Transparency and Policymaking with Endogenous Information Provision

How does the politician's reputation concern affect information provision when the information is endogenously provided by a biased lobbyist? I develop a model to study this problem and show that the answer depends on the transparency design. When the lobbyist's preference is publicly known, the politician's reputation concern induces the lobbyist to provide more information. When the lobbyist's preference is unknown, the politician's reputation concern may induce the lobbyist to provide less information. One implication of the result is that given transparent preferences, the transparency of decision consequences can impede information provision by moderating the politician's reputational incentive

Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning

This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the network, the collected data is sent to the cloud for statistical analysis, prediction, and data analysis to achieve business objectives. However, because the cloud computing model is limited by distance, it can be problematic in environments where the quality of the Internet connection is not ideal for critical operations. Therefore, edge computing, as a distributed computing architecture, moves the location of processing applications, data and services from the central node of the network to the logical edge node of the network to reduce the dependence on cloud processing and analysis of data, and achieve near-end data processing and analysis. The combination of iot and edge computing can reduce latency, improve efficiency, and enhance security, thereby driving the development of intelligent systems. The paper also introduces the development of iot monitoring and control technology, the application of edge computing in iot monitoring and control, and the role of machine learning in data analysis and fault detection. Finally, the application and effect of intelligent Internet of Things monitoring and control system in industry, agriculture, medical and other fields are demonstrated through practical cases and experimental studies

Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks

To enable large-scale and efficient deployment of artificial intelligence (AI), the combination of AI and edge computing has spawned Edge Intelligence, which leverages the computing and communication capabilities of end devices and edge servers to process data closer to where it is generated. A key technology for edge intelligence is the privacy-protecting machine learning paradigm known as Federated Learning (FL), which enables data owners to train models without having to transfer raw data to third-party servers. However, FL networks are expected to involve thousands of heterogeneous distributed devices. As a result, communication efficiency remains a key bottleneck. To reduce node failures and device exits, a Hierarchical Federated Learning (HFL) framework is proposed, where a designated cluster leader supports the data owner through intermediate model aggregation. Therefore, based on the improvement of edge server resource utilization, this paper can effectively make up for the limitation of cache capacity. In order to mitigate the impact of soft clicks on the quality of user experience (QoE), the authors model the user QoE as a comprehensive system cost. To solve the formulaic problem, the authors propose a decentralized caching algorithm with federated deep reinforcement learning (DRL) and federated learning (FL), where multiple agents learn and make decisions independentl

Homology Characteristics of EEG and EMG for Lower Limb Voluntary Movement Intention

In the field of lower limb exoskeletons, besides its electromechanical system design and control, attention has been paid to realizing the linkage of exoskeleton robots to humans via electroencephalography (EEG) and electromyography (EMG). However, even the state of the art performance of lower limb voluntary movement intention decoding still faces many obstacles. In the following work, focusing on the perspective of the inner mechanism, a homology characteristic of EEG and EMG for lower limb voluntary movement intention was conducted. A mathematical model of EEG and EMG was built based on its mechanism, which consists of a neural mass model (NMM), neuromuscular junction model, EMG generation model, decoding model, and musculoskeletal biomechanical model. The mechanism analysis and simulation results demonstrated that EEG and EMG signals were both excited by the same movement intention with a response time difference. To assess the efficiency of the proposed model, a synchronous acquisition system for EEG and EMG was constructed to analyze the homology and response time difference from EEG and EMG signals in the limb movement intention. An effective method of wavelet coherence was used to analyze the internal correlation between EEG and EMG signals in the same limb movement intention. To further prove the effectiveness of the hypothesis in this paper, six subjects were involved in the experiments. The experimental results demonstrated that there was a strong EEG-EMG coherence at 1 Hz around movement onset, and the phase of EEG was leading the EMG. Both the simulation and experimental results revealed that EEG and EMG are homologous, and the response time of the EEG signals are earlier than EMG signals during the limb movement intention. This work can provide a theoretical basis for the feasibility of EEG-based pre-perception and fusion perception of EEG and EMG in human movement detection

