bluuebunny/crossref_metadata_2025 huggingface dataset:
Phytoplankton Biodiversity in Two Tropical, High Mountain Lakes in Central Mexico
Authors: Cuna Estela , Alcocer Javier , Gaytán Martha , Caballero Margarita
Published: January 2022

Lakes El Sol and La Luna, inside the crater of the Nevado de Toluca volcano, Central Mexico, are the only two perennial high mountain lakes [HML] in the country. HML are considered among the most comparable ecosystems worldwide. These lakes are “extreme” environments with diluted, oligotrophic, and cold waters exposed to high UV radiation doses. In this paper, we document the phytoplankton species inhabiting these two extreme tropical ecosystems. The phytoplankton diversity of Lakes El Sol and La Luna is low compared to the global phytoplankton diversity from other Mexican inland waters. Nonetheless, the phytoplankton diversity turned out to be high compared to other HML worldwide, both temperate and tropical. The phytoplankton community in Lake El Sol was formed by 92 morphospecies and in Lake La Luna by 63; in both, the most diverse groups were Bacillariophyceae and Chlorophyceae. All species found in Lake La Luna were present in Lake El Sol, but 29 species present in Lake El Sol were absent in Lake La Luna. Nevertheless, 57% of the most frequent and abundant taxa in Lake El Sol were rare in La Luna, and 47% of the most frequent and abundant taxa in Lake La Luna were rare in Lake El Sol. Associated with their shallowness and polymictic thermal pattern, 87.5% of the Bacillariophyceae morphospecies are tychoplantonic, while the remaining 12.5% are truly planktonic. Dominant taxa were Chlorophyceae in Lake El Sol and Cyanobacteria in Lake La Luna. The most extreme conditions of Lake La Luna (ultraoligotrophy, very low pH, high ultraviolet radiation) most likely explained the differences in the dominant species composition and lower species richness compared to Lake El Sol. Herein, we provide for the first time a taxonomic list of the phytoplankton community of Lakes El Sol and La Luna. This information constitutes a baseline needed to use these HML as sentinels of global and climate change.
Laboratory Tests to Optimize the Milking Machine Settings with Air Inlet Teat Cups for Sheep and Goats
Authors: Kaskous Shehadeh
Published: January 2022

Milking machine design and performance are directly related to the milkability of sheep and goats, with the aim of milking quickly, completely and gently. This leads to an increase in the milk yield with improved quality, and the maintenance of healthy udders. The aim of this study was to carry out laboratory tests to determine the optimal level of vacuum, pulsation rate and pulsation ratio of new milking machines in high and low milk lines for sheep and goats. This study was conducted at the Department of Research and Development, Siliconform, Germany. For this purpose, different levels of vacuum (32, 34, 36, 38 and 40 kPa), milk jet (2, 2.5, 3 and 4 mm), milk line (high line and low line) and pulsation ratio (50:50 and 60:40) were used. First minute water flow (1st WF/kg) was used as an indicator for assessing the best combination in the milking machine. In addition, the cyclic vacuum fluctuation was measured in the inner chamber of the teat cup during the 1st WF/kg with the aid of a Vacuscope device. Statistical analysis was conducted using the mixed procedure in SAS. Our results show that the vacuum level, the milk jet and the pulsation ratio had a significant influence (p < 0.05) on the 1st WF/kg in the two milking machines for goats and sheep. In conclusion, the ideal conditions for milking goats with air inlet teat cups in the milking machine are a vacuum level of 36–38 kPa (low line) and 38–40 kPa (high line), a pulsation rate of 90 cycles/min and a pulsation ratio of 60:40, while the ideal conditions in the sheep milking machines are a vacuum level of 35–36 kPa (low line) and 36–38 kPa (high line), a pulsation rate of 120 cycles/min and a pulsation ratio of 60:40 or 50:50.
Knowledge Management Model for Smart Campus in Indonesia
Authors: Hidayat Deden Sumirat , Sensuse Dana Indra
Published: January 2022

The application of smart campuses (SC), especially at higher education institutions (HEI) in Indonesia, is very diverse, and does not yet have standards. As a result, SC practice is spread across various areas in an unstructured and uneven manner. KM is one of the critical components of SC. However, the use of KM to support SC is less clearly discussed. Most implementations and assumptions still consider the latest IT application as the SC component. As such, this study aims to identify the components of the KM model for SC. This study used a systematic literature review (SLR) technique with PRISMA procedures, an analytical hierarchy process, and expert interviews. SLR is used to identify the components of the conceptual model, and AHP is used for model priority component analysis. Interviews were used for validation and model development. The results show that KM, IoT, and big data have the highest trends. Governance, people, and smart education have the highest trends. IT is the highest priority component. The KM model for SC has five main layers grouped in phases of the system cycle. This cycle describes the organization’s intellectual ability to adapt in achieving SC indicators. The knowledge cycle at HEIs focuses on education, research, and community service.
Special Issue “The Next Generation of Upper Gastrointestinal Endoscopy”
Authors: Kurumi Hiroki , Isomoto Hajime
Published: January 2022

