Route Discovery Research Articles

Computer-aided Drug Design: Innovation and its Application in Reshaping Modern Medicine

Abstract: Computer-aided drug design has revolutionized the landscape of drug discovery, accompanied by a new era of innovation and efficiency of novel therapeutic agents. This article review explores the diverse innovations and practical applications that have propelled CADD into the forefront of modern medicine. CADD, a multidisciplinary field at the interaction of biology, chemistry, and computational science, offers a toolkit for the identification and development of pharmaceutical compounds. It has the ability to predict molecular interaction between drug and biological targets with remarkable precision, reducing the dependency on laborious and costly laboratory experiments. The review deals with two primary domains of CADD: structure-based and ligand-based design. This three-dimensional protein structure and screening of chemical libraries have led to rational changes. The analysis of known drug compounds' chemical and biological properties has enabled the creation of predictive models, opening new routes for drug discovery. The impact of CADD on the pharmaceutical industry is clear. This review highlights its instrumental role in the development of antiviral agents, cancer therapeutics, and treatment for various diseases. The transformation potential of CADD is not without challenges, including the need for substantial computational resources and the essential requirement of experimental analysis. The synergy between innovation and practical application is clear, driving unexpected efficiency and precision in the identification of therapeutic solutions. As pharmaceutical research continues to evolve, the role of CADD remains pivotal, assuring the rapid translation of scientific innovation into real-world medical advancements.

Segment routing for WSN using hybrid optimization with energy-efficient game theory-based clustering technique

This research focuses on Wireless Sensor Networks (WSNs) and proposes a three-phase approach to achieve energy-efficient routing. The approach consists of node deployment using Voronoi diagrams, clustering, and Cluster Head (CH) selection using energy-efficient game theory, and a routing strategy based on Improved Pelican Optimization (ImPe) segment routing. Random deployment of sensor nodes in WSNs can lead to coverage issues, and hence, in order to address this, Voronoi-based node deployment is employed to ensure uniform and balanced coverage of the monitoring area. An energy-efficient game theory-based approach is used for CH selection by considering the energy levels to select CHs for enhancing network longevity. The proposed routing mechanism utilizes segment routing, which provides deterministic routing paths from CHs to the sink (Base Station). Segment routing eliminates the need for route discovery and maintenance, making it energy-efficient. The ImPe algorithm that works on the characteristics of pelican search agents is employed to choose the optimal segment path for information sharing. The assessment based on delay, network lifetime, packet delivery ratio, residual energy, throughput, communication overhead, and energy utilization acquired the values of 2.57, 98.59, 98.29, 0.98, 238.51, 7.71, and 0.02 respectively.

Discovery of New Synthetic Routes of Amino Acids in Prebiotic Chemistry.

The origin of life on Earth remains one of the most perplexing challenges in biochemistry. While numerous bottom-up experiments under prebiotic conditions have provided valuable insights into the spontaneous chemical genesis of life, there remains a significant gap in the theoretical understanding of the complex reaction processes involved. In this study, we propose a novel approach using a roto-translationally invariant potential (RTIP) formulated with pristine Cartesian coordinates to facilitate the simulation of chemical reactions. By employing RTIP pathway sampling to explore the reactivity of primitive molecules, we identified several low-energy reaction mechanisms, such as two-hydrogen-transfer hydrogenation and HCOOH-catalyzed hydration and amination. This led to the construction of a comprehensive reaction network, illustrating the synthesis pathways for glycine, serine, and alanine. Further thermodynamic analysis highlights the pivotal role of formaldimine as a key precursor in amino acid synthesis, owing to its more favorable reactivity in coupling reactions compared to the traditionally recognized hydrogen cyanide. Our study demonstrates that the RTIP methodology, coupled with a divide-and-conquer strategy, provides new insights into the simulation of complex reaction processes, offering promising applications for advancing organic design and synthesis.

Structural basis of the mechanism and inhibition of a human ceramide synthase.

