Mobile Networks Research Articles

Transcriptome analysis reveals methanol metabolism variations for the growth damage caused by overexpression of chimeric transactivators in Pichia pastoris

Methanol is a promising substrate for sustainable biomanufacturing, and Pichia pastoris has become a commonly used yeast for methanol utilization due to its powerful methanol metabolic pathways and methanol inducible promoter. Previous reconstruction of gene circuits highly improved transcriptional activity, but excessive expression of chimeric transactivator damaged cell growth on methanol. Here we employed transcriptome analysis to investigate the effects of chimeric transactivator overexpression on cellular metabolism and regulatory networks. The results showed that strong expression of chimeric transactivator unexpectedly downregulated methanol metabolism, especially the alcohol oxidase 1 (AOX1), but without remarkable changes in expression of transcriptional factors. Meanwhile, the synthesis of peroxisomes also varied with chimeric transactivator expression. In addition, the enrichment analysis of differentially expressed genes revealed their impact on cellular metabolism. The gene expression patterns caused by different expression levels of chimeric transactivators have also been clarified. This work provides useful information to understand the transcriptional regulation of the AOX1 promoter and methanol signaling. It revealed the importance of balancing transcription factor expression for the host improvement.

Facilitating and Determining Turing Patterns in 3-D Memristor Cellular Neural Networks

Facilitating and Determining Turing Patterns in 3-D Memristor Cellular Neural Networks

Energy-Efficient and Accelerated Resource Allocation in O-RAN Slicing Using Deep Reinforcement Learning and Transfer Learning

Abstract Next Generation Wireless Networks (NGWNs) have two main components: Network Slicing and Open Radio Access Networks (O-RAN). NS is needed to handle various Quality of Services (QoS). O-RAN adopts an open environment for network vendors and Mobile Network Operators (MNOs). In recent years, Deep Reinforcement Learning (DRL) approaches have been proposed to solve some key issues in NGWNs. The primary obstacles preventing the DRL deployment are being slowly converged and unstable. Additionally, these algorithms have enormous carbon emissions that negatively impact climate change. This paper tackles the dynamic allocation problem of O-RAN radio resources for better QoS, faster convergence, stability, lower energy and power consumption, and reduced carbon emissions. Firstly, we develop an agent with a newly designed latency-based reward function and a top-k filtration mechanism for actions. Then, we propose a policy Transfer Learning approach to accelerate agent convergence. We compared our model to another two models.

Joint Traffic Admission, Resource Allocation and Mode Selection Protocol for NOMA-Based D2D Users Underlaying Cellular Network for 5G and Beyond Networks

Joint Traffic Admission, Resource Allocation and Mode Selection Protocol for NOMA-Based D2D Users Underlaying Cellular Network for 5G and Beyond Networks

On Impact of HPA Nonlinearity in NOMA Enabled Heterogeneous Cellular Network

On Impact of HPA Nonlinearity in NOMA Enabled Heterogeneous Cellular Network

Leveraging a Variety of Anchors in Cellular Network for Ubiquitous Sensing

Leveraging a Variety of Anchors in Cellular Network for Ubiquitous Sensing

Comparison of facial skin ageing in healthy Asian and Caucasian females quantified by in vivo line-field confocal optical coherence tomography 3D imaging.

Quantitative biomarkers of facial skin aging were investigated in 109 healthy Asian female volunteers, aged 20 to 70 years. In vivo 3D Line-field Confocal Optical Coherence Tomography (LC-OCT) imaging, enhanced by Artificial Intelligence (AI)-based quantification algorithms, was utilized to compute various metrics, including stratum corneum thickness (SC), viable epidermal (VE) thickness, and Dermal-Epidermal Junction (DEJ) undulation along with cellular metrics for the temple, cheekbone, and mandible. Comparison with data from a cohort of healthy Caucasian volunteers revealed similarities in the variations of stratum corneum and viable epidermis layers, as well as cellular shape and size with age in both ethnic groups. However, specific findings emerged, such as larger, more heterogeneous nuclei in both layers, demonstrated by an increase in nuclei volume and their standard deviation, and increased network atypia, all showing significant age-related variations. Caucasian females exhibited a flatter and more homogeneous epidermis, evidenced by a decreased standard deviation of the number of layers, and a less dense cellular network with fewer cells per layer, indicated by a decrease in cell surface density. Ethnicity-wise comparisons highlighted distinct biological features specific to each population. Asian individuals showed significantly higher DEJ undulation, higher compactness, and lower cell network atypia compared to their Caucasian counterparts across age groups. Differences in stratum corneum and viable epidermal thickness on the cheekbone were also significant. LC-OCT 3D imaging provides valuable insights into the aging process in different populations and underscores inherent biological differences between Caucasian and Asian female volunteers.