Upper gastrointestinal endoscopy is now widely used as a first-line procedure to investigate upper gastrointestinal symptoms in most countries around the world [...]
I Be Africa Man Original: Towards a Contextual Conceptualization of Father Involvement in the Education of Children with Disabilities in Kenya
Authors: Karisa Amani , McKenzie Judith
Published: January 2022

Father involvement could play a significant role in the lives of children with disabilities. Research is scarce on father involvement in the education of children with disabilities in Africa. We seek to provide a context for father involvement in the formal education of children with disabilities in Kenya, with the aim of contributing to the development of a conceptual understanding for father involvement in such a circumstance. We examine general research on father involvement in Kenya, explore the policy frameworks that guide fatherhood in the country, and look at the specific area of involvement in education. We then present a case study that examines father involvement in the formal education of children with disabilities in Kenya. Our analysis flags up a key opportunity in the pursuit of education for children with disabilities when fathers are involved; they can support their children with disabilities’ access, participation and success in education. We highlight the need for research that builds upon the voices of fathers to illuminate their role in education and we also make some suggestions toward a conceptual lens that will highlight the contextual realities involved, particularly in regard to the education of children with disabilities.
Machine-Learned Free Energy Surfaces for Capillary Condensation and Evaporation in Mesopores
Authors: Desgranges Caroline , Delhommelle Jerome
Published: January 2022

Using molecular simulations, we study the processes of capillary condensation and capillary evaporation in model mesopores. To determine the phase transition pathway, as well as the corresponding free energy profile, we carry out enhanced sampling molecular simulations using entropy as a reaction coordinate to map the onset of order during the condensation process and of disorder during the evaporation process. The structural analysis shows the role played by intermediate states, characterized by the onset of capillary liquid bridges and bubbles. We also analyze the dependence of the free energy barrier on the pore width. Furthermore, we propose a method to build a machine learning model for the prediction of the free energy surfaces underlying capillary phase transition processes in mesopores.
Velocity Multistability vs. Ergodicity Breaking in a Biased Periodic Potential
Authors: Spiechowicz Jakub , Hänggi Peter , Łuczka Jerzy
Published: January 2022

Multistability, i.e., the coexistence of several attractors for a given set of system parameters, is one of the most important phenomena occurring in dynamical systems. We consider it in the velocity dynamics of a Brownian particle, driven by thermal fluctuations and moving in a biased periodic potential. In doing so, we focus on the impact of ergodicity—A concept which lies at the core of statistical mechanics. The latter implies that a single trajectory of the system is representative for the whole ensemble and, as a consequence, the initial conditions of the dynamics are fully forgotten. The ergodicity of the deterministic counterpart is strongly broken, and we discuss how the velocity multistability depends on the starting position and velocity of the particle. While for non-zero temperatures the ergodicity is, in principle, restored, in the low temperature regime the velocity dynamics is still affected by initial conditions due to weak ergodicity breaking. For moderate and high temperatures, the multistability is robust with respect to the choice of the starting position and velocity of the particle.
Introduction and Analysis of a Method for the Investigation of QCD-like Tree Data
Authors: Jercic Marko , Jercic Ivan , Poljak Nikola
Published: January 2022

The properties of decays that take place during jet formation cannot be easily deduced from the final distribution of particles in a detector. In this work, we first simulate a system of particles with well-defined masses, decay channels, and decay probabilities. This presents the “true system” for which we want to reproduce the decay probability distributions. Assuming we only have the data that this system produces in the detector, we decided to employ an iterative method which uses a neural network as a classifier between events produced in the detector by the “true system” and some arbitrary “test system”. In the end, we compare the distributions obtained with the iterative method to the “true” distributions.
Does Decoherence Select the Pointer Basis of a Quantum Meter?
Authors: Kofman Abraham G. , Kurizki Gershon
Published: January 2022

