCH model and Asymmetric Slope model. The experimental results show that our approach provides a promising alternative to oil price prediction, and the framework also improve the performance of the traditional methods. The contribution of this paper is to improve the accuracy of the oil price forecasting model, and the framework applies to other energy prices as well.

Based on binocular stereo vision measurement and wireless sensing theory, this paper studies the measurement and analysis methods of athletes' movement displacement parameters. The main purpose is to explore the relationship between sports biomechanical parameters. The analysis reveals the laws of athletes 'movements, and provides theoretical guidance on athletes' attitude recognition and physical training. Based on the principle of binocular stereo vision, a non-contact displacement measurement system was developed. The displacement measurement system uses template matching to extract the image coordinates of the measurement points and restore the spatial coordinates of the measurement points through European 3D reconstruction, which improves the practicality of the measurement system. In the cantilever beam vibration test, the traditional displacement sensor and binocular image displacement measurement system are used to simultaneously measure the cantilever beam displacement. The experimental results show that the integrated displacement measurement system in this paper has higher accuracy and better stability.

In the virtual world, the authentication process is used to prove the identity for both of the server and the clients. Therefore, the authentication is an urgent issue for sharing data between users in the data warehouse systems. This paper presents the enhancement of Data Warehouse Signature (DWS) framework through proposing a novel authentication and group-key distribution model, based on the modular symmetric polynomials, to distribute a secure group-key between the manager and the chosen executors. To the best of our knowledge, we are the first to propose a group-key based on the symmetric polynomial and the modular arithmetic. This leads to increase the security level of the proposed model from t, where t is the symmetric polynomial degree, to ∑ i = 1 c c i × t , where c is the number of executors in the DWS system. In this paper, enhancing the DWS achieves the high performance using a parallel computing in our middleware. The proposed model has the lowest cost of computation, communication and complexity overheads among the existing security models. Additionally, security analysis shows that the proposed model achieves the key security, prevents insider and outsider attacks, and provides forward and backward secrecy.

الملخص: عادة ما تكون المقالات ما قبل الطباعة بمثابة مقدمة أولية لأوراق تمت مراجعتها من قِبل النظراء ويتم وضعها على الفور تقريبا، باستثناء بعض الفحص السطحي، في مستودع وصول مفتوح للجمهور للسماح للمعلومات بالوصول إلى القراء بسرعة، والتغلب على الخمول المرتبط عادة بالمعالجة بطريقة المجلات التي تعتمد على مراجعة الأقران. بالنسبة للباحثين في بداية حياتهم المهنية الذين قد يكونون متحمسين للحصول على بعض التقدير لجهودهم، أو الرغبة في الحصول على مدخلات مفتوحة وعلنية حول عملهم، فإن النشر قبل الطباعة هو بالتأكيد خيار نشر مفيد. ومع ذلك، إذا لم يتم فحص البيانات والمعلومات المتعلقة بالصحة بعناية، فقد تشكل خطرا، وقد تعمل أيضا كمصدر لمعلومات مضللة عن الصحة العامة. تشير زيادة النمو والمنافسة بين خوادم ما قبل الطباعة، إلى جانب حجم هائل من المطبوعات المسبقة المتعلقة بـ كوفيد-١٩، وخاصة على خادم ما قبل الطباعة لعلم الأحياء، وخادم ما قبل الطباعة للعلوم الطبية، بالإضافة إلى تحديد الفهرسة التي يتم اختبارها الآن على موقع بوب ميد، إلى أن المطبوعات المسبقة يتم استخدامها بشكل متزايد في العلوم الطبية الحيوية. وهناك حاجة إلى سياسات أخلاقية أقوى وأكثر قوة لفحص المطبوعات المسبقة قبل نشرها للجمهور، وحتى إذا كان هذا يعني تأخيرا طفيفا في النشر، فقد يؤدي ذلك إلى زيادة ثقة الأكاديميين في هذا الشكل من المعلومات العلمية والتواصل. يجب تقديم سياسات أخلاقية واضحة وصارمة بشكل عاجل من قبل مجموعات الأخلاقيات مثل لجنة أخلاقيات النشر، واللجنة الدولية لمحرري المجلات الطبية للمجلات الأعضاء التي تسمح بنشر مقالات ما قبل الطباعة قبل مراجعة النظراء التقليدية. ستفيد الإرشادات الأخلاقية الصارمة التي تتعامل مع سوء السلوك على قدم المساواة في مقالات ما قبل الطباعة والأبحاث التي راجعها النظراء، ستفيد نزاهة النشر الأكاديمي. Abstract: Preprints are typically crude precursors of peer-reviewed papers that are placed almost immediately, save for some superficial screening, on an open-access repository to allow the information to reach readers quickly, circumventing the long-drawn process typically associated with processing in peer-reviewed journals. For early-career researchers who might be enthusiastic about obtaining some recognition for their efforts, or wanting open and public input about their work, preprints are certainly a useful publication choice. However, if health-related data and information have not been carefully scrutinised, they may pose a risk and may even serve as a source of public health misinformation. Surging growth and competition among preprint servers, coupled with a massive volume of COVID-19-related preprints, mainly on bioRxiv and medRxiv, as well as select indexing now being tested on PubMed, suggests that preprints are being increasingly used in the biomedical sciences. Stronger and more robust ethical policies are needed to screen preprints before they are released to the public, and even if this implies a slight delay in publication, it may increase academics' trust in this form of scientific information and communication. Clear and stringent ethical policies need to be urgently introduced by ethics groups such as COPE and the ICMJE, whose many member journals allow preprints to be posted before traditional peer review. Stringent ethical guidelines that treat misconduct equally in preprints and peer-reviewed papers will boost the integrity of academic publishing.