Foreign Migrants in the Moscow Agglomeration: Spatio-Temporal Analysis Based on Mobile Network Operator Data

Using mobile network operator data, the study analyzes the modern ethnic landscape of the Moscow agglomeration formed by foreign migrants. Their total number and monthly dynamics from October 2021 to October 2022 were analyzed. Foreigners’ main areas of residence were identified, the ethnic diversity of municipalities was assessed, and the main types of settlement pattern of national-ethnic communities were identified. The study showed that the total number of foreign migrants in the agglomeration reaches 1.8 mln people, or 9% of the total population, remaining almost unchanged against the backdrop of the events of 2022, including the special military operation. The share of foreigners is minimal in closed administrative-territorial units (below 3%), and it is also small in the remote parts of Moscow Region, as well as in expensive areas of the capital. At the same time, eight municipalities in the agglomeration have crossed the 17% mark, the so-called tipping point, reflecting a sharp increase in the risks of interethnic conflicts and ghettoization of the urban space. The two most noticeable areas of increased concentration of foreign migrants have been identified in the southeast at the junction of Moscow and Moscow oblast (Lublino–Kotelniki) and in New Moscow (Mosrentgen–Sosenskoe). Calculation of the Ekkel’ ethnic mosaic index has confirmed the presence of pronounced interethnic contact zones here. Elevated index values were also noted in most areas of the center and southwest of the capital, which, with a lower share of foreign residents, is associated with a high density of office buildings, diplomatic institutions, and universities. Analysis of the settlement patterns of national-ethnic groups of migrants revealed three types determined in accordance with the adaptive capabilities of ethnic communities. A diffuse, relatively uniform settlement pattern is characteristic of both the largest ethnic groups (citizens of Tajikistan, Uzbekistan, and Kyrgyzstan) and those most culturally close to the local population (immigrants from Ukraine and Belarus). A concentric settlement pattern (in residential areas of Moscow and satellite cities) is typical of relatively large ethnic groups from post-Soviet countries (citizens of Azerbaijan, Armenia, and Georgia). The local type is characteristic of small ethnic communities that gravitate towards certain areas of the capital.

Analysis of 4G/LTE Network Performance in North-Central Nigeria: A Comprehensive Drive Test Approach

The proliferation of mobile devices and escalating demand for data services have resulted in a substantial increase in data traffic across Nigeria, especially in the North Central region due to heavy migration of individuals and organizations towards Abuja, the federal capital. However, despite the growing adoption of 4G/LTE networks in North Central Nigeria, users encounter persistent challenges in accessing high-speed internet and poor service network. This study investigates the performance of 4G/LTE networks in North-Central Nigeria using a comprehensive drive test methodology carried out in Abuja, Lafia and Makurdi. It covered a duration of 3hrs (12.00noon to 15.00pm) for 3days (3rd - 6th January, 2024). For each test day, drive tests covered a period of data measurements divided into 3,600 time-steps, with each time-step sized at 1sec; and scaled to 1:150 units. Key metrics including network speed, latency, uptime, coverage, and signal power were evaluated across major Mobile Network Operators. Result shows that D-NGN has the highest mean speed (9.543Mbps), packet loss (2.007%), uplink percentage (97.714%), and Network coverage (87.514%). It has the least latency (25.921) but high packet loss (2.007%). However, C-NGN shows lowest mean speed (6.638Mbps), uplink percentage (38.706%) and Signal power (-78.057dBm) but lowest mean packet loss (0.121%). A-NGN has the highest mean signal power (-61.867dBm) while B-NGN has highest latency (44.070ms) and lowest Network coverage (60.731%). This reveals that D-NGN has dominance in network performance, outperforming A-NGN, B-NGN, and C-NGN. It is recommended that MNOs in North-Central Nigeria should strategically enhance their network infrastructure to improve service delivery, particularly in suburban and rural areas where performance lags. D-NGN, the frontrunner in network speed and coverage, could further solidify its market leadership by addressing identified latency and packet loss issues especially in Makurdi. Moreover, MNOs with lower performance metrics could invest in expanding coverage and optimizing network configurations to meet user demands, thereby increasing competitiveness and customer satisfaction across the region.