The consensus regarding quantum measurements rests on two statements: (i) von Neumann’s standard quantum measurement theory leaves undetermined the basis in which observables are measured, and (ii) the environmental decoherence of the measuring device (the “meter”) unambiguously determines the measuring (“pointer”) basis. The latter statement means that the environment monitors (measures) selected observables of the meter and (indirectly) of the system. Equivalently, a measured quantum state must end up in one of the “pointer states” that persist in the presence of the environment. We find that, unless we restrict ourselves to projective measurements, decoherence does not necessarily determine the pointer basis of the meter. Namely, generalized measurements commonly allow the observer to choose from a multitude of alternative pointer bases that provide the same information on the observables, regardless of decoherence. By contrast, the measured observable does not depend on the pointer basis, whether in the presence or in the absence of decoherence. These results grant further support to our notion of Quantum Lamarckism, whereby the observer’s choices play an indispensable role in quantum mechanics.
Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation
Authors: Manicka Santosh , Levin Michael
Published: January 2022

What information-processing strategies and general principles are sufficient to enable self-organized morphogenesis in embryogenesis and regeneration? We designed and analyzed a minimal model of self-scaling axial patterning consisting of a cellular network that develops activity patterns within implicitly set bounds. The properties of the cells are determined by internal ‘genetic’ networks with an architecture shared across all cells. We used machine-learning to identify models that enable this virtual mini-embryo to pattern a typical axial gradient while simultaneously sensing the set boundaries within which to develop it from homogeneous conditions—a setting that captures the essence of early embryogenesis. Interestingly, the model revealed several features (such as planar polarity and regenerative re-scaling capacity) for which it was not directly selected, showing how these common biological design principles can emerge as a consequence of simple patterning modes. A novel “causal network” analysis of the best model furthermore revealed that the originally symmetric model dynamically integrates into intercellular causal networks characterized by broken-symmetry, long-range influence and modularity, offering an interpretable macroscale-circuit-based explanation for phenotypic patterning. This work shows how computation could occur in biological development and how machine learning approaches can generate hypotheses and deepen our understanding of how featureless tissues might develop sophisticated patterns—an essential step towards predictive control of morphogenesis in regenerative medicine or synthetic bioengineering contexts. The tools developed here also have the potential to benefit machine learning via new forms of backpropagation and by leveraging the novel distributed self-representation mechanisms to improve robustness and generalization.
Advances in Computer Recognition, Image Processing and Communications
Authors: Choraś Michał , Burduk Robert , Giełczyk Agata , Kozik Rafał , Marciniak Tomasz
Published: January 2022

This Special Issue aimed to gather high-quality advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity [...]
Socio-Economic Impact of the Interest-Free Community Investment Fund: A Case Study of Rural Sindh, Pakistan
Authors: Memon Pervaiz Ahmed , Kalhoro Muhammad Ramzan , Tariq Kiran , Sindhu Paras , Shaikh Suman
Published: January 2022

This study aims to measure the impact of an intervention, the Community Investment Fund (CIF), on the socio-economic life of rural women. CIF is a community-managed fund aimed at improving the living standards of women by empowering them to undertake income-generating projects to become financially more stable and self-governed in the Khairpur, Shikarpur, Kandhkot-Kashmore and Jacobabad districts of Sindh, Pakistan. This study used a quasi-experimental design approach that involved two groups, i.e., the treatment group (beneficiaries) and control group (non-beneficiaries). The sample size of this study was 708 respondents including the treatment and control group. The results of comparison of mean indicate that there is a significant difference between treatment and control group in terms of socio-demographic variables (including monthly income and consumption, saving amount, total asset value, an asset purchased value and household diet) and women empowerment’s indicators, thereby suggesting that CIF has resulted in women empowerment. Concerning the results of the poverty scorecard, the higher graduation of beneficiaries (treatment group) asserts that the intervention of CIF has also a positive impact on targeted beneficiaries. In particular, the findings indicate that 72% of beneficiaries (treatment group) have graduated from one poverty band to another higher band compared to 59.4% of non-beneficiaries (control group) in poverty score. In addition, the findings of the logistic regression analysis confirmed that participation in the CIF program empowers women beneficiaries. This study will support policymakers to further improve CIF so that it can become more effective and sustainable.
Muslim Clothing Online Purchases in Indonesia during COVID-19 Crisis
Authors: Salim Muhartini , Aprianto Ronal , Anwar Abu Bakar Syaiful , Rusdi Muhammad
Published: January 2022

