Limited Storage Capacity Research Articles

4D BIM integrated optimization of construction steel bar procurement plan for limited storage capacity

Purpose Due to its longer length compared to other construction materials and distinctive stacking patterns, obtaining construction steel bars in congested construction sites with limited storage capacity becomes challenging. Lack of storage space in crowded places prompts the need for building steel bar storage choice optimization. Therefore, this study aims to optimize the construction steel bar procurement plan by providing when and how much rebar to order and how to stack different sizes of rebar considering limited storage capacity. Design/methodology/approach A novel approach has been presented in this paper by integrating 4D building information modelling (BIM) and mixed-integer linear programming (MILP). This technique uses BIM to retrieve material quantities, including rebar, during the design phase. Following that, activities are scheduled depending on the duration determined by crew productivity data and material quantity. Then, based on the prior price, the price of each unit of rebar is projected for the duration of construction using the exponential smoothing method. After that, the MILP approach is used to generate an optimal steel bar procurement plan for limited storage space following the scheduled rebar-related operations. Findings The developed strategy minimizes overall procurement costs and ensures the storage of rebar as per standard guidelines. An optimal rebar procurement and storage plan to construct a six-storied RC frame has been presented in this paper as a demonstrative example to show the effectiveness of the proposed method. Originality/value This work partially satisfies a long-sought research need for establishing a comprehensive construction steel bar procurement system, making it a very useful source of information for practitioners and researchers. The proposed method can be used to minimize a key performance limitation that the conventional rebar procurement practice for crowded building sites may experience.

Analisis Faktor Kunci Keberhasilan Implementasi Last Planner System untuk Peningkatan Kinerja Waktu pada Proyek Bendungan

Infrastructure development is crucial to the development of a nation’s economy. Based on data from the 2021 World Competitiveness Yearbook, Indonesia's infrastructure is ranked 57th out of 64 countries and one of the assessment criteria is related to water resources. Water resource management in Indonesia is still faced with fundamental challenges, including the limited water storage capacity in Indonesia so alternative water resources development is needed, among which is through the construction of dams. In practice, dam projects are very vulnerable to time delays. One of the solutions to this problem is the application of the last planner system method. The implementation of the last planner system can help create major improvements in project planning, increase workflow and productivity, and reduce project time and site accidents. This research has an update regarding the implementation of the last planner system method on a dam project and analyzing the key success factors using SEM-PLS. According to the study's findings, the key success factors of the implementation of the last planner system method on dam projects are budget plan, work schedule, clarity of scope of work, work plan, planning process, and weekly plan

Novel blockchain deep learning framework to ensure video security and lightweight storage for construction safety management

In construction management, video data tampering behavior like manual forging and deletion can negatively impact on-site safety and accident accountability. Blockchain technology holds the potential to address this issue by leveraging distributed ledger characteristics. However, blockchain's limited storage capacity and block size make it difficult to upload large-sized construction data such as daily monitoring video. Furthermore, it is unnecessary to store all construction data in any case. Therefore, this study proposes a blockchain deep learning framework that focuses on how to efficiently extract and securely store key information (i.e., video summarization that involves worker’s unsafe behavior) on-blockchain for data traceability. The framework involves a novel data-driven and rule-based keyframe extraction (DRKE) model to lightweight large-sized construction video in the nascent field of deep learning and blockchain combination. To define parameters for the DRKE model, specific construction rules (e.g., people’s unsafe behavior-based and people-based rules) have been pre-defined. This framework has been evaluated, and the results demonstrate its capability for effective video security storage, facilitating practical needs in construction management. The study extends existing research and provides a practical solution for large-sized construction video storage with security and lightweight considerations. The proposed video security storage and data lightweight process offers substantial benefits to construction management, such as streamlined accident investigation and accountability and improved on-site work efficiency, contributing to the smooth progress of construction projects.

