Communication Capacity Research Articles

Silicon Nanoantennas for Ultra‐Compact, High‐Speed and Low‐Power Consumption Electro‐Optic Modulators

AbstractTo keep pace with the expanding data capacity in modern communications and increasing on‐chip integration density, it is highly desirable to have electro‐optic modulators featured with compact footprint, large modulation bandwidth, low energy consumption, and full complementary metal‐oxide‐semiconductor (CMOS) compatibility. Here, this work experimentally demonstrates a high‐speed electro‐optic modulator in the telecommunication wavelength range made of a 1D chain resonator of Mie‐resonant silicon nanoparticles. The modulator exploits the plasma dispersion effect and works in the carrier depletion configuration. The resulting large modulation bandwidth over 40 GHz and low energy consumption of 23 fJ bit−1 are attributed to its ultra‐compact footprint of 10 µm2. The open eye diagrams are measured up to 40 Gbits s−1. The modulators are fabricated at wafer level by a silicon photonic foundry using CMOS‐compatible process. This modulator shows promise for achieving higher integration density and larger modulation bandwidth. It can be added to the existing on‐chip device building blocks library and used for a wide range of applications, including data communication, quantum photonics, and non‐reciprocal optics.

Analysis and exploration of local participation in the construction of international communication capacity under a new model for human progress

Analysis and exploration of local participation in the construction of international communication capacity under a new model for human progress

High-capacity MIMO visible light communication integrated into mini-LED LCDs

Visible light communication (VLC) can be integrated into a liquid crystal display (LCD) by modulating its backlight while normally showing pictures. Received by ordinary cameras, such integrated display and communication (IDAC) systems are promising for the Internet of Things and Metaverse. However, in the premise of unaffected display function, the capacity of current IDAC systems is limited, with data rates of very few kbps. This work proposes a new architecture: multiple-input, multiple-output (MIMO) VLC integrated into a mini-LED LCD, whose many backlight segments act as multiple transmitters. A camera utilizes the rolling shutter effect with independent pixel columns to form multiple outputs. The communication capacity is thus significantly multiplied by the backlight column number. In addition, local dimming, which is favorable for an LCD’s contrast and power consumption, is exploited to achieve efficient signal modulation. We built a mini-LED LCD prototype with 8-by-20 backlight segments for experimental verification. The backlight segments multiplex a video-rate signal for local dimming and a high-frequency (∼34 kHz) signal modulated through multi-pulse position modulation (MPPM) for VLC. By taking photographs with a camera 1.1 m away from the screen, a record-high rate of 201.6 kbps (approximately ten times faster than current IDAC systems) was experimentally achieved with a bit error rate satisfying the forward error correction. Improved image contrast due to local dimming was also observed.

A congestion-aware user–cell association scheme to assist with traffic offloading in a three-tier heterogeneous network

In this work, we aim to expand the cell range in congestion areas. To achieve this goal, we integrate UAV network into macro–micro cellular network to enhance coverage. However, the gain of deploying heterogeneous nodes is limit if there are no CRE (cell range expanding)-based method to steer users towards low-power nodes. Therefore, in order to further improve system access capacity, we propose a congestion-aware UA (user–cell association) scheme to offload queued traffic of cellular network into UAV network. To verify the superiority of proposed scheme, we utilize the low-complexity SG (stochastic-geometry) methods. Then in simulation analysis phase, we obtain five primary results: (a). The SG-based methods have lower computation complexity, and the convergence of derived expressions are verified. (b). The SG-based methods do not need the exact users’ location but the users’ density, which have improved the operability and robustness in infrastructure construction of wireless network. (c). The congestion-aware UA scheme can prompt network to adjust association tier flexibly, which is very suitable for traffic unevenly-distributed regions. (d). The optimal offsets of virtual power are estimated as B2 = −4 dB for small-cell and B3 = 3 dB for drone-cell, the resulting improvement in communication capacity is 8%. (e). We suggest the deployment density of UAV sites will no need to exceed λ3 = 41 (pks/km2).

