Distributed Resource Allocation Assisted by Intercell Interference Mitigation in Downlink Multicell MC DS-CDMA Systems

This paper investigates the allocation of resources, including subcarriers and spreading codes, as well as intercell interference (ICI) mitigation for multicell downlink multicarrier direct-sequence code division multiple-access systems, which aim to maximize the system's spectral efficiency (SE). The analytical benchmark scheme for resource allocation and ICI mitigation is derived by solving or closely solving a series of mixed integer non-convex optimization problems. Based on the optimization objectives the same as the benchmark scheme, we propose a novel distributed resource allocation assisted by ICI mitigation scheme referred to as resource allocation assisted by ICI mitigation (RAIM), which requires very low implementation complexity and demands little backhaul resource. Our RAIM algorithm is a fully distributed algorithm, which consists of the subcarrier allocation (SA) algorithm named RAIM-SA, spreading code allocation (CA) algorithm called RAIM-CA and the ICI mitigation algorithm termed RAIM-IM. The advantages of the RAIM are that its CA only requires limited binary ICI information of intracell channels, and it is able to make mitigation decisions without any knowledge of ICI information. Our simulation results show that the proposed RAIM scheme, with very low complexity required, achieves significantly better SE performance than other existing schemes, and its performance is very close to that obtained by the benchmark scheme

Distributed Resource Allocation Assisted by Intercell Interference Mitigation in Downlink Multicell MC DS-CDMA Systems

This paper investigates the allocation of resources, including subcarriers and spreading codes, as well as intercell interference (ICI) mitigation for multicell downlink multicarrier direct-sequence code division multiple-access systems, which aim to maximize the system's spectral efficiency (SE). The analytical benchmark scheme for resource allocation and ICI mitigation is derived by solving or closely solving a series of mixed integer non-convex optimization problems. Based on the optimization objectives the same as the benchmark scheme, we propose a novel distributed resource allocation assisted by ICI mitigation scheme referred to as resource allocation assisted by ICI mitigation (RAIM), which requires very low implementation complexity and demands little backhaul resource. Our RAIM algorithm is a fully distributed algorithm, which consists of the subcarrier allocation (SA) algorithm named RAIM-SA, spreading code allocation (CA) algorithm called RAIM-CA and the ICI mitigation algorithm termed RAIM-IM. The advantages of the RAIM are that its CA only requires limited binary ICI information of intracell channels, and it is able to make mitigation decisions without any knowledge of ICI information. Our simulation results show that the proposed RAIM scheme, with very low complexity required, achieves significantly better SE performance than other existing schemes, and its performance is very close to that obtained by the benchmark scheme

Rice grain cadmium concentrations in the global supply-chain

Artículo escrito por un elevado número de autores, solo se referencia el que aparece en primer lugar, el nombre del grupo de colaboración, si lo hubiere, y los autores pertenecientes a la UAMOne of cadmium’s major exposure routes to humans is through rice consumption. The concentrations of cadmium in the global polished (white), market rice supply-chain were assessed in 2270 samples, purchased from retailers across 32 countries, encompassing 6 continents. It was found on a global basis that East Africa had the lowest cadmium with a median for both Malawi and Tanzania at 4.9 μg/kg, an order of magnitude lower than the highest country, China with a median at 69.3 μg/kg. The Americas were typically low in cadmium, but the Indian sub-continent was universally elevated. In particular certain regions of Bangladesh had high cadmium, that when combined with the high daily consumption rate of rice of that country, leads to high cadmium exposures. Concentrations of cadmium were compared to the European Standard for polished rice of 200 μg/kg and 5% of the global supply-chain exceeded this threshold. For the stricter standard of 40 μg/kg for processed infant foods, for which rice can comprise up to 100% by composition (such as rice porridges, puffed rice cereal and cakes), 25% of rice would not be suitable for making pure rice baby foods. Given that rice is also elevated in inorganic arsenic, the only region of the world where both inorganic arsenic and cadmium were low in grain was East Afric

Inferring Economic Condition Uncertainty from Electricity Big Data

Inferring the uncertainties in economic conditions are of significant importance for both decision makers as well as market players. In this paper, we propose a novel method based on Hidden Markov Model (HMM) to construct the Economic Condition Uncertainty (ECU) index that can be used to infer the economic condition uncertainties. The ECU index is a dimensionless index ranges between zero and one, this makes it to be comparable among sectors, regions and periods. We use the daily electricity consumption data of nearly 20 thousand firms in Shanghai from 2018 to 2020 to construct the ECU indexes. Results show that all ECU indexes, no matter at sectoral level or regional level, successfully captured the negative impacts of COVID-19 on Shanghai's economic conditions. Besides, the ECU indexes also presented the heterogeneities in different districts as well as in different sectors. This reflects the facts that changes in uncertainties of economic conditions are mainly related to regional economic structures and targeted regulation policies faced by sectors. The ECU index can also be easily extended to measure uncertainties of economic conditions in different fields which has great potentials in the future

Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis

This paper explores the application of deep learning techniques, particularly focusing on BERT models, in sentiment analysis. It begins by introducing the fundamental concept of sentiment analysis and how deep learning methods are utilized in this domain. Subsequently, it delves into the architecture and characteristics of BERT models. Through detailed explanation, it elucidates the application effects and optimization strategies of BERT models in sentiment analysis, supported by experimental validation. The experimental findings indicate that BERT models exhibit robust performance in sentiment analysis tasks, with notable enhancements post fine-tuning. Lastly, the paper concludes by summarizing the potential applications of BERT models in sentiment analysis and suggests directions for future research and practical implementations

Compact InGaAs/InP single-photon detector module with ultra-narrowband interference circuits

Gated InGaAs/InP avalanche photodiodes are the most practical device for detection of telecom single photons arriving at regular intervals.Here, we report the development of a compact single-photon detector (SPD) module measured just 8.8cm * 6cm * 2cm in size and fully integrated with driving signal generation, faint avalanche readout, and discrimination circuits as well as temperature regulation and compensation. The readout circuit employs our previously reported ultra-narrowband interference circuits (UNICs) to eliminate the capacitive response to the gating signal. We characterize a UNIC-SPD module with a 1.25-GHz clock input and find its performance comparable to its counterpart built upon discrete functional blocks. Setting its detection efficiency to 30% for 1,550-nm photons, we obtain an afterpulsing probability of 2.4% and a dark count probability of 8E-7 per gate under 3-ns hold-off time. We believe that UNIC-SPDs will be useful in important applications such as quantum key distribution

Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems

The integration of LLMOps into personalized recommendation systems marks a significant advancement in managing LLM-driven applications. This innovation presents both opportunities and challenges for enterprises, requiring specialized teams to navigate the complexity of engineering technology while prioritizing data security and model interpretability. By leveraging LLMOps, enterprises can enhance the efficiency and reliability of large-scale machine learning models, driving personalized recommendations aligned with user preferences. Despite ethical considerations, LLMOps is poised for widespread adoption, promising more efficient and secure machine learning services that elevate user experience and shape the future of personalized recommendation systems

Nonnegative Matrix Factorization Numerical Method for Integrated Photonic Cavity Based Spectroscopy

Nonnegative matrix factorization numerical method has been used to improve the spectral resolution of integrated photonic cavity based spectroscopy. Based on the experimental results for integrated photonic cavity device on Optics Letters 32, 632 (2007), the theoretical results show that the spectral resolution can be improved more than 3 times from 5.5 nm to 1.8 nm. It is a promising way to release the difficulty of fabricating high-resolution devices

Accurate power sharing of hybrid energy storage system in DC shipboard power system based on quadratic programming algorithm

The DC shipboard power system (DC-SPS) can be regarded as an island microgrid, supplying energy to propulsion systems, service devices and advanced equipment in future ships. Ensuring accurate power sharing among distributed power sources and maintaining the stability of DC bus voltage in DCSPS are prerequisites to run system in security and economy. Therefore, an accurate power sharing method based on the quadratic programming algorithm is proposed in this paper. That method aims at minimizing the cost of voltage regulation in the consideration of state of charge (SoC) of each energy storage device (ESD). In detail, the target power is determined by the DC bus voltage deviation, and further distributed among various energy storage by quadratic programming accurately. With the control method, the DC bus voltage is maintained within the desired voltage range. Moreover, the method can meet the plug-and-play requirements of distributed power. The effectiveness of the proposed control method is verified by real-time simulation

Ginsenosides Rg1 from Panax ginseng

Acute liver failure (ALF) is a rapidly progressing critical illness with a high mortality rate. Circulating inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), play a significant role in the pathophysiology of ALF through promoting hepatocellular apoptosis. Ginsenoside Rg1, the primary active ingredient in Panax ginseng (also termed Asian or Korean ginseng), has been reported to inhibit TNF-α production and has been shown to significantly attenuate liver fibrosis development. Here, we assessed ginsenoside Rg1’s potential as a therapy for ALF by investigating the effect of ginsenoside Rg1 treatment on circulating inflammatory markers, hepatocellular apoptosis, and relevant apoptotic signaling pathways in a well-established murine ALF model. We found that ginsenoside Rg1 significantly reduces liver damage in a murine ALF model through inhibiting TNF-α-induced, caspase-dependent hepatocellular apoptosis. These results support the further investigation of ginsenoside Rg1 as a therapeutic candidate for ALF