Network Location Research Articles

PLANNING FOR THE DEVELOPMENT OF A DRINKING WATER DISTRIBUTION SYSTEM NETWORK IN SUNGAI PINYUH

The increasing water demand due to population growth and territorial expansion has significantly outpaced the natural water supply in Sungai Pinyuh, Kalimantan Barat. This sub-district, part of the Mempawah Regency, faces challenges such as insufficient access to clean water, deteriorating pipe infrastructure, and environmental pollution affecting water quality. To address these issues, a study has been undertaken to plan a comprehensive development of the drinking water distribution system in Sungai Pinyuh, guided by the Regional Regulation of Mempawah Regency No. 3 of 2014. The planning utilizes EPANET software, a tool developed by the U.S. Environmental Protection Agency (EPA), which allows for the simulation of dynamic conditions in the water distribution system. EPANET helps create detailed maps of the pipe network, determine pump and reservoir locations, and specify pipe characteristics. The software’s ability to simulate various scenarios, such as changes in demand or disruptions, is crucial for ensuring a reliable water supply. The analysis incorporates primary data from field observations and secondary data from population statistics and existing water supply documents. The proposed distribution system will employ HDPE pipes of various diameters and a pump with a capacity of 200 L/s and a head of 5 meters. Simulation results indicate that the system's velocity, ranging from 0.3 to 0.72 m/s, and other parameters, such as head loss and pressure, are within acceptable limits. Additionally, reservoir capacity planning is based on the maximum daily water demand, with a calculated capacity of approximately 3,347.839 m³ to ensure adequate supply. This study provides a strategic framework for improving the drinking water distribution system, addressing current deficiencies, and preparing for future needs.

GPU-accelerated OCT imaging: Real-time data processing and artifact suppression for enhanced monitoring of 3D bioprinted tissues and vascular-like networks

Optical coherence tomography (OCT) imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues. However, it still faces the following challenges: including data processing speed, image quality, and improvements in three-dimensional (3D) visualization effects. OCT technology, especially functional imaging techniques like optical coherence tomography angiography (OCTA), requires a long acquisition time and a large data size. Despite the substantial increase in the acquisition speed of swept source optical coherence tomography (SS-OCT), it still poses significant challenges for data processing. Additionally, during in situ acquisition, image artifacts resulting from interface reflections or strong reflections from biological tissues and culturing containers present obstacles to data visualization and further analysis. Firstly, a customized frequency domain filter with anti-banding suppression parameters was designed to suppress artifact noises. Then, this study proposed a graphics processing unit (GPU)-based real-time data processing pipeline for SS-OCT, achieving a measured line-process rate of 800[Formula: see text]kHz for 3D fast and high-quality data visualization. Furthermore, a GPU-based real-time data processing for CC-OCTA was integrated to acquire dynamic information. Moreover, a vascular-like network chip was prepared using extrusion-based 3D printing and sacrificial materials, with sacrificial material being printed at the desired vascular network locations and then removed to form the vascular-like network. OCTA imaging technology was used to monitor the progression of sacrificial material removal and vascular-like network formation. Therefore, GPU-based OCT enables real-time processing and visualization with artifact suppression, making it particularly suitable for in situ noninvasive longitudinal monitoring of 3D bioprinting tissue and vascular-like networks in microfluidic chips.

Impacts of stream drying depend on stream network size and location of drying.

Stream drying is increasing globally, with widespread impacts on stream ecosystems. Here, we investigated how the impacts of drying on stream ecosystem connectivity might depend on stream network size and the location of drying within the stream network. Using 11 stream networks from across the United States, we simulated drying scenarios in which we varied the location and spatial extent of drying. We found that the rate of connectivity loss varied with stream network size, such that larger stream networks lost connectivity more rapidly than smaller stream networks. We also found that the rate of connectivity loss varied with the location of drying. When drying occurred in the mainstem, even small amounts of drying resulted in rapid losses in ecosystem connectivity. When drying occurred in headwater reaches, small amounts of drying had little impact on connectivity. Beyond a certain threshold, however, connectivity declined rapidly with further increases in drying. Given the increasing stream drying worldwide, our findings underscore the need for managers to be particularly vigilant about fragmentation when managing at large spatial scales and when stream drying occurs in mainstem reaches.