Performance Analysis of Filtered OFDM using MLE for Wireless Communication Networks

Introduction: OFDM has evolved as an effective modulation format for 4G mobile network technologies, Long-Term Evolution, and Worldwide Interoperability for Microwave Access. OFDM is the modulation technique adopted for communication standards such as DSL, wireless LAN, DVB, and DAB. However, because CP-OFDM has considerable out-of-band emission that may interfere with transmissions in adjacent bands and applies a single set of parameters to the whole band to fulfill a given service, it is unable to accommodate the diversity of services. The objective of the research is to develop a novel waveform with better adaptability than CP-OFDM that employs filters to increase spectrum containment. In this view, an FIR filter that uses the Blackman window function to lessen the OOBE is proposed in this manuscript. Filtered OFDM is sensitive to carrier frequency offset (CFO). CFO increases the Inter-Carrier Interference (ICI) in F-OFDM systems. However, research work that combines the CFO compensation technique with filtered OFDM has not been listed in the literature so far. Methods: In this paper, Maximum likelihood estimation is used to cancel the ICI caused by the CFO. The high PAPR of filtered OFDM is one of its drawbacks. The amplitude clipping method is used to reduce the PAPR. The distortion caused by amplitude clipping is prevented by filters used in the FOFDM system. Highlights: • A novel waveform addressing OOBE issues in OFDM is developed for 4G wireless communication networks. • An innovative solution using MLE is proposed to address the challenges faced by the filtered OFDM system due to the CFO. • Article addresses the high PAPR problem of filtered OFDM. Results: The performance of filtered OFDM with amplitude clipping is analyzed in terms of BER, PSD, and PAPR over the AWGN channel. Conclusion: Simulation results show that, when compared to the traditional OFDM system, such a combined method effectively suppresses PAPR while maintaining good BER (Bit Error Rate) and OOBE performance.

Guanxinning Tablet Alleviates Post-Ischemic Stroke Injury Via Regulating Complement and Coagulation Cascades Pathway and Inflammatory Network Mobilization.

Currently, ischemic stroke (IS) continues to significantly contribute to functional deterioration and reduced life quality. Regrettably, the choice of neuro-rehabilitation interventions to enhance post-IS outcomes is limited. Guanxinning tablet (GXNT), a multi-component medicine composed of Danshen and Chuanxiong, has demonstrated neuroprotective potential against ischemic brain injury and diabetic encephalopathy. However, the therapeutic impact of GXNT on post-IS functional outcomes and pathological injury, as well as the underlying molecular mechanisms and anti-IS active substances, remain unclear. To answer the above questions, neurological and behavioral assessment, cerebral lesions, and blood-brain barrier (BBB) integrity were combined to comprehensively investigate GXNT's pharmacodynamic effects against post-IS injury. The possible molecular mechanisms were revealed through transcriptome sequencing coupled with experimental verification. Furthermore, the brain tissue distribution of main components in GXNT, behavioral changes of IS zebrafish, and molecular docking were integrated to identify the anti-IS active compounds. Treatment with GXNT significantly mitigated the functional deficits, cerebral cortex lesions, and BBB disruption following IS. Transcriptome sequencing and bioinformatics analysis suggested that complement and coagulation cascades as well as inflammation might play crucial roles in the GXNT's therapeutic effects. Molecular biology experiments indicated that GXNT administration effectively normalized the abnormal expression of mRNA and protein levels of key targets related to complement and coagulation cascades (eg C3 and F7) and inflammation (eg MMP3 and MMP9) in the impaired cortical samples of IS mice. The locomotor promotion in IS zebrafish as well as favorable affinity with key proteins (C3, F7, and MMP9) highlighted anti-IS activities of brain-permeating constituents (senkyunolide I and protocatechuic acid) of GXNT. Taken together, these intriguing findings indicate that GXNT intervention exerts a beneficial effect against post-IS injury via regulating the complement and coagulation cascades pathway and mobilizing inflammatory network. Senkyunolide I and protocatechuic acid show promise as anti-IS active compounds.

