Network Programming Research Articles

Reversible Shape-Shifting of an Ionic Strength Responsive Hydrogel Enabled by Programmable Network Anisotropy.

Reversible shape-shifting hydrogels exhibit great potential in diverse fields. Repeatable programmability for the shape transformation has newly been enabled in thermally responsive hydrogels via engineering of the chain orientation of the polymer network, which substantially promotes the transformation capability. However, diversified responsive behavior and the enabling mechanism require further investigation. Herein, we develop an ionic strength (IS) responsive hydrogel enabling the programmable reversible shape transformation based on a semi-interpenetrating network of poly(acrylic acid) (PAA) and poly(vinyl alcohol) (PVA). Deformation of the hydrogel upon external force can be fixed due to crystallization of PVA that underwent cyclic freezing-thawing. Therefore, the chain orientation can be retained in the deformed area, enabling the programmable IS responsive actuation. In contrast to the thermally responsive actuation originated from the lower critical solution temperature phase transition, the IS responsive actuation does not accompany any phase change and the corresponding mechanism is proposed. Reversible bending providing an actuation angle as large as 80° can be achieved after optimization of the PVA content. The PVA crystals can be melted upon heating, and the responsive actuation can thus be reprogrammed. In addition, utilizing a digital light 3D printer, the hydrogels are further fabricated into arbitrary geometries, thus realizing more complex actuations. Overall, our work provides a general strategy to develop reversible shape-shifting hydrogels and paves the way for soft actuators.

Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks

Taxonomic classification, that is, the assignment to biological clades with shared ancestry, is a common task in genetics, mainly based on a genome similarity search of large genome databases. The classification quality depends heavily on the database, since representative relatives must be present. Many genomic sequences cannot be classified at all or only with a high misclassification rate. Here we present BERTax, a deep neural network program based on natural language processing to precisely classify the superkingdom and phylum of DNA sequences taxonomically without the need for a known representative relative from a database. We show BERTax to be at least on par with the state-of-the-art approaches when taxonomically similar species are part of the training data. For novel organisms, however, BERTax clearly outperforms any existing approach. Finally, we show that BERTax can also be combined with database approaches to further increase the prediction quality in almost all cases. Since BERTax is not based on similar entries in databases, it allows precise taxonomic classification of a broader range of genomic sequences, thus increasing the overall information gain.

Digital Innovation Hubs: Two-mode and Network-Based View on Technology and Services Provided

The Digital Innovation Hub (DIH) is a relatively young research area emerging on the basis of well-known ideas of technology parks or clusters. DIH is mainly focused on the digital transformation of enterprises and the development of a given region in the context of Industry 4.0 based on digital technology. The European Union is the initiator of the "Digital Europe" program for 2021-2027, aiming to promote the transfer of digital specialist knowledge to economic entities. The authors chose the DIHs operating in Poland as a starting point for network analysis in the context of the technology and services provided by Polish DIHs. This research aims to measure network cohesion, the centrality of individual DIH, and its specialty (exclusivity). It is based on secondary data collected using the Smart Specialization Platform based on which the two two-mode networks have been developed: actor x technology (ATij) and actor x services provided (ASPij). Measurements and visualization were made using two network programs (UCINET and ORA PRO). The following social network analysis techniques were used: 1) Two-mode cohesion: density, diameter, and fragmentation; 2) Two-mode centrality: degree and betweenness, and 3) Two-mode specialty (exclusivity). The results show two-mode networks consisting of the following nodes: 15 actors (A) or DIHs; 29 technologies (T), and 16 services provided (SP) forming a meta-network. They provide an insight into the network structure, most influential actors (DIHs), technologies, and services, as well as the specialty of each DIH in the European country, thus giving rise to further comparative research, using secondary data for the other 26 countries of the European Union.

Machine Learning-Based Fragility Assessment of Reinforced Concrete Buildings.

