Assisted and Hybrid Food Drying Routes: Mechanisms, Configurations, and Implementation Pathways

One of the major problems smart cities face is how to efficiently route traffic, especially when connected vehicles and sensors produce a very large amount of real, time data. This heavy traffic load results in delays, inefficient routing, and excessive processing of central units. Hence, this article presents FL-TrafficNet, a hybrid routing framework that enhances traffic management in the Internet of Vehicles (IoV) and Vehicular Ad Hoc Networks (VANETs) scenarios. FL-TrafficNet combines Transformer models, and Graph Neural Networks (GNNs) to capture not only the time-dependent traffic variation but also the layout of the road network. A dual-attention component enables the model to pinpoint the most significant features in both spatial and temporal domains. To avoid uploading all the raw data to the cloud, the solution employs Federated Learning (FL), whereby vehicles and roadside units (RSUs), train their models on the spot and only share the resultant updates. This ensures data confidentiality and significantly reduces the network traffic. A reinforcement component embedded in the model dynamically makes path decisions by analyzing real-time traffic updates and feedback. The model works continuously by learning from nearby changes in traffic, weather, or road status. Simulation results show that FL-TrafficNet reduces errors in prediction (Mean Absolute Error (MAE): 1.95, Root Mean Squared Error (RMSE): 2.87, Mean Absolute Percentage Error (MAPE): 3.12), improves data privacy (97.8% privacy score), and increases traffic delivery rate (TDR) to 38.4%, a clear improvement over existing recent methods. These results make it suitable for real-time, privacy-aware routing in urban traffic networks.

Design of a Hybrid Routing and Congestion Control Algorithm Used for Decreasing Latency and Packet Loss in Next-generation Wireless Networks

ABSTRACT Persistent luminescence (PersL) attracts growing attention due to its potential for energy‐efficient optical storage, sensing, and imaging technologies. Although extensively investigated in bulk and powder forms, its translation into transparent and functional thin films or glassy systems remains a major challenge. This review discusses the state of the art in the development of persistent luminescent thin films and glasses, emphasizing processing strategies, structure‐property correlations, and emerging device‐level applications. The mechanisms governing PersL are briefly described. This is followed by an analysis of thin‐film growth techniques, including chemical, physical, and hybrid routes, and highlighting their influence on defect engineering, dopant distribution, and luminescence performance. The discussion integrates advances on polymeric and glass matrices, where control over morphology and interfaces is essential to achieve optical transparency and environmental stability. This review also aims to present opportunities for studying and developing new materials through thin‐film fabrication techniques that contribute to the creation of devices beneficial to society, the environment, and energy. Current limitations and future directions are discussed focusing on trap modulation, compositional design, and scalable fabrication toward next‐generation photonic and optoelectronic devices.

Wireless Sensor Networks (WSNs) are fundamental to security and surveillance applications such as military defense, disaster management, and intrusion monitoring. The performance of these networks depends largely on the efficiency of routing protocols. This paper examines the Ad hoc On-Demand Distance Vector (AODV) routing protocol in multi-hop WSN environments for target tracking, evaluating critical metrics including Packet Delivery Ratio (PDR), End-to-End Delay, Energy Consumption, and Network Lifetime. Simulation results illustrate the impact of node depletion due to transmission loss, affecting network stability and robustness in target detection. In-network detection in WSNs presents trade-offs between real-time data transmission, energy efficiency, and trajectory lifespan. Inefficient routing optimization may result in increased latency, packet loss, and premature node failure, ultimately reducing localization accuracy. While AODV’s reactive path-based approach offers manageable overhead, its performance degrades under increased energy consumption and route rediscovery delays. This study systematically evaluates AODV’s strengths and limitations in time-sensitive detection scenarios. Findings indicate that AODV ensures reliable data transmission in early network stages but suffers significant performance deterioration as node energy declines, impacting coverage and responsiveness. To enhance AODV’s target tracking capabilities, this paper proposes adaptive energy-saving techniques and hybrid routing schemes. These strategies contribute to ongoing research aimed at optimizing routing protocols to balance accuracy and node longevity for real-time WSN applications.

