Transmission Capacity Research Articles

Kshoudra as Dahanopakarana in Snayugata Vata Roga

The father of surgery, Acharya Susrutha has mentioned different Parasurgical methods and agnikarma is one among them. Agnikarma is an asset of ayurveda which has been used widely in the clinical practise since ancient times. Agnikarma has been considered as superior than other parasurgical procedures since diseases treated with agnikarma never reoccurs, as well as it gives instant pain relief. Acharya susruta has indicated agnikarma as a chief parasurgical procedure which is an effective treatment in conditions of severe pain in twak, mamsa, sira, snayu, sandhi, asthi.[1] Acharya has mentioned different materials for agnikarma like pippali, ajasakruth, godanta, sara, salaka, jambavaushta, madhu, guda, sneha etc based on its specific heat retension and transmission capacity on specific tissues.[2] In sira, snayu, asthigata vikaras acharya susruta has mentioned snigdha dravyas like kshoudra, guda, sneha.[2] Snayugata Vata is described within the context of Vatavyadhi. As agni has ushna, Sukshma, theekshna, aashukari guna which are the opposites of vata and kapha doshas, agnikarma is thought to be the best therapy for vata and kapha doshas by eliminating the srotorodha(obstruction) and rectifying the amavastha. Eventhough kshoudra has mentioned for agnikarma it was not used widely, so this article explains the action of kshoudra as dahanopakarana in snayugata roga.

Design and Implementation of Intelligent Traffic Signal Light Control System based on LoRa Technology

The acceleration of urbanization has left cities around the world facing significant challenges, including traffic congestion and inefficient management. Intelligent transportation systems provide new technological means to address these issues, especially in traffic signal control, where advanced communication technologies play an important role in facilitating the process. The potential of long range radio (LoRa) technology in intelligent traffic signal control systems is attracting growing interest, largely due to its capacity for long-range transmission and low power consumption. This paper explores the potential benefits of LoRa technology in traffic flow monitoring and signal control by reviewing existing studies, especially in the field of long-distance transmission and low-power communication. The study indicates that LoRa technology can effectively boost the efficacy of traffic management, reduce traffic congestion, and provide technical support for the development of future intelligent transportation systems. Nevertheless, the stability of LoRa technology in high-density urban environments and its integration with other technologies still present challenges. Further research can investigate the anti-jamming ability, communication rate optimization, and its potential for wider application of LoRa technology in Intelligent Transport Systems (ITS) to promote more efficient urban traffic management

Evaluating a Public Health Assessment and Response Framework: SARS-CoV-2 Spread Under the Controlled Distancing Model of Rio Grande do Sul, Brazil.

In early 2020, to halt the spread of SARS-CoV-2, the state government of Rio Grande do Sul in Brazil established a public health assessment and response framework known as a "controlled distancing model." Using this framework, the government divided the state into 21 regions and evaluated them against a composite index of disease transmission and health service capacity. Regions were assessed using a color-coded scale of flags that was updated on a weekly basis and used to guide the adoption of nonpharmaceutical interventions. In this study, we aimed to evaluate the extent to which the controlled distancing model accurately assessed transmission and the effectiveness of its responses throughout 2020. We estimated the weekly effective reproduction number (Rt) of SARS-CoV-2 for each region using a renewal equation-based statistical model of notified COVID-19 deaths. Using Rt estimates, we explored whether flag colors assigned by the controlled distancing model either reflected or affected SARS-CoV-2 transmission. Results showed that flag assignments did reflect variations in Rt to a limited extent, but we found no evidence that they affected Rt in the short term. Medium-term effects were apparent in only 4 regions after 8 or more weeks of red flag assignment. Analysis of Google movement metrics showed no evidence that people moved differently under different flags. The dissociation between flag colors and the propagation of SARS-CoV-2 does not call into question the effectiveness of nonpharmaceutical interventions. Our results show, however, that decisions made under the controlled distancing model framework were ineffective both at influencing the movement of people and halting the spread of the virus.