Ceramides are bioactive sphingolipids crucial for regulating cellular metabolism. Ceramides and dihydroceramides are synthesized by six ceramide synthase (CerS) enzymes, each with specificity for different acyl-CoA substrates. Ceramide with a 16-carbon acyl chain (C16 ceramide) has been implicated in obesity, insulin resistance and liver disease and the C16 ceramide-synthesizing CerS6 is regarded as an attractive drug target for obesity-associated disease. Despite their importance, the molecular mechanism underlying ceramide synthesis by CerS enzymes remains poorly understood. Here we report cryo-electron microscopy structures of human CerS6, capturing covalent intermediate and product-bound states. These structures, along with biochemical characterization, reveal that CerS catalysis proceeds through a ping-pong reaction mechanism involving a covalent acyl-enzyme intermediate. Notably, the product-bound structure was obtained upon reaction with the mycotoxin fumonisin B1, yielding insights into its inhibition of CerS. These results provide a framework for understanding CerS function, selectivity and inhibition and open routes for future drug discovery.

Hybrid Optimization With Deep Spiking Equilibrium Neural Network for Software Defined Network Based Congestion Prevention Routing

ABSTRACTIn the Internet of Things (IoT) network, within a period of time it constructs a huge network of millions and billions of things that incorporate with each other, leading to various technical and application problems. Also, on‐time delivery of packets is significant in software‐defined networks. In the current software‐defined network, the bandwidth overhead is not considered when an enormous amount of traffic enters the network; this may lead to network congestion. To overcome this issue, a novel method is proposed to detect network congestion based on hybrid optimization, namely, the Hybrid Gannet with Pelican Optimization (HGPO) algorithm. The node‐level congestions and the link‐level congestions, which means the buffer overflow and the multiple nodes trying to utilize the channel at the same time, must be controlled efficiently. Once the network congestion gets controlled, the shortest route path selection and congestion prevention are performed perfectly with the aid of the proposed Deep Spiking Equilibrium Neural Network (DSENN). A few route discovery frequency vectors, such as the interroute discovery time and route discovery time of each node, are determined to prevent congestion. Finally, it is implemented in the Python platform successfully, and the achieved throughput, delay, packet loss ratio, packet delivery ratio, end‐to‐end delay, and performance measures of the proposed method are 140 Mbit/s, 17 ms, 3.8%, 0.35 s, and 100%, respectively, which outperforms the other compared traditional algorithms.

Discovery of a novel methionine biosynthetic route via O-phospho-l-homoserine.

Methionine (Met), a sulfur-containing amino acid, is essential for the underlying biological processes in living organisms. In addition to its importance as a starting building block for peptide chain elongation in protein biosynthesis, Met is a direct precursor of S-adenosyl-l-methionine, an indispensable methyl donor molecule in primary and secondary metabolism. Streptomyces bacteria are well known to produce diverse secondary metabolites, but many strains lack canonical Met pathway genes for l-homocysteine, a direct precursor of Met in bacteria, plants, and archaea. Here, we report the identification of a novel gene (metM) responsible for the Met biosynthesis in Streptomyces strains and demonstrate the catalytic function of the gene product, MetM. We further identified the metO gene, a downstream gene of metM, and showed that it encodes a sulfur-carrier protein (SCP). In in vitro analysis, MetO was found to play an important role in a sulfur donor by forming a thiocarboxylated SCP. Together with MetO (thiocarboxylate), MetM directly converted O-phospho-l-homoserine to l-homocysteine. O-Phospho-l-homoserine is also known as an intermediate for threonine biosynthesis in bacteria and plants, and MetM shares sequence homology with threonine synthase. Our findings thus revealed that MetM seizes O-phospho-l-homoserine from the threonine biosynthetic pathway and uses it as an intermediate of the Met biosynthesis to generate the sulfur-containing amino acid. Importantly, this MetM/MetO pathway is highly conserved in Streptomyces bacteria and distributed in other bacteria and archaea.IMPORTANCEMethionine (Met) is a sulfur-containing proteinogenic amino acid. Moreover, Met is a direct precursor of S-adenosyl-l-methionine, an indispensable molecule for expanding the structural diversity of natural products. Because Met and its derivatives benefit humans, the knowledge of Met biosynthesis is important as a basis for improving their fermentation. Streptomyces bacteria are well known to produce diverse and valuable natural products, but many strains lack canonical Met pathway genes. Here, we identified a novel l-homocysteine synthase (MetM) in Streptomyces and demonstrated that it converts O-phospho-L-homoserine to l-homocysteine using a thiocarboxylated sulfur-carrier protein as a sulfur donor. Since the metM is distributed in other bacteria and archaea, our pioneering study contributes to understanding Met biosynthesis in these organisms.