ABSTRACT Balancing melt processability with stiffness and heat resistance remains a central challenge for PA6 nanocomposites. Here, graphene oxide (GO) was synthesized by a modified Hummers method and incorporated into PA6 via a hybrid route (solution‐made masterbatch, melt dilution and injection molding) at 0.1–2.0 wt%. Melt rheology at 240°C shows higher zero‐shear viscosity and stronger shear thinning, while SAOS and Cole–Cole plots reveal the development of a GO‐induced network. Capillary rheometry over 80–11,500 s −1 indicates viscosity convergence to Pa s for all compositions, confirming preserved moldability. By tracking / before and after injection molding, we show that the second melt‐processing step relaxes, but does not destroy, the filler‐induced network and shifts the onset of rheological percolation to around 2 wt% GO. In the solid state, DMA and HDT demonstrate a increase of ≈6°C–8°C, a 21% rise in HDT (80.8°C–97.7°C) and higher rubbery modulus, whereas tensile tests reveal up to +22% yield stress and +59% ultimate strength at 2 wt% GO, at the cost of reduced ductility. Correlating unified rheological parameters with HDT and tensile data yields a concise rheology‐to‐performance map, with an optimum near 1 wt% GO. The results link injection relevant melt signatures to molded part performance, targeting electrical connector housings.

In an attempt to identify solutions to advance net-zero energy activities and accelerate the deployment of cutting-edge low-carbon technologies, hybrid approaches for solar energy harvesting and engineering materials have been developed. In this study, two different forms of TiO2were synthesized and applied as electron transport layers (ETL) in perovskite solar cells (PSCs). In addition, double-doped sputtered NiO was used and the fabricated NiO/TiO2heterostructures were examined for their photocatalytic activities against the decolorization of methylene blue (MB). The two forms of TiO2were the one-dimensional (1D) TiO2nanorods (TiO2-NRs), synthesized using a hydrothermal technique, and the three-dimensional (3D) mesoporous TiO2(m-TiO2) synthesized by spin-coating. The PSC formed by the 1D TiO2-NRs as ETL showed the same open-circuit voltage under solar illumination but twice the short-circuit current when compared to the PSC having the conventional m-TiO2as ETL. The photocatalytic activity of the 1D NiO/TiO2-NRs heterostructure was 23 wt% faster than the respective 3D NiO/TiO2one, while inducing about 83 wt% more MB degradation. These effects were attributed to the different effective surface areas and the diode properties of the NiO/TiO2heterostructures. The presented results provide a direct comparison between heterostructures synthesized via hybrid routes for optoelectronic applications in the fields of energy harvesting and photocatalysis.

The significance of effective on-chip communication has increased due to the increasing integration of computing cores in contemporary systems. Since routing techniques affect latency, throughput, and power consumption while reducing congestion and deadlock, they are essential to Network-on-Chip (NoC) performance. While adaptive and hybrid algorithms like Q-learning (Q), Path-based Randomized Oblivious Minimal (PROM), and Dynamic Adaptive Deterministic (DyAD) promise greater adaptability, deterministic techniques like XY provide simplicity but lack flexibility. The deterministic, adaptive, and hybrid routing algorithms in NoC are assessed in this study under bursty and constant bit rate (CBR) traffic. By combining delay, throughput, and power, a composite Performance Metric (PM) is used to measure routing efficiency. According to the results, PROM outperforms Q-routing by a substantial margin, achieving the highest efficiency under bursty traffic with a PM of 38.42%. DyAD performs best for CBR traffic, with a PM of 34.97%, compared to XY’s PM of 33.64%. The results show that traffic conditions affect the algorithm's applicability. DyAD performs well under constant loads and PROM is well suited for unpredictable traffic.

