Scarcity Problem Research Articles

SAM-GAN: An improved DCGAN for rice seed viability determination using near-infrared hyperspectral imaging

Viability is a significant indicator of rice seeds, affecting rice yield and quality. Existing viability determination methods cannot meet the requirements of rapidity, non-destructive and accuracy. In this study, near-infrared hyperspectral imaging was used to detect the viability of natural aging seeds. Generative Adversarial Network (GAN) is the main means of coping with Few-shot learning. Considering that natural aging seed samples were difficult to obtain and the number was scarce, this study used Spectral Angle Mapper Generative Adversarial Network (SAM-GAN) to generate rice seed spectral data based on the spectra of obtained natural aging seeds to solve the problem of sample scarcity. SAM-GAN is based on Deep Convolution GAN (DCGAN), introduced by SAM. SAM-GAN was compared with Wasserstein Generative Adversarial Nets with Gradient Penalty (WGAN-GP) and DCGAN, and the Convolutional Neural Network (CNN) model was established by three modeling methods: real data modeling, fake data modeling and mixed modeling of real data and fake data. The experimental results show that the accuracy of the CNN model established by mixing real data with fake data generated by SAM-GAN reaches nearly 100%. This study provides an effective method for rapid, non-destructive and accurate determination of rice seed viability with a limited sample number.

Refined self-attention mechanism based real-time structural response prediction method under seismic action

Accurate prediction of structural response under earthquake is of great significance for structural damage and performance evaluation. In order to improve the efficiency of structure time history response prediction, this paper proposes a novel SeisFormer model based on the self-attention mechanism and deep learning technology. Through autoregressive prediction, the SeisFormer can achieve real-time prediction of the response time histories of a large number of nodes in the structure under seismic action and can effectively solve the problem of data scarcity. Four case studies are performed to verify the accuracy and efficiency of the proposed methodology, including validation on datasets obtained from elastoplastic seismic analysis of a single-story structure, a three-story structure, and an eleven-story structure, and measured data of a shaking table test model. In addition, this paper further studies the prediction accuracy of the SeisFormer through ablation experiments and comparative experiments. The experimental results show that the SeisFormer can accurately predict the acceleration, velocity, and displacement time histories of numerous nodes in the structure. The prediction accuracy outperforms the LSTM model, and the prediction speed is 193–109,824 times faster than finite element method. Furthermore, with data augmentation through autoregressive prediction, the SeisFormer model can achieve efficient and accurate predictions when training data is exceptionally scarce, enabling engineering applications.

ProteinMAE: masked autoencoder for protein surface self-supervised learning.

The biological functions of proteins are determined by the chemical and geometric properties of their surfaces. Recently, with the booming progress of deep learning, a series of learning-based surface descriptors have been proposed and achieved inspirational performance in many tasks such as protein design, protein-protein interaction prediction, etc. However, they are still limited by the problem of label scarcity, since the labels are typically obtained through wet experiments. Inspired by the great success of self-supervised learning in natural language processing and computer vision, we introduce ProteinMAE, a self-supervised framework specifically designed for protein surface representation to mitigate label scarcity. Specifically, we propose an efficient network and utilize a large number of accessible unlabeled protein data to pretrain it by self-supervised learning. Then we use the pretrained weights as initialization and fine-tune the network on downstream tasks. To demonstrate the effectiveness of our method, we conduct experiments on three different downstream tasks including binding site identification in protein surface, ligand-binding protein pocket classification, and protein-protein interaction prediction. The extensive experiments show that our method not only successfully improves the network's performance on all downstream tasks, but also achieves competitive performance with state-of-the-art methods. Moreover, our proposed network also exhibits significant advantages in terms of computational cost, which only requires less than a tenth of memory cost of previous methods. https://github.com/phdymz/ProteinMAE.

Evaluation of water productivity of smallholder irrigation along a Sand River, in the Maigobo watershed of northern Ethiopia