Sistematika Ilmu Komunikasi

The purpose of this study is to evaluate the role that instructional communication plays in the learning process. This study use explanatory research methodologies in conjunction with a quantitative methodology. The study's findings demonstrate the significance of communication as a fundamental human need and as a means of communicating with others in order to exchange experiences, opinions, feelings, knowledge, and guidance. In order to transmit educational messages during the learning process, effective communication is also a crucial component of educational communication. Effective educational communication is defined as one in which the material presented by teachers is well-received, easily comprehended, and results in favorable student feedback. This research concludes that educators must acquire "communication science," as well as the fundamentals of educational communication, successful communication, intercultural communication, and the components, functions, and aims of communication. Because teachers' capacity for effective communication plays a major role in determining the effectiveness of education.

Optimizing Antenna Positioning for Enhanced Wireless Coverage: A Genetic Algorithm Approach.

The precise placement of antennas is essential to ensure effective coverage, service quality, and network capacity in wireless communications, particularly given the exponential growth of mobile connectivity. The antenna positioning problem (APP) has evolved from theoretical approaches to practical solutions employing advanced algorithms, such as evolutionary algorithms. This study focuses on developing innovative web tools harnessing genetic algorithms to optimize antenna positioning, starting from propagation loss calculations. To achieve this, seven empirical models were reviewed and integrated into an antenna positioning web tool. Results demonstrate that, with minimal configuration and careful model selection, a detailed analysis of antenna positioning in any area is feasible. The tool was developed using Java 17 and TypeScript 5.1.6, utilizing the JMetal framework to apply genetic algorithms, and features a React-based web interface facilitating application integration. For future research, consideration is given to implementing a server capable of analyzing the environment based on specific area selection, thereby enhancing the precision and objectivity of antenna positioning analysis.

An adaptive transmission strategy based on cloud computing in IoV architecture

Because of recent developments in wireless communication, sensor technology, and computing technology, researchers have recently shown a significant amount of interest in the Internet of Vehicles (IoV), which has become feasible as a result of these improvements. Because of the distinctive characteristics of IoV, such as the varied compute and communication capacities of network nodes, it is difficult to process jobs that are time-sensitive. The purpose of this study is to investigate the ways in which cloud computing may collaborate with the IoV to make the processing of time-sensitive procedures easier. We propose a vehicle design that makes advantage of cloud computing as a means of accomplishing this goal. Increasing the proportion of time-sensitive jobs that are ultimately completed was the motivation behind the development of the offloading model that we devised. Taking this into perspective, we present an adaptive task offloading and transmission method. Taking into account the ever-changing requirements and constraints on the available resources, this algorithm dynamically organizes all of the tasks into separate cloud link lists on the cloud. Following that, the tasks contained within each list are distributed in a cooperative manner to a number of different nodes, with the characteristics of those nodes being taken into consideration. Following the presentation of the simulation model, we carried out an experimental investigation into the effectiveness of the model that was proposed. It is abundantly evident that the proposed model is effective, as indicated by the findings.

FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update

As a promising approach to deal with distributed data, Federated Learning (FL) achieves major advancements in recent years. FL enables collaborative model training by exploiting the raw data dispersed in multiple edge devices. However, the data is generally non-independent and identically distributed, i.e., statistical heterogeneity, and the edge devices significantly differ in terms of both computation and communication capacity, i.e., system heterogeneity. The statistical heterogeneity leads to severe accuracy degradation while the system heterogeneity significantly prolongs the training process. In order to address the heterogeneity issue, we propose an Asynchronous Staleness-aware Model Update FL framework, i.e., FedASMU, with two novel methods. First, we propose an asynchronous FL system model with a dynamical model aggregation method between updated local models and the global model on the server for superior accuracy and high efficiency. Then, we propose an adaptive local model adjustment method by aggregating the fresh global model with local models on devices to further improve the accuracy. Extensive experimentation with 6 models and 5 public datasets demonstrates that FedASMU significantly outperforms baseline approaches in terms of accuracy (0.60% to 23.90% higher) and efficiency (3.54% to 97.98% faster).