640 Gbit/s FSO turbulence-resilient field trial utilizing the cylindrical vector beam.

Free-space optical (FSO) communication has the advantages of large bandwidth and high security and being license-free, making it the preferred solution for addressing the "last kilometer" of information transmission. However, it is susceptible to fluctuations in the received optical power (ROP) due to atmospheric turbulence and pointing errors, resulting in the inevitable free-space optical communication transmission performance degradation. In this work, we experimentally verified the turbulence resistance of the cylindrical vector beam (CVB) over a 3 km long free-space field trial link. A transmission capacity of 640 Gbit/s was conducted by utilizing a 20 GBaud 16-quadrature amplitude modulation (16QAM) through eight-wavelength multiplexing. The experimental results show that the CVB exhibits stronger turbulence resilience compared to the scalar circularly polarized Gaussian beam. Under moderately strong turbulence conditions, the communication stability probability of the first-order CVB improves by about 1/3 compared to the Gaussian beam, while for that of the second-order, the CVB is improved by about 1/5.

North American avian species that migrate in flocks show greater long-term non-breeding range shift rates.

Many species are exhibiting range shifts associated with anthropogenic change. For migratory species, colonisation of new areas can require novel migratory programmes that facilitate navigation between independently-shifting seasonal ranges. Therefore, in some cases range-shifts may be limited by the capacity for novel migratory programmes to be transferred between generations, which can be genetically and socially mediated. Here we used 50years of North American Breeding Bird Survey and Audubon Christmas Bird Count data to test the prediction that breeding and/or non-breeding range-shifts are more prevalent among flocking migrants, which possess a capacity for rapid social transmission of novel migration routes. Across 122 North American bird species, social migration was a significant positive predictor for the magnitude of non-breeding centre of abundance (COA) shift within our study region (conterminous United States and Southern Canada). Across a subset of 81 species where age-structured flocking was determined, migrating in mixed-age flocks produced the greatest shifts and solo migrants the lowest. Flocking was not a significant predictor of breeding COA shifts, which were better explained by absolute population trends and migration distance. Our results suggest that social grouping may play an important role in facilitating non-breeding distributional responses to climate change in migratory species. We highlight the need to gain a better understanding of migratory programme inheritance, and how this influences spatiotemporal population dynamics under environmental change.

Fractional-order vortex beam diffraction process recognition using machine learning

Fractional-order vortex beams possess Fractional Orbital Angular Momentum (FOAM) modes, which theoretically have the potential to increase transmission capacity infinitely. Therefore, they have significant application prospects in the field of measurement, optical communication and microparticle manipulation. However, when fractional-order vortex beams propagate in free space, the discontinuity of the helical phase makes them susceptible to diffraction in practical applications, thereby affecting the accuracy of OAM mode recognition and severely limiting the use of FOAM-based optical communication. The problem of achieving machine learning recognition of fractional-order vortex beams under diffraction conditions is currently an urgent and unreported issue. This paper proposes a deep learning (DL) method based on ResNet for accurate recognition of the propagation distance and topological charge of fractional-order vortex beam diffraction process. Utilizing both experimental measured and theoretically simulated intensity distributions, a dataset of vortex beam diffraction intensity patterns in atmospheric turbulence environments was created. An improved 101-layer ResNet structure based on transfer learning was employed to achieve accurate and efficient recognition of the FOAM model at different propagation distances. Experimental results show that the proposed method can accurately recognize FOAM modes with a propagation distance of 100 cm, an interval of 5 cm, and a mode spacing of 0.1 under turbulent conditions, with an accuracy of 99.69%. This method considers the effect of atmospheric turbulence during spatial transmission, allowing the recognition scheme to achieve high accuracy even in special environments. It has the distinguishing capability for ultra-fine FOAM modes and propagation distances that traditional methods cannot achieve. This technology can be applied to multidimensional encoding and sensing measurements based on FOAM beam.

Evaluation method of tie-line transmission capacity considering uncertainty of renewable energy and system frequency constraints

Evaluation method of tie-line transmission capacity considering uncertainty of renewable energy and system frequency constraints

Short-Term Transmission Capacity Prediction of Hybrid Renewable Energy Systems Considering Dynamic Line Rating Based on Data-Driven Model

Short-Term Transmission Capacity Prediction of Hybrid Renewable Energy Systems Considering Dynamic Line Rating Based on Data-Driven Model

PAPR Reduction Techniques in MIMO-OFDM Systems Using PSO and Advanced Modulation Schemes for Next-Generation Wireless Networks

The increasing need for rapid communication led to the development of two multicarrier regulating systems: Code Division Multiple Access (CDMA) and Orthogonal Frequency Division Multiplexing (OFDM). The OFDM is a type of frequency division multiplexing (FDM) plot used in complex multi carrier regulation. To transport information, many evenly spaced orthogonal sub-carriers are used. The data is split up into several equal bursts of streams for every sub-carrier. At a low image rate, each sub-carrier is adjusted using a traditional tweak plot (like QPSK) to maintain complete information rates that are comparable to traditional single carrier regulation methods at a similar capacity for transmission.

