Hydrogels Are Reinforced with Colombian Fique Nanofibers to Improve Techno-Functional Properties for Agricultural Purposes

Colombia is the world’s largest producer of fique fibers (Furcraea bedinghausii), with a net production of 30,000 tons per year. This work proposes to revalue waste from the Colombian fique agroindustry. For this purpose, cellulose nanofibers were obtained from fique and used as reinforcement material to create acrylic superabsorbent hydrogels. Unreinforced acrylic hydrogels (AHR0) and acrylic hydrogels reinforced with fique nanofibers at 3% w/w (AHR3), 5% w/w (AHR5), and 10 % w/w (AHR10) were synthesized using the solution polymerization method. The best hydrogel formulation for agricultural purposes was chosen by comparing their swelling behavior, mechanical properties, and using scanning electron microscopy (SEM). By raising the nanofiber concentration to 3% (AHR3), the best-chosen formulation, the interaction between the nanofibers and the polymer matrix increased, which favored the network stability. However, beyond AHR3, there was a higher viscosity of the reactive system, which caused a reduction in the mobility of the polymer chains, thus disfavoring the swelling capacity. The reinforced hydrogel proposed in this study (AHR3) could represent a contribution to overcoming the problems of land dryness present in Colombia, an issue that will worsen in the coming years due to the climate emergency.

Negative tension controls stability and structure of intermediate filament networks

Networks, whose junctions are free to move along the edges, such as two-dimensional soap froths and membrane tubular networks of endoplasmic reticulum are intrinsically unstable. This instability is a result of a positive tension applied to the network elements. A paradigm of networks exhibiting stable polygonal configurations in spite of the junction mobility, are networks formed by bundles of Keratin Intermediate Filaments (KIFs) in live cells. A unique feature of KIF networks is a, hypothetically, negative tension generated in the network bundles due to an exchange of material between the network and an effective reservoir of unbundled filaments. Here we analyze the structure and stability of two-dimensional networks with mobile three-way junctions subject to negative tension. First, we analytically examine a simplified case of hexagonal networks with symmetric junctions and demonstrate that, indeed, a negative tension is mandatory for the network stability. Another factor contributing to the network stability is the junction elastic resistance to deviations from the symmetric state. We derive an equation for the optimal density of such networks resulting from an interplay between the tension and the junction energy. We describe a configurational degeneration of the optimal energy state of the network. Further, we analyze by numerical simulations the energy of randomly generated networks with, generally, asymmetric junctions, and demonstrate that the global minimum of the network energy corresponds to the irregular configurations.

Semi-Adaptive Evolution with Spontaneous Modularity of Half-Chaotic Randomly Growing Autonomous and Open Networks

Up until now, studies of Kauffman network stability have focused on the conditions resulting from the structure of the network. Negative feedbacks have been modeled as ice (nodes that do not change their state) in an ordered phase but this blocks the possibility of breaking out of the range of correct operation. This first, very simplified approximation leads to some incorrect conclusions, e.g., that life is on the edge of chaos. We develop a second approximation, which discovers half-chaos and shows its properties. In previous works, half-chaos has been confirmed in autonomous networks, but only using node function disturbance, which does not change the network structure. Now we examine half-chaos during network growth by adding and removing nodes as a disturbance in autonomous and open networks. In such evolutions controlled by a ‘small change’ of functioning after disturbance, the half-chaos is kept but spontaneous modularity emerges and blurs the picture. Half-chaos is a state to be expected in most of the real systems studied, therefore the determinants of the variability that maintains the half-chaos are particularly important in the application of complex network knowledge.

Enhancing Route lifetime in Vehicular Ad Hoc ‎Networks Based on Skellam Distribution Model

The emergence of smart cities and the need to use intelligent transportation systems has led to an increased reliance on vehicle ad hoc networks (VANET). The topology of VANET is highly dynamic, which results in a short effective routing time. This paper presents a two-stage algorithm to select a route that can sustain communication between vehicles for as long as possible while taking into account the variables that affect the VANET topology. The first stage uses Skellam distribution model to assess the connectivity probability of paths in ‎a 2d road network based on traffic-flow and the number of vehicles ‎joining and leaving the ‎network, accordingly, the path with the highest connectivity is chosen. In the second stage, the control packets sent only to vehicles on the selected path to detect routes between source and destination, thus reducing the overhead of control packets and increasing network stability. ‎ the algorithm adopts the principle of global evaluation to ‎estimate the lifetime ‎of the ‎detected ‎routes within the chosen path. ‎the route with the ‎best estimated ‎lifetime ‎is ‎chosen to be ‎the active route. ‎in the event of route failure, the validity of the next route in lifetime is confirmed to be adopted as the alternate route. The proposed algorithm was compared with both on-‎demand distance ‎vector routing protocol (AODV) protocol and the modified location-aided routing ‎‎(LAR) ‎protocol. The proposed algorithm showed greater network stability, higher performance in terms of longer lifetime route detection, less energy consumption and higher throughput.

The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study

A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by 25.8% and data throughput by 26.86%. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.

Anxiety and depressive symptoms in college students during the late stage of the COVID-19 outbreak: a network approach

Mental health problems are common in college students even in the late stage of the coronavirus disease 2019 (COVID-19) outbreak. Network analysis is a novel approach to explore interactions of mental disorders at the symptom level. The aim of this study was to elucidate characteristics of depressive and anxiety symptoms network in college students in the late stage of the COVID-19 outbreak. A total of 3062 college students were included. The seven-item Generalized Anxiety Disorder Scale (GAD-7) and nine-item Patient Health Questionnaire (PHQ-9) were used to measure anxiety and depressive symptoms, respectively. Central symptoms and bridge symptoms were identified based on centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. The strongest direct relation was between anxiety symptoms “Nervousness” and “Uncontrollable worry”. “Fatigue” has the highest node strength in the anxiety and depression network, followed by “Excessive worry”, “Trouble relaxing”, and “Uncontrollable worry”. “Motor” showed the highest bridge strength, followed by “Feeling afraid” and “Restlessness”. The whole network was robust in both stability and accuracy tests. Central symptoms “Fatigue”, “Excessive worry”, “Trouble relaxing” and “Uncontrollable worry”, and critical bridge symptoms “Motor”, “Feeling afraid” and “Restlessness” were highlighted in this study. Targeting interventions to these symptoms may be important to effectively alleviate the overall level of anxiety and depressive symptoms in college students.

Influence of Scientific Collaboration Network on Academic Performance

Collaboration in science is a complex phenomenon that affects scientific performance in various ways. Thus, understanding the influences of the research collaboration network is important for researchers. This paper explores the relationship between research collaboration network structural and scientific research performance and conducts an empirical test with data from 416 scholars. Findings revealed that network stability reduces the scholars' research performance, and network centrality promotes research performance. The network structural holes that the scholar spans, moderate the detrimental effects of network stability. This research provides suggestions for scholars to build a reasonable scientific research collaboration network to improve their research performance.