Improving fiber coupling efficiency by shaping the transmission trajectory of the vortex beam

Vortex beams with orbital angular momentum (OAM) have the potential to significantly increase the communication capacity in long-distance free-space optical communication systems. In order to obtain optimal coupling efficiency, few-mode fibers (FMFs) are employed to receive spatial light. This paper presents a computer model that describes the process of coupling vortex beams into FMFs after they have been transmitted via free space. We propose to improve the coupling efficiency by controlling the transmission trajectory of the vortex beam in free space. The coupling efficiency of a vortex beam into FMFs can be enhanced at a specific transmission distance by pre-designing the transmission trajectory of the optical field and tweaking the parameters of the coupling lens and fiber. This paper also examines the coupling correlation between higher-order OAM modes vortex beams and FMF modes. The numerical results of this study show that controlling the radial phase distribution to manage the transmission trajectory of the optical field is an effective and flexible method for improving the efficiency of coupling light between space and fiber. The results of this study can be applied in the domain of free-space optical communications.

Spectral analysis of intricate orbital angular momentum modes in multiplexing communication using a residual neural network

Vortex beams carrying orbital angular momentum (OAM) modes offer a compelling avenue for increasing optical communication capacity. Despite significant efforts in OAM mode (de)multiplexing, challenges persist in signal demodulation and noise monitoring of multiplexed channels. This challenge largely stems from the absence of efficient feature extraction methods capable of analyzing the OAM spectrum, which delineates mode components and intensity weights. Herein, we introduce a novel strategy for OAM mode spectrum analysis using a residual neural network incorporating interference techniques. By introducing interference between target vortex beams and a spherical wave, resulting in the mapping of OAM distributions onto the stripe features observed in the interferogram. To effectively extract spectrum details from these interferograms, we construct a residual neural network exhibiting high feature processing capabilities, which enables precise analysis of intricate OAM mode spectrum. We demonstrate the effectiveness of this OAM spectrum analysis by obtaining the intensity weights for 15 superimposed modes ranging from −10 to +10, achieving a mean-square-error of 2.3 × 10−4. To validate its practicality, we showcase an 80-channel three-dimensional multiplexing communication system including five OAM modes, two polarizations, and eight wavelengths. The 4 Tbit/s quadrature-phase-shift-keying signals are successfully demodulated with a bit-error rate below 3.34 × 10−6. Additionally, the measured optical signal-to-noise ratio reaches 11.5 dB, indicating the potential applications of this OAM spectrum analysis strategy in signal demodulation and noise monitoring.

Optimizing High Data Rate Up to 2.5 Tbps Transmission using 64-Channel DWDM System with DCF and NRZ Modulation

Objectives: This study aims to enhance and optimize a modern communication system for high data rate transmission using Dense Wavelength Division Multiplexing (DWDM). The objectives include employing a 64-channel DWDM system, implementing different data speeds, and utilizing a dispersion compensation method. Methods: To achieve the objectives, we simulate and analyze the effects of Dispersion Compensation Fiber (DCF) in a DWDM system with 64 channels. The system employs Non-Return-to-Zero (NRZ) modulation format at varied bit rates and multiple energy levels. We propose a method for achieving data rates of 10 to 40 Gbps using NRZ modulation and Erbium-Doped Fiber Amplifier (EDFA) over a single-mode fiber transmission distance of 40 to 160 km, along with a dispersion compensation fiber of 8 to 32 km (DCF). The performance of the developed model is evaluated based on Quality Factor, Bit Error Rate (BER), Eye Height, and Threshold. These metrics are measured at two input energy levels from an optical power source, covering a communication capacity ranging from 0.625 Tbps to 2.5 Tbps. Findings: Through the simulations and analyses, we uncover the impact of dispersion and demonstrate the effectiveness of the proposed method in compensating for dispersion effects. Performance analysis of two different input transmitter power levels, -10 dBm is more efficient compared to 0 dBm input transmitter power in terms of bit error rate and quality factor. Novelty: This study presents a novel approach to improving modern communication systems by utilizing DWDM with a dispersion compensation method. The use of a 64-channel DWDM system, combined with the proposed NRZ modulation technique and dispersion compensation fiber, provides an efficient solution for achieving high data rates over long transmission distances while minimizing the effects of dispersion. The findings contribute to the advancement of communication systems for high data rate transmission. Keywords Dispersion effect, Bit Error Rate, Q-factor, Optisystem software, Erbium doped fiber amplifier

The Systematic Construction of Emergency Medical Security System in Long-Distance Walking Movement on Urban Paved Roads