Legitimacy considerations in regulation for local mobile communication network business in Finland and the UK

The article explores the legitimacy of local 5G and upcoming 6G mobile communication networks in Europe. The paper paves the way for a new perspective for understanding the legitimacy of local 5G/6G mobile communication networks by applying the business model-based ecosystem perspective and legitimacy as a theoretical foundation. As regulations are expanding to consider the novel deployment model of local 5G and 6G mobile communication networks, the legitimacy of such networks is under-explored. Analyzing the regulatory approaches taken by two national regulatory authorities (NRA) in Europe, Finland and the UK, we identify seven legitimacy-related themes including innovation orientation, awareness and knowledge of how to build and design local networks, European harmonized spectrum management, competition, cybersecurity and risk assessment and equipment availability. Our study lays the groundwork for exploring how the application of the business model-based ecosystem perspective can be utilized to advance regulatory initiatives for local mobile communication network businesses. It also identifies which legitimacy-related aspects are involved in setting regulatory priorities.

A dual incentive mechanism based on graph attention neural network and contract in mobile opportunistic networks

In mobile opportunistic networks, messages are transmitted through opportunistic contacts between nodes. Hence, the successful delivery of messages heavily relies on the mutual cooperation among nodes in the network. However, due to limited network resources such as node energy and cache space, nodes tend to be selfish, and they are unwilling to actively participate in message forwarding. In response to this challenge, lots of incentive mechanisms have been proposed. However, most of them rely on single incentives, there are issues such as inadequate handling of selfish nodes and vulnerability to malicious attacks, which ultimately lead to poor incentive effects. Therefore, in this paper, a Dual Incentive mechanism based on Graph attention neural network and Contract (DIGC) is introduced to encourage active participation of network nodes in data transmission. This incentive mechanism is divided into two steps. In the first step, the graph attention neural network is used to evaluate the reputation of nodes to achieve the goal of reputation-based incentive, and blockchain is employed to store and manage node reputation to ensure security and transparency. In the second step, an incentive based on contract theory is introduced, where personalized contracts were designed based on the different resources owned by nodes, thereby establishing a reward mechanism to encourage collaborative transmission. Extensive simulations based on two real-life mobility traces have been done to evaluate the performance of our DIGC compared with other existing incentive mechanisms. The results show that, our proposed mechanism can greatly improve throughput and reduce average delay while ensuring the overall delivery performance of the network.

사회적 연결은 득인가 실인가: 사회적 연결의 이점과 비용에 관한 연구 동향과 과제

Network theory has traditionally focused on social ties as its primary subject of study, investigating patterns of linkages and relationships among actors and the ensuing social structures from various perspectives. This paper reviews the extant literature on both the positive and negative effects of social ties and social networks. It transcends the mainstream research tradition that has predominantly emphasized positive impacts, presenting domestic and international studies that explore the constraints and costs engendered by social ties, including the excessive homogenization they can cause. Specifically, the paper delves into Granovetter’s (1973) “strength of weak ties” theory, Burt’s (1992, 1995) structural hole theory, which addresses both the patterns of connections and relationships between actors and the resultant structures, and Coleman's (1990) concept of network closure, which underscores the overall structure of social networks. In doing so, this paper presents core tenets of these theories and critically evaluates the current state of knowledge as presented in leading domestic and international management and sociology journals. Finally, considering the implications of networks and their social ties for organizational research, alongside the ease with which individuals can now join various networks through the rapid development of online and mobile social networking sites, the paper underscores the importance of focusing on the costs and constraints that actors face when navigating numerous social connections. It also highlights the necessity of being wary of confirmation bias caused by suggestion algorithms embedded in online sites and proposes directions for future research.