Sand dams are a type of rainwater harvesting technology that can increase groundwater recharge in dry land environments. They are constructed across seasonal sandy rivers and trap sand and water during floods. The sand acts as a natural filter and storage medium for the water, which can be extracted by the local communities through wells, pumps, or scoop holes. This research aims to evaluate agricultural water productivity based on water consumption (ETa) and biomass production of smallholder irrigation schemes along a sand river with a sand dam in the riverbeds of northern Ethiopia. Remotely sensed satellite data, WaPOR (Water Productivity through Open access of Remotely sensed derived data) database was used to estimate water productivity in the arid and semi-arid area. Following the installation of the sand dam at the irrigated parts of the Mai Gobo watershed, both the annual and seasonal values increased over a number of years, according to the spatial analysis of ETa and biomass production. For instance, the maximum seasonal ETa before the dam was about 70 mm/season, whereas after the dam the spatially analysed ETa reached 174 mm/season. Likewise, maximum seasonal biomass production of 1117 kg/ha and 4433 kg/ha were obtained before and after the construction of the sand dam, respectively. Agricultural water productivity at the irrigated plots of the Mai Gobo watershed was also improved from 1.4 kg/m3 to 2.9 kg/m3 before and after the sand dam respectively. Generally, the output of the study can be used as an input for the concerned stakeholder to take measures against the water scarcity problem in the study area. The study also contributes to the scientific knowledge and understanding of the hydrological and agronomical impacts of the sand dam in the arid and semi-arid regions.

A QoS-aware framework for spectrum characterization and switching decision in cognitive radio networks

The increasing demand for seamless wireless services coupled with underutilization of limited spectrum due to fixed channel allocation policy have posed numerous challenges to wireless communications. There is need to design appropriate channel allocation strategies for continuous communication by mobile users for higher spectral efficiency. Cognitive radio network (CRN) offers a solution to the spectrum scarcity problem inherent in 4G and other networks through dynamic spectrum access, by allowing unlicensed secondary users (SUs) with cognitive devices to opportunistically access the spectrum holes when the licensed primary users (PUs) are not occupying them. Often times, measurements taken by SUs during the sensing process are uncertain due to multipath fading, shadowing and varying channel conditions. This results in imprecise spectrum detection and selection by SUs during switching decisions causing incessant spectrum handoff and undesirable ping-pong effect. In this paper, a support vector machine (SVM) classifier is used to categorize spectrum into two classes of busy and idle. Then, based on heterogeneous quality of service (QoS) requirements of the SUs, dynamic activities of PUs, and the fluctuating channel state information, a QoS-aware Adaptive Neuro-Fuzzy Inference System (ANFIS) framework is developed for spectrum switching decision using underlay spectrum access model. SVM predicted spectrum holes with 98.8% accuracy. ANFIS model yielded a 91.62% accuracy in the task of allocating spectrum holes to SUs for coexistent with PUs. Results further indicate that the intelligent framework can ensure fairness among SUs, reduce interference, improve throughput, and spectral efficiency. It can be deployed in disaster relief and emergency, public safety, and battlefield environments.

Crack modeling via minimum-weight surfaces in 3d Voronoi diagrams

As the number one building material, concrete is of fundamental importance in civil engineering. Understanding its failure mechanisms is essential for designing sustainable buildings and infrastructure. Micro-computed tomography (μCT) is a well-established tool for virtually assessing crack initiation and propagation in concrete. The reconstructed 3d images can be examined via techniques from the fields of classical image processing and machine learning. Ground truths are a prerequisite for an objective evaluation of crack segmentation methods. Furthermore, they are necessary for training machine learning models. However, manual annotation of large 3d concrete images is not feasible. To tackle the problem of data scarcity, the image pairs of cracked concrete and corresponding ground truth can be synthesized. In this work we propose a novel approach to stochastically model crack structures via Voronoi diagrams. The method is based on minimum-weight surfaces, an extension of shortest paths to 3d. Within a dedicated image processing pipeline, the surfaces are then discretized and embedded into real μCT images of concrete. The method is flexible and fast, such that a variety of different crack structures can be generated in a short amount of time.

PH-responsive color indicator films based on chitosan and purple yam extract for in-situ monitoring food freshness

The development and utilization of abundant biomass resources is a promising solution to the problems of fossil resource scarcity and the difficulty in the biodegradation of waste faced by traditional food packaging films. Novel biomass-based films are not only biodegradable but can also be endowed with a variety of functional characteristics, including smart pH indicating film that can show color changes according to pH changes during food storage to achieve real-time visual detection of the degree of food spoilage, which is intuitive, sensitive, and convenient compared to traditional food packaging films. In this work, an environmentally friendly smart pH indicating film was prepared by incorporating natural polymer chitosan (CS) with anthocyanin-rich purple yam extract (PYE). The effect of different additions (1.5, 3, 4.5, 6 wt%) of PYE on the mechanical properties, water vapor barrier properties, antioxidant, antibacterial and pH sensitivity properties of chitosan films were investigated. The results showed that the films supplemented with 4.5% and 6% PYE demonstrated higher antioxidant capacity and antibacterial properties, while showing a more pronounced color change in different pH buffers. The results of FT-IR, scanning electron microscopy and X-ray diffraction analysis indicated the presence of hydrogen bonding between CS and PYE molecules. The application of films for in-situ monitoring the freshness of food (e.g., shrimp) was also studied, and films with PYE additions between 3% and 6% all showed visible color changes. Consequently, CS-PYE films are of great potential as smart and active packaging materials for keeping and monitoring the freshness of foods.