Pre-Coded Inter-Satellite Routing Algorithm with Load Balancing for Mega-Constellation Networks

In order to achieve global multiple seamless coverage, space-based internet usually adopts low Earth orbit (LEO) mega-constellation networks structure, which has the characteristics of high network topology dynamics, limited on-board computing and storage capacity, and uneven distribution of ground traffic. Such features may cause problems such as high transmission delay, network congestion and link interruption. Establishing a stable, efficient and balanced satellite communication link can effectively alleviate the performance of the transmission delay, load balancing, and network throughput. Taking advantage of the regularity of network topology, a pre-coded inter-satellite routing algorithm with load balancing is proposed, which includes 3 parts: (a) the routing sequence coding method and the concept of gateway satellite Service Region (GSSR) are proposed; (b) the initial routing sequence of GSSR is generated based on the maximum network flow method under the ideal situation of uniform satellite traffic distribution; (c) aiming at the uneven distribution of traffic, the Sinkhorn algorithm is used to improve the load balancing performance of inter-satellite links. Simulation results show that, for the Starlink Group-4 constellation, the proposed method can maintain a low transmission delay and improve the load balancing together with the network throughout performance with minimal hops and low time complexity.

Joint Peak Shaving Mechanism of Pipeline Gas Storage: Case Study of West-East Gas Pipeline

Demand imbalance owing to gas supply is considered in the context of a surge in natural gas demand. Currently, a single method of storing gas at the end of a pipeline is commonly used to alleviate the peak shaving pressure. This method is economical, convenient, and able to respond quickly to fluctuations in user gas demand. However, because of its limited gas storage capacity, gas storage for peak shaving is still considered when the demand for peak shaving is high. With the increasing demand for natural gas inclined towards residential life, it is difficult to achieve a relative balance between the supply and demand of natural gas solely by relying on traditional pipeline-end storage and peak shaving. Based on this, a “pipeline gas storage integration” peak-shaving model is constructed. Furthermore, the model is combined with real-time analysis of the required peak-shaving amount of the pipeline to achieve synergistic effects of multiple peak-shaving methods. The established model is used to study the collaborative peak-shaving mechanism between pipelines and gas storage facilities under different supply and demand conditions. In addition, a quantitative relationship between the optimal economic benefits and different peak-shaving methods is constructed using the model. A peak-shaving mechanism of gas transmission considering different uniformity coefficients is also studied. Considering the case of the Jiangsu section of the West-East Gas Pipeline for simulation analysis, we reasonably plan the capacity and layout of gas storage facilities, optimize the peak shaving capacity of pipelines and gas storage facilities, and provide specific measures and guidance for actual engineering. Furthermore, we improve the efficiency and flexibility of the natural gas supply chain and achieve maximum economic benefits.

FEB-YOLOv8: A multi-scale lightweight detection model for underwater object detection.

Underwater object detection plays a crucial role in safeguarding and exploiting marine resources effectively. Addressing the prevalent issues of limited storage capacity and inadequate computational power in underwater robots, this study proposes FEB-YOLOv8, a novel lightweight detection model. FEB-YOLOv8, rooted in the YOLOv8 framework, enhances the backbone network by refining the C2f module and introducing the innovative P-C2f module as a replacement. To compensate for any potential reduction in detection accuracy resulting from these modifications, the EMA module is incorporated. This module augments the network's focus on multi-scale information, thus boosting its feature extraction capabilities. Furthermore, inspired by Bi-FPN concepts, a new feature pyramid network structure is devised, achieving an optimal balance between model lightness and detection precision. The experimental results on the underwater datasets DUO and URPC2020 reveal that our FEB-YOLOv8 model enhances the mAP by 1.2% and 1.3% compared to the baseline model, respectively. Moreover, the model's GFLOPs and parameters are lowered to 6.2G and 1.64M, respectively, marking a 24.39% and 45.51% decrease from the baseline model. These experiments validate that FEB-YOLOv8, by harmonizing lightness with accuracy, presents an advantageous solution for underwater object detection tasks.