Heterogeneous Social Network Location Aware Multi Party Data Protection and Privacy System

Social networks are large online communities that allow users to interact and share information. They can be used to connect with friends, family, or potential romantic partners, build a business, or even find a job. The evolution of social media has led to a trend of posting daily photos on online Social Network Platforms (SNPs). The privacy of online photos is often protected carefully by security mechanisms. However, these mechanisms will lose effectiveness when someone spreads the photos to other platforms. This project proposes a blockchain based secure photo sharing framework that provides powerful dissemination control for heterogeneous social network photo sharing. In contrast to security mechanisms running separately in centralized servers that do not trust each other, this framework achieves consistent consensus on photo dissemination control through carefully designed smart contract-based protocols

Optimal spatial distribution of seismic stations to detect magma migration using the seismic amplitude ratio analysis

Magma migrations frequently trigger seismic swarms, resulting in seismic events that overlap in time and hinder real-time phase picking commonly used for hypocenter location. Addressing this challenge, seismic amplitude ratio analysis (SARA) allows identification of seismic migrations in real-time by simply tracking the relative seismic amplitude between a pair of seismic stations.This paper aims to identify key statistical features of the seismic network array locations that improve their ability to detect seismic migrations using SARA. We evaluated the capability to detect the most frequently oriented magma migrations in over 100 volcanoes, using a criterion previously proposed to study vertical magma migrations in Piton de la Fournaise. Additionally, we investigate the influence of vent-station proximity on magma conduit coverage and identify the distance ratio that yields improved detection.Furthermore, we estimate the seismic network efficiency by calculating the detection capability volume per station. We then use the random forest regression algorithm to identify which statistical features of the seismic network location contribute more to the efficiency disparity among different volcanoes. Notably, our findings reveal that optimizing seismic network coverage entails maximizing the standard deviation of relative pair station distances, while maintaining a prescribed minimum separation distance between station pairs. Our results reveal important criteria that can be used to optimize seismic network location design.

Combination of Matrix and Genetic Algorithms for Fault Location of Active Distribution Network

Abstract Timely and effective location of faults in distribution networks is important for restoring power supply and dealing with faults. In this paper, a method combining matrix algorithm and genetic algorithm (GA) is proposed to determine the fault section. Firstly, the matrix algorithm is applied to locate the fault section initially by combining the alarm information uploaded from the feeder terminal unit (FTU). Secondly, in order to deal with the problem of outputting wrong location results, the switching function of the active distribution network is constructed to check the location results calculated by the matrix, and the results that do not meet the checking will be treated as a suspicious fault set, and the relevant evaluation function is constructed and optimized using the genetic algorithm, so as to arrive at the correct fault zone location results. The results of the example simulation show that the method used in this paper can achieve fast and fault-tolerant fault location in active distribution networks.

Deep Learning-based Intelligent Fault Diagnosis for Power Distribution Networks

Power distribution networks with distributed generation (DG) face challenges in fault diagnosis due to the high uncertainty, randomness, and complexity introduced by DG integration. This study proposes a two-stage approach for fault location and identification in distribution networks with DG. First, an improved bald eagle search algorithm combined with the Dijkstra algorithm (D-IBES) is developed for fault location. Second, a fusion deep residual shrinkage network (FDRSN) is integrated with IBES and support vector machine (SVM) to form the FDRSN-IBS-SVM model for fault identification. Experimental results showed that the D-IBES algorithm achieved a CPU loss rate of 0.54% and an average time consumption of 1.70 seconds in complex scenarios, outperforming the original IBES algorithm. The FDRSN-IBS-SVM model attained high fault identification accuracy (99.05% and 98.54%) under different DG output power levels and maintained robustness (97.89% accuracy and 97.54% recall) under 5% Gaussian white noise. The proposed approach demonstrates superior performance compared to existing methods and provides a promising solution for intelligent fault diagnosis in modern distribution networks.