Recent Climate Change in the Republic of Srpska and Bosnia and Herzegovina

The paper gives an overview of the observed climate change in the Republic of Srpska and Bosnia and Herzegovina; primarily the changes in air temperature and precipitation regime. In the period 1961‒2015 the positive trends of mean, maximum and minimum annual and seasonal air temperatures were determined, which were most pronounced in the summer season, whereas in the autumn season there is only a slight upward trend of temperatures. Warming is most pronounced in the northern part of the territory, whereas the increase is much lower in the higher parts of the Herzegovina region. Unlike air temperature, precipitation trends are, both seasonally and regionally, very variable and of different signs (positive and negative), but mostly insignificant. However, in some areas, the interannual variability of precipitation and the frequency of extreme precipitation have increased. In addition, the paper presents the results of research on possible climate change (based on expected changes in air temperature and precipitation) by the end of the 21st century in the Republic of Srpska and Bosnia and Herzegovina according to the climate scenario RCP8.5. The results indicate more pronounced climate change related to increasing annual temperatures up to 5 °C and reducing annual precipitation by up to 30%, whereas the deficit in the summer season (June, July and August) could go up to 40% by the end of the 21st century. Such changes point to the serious problem of drought and water scarcity and the need for more significant integration of climate change issues into planning and strategic documents.

Performance analysis and techno-economic assessment of a developed cooling/preheating small PVT-RO desalination plant

Middle East and North Africa (MENA) countries are experiencing rapid population growth, so water and electricity consumption plays a crucial role in the sustainable development of these countries. To overcome the water scarcity and electricity problems facing the MENA region, the developed cooling/preheating small PVT-RO desalination plants have been proposed as a practical solution. To achieve sustainable water and energy development in the MENA region, this study presents a commendable and highly efficient renewable energy project for freshwater production and electricity generation to solve the energy crisis and water scarcity in the MENA countries. Therefore, this study aims to develop a cooling/preheating small PVT-RO desalination plant to facilitate freshwater supply to remote regions and produce electricity. This was done by connecting photovoltaic/thermal (PVT) collectors with reverse osmosis (RO) desalination systems, where seawater is used as a medium to cool photovoltaic cells to increase electric power generation and at the same time recover thermal energy and use it in the initial heating of feed seawater before it is fed into the RO plants, thus increasing its productivity. The results indicate that using the photovoltaic thermal panels as preheating units will lead to a 0.135 kWh/m3 reduction in the rate of specific electricity consumption for the RO desalination plant, as well as increase the electricity generation from PVT panels by a rate of 8%. The economic feasibility presented that the proposed developed cooling/preheating small PVT-RO desalination plant represents an effective technology that reduced the freshwater cost by a rate of 49.5%.

Evaluating Mesh Geometry and Shade Coefficient for Fog Harvesting Collectors

The most valuable resource for sustaining life on earth is water. In dry and semi-arid areas, the problem of water scarcity can be resolved with the aid of fog collection techniques employing fog collectors. Fog collection is greatly influenced by a variety of factors. Some are design parameters, while others depend on ambient circumstances. Geometry and the mesh’s shade coefficient are important design factors that can be modified and have an impact on the rate at which fog collects in fog collectors. The shape of the mesh holes and the process used to create the mesh serve to identify geometry and measure the shade coefficient. In this paper, a straightforward mathematical technique is proposed to make it easier to calculate the shade coefficient of various mesh shapes used in fog harvesting and to provide an approximation of the mesh volume and cost. Five alternative geometries were used: the rectangular mesh, square mesh, Raschel mesh, triangular mesh, and hexagonal mesh. The current simple method will facilitate the design of the fog mesh collector and can assist in achieving the ideal shade coefficient and most effective mesh geometry for fog harvesting. Rectangular meshes were solely used as an example to evaluate the results. Stainless steel rectangular meshes with various shade coefficients were tested for fog collection, and the amount of water collected by each mesh varied. It was concluded that the optimum shade coefficient ranged 50–60% for fog collection.