Objective To explore the scope of building emergency medical security mechanism in urban paved road environment, and to provide reference, sample data and improvement suggestions for building emergency medical security mechanism in such specific environment. Methods The data of rescue personnel deployment, rescue materials allocation, rescue force monitoring, actual rescue situation, emergency plan and relevant regulations setting, training and other aspects of the emergency medical security mechanism in March and April 2023 were collected, and the data were analyzed. Results (1) The hiking distance was 40km and the road conditions were urban paved roads. There were 1150 official trekkers and 65 people were treated medically. (2) The most common symptoms of outdoor hiking included skeletal muscle injury (19.48%), foot skin and soft tissue injury (18.17%), fever (2.61%) and diarrhea (0.61%). (3) In March, 18.36% of the core medical team members and 20.41% of the first aid volunteers who underwent systematic training took up the task of supporting hiking activities. In April, the core medical team personnel who undertook the task of supporting hiking activities accounted for 1.23% of the total number of the security team, and the first aid volunteers who underwent systematic training accounted for 37.04%. (4) According to local conditions, the multi-vehicle rescue and transport mode was adopted, which was combined with the modified commercial rescue vehicle (42.86%) with the on-board emergency medical unit (EMU) and the ordinary ambulance (28.57%). (5) More than 700 pieces of 34 kinds of medical materials and 40 sets of mobile communication equipment, which were put into use after the feasibility study of the expert group and the technical group, guaranteed the competition from the hardware level. Conclusion The construction of emergency medical support mechanism in outdoor walking sports needs to pay attention to environmental characteristics, operation mode of support team, personnel and adaptability training, equipment and deployment of medical support materials, deployment and cooperation of communication and transport capacity, reasonable support plan and other factors. Medical volunteers who have been systematically trained can undertake part of outdoor medical rescue work. Pre-activity training and regular re-training of medical volunteers can ensure the safety of outdoor activities.

Energy efficiency in 5G systems: A systematic literature review

To ensure Energy Efficiency (EE) and better Quality of service (QoS), it is necessary to analyze the energy saving possibilities for low resource utilization in the current networks caused by rigorous QoS requirements and implementing EE approach in the planned model for performance improvement. Distributed Denial of Service (DDoS) attacks aim to exhaust the network’s processing and communication capacity by saturating it with packets and generating malicious traffic. There are numerous advantages that make Digital Twin (DT) and Intrusion Detection technique (ID) an effective remedy for a range of (fifth generation) 5G problems. A DDoS attack must be immediately detected and stopped before a legitimate user can access the target of the attacker for the 5G network to provide an efficient energy service. Although they clearly show promise in assisting with the creation and implementation of the challenging 5G environment, Digital Twins is still a relatively new technology for 5G networks but will increase EE. In this research, a thorough examination of the materials was carried out to identify the most cutting-edge DT and ID methods. The purpose of this study was to comprehend the problems with Energy Efficiency, the need for DT, and the methods for dealing with large-scale attack by DDoS on Energy Efficient networks. Only 94 of the 1555 articles produced by the procedure were determined to be relevant using inclusion and exclusion criteria. The outcome demonstrates that in 5G networks, DT, and its fundamental approaches, like QoS and DDoS attack mitigation, can be used to regulate the network’s Energy Efficiency. Numerous practical applications focusing on 5G Systems use their own principles. The effectiveness of these strategies was evaluated using several assessment criteria, including DT, Intrusion Detection, QoS, Energy Efficiency, and 5G Systems. Each study issue is thoroughly explained, along with typical methods, advantages, disadvantages, and performance metrics. Energy economy, network reliability, privacy, and cost reduction are all considerably increased by the implementation of intrusion detection technology in 5G systems. The decision is supported by the technology’s demonstrated efficacy, scalability, real-time detection capacities, low error level, and personalized learning attributes, all of which contribute to the long-term viability of 5G networks as an entire system.

