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Background: Apathy is a prevalent psychiatric condition after stroke, affecting approximately 30% of stroke survivors. It is associated with slower recovery and an increased risk of depression. Understanding the pathophysiological mechanisms of post stroke apathy (PSA) is crucial for developing targeted rehabilitation strategies. Methods: In this study, we recruited a total of 18 PSA patients, 18 post-stroke non-apathy (NPSA) patients, and 18 healthy controls (HCs). Apathy was measured using the Apathy Evaluation Scale (AES). Resting-state functional magnetic resonance imaging (rs-fMRI) was utilized to investigate spontaneous brain activity. We estimated the amplitude of low-frequency fluctuation (ALFF) across three different frequency bands (typical band: 0.01–0.08 Hz; slow-4: 0.027–0.073 Hz; slow-5: 0.01–0.027 Hz) and the fractional amplitude of low-frequency fluctuation (fALFF). Results: Band-specific ALFF differences among the three groups were analyzed. Significant differences were found in the typical band within the left lingual gyrus, right fusiform gyrus, right superior temporal gyrus (STG), and left insula. In the slow-4 band, significant differences were observed in the left middle frontal gyrus (MFG) and right STG. In the slow-5 band, significant differences were identified in the left calcarine cortex and right insula. For fALFF values, significant differences were found in the left lingual gyrus and right thalamus. Moreover, positive correlations were observed between AES scores and the ALFF values in the right STG (r = 0.490, p = 0.002) in the typical band, left MFG (r = 0.478, p = 0.003) and right STG (r = 0.451, p = 0.006) in the slow-4 band, and fALFF values of the right thalamus (r = 0.614, p < 0.001). Conclusion: This study is the first to investigate the neural correlates of PSA using voxel-level analysis and different ALFF banding methods. Our findings indicate that PSA involves cortical and subcortical areas, including the left MFG, right STG, and right thalamus. These results may help elucidate the neural mechanisms underlying PSA and could serve as potential neuroimaging indicators for early diagnosis and intervention. [ABSTRACT FROM AUTHOR]

Introduction: Prognostication in patients with prolonged disorders of consciousness (pDoC) remains a challenging task. Electroencephalography (EEG) is a neurophysiological method that provides objective information for evaluating overall brain function. In this study, we aim to investigate the multiple features of pDoC using EEG and evaluate the prognostic values of these indicators. Methods: We analyzed the EEG features: (i) spectral power; (ii) microstates; and (iii) mismatch negativity (MMN) and P3a of healthy controls, patients in minimally conscious state (MCS), and unresponsive wakefulness syndrome (UWS). Patients were followed up for 6 months. A combination of machine learning and SHapley Additive exPlanations (SHAP) were used to develop predictive model and interpret the results. Results: The results indicated significant abnormalities in low-frequency spectral power, microstate parameters, and amplitudes of MMN and P3a in MCS and UWS. A predictive model constructed using support vector machine achieved an area under the curve (AUC) of 0.95, with the top 10 SHAP values being associated with transition probability (TP) from state C to F, time coverage of state E, TP from state D to F and D to F, mean duration of state A, TP from state F to C, amplitude of MMN, time coverage of state F, TP from state C to D, and mean duration of state E. Predictive models constructed for each component using support vector machine revealed that microstates had the highest AUC (0.95), followed by MMN and P3a (0.65), and finally spectral power (0.05). Discussion: This study provides preliminary evidence for the application of microstate-based multiple EEG features for prognosis prediction in pDoC. Clinical trial registration: chictr.org.cn , identifier ChiCTR2200064099. [ABSTRACT FROM AUTHOR]

Spatiotemporal Mobile CrowdSourcing (MCS) is a new intelligent sensing paradigm for large-scale data acquisition where requesters can recruit a crowd of workers to perform data collection tasks. How to recruit suitable workers in a dynamic environment to maximize platform utility is a key issue and has become a research hotspot. Many past studies have made great efforts in this regard, but most of them either assume that the worker quality is known in advance or ignore the limitations of workers' short-term ability to provide resources. In this article, we consider a platform-centered online spatiotemporal MCS system where mobile workers have both long-term and short-term constraints for providing resources, and their quality is unknown to the platform, while the platform has a long-term budget constraint for recruiting workers. We aim to find an online worker scheduling scheme to maximize the platform's long-term utility without violating the constraints of both workers and the platform. To address this problem, we first transform the long-term utility maximization problem into a real-time utility maximization problem by leveraging the Lyapunov optimization, then design algorithms based on the Upper Confidence Bound (UCB) and Markov approximation to solve each real-time utility maximization problem with unknown worker quality. We demonstrate that our UCB-based algorithm has a sublinear regret and prove that our proposed framework has a performance guarantee for the addressed problem. Finally, we evaluate our design through numerical simulation experiments, and the results demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

