Network Strengths Research Articles

Intrinsic functional connectivity mediates the effect of personality traits on depressive symptoms.

Personality traits have been proposed as risk factors for depressive symptoms. However, the neural mechanism behind these relationships is unclear. This study examined the possible mediating effect of resting-state functional connectivity networks on these relationships. Data from 153 healthy Germans were obtained from the MPI-Leipzig Mind-Brain-Body: Neuroanatomy & Connectivity Protocol database. Network-based statistics were used to identify significant functional connectivity networks that were positively and negatively associated with the personality traits of neuroticism, conscientiousness, and extraversion, with and without demographical covariates. Mediation analyses were performed for each personality trait and depressive symptoms with the significant positive and negative network strengths of the respective personality traits as mediators. Neuroticism, conscientiousness, and extraversion were significantly correlated with depressive symptoms. Network-based statistics identified patterns of functional connectivity that were significantly associated with neuroticism and conscientiousness. After controlling for demographical covariates, significant conscientiousness-associated and extraversion-associated networks emerged. Mediation analysis concluded that only the neuroticism-positive network mediated the effect of neuroticism on depressive symptoms. When age and sex were controlled, the extraversion-positive network completely mediated the effect of extraversion on depressive symptoms. These findings revealed that patterns of intrinsic functional networks predict personality traits and suggest that the relationship between personality traits and depressive symptoms may in part be due to their common patterns of intrinsic functional networks.

Strategi Peningkatan Efisiensi Dan Efektivitas Operasi Agrorelasi

This research aims to analyze the business development strategy of an agricultural community, namely Agrorelasi, through SWOT analysis. The research method used was descriptive, with primary and secondary data collection. The results showed that Agrorelasi has strengths in large mass numbers, a lot of networking, actors have extensive knowledge and specific expertise, have a lot of collaboration, and commitment to sustainability. The suggested strategy is to integrate network strengths, optimize sustainability commitments and build strategic partnerships. Overall, the research suggests Agrorelations leverage internal strengths, minimize weaknesses with external support, and build cross-sector collaboration to improve efficiency and effectiveness.

Development strategy of halal tourism village in Gedepangrango tourism village, Sukabumi district

Tourism is an industry that significantly contributes to the country's economy. Halal tourism is a type of tourism with promising potential. The number of tourism villages with halal concepts in Indonesia is still limited. However, Gedepangrango Tourism Village shows potential for development as a halal tourism village. This study analyses the strategy for developing a halal tourism village in Gedepangrango Tourism Village, Sukabumi Regency, utilizing the Analytical Network Process (ANP) method and Strengths, Weaknesses, Opportunities, and Threats (SWOT) approaches. The study's findings indicate that strengths, weaknesses, and opportunities significantly impact the development of a halal tourism village in Gedepangrango Tourism Village. The analysis of strengths, weaknesses, opportunities, and threats reveals the tourism potential in agriculture, nature, and culture within Gedepangrango Tourism Village. However, governance in the village has not been optimal. Notably, the development plans for the halal tourism village correspond to the Regional Medium-Term Development Plan of Sukabumi Regency. Additionally, several other tourist village objects present competition. The primary focus lies in raising human resources' capacity to develop halal tourism villages.

Exploratory Study of Multidimensionality Elements of Small Entrepreneurs' Network Change and Development in the Southern Region of Malaysia

The research aims to empirically explore the multidimensionality elements in small entrepreneurs' network change and development during periods when the entrepreneurs face tipping points. Prior scholars have examined the mix of strong and weak links in small businesses and linked them to business development stages. For small firms to continue experiencing business growth, current literature must emphasize the connection between network strengths and overcoming tipping points. This research explores the multidimensional elements of small entrepreneurs' network change and development that contribute to their survival during the era of tipping points in the Southern Region of Malaysia. This qualitative research uses a multiple case study approach involving eight small firms in the southern region of Malaysia. The data collection methods include analysis of secondary documentation and in-depth interviews with the entrepreneurs using the critical incident interview technique. The data has been analyzed using NVIVO software and has employed Miles and Huberman's (1994) flows of analysis activity. Data triangulation was carried out by interviewing individuals who were seen as having strong or weak ties within the network and who were identified through interviews with the entrepreneurs. The findings enlighten two themes: the entrepreneur-level context and the dyadic-level context. The findings show that the multidimensionality of the individual entrepreneur is demonstrated through their demographic factors, personal values, experience, and capabilities. Therefore, they differ in responding to changes in their networks. Dyadic multidimensionality is found in the tie's content, structure, governance, and the dyadic life cycle. Finally, this research provides theoretical and methodological contributions and offers implications for entrepreneurs, government agencies and policy-makers.