Mobile Ad-hoc Networks (MANETs) are decentralized, self-organizing wireless networks that operate without fixed infrastructure. Despite their flexibility, they are highly susceptible to congestion and black hole attacks, which significantly degrade throughput, packet delivery ratio (PDR), and overall Quality of Service (QoS). This paper introduces an enhanced hybrid routing protocol Black Hole and Congestion Overcome AOMDV (IH-AOMDV) as an optimized extension of the Ad-hoc On-Demand Multipath Distance Vector (AOMDV) protocol. The proposed approach integrates congestion awareness and security mechanisms by combining throughput-based congestion detection with sequence-number-based route validation to identify and isolate congested and malicious nodes in real time. Simulation experiments conducted in NS2.35 across a 1000×1000 m network with 15–35 nodes demonstrate that IH-AOMDV achieves up to 90–100% throughput, improves the packet delivery ratio by 35%, reduces end-to-end delay by 45%, and decreases packet loss by approximately 40% compared to standard AOMDV. The results confirm the robustness, scalability, and adaptability of IH-AOMDV, establishing it as a reliable routing framework for secure and congestion-free MANET communication in dynamic environments such as military, vehicular, and disaster response networks.

В работе предложен новый гибридный протокол маршрутизации AOHPR (Ad-hoc On-demand Hybrid Proactive-Reactive) для беспроводных самоорганизующихся сетей Ad-hoc. Он реализован на языке C++ в симуляторе NS-2 и сочетает проактивные и реактивные принципы маршрутизации, объединяя сильные стороны протоколов. Протокол разработан в виде класса Agent/AOHPR и интегрируется в симулятор NS2 через механизм Tcl Class. Проведено сравнительное моделирование работы протокола AOHPR с существующими протоколами маршрутизации в сетях Ad-hoc. Introduction. This paper introduces a novel hybrid routing protocol, AOHPR (Ad-hoc On-demand Hybrid Proactive-Reactive), designed for mobile ad hoc networks. The protocol integrates proactive and reactive approaches by implementing mechanisms for local proactivity, adaptive caching, and automatic route restoration. The implementation was realized in the NS-2 simulator using C++ as the Agent/AOHPR class and integrated via the Tcl Class mechanism. The objective of this work is to enhance routing efficiency in wireless ad-hoc networks under conditions of variable node density through the development and analysis of the hybrid AOHPR protocol. Simulation of the proposed protocol in NS-2. As AOHPR is a new hybrid routing protocol not included in the standard NS-2 distribution, its implementation was written in C++, comprising a header file (aohpr.h) and an implementation file (aohpr.cc). This code was integrated into the NS-2 architecture and implements the core functions described in this paper: local proactivity, reactive routing for remote destinations, adaptive caching, and an automatic route recovery mechanism. (The files aohpr.cc and aohpr.h are C++ source codes not part of the standard NS-2 build). Results.A novel hybrid routing protocol, AOHPR (Ad hoc Optimized Hybrid Proactive-Reactive), is proposed. It combines four key principles – local proactivity, a reactive mechanism, adaptive caching, and automatic recovery to improve the efficiency of routing and data transmission in wireless ad-hoc networks. To enable its simulation, the AOHPR protocol was developed in C++ as the Agent/AOHPR class and integrated into the NS-2 simulator via the Tcl Class mechanism. Simulation of the proposed protocol allowed for an evaluation of its performance, confirming its effectiveness in networks with varying node density. The new protocol was compared against existing protocols, such as AODV and OLSR, using metrics including packet delivery delay, packet delivery ratio (PDR), and packet loss ratio. In networks comprising 25–100 nodes with a mobility speed of 5 m/s, the simulation results showed: AOHPR reduces average data transmission delay, particularly in larger networks (≥ 75 nodes), achieving delays as low as 200 ms; AOHPR increases the packet delivery ratio (PDR) by up to 10% compared to AODV and up to 20% compared to OLSR, depending on the network size; AOHPR reduces packet loss by 3–20% relative to AODV and OLSR, attributed to its rapid route restoration capability upon loss of neighboring nodes.