Developing power control algorithms for dynamic multi-connection industrial wireless sensor networks

In this paper, we propose a Dynamic Multi-Connection Industrial Wireless Sensor Network (DMC IWSN) model to improve data transmission efficiency in a factory workshop. We introduce a new power control algorithm called Window-Selection (WS), which adjusts sensor power based on predefined upper and lower limits. The algorithm reduces power when it exceeds the upper limit, increases power when it falls below the lower limit, and maintains power within these bounds. We compare the performance of the WS algorithm with the Max-Min (MM) algorithm, and a scenario no power control in terms of the weakest sensor capacity and total capacity in uplink transmission. Simulation results show that the WS algorithm achieves the highest capacities, while the MM algorithm moderately improves system performance in the DMC IWSN model.

Capacity Planning Method for Wind–Solar–Thermal-Storage Bundled HVDC Sending System Considering Transient Overvoltage Constraints

High-voltage direct current (HVDC) sending systems have been the main means of renewable power cross-regional sharing and consumption. However, the transient overvoltage problems restrict the transmission capacity and renewable energy accommodation. The allocation of wind–solar–thermal storage capacity has become an important factor affecting the safety and stability of renewable energy sending. A capacity planning method is proposed for a wind–solar–thermal-storage bundled HVDC sending system considering transient overvoltage constraints. Firstly, based on quantile regression analysis and Gaussian mixture modeling, the typical scenario generation method is proposed to depict the uncertainty of renewable energy. Then, the transient overvoltage characteristics of the integrated HVDC transmission system are analyzed. The relationship between the power output of power sources and the system short-circuit capacity is derived. Meanwhile, the calculation method of the minimum short-circuit capacity of the HVDC system is proposed. Based on the calculation method, the transient overvoltage constraint corresponding to the voltage support strength is constructed. Finally, considering the transient overvoltage constraints, the capacity planning model of the wind–solar–thermal storage is established. The upper-layer model optimizes the configuration scheme of the wind–solar–thermal storage to minimize the total system cost. The lower-layer model optimizes the operation scheduling under the typical operation scenarios of renewable energy and delivery load. The optimal capacity planning scheme for the wind–solar–thermal storage is determined through the coordinated optimization of the two-layer model. The feasibility and effectiveness of the proposed method are verified through a case analysis. The results show that the proposed planning method can effectively maintain a higher short-circuit ratio and improve the voltage support strength under the premise of completing the sending plan.

Research on Orbital Angular Momentum Mode Detection in an Atmospheric Environment with Fusion Transfer Learning

The vortex beam carrying Orbital Angular Momentum (OAM) has infinite orthogonal characteristic states, which theoretically can infinitely increase the communication transmission capacity, thus attracting much attention in the field of optical communication. Due to the large amount of data required for training each OAM mode, the increase in channel capacity leads to an exponential growth in the required data volume. At the same time, the phase wavefront distortion caused by atmospheric turbulence (AT) further increases the difficulty of OAM pattern recognition. This article introduces transfer learning into the field of OAM modal detection and establishes an OAM modal classifier for detecting the topological charge of distorted vortex beams. The influence of different data volumes, turbulence intensities, and propagation distances on the accuracy of OAM modal detection during the transmission of Laguerre Gaussian beams in atmospheric turbulent channels is studied, and the generalization ability of the model is analyzed. The results show that compared with traditional convolutional neural networks, the modal classifier proposed in this paper reduces the dataset size to 1/10 of the original and successfully improves the OAM detection accuracy by 15.84%. It also exhibits good generalization under unknown atmospheric turbulence strengths, providing a new approach for identifying OAM modes.

African swine fever from Kenya to five continents: the role of wild boar.

African swine fever (ASF) is currently the largest threat to world pork production. The complexity of the virus, its persistence in the environment, the particular immune response it elicits without significant neutralising antibodies, its capacity for transmission by several routes and the presentation of different clinical forms, from acute with high mortality to attenuated to chronic, all pose significant challenges. This article provides an overview of the epidemiological situation of ASF across five continents, the role of wild boar in virus transmission, the development of new immunological tools that aim to enhance protection against this complicated virus in wild boar, and the protection studies that are under way.