In the past, large earthquakes caused the collapse of infrastructure and killed thousands of people in Pakistan, a seismically active region. Therefore, the seismic assessment of infrastructure is a dire need that can be done using the fragility analysis. This study focuses on the fragility analysis of school buildings in Muzaffarabad district, seismic zone-4 of Pakistan. Fragility curves were developed using incremental dynamic analysis (IDA); however, the numerical analysis is computationally time-consuming and expensive. Therefore, soft computing techniques such as Artificial Neural Network (ANN) and Gene Expression Programming (GEP) were employed as alternative methods to establish the fragility curves for the prediction of seismic performance. The optimized ANN model [5-25-1] was used. The feedforward backpropagation network was considered in this study. To achieve a reliable model, 70% of the data was selected for training and 15% for validation and 15% of data was used for testing the model. Similarly, the GEP model was also employed to predict the fragility curves. The results of both ANN and GEP were compared based on the coefficient of determination, R 2. The ANN model accurately predicts the global drift values with R 2 equal to 0.938 compared to the GEP model having R 2 equal to 0.87.

The Integration of the "Floop" Platform for Enhancing the Effectiveness of Feedback in the Postsecondary Education Context

During the pandemic period, teaching has become increasingly challenging. One of the hardest parts of educating is considered providing effective feedback which will enhance students’ comprehension and lead them to learner autonomy. Firstly, while studying online there is less teacher-student interaction and secondly, students are not sure how to use provided feedback. The “FLOOP” feedback platform was found very helpful for solving both of these issues. It was used for OPEN (Online Professional English Network Program) teacher development sessions and was integrated into online teacher training that was called “Teaching process writing with the integration of technologies for online lessons in postsecondary education.” The basic reason for it is that it saves the teacher’s time by saving ready templates for the same tasks and it can be replied to on spot. There is a possibility of providing peer feedback for students which would be helpful for student feedback literacy and assist them to develop their self-study skills. This platform is extremely efficient to facilitate one of the most challenging teaching contexts, specifically large multilevel classes. Therefore, it might contribute to the professional expertise in providing constructive feedback for all the teachers around the world.

A novel AI approach for modeling land surface temperature of Freetown, Sierra Leone, based on land-cover changes

ABSTRACT Land use/land cover (LULC) indices can be considered while developing land surface temperature (LST) models. The relationship between LST and LULC indices must be established to accurately estimate the impacts of LST changes. This study developed novel machine learning models for predicting LST using multispectral Landsat images data of Freetown city in Sierra-Leon. Artificial neural network (ANN) and gene expression programming (GEP) were employed to develop LST prediction models. Images of multispectral bands were obtained from Landsat 4-5 and 8 satellites to develop the proposed models. The extracted data of LULC indices, such as normal difference vegetation index (NDVI), normal difference built-up index (NDBI), urban index (UI), and normal difference water index (NDWI), were utilized as attributes to model LST. The results show that the root-mean-square error (RMSE) of the ANN and GEP models were 0.91oC and 1.08 oC, respectively. The GEP model was used to yield a relationship between LULC indices and LST in the form of a mathematical equation, which can be conveniently used to test new data regarding the thematic area. The sensitivity analysis revealed that UI is the most influential parameter followed by NDBI, NDVI, and NDWI towards contributing LST.

Shattering the second glass ceiling: an empirical study on women entrepreneurs in Egypt

PurposeThe purpose of this paper is to analyze women's entrepreneurship in Egypt by examining their drivers, motives and challenges while applying a gender-lens perspective. This research tries to close the gaps in previous research and determine the impact of systematic gendered policies in empowering women's entrepreneurship and in return, aid in the overall economic growth and development nation-wide. This paper addresses one main question: How can the Egyptian government empower women entrepreneurs through key policy interventions?Design/methodology/approachThe research paper is based on quantitative methodology using three sets of the latest data available from Egypt's Global Entrepreneurship Monitor Report for 2019 and 2018, in addition to the Egyptian Labor Market Panel Survey of 2018. It examines the probability for women to become entrepreneurs in Egypt's current ecosystem. This allowed for a better contextualization and understanding of the nature of developing Egyptian female-led entrepreneurial ventures.FindingsThe findings of this research emphasized the importance of the existence of policies for training, networking and educational programs tailored for women. In addition to, the unique needs for women entrepreneurs when it comes to funding and applying for loans. Such needs can determine the amounts of funding given out to women, while setting out a repayment policy that is well-designed for women entrepreneurs, ensuing a further discussion of the policies and their implications.Originality/valueThis paper uses and incorporates a variety of data sets that are very focused and specialized in the Egyptian context. Through which the paper aims to inform and analyze the current environment, challenges and opportunities that women entrepreneurs are operating – in Egypt. In addition, this paper provides a set of actionable policy recommendations to strengthen the government's role in empowering women entrepreneurs in Egypt.