Each organization faces threats and risks in daily operations. One of the main risks is how to assess the security level to protect from the increasing risks associated with technology evolution. So, organizations can specify the required approaches and skills. In this paper, we propose a security maturity model that classifies the organizations into five levels. Each level determines the technologies and process capability used by the organizations. There is a set of factors that can help in determining the security maturity level, such as technology, people, and infrastructure. This paper adopts an Information Security Management model to assess organization’s security level. The authors make a correspondence between maturity levels and security levels in an organization. Also, the proposed process capability controls influence both levels. The proposed model helps the organizations bridging the cybersecurity gaps. These gaps relate to talent, technology, organizational units, financial, management and operations gaps. Thus, the model helps the cybersecurity auditors to create a comprehensive plan for measuring the security level of the organization. This plan can manage and develop the organization’s automated countermeasures. Also, it can help in applying the suitable standard and framework based on the organization’s daily operation. Cybersecurity auditors use cybersecurity techniques and tools to assess the organization’s postures. Finally, the authors applied the security maturity controls in two case studies: retirement organization and public telecommunication corporation in the Republic of Yemen.

With the advent of digitalization and industry 4.0, education in chemical and biochemical engineering has undergone significant revamping over the last two decades. However, undergraduate students sometimes do lack industrial exposure and are unable to visualise the complexity of actual process plants. Thereby, students might graduate without adequate professional hands-on experience. Similarly, in the process industry, operator training-simulators are widely used for the training of new and skilled operators. However, conventional training-simulators often fail to simulate reality and do not provide the user with the opportunity to experience unexpected and hazardous scenarios. In these regards, virtual reality appears to be a promising technology that can cater to the needs of both academia and industry. This paper discusses the opportunities and challenges for the incorporation of virtual reality into chemical and biochemical engineering education with an emphasis on the fundamental areas of technology, pedagogy and socio-economics. The paper emphasises the need for augmenting virtual reality interfaces with mathematical models to develop advanced immersive learning applications. Further, the paper stresses upon the need for novel educational impact assessment methodologies for the evaluation of virtual-reality-based learning. Finally, an ongoing case study application is presented to briefly discuss the social and economic implications, and to identify the bottlenecks involved in the adoption of virtual reality tools across chemical and biochemical engineering education.

In this paper, different optimization algorithms are introduced to solve the security-constrained optimal power flow between two connected regions, applying two control modes, namely the centralized and decentralized modes. Traditionally, the decentralized control, i.e., each power system controls its variables regardless of the boundary buses, is the most commonly used approach. However, the centralized control has been recently utilized to enhance the system security, because the active power of the generation units and/or the voltage buses on the boundaries are the two variables, which can mitigate the changes, when compared to the decentralized control solution. The optimization techniques applied propose using the Genetic Algorithm (GA) as a tool to validate the solution reached through the Whale Optimization Algorithm. The proposed methodology is investigated on the IEEE 48-bus electric power system, which consists of two regions connected to each other through three transmission lines. The problem can be solved by applying both the AC and DC Optimal Power Flow respectively, using three approaches, namely: 1- Changing the active power of generation unit; 2- Controlling the voltage magnitude and 3- Combining the changes in the real power of generation units and the voltage variations. All the proposed approaches are implemented taking into consideration the technical constraints. All of these different techniques are constructed and modeled by utilizing the Matlab code. The results of each case study are presented; and a final comparison between all the methods utilized is drawn to demonstrate the advantages of each method. The novelty of this paper is represented in investigating the effectiveness of applying the AC Optimal Power Flow and the DC Power Flow, utilizing meta-heuristic techniques.