COVID-19 REINFECTION PROCESSES: MATHEMATICAL MODELING, DATA FITTING WITH TIME-VARYING PARAMETERS, AND OPTIMAL CONTROL STUDIES

In this paper, we construct a COVID-19 dynamic model that included both the initial and reinfected population compartments, and conduct a structural identifiability analysis of the model parameters to ensure the robustness of the parameter fitting results. We use some actual statistical data from North Carolina to fit the model and estimate the values of some important parameters. In order to accurately fit the parameters in the model, we improve the physics-informed neural networks (PINNs) method in this paper, so that the fitting results can be reproduced on Matlab. The results of this study show that the transmission capacity of the virus in the reinfected person is only slightly lower than that of the first infected person, and vaccination is not effective in reducing the transmission rate of the virus. The death rate of the reinfected is much higher than that of the first infected person. Finally, we conduct a cost-effectiveness study using optimal control methods and found that, while it is easier to reduce reinfection by combining multiple strategies, the most effective strategy for reducing reinfection is to increase treatment cure rates and reduce direct or indirect contact among those who have recovered.

Optimization of Active Distribution Network Operation with SOP Considering Reverse Power Flow

As the penetration of distributed renewable energy increases, the phenomenon of bidirectional power flow in distribution networks becomes increasingly severe. Traditional regulation devices like OLTC (on-load tap changer) and CB (capacitor bank) cannot effectively mitigate reverse power flow in distribution networks due to their limitations. The transmission capacity of the distribution network under reverse power flow is approximately 50% of the rated capacity of the OLTC, leading to issues such as voltage limit violations and high wind and solar curtailment rates. This paper proposes a method for calculating the reverse power flow delivery capacity of distribution networks, quantitatively describing the distribution network’s delivery limits for reverse power flow. Based on this, a joint optimization model for multiple distribution networks with an SOP is established. The SOP is utilized to share reverse power flow delivery capacity among multiple distribution networks, enhancing operational economy and increasing the accommodation of the DG. Finally, the method’s effectiveness and correctness are verified in the IEEE 33-node system. The results validate that while joint operation does not enhance the reverse flow transmission capacity of a single distribution network, it can, through the shared reverse flow transmission capacity approach, elevate the reverse flow transmission capacity to approximately 70% during the majority of time periods.

Heat stress promotes the accumulation of tomato yellow leaf curl virus in its insect vector by activating heat shock factor

High temperature tends to be a contributing factor to the spread of vector-borne viral diseases by enhancing vector competence. However, the underlying mechanisms of the increased virus transmission capacity of vectors under heat stress are still largely unknown. Tomato yellow leaf curl virus (TYLCV) is one of the most devastating plant DNA viruses worldwide and is transmitted exclusively by Bemisia tabaci. Here, we identified heat shock factor (HSF) as a key transcription factor that mediates TYLCV accumulation in whitefly vectors under heat stress. Quantitative analysis revealed that the amount of TYLCV DNA in whiteflies gradually increased with increasing temperature (above 38 °C). To determine the underlying mechanism, RNA-seq analysis was performed, which revealed that 1 h of heat stress caused a dramatic increase in heat shock protein (HSP) expression. Moreover, the whitefly HSP transcription factor HSF was shown to specifically interact with the intergenic region of TYLCV via yeast one-hybrid and dual-luciferase analyses. Additionally, the transcriptional activity of HSF gradually increased with increasing heat treatment temperature, indicating a similar pattern of virus accumulation under heat stress. Knocking down HSF in whiteflies caused a significant decrease in TYLCV DNA and coat protein. Our results reveal the important role of HSF in mediating virus accumulation in insect vectors under high temperature and provide insights into how environmental factors affect plant virus‒vector interactions.