Quantitative Trading Model of Two Shares Based on NARX Neural Network and Dynamic Programming

This paper focuses on the quantitative trading of two shares based on historical data. In modern financial research, quantitative transaction is an important research topic. With the rapid development of the financial market, it is of great practical significance to accurately predict the future data through historical data and make reasonable decisions. This paper aims to help traders improve their decision-making ability and optimize asset allocation by building a quantitative trading decision-making model. This paper analyzes the advantages and disadvantages of the model, and proves that the established model has high robustness, accuracy and popularization significance. According to the experimental results and research conclusions, the memorandum provides information and suggestions for investors' investment choices.

Organizationally Imprinted Networks and Gender Disparities in the Labor Market

How do organizations gender network hiring? There is ample research suggesting that network hiring contributes to gendered outcomes for workers and organizations and that the hiring organization is key to this process. However, the hiring firm is not the only relevant organizational context for labor market networks, since workers’ past labor market experiences are likely to have shaped the networks they carry forward as they search for new employment opportunities. The settings in which workers originally formed connections may therefore also influence the value of job movers’ contacts. This paper uses administrative data on job moves across the Danish labor market from 2014-2016 and an innovative matching strategy to examine how the gender composition of two organizations - hiring establishments and the establishments in which a mover and their contact previously worked together - influences the value of contacts. We find that ties to male managers are particularly useful overall, and in both organizational settings, ties are most useful when employees are in the gender majority. Our findings show how successfully managing a diverse workforce is important for the long-term careers of workers and suggests that access to authority figures should be a priority of organizational networking and mentorship programs.

How Women Use Network Strategies to Break Male Elite Cliques and to be Elected on Venture Boards

Based upon a qualitative study of 120 interviews and a statistical analysis of women active in the board scene around VC-backed ventures, we contrast previous claims and the agenda of network programs by and for women who highlight fighting network disadvantages by homophily. By forming cliques with other women, minority disadvantages persist, and these networks are losing rather than gaining efficiency. We outline how women networking with males, and how alternative networking styles break network cliques of men that act as barriers to women. Such networking strategies also contribute to progressive change in the total network reducing stereotypes that are restricting women entrepreneurs.

Research on an Extensible Monitoring System of a Seafloor Observatory Network in Laizhou Bay

An extensible remote monitoring system for a seafloor observatory network in Laizhou Bay was established to realize long-term, continuous and on-line monitoring for a marine ranching environment. This paper deals with data communication, device management and data quality control. A control model is introduced that is structured into four layers, enabling bidirectional information flow. Based on the control model, the standardized communication protocol and device object model-oriented dynamic management method are designed as plug-and-play, for data processing and control of a large number of devices. An improved data quality control method is proposed to reduce the data error rate. The monitoring system was developed based on socket network programming, MySQL database technologies and modular ideas. The seafloor observatory network was successfully deployed in Laizhou Bay marine ranching. The experimental results demonstrate that the monitoring system obtains better performance. The proposed algorithms can also be used in many other similar systems with adaptive requirements.

Network Topology Classification in SDN Ecosystem using Machine Learning

To meet the increasing network demands of enterprise environments and data centers, traditional network architectures have been replaced by software-enabled hardware devices for developing agile, dynamic, and programmable networks. Software Defined Networking (SDN) is a new paradigm shift that abstracts network design and infrastructure in the software and then implements it by using software across hardware devices. SDN is used to build and manage the network in a customized way. SDN architecture offers network virtualization, network programmability, and flexibility by decoupling control and data planes which further enriches the network performance. As such, the SDN controller is a tactical control point in SDN. An SDN normally allows data flow control to the switches and/or routers and the rationale of the application's logic for deploying intelligent networks. SDN with Machine Learning (ML) and Artificial Intelligence (AI) techniques build network models, which essentially can take decisions based on self-learning and self-management capabilities. Accurate classification of topology is of prime importance to satisfy future network prerequisites like unpredictable traffic patterns, dynamic scaling, flexibility, and centralized control. The controller needs to have exact information about the topology of the network in order to configure and manage the network. Subsequently, topology classification is an important component of any Software Defined Network architecture. This paper presents the classification of topologies using different supervised ML algorithms. The accuracy obtained from Support Vector Machine (SVM) and Classification and Regression (CART) is 95% and 90% respectively. The experimental results show that according to the k Cross-Fold validation technique, SVM algorithm has been found to be the most accurate amongst the other ML algorithms with a mean accuracy value of 85%.