BENEFIT OF USING APPLIED BEHAVIOR ANALYSIS (ABA) IN STIMULUSING SPEECH IN AUTISTIC CHILDREN

This Course Conclusion Paper’s main focus is to address the benefit of using Applied Behavior Analysis (ABA) in stimulating speech in children with Autism Spectrum Disorder (ASD). ABA is a methodology based on behavioral principles that aims to improve and strengthen socially important behaviors, including communication skills. The main objective of this research is to indicate the benefits of using Applied Behavior Analysis (ABA) to stimulate speech in autistic children. The aim is to analyze how this methodology can contribute to the development of these children’s communicative skills, promoting their better social inclusion and quality of life. The research question that guides this study is: “What are the benefits of using Applied Behavior Analysis (ABA) in stimulating autistic children? And why does this method have better results?” To answer this question, a literature review, case study analysis and direct observation in therapeutic contexts were carried out. The results indicate that ABA, when applied correctly and consistently, can generate significant progress in the development of speech and the general communicative capacity of children with autism. This should be an individualized structure and the focus on positive reinforcement that characterizes this approach. This work hopes to contribute to the understanding of the benefits of ABA in the field of Speech Therapy and to the promotion of more effective therapeutic practices in the treatment of children with ASD.

Co‐channel interference cancellation based on BSS for multi‐satellite MIMO communication systems with FFR

SummaryMultiple‐input multiple‐output (MIMO) technology can boost the capacity of conventional satellite communications, and full frequency reuse (FFR) can further improve the capacity by making full use of frequency resources. However, FFR will cause severe co‐channel interference and lead to a degradation of channel capacity. Blind source separation (BSS) algorithm can be used to eliminate co‐channel interference and does not require prior information, which avoids the extra occupation of channel frequency resources. While the multi‐satellite MIMO communication system have delay mixing characteristic, the separation performance will be degraded if the delay is ignored. In this paper, DMBSS algorithm for delay mixing is proposed. Firstly, the DMBSS algorithm utilizes Taylor expansion to achieve dimension expansion of observed signals and so as to transform the delay mixing into instantaneous mixing with extended dimension. Secondly, complex non‐orthogonal joint diagonalization algorithm is used to solve the separation matrix equation of extended observation signals and extended source signals and thus to eliminate the co‐channel interference. Simulation results show that for multi‐satellite MIMO communication scenarios with FFR, the proposed algorithm can effectively improve the performance of co‐channel interference cancellation and reduce the value of BER. Furthermore, the influence of order of Taylor expansion on the separation performance is also analyzed and simulated in this paper to show the cost performance of the proposed algorithm.

Childhood vertigo: A retrospective series of 791 cases

Background: Vertigo is one of the most common complaints leading to admissions in pediatric neurology clinics. Diagnosis of vertigo in children is often challenging due to limited communication capacity in defining symptoms, the unreliability of neurovestibular investigations in children and the variability of etiologies. The aim of this study is to evaluate clinical and etiological profile of vertigo in childhood and to assess the diagnostic yield of neuroimaging and electroencephalography (EEG). Methods: All patients under the age of 18 years presented with vertigo to the pediatric neurology outpatient clinic of Ankara Keçiören Research and Training Hospital were included in the study. Demographic data, accompanying symptoms, family history, physical and neurological examination findings, neuroimaging, and EEG results were retrospectively analyzed. The etiological spectrum was also evaluated according to age groups. Results: A total of 791 patients (female/male=2.1) with a mean age of 13.1 years were included in the study. The most common etiologies were vestibular migraine (27.3%) and psychogenic vertigo (14.9%) in the entire cohort; whereas benign paroxysmal vertigo of childhood was the top diagnosis in children ≤12 years (25%). The most common accompanying symptom was headache (36.7%) which was most frequently associated with vertigo attacks in migrainous patients. In most cases there was no correlation between neuroimaging findings and vertigo. Additionally, the contribution of EEG to the diagnosis was found to be limited. Conclusion: Most of the childhood vertigo cases can be diagnosed accurately by a detailed medical history, physical and neurological examination. A multidisciplinary approach is essential for evaluating childhood vertigo and all the signs and symptoms accompanying vertigo should be considered while planning further investigations including neuroimaging and EEG.

Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework

The 5 G networks are effectively deployed worldwide, and academia and industries have begun looking at 6 G network communication technology for consumer electronics applications. 6 G will be built on pervasive artificial intelligence (AI) to enable data-driven Machine Learning (ML) applications in massively scalable and heterogeneous networks. Conventional ML technique involves centralizing train data in data centers where centralized ML algorithms can be employed for data inference and analysis. The data inference and analysis are frequently inconvenient or impracticable for the devices to submit information to the preset sever because of privacy concerns and inadequate communication capabilities in wireless networks. However, privacy limitations and restrictions in wireless network communication capacity are frequently impractical or undesirable for the devices to acquiesce data to the parameter server. Federated learning (FL) enables the devices to train a practical and standard model while needing data exchange and transfer, which might solve these issues. This paper presents an overview of FL, 6 G, and FL enables 6 G communication technology. In particular, 6 G requirements and applications, and the proposed FL framework algorithm with evaluation are described. Finally, FL-enabling 6 G communication technologies open challenges, and research directions are discussed to help future researchers improve the FL-enabled 6 G network.