A trust model for VANETs using malicious-aware multiple routing

Vehicular ad hoc networks (VANETs) enable multi-hop communication among vehicles, promoting information sharing and smarter collaborative driving. However, VANETs are facing several challenges due to the open wireless communication environment. Attackers may maliciously drop or alter packets so that the receiver cannot obtain correct information. In addition, the high mobility of vehicles may lead to link failures, consequently resulting in packet loss. In this paper, we propose a multipath-based trust model (MPTM), in which the reliability of packet transmission is guaranteed by data redundancy and the detection of potential attackers is achieved by trust evaluation. Specifically, we present a route discovery mechanism to find multiple routes that avoid potential attackers, which reduces the risk of attacks on redundant packets. The receivers identify correct information based on two factors including content consistency and route information. An attacker detection module is presented to evaluate the trustworthiness of vehicles involved in packet transmission and vehicles with trust value below a threshold are detected as attackers. We conducted extensive experiments using OMNeT++ simulation platform, considering various attack scenarios. Experiment results show that MPTM can reach 90% packet delivery ratio and effectively detect attackers in terms of 90% detection precision.

Research on intelligent lighting Bluetooth Mesh networking control based on AODV

This paper discusses the deficiencies of the flooding broadcast mechanism in Bluetooth Mesh networks and puts forth an enhanced routing protocol Olq-AODV. The limitations of traditional AODV routing protocols are particularly evident in smart light networks with high node density, where packet redundancy and link quality judgment are crucial. To this end, Olq-AODV introduces an optimized link quality judgment mechanism in the process of route discovery, considering delay, hop count, and cascade PRR, with the aim of improving the transmission efficiency and reliability of the network. The test results demonstrate that the Olq-AODV routing protocol outperforms the traditional flooding algorithm in terms of network transmission delay and packet delivery rate, and effectively improve the performance of Bluetooth Mesh networks.

Revolutionizing MANET Route Discovery with INTSM: An Innovative Load Balancing Approach

Communication challenges in ad hoc networks arise due to the mobility of nodes, causing frequent changes in connections and locations. Maintaining network equilibrium to prevent node overload and underutilization is crucial. However, imposing static behaviors on nodes to improve performance can lead to delays, especially in core nodes. Addressing these issues, this research proposes the Intermediate Node Traffic Sharing Model (INTSM) for ad hoc networks. INTSM prioritizes congestion control and load balancing during route discovery, aiming to optimize network resource utilization and traffic distribution, thereby reducing packet delays. The model employs dynamic traffic sharing algorithms that consider real-time network conditions, enabling nodes to adjust their behaviour adaptively. This approach minimizes congestion by distributing traffic loads more evenly across the network, preventing bottlenecks at central nodes. Additionally, INTSM incorporates predictive analysis to foresee potential congestion points and reroute traffic proactively, enhancing overall network stability and performance. Extensive simulations demonstrate that INTSM significantly reduces average packet delay and improves throughput compared to traditional routing protocols. The results highlight the model's efficacy in diverse scenarios, including high mobility and varying traffic loads, proving its robustness and scalability. The primary objective of this study is to enhance navigation and equilibrium mechanisms to improve the performance of ad hoc networks, contributing to more reliable and efficient wireless communication systems. The findings of this research have significant implications for the design of future ad hoc networks, particularly in applications requiring high reliability and quick adaptation to changing network conditions, such as disaster recovery, military operations, and mobile sensor networks. By addressing the critical challenges of congestion control and load balancing, INTSM offers a promising solution to enhance the resilience and efficiency of ad hoc networks.