In terms of both economy and sustainability, rural areas can greatly benefit from adopting E-commerce. The Chinese government is currently devoting significant efforts to developing agricultural E-commerce. However, one of the most significant problems is the lack of effective tools for evaluating regional potentials in this regard, possibly leading to inappropriate policymaking, investment allocation, and regional planning. To address this issue, this study proposes a novel and effective method for evaluating regional potentials for agricultural E-commerce development, integrating the method based on the removal effects of criteria (MEREC), Heronian mean operator, and combined compromise solution (CoCoSo) method. The method's effectiveness is then tested and confirmed in the Chinese city of Yibin through an evaluation of its ten regions. The results suggest that such a method is robust, objective, and able to consider indicator interactions effectively. By applying this method, regional agricultural E-commerce development potentials can be thoroughly evaluated and ranked. This study contributes to the literature by providing new analytical techniques for agricultural studies, as well as by supporting political and investment decision-making for governments and E-commerce practitioners in the agriculture sector. [ABSTRACT FROM AUTHOR]

Background: The current application effects of computerized cognitive intervention are inconsistent and limited to hospital rehabilitation settings. Objective: To investigate the effect of mobile intelligent cognitive training (MICT) on patients with post-stroke cognitive impairment (PSCI). Methods: This study was a multicenter, prospective, open-label, blinded endpoint, cluster-randomized controlled trial (RCT). 518 PSCI patients were stratified and assigned to four rehabilitation settings, and then patients were randomized into experimental and control groups in each rehabilitation setting through cluster randomization. All patients received comprehensive management for PSCI, while the experimental group additionally received MICT intervention. Treatment was 30 minutes daily, 5 days per week, for 12 weeks. Cognitive function, activities of daily living (ADL), and quality of life (QOL) were assessed before the treatment, at weeks 6 and 12 post-treatment, and a 16-week follow-up. Results: Linear Mixed Effects Models showed patients with PSCI were better off than pre-treatment patients on each outcome measure (p < 0.05). Additionally, the improvement of these outcomes in the experimental group was significantly better than in the control group at week 6 post-treatment and 16-week follow-up (p < 0.05). The rehabilitation setting also affected the cognitive efficacy of MICT intervention in improving PSCI patients, and the degree of improvement in each outcome was found to be highest in hospital, followed by community, nursing home, and home settings. Conclusions: Long-term MICT intervention can improve cognition, ADL, and QOL in patients with PSCI, with sustained effects for at least one month. Notably, different rehabilitation settings affect the cognitive intervention efficacy of MICT on PSCI patients. However, this still needs to be further determined in future studies. [ABSTRACT FROM AUTHOR]

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) system over Nakagami-m channels. The single-antenna base station serves two single-antenna NOMA users based on statistical channel state information (CSI). We derive the closed-form expression of the exact outage probability under a given decoding order, and we also deduce the asymptotic outage probability and diversity order in a high-SNR regime. Then, we analyze all the possible power allocation ranges and theoretically prove the optimal power allocation range under the corresponding decoding order. The demarcation points of the optimal power allocation ranges are affected by target data rates and total power, without an effect from the CSI. In particular, the values of the demarcation points are proportional to the total power. Furthermore, we formulate a joint decoding order and power allocation optimization problem to maximize the sum throughput, which is solved by efficiently searching in our obtained optimal power allocation ranges. Finally, Monte Carlo simulations are conducted to confirm the accuracy of our derived exact outage probability. Numerical results show the accuracy of our deduced demarcation points of the optimal power allocation ranges. And the optimal decoding order is not constant at different total transmit power levels. [ABSTRACT FROM AUTHOR]

Repetitive transcranial magnetic stimulation (rTMS) has been widely used in motor rehabilitation after stroke, and functional magnetic resonance imaging (fMRI) has been used to investigate the neural mechanisms of motor recovery during stroke therapy. However, there is no review on the mechanism of rTMS intervention for motor recovery after stroke based on fMRI explicitly. We aim to reveal and summarize the neural mechanism of the effects of rTMS on motor function after stroke as measured by fMRI. We carefully performed a literature search using PubMed, EMBASE, Web of Science, and Cochrane Library databases from their respective inceptions to November 2022 to identify any relevant randomized controlled trials. Researchers independently screened the literature, extracted data, and qualitatively described the included studies. Eleven studies with a total of 420 poststroke patients were finally included in this systematic review. A total of 338 of those participants received fMRI examinations before and after rTMS intervention. Five studies reported the effects of rTMS on activation of brain regions, and four studies reported results related to brain functional connectivity (FC). Additionally, five studies analyzed the correlation between fMRI and motor evaluation. The neural mechanism of rTMS in improving motor function after stroke may be the activation and FCs of motor-related brain areas, including enhancement of the activation of motor-related brain areas in the affected hemisphere, inhibition of the activation of motor-related brain areas in the unaffected hemisphere, and changing the FCs of intra-hemispheric and inter-hemispheric motor networks. [ABSTRACT FROM AUTHOR]

