Long-term Transmission Research Articles

Efficient IoT Big Data Streaming With Deep-Learning-Enabled Dynamics

Internet of Medical Things (IoMT) is igniting many emerging smart health applications, by continuously streaming the big data for data-driven innovations. One critical obstacle in IoMT big data is the power hungriness of long-term data transmission. Targeting this challenge, we propose a novel framework called, IoMT big-data Bayesian-backward deep-encoder learning (IBBD), which mines deep autoencoder (AE) configurations for data sparsification and determines optimal tradeoffs between information loss and power overhead. More specifically, the IBBD framework leverages an additional external Bayesian-backward loop that recommends AE configurations, on top of a traditional deep learning loop that executes and evaluate the AE quality. The IBBD recommendation is based on confidence to further minimize the regularized metrics that quantify the quality of AE configurations, and it further leverages regularization techniques to allow adjusting error–power tradeoffs in the mining process. We have conducted thorough experiments on a cardiac data streaming application and demonstrated the superiority of IBBD over the common practices such as discrete wavelet transform, and we have further generalized IBBD through validating the optimal AE configurations determined on one user to other users. This study is expected to greatly advance IoMT big data streaming practices toward precision medicine.

The Evolution and Transmission Dynamics of Multidrug-Resistant Tuberculosis in an Isolated High-Plateau Population of Tibet, China.

On the Tibetan Plateau, most tuberculosis is caused by indigenous Mycobacterium tuberculosis strains with a monophyletic structure and high-level drug resistance. This study investigated the emergence, evolution, and transmission dynamics of multidrug-resistant tuberculosis (MDR-TB) in Tibet. The whole-genome sequences of 576 clinical strains from Tibet were analyzed with the TB-profiler tool to identify drug-resistance mutations. The evolution of the drug resistance was then inferred based on maximum-likelihood phylogeny and dated trees that traced the serial acquisition of mutations conferring resistance to different drugs. Among the 576 clinical M. tuberculosis strains, 346 (60.1%) carried at least 1 resistance-conferring mutation and 231 (40.1%) were MDR-TB. Using a pairwise distance of 50 single nucleotide polymorphisms (SNPs), most strains (89.9%, 518/576) were phylogenetically separated into 50 long-term transmission clusters. Eleven large drug-resistant clusters contained 76.1% (176/231) of the local multidrug-resistant strains. A total of 85.2% of the isoniazid-resistant strains were highly transmitted with an average of 6.6 cases per cluster, of which most shared the mutation KatG Ser315Thr. A lower proportion (71.6%) of multidrug-resistant strains were transmitted, with an average cluster size of 2.9 cases. The isoniazid-resistant clusters appear to have undergone substantial bacterial population growth in the 1970s to 1990s and then subsequently accumulated multiple rifampicin-resistance mutations and caused the current local MDR-TB burden. These findings highlight the importance of detecting and curing isoniazid-resistant strains to prevent the emergence of endemic MDR-TB. IMPORTANCE Emerging isoniazid resistance in the 1970s allowed M. tuberculosis strains to spread and form into large multidrug-resistant tuberculosis clusters in the isolated plateau of Tibet, China. The epidemic was driven by the high risk of transmission as well as the potential of acquiring further drug resistance from isoniazid-resistant strains. Eleven large drug-resistant clusters consisted of the majority of local multidrug-resistant cases. Among the clusters, isoniazid resistance overwhelmingly evolved before all the other resistance types. A large bacterial population growth of isoniazid-resistant clusters occurred between 1970s and 1990s, which subsequently accumulated rifampicin-resistance-conferring mutations in parallel and accounted for the local multidrug-resistant tuberculosis burden. The results of our study indicate that it may be possible to restrict MDR-TB evolution and dissemination by prioritizing screening for isoniazid (INH)-resistant TB strains before they become MDR-TB and by adopting measures that can limit their transmission.