Today, online Muslim clothing providers in Indonesia are faced with increasing competition in business openness. This condition requires online Muslim clothing providers to be more creative, innovative, effective and efficient by offering Muslim clothing products that are more valuable than competitors’. Therefore, a sophisticated and smart technology planning concept is needed for Muslim fashion consumers and to continue to achieve the benefits obtained by online Muslim clothing providers. This study aims to determine: (1) the influence of attitudes on the online buying intention of Muslim clothing in Indonesia during the COVID-19 crisis (2) the influence of subjective norm on the online buying intention of Muslim clothing in Indonesia during the COVID-19 crisis (3) the influence of perceived behavioral control on the online buying intention of Muslim clothing in Indonesia during the COVID-19 crisis (4) the effect of attitudes on the online buying intention of Muslim clothing in Indonesia during the COVID-19 crisis moderated by religious belief, (5) the effect of subjective norm on the online buying intention of Muslim clothing in Indonesia during the COVID-19 crisis moderated by religious belief, and (6) the effect of perceived behavioral control on the online buying intention of Muslim clothing in Indonesia during the COVID-19 crisis moderated by religious belief. This study uses 1. independent variables, namely: attitudes, subjective norms, and perceived behavioral control. 2. The dependent variable is: Purchase intention. 3. The moderating variable is: religion. The data in this study are obtained from questionnaires distributed to 762 respondents. The method used is purposive sampling to all respondents who shopped online. The method is through the LISREL 8.7 program and t-test. These results indicate that the variables of attitude, subjective norm and perceived behavioral control influence the online buying intention of Muslim clothing in Indonesia during the COVID-19 crisis. They also find that the religious belief variable can moderate the variable of attitude towards the online buying intention of Muslim clothing in Indonesia during the COVID-19 crisis but not the subjective norm and perceived behavioral control variables.
Falling through the Cracks: Deaf New Americans and Their Unsupported Educational Needs
Authors: Schwartz Michael A. , Elder Brent C. , Chhetri Monu , Preli Zenna
Published: January 2022

Members of the Deaf New American community reported they arrived in the United States with no formal education, unable to read or write in their native language, and had zero fluency in English. Efforts to educate them have floundered, and the study aims to find out why and how to fix the problem. Interviews of eight Deaf New Americans yielded rich data that demonstrates how education policy in the form of the Individuals with Disabilities Education Act (IDEA) and other laws fail to address their needs, because these laws do not include them in their coverage. The study’s main findings are the deleterious effect of the home country’s failure to educate their Deaf citizens, America’s failure to provide accessible and effective instruction, and the combined effect of these institutional failures on the ability of Deaf New Americans to master English and find gainful employment. This article is an argument for a change in education policy that recognizes the unique nature of this community and provides for a role of Deaf educators in teaching Deaf New Americans.
Outside Training of Spanish University Students of Education for the Didactic Application of Cinema: Formal, Non-Formal, and Informal Perspectives
Authors: Lorenzo-Lledó Alejandro , Lledó Asunción , Lorenzo Gonzalo , Gilabert-Cerdá Alba
Published: January 2022

Nowadays, audiovisual media play a central role in access to information and in personal relationships. Among the audiovisual media is cinema, which due to its heterogeneous nature, can fulfill diverse educational functions. The objective of this study was to learn about the training that future teachers in Spain receive outside of their teaching degree for the didactic use of cinema. In addition, we sought to understand the influence of training on perceptions regarding the educational potential of cinema and the predisposition to its use. Using a quantitative approach, information was collected from 4659 students from 58 Spanish universities. The questionnaire used covered perceptions about the potencialities of cinema as a didactic resource in pre-school and primary classrooms (PECID). The results showed that 95.1% of the students had not received training. In addition, we found a significant influence of training on their perceptions of the educational possibilities of cinema. Furthermore, we found an influence on their predisposition to use training in their future teaching practice. Overall, it is necessary to implement training actions to fill the gaps detected in favor of a quality education with active learning and linked to society.
Educator Perceptions of Early Learning Environments as Places for Privileging Social Justice in Rural and Remote Communities
Authors: Christopher Vicki , Turner Michelle , Green Nicole C.
Published: January 2022

Early childhood education and care (ECEC) in Australia has long been associated with the concept of social justice, however, a clear understanding of what it looks like across diverse services and communities is not available. This article reports the process of inquiry, as well as the outcomes, of a small-scale study designed to uncover the perceptions of ECEC educators working in rural and remote communities in the state of Queensland. Data were collected through individual semi-structured interviews with five educators from rural and remote settings identified as areas experiencing significant growth in population diversity. An initial thematic analysis of the data revealed three key themes. A secondary analysis using a place and space conceptual framework uncovered deeper, more sophisticated meanings of the educator experience of social justice. The research is important in bringing pedagogical conversations to the forefront regarding ECEC educator perceptions of their role in creating a socially just learning environment. In addition to identifying future research possibilities, implications from the findings indicate opportunities for re-examining and rethinking initial teacher education and ongoing professional learning.
Distributed Cooperative Jamming with Neighborhood Selection Strategy for Unmanned Aerial Vehicle Swarms
Authors: Zhou Yongkun , Song Dan , Ding Bowen , Rao Bin , Su Man , Wang Wei
Published: January 2022