Spatial-temporal distribution and transport flux of polycyclic aromatic hydrocarbons in a large hydropower reservoir of Southeast China: Implication for impoundment impacts

Abstract(#br)In order to investigate the impacts of dam-related water impoundment on the spatial-temporal variations and transport of anthropogenic organic pollutants, 15 priority polycyclic aromatic hydrocarbons (PAHs) were analyzed in water samples from the Shuikou Reservoir (SKR) of the Minjiang River. The SKR was formed after the construction of the Shuikou Dam, which is the largest hydropower station in Southeast China. The water samples were collected from the backwater zone of the SKR, in both the wet and dry seasons, corresponding to the drainage and impoundment periods of water flow, respectively. The concentrations of the dissolved PAHs in surface water from the wet season (average of 161 ± 97 ng L −1 ) were significantly higher (ANOVA, p < 0.01) than those from the dry season (average of 43 ± 21 ng L −1 ). PAH concentrations in the SKR decreased from upstream (industrialized cities) to downstream (rural towns or counties), indicating high PAH loads caused by intensive urbanization effects. The high proportions of 3-ring PAHs in the wet season were from local sources via surface runoff; while the elevated proportions of 4- to 6- ring PAHs in the dry season reflected atmospheric deposition emerged of these PAHs and/or volatilization of 3-ring PAHs enhanced. Molecular diagnostic ratios of PAH isomers in multimedium and principal component analysis indicated that PAH presence in the SKR was mainly attributed to pyrogenic origin. The isomeric ratios of fluoranthene to fluoranthene plus pyrene in the wet season were homogeneous, implying that there were continuous new inputs along the riverine runoff. However, these ratios showed spatial downward trend in the dry season, indicating continued degradation of PAHs occurred along the transport path during the impoundment period. The input and output fluxes of PAHs in the SKR were 5330 kg yr −1 and 2991 kg yr −1 , revealing that the reservoir retained contaminants after impoundment of the hydropower dam

Spatial-temporal distribution and transport flux of polycyclic aromatic hydrocarbons in a large hydropower reservoir of Southeast China: Implication for impoundment impacts.

In order to investigate the impacts of dam-related water impoundment on the spatial-temporal variations and transport of anthropogenic organic pollutants, 15 priority polycyclic aromatic hydrocarbons (PAHs) were analyzed in water samples from the Shuikou Reservoir (SKR) of the Minjiang River. The SKR was formed after the construction of the Shuikou Dam, which is the largest hydropower station in Southeast China. The water samples were collected from the backwater zone of the SKR, in both the wet and dry seasons, corresponding to the drainage and impoundment periods of water flow, respectively. The concentrations of the dissolved PAHs in surface water from the wet season (average of 161 ± 97 ng L-1) were significantly higher (ANOVA, p < 0.01) than those from the dry season (average of 43 ± 21 ng L-1). PAH concentrations in the SKR decreased from upstream (industrialized cities) to downstream (rural towns or counties), indicating high PAH loads caused by intensive urbanization effects. The high proportions of 3-ring PAHs in the wet season were from local sources via surface runoff; while the elevated proportions of 4- to 6- ring PAHs in the dry season reflected atmospheric deposition emerged of these PAHs and/or volatilization of 3-ring PAHs enhanced. Molecular diagnostic ratios of PAH isomers in multimedium and principal component analysis indicated that PAH presence in the SKR was mainly attributed to pyrogenic origin. The isomeric ratios of fluoranthene to fluoranthene plus pyrene in the wet season were homogeneous, implying that there were continuous new inputs along the riverine runoff. However, these ratios showed spatial downward trend in the dry season, indicating continued degradation of PAHs occurred along the transport path during the impoundment period. The input and output fluxes of PAHs in the SKR were 5330 kg yr-1 and 2991 kg yr-1, revealing that the reservoir retained contaminants after impoundment of the hydropower dam