EA-GAN: restoration of text in ancient Chinese books based on an example attention generative adversarial network

Ancient Chinese books are of great significance to historical research and cultural inheritance. Unfortunately, many of these books have been damaged and corroded in the process of long-term transmission. The restoration by digital preservation of ancient books is a new method of conservation. Traditional character restoration methods ensure the visual consistency of character images through character features and the pixels around the damaged area. However, reconstructing characters often causes errors, especially when there is large damage in critical locations. Inspired by human’s imitation writing behavior, a two-branch structure character restoration network EA-GAN (Example Attention Generative Adversarial Network) is proposed, which is based on a generative adversarial network and fuses reference examples. By referring to the features of the example character, the damaged character can be restored accurately even when the damaged area is large. EA-GAN first uses two branches to extract the features of the damaged and example characters. Then, the damaged character is restored according to neighborhood information and features of the example character in different scales during the up-sampling stage. To solve problems when the example and damaged character features are not aligned and the convolution receptive field is too small, an Example Attention block is proposed to assist in restoration. Qualitative and quantitative analysis experiments are carried out on a self-built dataset MSACCSD and real scene pictures. Compared with current inpainting networks, EA-GAN can get the correct text structure through the guidance of the additional example in the Example Attention block. The peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) value increased by 9.82% and 1.82% respectively. The learned perceptual image patch similarity (LPIPS) value calculated by Visual Geometry Group (VGG) network and AlexNet decreased by 35.04% and 16.36% respectively. Our method obtained better results than the current inpainting methods. It also has a good restoration effect in the face of untrained characters, which is helpful for the digital preservation of ancient Chinese books.

On Volatility Transmission between Gold and Silver Markets: Evidence from A Long-Term Historical Period

Several studies estimate the volatility spillover effects between gold and silver returns, but none of them used the implied volatility to evaluate the long-term relationship between these two metal markets. Our paper aims to fill this gap in the existing literature. This paper investigates the long-term volatility transmission between gold and silver; by using GARCH and VAR modelling, it finds that the volatility transmission from gold to silver is unidirectional. Volatility strategies using options can be designed to take advantage of this especially in times where the volatility transmission is not captured by the markets. Additionally, the results appear to be useful for gaining better portfolio diversification benefits. Investors, for instance, could use the results of this study for making proper investment decisions during the period of economic down-turns or inflation surges.

Food insecurity and sovereignty threat to uncontrolled price spillover effects in financialized agricultural products: The red meat case in Turkiye

The objective of this study is to examine the long-term uncertainty transmission between cattle and lamb carcass markets and the feed wheat market in terms of exogenous variables (e.g., gasoline price, exchange rate, and import variables) and whether the volatility is symmetrical, using the daily data from January 2005 to June 2019, and VAR (1)–BEKK–GARCH (1, 1) Model. The empirical results indicate that short- and long-term indirect and direct shocks and long-term uncertainty propagation significantly affect the conditional variances of the beef carcass, lamb carcass, and feed wheat returns, which poses a threat to producers in terms of product sovereignty and consumers in terms of food security. We also discovered that the conditional variances of the beef carcass, lamb carcass, and feed wheat returns were significantly affected by any increase in foreign exchange rates in times of import in comparison with those in the times of nonimport. Similarly, the fluctuations in the energy (gasoline) market increased the ongoing risk propagations in the lamb carcass and feed wheat markets, whereas they decreased it in the beef carcass market. Meanwhile, asymmetrical effects played a role in uncertainty in transitions for product markets. Additionally, the optimal portfolio weight of beef carcasses on lamb carcasses and feed wheat was 0.645 and 0.553, respectively, whereas the optimal portfolio weight of lamb carcasses on feed wheat remained at approximately 0.188.

Increasing the productivity of shallot planting and its impact on the agricultural market, West Java.

The main objective of this study is to increase the productivity of shallot planting and its impact on the agricultural market. This study analyzes the transmission of shallot prices at the Caringin Main Market (wholesale) level with shallot prices at the farmer (producer) level using quantitative analysis of the Asymmetric Error Correction Model (AECM). The results showed that long-term transmission with positive ECT coefficients and negative ECT showed the same and significant positive values. Positive ECT is 0.286909 and negative ECT is 0.289266, where when there is a price deviation above and below the average balance line of onion prices at the farmer level, it takes three weeks to adjust to the balance of changes in shallot prices. The onion market at the wholesale or parent market occurs asymmetric price transmission from the wholesale price level (Caringin Main Market) to production prices (at the farmer level). A significant t-value of one percent evidences this. Asymmetric price transmission can be caused by abuse of market power.

Markov modeling and performance analysis of infectious diseases with asymptomatic patients.

