Search results for: Functional Networks (FN)

Authors: R. Schweighoffer, N. Nagy, E. Reeves, B. Liebig

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Due to aging populations, the need for seamless palliative care provision is of central interest for western societies. An essential aspect of palliative care delivery is the quality of collaboration amongst palliative care providers. Therefore, the current research is based on Bainbridge’s conceptual framework, which provides an outline for the evaluation of palliative care provision. This study is the first one to investigate the predictive validity of spatial distribution on the quantity of interaction amongst various palliative care providers. Furthermore, based on the familiarity principle, we examine whether the extent of collaboration influences the perceived quality of collaboration among palliative care providers in urban versus rural areas of Switzerland. Based on a population-representative survey of Swiss palliative care providers, the results of the current study show that professionals in densely populated areas report higher absolute numbers of interactions and are more satisfied with their collaborative practice. This indicates that palliative care providers who work in urban areas are better embedded into networks than their counterparts in more rural areas. The findings are especially important, considering that efficient collaboration is a prerequisite to achieve satisfactory patient outcomes. Conclusively, measures should be taken to foster collaboration in weakly interconnected palliative care networks.

Keywords: collaboration, healthcare networks, palliative care, Switzerland

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

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Authors: Nicolas Haber

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The study addresses the conceptual design of Product-Service Systems (PSSs) as a means of innovating solutions with the aim of reducing the environmental load of conventional product based solutions. Functional approaches targeting PSS solutions are developed in instinctive methods within the constraints of the setting in which they are conceived. Adopting morphological matrices in designing PSS concepts allows a thorough understanding of the settings, stakeholders, and functional requirements. Additionally, such a methodology is robust and adaptable to product-oriented, use-oriented and result-oriented systems. The research is based on a functional decomposition of the task in a similar way as in product design; while extended to include service components, providers, and receivers, while assessing the adaptability and homogeneity of the selected components and actors. A use-oriented concept is presented via a practical case study at an agricultural boom-sprayer manufacturer to demonstrate the effectiveness of the morphological approach to justify its viability. Additionally, a life cycle analysis is carried out in order to evaluate the environmental advantages inherited in a PSS solution versus a conventional solution. In light of the applications presented, the morphological approach appears to be a valid and generic tactic to conceiving integrated solutions whilst capturing the interrelations between the actors and elements of an integrated product-service system.

Keywords: conceptual design, design for sustainability, functional decomposition, product-service systems

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Authors: D. S. Palimkar

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The FRIGDI (functional random integrodifferential inclusion) seems to be new and includes several known random differential inclusions already studied in the literature as special cases have been discussed in the literature for various aspects of the solutions. In this paper, we prove the existence result for FIGDI under the non-convex case of multi-valued function involved in it.Using random fixed point theorem of B. C. Dhage and caratheodory condition. This result is new to the theory of differential inclusion.

Keywords: caratheodory condition, random differential inclusion, random solution, integro-differential inclusion

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Authors: A. Kellou, L. Rouaiguia, L. Rabahi

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In the present work, the atomic hydrogen absorption trend in the GdNi5 and GdNi4Cu rare earth compounds within the hexagonal CaCu5 type of crystal structure (space group P6/mmm) is investigated. The density functional theory (DFT) combined with the generalized gradient approximation (GGA) is used to study the site preference of atomic hydrogen at 0K. The octahedral and tetrahedral interstitial sites are considered. The formation energies and structural properties are determined in order to evaluate hydrogen effects on the stability of the studied compounds. The energetic diagram of hydrogen storage is established and compared in GdNi5 and GdNi4Cu. The magnetic properties of the selected compounds are determined using spin polarized calculations. The obtained results are discussed with and without hydrogen addition taking into account available theoretical and experimental results.

Keywords: density functional theory, hydrogen storage, rare earth compounds, structural and magnetic properties

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Authors: Shahed Vahedi, Mohd Salmi Md Noorani

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This paper investigates cluster synchronization phenomena between community networks. We focus on the situation where a variety of dynamics occur in the clusters. In particular, we show that different synchronization states simultaneously occur between the networks. The controller is designed having an adaptive control gain, and theoretical results are derived via Lyapunov stability. Simulations on well-known dynamical systems are provided to elucidate our results.

