Explore the vast range of titles available.

The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers. To address this issue, an incentive-based demand response (DR) model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers. The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions. The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price. Then, the sensitivity of incentive price on retailers’ benefits is analyzed and the optimal incentive price is obtained according to the DR model. The case study verifies the effectiveness of the proposed model.

Over the past decades, both agriculture and power systems have faced serious problems, such as the power supply shortage in agriculture, and difficulties of clean energy consumption in the power system. To address and overcome these issues, this paper proposes an idea to combine smart agriculture and clean energy consumption, use surplus clean energy to supply agriculture production, and utilize smart agriculture to support power system with clean energy penetration. A comprehensive review has been conducted to first depict the roadmap of coupling a agriculture-clean energy system, analyze their feasibilities and advantages. The recent technologies and bottlenecks are summarized and evaluated for the development of a combined system consisting of smart agriculture production and clean energy consumption. Several case studies are introduced to explore the mutual benefits of agriculture-clean energy systems in both the energy and food industries.

The threat of environmental degradation attracts great attention to clean energy production and transportation. However, the limited scope of energy consumption causes large-scale of clean energy sources to be abandoned in Sichuan province. In the meantime, the development of modern greenhouse cultivation has transformed the agriculture industry to have a brand-new type of electrical load in the grid. Consequently, the agricultural load can be used to consume the clean energy while facilitating plant growth. In this paper, an innovative agricultural load model is proposed based on crop evapotranspiration and daily light integration. Furthermore, the proposed agricultural load model is also applied to investigate the electricity consumption of five types of crop planting. The results illustrate that the power consumption is primarily driven by artificial lighting compensation system.

In recent years, how to build a reliable, high-efficient, high-tech and environmental-friendly distribution network infrastructure and service system, especially to accurately assess the reliability impact of various technical measures and establish optimal investment decision model for the future distribution network planning, has great theoretical and practical significance. However, the traditional analysis of investment decision model of distribution network is based on complex power flow calculation, which is not suitable for current construction of large and smart distribution network with multi-agent interaction under electrical market environment. Therefore, in this paper, based on the sample data, the correlation mining methods of the reconstruction measures and loss load index, such as neural network method, are applied to analyse the relationship between different types of reconstruction measures and loss load index. Based on the correlation mining methods, the relationship between the reconstruction measures and loss load index can be obtained and the value of loss load can be fast predicted. Through the comparison the relationship between reconstruction measures and loss load index, the optimal reconstruction measures can be chosen, and the data relationship based accurate investment decision model can be built. Experimental result shows the accuracy and effectiveness of the presented methodology.

Chimeric antigen receptor (CAR)-engineered T cell therapy holds promise for treating myeloid malignancies, but challenges remain in bone marrow (BM) infiltration and targeting BM-resident malignant cells. Current autologous CAR-T therapies also face manufacturing and patient selection issues, underscoring the need for off-the-shelf products. In this study, we characterize primary patient samples and identify a unique therapeutic opportunity for CAR-engineered invariant natural killer T (CAR-NKT) cells. Using stem cell gene engineering and a clinically guided culture method, we generate allogeneic CD33-directed CAR-NKT cells with high yield, purity, and robustness. In preclinical mouse models, CAR-NKT cells exhibit strong BM homing and effectively target BM-resident malignant blast cells, including CD33-low/negative leukemia stem and progenitor cells. Furthermore, CAR-NKT cells synergize with hypomethylating agents, enhancing tumor-killing efficacy. These cells also show minimal off-tumor toxicity, reduced graft-versus-host disease and cytokine release syndrome risks, and resistance to allorejection, highlighting their substantial therapeutic potential for treating myeloid malignancies. Yang and colleagues have previously reported a clinically guided culture method to generate allogeneic CAR-NKT cells by engineering human hematopoietic stem and progenitor cells. As potential application, here the authors describe the design and characterization of allogeneic CD33-targeting CAR-NKT cells, showing anti-tumor activity in preclinical models of bone marrow-resident myeloid malignancies.