In system science, a swarm possesses certain characteristics which the isolated parts and the sum do not have. In order to explore emergence mechanism of a large–scale electromagnetic agents (EAs), a neighborhood selection (NS) strategy–based electromagnetic agent cellular automata (EA–CA) model is proposed in this paper. The model describes the process of agent state transition, in which a neighbor with the smallest state difference in each sector area is selected for state transition. Meanwhile, the evolution rules of the traditional CA are improved, and performance of different evolution strategies are compared. An application scenario in which the emergence of multi–jammers suppresses the radar radiation source is designed to demonstrate the effect of the EA–CA model. Experimental results show that the convergence speed of NS strategy is better than those of the traditional CA evolution rules, and the system achieves effective jamming with the target after emergence. It verifies the effectiveness and prospects of the proposed model in the application of electronic countermeasures (ECM).
Locating Faults in Thyristor-Based LCC-HVDC Transmission Lines Using Single End Measurements and Boosting Ensemble
Authors: Swetapadma Aleena , Agarwal Shobha , Chakrabarti Satarupa , Chakrabarti Soham , El-Shahat Adel , Abdelaziz Almoataz Y.
Published: January 2022

Most of the fault location methods in high voltage direct current (HVDC) transmission lines usemethods which require signals from both ends. It will be difficult to estimate fault location if the signal recorded is not correct due to communication problems.Hence a robust method is required which can locate fault with minimum error. In this work, faults are located using boosting ensembles in HVDC transmission lines based on single terminal direct current (DC) signals. The signals are processed to obtain input features that vary with the fault distance. These input features are obtained by taking maximum of half cycle current signals after fault and minimum of half cycle voltage signals after fault from the root mean square of DC signals. The input features are input to a boosting ensemble for estimating the location of fault. Boosting ensemble method attempts to correct the errors from the previous models and find outputs by combining all models. The boosting ensemble method has been also compared with the decision tree method and thebagging-based ensemble method. Fault locations are estimated using three methods and compared to obtain an optimal method. The boosting ensemble method has better performance than all the other methods in locating the faults. It also validated varying fault resistance, smoothing reactors, boundary faults, pole to ground faults and pole to pole faults. The advantage of the method is that no communication link is needed. Another advantage is that it allowsreach setting up to 99.9% and does not exhibitthe problem of over-fitting. Another advantage is that the percentage error in locating faults is within 1% and has a low realization cost. The proposed method can be implemented in HVDC transmission lines effectively as an alternative to overcome the drawbacks of traveling wave methods.
Transfer Learning with Social Media Content in the Ride-Hailing Domain by Using a Hybrid Machine Learning Architecture
Authors: de Pablo Álvaro , Araque Oscar , Iglesias Carlos A.
Published: January 2022

The analysis of the content of posts written on social media has established an important line of research in recent years. The study of these texts, as well as their relationship with each other and their dependence on the platform on which they are written, enables the behavior analysis of users and their opinions with respect to different domains. In this work, a hybrid machine learning-based system has been developed to classify texts using topic modeling techniques and different word-vector representations, as well as traditional text representations. The system has been trained with ride-hailing posts extracted from Reddit, showing promising performance. Then, the generated models have been tested with data extracted from other sources such as Twitter and Google Play, classifying these texts without retraining any models and thus performing Transfer Learning. The obtained results show that our proposed architecture is effective when performing Transfer Learning from data-rich domains and applying them to other sources.
Techno-Economic Evaluation of 5G Technology for Automated Guided Vehicles in Production
Authors: Kiesel Raphael , Henke Leonhard , Mann Alexander , Renneberg Florian , Stich Volker , Schmitt Robert H.
Published: January 2022

The fifth generation of mobile communication (5G) is expected to bring immense benefits to automated guided vehicles by improving existing respectively enabling 5G-distinctive network control systems, leading to higher productivity and safety. However, only 1% of production companies have fully deployed 5G yet. Most companies currently lack an understanding of return on investment and of technical use-case benefits. Therefore, this paper analyses the influence of 5G on an automated guided vehicle use case based on a five-step evaluation model. The analysis is conducted with a use case in the Digital Experience Factory in Aachen. It shows a difference of net present value between 4G and 5G of 1.3 M€ after 10 years and a difference of return of investment of 66%. Furthermore, analysis shows an increase of mobility (13%), productivity (20%) and safety (136%). This indicates a noticeable improvement of a 5G-controlled automated guided vehicle compared to a 4G-controlled automated guided vehicle.
Theoretical and Empirical Verification of Electrical Impedance Matching Method for High-Power Transducers
Authors: Lee Jungwoo , Kim Jinhyuk
Published: January 2022