Mechanical properties and micro-mechanism of cement-based materials strengthened by in-situ organic-inorganic polymerization

Ordinary cement-based materials face defects in practical applications, such as poor tensile strength, brittleness, and high cracking risk. Compound modification has great potential to improve cement-based material structural and mechanical performance. In this paper, cement-based materials with a three-dimensional (3D) plug-in network structure are formed by in-situ organic-inorganic polymerization. Partial acrylamide (AM) monomer is inserted into the zirconium phosphate (ZrP) interlayer, and zirconium phosphate-acrylamide (ZrP-AM) composite materials are successfully prepared. During cement hydration, AM undergoes in-situ self-polymerization and co-polymerization with ZrP-AM, forming a complete plug-in network with ZrP distributed on the polymer network. Meanwhile, the plug-in network structure is closely bound to the cement matrix due to the formation of chemical bonds. In the best mix ratio design, the ZrP-PAM-modified sample with 1 wt% ZrP and 3 wt% AM has a 105% higher flexural strength than the cement paste. Moreover, due to the enhancing effect of ZrP on the cement matrix and polymer network, the flexural and compressive strengths of the modified sample increased by 28.5% and 17%, respectively, compared to the single AM modified sample with respect to the same dosage. The formation of the plug-in network structure and chemical bonds greatly enhances the mechanical strength of cement-based materials. This synergistic modification method fully exploits the modifying effects of polymers and nanomaterials and provides a novel strategy to improve the mechanical properties of cement-based materials.

66 GRIP STRENGTH, ONLY A PHYSICAL TASK? ASSOCIATIONS BETWEEN RESTING-STATE BRAIN CONNECTIVITY AND GRIP STRENGTH

Abstract Background Grip strength (GS) is a simple measure used in the assessment of frailty and sarcopenia. Despite being physical, studies show that it is also related with depression and cognition. Our aim was to explore if brain connectivity is implicated in GS. Methods Participants from Wave 3 of TILDA with adequate resting-state functional-MRI data were included. Connectome-based predictive modelling was used on Shen parcellated brain connectivity matrices to find connections that are positively and negatively associated with grip strength: positive and negative networks (PN and NN). On each iteration of a randomized 10-fold cross-validation, the training folds were utilized to perform partial Spearman correlations (age-, sex-adjusted) between each connection and GS. Significant (p<0.001) connections were selected. PN and NN strengths were computed by summing across all significant correlations. Linear models regressing GS on network strengths were built on training data and used to predict GS in the test fold. After 10 folds were complete the performance was measured via Pearson R between predicted and true GS values. This 10-fold process was repeated 1000 times and a mean R obtained. Permutation significance testing was employed where the cross-validation procedure was repeated 1000 times, with GS randomly shuffled each time. The P-value was the proportion of permuted R values >= the mean R. Results 317 participants were included (mean(SD) age 67.3(7.2) and 49.8% female). For PN: R[95% intervals]=0.29[0.26, 0.32], P<0.001 and for NN: R=0.24[0.20, 0.28], P<0.001. In both, the default mode, dorsal attention, and cerebellum networks were highly involved but with differing patterns: most notable was the presence of high connectivity between both cerebellar hemispheres in the PN but not in the NN. Conclusion Grip strength is related with different brain connectivity patterns suggesting the involvement of networks beyond motor areas. Further studies are required to disentangle the neuroscience behind this clinically relevant physio-cognitive task.

Comparative Analysis of PI and ADRC Control through CHIL Real Time Simulations of a DC-DC DAB into a Multi-Terminal MVDC/LVDC Distribution Network

This article presents a deep theoretical analysis of the Active Disturbance Rejection Control (ADRC) regulator for the control of first-order systems, directly compared to a “traditional” Proportional Integral (PI) regulator. To complete the theoretical study, ADRC and PI are implemented into the model of a single-phase Dual Active Bridge (DAB) converter to regulate the voltage of a Direct Current (DC) network. Facing different types of disturbances and DC network parameters variations, strengths and weaknesses of the two controllers are highlighted. ADRC and PI controls are discretized and implemented in Control Hardware In the Loop (CHIL) simulations of a single-phase DAB converter to regulate the voltage of a node of multi-terminal and multi-level DC network. By changing the DAB connection points along the MVDC network, the controlled system is stressed with different disturbances, extending the result of single-terminal network software simulations.