Issues and approaches in aquaculture of rural ponds of Southern West Bengal with introspection over the years

Utilization and aquaculture scenarios of rural ponds of various sizes in West Bengal especially southern West Bengal have been investigated and observed for more than sixty years. These ponds are of various shapes, sizes, and depths, and used by the rural people for domestic, agricultural irrigation, pisciculture, livelihood dependency, firefighting, and community wellbeing. During the post-independence period, these ponds have undergone succession of shifting attention and shared interests towards their service to the rural community and the society. In recent years, these ponds suffer shrinkage due to silt accumulation and waste disposal, becoming increasingly semi-derelict and derelict, and encroachment continued rampant. Productivity as well as aesthetic values has gone down along with the increase in pollution and environmental degradation. Indigenous management option of Parha Level Pond Care (PLPC) aqua cultural production with annual management action plan at the village level local unit (Parha / Ward/ Neighborhood) in conjunct with local Panchayat is suggested for agri-piscicultural integration with Integrated Area Development Network Programme (IADNP) at the Block Development level.

A novel method to find the best path in SDN using firefly algorithm

Abstract Over the previous three decades, the area of computer networks has progressed significantly, from traditional static networks to dynamically designed architecture. The primary purpose of software-defined networking (SDN) is to create an open, programmable network. Conventional network devices, such as routers and switches, may make routing decisions and forward packets; however, SDN divides these components into the Data plane and the Control plane by splitting distinct features away. As a result, switches can only forward packets and cannot make routing decisions; the controller makes routing decisions. OpenFlow is the communication interface between the switches and the controller. It is a protocol that allows the controller to identify the network packet’s path across the switches. This project uses the SDN environment to implement the firefly optimization algorithm to determine the shortest path between two nodes in a network. The firefly optimization algorithm was implemented using Ryu control. The results reveal that using the firefly optimization algorithm improves the selected short path between the source and destination.

Perceptions of workplace climate and diversity, equity, and inclusion within health services and policy research.

To describe the perception of professional climate in health services and policy research (HSPR) and efforts to advance diversity, equity, and inclusion (DEI) in the HSPR workforce and workplaces. We administered the HSPR Workplace Culture Survey online to health services and policy researchers. Our survey examined participants' sociodemographic, educational, and professional backgrounds, their perception on DEI in HSPR, experience with DEI initiatives, feeling of inclusion, and direct and witnessed experiences of discrimination at their institutions/organizations. We calculated sample proportions of responses by gender identity, sexual orientation, race/ethnicity, and disability status and compared them with Fisher's exact test. We administered the survey online from July 28 to September 4, 2020. HSPR professionals and trainees aged 18 and older were eligible to participate. Analyses used complete cases only (n=906; 70.6% completion rate). 53.4% of the participants did not believe that the current workforce reflects the diversity of communities impacted by HSPR. Although most participants have witnessed various DEI initiatives at their institutions/organizations, nearly 40% characterized these initiatives as "tokenistic." Larger proportions of participants who identified as female, LGBQI+, underrepresented racial/ethnic groups, and those with a disability held this perception than their male, heterosexual, White, and non-disabled counterparts. Current DEI initiatives focused on "planning" activities (e.g., convening task forces) rather than "implementation" activities (e.g., establishing mentoring or network programs). 43.7% of the participants felt supported on their career development, while female, Black, Hispanic/Latino, LGBQI+ participants and those with a disability experienced discrimination at their workplace. Despite an increasing commitment to increasing the diversity of the HSPR workforce and improving equity and inclusion in the HSPR workplace, our results suggest that there is more work to be done to achieve such goals.