After over three years of COVID-19, it has become clear that infectious diseases are difficult to eradicate, and humans remain vulnerable under their influence in a long period. The presence of presymptomatic and asymptomatic patients is a significant obstacle to preventing and eliminating infectious diseases. However, the long-term transmission of infectious diseases involving asymptomatic patients still remains unclear. To address this issue, this paper develops a novel Markov process for infectious diseases with asymptomatic patients by means of a continuous-time level-dependent quasi-birth-and-death (QBD) process. The model accurately captures the transmission of infectious diseases by specifying several key parameters (or factors). To analyze the role of asymptomatic and symptomatic patients in the infectious disease transmission process, a simple sufficient condition for the stability of the Markov process of infectious diseases is derived using the mean drift technique. Then, the stationary probability vector of the QBD process is obtained by using RG-factorizations. A method of using the stationary probability vector is provided to obtain important performance measures of the model. Finally, some numerical experiments are presented to demonstrate the model's feasibility through analyzing COVID-19 as an example. The impact of key parameters on the system performance evaluation and the infectious disease transmission process are analyzed. The methodology and results of this paper can provide theoretical and technical support for the scientific control of the long-term transmission of infectious diseases, and we believe that they can serve as a foundation for developing more general models of infectious disease transmission.

A review on monkeypox virus outbreak: New challenge for world.

Monkeypox is a viral zoonotic disease caused by the monkeypox virus, a double-stranded DNA-enveloped virus that can be transmitted from animal to human or human to human. Consequently, it emerged as the most important orthopoxvirus for public health. Based on available online literature, this study reviewed the majority of the data representing the outbreak, diagnosis, treatment, and prevention of monkeypox. The literature search was conducted between July5 and September15, 2022. In addition to reviewing the databases of World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), Africa CDC, and United Kingdom Health Security Agency monkey pox advice, 43 papers were studied in depth. Human monkeypox was first identified in 1970 in a child in the Democratic Republic of the Congo. Until May 6, 2022, it was endemic in West and Central African countries and infrequently occurred outside of Africa. However, many cases have been identified in several nonendemic countries since May13, 2022, with no prior human or animal travel from endemic areas; that was the first time to document the cases and long-term transmission in countries with no epidemiological ties to endemic African countries. Seven travel-related human monkeypox cases were recorded outside of Africa from September 2018 to November 2021: one in Israel, one in Singapore, and two in the US Youth are most affected. Monkeypox's unanticipated development in places with no known epidemiological linkages raises concerns about the virus's evolution, which permits undetected transmission for a long period. Monkeypox is no longer a rare, self-limiting disease limited to endemic countries. Its ever-changing epidemiology and transmission dynamics have increased the possibility of its evolving into a much deadlier pathogen. Therefore, improved surveillance and detailed case and contact investigation are required to comprehend the ever-changing epidemiology of monkeypox.

Optimized Data Sampling and Energy Consumption in IIoT: A Federated Learning Approach

Real-time environment monitoring is a key application in Industrial Internet of Things, where sensors proactively collect and transmit environmental data to the controller. However, due to limited wireless resources, keeping sensors’ sampled data fresh at the controller is critical. This work aims to investigate the trade-off between the sensor’s data-sampling frequency and long-term data transmission energy consumption while maintaining information freshness. Leveraging the entropic risk measure (ERM), we jointly minimize the global transmission energy’s mean and variance subject to probabilistic constraints on information freshness. Furthermore, while jointly saving the model training energy, we adopt the federated learning (FL) paradigm and propose an FL-based two-stage iterative optimization framework to optimize the aforementioned objective. Specifically, we iteratively learn the sampling frequency via Bayesian optimization and minimize the long-term ERM of the global energy consumption via Lyapunov optimization. Numerical results show that the proposed FL-based scheme saves substantial executing energy with less performance loss. Quantitatively, compared with the centralized learning baseline, the proposed FL-based framework saves up to 69% model training energy at the expense of a mere increased objective outcome, i.e., 6.3% in the global data transmission energy consumption ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9.936\times 10^{-5}$ </tex-math></inline-formula> in ERM) under 0.4% bias from the global optimal data-sampling frequency.

Phenotypic and Genotypic Characterization of Carbapenem-Resistant Enterobacteriaceae Recovered from a Single Hospital in China, 2013 to 2017.