Designing green infrastructure and sustainable drainage systems in urban development to achieve multiple ecosystem benefits

· Develops a new methodology for assessing green infrastructure network potential. · Uses widely available datasets and simple metrics for a simple and accessible approach. · Assesses contribution of different sustainable drainage infrastructure to this potential. · Examines how development design impacts upon network strengths and shapes. Urbanisation is one of the greatest threats to biodiversity and ecological connectivity. Green infrastructure (GI) networks and corridors are promoted as a way to preserve habitat connectivity even in the context of urbanisation. Yet, previous research has a primary focus on the ecological benefits that may be provided by GI networks without also valuing other potential ecosystem services. There has also been a lack of research into how the design of urban developments, including the potential of sustainable drainage systems (SuDS), may contribute to such networks. Using a case study region in south-east England, different development scenarios and urban designs were assessed under metrics quantifying four aspects of GI provision – ecological services, ecological status, ecological connectivity, and proximity to the population. These were then combined into a single score to compare the overall spatiality of GI potential across the region under the different design and development approaches. Three key considerations for planners were found: first, the positive role urban spaces can play, even without SuDS or considering the proximity to the population, in contributing to GI potentials; second, the balance required between different development approaches to manage the trade-offs between each; and third, the range of positive and negative impacts different SuDS infrastructure have on GI potentials under different urban designs.

Collaborative Edge Computing Advances in Children's Behavior Observation for Social Internet of Things Systems in Preschool Education

Teachers who observe and analyze children's conduct can learn more about children and the reasons for their behavior. Promoting the fusion of adult and preschool vision through discussion is a basic competency of preschool teachers.Social Internet of Things (SIoT) uses recent advances in collaborativeand social relationships among SIoT users to take advantage of the network's strengths while posing new challenges.With the help of the theory of development, behavioral cues, and individual circumstances, a thorough and holistic interpretation can be made of children's behavior.There is a brief introduction to the concepts of collaborative edge computing and data storage in addition to examples of various socially-minded solutions; for long last, a list of research obstacles is provided. When measured numerically, the ECBO has a positive impact on student accomplishment (97.9%), developmental progress (95.3%), interpersonal interaction (94.6%), parental participation (98.5%), and attention ratio (98%).

Crucial interfacial shear strength to consider an imperfect interphase in halloysite-nanotube-filled biomedical samples

“τc” is defined as crucial interfacial shear strength for effectual shifting of stress through a deficient interphase and its role in the strength of nanocomposites filled with halloysite nanotubes (HNT) is highlighted by progressing the Kolarik model. The developed model assumes mechanical percolation, defective interphase part, and filler/interphase nets. The outputs of the model suitably follow the experimented levels of several samples. The strength of the nanocomposite improves by 15% and 45% at network strengths of 2 and 6 GPa, respectively. Additionally, the crucial interfacial shear strength of 10 and 150 MPa increase the strength of samples by 80% and 10%, respectively. Moreover, the strength of the nanocomposite improves by 18% at an interfacial shear strength of 40 MPa, while the interfacial shear strength of 80 MPa improves the strength of the system by 34%. Furthermore, the strength of samples enhances by 51% at the beginning of percolation at 0.001; however, the strength of the nanocomposite only grows by 25% at the beginning of percolation at 0.01. Accordingly, both network strength and interfacial shear strength directly govern the strength of the nanocomposite, while the crucial interfacial shear strength and the beginning of percolation adversely affect the strengthening effect.

Changes in specific and nonspecific psychopathology network structure after intensive cognitive behavior therapy in patients with anorexia nervosa.