Keywords: cluster synchronization, adaptive control, community network, simulation

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Authors: Nada Souissi, Mourad Mroua

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The main purpose of this study is to examine the interaction between financial asset volatility, economic factors and investor's behavioral indicators related to both the company's and the markets stocks for the period from January 2000 to January2020. Using multiple linear regression and Bayesian Networks modeling, results show a positive and negative relationship between investor's psychology index, economic factors and predicted stock market return. We reveal that the application of the Bayesian Discrete Network contributes to identify the different cause and effect relationships between all economic, financial variables and psychology index.

Keywords: Financial asset return predictability, Economic factors, Investor's psychology index, Bayesian approach, Probabilistic networks, Parametric learning

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Authors: Wen-Bin Liau, Wan-Ting Wang, Chiang-Jen Hsiao, Sheng-Mao Tseng

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In this paper, an interesting and easy method to prepare one-dimensional nanostructured polyaniline/dendritic silver composites is reported. It is well known that the morphology of metal particle is a very important factor to influence the properties of polymer-metal composites. Usually, the dendritic silver is prepared by kinetic control in reduction reaction. It is not a thermodynamically stable structure. It is the goal to reduce silver ion to dendritic silver by polyaniline polymer via kinetic control and form one-dimensional nanostructured polyaniline/dendritic silver composites. The preparation is a two steps sequential reaction. First step, the polyaniline networks composed of nano fibrillar polyaniline are synthesized from aniline monomers aqueous with ammonium persulfate as the initiator at room temperature. In second step, the silver nitrate is added into polyaniline networks dispersed in deionized water. The dendritic silver is formed via reduction by polyaniline networks under the kinetic control. The formation of polyaniline is discussed via transmission electron microscopy (TEM). Nanosheets, nanotubes, nanospheres, nanosticks, and networks are observed via TEM. Then, the mechanism of formation of one-dimensional nanostructured polyaniline/dendritic silver composites is discussed. The formation of dendritic silver is observed by TEM and X-ray diffraction.

Keywords: 1D nanostructured polyaniline, dendritic silver, synthesis

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Authors: Farouk Shehu Abdulwahab

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The concept of farmer-farmer knowledge sharing was introduced to bridge the extension worker-farmer ratio gap in developing countries. However, the idea was poorly accepted, especially in typical agrarian communities. Therefore, the study explores the concept of a farmer-to-farmer knowledge-sharing network to enhance extension service delivery. The study collected data from 80 farmers randomly selected through a series of multiple stages. The Data was analysed using a 5-point Likert scale and descriptive statistics. The Likert scale results revealed that 62.5% of the farmers are satisfied with farmer-to-farmer knowledge-sharing networks. Moreover, descriptive statistics show that lack of capacity building and low level of education are the most significant problems affecting farmer-farmer sharing networks. The major implication of these findings is that the concept of farmer-farmer knowledge-sharing networks can work better for farmers in developing countries as it was perceived by them as a reliable alternative for information sharing. Therefore, the study recommends introducing incentives into the concept of farmer-farmer knowledge-sharing networks and enhancing the capabilities of farmers who are opinion leaders in the farmer-farmer concept of knowledge-sharing to make it more sustainable.

Keywords: agricultural productivity, extension, farmer-to-farmer, livelihood, technology transfer

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Authors: Derya Nizam

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An alternative understanding of "localization" has gained significance as the ecological and social issues associated with the growing pressure of agricultural homogeneity and standardization become more apparent. Through an analysis of a case study on an alternative food networks in Turkey, this research seeks to critically examine the localization movement. The results indicate that the idea of localization helps to create new niche markets by creating place-based labels, but it also strengthens local identities through social networks that connect rural and urban areas. In that context, localization manifests as a commodification movement that appropriates local and cultural values to generate capitalist profit, as well as a grassroots movement that strengthens the resilience of local communities. This research addresses the potential of community development approaches in the democratization of global agro-food networks.