Background: The mechanism(s) of cognitive impairment remains complex, making it difficult to confirm the factors influencing poststroke cognitive impairment (PSCI). Objective: This study quantitatively investigated the degree of influence and interactions of clinical indicators of PSCI. Methods: Information from 270 patients with PSCI and their Wechsler Adult Intelligence Scale (WAIS-RC) scores, totaling 18 indicators, were retrospectively collected. Correlations between the indicators and WAIS scores were calculated. Multiple linear regression model(MLR), genetic algorithm modified Back-Propagation neural network(GA-BP), logistic regression model (LR), XGBoost model (XGB), and structural equation model were used to analyze the degree of influence of factors on the WAIS and their mediating effects. Results: Seven indicators were significantly correlated with the WAIS scores: education, lesion side, aphasia, frontal lobe, temporal lobe, diffuse lesions, and disease course. The MLR showed significant effect of education, lesion side, aphasia, diffuse lesions, and frontal lobe on the WAIS. The GA-BP included five factors: education, aphasia, frontal lobe, temporal lobe, and diffuse lesions. LR predicted that the lesion side contributed more to mild cognitive impairment, while education, lesion side, aphasia, and course of the disease contributed more to severe cognitive impairment. XGB showed that education, side of the lesion, aphasia, and diffuse lesions contributed the most to PSCI. Aphasia plays a significant mediating role in patients with severe PSCI. Conclusions: Education, lesion side, aphasia, frontal lobe, and diffuse lesions significantly affected PSCI. Aphasia is a mediating variable between clinical information and the WAIS in patients with severe PSCI. [ABSTRACT FROM AUTHOR]

Intelligent reflective surface (IRS) technology is emerging as a promising performance enhancement technique for next-generation wireless networks. Hence, we investigate the physical layer security of the downlink in IRS-aided non-orthogonal multiple access networks in the presence of an eavesdropper, where an IRS is deployed for enhancing the quality by assisting the cell-edge user to communicate with the base station. To characterize the network's performance, the expected value of the new channel statistics is derived for the reflected links in the case of Nakagami- $m$ fading. Furthermore, the performance of the proposed network is evaluated both in terms of the secrecy outage probability (SOP) and the average secrecy capacity (ASC). The closed-form expressions of the SOP and the ASC are derived. We also study the impact of various network parameters on the overall performance of the network considered. To obtain further insights, the secrecy diversity orders and the high signal-to-noise-ratio (SNR) slopes are obtained. We finally show that: 1) the expectation of the channel gain in the reflected links is determined both by the number of IRS elements and by the Nakagami- $m$ fading parameters; 2) If the Nakagami- $m$ parameter is no less than 2, the SOP of both User 1 and User 2 becomes unity, when the number of IRS elements tends to infinity; 3) The secrecy diversity orders are affected both by the number of IRS elements and by the Nakagami- $m$ fading parameters, whereas the high-SNR slopes are not affected by these parameters. Our Monte-Carlo simulations perfectly demonstrate the analytical results. [ABSTRACT FROM AUTHOR]

Fog Computing (FC) is a flexible architecture to support distributed domain-specific applications with cloud-like quality of service. However, current FC still lacks the mobility support mechanism when facing many mobile users with diversified application quality requirements. Such mobility support mechanism can be critical such as in the industrial internet where human, products, and devices are moveable. To fill in such gaps, in this paper we propose novel container migration algorithms and architecture to support mobility tasks with various application requirements. Our algorithms are realized from three aspects: 1) We consider mobile application tasks can be hosted in a container of a corresponding fog node that can be migrated, taking the communication delay and computational power consumption into consideration; 2) We further model such container migration strategy as multiple dimensional Markov Decision Process (MDP) spaces. To effectively reduce the large MDP spaces, efficient deep reinforcement learning algorithms are devised to achieve fast decision-making and 3) We implement the model and algorithms as a container migration prototype system and test its feasibility and performance. Extensive experiments show that our strategy outperforms the existing baseline approaches 2.9, 48.5 and 58.4 percent on average in terms of delay, power consumption, and migration cost, respectively. [ABSTRACT FROM AUTHOR]

Abstract We aimed to explore the cognitive characteristics of patients with post-stroke cognition impairment (PSCI) on the basis of the Wechsler Adult Intelligence Scale-Revised in China (WAIS-RC) and the individual contribution of the subtests to WAIS score. We included 227 patients with PSCI who were assessed using the WAIS-RC. We described the characteristics and score distribution of the scale and subtests individually and compared them with those of the normal group to measure the damage degree of these patients. We performed item response theory analysis to explore the best criterion score for all dimensions that allowed ideal discrimination and difficulty for reflecting cognitive level. Finally, we analyzed the contribution of each dimension to the overall cognitive function. Patients with PSCI showed worse cognition levels than healthy individuals in terms of overall intelligence quotient (73.26–100, −1.78 SD), with a difference of 4.54–7.96 points in each dimension (–0.68 to −1.82 SD), and a range of 5–7 points is the appropriate range for reflecting cognitive ability in patients with PSCI. The average cognitive level of patients with PSCI was significantly inferior to normal people (–1.78 SD, 96.25%). Vocabulary contributes most to WAIS score. [ABSTRACT FROM AUTHOR]