In the modern era, multi-core technology is one of the effective communication between the various IP-enabled cores and it is a critical challenge. As more and more IP cores are integrated into multi-core system-on-chip (M-SoC), traditional bus architectures are inadequate to meet the performance demands of modern systems.  Also, interconnection architecture for on-chip multi-core communication has an increasing presence in research due to its scalability and efficient communication. M-SoC is the integration of multiple processing cores to implement the communication performance effectively within the SoC. Consequently, conventional messaging protocols fail to optimize the clock distribution challenges and imperfect fault tolerance. To achieve better performance, adaptive routing algorithms need to monitor network status and make routing decisions informed by congestion data, which unavoidably increases design complexity and overhead. Therefore, this paper developed an Adaptive Fault Tolerant with Hybrid Congestion Aware routing protocol (AFT-HCARP) is designed to improve the performance while ensuring the asynchronous communication protocol. Here, a deterministic-adaptive hybrid routing strategy was utilized to reroute the transmitted messages around the overloaded modes. In addition, the proposed AFT-HCARP protocol is integrated with dynamic routing adaptation as well as a destination-based adaptive routing algorithm to validate the finest message transferring under several network conditions, which are varied under the fault scenarios. Moreover, the experimental outcomes were verified in terms of throughput performance under various fault conditions, latency to load, and finally comparative analysis is performed across the baseline protocols. Furthermore, the results show that developed AFT-HCARP reduces average packet delay as 3.8cycles and enhances the throughput as 0/0098 flit/node/cycle. From the comparison, fault recovery time is 4 cycles, and packet delivery ratio is 97.60% than the conventional routing schemes. 

ABSTRACT Wireless Body Area Networks (WBANs) play a vital role in real‐time healthcare monitoring by enabling continuous acquisition and transmission of physiological data through wearable and implantable sensor nodes. However, their practical deployment is constrained by critical challenges such as limited energy resources, frequent topology variations caused by body movements, high communication overhead, and reduced network lifetime, which collectively affect reliable data transmission in long‐term medical applications. To address these issues, this work proposes an energy‐efficient hybrid clustering and routing protocol for WBANs based on the integration of an Adaptive Binary Bird Swarm Optimization Algorithm (ABBSOA) and an Enhanced Golden Eagle Optimization Algorithm (EGEOA). ABBSOA is employed for optimal cluster formation and dynamic cluster head (CH) selection by jointly considering residual energy, link quality, and communication cost, thereby ensuring balanced energy utilization among sensor nodes. Subsequently, EGEOA is utilized to establish reliable and energy‐aware multihop routing paths, minimizing transmission overhead and improving data delivery reliability. The effectiveness of the proposed ABBSOA–EGEOA protocol is validated through extensive simulations under two distinct scenarios and compared with state‐of‐the‐art WBAN protocols, including MT‐MAC, DHCO, ALOC, DECR, EHCRP, and MGWO. Simulation results demonstrate that the proposed approach achieves higher throughput, improved packet delivery ratio, reduced end‐to‐end delay, lower energy consumption, and a significantly extended network lifetime. Overall, the proposed protocol enhances energy efficiency, reliability, and scalability, making it well‐suited for sustainable and long‐term WBAN‐based healthcare monitoring applications. The ABBSOA‐EGEOA framework significantly extends network longevity, surpassing ALOC, DECR, EHCRP, and M‐GWO by 25%, 16.27%, 11.11%, and 6.38%, respectively. These results confirm that the proposed protocol enhances energy efficiency, reliability, and scalability in WBAN environments.

A cross-scale traffic-communication control framework for improving safety through proactive congestion mitigation in mixed traffic.