Keywords: community building, local food, alternative food movements, localization

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Authors: J. Prathap Kumar, G. Vaitheeswaran

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The present study investigates the role of ammonium and potassium ion in the electronic, bonding and optical properties of dinitramide salts due to their stability and non-toxic nature. A detailed analysis of bonding between NH₄ and K with dinitramide, optical transitions from the valence band to the conduction band, absorption spectra, refractive indices, reflectivity, loss function are reported. These materials are well known as oxidizers in solid rocket propellants. In the present work, we use full potential linear augmented plane wave (FP-LAPW) method which is implemented in the Wien2k package within the framework of density functional theory. The standard DFT functional local density approximation (LDA) and generalized gradient approximation (GGA) always underestimate the band gap by 30-40% due to the lack of derivative discontinuities of the exchange-correlation potential with respect to an occupation number. In order to get reliable results, one must use hybrid functional (HSE-PBE), GW calculations and Tran-Blaha modified Becke-Johnson (TB-mBJ) potential. It is very well known that hybrid functionals GW calculations are very expensive, the later methods are computationally cheap. The new developed TB-mBJ functionals use information kinetic energy density along with the charge density employed in DFT. The TB-mBJ functionals cannot be used for total energy calculations but instead yield very much improved band gap. The obtained electronic band gap at gamma point for both the ammonium dinitramide and potassium dinitramide are found to be 2.78 eV and 3.014 eV with GGA functional, respectively. After the inclusion of TB-mBJ, the band gap improved by 4.162 eV for potassium dinitramide and 4.378 eV for ammonium dinitramide. The nature of the band gap is direct in ADN and indirect in KDN. The optical constants such as dielectric constant, absorption, and refractive indices, birefringence values are presented. Overall as there are no experimental studies we present the improved band gap with TB-mBJ functional following with optical properties.

Keywords: ammonium dinitramide, potassium dinitramide, DFT, propellants

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Authors: Vaidehi Iyer, Konstantin Borozdin

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Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

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Authors: Jaime E. Fernandez, Pablo J. Valverde

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This paper presents a methodological approach that aims to contrast/validate theoretical results from a corruption network game through probabilistic analysis of simulated microdata using Bayesian Networks (BNs). The research develops a public corruption model in a game theory framework. Theoretical results suggest a series of 'optimal settings' of model's exogenous parameters that boost the emergence of corruption. The paper contrasts these outcomes with probabilistic inference results based on BNs adjusted over simulated microdata. Principal findings indicate that probabilistic reasoning based on BNs significantly improves parameter specification and causal analysis in a public corruption game.

Keywords: Bayesian networks, probabilistic reasoning, public corruption, theoretical games

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Authors: Ameen Jameel Alawneh

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A Mobile ad-hoc network (MANET) is a collection of wireless mobile hosts that dynamically form a temporary network without the aid of a system administrator. It has neither fixed infrastructure nor wireless ad hoc sessions. It inherently reaches several nodes with a single transmission, and each node functions as both a host and a router. The network maybe represented as a set of clusters each managed by clusterhead. The cluster size is not fixed and it depends on the movement of nodes. We proposed a clusterhead size algorithm (CHSize). This clustering algorithm can be used by several routing algorithms for ad hoc networks. An elected clusterhead is assigned for communication with all other clusters. Analysis and simulation of the algorithm has been implemented using GloMoSim networks simulator, MATLAB and MAPL11 proved that the proposed algorithm achieves the goals.

Keywords: simulation, MANET, Ad-hoc, cluster head size, linked cluster algorithm, loss and dropped packets

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Authors: Semih Demir, Anil Celebi

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Circuit switched networks widely used until the end of the 20^th century have been transformed into packages switched networks. Time Division Multiplexing over Internet Protocol (TDMoIP) is a system that enables Time Division Multiplexing (TDM) traffic to be carried over packet switched networks (PSN). In TDMoIP systems, devices that send TDM data to the PSN and receive it from the network must operate with the same clock frequency. In this study, it was aimed to implement clock synchronization process in Field Programmable Gate Array (FPGA) chips using time information attached to the packages received from PSN. The designed hardware is verified using the datasets obtained for the different carrier types and comparing the results with the software model. Field tests are also performed by using the real time TDMoIP system.