Carbapenem-resistant Enterobacteriaceae (CRE) have become an increasingly common cause of healthcare-related infections and present a serious challenge to clinical treatment. This study examined the phenotypic, genotypic characterization, clinical, and microbiological data of CRE in the Huizhou Municipal Central Hospital. We conducted a phenotypic susceptibility evaluation and whole genome sequence analysis for 52 CRE strains isolated from 37 patients and 2 medical device-related samples during 2013-2017 to characterize risk factors, antimicrobial resistance profiles, dominant clones and hospital transmission. Long-term hospitalization, treatment time with antibiotics and use of invasive devices were linked to the risk of CRE infection. The carbapenem resistance genes (CRGs) we found included blaNDM (82.7%), blaIMP (19.2%) and blaKPC (3.8%), Escherichia coli (44.2%) and Klebsiella pneumoniae (44.2%) were the dominant species we identified, and the type of CRG carried by isolates was highly correlated with species. The coexistence of CRGs with a variety of other antibiotic resistance genes leads to an increased prevalence of high resistance levels for CRE to β-lactams and other antibiotic classes such as aminoglycosides and fluoroquinolones. These isolates were sensitive only to colistin and tigecycline. In addition to this, we observed significantly genomic diversity of CRE isolates in this hospital. Importantly, we found that long-term transmission of multiple CRE clones had occurred at this hospital between various wards. Evaluating and improving the current infection control strategies may be necessary, and reducing nosocomial transmission remains the primary control element for CRE infections in China.

From Miscellany to Cultural Memory: The Long-Term Transmission of Poems from Sixteenth-Century Poetry Anthologies

From Miscellany to Cultural Memory: The Long-Term Transmission of Poems from Sixteenth-Century Poetry Anthologies

GluN1 antibody causes behavioral deficits in prepulse inhibition and memory through CaMKIIβ signaling

Accumulating evidence suggests that some patients with schizophrenia have high production of autoantibodies against the N-methyl-d-aspartate receptor (NMDAR) subunit GluN1 and that these antibodies lead to cognitive impairment. However, the molecular mechanisms of the deficits seen in these patients are largely unknown. In the present study, we found that passive infusion of GluN1 antibody into the hippocampus of mice for 7 days led to decreased expression of GluN1, phosphor-Ser897-GluN1, and EphrinB2 receptor (EphB2R); deficits in long-term potentiation (LTP) and synaptic transmission in the hippocampal CA1 area; impairment in prepulse inhibition (PPI); and deterioration of recognition memory in novel object recognition test. We also found decreased expression of CaMKIIβ, ERK1/2, CREB, and NF-κB after 7 days of GluN1 antibody exposure, as was the phosphorylation of these signaling molecules. The decrease in GluN1 and phosphor-Ser897-GluN1 expression and the deficits in LTP, PPI, and recognition memory were ameliorated by CaMKIIβ overexpression. These results suggest that downregulation of CaMKIIβ–ERK1/2–CREB–NF-κB signaling is responsiable for GluN1 antibody-associated impairment in PPI and memory and that GluN1 antibody-induced NMDAR hypofunction is the underlying mechanism of this impairment. Our findings indicate possible strategies to ameliorate NMDAR antibody-associated cognitive impairment in neuropsychiatric disease. They also provide evidence that NMDAR hypofunction is an underlying mechanism for cognitive impairment in schizophrenia.

Asymmetric Price Transmission and Market Power: A Case of the Aquaculture Product Market in China

In the aquaculture product market, the efficiency of vertical price transmission is an important manifestation, representing the marketization level of aquatic products. When the price of any sector in the aquatic product industry chain fluctuates slightly, the welfare of each sector will be affected accordingly. This study focuses on carp, the main freshwater fish in China, to explore the relationship between asymmetric price transmission and market power. We use the nonlinear autoregressive distributed lag model to simultaneously describe the short- and long-term asymmetric effects of wholesale-to-retail prices for carp, and apply the asymmetric autoregressive conditional heteroskedasticity model to analyze the differences in market power between wholesalers and retailers. The results confirm the evidence of a nonlinear cointegration between wholesale and retail prices in the carp market. There are also significant asymmetric effects in both the short- and long-term transmission speed and degree of price transmission from the wholesale to the retail market. It is evident that carp wholesalers have strong control over the price, resulting in an asymmetric effect in vertical price transmission, indicating a need to improve the development level and integration degree in the Chinese aquatic products market. Subsequently, the government should develop effective systems for aquatic product circulation, strengthen supervision over the aquatic product wholesale market, and establish a public aquatic product price platform to promote the healthy and stable development of China’s aquatic product market.