The efficacy of a cellular approach to resilience-oriented operation of distribution grids based on eigenvectors of the Laplacian matrix

This study aims to address the following research query: In the event of an imminent disaster poised to impact distribution grids, what constitutes the optimal course of action for the distribution system operators to keep the lights on? To address this challenge, we propose a cost-efficient cellular model for enhancing the resilience of smart distribution grids. This model prioritizes resilience in the face of natural disasters or other disruptions that could impact service delivery. This method benefits both grid operators and consumers by ensuring reliable power supply while minimizing energy costs. Furthermore, the model's scalability allows it to be applied to distribution systems of varying sizes. The proposed method utilizes an innovative approach to form optimal cellular network configurations within the grid. As the first step in the formation of cellular topology for the grid, the eigenvectors of the Laplacian matrix of the grid will be used to decide on the optimal configurations. Subsequently, a bi-level mixed-integer linear programming model is proposed to decrease the network costs while simultaneously consider potential power transfer scenarios between the cells and the upstream network during both normal and emergency conditions. The researchers validated the effectiveness of the proposed method through simulations on an IEEE 33-bus test system. The results demonstrate outstanding performance, with a significant increase in the resilience index (96 %) and a substantial reduction in load-shedding costs (80 %), making the network considerably more robust.

Exploiting Conventional MANET Routing in UAV’s Based Environment

Nowadays, Unmanned Aerial Vehicles (UAVs), have indeed revolutionized various industries and opened up new possibilities. Additionally, a Flying Ad hoc Network (FANET), a group of UAVs can perform specific functions like delivering goods, assisting people after a disaster, monitoring the weather and environment, among others. FANET is another kind of ad-hoc mobile network. It is capable of connecting with its environment and other nodes to obtain critical information. It is particularly difficult to route across FANETs because of how mobility they are and how fast they may change layouts. Therefore, routing algorithms are crucial for improving the performance of ad-hoc networks. This article will analyse the performance of the AODV, DSDV, and TORA routing protocols in the FANET environment. The NS-2 simulator has been used for testing parameters such as jitter, throughput, End-to-End delay, and packet delivery ratio (PDR). The simulation results indicate that TORA outperforms AODV and DSDV routing protocols across all performance metrics except End-to-End Delay.

Deep Convolutional Neural Network Algorithm Based on Optical Sensors and Wireless Mobile Networks for Real time Monitoring of Physical Health

Deep Convolutional Neural Network Algorithm Based on Optical Sensors and Wireless Mobile Networks for Real time Monitoring of Physical Health

Artificial Intelligence Workload Allocation Method for Vehicular Edge Computing

Real-time applications such as smart transportation systems require minimum response time to increase performance. Incorporating edge computing, processing units near end devices, achieving fast response time. The collaboration between edge servers and cloud servers is beneficial in achieving the lowest response time by using edge servers and high computational resources by using cloud servers. The workload allocation between edge–cloud servers is challenging, especially in a highly dynamic system with multiple factors varying over time. In this paper, the workload allocation decisions among the edge servers and cloud are considered for autonomous vehicle systems. The autonomous vehicle system generates multiple tasks belonging to different AI applications running on the vehicles. The proposed method considers allocating the tasks to edge or cloud servers. The cloud servers can be reached through a cellular network or a wireless network. The proposed method is based on designing a neural network model and using a high number of features that contribute to the decision-making process. A huge dataset has also been generated for the implementation. The EdgeCloudSim is used as a simulator for implementation. The competitor's methods considered for the comparison are random, simple moving average (SMA) based, multi-armed bandit (MAB) theory-based, game theory-based, and machine learning-based workload allocation methods. The result shows an improvement in the average Quality of Experience (QoE), ranging from 8.33% to 28.57%, while the average failure rate achieved enhancement up to 50%.