This study aimed to compare eating disorder-specific and nonspecific clinical features in patients with anorexia nervosa before and after intensive enhanced cognitive behavior therapy (CBT-E) via network analysis. All consecutive patients admitted to intensive CBT-E were eligible, and the sample comprised patients aged ≥16 years who completed a 20-week intensive CBT-E program. Body mass index (BMI), Eating Disorder Examination Questionnaire and Brief Symptoms Inventory responses were gathered at baseline and end of treatment, and used to generate statistical networks of the connections between symptoms (nodes) and the strength and centrality thereof. A total of 214 patients were included. Most nodes had relatively similar centrality compared to other nodes in the networks. "Eating concern" and "phobic anxiety" showed the greatest bridge centrality at both time points. No differences were found between baseline and the end of treatment in either global network or individual connection strengths. These findings suggest that some clinical expressions not specific to eating-disorder psychopathology remain strongly connected in the generalized network of patients with anorexia nervosa after CBT-E. Future research should examine whether additional procedures specifically designed to target these symptoms should be integrated into this and other treatments.

Selected Articles from This Issue

Cancer mortality rates are high in rural areas in the United States, and practitioner uses of rural informal collaborative networks are not well understood. Carothers, Allen, and colleagues conducted network analyses in five rural service areas with agencies that address cancer risk, measuring collaborations on cancer prevention activities and health service referrals. Results were disseminated to practitioners through infographics, workshops, and online interactive applications. Practitioners used the analyses for strategic planning, to identify relationship improvement opportunities, and demonstrate network strengths to funders. Dissemination of findings to network members facilitates real-world use of results to benefit programs and policies.Aboriginal and Torres Strait Islander peoples experience lower life expectancy and poorer cancer survival rates than other Australians. Dasgupta and colleagues examine this disparity using a study cohort of more than 700,000 Australians diagnosed with an invasive solid cancer from 2005–2016. Aboriginal and Torres Strait Islanders experienced consistently lower remaining life expectancy after a cancer diagnosis than other Australians. One-third of the difference was due to cancer mortality. If the differences in cancer mortality were removed, the average life years remaining following a cancer diagnosis would increase by 17%. These important differences highlight the need for culturally appropriate, accessible and effective health services and cancer care.Prior studies of breast cancer disparities have focused primarily on differences between Black and White women, yet contemporary patterns of disparity for other groups are not well understood. Primm and colleagues used the most recent data available from the National Cancer Institute's Surveillance, Epidemiology, and End Results Program (SEER) to examine breast cancer disparities by stage at diagnosis across nine racial and ethnic groups. Analysis of breast cancer outcomes by racial and ethnic groups, the authors found persistent disparities in both the likelihood of being diagnosed at an early stage, and also in survival among women diagnosed at early stages. The results have implications for efforts to reduce entrenched racial and ethnic disparities in breast cancer early detection and survival.Metabolomics studies are usually conducted in a relatively small number of participants. Shu and colleagues performed integrative analyses to combine circulating metabolomics data with large-scale genome-wide association data to infer the associations between genetically predicted circulating metabolite levels and colorectal cancer (CRC) risk. The study identified 21 metabolites (mainly lipids) that were significantly associated with CRC risk in both European and East Asian populations. These identified metabolites should be investigated further for their potential to be developed into new tools for risk assessment of CRC in the two populations.

Abnormal Brain Network Interaction Associated With Positive Symptoms in Drug-Naive Patients With First-Episode Schizophrenia.

Positive symptoms are marked features of schizophrenia, and emerging evidence has suggested that abnormalities of the brain network underlying these symptoms may play a crucial role in the pathophysiology of the disease. We constructed two brain functional networks based on the positive and negative correlations between positive symptom scores and brain connectivity in drug-naive patients with first-episode schizophrenia (FES, n = 45) by using a machine-learning approach (connectome-based predictive modeling, CPM). The accuracy of the model was r = 0.47 (p = 0.002). The positively and negatively associated network strengths were then compared among FES subjects, individuals at genetic high risk (GHR, n = 41) for schizophrenia, and healthy controls (HCs, n = 48). The results indicated that the positively associated network contained more cross-subnetwork connections (96.02% of 176 edges), with a focus on the default-mode network (DMN)-salience network (SN) and the DMN-frontoparietal task control (FPT) network. The negatively associated network had fewer cross-subnetwork connections (71.79% of 117 edges) and focused on the sensory/somatomotor hand (SMH)-Cingulo opercular task control (COTC) network, the DMN, and the visual network with significantly decreased connectivity in the COTC-SMH network in FES (FES < GHR, p = 0.01; FES < HC, p = 0.01). Additionally, the connectivity strengths of the right supplementary motor area (SMA) (p < 0.001) and the right precentral gyrus (p < 0.0001) were reduced in FES. To the best of our knowledge, this is the first study to generate two brain networks associated with positive symptoms by utilizing CPM in FES. Abnormal segregation, interactions of brain subnetworks, and impaired SMA might lead to salience attribution abnormalities and, thus, as a result, induce positive symptoms in schizophrenia.