북부 규슈 마한 교류 네트워크의 변천 및 구조 연구

Records implying the interaction between the Korean Peninsula and the Japanese archipelago are found in works such as the 『漢書地理志』 and the 『魏志倭人伝』. In addition, considering that the Nakrang (樂浪)·Daebang (帶方) route was inevitably through the Mahan region, it can be seen that Mahan was also located within the Korea-Japan interaction network. This is evidenced by the large number of Mahan pottery found in northern Kyushu, where various Korean Peninsula-type pottery are concentrated. This article attempts to estimate the location and operational strategy of the Mahan interaction network from a comparative perspective, paying attention to the specificity of the logistics environment in northern Kyushu. The temporal and spatial aspects of Mahan type pottery excavated from northern Kyushu are re-examined and compared with those of the Nakrang and Byeon·jinhan(弁·辰韓) type pottery that were introduced at the same time. Through this, it can be admitted that the Mahan interaction network has been organized since the second half of the late Yayoi period, but it is judged that they have taken advantage of and participated in the network established around Nakrang in the past. Mahan's new initiative is recognized in the process of dismantling and reorganizing existing networks from the beginning to the first half of the Early Kofun Period. Furthermore, by looking at the background of the establishment and dismantling of the Nishijinmachi[西新町] ruins, which are the inflection points, from a relative perspective and operational strategy, the possibility that they had a trade structure vulnerable to risk management was pointed out. Through the above results, it was possible to estimate the specific status and role of the Mahan interaction network identified in northern Kyushu. It was possible to evaluate Mahan's external capabilities in a new position during the Proto-Three Kingdom Period, which had previously received relatively little attention as part of the Samhan or as part of the Baekje. Through this, it is expected to be a study that can contribute to the reconstruction of the multifaceted and comprehensive aspects of external relations formed by Mahan while evoking research on Korea-Japan interactions during the Proto-Three Kingdom Period.

METHODOLOGY FOR IMPROVING THE CYBER THREAT PROTECTION SYSTEM

The article presents the results of a study of the enterprise information security system and the development of proposals for its implementation. The article considers security as a system that includes tools and processes used by organizations to protect information. This includes policy settings that prevent unauthorized persons from accessing business or personal information. Information security is positioned as a growing and developing industry, covering a wide range from network and infrastructure protection to testing and auditing. The article analyzed IT products designed to ensure information security that are used in small and medium-sized businesses, explored problem areas and current applications that entrepreneurs use. One of the most pressing problems in the IT industry today, in the era of big data, is the rapid growth of the volume of unstructured content. Virtually every corporate data center (Docs) has file servers, corporate portals, Microsoft Exchange folders, network and cloud storage locations that contain many documents, including the content of important information. In addition, the repeated increase in volume and the growing variety of information stored and processed significantly complicates the task of managing this data.

Investigation of Networked Minimarket Development

The growth of network minimarkets in Bali is growing very rapidly, including in Tabanan district. This needs to get the attention of the regional government and local village officials to reduce the negative impact of network minimarkets which are increasingly mushrooming. This research uses primary data in the form of interviews and questionnaires which will later be processed using SWOT analysis to obtain indicators regarding the strengths, weaknesses, opportunities and challenges of building a new minimarket in Samsam Village. Of course, validity and reliability tests are also carried out to ensure that the questionnaire form that has been prepared based on interviews is truly valid and reliable to be used as a tool for obtaining research data. This research took the study area in Samsam Village, Kerambitan District, Tabanan Regency, Bali . It is hoped that this research can provide meaningful input to the regional government and local village officials in making policies on granting permits to build networked minimarkets in Samsam Village. So that the existence of the Bali Land Transportation Polytechnic Campus in Samsam Village can provide positive color and benefits to Samsam Village, Tabanan Regency, Bali. The results of this research show that the SWOT Indicator Analysis of networked minimarket development in Samsam Village, Tabanan Regency, Bali has 4 strength factors (Strength). ), 2 weakness factors (Weakness), 3 opportunity factors (Opportunities), and 3 threat factors (Threats). Where the results of the analysis are in quadrant 1, which means that building a new network minimarket in the Samsam Village area, Tabanan Regency, Bali is still feasible. Meanwhile, based on factor analysis and priority matrix, the results showed that the new networked minimarket construction location that received a positive response from local village officials and communities was in the Banjar Dinas Samsam 1 area (Jalan Cempaka Putih).

Fault Location in Distribution Network Based on RNN and Transfer Learning

Fault location in distribution networks is a major challenge that needs to be addressed in power distribution systems. Currently, fault location methods based on matrix algorithms, genetic algorithms, deep learning, and other algorithms have received wide attention from the industry. However, such methods have some drawbacks: (1) they require high accuracy in fault information uploads, which can lead to low fault tolerance; (2) they tend to converge early and get stuck in local optimal solutions; (3) they involve high computational complexity, leading to location delays. In this study, we propose a fault location framework based on recurrent neural network (RNN) and transfer learning. In this method, we first encode the information data collected from distribution terminals, and then use RNN to establish a nonlinear mapping relationship between fault features and fault location intervals, which effectively improves fault tolerance and reduces misjudgment issues. We then use transfer learning to load the pre-trained model onto the target task to address the problem of insufficient data for fault location in distribution networks. Experimental results show that after 15 rounds of training, our T-RNN model has achieved over 80% accuracy. Benefiting from Glorot weight initialization adopted after transfer learning, the model achieves good performance early on compared to the BP model, converges faster, and ultimately achieves a prediction accuracy of 96.5%.