Inventory Control for Omnichannel Retailing Between One Warehouse and Multiple Stores

In this article, we research a multiperiod two-echelon inventory control problem for omnichannel retailing [i.e., online channel, offline channel, and buy-online-and-pick-up-in-store (BOPS) channel]. Especially, we jointly consider the limited storage capacity of the stores and the delivery lead time of the warehouse. For the out of stock, the unsatisfied demands for multiple purchasing channels have different processes (i.e., lost sales or backlog), based on the different characteristics of multiple channels. First, we divide the multiperiod inventory control problem for omnichannel retailing into two subproblems (the inventory control problem for the stores and the inventory control problem for the warehouse). Then, to reduce the scale of the first subproblem and the computation effort, we propose a method based on Lagrangian relaxation and value iteration to solve the inventory control problem of stores. Further, based on the results of the inventory control problem of stores, we construct a delay-aware Markov decision process (DAMDP) for the inventory control problem of the warehouse and employ the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning technique based on the constructed DAMDP to solve the second subproblem. Besides, we also extend our analysis to the case of the low-level backlog in buying-online channels (i.e., online and BOPS channels). Finally, some numerical experiments are conducted to illustrate the performance of the inventory control policy for omnichannel retailing and reveal some managerial insights.

An Inception of Value Chain Development of Sweet Orange; Kinesis to Food Security in the Developing Countries Scenario

Sweet orange is one of the major citrus fruits of Nepal which is widely cultivated in mid-hills. This paper assesses the value chain of sweet oranges cultivated in the Ramechhap district of Nepal, considering its economic significance for farmers. The study adopts a qualitative research approach, utilizing key informant interviews and focus group discussions with various stakeholders. Participants were selected based on a list of producers collected from the Prime-Minister Agricultural Modernization Project (PMAMP) Ramechhap district, and the snowball method was employed to identify other actors. This study revealed that the value chain of sweet oranges in the Ramechhap district involves growers, vendors, wholesalers, retailers, and consumers as the main actors, while federal, provincial, and local agricultural service centres serve as enablers. Additionally, findings show vendors play a dominant role, in determining the per unit price and pressuring farmers to sell immediately after harvest due to limited storage capacity. Vendors are enjoying increased profit margins because they have greater bargaining power over farmers, who have inadequate storage facilities. The study suggests areas for improvement: using quality seedlings, managing nutrients effectively, and employing proper storage techniques like cold stores to prolong shelf-life and enhance quality, taste, and colour. Enhancing productivity via trifoliate rootstock, optimizing fruit quality, and extending shelf-life through improved storage facilities can substantially boost revenue for producers. Understanding the entire value chain provides insights into enhancing the overall performance of value chain actors. Further studies can explore additional dimensions of the chain in more depth.

An adaptive image compression algorithm based on joint clustering algorithm and deep learning

AbstractIn recent years, deep artificial neural networks have attracted much attention and have been applied in various fields because they surpass the parameter fitting effect of traditional methods under the condition of data convergence. On the other hand, limited transmission bandwidth and storage capacity make image compression necessary in communication. Here, a compression algorithm that combines the K‐means clustering algorithm with the neural network algorithm is proposed. First, the pixel points of the image are clustered by K‐means algorithm in order to reduce the amount of data input to the neural network algorithm. Secondly, neural network is used to extract image features which realizes further compression. The experiment results show that the peak signal‐to‐noise ratio (PSNR) is 33.48 dB at most with compression ratio at 32:1. The ablation experiment shows that the run time speeds up 9.5% compared to the algorithm without K‐means clustering. Comprehensive comparison experiment shows that the average PSNR is 30.09 dB, which is larger than other baseline approaches. The proposed algorithm is an efficient solution for image compression.

Understanding the gut microbiota by considering human evolution: a story of fire, cereals, cooking, molecular ingenuity, and functional cooperation.