Efficient vehicle and data routing in traffic-aware vehicular networks remains a challenging task. Continuous vehicle movement results in dynamic traffic congestion and frequent changes in network topology. Since shortest-path algorithms such as Dijkstra's algorithm compute optimal routes for static networks, their effectiveness degrades in highly dynamic vehicular environments as frequent topology changes require continuous route re-computation. This makes routing computationally expensive and delay prone. In this work, a hybrid routing framework is proposed that integrates Dijkstra's algorithm with a Graph Neural Network (GNN) to enable traffic-aware routing decisions. Road network is modeled as a grid-based graph, where intersections are represented as nodes and road segments as edges. Then GNN is used to predict areas of traffic congestion in advance, considering parameters such as vehicle density, time of day, and average speed of vehicles. This information is fed back to vehicles to guide dynamic re-execution of Dijkstra's algorithm for adaptive route computation, after dynamically adjusting edge weights. Simulation results demonstrate that the proposed GNN approach improves adaptability to traffic disturbances as unnecessary recomputations are reduced. This results in efficient and accurate routing in dynamic vehicular environments.

Abstract In response to the ‘Dual Carbon’ goals (China’s strategy of ‘carbon peak and carbon neutrality,’ aiming to reach peak carbon emissions by 2030 and achieve net-zero carbon emissions by 2060), this study proposes a Carbon Emissions and Closeness Centrality Hybrid (CCH) routing strategy for multi-layer transportation networks. The strategy integrates node closeness centrality and carbon emissions metrics into a tunable cost function, enabling the balanced optimization of traffic capacity and environmental performance. The three-layer architecture—comprising logical, low-speed, and high-speed physical layers—emulates real-world multimodal systems, where inter-layer coupling reflects mode transfers, such as between subway stations and major bus stops. Simulation results indicate that when the parameter β is approximately 0.8, the system reaches an ideal state. The CCH strategy outperforms the conventional Carbon Emissions Hybrid (CEH) method (a path optimization approach that only considers node degree and carbon emissions), increasing traffic capacity by up to 17.8% in large-scale networks ( N = 1000). Moreover, the carbon emission ratio consistently remains below 1 across different network scales, confirming the strategy’s effectiveness in achieving a sustainable balance between throughput and low-carbon objectives.

ABSTRACT WSNs play a pivotal role in enabling ubiquitous data collection in many areas including environmental monitoring, smart infrastructure, and industrial automation. Despite their benefits, WSNs are limited by the scarcity of energy sources and are extremely vulnerable to link failures, buffer overflow, and random SN failures. Such problems tend to cause more packet loss, transmission delays, and shorter network life. To resolve these concerns, this work proposes a smart hybrid routing framework, which is the combination of Learning Automata (LA), Genetic Algorithms (GA), and Machine Learning (ML) to achieve reliable data delivery and enhance the energy efficiency of the network. In the initial phase, LA is applied to create a context‐sensitive population of candidate routes based on the SN's parameters like proximity, residual energy, link quality, and buffer occupancy. Furthermore, GA is used to optimize these paths as directed by a multiparameter fitness function. This framework is uniquely designed as it employs ML in the routing process to achieve: (i) predictive link quality estimation with supervised learning, (ii) predicting energy depletion and buffer congestion with time‐series models, (iii) dynamic adaptation of fitness function weights with reinforcement learning, and (iv) anomaly detection with unsupervised learning to isolate unstable or compromised SNs. Simulation results also verify that the proposed ML‐enhanced LA‐GA framework is energy efficient, optimizes the delay in packet delivery, diminishes the retransmission overhead, as well as boosts the network life when compared to the traditional routing schemes based on GA.

Abstract In the era of digital technology, the Internet of Things (IoT) plays an imperative role by connecting smart devices over divergent domains. Low Power and Lossy Networks (LLNs) balance the energy efficiency among multiple devices that enable real-time insights for numerous IoT applications particularly from simple automation systems to complex surveillance. However, the IoT–LLN is highly susceptible to a variety of attacks due to memory consumption, constrained battery storage and increased control packet overhead in the routing environment. These attacks severely impact on system responsiveness, data integrity and Quality of Service (QoS). To address these challenges, this paper introduces a novel Hybrid Routing Attack Detection and Mitigation Technique (HRADMT) for securing the IoT–LLN environment. The proposed protocol empowers them to identify the different vulnerabilities by detecting and classifying attacks before they can compromise the system. Primarily, the proposed HRADMT utilizes feature selection methods to detect attacks based on the behavior of nodes and energy patterns. It further invokes the attack classification phase in order to detect and categorize the different attacks like rank, wormhole, and distributed denial of service attacks. The performance of the proposed HRADMT has been evaluated under two different environments (Sparse LLN and Dense LLN). The simulation outcomes prove that the proposed HRADMT protocol achieves 96.18% accuracy when compared with the existing detection techniques.