Application of GPR to Detect Lost Valve Chambers in a Utility Network

The exact geographic location of the fresh water transmission network of Dhaka WASA was not known to the agency. With time and development works, access to many such underground valve chambers were lost. Their recovery was usually done through manual digging and trenching. This was expensive, time- and labour-consuming, often led to traffic jams, and the rate of success was low. This study was conducted to use the non-intrusive method of Ground Penetrating Radar to locate those valves as an alternative. A total of 27 out of the 41 lost nodes in the network was successfully recovered and their precise location was recorded using RTK-GNSS. The article presents the detail procedure of locating the valve chambers of underground pipeline network. It also highlights how the lack of collaboration between agencies are causing water supply and maintenance problems to this day, and how infrastructure development of one agency may erode successes of another. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(2), 2023, P 139-151

Automatic classification of underground utilities in Urban Areas: A novel method combining ground penetrating radar and image processing

Precise determination of the location of underground utility networks is crucial in the field of civil engineering for: the planning and management of space with densely urbanized areas, infrastructure modernization, during construction and building renovations. In this way, damage to underground utilities can be avoided, damage risks to neighbouring buildings can be minimized, and human and material losses can be prevented. It is important to determine not only the location but also the type of underground utility network. Information about location and network types improves the process of land use design and supports the sustainable development of urban areas, especially in the context of construction works in build-up areas and areas planned for development. The authors were inspired to conduct research on this subject by the development of a methodology for classifying network types based on images obtained in a non-invasive way using a Leica DS2000 ground penetrating radar. The authors have proposed a new classification algorithm based on the geometrical properties of hyperboles that represent underground utility networks. Another aim of the research was to automate the classification process, which may support the user in selecting the type of network in images that are sometimes highly noise-laden. The developed algorithm shortens the time required for image interpretation and the selection of underground objects, which is particularly important for inexperienced operators. The classification results revealed that the average effectiveness of the classification of network types ranged from 42% to 70%, depending on the type of infrastructure.

Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks.

Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using SaTScan. SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease's systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve.

A spatial statistical approach to estimate bus stop demand using GIS-processed data

This study integrates the fields of geography, urban transit planning, and statistical learning to develop a sophisticated methodology for predicting bus demand at the stop level. It uses a Generalized Additive Model that captures non-linear relationships and incorporates spatial dependence, improving traditional methods. It showcases a high predictive capacity with a pseudo R-squared of 0.79 during its validation, ensuring substantial explanatory power for new observations. A large number of variables, including land-use characteristics, socioeconomic factors, and transit supply, are analysed. These widely available predictors facilitate the transferability of the methodology to other urban areas. Transit supply predictor considers the number of annual trips per stop and area as well as the location of stops along the lines that serve them. GIS processing of the data allows the calculation of variables within the areas of influence of each stop, obtained by following the walkable street network. For the case study, the presence of universities, hospitals, and lodgings areas, as well as inhabitants and ratio of bus trips show a positive impact on bus demand. This geo-analysis process employs accurate disaggregated data, such as information on uses in each building, as well as methods for assigning socioeconomic information from local areas to residential buildings. This study highlights the complex relationship between the location of transit network stops, both along the bus line and in terms of geographical proximity, their transit supply, and its surrounding factors. The results indicate that there is spatial dependence for stops less than 1.15 km apart. The developed methodology provides reliable information to transit network planners for decision making. Specifically, this proposed methodology can contribute to designing new routes, optimizing stop locations, and estimating the impact of changes in the transit network or urban planning on bus demand. All these improvement measures promote sustainable urban mobility, consequently fostering environmental and social benefits.

Improving the Accuracy of DDos Attack Detection by Fine Tuning CNN-LSTM Classifier

Abstract: DDoS attacks also known as distributed denial of service (DDoS) attacks have emerged as one of the most serious and fastest-growing threats on the Internet. Denial-of-service (DDoS) attacks are an example of cyber-attacks that target a specific system or network in an attempt to render it inaccessible or unusable for a period of time. As a result improving the detection of diverse types of DDoS cyber threats with better algorithms and higher accuracy while keeping the computational cost under control has become the most significant component of detecting DDoS cyber threats. DDoS (distributed denial-of-service) attack originates from many sources scattered over multiple network locations. DoS attacks are primarily motivated by the desire to significantly degrade the performance or completely consume a certain resource and a process to exploit a machine defect and cause failure of a processing or exhausting the system resources by exploiting a system flaw. Yet another method of assaulting the target system is to flood the network and monopolies it so preventing anyone else from utilizing it. DDoS attack has a high impact on crashing the network resources making the target servers unable to support the valid users.