Assessment of Climate Change’s Impact on Flow Quantity of the Mountainous Watershed of the Jajrood River in Iran Using Hydroclimatic Models

Rivers are the main source of fresh water in mountainous and downstream areas. It is crucial to investigate the possible threats of climate change and understand their impact on river watersheds. In this research, climate change’s impact on the mountainous watershed of the Jajrood River, upstream of Latyan Dam in Iran, was assessed by using a multivariate recursive quantile-matching nesting bias correction (MRQNBC) and the soil and water assessment tool (SWAT). Also, this study considered ten global circulation models (GCMs) from the coupled model intercomparison project phase VI (CMIP6). With a higher correlation coefficient, the MIROC6 model was selected among other models. For the future period of 2031–2060, the large-scale outputs of the MIROC6 model, corresponding to the observational data were extracted under four common socioeconomic path scenarios (SSPs 1–2.6, 2–4.5, 3–7.0, 5–8.5). The bias was corrected and downscaled by the MRQNBC method. The downscale outputs were given to the hydrological model to predict future flow. The results show that, in the period 2031–2060, the flow will be increased significantly compared to the base period (2005–2019). This increase can be seen in all scenarios. In general, changes in future flow are caused by an increase in precipitation intensity, as a result of an increase in temperature. The findings indicate that, although the results show an increase in the risk of flooding, considering the combined effects of three components, i.e., increased precipitation concentration, temperature, and reduced precipitation, climate change is intensifying the problem of water scarcity.

Using Augmented Small Multimodal Models to Guide Large Language Models for Multimodal Relation Extraction

Multimodal Relation Extraction (MRE) is a core task for constructing Multimodal Knowledge images (MKGs). Most current research is based on fine-tuning small-scale single-modal image and text pre-trained models, but we find that image-text datasets from network media suffer from data scarcity, simple text data, and abstract image information, which requires a lot of external knowledge for supplementation and reasoning. We use Multimodal Relation Data augmentation (MRDA) to address the data scarcity problem in MRE, and propose a Flexible Threshold Loss (FTL) to handle the imbalanced entity pair distribution and long-tailed classes. After obtaining prompt information from the small model as a guide model, we employ a Large Language Model (LLM) as a knowledge engine to acquire common sense and reasoning abilities. Notably, both stages of our framework are flexibly replaceable, with the first stage adapting to multimodal related classification tasks for small models, and the second stage replaceable by more powerful LLMs. Through experiments, our EMRE2llm model framework achieves state-of-the-art performance on the challenging MNRE dataset, reaching an 82.95% F1 score on the test set.

Water and Sanitation Services at the Local Government Level in Bangladesh: An Analysis of SDG 6 Implementation Status and Way Forward

This study looks into the state of SDG 6 implementation at the local government level in Rajshahi City Corporation (RCC) with a focus on water and sanitation services. The research adopts a mixed-method approach and in-depth interviews to collect primary data. The study analyses the collected data using both quantitative and qualitative techniques. It has been found that large numbers of people do not always have access to enough water, demonstrating the problem of water scarcity in the region. According to the study, only a small percentage of participants tested their drinking water, which suggests that participants were not aware of the importance of water quality. Additionally, the lack of personal water purification and reuse systems is revealed by the research, indicating a gap in sustainable water management practices. Most of the city dwellers expressed dissatisfaction with the authority’s management of the water supply, indicating insufficient oversight and governance. The findings highlight the need for improved infrastructures, water treatment techniques, education campaigns, and governance mechanisms to effectively meet SDG 6 targets and guarantee residents of RCC access to clean water and sanitary facilities.

Evaluation of the Providing Extension Services to Vegetable Growers in the Field of Rationalizing Irrigation Water in Baghdad Province

The aim of research is to Evaluating the extension service provided to vegetable growers in the field of rationalizing irrigation water, as well as identifying the problems facing vegetable growers in this field. The Baghdad governorate was chosen as a region to conduct the research because it is one of the governorates that are interested in growing vegetable crops. In order to achieve the objectives of the research, a plan was prepared as a result of the evaluation study of the extension service an introduction to vegetable growers in the field of irrigation water rationalization, consisting of 47 paragraphs distributed over 5 areas (Organization, Planning, Implementation, Monitoring, and Evaluation). A random sample of 50 vegetable growers was selected at a rate of 2% of the total vegetable growers in the research governorate, totaling 3350 farmers, to study the problems they face in the field of rationalizing irrigation water according to a quartet scale consisting of 30 items distributed over 5 areas: (climatic problems, financial problems, technology problems, extension activities problems, equipment problems). The research concluded that there is a weakness in the extension service provided to vegetable growers in the field of rationalizing irrigation water, as well as the absence of effective plans strategies by the relevant authorities in facing the problem of scarcity of irrigation water and the advancement of the agricultural reality in the country, and accordingly the researcher recommends the relevant authorities to achieve food security and security water sector by developing long-term strategies to confront climatic changes that have a direct impact on the problem of water scarcity in the country.

SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data.

Medical genomics faces significant challenges in interpreting disease phenotype and genetic heterogeneity. Despite the establishment of standardized disease phenotype databases, computational methods for predicting gene-phenotype associations still suffer from imbalanced category distribution and a lack of labeled data in small categories. To address the problem of labeled-data scarcity, we propose a self-supervised learning strategy for gene-phenotype association prediction, called SSLpheno. Our approach utilizes an attributed network that integrates protein-protein interactions and gene ontology data. We apply a Laplacian-based filter to ensure feature smoothness and use self-supervised training to optimize node feature representation. Specifically, we calculate the cosine similarity of feature vectors and select positive and negative sample nodes for reconstruction training labels. We employ a deep neural network for multi-label classification of phenotypes in the downstream task. Our experimental results demonstrate that SSLpheno outperforms state-of-the-art methods, especially in categories with fewer annotations. Moreover, our case studies illustrate the potential of SSLpheno as an effective prescreening tool for gene-phenotype association identification. https://github.com/bixuehua/SSLpheno.

Network of Water Problems in the Press of Mexico City During the COVID-19 Era

The pandemic generated containment and mitigation policies, as well as distancing and confinement strategies that limited the supply of water resources to social sectors. Residential areas- The offer is maintained, but with an increase in rates. Marginalized areas were subsidized and exempted from paying for an increasingly intermittent supply. The anti-COVID-19 policies guided water policies in two ways: The first consisted of disseminating anti-COVID-19 policies in water management agencies. Another second consisted of the autonomy of the institutions and their disassociation or agreement with the anti-COVID-19 policies. In this way, the literature from 2020 to 2023 around anti-COVID-19 policies in their water dimensions, registers problems of scarcity, famine and unsanitary conditions. Scarcity had already been observed in marginalized sectors, famine in residential neighborhoods, but unsanitary conditions were appreciated in migrant communities. In fact, the type of exposure to occupational hazards determined the health status of the migrants. The water problems were recorded in the circulation press to highlight the asymmetries of the anti-COVID-19 policies in the public and private sectors, as well as in the political and social actors. The objective of the study was to reveal the network structure of relationships between nodes and edges related to press releases on water issues. A documentary, cross-sectional and retrospective study was carried out with national circulation newspapers: El País, El Reforma, La Jornada and El Universal, considering the water problems of scarcity, unsanitary conditions and famine. The results show a structure of nodes where the water problems were initiated by La Jornada and ended by El Reforma. Both findings are relevant considering the ideology of the newspaper. La Jornada, a newspaper identified with the political ideology of the left, began the diffusion of water problems in a city administered by a government of the same ideology. El Reforma, a newspaper designated by the executive as a spokesperson for the opposition ideology, culminates the network of notes on water problems. In other words, regardless of the type of political ideology attributed to newspapers, the problems of scarcity, unsanitary conditions and famine are spread. In relation to the state of the art where it is shown that ideology does not influence the establishment of the agenda, the present work corroborates and recommends expanding the study to other entities administered by the opposition such as the cities of Guadalajara and Monterrey.

Out-of-vocabulary word embedding learning based on reading comprehension mechanism

Currently, most natural language processing tasks use word embeddings as the representation of words. However, when encountering out-of-vocabulary (OOV) words, the performance of downstream models that use word embeddings as input is often quite limited. To solve this problem, the latest methods mainly infer the meaning of OOV words based on two types of information sources: the morphological structure of OOV words and the contexts in which they appear. However, the low frequency of OOV words themselves usually makes them difficult to learn in pre-training tasks by general word embedding models. In addition, this characteristic of OOV word embedding learning also brings the problem of context scarcity. Therefore, we introduce the concept of “similar contexts” based on the classical “distributed hypothesis” in linguistics, by borrowing from the human reading comprehension mechanisms to make up for the deficiency of insufficient contexts in previous OOV word embedding learning work. The experimental results show that our model achieved the highest relative scores in both intrinsic and extrinsic evaluation tasks, which demonstrates the positive effect of the “similar contexts” introduced in our model on OOV word embedding learning.