SUMMARYThe microbial community inhabiting the human colon, referred to as the gut microbiota, is mostly composed of bacterial species that, through extensive metabolic networking, degrade and ferment components of food and human secretions. The taxonomic composition of the microbiota has been extensively investigated in metagenomic studies that have also revealed details of molecular processes by which common components of the human diet are metabolized by specific members of the microbiota. Most studies of the gut microbiota aim to detect deviations in microbiota composition in patients relative to controls in the hope of showing that some diseases and conditions are due to or exacerbated by alterations to the gut microbiota. The aim of this review is to consider the gut microbiota in relation to the evolution of Homo sapiens which was heavily influenced by the consumption of a nutrient-dense non-arboreal diet, limited gut storage capacity, and acquisition of skills relating to mastering fire, cooking, and cultivation of cereal crops. The review delves into the past to gain an appreciation of what is important in the present. A holistic view of "healthy" microbiota function is proposed based on the evolutionary pathway shared by humans and gut microbes.

An End-to-End Security Scheme for Protection from Cyber Attacks on Internet of Things (IoT) Environment

The Internet of Things (IoT) technology has recently emerged as a potential global communication medium that efficiently facilitates human-to-human, human-to-machine, and machine-to-machine communications. Most importantly, unlike the traditional Internet, it supports machine-to-machine communication without human intervention. However, billions of devices connected to the IoT environment are mostly wireless, small, hand-held, and resourced-constrained devices with limited storage capacities. Such devices are highly prone to external attacks. These days, cybercriminals often attempt to launch attacks on these devices, which imposes the major challenge of efficiently implementing communications across the IoT environment. In this paper, the issue of cyber-attacks in the IoT environment is addressed. An end-to-end encryption scheme was proposed to protect IoT devices from cyber-attacks.

A Fuzzy Logic Based Cluster Head Election Technique for Energy Consumption Reduction in Wireless Sensor Networks

Wireless sensor networks deploy sensor nodes to different areas for data collection. The small size of these sensor nodes allows limited energy storage capacity, and most applications of the networks do not support recharging the batteries once their energy is depleted. Research on energy efficiency in wireless sensor networks is thus an active area that seeks to minimize energy consumption so that the sensor nodes can live longer. Clustering, one of the energy consumption optimization techniques, is employed in this research. It splits the network into smaller groups for data collection and forwards the data to the base station via appointed cluster heads. A fuzzy-based cluster head election strategy is proposed here to improve energy efficiency in wireless sensor networks. The input parameters of the fuzzy inference system are chosen as the residual energy, the node centrality, and the mobility factor. The system generates an output of the chance of a node being selected as a cluster head based on the combination of the values of the given inputs. The simulation results show that the proposed model reduces the network’s overall energy consumption and extends the sensor nodes’ lifetime.

DAO2: Overcoming Overall Storage Overflow in Intermittently Connected Sensor Networks

Many emerging sensor network applications operate in challenging environments wherein the base station is unavailable. Data generated from such intermittently connected sensor networks (ICSNs) must be stored inside the network for some unpredictable time before uploading opportunities become available. Consequently, sensory data could overflow the limited storage capacity available in the entire network, making discarding valuable data inevitable. To overcome such overall storage overflow in ICSNs, we propose and study a new algorithmic framework called data aggregation for overall storage overflow ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{DAO}^2$</tex-math> </inline-formula> ). Utilizing spatial data correlation that commonly exists among sensory data, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{DAO}^2$</tex-math> </inline-formula> employs data aggregation techniques to reduce the overflow data size while minimizing the total energy consumption in data aggregation. At the core of our framework are two new graph theoretical problems that have not been studied. We refer to them as traveling salesmen placement problem ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{TSP}^2$</tex-math> </inline-formula> ) and quota traveling salesmen placement problem (Q- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{TSP}^2$</tex-math> </inline-formula> ). Different from the well-known multiple traveling salesman problem (mTSP) and its variants, which mainly focus on the routing of multiple salesmen initially located at fixed locations, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{TSP}^2$</tex-math> </inline-formula> and Q- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{TSP}^2$</tex-math> </inline-formula> must decide the placement as well as the routing of the traveling salesmen. We prove that both problems are NP-hard and design approximation, heuristic, and distributed algorithms. Our algorithms outperform the state-of-the-art data aggregation work with base stations by up to 71.8% in energy consumption.