Data-driven traffic sensor location and path flow estimation using Wasserstein metric

This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality.

Cultural mobilities and cultural heritage: concepts for an Asia-centric approach

This article sets out an original and exploratory framework for examining emerging concepts of cultural mobilities and heritage with a key focus on the infrastructure and spatialities of cultural mobilities in, of and through Asia—specifically China. To date, the scholarly analysis of mobilities has been dominated by the social sciences in relation to central themes of migration, national borders, crisis and transnational flows of objects and people. This paper seeks to expand the focus in mobilities discourses to humanities, not only to research how infrastructures and spatialities are shaped by culture and heritage but also to analyse cultural mobility through infrastructure and spatialities. We set out epistemic considerations for approaching cultural mobilities through an interdisciplinary lens that seeks to address heritage studies of diverse kinds, from environmental to religious to architectural. By centring Asian epistemes, the paper also challenges recognition and interpretation biases in the humanities and social sciences that continue to privilege Eurocentric hegemonies. Together, the co-authors examine: how Asian cultures operate through material and non-material infrastructures that defy singular location in geopolitical crossings and networks; how culture is mobilised in different ways through infrastructure; the entanglement of cultural heritage with political infrastructure and living practice as well as embedded values. The article discusses China’s Belt and Road Initiative (BRI), yoga as a cultural infrastructure between India and China, and the infrastructure and multiple spatialities of Chinatown as a evolving practice of cultural heritage.

Deep-Pathfinder: a boundary layer height detection algorithm based on image segmentation

Abstract. A novel atmospheric layer detection approach has been developed based on deep learning techniques for image segmentation. Our proof of concept estimated the layering in the atmosphere, distinguishing between pollution-rich layers closer to the surface and cleaner layers aloft. Knowledge of the spatio-temporal development of atmospheric layers, such as the mixing boundary layer height (MBLH), is important for the dispersion of air pollutants and greenhouse gases, as well as for assessing the performance of numerical weather prediction systems. Existing lidar-based layer detection algorithms typically do not use the full resolution of the available data, require manual feature engineering, often do not enforce temporal consistency of the layers, and lack the ability to be applied in near-real time. To address these limitations, our Deep-Pathfinder algorithm represents the MBLH profile as a mask and directly predicts it from an image with backscatter lidar observations. Deep-Pathfinder was applied to range-corrected signal data from Lufft CHM15k ceilometers at five locations of the operational ceilometer network in the Netherlands. Input samples of 224 × 224 px were extracted, each covering a 45 min observation period. A customised U-Net architecture was developed with a nighttime indicator and MobileNetV2 encoder for fast inference times. The model was pre-trained on 19.4×106 samples of unlabelled data and fine-tuned using 50 d of high-resolution annotations. Qualitative and quantitative results showed competitive performance compared to two reference methods: the Lufft and STRATfinder algorithms, applied to the same dataset. Deep-Pathfinder enhances temporal consistency and provides near-real-time estimates at full spatial and temporal resolution. These properties make our approach valuable for application in operational networks, as near-real-time and high-resolution MBLH detection better meets the requirements of users, such as in aviation, weather forecasting, and air quality monitoring.

Research on the Relationship Between Institutional Investor Network, Government R&D Subsidy and Corporate R&D Investment: Evidence from China

This paper studies the relationship between institutional investor network, government subsidy and R&D investment in Chinese market. This paper selects the data of A-share listed companies from 2015 to 2020 as samples, and uses UCIENT and Stata to process and analyze the data. In this study, companies in the financial sector were excluded to avoid cross-pollination. Through empirical analysis, the research results show that the higher the degree of institutional investors' network centrality, the higher the R&D investment of enterprises. This means that a well-positioned institutional investor network can obtain more professional, high-value information, and promote corporate R&D investment through joint pressure and voting by institutional investors. In addition, this study finds that government subsidies have an incentive effect on corporate R&D investment. Further analysis verifies the moderating effect of government R&D subsidies on institutional investor network and firm R&D investment. The finding shows that government subsidies can improve the promotion effect of institutional investor network location on firm R&D investment.