Device to Device communication involves communique between two devices without routing the packet through the cellular network. Ideally, the process involves reutilizations of the ideal resources of the cellular network for communication purpose. This ensures better management of radio spectrum and reduces communication delay. However, resource allocation has also been a major challenge because it contributes to various problems like interference, throughput issues and many other. A lot of work has been carried out in the recent past to address the above issue. The current paper reviews the work done in the area of resource allocation in the context of device to device communication. Among various mathematical tools available it was bring into being that game theory has been extensively used to address the problem. The main contribution of the paper, it provides the insight into the evolvement that has been made in the area of resource allocation and highlight various open issues that needs to be addressed.

Facial Expression Recognition (FER) is an attractive and demanding problem in human computer interaction. Facial expressions are one of the most dominant and usual means for a human to communicate their feelings and intentions. This paper proposes the Enhanced Modified Decision Based Unsymmetric Trimmed Median Filter (EMDBUTMF) method which removes the noisy pixels from the face image. Also, this paper proposes the Local Directional Number (LDN) pattern, Dominant Gradient Local Ternary Pattern (DGLTP) descriptor for feature extraction and Support Vector Machine (SVM) classifier for classification. The histogram features are extracted from face images using the LDN and DGLTP descriptors. The experimental result shows that the proposed method gives an improved performance of 88% accuracy on two publicly available facial expression datasets JAFFE and CK.

Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE) are two of the essential tools to design and develop new products. CAD systems help to design a new product efficiently, whereas CAE systems are used to predict and assess the future functionalities (durability, structural strength, etc.) of a product. Usually, these two systems work independently; however, their mutual dependency is a topic of interest for the researchers. This paper discusses the contemporary research efforts in the domain of CAD to CAE integration and later find the research gap. Based on the research gap this paper develop a methodology to integrate a CAD to CAE systems where scientific data format HDF (Hierarchical Data Format) acts as a bridge to link the gap between a CAD and CAE system. The virtual prototype of a product is getting accurate with the advancement of CAD systems; hence, the transferability of those models to a CAE system has become more sophisticated using traditional (e.g., .step, .iges files) methodologies. Since HDF is often used to handle massive and complex datasets for various research domains; hence, in this research effort HDF acts as a data repository neutral system to interact with the CAD and CAE systems. Based on the comprehensive literature review, this research effort aims to develop an uninterrupted integrated platform to handle complex design and analysis models using a big open-source data handling platform HDF (Hierarchical Data Format). MATLAB is used to bridge from the CAD system to HDF primarily and later from the HDF to CAE. A simple case study of analyzing a classic fluid mixing elbow demonstrates the preliminary implementation effort towards the proposed HDF-based integrated platform.

Enhancing traffic safety on freeways is the main goal for all transportation agencies. However, to achieve this goal, many analysis protocols of network screening models need to be improved through considering human factors while analyzing traffic data. This paper introduces one on the new analysis protocol of identifying and discriminating between normal and risky driving in clear and rainy weather. The introduced analysis protocol will consider the effect of human factors on updating the networking screening process of identifying hotspots of crash risk. This paper employs the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data to investigate the behavior of normal and risky driving under both rainy and clear weather conditions. Near-crash events on freeways, which were used as Surrogate Measure of Safety (SMoS) for crash risk, were identified based on the changes in vehicle kinematics, including speed, longitudinal and lateral acceleration and deceleration rates, and yaw rates. Through a trajectory-level data analysis, there were significant differences in driving patterns between rainy and clear weather conditions; factors that affected crash risk mainly included driver reaction and response time, their evasive maneuvers such as changes in acceleration rates and yaw rates, and lane-changing maneuvers. A cluster analysis method was employed to classify driving patterns into two clusters: normal and risky driving condition patterns, respectively. Statistical results showed that risky driving patterns started on average one second earlier in rainy weather conditions than in clear weather conditions. Furthermore, risky driving patterns extended in average three seconds in rainy weather conditions, while it was two seconds in clear weather conditions. The identification of these patterns is considered as a primary step towards an automated development that would distinguish between different driving patterns in a Connected Vehicle CV environment using Basic Safety Messages (BSM) and to enhance the network screening analysis for increased crash risk hotspots.