A novel chaotic map-based encryption scheme for surveillance videos

The widespread use of surveillance cameras has initiated privacy concerns among individuals being captured by these devices. Due to the limited internal storage capacity, these cameras often transmit recorded videos over unreliable networks. This situation creates opportunities for attackers to exploit vulnerabilities, such as launching Man-in-the-Middle attacks, replayed scene attacks, and tampering with the video content. Considering these risks, this paper introduces a new chaotic map-based encryption scheme for surveillance videos. The work proposes a novel chaotic map using cascading and coupling operations on functions and is referred to as a Cascaded Coupled Logistic-Sine-Cosine (CCLSC) map. The chaotic performance of the novel CCLSC map has been evaluated using the Lyapunov exponent and the Bifurcation diagram. The scheme to encrypt surveillance videos is designed using the novel CCLSC map and our earlier designed Sine-Tangent-Sine (STS) chaotic map. The process involves extracting individual frames from the video and subsequently encrypts each frame as a digital image. The encryption of each frame involves three steps. The first step generates a Dynamic Chaotic Matrix (DCM) using CCLSC and STS map. The second phase is pixel scrambling using DCM and the final step changes the pixel values using DCM. Our results validate the security, robustness, and effectiveness of the proposed method.

A Two-Warehouse Lot Sizing Problem for Defective Items with a Completely Backlogged Shortage Under Limited Storage Capacity for Rented Warehouses

Determining an economic order quantity across inventory management is vital for any production or distribution company. There is an assumption in classical lot-sizing problems that the inventories are stored in a single warehouse with unlimited capacity. However, the mentioned assumption may be unrealistic, and in many markets, owned warehouses (OW) have limited capacity, therefore, in some cases, a rented warehouse (RW) is considered for storing goods to create more flexibility in terms of space capacity. In this study, we proposed a more realistic extension of a two-warehouse system, which intends to investigate an optimal EOQ considering limited storage for both OW and RW. This paper contributes to considering multiple RWs with limited capacity. For this purpose, a two-objective MINLP lot-sizing problem with a discreet period and finite time horizon is taken into account. The complexity of the model is to determine if there is a need to rent one or multiple warehouses regarding limited storage on OWs and RWs as well as budget restrictions to hire one or multiple warehouses. Also, all parameters are considered to be stochastic which means they follow a specific probability distribution. As the capacity of RWs and the total available budget to rent them are random parameters, the constraint on budget, as well as RW’s capacity in a period, can be formulated as chance constraints. Finally, four multi-objective decision-making methods namely Goal programming, Lp-Metric, Goal Attainment, and Augmented E-Constraint are utilized to solve the concerned problem. Then, the best method is chosen based on graphical, statistical, and TOPSIS analysis. Moreover, the Lagrangian Relaxation method is applied since solving this optimization model in large-size problems requires a considerable amount of time. In the end, the effect of changing the rates of three parameters on objective function values is evaluated through sensitivity analysis.

Enhanced Dynamic Control Strategy for Stacked Dynamic Regulation Frequency Response Services in Battery Energy Storage Systems