Head-to-tail polymerization by VEL proteins underpins cold-induced Polycomb silencing in flowering control.

Transcriptional silencing through the Polycomb silencing machinery utilizes a "read-write" mechanism involving histone tail modifications. However, nucleation of silencing and long-term stable transmission of the silenced state also requires P-olycomb Repressive Complex 2 (PRC2) accessory proteins, whose molecular role is poorly understood. The Arabidopsis VEL proteins are accessory proteins that interact with PRC2 to nucleate and propagate silencing at the FLOWERING LOCUS C (FLC) locus, enabling early flowering in spring. Here, we report that VEL proteins contain a domain related to an atypical four-helix bundle that engages in spontaneous concentration-dependent head-to-tail polymerization to assemble dynamic biomolecular condensates. Mutations blocking polymerization of this VEL domain prevent Polycomb silencing at FLC. Plant VEL proteins thus facilitate assembly of dynamic multivalent Polycomb complexes required for inheritance of the silenced state.

Inclusive infrastructure development, green innovation, and sustainable resource management: Evidence from China’s trade-adjusted material footprints

Depleting scarce resources and environmental deterioration are critical issues to address in the contemporary world. Particularly, China has shown exponential growth in infrastructure construction, international trade, and embodied innovation in the last few years. Aggressive infrastructure-led growth and urbanization have created unremitting pressure on China’s resource consumption. Therefore, this study analyzes the asymmetric impact of resource depletion associated with Chinese infrastructure development and urbanization by controlling the effects of green innovation and economic growth from 1990 to 2018. Consumption-based material footprint (MF) is used to quantify resource consumption from domestic activity. This study employ novel Quantile ARDL approach to integrating aromaticity arising from structural changes, policy shifts, financial or economic shocks, and data abnormality confirmed through preliminary tests. The long-run results show that infrastructure development, urbanization, and economic growth significantly contribute to MF. However, these effects substantially varied across lower, medium, and higher quantiles. The positive impact of infrastructure development is highest for higher quantiles of MF, while the impact of urbanization is highest at lower quantiles. Green innovation significantly decreases the MF, mainly at higher quantiles. Besides, the study validates Environmental Kuznets Curve hypothesis in the long run. Similar results are observed in the shorter run; however, their significance and magnitude are substantially lower due to the long-term transmission of model variables. These results offer valuable policy recommendations.

Modeling Covid-19 contagious effect between asset markets and commodity futures in India

For the purpose of optimizing an investment portfolio in the Indian market, this research provides an understanding of the connectedness, volatility transmission and investment scenario between different pairs of commodity futures and assets, observing the impact of Covid-19. To do that, we derived a daily dataset spanning from 2016 to 2021 bifurcated into two panels comprising the pre & post-Covid-19 onset periods. First, we employed wavelet coherence plots to check the directional connectedness between asset and commodity futures. The DCC-GARCH model was then applied on both the data panels to investigate and compare volatility transmission. Hedge ratios and optimum portfolio weights have been estimated to demonstrate a comparative investment scenario. We observe a significant long-term volatility transmission from the asset markets to commodity futures for both the onset panel. Commodity futures provide effective hedging and diversification efficiency against the asset markets. DCC, hedge ratio and optimum portfolio weights show the heterogeneous patterns of investment for pre & post-Covid-19 period. Gold, bond, bullion futures and exchange rates are recognized as a gauge of the economy in crisis. This work will provide significant insight to readers about portfolio construction and required changes in portfolio weights in normal and crisis market conditions.

Long-term intergenerational transmission of memories of the Vajont disaster.