An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm

One of the most exciting areas of rehabilitation research is brain-controlled prostheses, which translate electroencephalography (EEG) signals into control commands that operate prostheses. However, the existing brain-control methods have an obstacle between the selection of brain computer interface (BCI) and its performance. In this paper, a novel BCI system based on a facial expression paradigm is proposed to control prostheses that uses the characteristics of theta and alpha rhythms of the prefrontal and motor cortices. A portable brain-controlled prosthesis system was constructed to validate the feasibility of the facial-expression-based BCI (FE-BCI) system. Four types of facial expressions were used in this study. An effective filtering algorithm based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and sample entropy (SampEn) was used to remove electromyography (EMG) artifacts. A wavelet transform (WT) was applied to calculate the feature set, and a back propagation neural network (BPNN) was employed as a classifier. To prove the effectiveness of the FE-BCI system for prosthesis control, 18 subjects were involved in both offline and online experiments. The grand average accuracy over 18 subjects was 81.31 ± 5.82% during the online experiment. The experimental results indicated that the proposed FE-BCI system achieved good performance and can be efficiently applied for prosthesis control

Sentiment Analysis and Opinion Mining on Twitter with GMO Keyword

Twitter are a new source of information for data mining techniques. Messages posted through Twitter provide a major information source to gauge public sentiment on topics ranging from politics to fashion trends. The purpose of this paper is to analyze the Twitter tweets to discern the opinions of users regarding Genetically Modified Organisms (GMOs). We examine the effectiveness of several classifiers, Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Logistic Regression and Linear Support Vector Classifier (SVC) in identifying a positive, negative or neutral category on a tweet corpus. Additionally, we use three datasets in this experiment to examine which dataset has the best score. Comparing the classifiers, we discovered that GMO_NDSU has the highest score in each classifier of my experiment among three datasets, and Linear SVC had the highest consistent accuracy by using bigrams as feature extraction and Term Frequency, Chi Square as feature selection

Reconstruction of OFDM Signals Using a Dual Discriminator CGAN with BiLSTM and Transformer

Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods’ intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using the traditional CNN network, it becomes challenging to extract intricate temporal information. Therefore, the BiLSTM network is incorporated into the first discriminator to capture timing details of the IQ (In-phase and Quadrature-phase) sequence and constellation map information of the AP (Amplitude and Phase) sequence. Subsequently, following the addition of fixed position coding, these data are fed into the core network constructed based on the Transformer Encoder for further learning. Simultaneously, to capture the correlation between the two IQ signals, the VIT (Vision in Transformer) concept is incorporated into the second discriminator. The IQ sequence is treated as a single-channel two-dimensional image and segmented into pixel blocks containing IQ sequence through Conv2d. Fixed position coding is added and sent to the Transformer core network for learning. The generator network transforms input noise data into a dimensional space aligned with the IQ signal and embedding vector dimensions. It appends identical position encoding information to the IQ sequence before sending it to the Transformer network. The experimental results demonstrate that, under commonly utilized OFDM modulation formats such as BPSK, QPSK, and 16QAM, the time series waveform, constellation diagram, and spectral diagram exhibit high-quality reconstruction. Our algorithm achieves improved signal quality while managing complexity compared to other reconstruction methods

Young Women in Cities

Young women outnumber young men in cities in many countries during periods of economic growth and urbanization. This gender imbalance among young urbanites is more pronounced in larger cities. We use the gradual rollout of special economic zones across China as a quasi-experiment to establish the causes of this gender imbalance. Our analysis suggests that a key contributor is gender-differential incentives to migrate due to rural women's higher likelihood of marrying and marrying up in cities when urbanization creates more economic opportunities and an abundance of high-income marriage-age men