Reducing Transmission Signal Collisions on Optimized Link State Routing Protocol Using Dynamic Power Transmission

Many devices connected to a network inevitably result in clashes between communication signals. These collisions are an important factor that causes a decrease in network performance, especially affecting Quality of Service (QoS) like throughput, Packet Delivery Ratio (PDR), and end-to-end de- lay, which has a direct impact on the success of data transmission by potentially causing data loss or damage. The aim of this research is to integrate the Dynamic Power Transmission (DPT) algorithm into the Optimized Link State Routing (OLSR) routing protocol to regulate the communication sig- nal strength range. The DPT algorithm dynamically adapts the signal coverage distance based on the density of neighboring nodes to reduce signal collisions. In our protocol, the basic mechanism of a DPT algorithm includes four steps. The Hello message structure of OLSR has been modified to incorporate the ”x-y position” coordinate field data. Nodes calculate distances to neighbors using these coordinates, which is crucial for route discovery, where all nearby nodes can process route re-quests. The results of this research are that DPT-OLSR improves network efficiency in busy areas. In particular, the DPT-OLSR routing protocol achieves an average throughput enhancement of 0.93%, a 94.79% rise in PDR, and reduces end-to-end delay by 45.69% across various variations in node density. The implication of this research result is that the algorithm proposed automatically adapts the transmission power of individual nodes to control the number of neighboring nodes within a de-fined range. This effectively avoids unwanted interference, unnecessary overhearing, and excessive processing by other nodes, ultimately boosting the network’s overall throughput.

A dynamic multicast routing protocol for visible light communication networks

Due to the current challenges of wireless communications, optical wireless communications, like visible light communication (VLC), have become very popular in the last decade for local area networks. However, current VLC systems focus mainly on one-hop direct transmissions, ignoring that many networking applications require multicast communications. In this sense, we propose MCAST-VLC, a fully functional cross-layer dynamic multicast scheme over a dynamic routing protocol for VLC. MCAST-VLC considers the particular characteristics of the VLC as the need for group management, traffic isolation, a challenging multi-hop route discovery protocol, and a complex cross-layer VLC mechanism to maintain the proper system state. We evaluate our protocol following close real indoor VLC scenarios, previously discussed in the literature, where a light source in a room streams data to a few other VLC devices, and we extend our results numerically. Our results show that MCAST-VLC can handle considerably more network flows than a unicast VLC system under a negligible overhead.

Exploiting data transmission for route discoveries in mobile ad hoc networks

Exploiting data transmission for route discoveries in mobile ad hoc networks

Survey of Multiple Destination Route Discovery Protocols

Route discovery protocols for multiple destination is one of the most interesting research topics since it is applied for real-world applications and needed in smart cities services such as: delivery services. The inclusion of artificial intelligence can improve the performance of multiple destination route discovery protocols. In this paper, we studied and analyzed multiple destination route discovery protocols based on different search strategies especially artificial intelligent methods. The survey compares between multiple destination route discovery protocols relate to its applications and implementation tools.

Правовое становление и развитие Северного морского пути

The main goal of the article is to show stages of discovery and development of the Northern Sea Route, describe its establishment on the legislative and economic level. The paper reviews prospects of the new strategy of development of this region, where one of the main goals is a change of living conditions in the Arctic for the better, and stages of elaboration of new scientific technologies applied at transportation of a broad range of cargo, extraction of resources, the implementation of which is as complex as space exploration. The author shows changes in the foreign policy of Russia that should aim at multipolarity for the development of this region, attraction of cutting-edge technologies and global investors subject to predominant consideration of interests of indigenous small-numbered peoples.