Use of Structural Equation Modelling and Neural Network to Analyse Shared Parking Choice Behaviour

The shared parking mode represents a feasible solution to the persistent problem of parking scarcity in urban areas. This paper aims to examine the shared parking choice behaviours using a combination of structural equation modelling (SEM) and neural network, taking into account both the parking location characteristics and the travellers’ characteristics. Data were collected from a commercial district in Nanjing, China, through an online questionnaire survey covering 11 factors affecting shared parking choice. The method involved two steps: firstly, SEM was applied to examine the influence of these factors on shared parking choice. Following this, the seven factors with the strongest correlation to shared parking choice were used to train a neural network model for shared parking prediction. This SEM-informed model was found to outperform a neural network model trained on all eleven factors across precision, recall, accuracy, F1 and AUC metrics. The research concluded that the selected factors significantly influence shared parking choice, reinforcing the hypothesis regarding the importance of parking location and traveller characteristics. These findings provide valuable insights to support the effective implementation and promotion of shared parking.

Relationship between the trait response of aboveground and belowground parts of dominant plant species to groundwater depth change in Horqin Sandy Land, eastern China

Climate change and human development will lead to an increase in groundwater levels. This will have a particularly severe impact on arid ecosystems that are highly dependent on groundwater, as overconsumption and pollution of water resources can lead to faster depletion of groundwater. This not only exacerbates the problem of groundwater scarcity, but also may have profound impacts on local ecological environment and biodiversity. However, previous research has focused on aboveground trait responses to precipitation deficits, with less emphasis on concomitant belowground impacts and deep soil drought from groundwater depletion. Therefore, it remains unclear how groundwater depletion affects above and belowground traits of plant to jointly regulate aboveground biomass. In this study, we assessed morphological and ecophysiological traits of two dominant species (herb: Pennisetum centrasiaticum and shrub: Artemisia halodendron) across three groundwater depth (50 cm, 100 cm, and 200 cm) in the Horqin Sandy Land in China, within which photosynthetic rate (Photo), stomatal conductance (Cond), transpiration rate (Tr), internal CO2 concentration (Ci), instantaneous water-use efficiency of leaves (Lwue), total nitrogen (LN) and phosphorus (LP) content of the leaves, root to shoot ratio (RSR), root length density (RLD), total root length (RTL), root length ratio (RLR) were measured. The result showed that the two species growing with increased groundwater depth had reduced photosynthetic rate, and regulated stomatal close to aggregate ability of instantaneous water use efficiency, which may allow plants to meet resource demands under drought stress avoiding transpiration water loss. Plants also reduced the density of all roots and adjusted the vertical distribution of coarse root by changing biomass distribution to acquire deeper soil available water. Correlation analysis showed that the vital traits for aboveground biomass responses appear to be groundwater depth, soil moisture, leaf area, photosynthesis, water use efficiency, and root traits. Overall, the biomass production response of two dominant species may be greatly influenced by the interaction between aboveground and belowground traits in response to changes in groundwater depth. This result contributes to our understanding of adaptive strategies of vegetation in sandy soils and provides additional support for enhancing vegetation productivity in semi-arid ecosystems. The ability of semi-arid ecosystems to predict vegetation dynamics under current and future increasing groundwater depths is crucial.

New Construction of Asynchronous Channel Hopping Sequences in Cognitive Radio Networks.

The channel-hopping-based rendezvous is essential to alleviate the problem of under-utilization and scarcity of the spectrum in cognitive radio networks. It dynamically allows unlicensed secondary users to schedule rendezvous channels using the assigned hopping sequence to guarantee the self-organization property in a limited time. In this paper, we use the interleaving technique to cleverly construct a set of asynchronous channel-hopping sequences consisting of d sequences of period xN2 with flexible parameters, which can generate sequences of different lengths. By this advantage, the new designed CHSs can be used to adapt to the demands of various communication scenarios. Furthermore, we focus on the improved maximum-time-to-rendezvous and maximum-first-time-to-rendezvous performance of the new construction compared to the prior research at the same sequence length. The new channel-hopping sequences ensure that rendezvous occurs between any two sequences and the rendezvous times are random and unpredictable when using licensed channels under asynchronous access, although the full degree-of-rendezvous is not satisfied. Our simulation results show that the new construction is more balanced and unpredictable between the maximum-time-to-rendezvous and the mean and variance of time-to-rendezvous.