Nonblocking conditions for Clos fabrics with non-uniform switch radixes

Datacenter networks (DCNs) evolve over years and so comprise switches from different generations. Thus, each stage/layer of the Clos fabric may consist of switches with varying radixes (i.e., different port counts), leading to non-uniform stages. While optical circuit switches are increasingly deployed in DCNs to enhance transmission capacity and energy efficiency, the nonblocking condition, crucial for determining the performance of circuit-switched networks, has been established only for Clos fabrics with uniform stages. This study extends the nonblocking condition to Clos fabrics with non-uniform stages. To facilitate practicality, we formulate the condition using integer linear programming (ILP). Using our novel, to our knowledge, condition, we quantitatively demonstrate how much the nonblocking property is compromised under two practical scenarios, random link failures and network expansion, which would break network uniformity. In particular, we reveal that network expansion, common in DCN evolution, could significantly undermine the nonblocking property. Additionally, we assess the computational efficiency of our ILP formulation, which can successfully evaluate the nonblocking property of a large Clos fabric accommodating 32K terminals/uplinks in just 19 min.

Green Hydrogen Blending into the Tunisian Natural Gas Distributing System

It is likely that blending hydrogen into natural gas grids could contribute to economy-wide decarbonization while retaining some of the benefits that natural gas networks offer energy systems. Hydrogen injection into existing natural gas infrastructure is recognised as a key solution for energy storage during periods of low electricity demand or high variable renewable energy penetration. In this scenario, natural gas networks provide an energy vector parallel to the electricity grid, offering additional energy transmission capacity and inherent storage capabilities. By incorporating green hydrogen into the NG network, it becomes feasible to (i) address the current energy crisis, (ii) reduce the carbon intensity of the gas grid, and (iii) promote sector coupling through the utilisation of various renewable energy sources. This study gives an overview of various interchangeability indicators and investigates the permissible ratios for hydrogen blending with two types of natural gas distributed in Tunisia (ANG and MNG). Additionally, it examines the impact of hydrogen injection on energy content variation and various combustion parameters. It is confirmed by the data that ANG and MNG can withstand a maximum hydrogen blend of up to 20%. The article’s conclusion emphasises the significance of evaluating infrastructure and safety standards related to Tunisia’s natural gas network and suggests more experimental testing of the findings. This research marks a critical step towards unlocking the potential of green hydrogen in Tunisia.

High spectral-efficiency, ultra-low MIMO SDM transmission over a field-deployed multi-core OAM fiber

Space-division multiplexing (SDM) systems based on few-mode multi-core fibers (FM-MCFs) utilize both spatial channels (fiber cores) and modes (optical modes per core) to maximize transmission capacity. Unlike laboratory FM-MCFs or field-deployed single-mode multi-core fibers (SM-MCFs), SDM transmissions over field-deployed FM-MCFs in outdoor settings have not been reported. Therefore, concerns remain that environmental interference and cabling stress could worsen inter-core and intra-core modal crosstalk and impact the performance of SDM systems over FM-MCFs. In this paper, we demonstrate successful bidirectional SDM transmission over a 5-km, field-deployed seven ring-core fiber (7-RCF) with a cladding diameter of 178 μm. Our measurements show no significant differences in attenuation and mode coupling compared to pre-cabling conditions, confirming the fiber’s resilience to environmental disturbances and adaptability to cable deployment. Using the field-deployed 7-RCF, bi-directional SDM transmission is implemented, achieving spectral efficiency (SE) of 2×201.6 bit/(s Hz) which sets a new record in field-deployed fiber cables that is a tenfold increase over previous systems. Furthermore, these results were achieved using a small-scale 4×4 multiple-input multiple-output (MIMO) scheme with a time-domain equalization (TDE) tap number not exceeding 15. These results demonstrate the substantial potential of using SDM techniques to significantly enhance SE and expand capacity in practical fiber-optic transmission applications.

An effective logarithm-degree resource allocation strategy for three-layer networks

Conventional resource allocation strategies, such as the Simplest Average (SA) resource allocation strategy, often assume identical transmission capacity for all nodes, failing to account for the actual importance of nodes within the network, especially in three-layer networks where inadequate resource allocation to hub nodes can significantly impact performance. Existing methods are primarily designed for single-layer networks and struggle to address the complexity and interdependencies of real-world multi-layer structures, thus limiting the optimization of network traffic capacity. This paper proposes an Effective Logarithm-Degree (EL) resource allocation strategy that allocates node delivery capacity based on the ratio of the logarithmic degrees of nodes across the logical and physical layers. Compared to the Simplest Average resource allocation strategy, the Effective Logarithm-Degree strategy significantly enhances network traffic capacity, increasing it by over 1.4 times and reducing traffic load by 60%, while also optimizing the load balance. Simulation results validate the superiority of the Effective Logarithm-Degree strategy in improving performance and resource utilization efficiency in three-layer network environments.