In complex indoor environments, traditional localization methods often suffer from non-line-of-sight (NLOS) and multipath problems, which lead to unsolvable or incorrectly solved mathematical localization models, thereby limiting localization accuracy. A localization method based on swarm intelligence optimization has been proposed to address this issue. The swarm intelligence optimization algorithm does not require solving matrix inversions and transforms the localization problem into a function optimization problem, which can obtain approximate optimal solutions. Nevertheless, optimization algorithms are beset with issues like sluggish convergence speed and proneness to getting trapped in local optima, thereby failing to satisfy the current practical requirements for localization. This paper proposes a new method that applies the grey wolf optimization (GWO) algorithm to ultra-wideband (UWB) indoor localization to enhance localization accuracy. It improves the GWO algorithm with four strategies. Firstly, a small-area optimization strategy near the target point is proposed. The Chan algorithm is adopted for initial tag localization, and the initial localization result is taken as a constraint to construct the search area of the GWO algorithm, thereby reducing the large space region to a small space region and enhancing optimization efficiency. Secondly, an improved Tent mapping, a nonlinear convergence factor, a fitness-weighted location update strategy, and an out-of-bounds reflection mechanism are designed to improve the GWO algorithm, referred to as the TIGWO algorithm. Finally, apply the TIGWO algorithm to determine the optimal location of the tag. The experimental results indicate that the proposed algorithm significantly enhances indoor localization accuracy. Compared to the Chan, Chan-Taylor, PSO, WOA, and GWO algorithms, the average localization accuracy has been enhanced by 59.65%, 63.41%, 40.97%, 45.97%, and 35.44%, respectively. In an equipment warehouse scenario, the X-axis, Y-axis, and Z-axis localization errors are 0.129 m, 0.101 m, and 0.154 m, respectively.

Background. Bortezomib is used for treating multiple myeloma (MM); however, it has considerable adverse effects. Emodin has been reported to exhibit inhibitory effects on MM cell lines. We investigated the efficacy of emodin 35 (E35), an emodin derivative, using U266 and MM1s cell lines in treating MM and the efficacy of combining bortezomib and E35. Methods. MTT assays were used to observe the effects of E35 on MM cell growth. The effects on cellular apoptosis were then observed using Annexin V/propidium iodide (PI) staining assay. The expression of apoptosis-related genes, including the caspase family, was examined. The efficacy of combining bortezomib and E35 was investigated by examining the expression of the Akt/mTOR/4EBP1 signaling pathway-related proteins. Results. We report that E35 inhibited the growth of U266 and MM1s cells by inducing cellular apoptosis. Moreover, E35 downregulated the expression of apoptosis-related genes and suppressed the phosphorylation of Akt/mTOR/4EBP1 signaling pathway-related genes, thus exhibiting synergistic effects with bortezomib. All observed effects were dose-dependent. Conclusion. The results showed that E35 exhibited cytotoxic effects in MM cell lines in protein levels. Thus, E35, particularly in combination with bortezomib, may be considered as a promising treatment for MM; however, this requires further investigation in vivo.

Purpose Conventional obstructive sleep apnea (OSA) diagnosis via polysomnography can be costly and inaccessible. Recent advances in artificial intelligence (AI) have enabled the use of craniofacial photographs to diagnose OSA. This meta-analysis aims to clarify the diagnostic accuracy of this innovative approach. Methods Two blinded reviewers searched PubMed, Embase, Scopus, Web of Science, and IEEE Xplore databases, then selected and graded the risk of bias of observational studies of adults (≥ 18 years) comparing the diagnostic performance of AI algorithms using craniofacial photographs, versus conventional OSA diagnostic criteria (i.e. apnea-hypopnea index [AHI]). Studies were excluded if they detected apneic events without diagnosing OSA. AI models evaluated with a random split test set or k-fold cross-validation were included in a Bayesian bivariate meta-analysis. Results From 5,147 records, 6 studies were included, containing 10 AI models trained/tested on 1,417/983 participants. The risk of bias was low. AI trained on craniofacial photographs achieved a pooled 84.9% sensitivity (95% credible interval [95% CrI]: 77.1–90.7%) and 71.2% specificity (95% CrI: 60.7–81.4%). Bayesian meta-regression identified deep learning (convolutional neural networks) as the most accurate AI algorithm (91.1% sensitivity, 79.2% specificity) comparable to home sleep apnea tests. AHI cutoffs, OSA prevalence, feature engineering, input data, camera type and informativeness of Bayesian prior did not alter diagnostic accuracy. There was no substantial publication bias. Conclusion AI trained on craniofacial photographs have high diagnostic accuracy and should be considered as a low-cost OSA screening tool. Future work focused on deep learning using smartphone images could improve the feasibility of this approach in primary care.