This study investigates a hybrid processing route that integrates localized fusion-based additive manufacturing and hot forging for the production of complex-shaped components, with emphasis on metallurgical integrity and mechanical performance. The DIN 8555 E6-UM-60 alloy, traditionally classified as martensitic and applied under severe wear conditions, exhibited atypical metallurgical behavior during hybrid processing, notably the consistent formation of chromium carbides under specific thermomechanical conditions. Metallographic analyses, microhardness measurements, thermographic monitoring, hot tensile tests, and room-temperature tensile tests were performed to establish correlations between microstructure, thermal history, and mechanical response. Specimens produced by additive manufacturing and subsequently hot forged showed a significant reduction in porosity, improved microstructural homogeneity, and partial retention of hardening phases, enabling discussion of recrystallization mechanisms, phase stabilization, and precipitation phenomena in martensitic alloys processed by additive manufacturing. Hot tensile tests revealed limited hot workability of the alloy, while room-temperature tensile tests led to premature fracture, with failure consistently initiating at pre-existing microcracks formed during the forging stage. Although detrimental, these microcracks provide valuable insight into critical processing conditions and ductility limits of the material. Overall, the hybrid route demonstrates strong potential for industrial applications, highlighting the importance of precise thermomechanical cycle control to mitigate defects and enhance structural reliability.

The design of millimeter-wave (mmWave) integrated circuits (ICs) is traditionally a slow process that requires extensive iterations and computationally intensive full-wave electromagnetic (EM) simulations. This paper presents a fully automatic radio-frequency layout generation (FARFLAG) method for the end-to-end synthesis of mmWave ICs, from device generation to final layout. The proposed method utilizes a mixture of machine-learning-assisted optimization, lookup tables, and fixed parameters for device generation. We introduce a hybrid placement and routing algorithm tailored for mmWave ICs, which is enhanced with a two-restart mechanism to improve placement robustness and exploration. The synthesis is driven by an improved Bayesian optimization approach with constrained multi-acquisition functions to navigate the design space efficiently. We demonstrate the effectiveness of the proposed framework by synthesizing two mmWave low-noise amplifiers. By integrating high-fidelity EM simulations, the FARFLAG method significantly reduces the search space compared to conventional methods, enabling the rapid synthesis of complex mmWave ICs with superior performance.

The swift improvement of wireless communication technology has aided the creation of vehicular ad hoc networks (VANETs) as a revolutionary option for the realization of intelligent transportation systems (ITSs). VANETs facilitate seamless communication between vehicles and infrastructure (V2I) and among the vehicles themselves (V2V). These networks can enhance safety and traffic control while providing various infotainment options. To ensure stable and efficient communication in highly dynamic and quickly changing vehicular contexts, effective data packet routing is a crucial component of VANETs. This survey delves further into the VANET routing algorithms for ITS. The study aims to evaluate the effectiveness and acceptability of various routing protocols suggested for VANETs while considering their unique requirements and difficulties. The review covers ad hoc on‐demand distance vector (AODV) and dynamic source routing (DSR), as well as more current strategies designed specifically for VANETs, such as geographic routing, cluster‐based routing, and hybrid routing protocols. The review assesses the routing algorithms based on several vital parameters, including packet delivery ratio (PDR), end‐to‐end delay, throughput, network overhead, scalability, and robustness. This detailed assessment supports aspiring researchers in acquiring a better knowledge of the benefits and drawbacks of the routing algorithms currently used in VANETs.