A comprehensive survey on software‐defined networking for smart communities

SummaryThe need to provide services closer to the end‐user proximity leads to the exchange of a large volume of data generated from the smart devices deployed at different geo‐distributed sites. The massive amount of data generated from the smart devices need to be transmitted, analyzed, and processed. This requires seamless data exchanges among geo‐separated nodes, which results in a considerable burden on the underlying network infrastructure and can degrade the performance of any implemented solution. Therefore, a dynamic, agile, and programmable network management paradigm is required. To handle the challenges mentioned above, software‐defined networking (SDN) gained much attention from academia, researchers, and industrial sectors. Shifting the computational load from forwarding devices to a logically centralized controller is a dream of every network operator who wants to have complete control and global visibility of the network. Also, the concept of network functions virtualization (NFV) in SDN controller is required to increase resource utilization efficiency. Thus, in this paper, a comprehensive survey on SDN for various smart applications is presented. This survey covers the infrastructural details of SDN hardware and OpenFlow switches, controllers, simulation tools, programming languages, open issues, and challenges in SDN implementation with advanced technologies such as 5G and microservices. In addition, the challenges on the control plane and data plane are highlighted in detail, such as fault tolerance, routing, scheduling of flows, and energy consumption on OpenFlow switches. Finally, various open issues and challenges future scope of SDN are discussed and analyzed in the proposal.

Research on Sustainable Development Indicator Prediction of Regional Economic Subsystem Based on Artificial Neural Network

Sustainable development system is a “black box,” or at least a “grey box,” and it is not very clear whether there is a causal relationship between various factors. Artificial neural network can simulate the system realistically. In this paper, the artificial neural network BP model is used as a tool, and Matlab language is used to predict the indexes of regional sustainable development economic subsystem. Through data collection, network structure model design, program design, and network training, this study drew the indicator forecast chart, analyzes it, and finally puts forward suggestions. The results show that it is feasible to use an artificial neural network to simulate a sustainable economic subsystem.

Random Fourier Approximation of the Kernel Function in Programmable Networks

Abstract Random Fourier features represent one of the most influential and wide-spread techniques in machine learning to scale up kernel algorithms. As the methods based on random Fourier approximation of the kernel function can overcome the shortcomings of machine learning methods that require a large number of labeled sample, it is effective to be applied to the practical areas where samples are difficult to obtain. Network traffic forwarding policy making is one such practical application, and it is widely concerned in the programmable networks. With the advantages of kernel techniques and random Fourier features, this paper proposes an application of network traffic forwarding policy making method based on random Fourier approximation of kernel function in programmable networks to realize traffic forwarding policy making to improve the security of networks. The core of the method is to map traffic forwarding features to Hilbert high-dimensional space through random Fourier transform, and then uses the principle of maximum interval to detect adversarial samples. Compared with the traditional kernel function method, it improves the algorithm efficiency from square efficiency to linear efficiency. The AUC on the data set from real-world network reached 0.9984, showing that the method proposed can realize traffic forwarding policy making effectively to improve the security of programmable networks.

How is the situation of women in leadership positions in medical education in Germany?

In Germany, about two thirds of students and doctoral candidates in medicine are female. The proportion is only about 35% for post-doctoral degrees and much lower for many leadership positions at medical schools and on medical education committees. Although reasons for this have long been known, changes are slow in coming. Therefore, with this commentary, we would like to shed light on the current situation regarding gender equality in Germany in medical education and identify and discuss measures. These include, for example, mentoring and networking programs as well as greater consideration of women in committees.

Optimal Trading Strategy Based on BP Neural Network and Dynamic Programming Model

In order to avoid the risks brought by blind investment, it is necessary to forecast the trading price of gold and bitcoin on that trading day before investment. To aim at this prediction problem, this paper puts forward the price prediction model of the BP neural network. The experimental results show that the model can reasonably predict the trading price of gold and bitcoin on the next trading day, and the relative error between them is less than 10%.However, predicting the trading price of gold and bitcoin can roughly determine the future direction, and it cannot provide scientific and reasonable trading strategies for traders, so it is necessary to design a trading strategy planning model. Considering that every trading activity except holding financial products will charge additional trade costs, the trade costs of gold and bitcoin are 1% and 2% of the trade amount, respectively. By comparing various classic trading models, we propose to use a dynamic programming model to provide traders with effective trading strategies, which takes the trading changes of gold and bitcoin in the next three trading days as decision variables. Meanwhile, it takes the highest value of the Sharp ratio in the next trading day as the objective function. It takes the proportion of each part as equal to or greater than 0 as the constraint condition to establish a trading strategy planning model to obtain the maximum profit.