Keywords: clock recovery on TDMoIP, FPGA, MATLAB reference model, clock synchronization

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Authors: Morteza Keshavarz

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In this work, using density functional theory (DFT) thermodynamic stability and quantum molecular descriptors of cyclophoshphamide (an anticancer drug)-functionalized zigzag (8,0) CNT, armchair (4,4) CNT and nanocone complexes in water, for two attachment namely the sidewall and tip, is considered. Calculation of the total electronic energy (Et) and binding energy (Eb) of all complexes indicates that the most thermodynamic stability belongs to the sidewall-attachment of cyclophosphamide into functional nanocone. On the other hand, results from chemical hardness show that drug-functionalized zigzag (8,0) and armchair (4,4) complexes in the tip-attachment configuration possess the smallest and greatest chemical hardness, respectively. By computing the solvation energy, it is found that the solution of the drug and all complexes are spontaneous in water. Furthermore, chirality, type of nanovector (nanotube or nanocone), or attachment configuration have no effects on solvation energy of complexes.

Keywords: carbon nanotube, drug delivery, cyclophosphamide drug, density functional theory (DFT)

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Authors: Y. Kourd, D. Lefebvre, N. Guersi

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This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty behaviors Models (NNFM's). NNFM's are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it becomes possible to take the appropriate decision regarding the actual process behavior by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.

Keywords: neural networks, decision trees, diagnosis, behaviors

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Authors: Riyadh Alsultani, Ali Majdi

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It is necessary to develop a design methodology that includes the possibility of seismic events occurring in a region, the vulnerability of the civil hydraulic structure, and the effects of the occurrence hazard on society, environment, and economy in order to evaluate the flood and earthquake risks of coastal bridge networks. This paper presents a design approach for the assessment of the risk and sustainability of coastal bridge networks under time-variant flood-earthquake conditions. The social, environmental, and economic indicators of the network are used to measure its sustainability. These consist of anticipated loss, downtime, energy waste, and carbon dioxide emissions. The design process takes into account the possibility of happening in a set of flood and earthquake scenarios that represent the local seismic activity. Based on the performance of each bridge as determined by fragility assessments, network linkages are measured. The network's connections and bridges' damage statuses after an earthquake scenario determine the network's sustainability and danger. The sustainability measures' temporal volatility and the danger of structural degradation are both highlighted. The method is shown using a transportation network in Baghdad, Iraq.

Keywords: sustainability, Coastal bridge networks, flood-earthquake risk, structural design

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Authors: Juan Sebastián Hernández

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The Nevado del Ruíz volcano, located between the limits of the Departments of Caldas and Tolima in Colombia, presented an unstable behaviour in the course of the year 2020, this volcanic activity led to secondary effects on the crust, which is why the prediction of deformations becomes the task of geoscientists. In the course of this article, the use of tropospheric variables such as evapotranspiration, UV aerosol index, carbon monoxide, nitrogen dioxide, methane, surface temperature, among others, is used to train a set of neural networks that can predict the behaviour of the resulting phase of an unrolled interferogram with the DInSAR technique, whose main objective is to identify and characterise the behaviour of the crust based on the environmental conditions. For this purpose, variables were collected, a generalised linear model was created, and a set of neural networks was created. After the training of the network, validation was carried out with the test data, giving an MSE of 0.17598 and an associated r-squared of approximately 0.88454. The resulting model provided a dataset with good thematic accuracy, reflecting the behaviour of the volcano in 2020, given a set of environmental characteristics.

Keywords: crustal deformation, Tropomi, neural networks (ANN), volcanic activity, DInSAR

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Authors: Shiyin He, Zheng Huang

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In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable.