In our prior study, a systematic approach was used to devise Langevin transducers for high-power applications where the energy efficiency was not considered in the design criteria. In this paper, the impedance matching methods are thus proposed to evaluate what matching topology is appropriate for their use. Both the series inductor scheme and low pass filter composed of a series inductor and shunt capacitor are examined as matching circuits. According to MATLAB simulation, the resonance frequency is seen at 36.79 kHz due to a series L circuit, and its associated impedance is reduced by 70.45% from that of its non-matching condition. The measured resonance frequency is 36.77 kHz and the corresponding impedance is decreased by 59.52%. Furthermore, the acoustic pressure is measured to determine the effect of the matching circuit on the transducer’s actual behavior. The transducer with a series L circuit shows more efficient matching results, 2.28 kPa of positive acoustic pressure is emitted without matching and 3.35 kPa is emitted with a series L element, respectively. As a result, this study demonstrates how to evaluate the influence of matching circuits by using our customized approach rather than commercial SPICE programs, as well as how to experimentally verify the acoustic behavior of high-power Langevin transducers.
Multi-Scale Presentation of Spatial Context for Cultural Heritage Applications
Authors: Partarakis Nikolaos , Zabulis Xenophon , Patsiouras Nikolaos , Chatjiantoniou Antonios , Zidianakis Emmanouil , Mantinaki Eleni , Kaplanidi Danae , Ringas Christodoulos , Tasiopoulou Eleana , Dubois Arnaud , Carre Anne Laure
Published: January 2022

An approach to the representation and presentation of spatial and geographical context of cultural heritage sites is proposed. The goal is to combine semantic representations of social and historical context with 3D representations of cultural heritage sites acquired through 3D reconstruction and 3D modeling technologies, to support their interpretation and presentation in education and tourism. Several use cases support and demonstrate the application of the proposed approach including immersive craft and context demonstration environment and interactive games.
Optimal Operation of a Microgrid with Hydrogen Storage Based on Deep Reinforcement Learning
Authors: Zhu Zhenshan , Weng Zhimin , Zheng Hailin
Published: January 2022

Microgrid with hydrogen storage is an effective way to integrate renewable energy and reduce carbon emissions. This paper proposes an optimal operation method for a microgrid with hydrogen storage. The electrolyzer efficiency characteristic model is established based on the linear interpolation method. The optimal operation model of microgrid is incorporated with the electrolyzer efficiency characteristic model. The sequential decision-making problem of the optimal operation of microgrid is solved by a deep deterministic policy gradient algorithm. Simulation results show that the proposed method can reduce about 5% of the operation cost of the microgrid compared with traditional algorithms and has a certain generalization capability.
Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review
Authors: Abdullahi Mujaheed , Baashar Yahia , Alhussian Hitham , Alwadain Ayed , Aziz Norshakirah , Capretz Luiz Fernando , Abdulkadir Said Jadid
Published: January 2022

In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.
Data Rate Maximization in RIS-Assisted D2D Communication with Transceiver Hardware Impairments
Authors: Zheng Hongxia , Zhang Chiya , Yang Yatao , Li Xingquan , He Chunlong
Published: January 2022

We maximize the transmit rate of device-to-device (D2D) in a reconfigurable intelligent surface (RIS) assisted D2D communication system by satisfying the unit-modulus constraints of reflectin elements, the transmit power limit of base station (BS) and the transmitter in a D2D pair. Since it is a non-convex optimization problem, the block coordinate descent (BCD) technique is adopted to decouple this problem into three subproblems. Then, the non-convex subproblems are approximated into convex problems by using successive convex approximation (SCA) and penalty convex-concave procedure (CCP) techniques. Finally, the optimal solution of original problem is obtained by iteratively optimizing the subproblems. Simulation results reveal the validity of the algorithm that we proposed to solve the optimization problem and illustrate the effectiveness of RIS to improve the transmit rate of the D2D pair even with hardware impairments.
PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval
Authors: Qasim Muhammad , Mahmood Danish , Bibi Asifa , Masud Mehedi , Ahmed Ghufran , Khan Suleman , Jhanjhi Noor Zaman , Hussain Syed Jawad
Published: January 2022