Mapping the Lay of the Land: Using Interactive Network Analytic Tools for Collaboration in Rural Cancer Prevention and Control.

Cancer mortality rates in the United States are higher in rural than urban areas, especially for colorectal cancer. Modifiable cancer risks (e.g., tobacco use, obesity) are more prevalent among U.S. rural than urban residents. Social network analyses are common, yet rural informal collaborative networks for cancer prevention and control and practitioner uses of network findings are less well understood. In five service areas in rural Missouri and Illinois, we conducted a network survey of informal multisector networks among agencies that address cancer risk (N = 152 individuals). The survey asked about contact, collaborative activities, and referrals. We calculated descriptive network statistics and disseminated network visualizations with rural agencies through infographics and interactive Network Navigator platforms. We also collected feedback on uses of network findings from agency staff (N = 14). Service areas had more connections (average degree) for exchanging information than for more time-intensive collaborative activities of co-developing and sustaining ongoing services and programs, and co-developing and sharing resources. On average, collaborative activities were not dependent on just a few agencies to bridge gaps to hold networks together. Users found the network images and information useful for identifying gaps, planning which relationships to establish or enhance to strengthen certain collaborative activities and cross-referrals, and showing network strengths to current and potential funders. Rural informal cancer prevention and control networks in this study are highly connected and largely decentralized. Disseminating network findings help ensure usefulness to rural health and social service practitioners who address cancer risks.

Leveraging communities’ network strengths to support climate change adaptation information-sharing: a study with coffee farmers in Risaralda, Colombia

Leveraging communities’ network strengths to support climate change adaptation information-sharing: a study with coffee farmers in Risaralda, Colombia

FUZZY APPROACH TO DEVICE LOCALIZATION BASED ON WIRELESS NETWORK SIGNAL STRENGTH

The paper presents an original approach to device location detection in a building. The new method is based on a map of individual interiors, drawn up based on the measurements of the strength of wireless network signals for each building venue. The device is initially assigned to all venues whose descriptions sufficiently correspond with the current measurements taken by the device. A fuzzy assignment level for each of the potentially considered venues depends on the difference between the averaged network strengths for the venue and the signal strengths currently measured with the device for localization purposes. Ultimately, the device is assigned to the venue with the highest level of assignment.

Enhanced performance of PMSG wind turbines during grid disturbance at different network strengths considering fault current limiter

With the recent proliferation and penetration of wind farms into existing power grids, it is paramount to conduct numerous studies to counter grid disturbances based on operational grid codes. One of the ways of generating electric power from wind energy is by employing the promising technology of the permanent magnet synchronous generator (PMSG) wind turbine. In order to solve the transient stability intricacies posed by the stochastic nature of wind energy during grid faults, this paper proposes a fault current limiter for a PMSG-based wind turbine control. The fault current limiter used in this study is a series dynamic braking resistor (SDBR). The best location to place the SDBR on the machine-side and grid-side converters (GSCs) of the wind generator was investigated, considering the grid voltage as its switching signal during transient state. Efforts have been given to find a suitable sizing of the SDBR. The performance of the SDBR has been investigated at various network strengths in weak and strong grids. A severe three-line-to-ground fault and asymmetrical faults were used to test the robustness and rigidity of the controllers of the wind generator. The system performance was evaluated using power system computer aided design and electromagnetic transient including DC (PSCAD/EMTDC) platform. The same conditions of operation were used in investigating the various scenarios considered in this study, for effective comparison.