Good use, non-use and misuse: safe sport reporting systems in context

ABSTRACT Reporting systems constitute an essential part of today’s safe sport initiatives across the world. Informed by literature on reporting wrongdoings in organisational contexts and a political sociology approach to policy instrumentation, this article examines how abuse reporting systems are utilised in youth high-performance sport environments. Drawing from 51 interviews with both user and provider groups of South Korea’s reporting facilities, the results offer three main uses of the country’s safe sport reporting mechanisms: (1) ‘good use’ that relies on their communicative capacity to signal changing organisational culture; (2) ‘non-use’ that derives not only from the fear of reprisals, but from more subtle relational and situational concerns, such as teams’ dissolution; and (3) ‘misuse’ of the systems as a tool to advance individual agendas as opposed to protecting victims. The findings of this study not only provide evidence for both positive and perverse effects of safe sport reporting facilities per se, but also illuminate the importance of social and institutional conditions that can both enable and constrain this newly implemented policy measure for athlete safeguarding.

Disrupted Structural Brain Networks and Structural-Functional Decoupling in First-Episode Drug-Naïve Adolescent Major Depressive Disorder

PurposeMajor depressive disorder (MDD) tends to emerge during adolescence, but the neurobiology of adolescent MDD is still poorly understood. This study aimed to explore the topological organization of white matter structural networks and the relationship between structural and functional connectivity in adolescent MDD. MethodsStructural and functional magnetic resonance imaging data were acquired from 94 first-episode drug-naïve adolescent MDD patients and 78 healthy adolescents. Whole brain structural and functional brain networks were constructed for each subject. Then, the topological organization of structural brain networks and the coupling strength between structural and functional connectivity were analyzed. ResultsCompared with controls, adolescent MDD patients showed disrupted small-world, rich-club, and modular organizations. Nodal centralities in the medial part of bilateral superior frontal gyrus, bilateral hippocampus, right superior occipital gyrus, right angular gyrus, bilateral precuneus, left caudate nucleus, bilateral putamen, right superior temporal gyrus, and right temporal pole part of superior temporal gyrus were significantly lower in adolescent MDD patients compared with controls. The coupling strength between structural and functional connectivity was significantly lower in adolescent MDD patients compared with controls. DiscussionOur findings suggest widespread disruption of structural brain networks and structural-functional decoupling in adolescent MDD, potentially leading to reduced network communication capacity.

Higher water ecological service values have better network connectivity in the middle Yellow River basin

There are numerous ecological challenges that are of considerable concern caused by urbanization and natural calamities. The spatial pattern of the landscape is of great significance in maintaining species diversity and regulating material cycles and individual ecophysiological processes as a medium for ecosystem service functions. Resolving the conflicting relationships between people and land necessitates the precise identification of essential ecological sources. Therefore, there is an urgent need to investigate the relationship between ecological sources and ecosystem service values (ESVs) and to optimize the matter and energy.connectivity network. The study optimizes ecological network simulation using a penetration threshold, population density inverse distance formula, and geodetector weight analysis, constructs ecological spatial networks and evaluates their structure using the minimum cumulative resistance (MCR) model and complex network analysis, and discusses the relationship between network topological characteristics (NTCs) and the spatial pattern of the various ESVs. The results show that the major sources and corridors are primarily along the banks of the Wei River and the Fen River. The clustering coefficient has a significant negative correlation with both water supply and water regulation service values. It is found that the sources with higher ecological service values related to water resources have better network connectivity. This result plays a major reference role for the identification of several crucial ecological components, such as sources, nodes and stepping stones, in the middle Yellow River basin (MYRB). Ultimately, we propose strengthening the ecological protection of the eastern part of the Qinling Mountains and increasing the ecological communication capacity of the forestland on both sides of the river valley to create an optimized ecological pattern.