Keywords: cell detection, cell recognition, deep learning, Mask-RCNN, ResNet

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Authors: Massimiliano Nastri

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The study examines the façade systems according to the constitutive and typological characters, as well as the functional and applicative requirements such as the expressive, constructive, and interactive criteria towards the environmental, perceptive, and energy conditions. The envelope systems are understood as instruments of mediation, interchange, and dynamic interaction between environmental conditions. The façades are observed for the sustainable concept of eco-efficient envelopes, selective and multi-purpose filters, adaptable and adjustable according to the environmental performance.

Keywords: typologies of façades, environmental and energy sustainability, interaction and perceptive mediation, technical skins

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Authors: Mahmoud Meckawey

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This research is dealing with one of the most important aspects of maintenance fields, that is Maintenance Strategy. It's the branch which concerns the concepts and the schematic thoughts in how to manage maintenance and how to deal with the defects in the engineering products (buildings, machines, etc.) in general. Through the papers we will act with the followings: i) The Engineering Product & the Technical Systems: When we act with the maintenance process, in a strategic view, we act with an (engineering product) which consists of multi integrated systems. In fact, there is no engineering product with only one system. We will discuss and explain this topic, through which we will derivate a developed definition for the maintenance process. ii) The factors or basis of the functionality efficiency: That is the main factors affect the functional efficiency of the systems and the engineering products, then by this way we can give a technical definition of defects and how they occur. iii) The legality of occurrence of defects (Legal defects and Illegal defects): with which we assume that all the factors of the functionality efficiency been applied, and then we will discuss the results. iv) The Guarantee, the Functional Span Age and the Technical surplus concepts: In the complementation with the above topic, and associated with the Reliability theorems, where we act with the Probability of Failure state, with which we almost interest with the design stages, that is to check and adapt the design of the elements. But in Maintainability we act in a different way as we act with the actual state of the systems. So, we act with the rest of the story that means we have to act with the complementary part of the probability of failure term which refers to the actual surplus of the functionality for the systems.

Keywords: engineering product and technical systems, functional span age, legal and illegal defects, technical and functional surplus

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Authors: Mohammad Mehdizadeh

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COVID-19 forced marketers to change their marketing strategies, focusing less on reactive approaches and more on proactive approaches, primarily social media. The next few years will be dominated by employee engagement and customer experience, leading to businesses focusing more on "long-term customer relationships." A large number of marketing strategies need to be employed in an ever-evolving online environment, which is both filled with opportunities and dangers, as well as being an intimidating platform to use, incorporating new and exciting opportunities for businesses and organizations as it constantly evolves. In this article, we examine the effect of social networks on marketing in post-pandemic environments. A descriptive survey is used as the research method. The results show that social networks have a positive and significant impact on marketing in a post-pandemic environment. Among the social networks studied, Instagram, Facebook, and Twitter have the most positive effect on marketing advancement.

Keywords: COVID-19, customers, marketing, post-pandemic

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Authors: Umair Shafique, Hafiz Usman Zia, Fiaz Majeed, Samina Naz, Javeria Ahmed, Maleeha Zainab

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The Internet of Things (IoT) has become a hot topic for the last couple of years. This innovative technology has shown promising progress in various areas, and the world has witnessed exponential growth in multiple application domains. Researchers are working to investigate its aptitudes to get the best from it by harnessing its true potential. But at the same time, IoT networks open up a new aspect of vulnerability and physical threats to data integrity, privacy, and confidentiality. It's is due to centralized control, data silos approach for handling information, and a lack of standardization in the IoT networks. As we know, blockchain is a new technology that involves creating secure distributed ledgers to store and communicate data. Some of the benefits include resiliency, integrity, anonymity, decentralization, and autonomous control. The potential for blockchain technology to provide the key to managing and controlling IoT has created a new wave of excitement around the idea of putting that data back into the hands of the end-users. In this manuscript, we have proposed a model that combines blockchain and IoT networks to address potential security and privacy issues in the healthcare domain. Then we try to describe various application areas, challenges, and future directions in the healthcare sector where blockchain platforms merge with IoT networks.