A novel passive frequency beam-steering linear antenna array is proposed in this paper to be utilized at the transmit side of a long-range wireless power transfer (WPT) system. To assure high power handling for a long-range transfer, the proposed antenna is designed based on stripline technology. Moreover, unlike other reported designs, the antenna array in this paper uses parallel feed while frequency beam-steering. This permits to apply a special synthesis at the radiating elements to minimize the energy wasted at the side lobes. The antenna array uses common-fed dipoles as radiating elements to eliminate the surface current at the ground planes that is considered a critical issue in high power applications. The antenna covers the frequency band from 1.1 to 1.5 GHz with a reflection coefficient better than −10 dB (VSWR

Software defined networks are an emerging category of networks in which the data plane and control plane are separated. This separation of planes opens the door for designing sophisticated routing algorithms that would overwhelm the computing power of traditional networking nodes. In this paper, we consider the possibility of introducing node trust into the routing problem. There are many ways for measuring node trust. However, in this paper, we focus on denial of service attacks. We develop a hybrid method for detecting denial of service attacks and incorporate this information in routing decisions so that nodes that are part of a botnet can be quickly identified and excluded from the network. The proposed method is flexible enough to allow nodes that have been suspected of participating in a denial of service attack to be “rehabilitated” if they cease their malicious behavior. The technique is also able to detect the start of a second attack while another one is on-going. Our results indicate that the proposed method for detecting denial of service attacks performs better than non-hybrid techniques.

This paper describes how a simple novel Galois Power-of-Two (GPOW2) real-time embedding scheme is used to improve the performance and accuracy of downstream NLP tasks. GPOW2 computes embeddings live on the fly (real time) in the context of target NLP tasks without the need for tabulated pre-embeddings. One excellent feature of the method is the ability to capture multilevel embeddings in the same pass. It simultaneously computes character, word and sentence embeddings on the fly. GPOW2 has been derived in the context of attempts to improve the performance of the SWAM Arabic morphological engine, which is a multipurpose tool that supports segmentation, classification, POS tagging, spell checking, word embeddings, sematic search, among other tasks. SWAM is a pattern-oriented algorithm that relies on morphological patterns and POS tagging to perform NLP tasks. The paper demonstrates how GPOW2 led to improvements in the accuracy of POS tagging and pattern matching, and accordingly the performance of the whole engine. The accuracy for pattern prediction is 99.47% and is 98.80% for POS tagging.

As the most basic travel mode of residents, walking trip takes place more frequently in residential districts. It is of great significance to study the land use characteristics of the residential district suitable for walking in order to create a good walking environment and land use model. In this paper, 18 residential blocks in Xi'an are taken as the research object, based on the relevant data of the pedestrian flow rate and land use of residential blocks, this paper analyzes the interactive relationship between the non-commuting hours during the week, the commuting hours during the week and the weekends by using the multiple linear regression model, and the land use characteristics of residential blocks under the guidance of walking trip are revealed. The results show that the land use factors related to pedestrian flow rate are mainly 9 factors, such as building density, buildings mixing degree, mixing degree of business functions and density of functional formats. In different hours, walking trips have different demands on various elements of land use. During the non-commuting hours during the week, the demand for walking trips for building density, mixing degree of business functions, density of functional formats and footpath area ratio is large. During the commuting hours during the week, the demand for walking trips for buildings mixing degree, density of functional formats, road network connectivity, and green area ratio of footpath is large. During the weekends, the demand for building density, density of functional formats, road network density, and footpath density is large. Through the above analysis, the optimization strategy is proposed for residential blocks in Xi 'an, which provides a theoretical basis for the optimization and transformation of the space environment of residential blocks to promote walking trip.

This paper exhibits a novel approach for solving fully intuitionistic fuzzy multi-objective fractional transportation problem (FIF-MOFTP). Because of the change of market policies, we expect that the transportation cost, the shipped quantity, the source, and the destination parameters are not generally exact. In this paper, a theorem which demonstrate that FIF-MOFTP is ever solvable was presented. In our approach, the problem is converted into a linear one based on some transformations. Then the linearized model is reduced to a crisp multi-objective transportation problem utilizing the accuracy function for each objective. Moreover, various theorems that set up the relation among the FIF-MOFTP and its equivalent crisp model using linear, hyperbolic, and parabolic membership functions are also proofed. To approve the proposed approach, a numerical example is incorporated. Deductions and future exploration of this paper are depicted at end.

Cyber-physical system (CPS) is the core technology of Industry 4.0. The deterministic behaviors of the CPS require real-time deterministic guarantee. Therefore, this paper improves the ant colony optimization (ACO) into a scheduling algorithm for time-triggered flows in time-sensitive network (TSN), a standard developed by the IEEE 802.1 Working Group that fully satisfies the strict end-to-end delay requirements of industrial applications. Simulation results show that the improved ACO (IACO) can schedule the time-triggered flows in the TSN excellently, and outperform the traditional ACO in convergence speed, optimization ability and the proneness to local optimum trap. To sum up, this paper provides an effective real-time guarantee for the TSNs.