Energy storage systems are undergoing a transformative role in the electrical grid, driven by the introduction of innovative frequency response services by system operators to unlock their full potential. However, the limited energy storage capacity of these systems necessitates the development of sophisticated energy management strategies. This paper investigates the newly introduced frequency response service, Dynamic Regulation, within the Great Britain electrical grid. Our study not only establishes control parameters but also demonstrates a novel approach to energy management that pushes the boundaries of the allowable service envelope. We present two distinctive control methods, the first serving as a reference for standard response, and the second as a dynamic control approach, exploiting the extremities of the allowable service envelope. A comprehensive sensitivity analysis that considers availability, the number of equivalent full cycles, and cost–revenue analysis based on grouped dynamic control state of charge setpoints is carried out. Our results underscore that the optimization of average availability takes precedence over merely minimizing the number of cycles, which leads us to define a target state of charge range of between 40% and 45% for a 1-h battery to achieve an availability &gt;95%. Furthermore, our study presents simulated results utilizing real-world frequency data, which reveal the transformative potential of the latter control method. By enhancing the availability of battery energy storage systems, this innovative approach promises not only higher revenues for the asset owner but also assists the system operator in managing frequency.

MXene derived Ti3C2/TiO2/Ag persistent photocatalyst with enhanced electron storage capacity for round-the-clock degradation of organic pollutant

Persistent photocatalysis has garnered significant attention due to its ability to sustain catalytic activity in dark by storing electrons. However, the practical application of persistent photocatalysis is hindered by limited electron storage capacity. Herein, we synthesized and demonstrated that Ti3C2/TiO2/Ag persistent photocatalyst has good electron storage capability. The electron storage capacity of Ti3C2/TiO2/Ag is up to 0.125 μmol/mg, which is 2.5 times that of Ti3C2/TiO2. The enhanced electron storage capacity resulted in improved dark-reaction activity because more electrons react with oxygen to form more radicals, as evidenced by degradation experiments of various organics. Especially, persistent photocatalytic degradation of tetracycline hydrochloride by Ti3C2/TiO2/Ag was achieved under natural outdoor conditions (from 2:00p.m. to 8:00p.m.). Additionally, the aid of oxidants such as peroxymonosulfate (PMS) can further improve the dark-reaction activity. TiO2/Ti3C2/Ag/PMS system exhibits excellent efficacy in removing tetracycline hydrochloride, oxytetracycline, rhodamine b, methyl orange, and methylene blue, with removal rates reaching 79.5 %, 81.4 %, 98.9 %, 99.1 %, and 99.2 %, respectively (15 min of light-reaction and 45 min of dark-reaction). This work provides a new strategy to enhance electron storage capacity and demonstrates that decoupling of light-reaction and dark-reaction may provide a new opportunity for photocatalytic removal of pollutants around the clock.

Deep Learning-Based Video Compression for Surveillance Footage

The utilization of closed-circuit television (CCTV) monitoring plays a pivotal role in the realm of video processing, providing an effective means for vigilant surveillance. However, a significant challenge associated with this practice is the substantial demand it places on storage space. Traditionally, surveillance footage is stored on hard disk drives, and due to limited storage capacities, it often necessitates periodic deletion. To tackle this issue, we have introduced an innovative method for compressing CCTV video, named “Detection-Based video Compression” (DBVC). Our DBVC model is a two-step process. In the first step, we employ advanced neural network approaches such as Mask-RCNN and YOLOv4 to determine active and idle frames within the surveillance video. These cutting-edge techniques enable precise identification of objects and events of interest in the video feed. In the second step, we construct a new video composed solely of the active frames, eliminating redundant or uneventful segments. After conducting a comprehensive analysis of the experimental results, it is evident that Mask-RCNN stands out with an impressive detection accuracy of 98% on the COCO dataset, making it a robust choice for identifying objects and events in the surveillance footage. Consequently, we chose to leverage the output generated by Mask-RCNN and YOLOv4 for subsequent processing stages. Our DBVC approach is a breakthrough in video compression technology, significantly reducing the storage space required for CCTV footage. In fact, it achieves an average compression ratio of up to 85% when using YOLOv4, surpassing the capabilities of existing state-of-the-art compression methods. This innovation not only optimizes storage efficiency but also maintains a high level of surveillance data integrity, making it a valuable advancement in the field of CCTV video processing and storage management.

The storage capacity of a directed graph and nodewise autonomous, ubiquitous learning.