Connectome-based model predicts episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment

ObjectiveTo explore whether the whole brain resting-state functional connectivity (rs-FC) could predict episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment. MethodThis study included 33 cognitive normal (CN), 26 subjective cognitive decline (SCD) and 27 amnestic mild cognitive impairment (aMCI) patients, and all the participants completed resting-state fMRI (rs-fMRI) scan and neuropsychological scale test data. Connectome-based predictive modeling (CPM) based on the rs-FC data was used to predict the auditory verbal learning test-delayed recall (AVLT-DR) scores, which measured episodic memory in individuals. Pearson correlation between each brain connection in the connectivity matrices and AVLT-DR scores was computed across the patients in predementia stages of Alzheimer’s disease (AD). The Pearson correlation coefficient values separated into a positive network and a negative network. Predictive networks were then defined and employed by calculating positive and negative network strengths. CPM with leave-one-out cross-validation (LOOCV) was conducted to train linear models to respectively relate positive and negative network strengths to AVLT-DR scores in the training set. During the testing procedure, each left-out testing subject's strengths of positive and negative network was normalized using the parameters acquired during training procedure, and then the trained models were used to predict the testing participant’s AVLT-DR score. ResultsThe negative network predictive model tested LOOCV significantly predicted individual differences in episodic memory from rs-FC. Key nodes that brain regions contributed to the prediction model were mainly located in the prefrontal cortex, frontal cortex, parietal cortex and temporal lobe. ConclusionOur findings demonstrated that rs-FC among multiple neural systems could predict episodic memory at the individual level.

Using network analysis to compare diagnosis-specific and age-specific symptom networks in eating disorders.

The network theory of mental disorders conceptualizes eating disorders (EDs) as networks of interacting symptoms. Network analysis studies in EDs mostly have examined transdiagnostic and/or mixed age samples. The aim of our study was to investigate similarities and differences of networks in adolescents and adults with anorexia nervosa (AN) or bulimia nervosa (BN). Participants were 2,535 patients (n=991 adults with AN, n=821 adolescents with AN, n=473 adults with BN, and n=250 adolescents with BN) who completed the Eating Disorder Inventory-2. Twenty-seven items were selected. Cross-sectional networks were estimated via Joint Graphical Lasso. Core symptoms were identified using strength centrality. Spearman correlations and network comparison tests (NCTs) were used to compare groups. Across diagnoses and ages, feeling ineffective, desire to be thinner, worries that feelings will get out of control, guilt after overeating as well as doing things perfectly emerged as most central symptoms. There were moderate to high correlations between symptom profiles (0.62-0.97, mean: 0.78) as well as high correlations between network structures (0.83-0.93, mean: 0.87) and network strengths (0.73-0.95, mean: 0.85). Global strength significantly differed in two of the six NCTs, and 2.5-10% of edges differed between networks. Considerable similarities in network structures and strengths across diagnoses and ages speak in favor of the transdiagnostic approach to EDs. Besides drive for thinness, ineffectiveness, emotion regulation difficulties, and perfectionism might be the most consistent factors in ED networks. These symptoms as well as their symptom connections should be especially focused in treatment regardless of age and diagnosis.

Application of complex networks for monthly rainfall dynamics over central Vietnam

Adequate understanding of the temporal connections in rainfall is important for reliable predictions of rainfall and, hence, for water resources planning and management. This research aims to study the temporal connections in rainfall using complex networks concepts. First, the single-variable rainfall time series is represented in a multi-dimensional phase space using delay embedding (i.e. phase-space reconstruction), where the appropriate delay time and optimal embedding dimension of the time series are determined by using average mutual information and false nearest neighbors methods, respectively. Then, this reconstructed phase space is treated as a ‘network,’ with the reconstructed vectors serving as ‘nodes’ and the connections between them serving as ‘links’. Finally, the strength of the nodes are calculated to identify some key properties of the temporal rainfall network. The approach is employed independently to monthly rainfall data observed over a period of 38 years (1979–2016) from 14 rain gauge stations in the Vu Gia Thu Bon River basin in central Vietnam. Moreover, entropy values of the original rainfall time series are calculated for obtaining additional information on the properties of the rainfall dynamics. The average node strengths are also examined in terms of the mean annual rainfall, entropy of the time series, and elevation of the rain gauge station. The results indicate that: (1) while some adjacent stations (i.e. networks) have somewhat similar strength (average node strength) values, several others that are geographically close show significantly different network strengths; (2) similar entropies for adjacent stations are found more frequently than similar average node strengths; (3) there is generally a positive and proportional relationship between average strengths of nodes and entropies; and (4) the average node strengths of different months have some distinct temporal patterns (3-month, 4-month, and 6-month patterns) in rainfall dynamics, depending upon the specific region of the study area. These results have important implications for prediction, interpolation, and extrapolation of rainfall data.