This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall.
Techno-Economic Evaluation of Hybrid Energy Systems Using Artificial Ecosystem-Based Optimization with Demand Side Management
Authors: Omotoso Hammed Olabisi , Al-Shaalan Abdullah M. , Farh Hassan M. H. , Al-Shamma’a Abdullrahman A.
Published: January 2022

Electrification of remote rural areas by adopting renewable energy technologies through the advancement of smart micro-grids is indispensable for the achievement of continuous development goals. Satisfying the electricity demand of consumers while adhering to reliability constraints with docile computation analysis is challenging for the optimal sizing of a Hybrid Energy System (HES). This study proposes the new application of an Artificial Ecosystem-based Optimization (AEO) algorithm for the optimal sizing of a HES while satisfying Loss of Power Supply Probability (LPSP) and Renewable Energy Fraction (REF) reliability indices. Furthermore, reduction of surplus energy is achieved by adopting Demand Side Management (DSM), which increases the utilization of renewable energy. By adopting DSM, 28.38%, 43.05%, and 65.37% were achieved for the Cost of Energy (COE) saving at 40%, 60%, and 80% REF, respectively. The simulation and optimization results demonstrate the most cost-competitive system configuration that is viable for remote-area utilization. The proposed AEO algorithm is further compared to Harris Hawk Optimization (HHO) and the Future Search Algorithm (FSA) for validation purpose. The obtained results demonstrate the efficacy of AEO to achieve the optimal sizing of HES with the lowest COE, the highest consistent level, and minimal standard deviation compared with HHO and FSA. The proposed model was developed and simulated using the MATLAB/code environment.
Design and Analysis Considering Magnet Usage of Permanent Magnet Synchronous Generator Using Analytical Method
Authors: Lee Ji-Hun , Lee Hoon-Ki , Lee Young-Geun , Lee Jeong-In , Jo Seong-Tae , Kim Kyong-Hwan , Park Ji-Yong , Choi Jang-Young
Published: January 2022

In this study, the characteristic analysis of a permanent magnet synchronous generator was performed using the analytical method, and the validity of the analytical method was compared with that of the finite element method (FEM). For the initial design, the rotor size was selected using the torque per rotor volume method, and the stator size was selected according to the saturation of the stator iron core. In addition, fast Fourier transform analysis was performed to determine the appropriate magnet thickness point, and it was confirmed that the open circuit and armature reaction magnetic flux densities were consistent with the FEM analysis results. Based on the analytical method, the generator circuit constants (phase resistance, back EMF, and inductance) were derived to construct an equivalent circuit. By applying the equivalent circuit method to the derived circuit constants, the accuracy of the equivalent circuit method was confirmed by comparing the FEM and experimental results.
Nano/Microrobots Line Up for Gastrointestinal Tract Diseases: Targeted Delivery, Therapy, and Prevention
Authors: Děkanovský Lukáš , Li Jinhua , Zhou Huaijuan , Sofer Zdenek , Khezri Bahareh
Published: January 2022

Nano/microrobots (NMRs) are tiny devices that can convert energy into motion and operate at nano/microscales.54 Especially in biomedical research, NMRs have received much attention over the past twenty years because of their excellent capabilities and great potential in various applications, including on-demand drug delivery, gene and cell transport, and precise microsurgery. Reports published in recent years show that synthetic nano/microrobots have promising potential to function in the gastrointestinal (GI) region, particularly in terms of drug delivery. These tiny robots were able to be designed in such a way that they propel in their surroundings (biological media) with high speed, load cargo (drug) efficiently, transport it safely, and release upon request successfully. Their propulsion, retention, distribution, and toxicity in the GI tract of mice has been evaluated. The results envisage that such nano/microrobots can be further modified and developed as a new-generation treatment of GI tract diseases. In this minireview, we focus on the functionality of micro/nanorobots as a biomedical treatment system for stomach/intestinal diseases. We review the research progress from the first in vivo report in December 2014 to the latest in August 2021. Then, we discuss the treatment difficulties and challenges in vivo application (in general) and possible future development routes.
Road Thermal Collector for Building Heating in South Europe: Numerical Modeling and Design of an Experimental Set-Up
Authors: D’Amico Antonino , Ciulla Giuseppina , Buscemi Alessandro , Panno Domenico , Zinzi Michele , Beccali Marco
Published: January 2022