Keywords: IoT, blockchain, cryptocurrency, healthcare, consensus, data

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Authors: Mikel Alonso López

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In the first decade of the twenty-first century, advanced imaging techniques began to be applied for neuroscience research. The Functional Magnetic Resonance Imaging (fMRI) is one of the most important and most used research techniques for the investigation of emotions, because of its ease to observe the brain areas that oxygenate when performing certain tasks. In this research, we make a review about the main research carried out on the influence of the emotions in the decision-making process that is exposed by using the fMRI.

Keywords: decision making, emotions, fMRI, consumer behaviour

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Authors: Chen Xi, Lao Xuerui, Li Junjie, Jiang Yike, Wang Hanwei, Zeng Zihao

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behaviour recognition models, to provide empirical data such as 'pedestrian flow data and human behavioural characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, convolutional neural network

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Authors: Radhika Ranjan Roy

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Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

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Authors: Alya A. Arabi

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Bioisosteres are functional groups that can be interchangeably used without affecting the potency of the drug. Bioisosteres have similar pharmacological properties. Bioisosterism is useful for modifying the physicochemical properties of a drug while obeying the Lipinski’s rules. Bioisosteres are key in optimizing the pharmacokinetic and pharmacodynamics properties of a drug. Tetrazole and carboxylate anions are non-classic bioisosteres. Density functional theory was used to obtain the wavefunction of the molecules and the optimized geometries. The quantum theory of atoms in molecules (QTAIM) was used to uncover the similarity of the average electron density in tetrazole and carboxylate anions. This similarity between the bioisosteres capped by a methyl group was valid despite the fact that the groups have different volumes, charges, energies, or electron populations. The biochemical correspondence of tetrazole and carboxylic acid was also determined to be a result of the similarity of the topography of the electrostatic potential (ESP). The ESP demonstrates the pharmacological and biochemical resemblance for a matching “key-and-lock” interaction.

Keywords: bioisosteres, carboxylic acid, density functional theory, electrostatic potential, tetrazole

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Authors: Angela Justina Kumalaputri

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Indonesia, as a maritime country, has abundant marine living resources yet has not been optimally utilized. So far, we only focusing on fisheries. In the other hand, Indonesia, as the country with the fourth longest coastline, is a very good cultivation place for microalgae. Microalgae can be diversified to many important products, such as food, fuel, pharmaceutical products, functional food, and cosmetics.This research is focusing on the literature study about types of microalgae as sources for functional foods (such as antioxidants), including the contents and the separation methods. The research methods which we use are: (1) Literature study about various microalgaes (2) Literature study about extractions using supercritical fluid of CO₂, which are free from toxic organic solvents, environmentally friendly, and safe for food products. Supercritical fluid extraction using CO₂ (low critical points: temperature at 31.1 oC and pressure at 72.9 bars) could be done at a low temperature which are suitable for temperature labile compounds, low energy, and faster extraction time compared with conventional method of extraction.

Keywords: antioxidants, supercritical fluid extraction, solvent-free extraction, microalgae

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Authors: Nicole Pearcy, Jonathan J. Crofts, Nadia Chuzhanova

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In this paper, we employ a directed hypergraph model to investigate the extent to which environmental variability influences the set of available biochemical reactions within a living cell. Such an approach avoids the limitations of the usual complex network formalism by allowing for the multilateral relationships (i.e. connections involving more than two nodes) that naturally occur within many biological processes. More specifically, we extend the concept of network reciprocity to complex hyper-networks, thus enabling us to characterize a network in terms of the existence of mutual hyper-connections, which may be considered a proxy for metabolic network complexity. To demonstrate these ideas, we study 115 metabolic hyper-networks of bacteria, each of which can be classified into one of 6 increasingly varied habitats. In particular, we found that reciprocity increases significantly with increased environmental variability, supporting the view that organism adaptability leads to increased complexities in the resultant biochemical networks.

Keywords: complexity, hypergraphs, reciprocity, metabolism

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