The paradoxes of Vietnam’s ties to India

ABSTRACT India’s amiable history with Vietnam began with peaceful migration to Southeast Asia from the beginning of the Common Era and discovery of new land and sea routes. Yet, modern Vietnamese consider India distant and remote. Indian culture reached Vietnam in “benign and peaceful” waves, while China’s onslaught was “forceful and potent.” India is neither a threat nor concerning runs the argument. Without surveys, we cannot be sure this is a consensus view. Clearer is that Vietnamese do not write revisionist histories to deny the migration of Indian princes, merchants, and philosophers in the first millennium. There is recognition of the Hinduized Champa as part of Vietnamese culture.

A local filtering-based energy-aware routing scheme in flying ad hoc networks

Flying ad hoc network (FANET) is a new technology, which creates a self-organized wireless network containing unmanned aerial vehicles (UAVs). In FANET, routing protocols deal with important challenges due to limited energy, frequent link failures, high mobility of UAVs, and limited communication range of UAVs. Thus, a suitable path is always essential to transmit data between UAVs. In this paper, a local filtering-based energy-aware routing scheme (LFEAR) is proposed for FANETs. LFEAR improves the template of the route request (RREQ) packet by adding three fields, namely the energy, reliable distance, and movement similarity of the relevant route to create stable and energy-efficient paths. In the routing process, LFEAR presents a local filtering construction technique to avoid the broadcasting storm issue. This filter limits the broadcasting range of RREQs in the network. Accordingly, only UAVs inside this local filtered area can rebroadcast RREQs and other UAVs must eliminate these packets. After ending the route discovery process, the destination begins the route selection phase and extracts information about each discovered route, including the number of hops, route energy, reliable distance, and movement similarity. Then, the destination node calculates a score for each path based on the extracted information, selects the route with the highest score, and sends a route reply (RREP) packet to the source node through this route. Finally, the simulation process of LFEAR is performed using the NS2 simulator, and two simulation scenarios, namely change in network density and change in the speed of UAVs, are defined to evaluate network performance. In the first scenario, LFEAR improves energy consumption, packet delivery rate, network lifespan, and delay by 1.33%, 1.77%, 6.74%, and 1.71%, while its routing overhead is about 16.51% more than EARVRT. In the second scenario, LFEAR optimizes energy consumption and network lifetime by 5.55% and 5.67%, respectively. However, its performance in terms of routing overhead, packet delivery rate, and delay is 23%, 2.29%, and 6.67% weaker than EARVRT, respectively.

Extreme mixing in nanoporous high-entropy oxides for highly durable energy storage

The ability to mix many different metal cations in a single-phase nanoscale oxide is critical for property adjusting and new material discovery. However, synthesizing multicomponent high-entropy oxides (HEOs) consisting of over ten metal cations remains a challenge due to dissimilarity and immiscibility among these elements. Herein, we explore the accommodation ability of Al3Ni-type and Al3Ti-type intermetallic phases and find that the Al3Ni-type phase is powerful to accommodate many different kinds metal elements. By chemically dealloying the Al from the multicomponent Al3Ni-type intermetallic phase, multicomponent spinel oxides such as 7-component (AlNiCoRuMoCrFe)3O4, 8-component (AlNiCoRuMoCrFeTi)3O4, 13-component (AlNiCoCrFeCuMoVTaNbHfZrTi)3O4, 16-component (AlNiCoCrFeCuMoTaNbHfZrTiRuVPdY)3O4 et al., with nanoporous structure and poor crystallity are obtained. As a case study, we find that when applied in Li-ion battery anode, our 16-component nanoporous HEO exhibits a high capacity of ∼1141.2 mAh g−1 after 290 cycles at 0.1 A g−1 and excellent cycling stability. This study greatly expands the composition space of nanoscale HEOs and provides an interesting route for new materials discovery.