Insight of the molecular mechanism of inhibitors located at different allosteric sites regulating the activity of wild type and mutant KRAS (G12)

As the important hub of many cellular signaling networks, KRAS (Kirsten rat sarcoma viral oncogene homologue) has been identified as a tumor biomarker. It is the frequently mutated oncogene in human cancers, and KRAS protein activation caused by mutations, such as G12D, has been found in many human tumors tissues. Although, there are two specific allosteric sites (AS1 and AS2) on the KRAS protein that can be used as the targets for inhibitor development, the difference of regulatory mechanisms between two individual allosteric sites still not be reported. Here, using molecular dynamics simulations combined with molecular mechanics generalized born surface area (MM/GBSA) analysis, we found that both of the inhibitors, located at AS1 and AS2, were able to reduce the binding free energy between wild type, mutant KRAS (G12/D/V/S/C) and GTP remarkably, however the effect of inhibitors on the binding free energy between wild type, mutant KRAS and GDP was limited. In addition, the degree of decrease of binding free energy between KRAS and GTP caused by inhibitors at AS2 was significantly greater than that caused by inhibitors at AS1. Further analysis revealed that both inhibitors at AS1 and AS2 were able to regulate the fluctuation of Switch Ⅰ and Switch Ⅱ to expand the pocket of the orthosteric site (GTP binding site), thereby reducing the binding of KRAS to GTP. Noteworthy there was significant differences in the regulatory preferences on Switch Ⅰ and Switch Ⅱ between two type inhibitor. The inhibitor at AS2 mainly regulated Switch Ⅱ to affect the pocket of the orthosteric site, while the inhibitor at AS1 mainly expand the pocket of the orthosteric site by regulating the fluctuation of Switch Ⅰ. Our study compared the differences between two type inhibitors in regulating the KRAS protein activity and revealed the advantages of the AS2 as the small molecule drug target, aiming to provide theoretical guidance for the research of novel KRAS protein inhibitors.

Defining cell type-specific immune responses in a mouse model of allergic contact dermatitis by single-cell transcriptomics.

Allergic contact dermatitis (ACD), a prevalent inflammatory skin disease, is elicited upon repeated skin contact with protein-reactive chemicals through a complex and poorly characterized cellular network between immune cells and skin resident cells. Here, single-cell transcriptomic analysis of the murine hapten-elicited model of ACD reveals that upon elicitation of ACD, infiltrated CD4+ or CD8+ lymphocytes were primarily the IFNγ-producing type 1 central memory phenotype. In contrast, type 2 cytokines (IL4 and IL13) were dominantly expressed by basophils, IL17A was primarily expressed by δγ T cells, and IL1β was identified as the primary cytokine expressed by activated neutrophils/monocytes and macrophages. Furthermore, analysis of skin resident cells identified a sub-cluster of dermal fibroblasts with preadipocyte signature as a prominent target for IFNγ+ lymphocytes and dermal source for key T cell chemokines CXCL9/10. IFNγ treatment shifted dermal fibroblasts from collagen-producing to CXCL9/10-producing, which promoted T cell polarization toward the type-1 phenotype through a CXCR3-dependent mechanism. Furthermore, targeted deletion of Ifngr1 in dermal fibroblasts in mice reduced Cxcl9/10 expression, dermal infiltration of CD8+ T cell, and alleviated ACD inflammation in mice. Finally, we showed that IFNγ+ CD8+ T cells and CXCL10-producing dermal fibroblasts co-enriched in the dermis of human ACD skin. Together, our results define the cell type-specific immune responses in ACD, and recognize an indispensable role of dermal fibroblasts in shaping the development of type-1 skin inflammation through the IFNGR-CXCR3 signaling circuit during ACD pathogenesis.