Previous literature documented the traumatic consequences of exposure to disasters on psychological functioning, but little attention has been paid to the intergenerational transmission of the memory of disasters. We explored long-term effects on the memory of the Vajont dam disaster in Northeast Italy that claimed 1,910 lives in 1963. We collected data from 52 two-generation families in which the first generations were born before the disaster and the second generations after. The families were divided into an experimental group whose first generation survived the disaster and a control group whose first generation had moved there afterward. The interviews included an open-ended narrative on the memory of the disaster. We coded free narratives focusing on the richness of the memories (i.e., length, causes, core, aftermath), analyzing negative emotions and salience of the natural and psychological domains. We applied generalized linear mixed models. The richness of the memories, including references to negative emotions, diminished with lower exposure and with intergenerational transmission. Moreover, the participants built a shared representation of the disaster that did not markedly differ across exposure or generation. The reported causes were attributed more to the natural rather than the human domain; the consequences more to the psychological compared to the material domain. Our findings highlight the processes through which collective memories of historical traumatic events are built over the long term and the way a collective identity develops to bear the burden of highly dramatic events and to transmit intergenerational lessons from the past. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Long-Term Behavior of Clinched Electrical Contacts

Joining by forming operations presents powerful and complex joining techniques. Clinching is a well-known joining process for use in sheet metalworking. Currently, clinched joints are focusing on mechanically enhanced connections. Additionally, the demand for integrating electrical requirements to transmit electrical currents will be increased in the future. This integration is particularly important, for instance, in the e-mobility sector. It enables connecting battery cells with electrical joints of aluminum and copper. Systematic use of the process-specific advantages of this joining method opens up the possibility to find and create electrically optimized connections. The optimization for the transmission of electrical currents will be demonstrated for clinched joints by adapting the tool geometry and the clinched joint design. Based on a comparison of the electrical joint resistance, the limit use temperature is defined for the joining materials used based on the microstructural condition and the aging condition due to artificial aging. As a result of the investigations carried out, reliable current transmission at a constant conductor temperature of up to 120 °C can be achieved for clinched copper–copper joints. In the case of pure aluminum joints and mixed joints of aluminum and copper, long-term stable current transmission can be ensured up to a conductor temperature of 100 °C.

Comparing different computational approaches for detecting long-term vertical transmission in host-associated microbiota.

Long-term vertical transmissions of gut bacteria are thought to be frequent and functionally important in mammals. Several phylogenetic-based approaches have been proposed to detect, among species-rich microbiota, the bacteria that have been vertically transmitted during a host clade radiation. Applied to mammal microbiota, these methods have sometimes led to conflicting results; in addition, how they cope with the slow evolution of markers typically used to characterize bacterial microbiota remains unclear. Here, we use simulations to test the statistical performances of two widely-used global-fit approaches (ParaFit and PACo) and two event-based approaches (ALE and HOME). We find that these approaches have different strengths and weaknesses depending on the amount of variation in the bacterial DNA sequences and are therefore complementary. In particular, we show that ALE performs better when there is a lot of variation in the bacterial DNA sequences, whereas HOME performs better when there is not. Global-fit approaches (ParaFit and PACo) have higher type I error rates (false positives) but have the advantage to be very fast to run. We apply these methods to the gut microbiota of primates and our results suggest that only a small fraction of their gut bacteria is vertically transmitted.

Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer

A simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multi-user downlink multiple-input single-output (MISO) communication system is investigated. In contrast to the existing ideal STAR-RIS model assuming an independent transmission and reflection phase-shift control, a practical coupled phase-shift model is considered. Then, a joint active and passive beamforming optimization problem is formulated for minimizing the long-term transmission power consumption, subject to the coupled phase-shift constraint and the minimum data rate constraint. Despite the coupled nature of the phase-shift model, the formulated problem is solved by invoking a hybrid continuous and discrete phase-shift control policy. Inspired by this observation, a pair of hybrid reinforcement learning (RL) algorithms, namely the hybrid deep deterministic policy gradient (hybrid DDPG) algorithm and the joint DDPG & deep-Q network (DDPG-DQN) based algorithm are proposed. The hybrid DDPG algorithm controls the associated high-dimensional continuous and discrete actions by relying on the hybrid action mapping. By contrast, the joint DDPG-DQN algorithm constructs two Markov decision processes (MDPs) relying on an inner and an outer environment, thereby amalgamating the two agents to accomplish a joint hybrid control. Simulation results demonstrate that the STAR-RIS has superiority over other conventional RISs in terms of its energy consumption. Furthermore, both the proposed algorithms outperform the baseline DDPG algorithm, and the joint DDPG-DQN algorithm achieves a superior performance, albeit at an increased computational complexity.