The combination/integration of renewable energy and storage systems appears to have significant potential, achieving high-energy results with lower costs and emissions. One way to cover the thermal needs of a building is through solar energy and its seasonal storage in the ground. The SMARTEP project aims to create an experimental area that provides for the construction of a road solar thermal collector directly connected to a seasonal low-temperature geothermal storage with vertical boreholes. The storage can be connected to a ground-to-water heat pump for building acclimatization. This system will meet the requirements of visual impact and reduction of the occupied area. Nevertheless, several constraints related to the radiative properties of the surfaces and the lack of proper thermal insulation have to be addressed. The project includes the study of several configurations and suitable materials, the set-up of a dynamic simulation model and the construction of a small-scale road thermal collector. These phases allowed for an experimental area to be built. Thanks to careful investigation in the field, it will be possible to identify the characteristics and the best operation strategy to maximize the energy management of the whole system in the Mediterranean area.
Optimal Configuration and Sizing of Seaport Microgrids including Renewable Energy and Cold Ironing—The Port of Aalborg Case Study
Authors: Bakar Nur Najihah Abu , Guerrero Josep M. , C. Vasquez Juan , Bazmohammadi Najmeh , Othman Muzaidi , Rasmussen Brian Dalby , Al-Turki Yusuf A.
Published: January 2022

Microgrids are among the promising green transition technologies that will provide enormous benefits to the seaports to manage major concerns over energy crises, environmental challenges, and economic issues. However, creating a good design for the seaport microgrid is a challenging task, considering different objectives, constraints, and uncertainties involved. To ensure the optimal operation of the system, determining the right microgrid configuration and component size at minimum cost is a vital decision at the design stage. This paper aims to design a hybrid system for a seaport microgrid with optimally sized components. The selected case study is the Port of Aalborg, Denmark. The proposed grid-connected structure consists of renewable energy sources (photovoltaic system and wind turbines), an energy storage system, and cold ironing facilities. The seaport architecture is then optimized by utilizing HOMER to meet the maximum load demand by considering important parameters such as solar global horizontal irradiance, temperature, and wind resources. Finally, the best configuration is analyzed in terms of economic feasibility, energy reliability, and environmental impacts.
Available Technologies and Commercial Devices to Harvest Energy by Human Trampling in Smart Flooring Systems: A Review
Authors: Visconti Paolo , Bagordo Laura , Velázquez Ramiro , Cafagna Donato , De Fazio Roberto
Published: January 2022

Technological innovation has increased the global demand for electrical power and energy. Accordingly, energy harvesting has become a research area of primary interest for the scientific community and companies because it constitutes a sustainable way to collect energy from various sources. In particular, kinetic energy generated from human walking or vehicle movements on smart energy floors represents a promising research topic. This paper aims to analyze the state-of-art of smart energy harvesting floors to determine the best solution to feed a lighting system and charging columns. In particular, the fundamentals of the main harvesting mechanisms applicable in this field (i.e., piezoelectric, electromagnetic, triboelectric, and relative hybrids) are discussed. Moreover, an overview of scientific works related to energy harvesting floors is presented, focusing on the architectures of the developed tiles, the transduction mechanism, and the output performances. Finally, a survey of the commercial energy harvesting floors proposed by companies and startups is reported. From the carried-out analysis, we concluded that the piezoelectric transduction mechanism represents the optimal solution for designing smart energy floors, given their compactness, high efficiency, and absence of moving parts.
Strategies for the Mobilization and Deployment of Local Low-Value, Heterogeneous Biomass Resources for a Circular Bioeconomy
Authors: Schipfer Fabian , Pfeiffer Alexandra , Hoefnagels Ric
Published: January 2022

With the Bioeconomy Strategy, Europe aims to strengthen and boost biobased sectors. Therefore, investments in and markets of biobased value chains have to be unlocked and local bioeconomies across Europe have to be deployed. Compliance with environmental and social sustainability goals is on top of the agenda. The current biomass provision structures are unfit to take on the diversity of biomass residues and their respective supply chains and cannot ensure the sustainability of feedstock supply in an ecological, social and economical fashion. Therefore, we have to address the research question on feasible strategies for mobilizing and deploying local, low-value and heterogeneous biomass resources. We are building upon the work of the IEA Bioenergy Task40 scientists and their expertise on international bioenergy trade and the current provision of bioenergy and cluster mobilization measures into three assessment levels; the legislative framework, technological innovation and market creation. The challenges and opportunity of the three assessment levels point towards a common denominator: The quantification of the systemic value of strengthening the potentially last remaining primary economic sectors, forestry, agriculture and aquaculture, is missing. With the eroding importance of other primary economic sectors, including fossil fuel extraction and minerals mining, the time is now to assess and act upon the value of the supply-side of a circular bioeconomy. This value includes the support the Bioeconomy can provide to structurally vulnerable regions by creating meaningful jobs and activities in and strengthening the resource democratic significance of rural areas.