The brain, an exceedingly intricate information processing system, poses a constant challenge to memory research, particularly in comprehending how it encodes, stores, and retrieves information. Cognitive psychology studies memory mechanism from behavioral experiment level and fMRI level, and neurobiology studies memory mechanism from anatomy and electrophysiology level. Current research findings are insufficient to provide a comprehensive, detailed explanation of memory processes within the brain. Numerous unknown details must be addressed to establish a complete information processing mechanism connecting micro molecular cellular levels with macro cognitive behavioral levels. Key issues include characterizing and distributing content within biological neural networks, coexisting information with varying content, and sharing limited resources and storage capacity. Compared with the hard disk of computer mass storage, it is very clear from the polarity of magnetic particles in the bottom layer, the division of tracks and sectors in the middle layer, to the directory tree and file management system in the high layer, but the understanding of memory is not sufficient. Biological neural networks are abstracted as directed graphs, and the encoding, storage, and retrieval of information within directed graphs at the cellular level are explored. A memory computational model based on active directed graphs and node-adaptive learning is proposed. First, based on neuronal local perspectives, autonomous initiative, limited resource competition, and other neurobiological characteristics, a resource-based adaptive learning algorithm for directed graph nodes is designed. To minimize resource consumption of memory content in directed graphs, two resource-occupancy optimization strategies-lateral inhibition and path pruning-are proposed. Second, this paper introduces a novel memory mechanism grounded in graph theory, which considers connected subgraphs as the physical manifestation of memory content in directed graphs. The encoding, storage, consolidation, and retrieval of the brain's memory system correspond to specific operations such as forming subgraphs, accommodating multiple subgraphs, strengthening connections and connectivity of subgraphs, and activating subgraphs. Lastly, a series of experiments were designed to simulate cognitive processes and evaluate the performance of the directed graph model. Experimental results reveal that the proposed adaptive connectivity learning algorithm for directed graphs in this paper possesses the following four features: (1) Demonstrating distributed, self-organizing, and self-adaptive properties, the algorithm achieves global-level functions through local node interactions; (2) Enabling incremental storage and supporting continuous learning capabilities; (3) Displaying stable memory performance, it surpasses the Hopfield network in memory accuracy, capacity, and diversity, as demonstrated in experimental comparisons. Moreover, it maintains high memory performance with large-scale datasets; (4) Exhibiting a degree of generalization ability, the algorithm's macroscopic performance remains unaffected by the topological structure of the directed graph. Large-scale, decentralized, and node-autonomous directed graphs are suitable simulation methods. Examining storage problems within directed graphs can reveal the essence of phenomena and uncover fundamental storage rules hidden within complex neuronal mechanisms, such as synaptic plasticity, ion channels, neurotransmitters, and electrochemical activities.

Unmanned Aerial Vehicle-Based Compressed Data Acquisition for Environmental Monitoring in WSNs.

With the increasing concerns for the environment, the amount of the data monitored by wireless sensor networks (WSNs) is becoming larger and the energy required for data transmission is greater. However, sensor nodes have limited storage capacity and battery power. The WSNs are faced with the challenge of handling larger data volumes while minimizing energy consumption for transmission. To address this issue, this paper employs data compression technology to eliminate redundant information in the environmental data, thereby reducing energy consumption of sensor nodes. Additionally, an unmanned aerial vehicle (UAV)-assisted compressed data acquisition algorithm is put forward. In this algorithm, compressive sensing (CS) is introduced to decrease the amount of data in the network and the UAV serves as a mobile aerial base station for efficient data gathering. Based on CS theory, the UAV selectively collects measurements from a subset of sensor nodes along a route planned using the optimized greedy algorithm with variation and insertion strategies. Once the UAV returns, the sink node reconstructs sensory data from these measurements using the reconstruction algorithms. Extensive experiments are conducted to verify the performance of this algorithm. Experimental results show that the proposed algorithm has lower energy consumption compared to other approaches. Furthermore, we employ different data reconstruction algorithms to recover data and discover that the data can be better reconstructed in a shorter time.