A Day-to-Day Dynamical Approach to the Most Likely User Equilibrium Problem

The lack of a unique user equilibrium (UE) route flow in traffic assignment has posed a significant challenge to many transportation applications. The maximum-entropy principle, which advocates for the consistent selection of the most likely solution, is often used to address the challenge. Built on a recently proposed day-to-day discrete-time dynamical model called cumulative logit (CumLog), this study provides a new behavioral underpinning for the maximum-entropy user equilibrium (MEUE) route flow. It has been proven that CumLog can reach a UE state without presuming that travelers are perfectly rational. Here, we further establish that CumLog always converges to the MEUE route flow if (i) travelers have no prior information about routes and thus, are forced to give all routes an equal initial choice probability or if (ii) all travelers gather information from the same source such that the general proportionality condition is satisfied. Thus, CumLog may be used as a practical solution algorithm for the MEUE problem. To put this idea into practice, we propose to eliminate the route enumeration requirement of the original CumLog model through an iterative route discovery scheme. We also examine the discrete-time versions of four popular continuous-time dynamical models and compare them with CumLog. The analysis shows that the replicator dynamic is the only one that has the potential to reach the MEUE solution with some regularity. The analytical results are confirmed through numerical experiments. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This research was funded by the United States National Science Foundation’s Division of Civil, Mechanical and Manufacturing Innovation [Grant 2225087]. The work of J. Xie was funded by the National Natural Science Foundation of China [Grant 72371205]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0525 .

Reliable Air Quality Assessment in Dynamic Environments using Reward-learning Routing with Flying MANET Technology

<p>Air pollution poses significant threats to ecosystems, atmosphere, and the health of both humans and wildlife. It is primarily caused by industrial and chemical pollutants, leading to deterioration in air, water, and soil quality. As a result, effective air quality monitoring is crucial for environmental management. Flying Mobile Ad hoc Networks (FMANETs) offer a promising solution for intelligent air quality monitoring and assessment by employing unmanned aerial vehicles (UAVs) to measure air pollutants. However, FMANET-based systems face unique challenges, including UAVs' three-dimensional movement, high dynamism, rapid topological changes, resource constraints, and low UAV density. Therefore, routing optimization becomes a fundamental issue in these systems. In this paper, we propose a novel routing method named RFMAN (Reward-learning-based FMANET) for intelligent air quality monitoring systems. RFMAN consists of two main components: route discovery and route maintenance. In the route discovery phase, we introduce a Reward-learning-based mechanism to identify optimal routes, while also implementing a filtering parameter to refine the UAV selection process and reduce the search space. The route maintenance phase of RFMAN focuses on detecting and correcting paths nearing breakdowns, as well as promptly replacing failed paths to ensure continuous monitoring efficiency. The result section pictures that RFMAN outperforms other methods. Overall, RFMAN offers a promising solution for enhancing the effectiveness and reliability of air quality monitoring in FMANET-based systems.</p>

Ground vehicle path planning on Uneven terrain Using UAV Measurement point clouds

Abstract. The objective of this study is to develop a system to support rapid ground vehicle activities by planning safe travel routes for ground vehicles from point clouds of wide-area uneven terrain environments measured using UAVs. However, fast path planning is difficult in complex environments such as large, uneven terrain environments. Therefore, this paper proposes a new RRT method based on the RRT algorithm that can perform fast path planning, even in complex environments. In the proposed method, narrow areas that are difficult to be explored by ordinary RRTs are first identified in advance, and nodes are placed in these areas to guide the search. When searching with RRTs, the tree is extended via these guide nodes to efficiently traverse the narrow area. In the validation of the proposed method, a comparison was made with RRT and RRT-Connect in two environments, including narrow areas. The results show that the proposed method has a higher route discovery capability, at least two times fewer search nodes and five times faster path planning capability than other RRTs.