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The success of future 5G/6G communication relies heavily on efficient and low-latency wireless cellular device-to-device communication, which enhances network spectrum, energy efficiency, and data transfer rate. To achieve this, advanced technologies such as deep learning and swarm optimization are utilized to improve energy-efficient communication while considering latency. The fifth and beyond-generation networks require intelligent and efficient technologies to control and transfer data. Unmanned Aerial Vehicles (UAVs) are employed to establish high-transfer-rate data communication between devices using minimal energy consumption. This study also focuses on integrating UAV-based D2D communication with other advanced technologies, highlighting the significance of integration with possible solutions to enhance network performance. The development of 5G/6G communication technologies aims to meet the increasing demands of wireless networks, such as faster data rates, lower latency, improved reliability, and more excellent device and application support. However, the current network advantages need to be enhanced to meet the needs of the upcoming digital environment. Thus, to address these challenges, we propose a novel approach that utilizes optimized deep learning models in three ways for UAV-based device-to-device communication: 1. Improved Hybrid Particle Swarm Optimization and Effective K-means clustering approach (IHPSO-K) 2. Hybrid Fuzzy C means (HFCM) approach, and 3—greedy algorithm to overcome the restrictions faced by UAVs in meeting the latest technological requirements. With the above considering features, we use two methods to execute the efficient performance of the proposed algorithm, namely the device-centric approach and the network-centric approach. Combining these techniques can help achieve a more accurate and efficient clustering of data points in UAV-based D2D communication. This can lead to improved performance in terms of throughput, latency, and energy consumption, which are critical factors in such communication scenarios. [ABSTRACT FROM AUTHOR]

Molecule-based selective contacts have become a crucial component to ensure high-efficiency inverted perovskite solar cells1–5. These molecules always consist of a conjugated core with heteroatom substitution to render the desirable carrier-transport capability6–9. So far, the design of successful conjugation cores has been limited to two N-substituted π-conjugated structures, carbazole and triphenylamine, with molecular optimization evolving around their derivatives2,5,10–12. However, further improvement of the device longevity has been hampered by the concomitant limitations of the molecular stability induced by such heteroatom-substituted structures13,14. A more robust molecular contact without sacrificing the electronic properties is in urgent demand, but remains a challenge. Here we report a peri-fused polyaromatic core structure without heteroatom substitution that yields superior carrier transport and selectivity over conventional heteroatom-substituted core structures. This core structure produced a relatively chemically inert and structurally rigid molecular contact, which considerably improved the performance of perovskite solar cells in terms of both efficiency and durability. The champion device showed an efficiency up to 26.1% with greatly improved longevity under different accelerated-ageing tests.A peri-fused polyaromatic core structure is used to produce a relatively chemically inert and structurally rigid molecular contact that improves the efficiency and durability of perovskite solar cells. [ABSTRACT FROM AUTHOR]

Background: To provide patients the chance of accepting curative transjugular intrahepatic portosystemic shunt (TIPS) rather than palliative treatments for portal hypertension-related variceal bleeding and ascites, we aimed to assess hepatic-associated vascular morphological change to improve the predictive accuracy of overt hepatic encephalopathy (HE) risks. Methods: In this multicenter study, 621 patients undergoing TIPS were subdivided into training (413 cases from 3 hospitals) and external validation datasets (208 cases from another 3 hospitals). In addition to traditional clinical factors, we assessed hepatic-associated vascular morphological changes using maximum diameter (including absolute and ratio values). Three predictive models (clinical, hepatic-associated vascular, and combined) were constructed using logistic regression. Their discrimination and calibration were compared to test the necessity of hepatic-associated vascular assessment and identify the optimal model. Furthermore, to verify the improved performance of ModelC−V, we compared it with four previous models, both in discrimination and calibration. Results: The combined model outperformed the clinical and hepatic-associated vascular models (training: 0.814, 0.754, 0.727; validation: 0.781, 0.679, 0.776; p < 0.050) and had the best calibration. Compared to previous models, ModelC−V showed superior performance in discrimination. The high-, middle-, and low-risk populations displayed significantly different overt HE incidence (p < 0.001). Despite the limited ability of pre-TIPS ammonia to predict overt HE risks, the combined model displayed a satisfactory ability to predict overt HE risks, both in the low- and high-ammonia subgroups. Conclusion: Hepatic-associated vascular assessment improved the predictive accuracy of overt HE, ensuring curative chances by TIPS for suitable patients and providing insights for cirrhosis-related studies. [ABSTRACT FROM AUTHOR]

Multi-instance learning (MIL) aims to learn mappings between bags of instances and bag-level labels. Therefore, the relationships among instances are very important for learning mappings. In this study, we propose an MIL algorithm based on a graph built by the structural relationships among instances within a bag. Then, we use a graph convolutional network (GCN) and a new graph-attention mechanism to learn the bag-embedding. In the task of high-resolution medical image classification, the GCN-based MIL algorithm makes full use of structural relationships among patches (instances) in an original image spatial domain. Experimental results verify that the proposed method is more suitable for handling high-resolution medical images. We also experimentally verify that the proposed method achieves better results than previous methods on five benchmark MIL datasets and five medical image datasets. Moreover, we verify that GCN and the proposed graph-attention mechanism outperform GNN and attention-MIL by ablation experiments on MIL benchmark datasets. [ABSTRACT FROM AUTHOR]

Identifying unknown types of diseases is a crucial step in preceding retinal imaging classification for the sake of safety, which is known as anomaly detection of retinal imaging. However, the widely-used supervised learning algorithms are not suitable for this problem, since the data of the unknown category is unobtainable. Moreover, for retinal imaging with different types of anomalous regions, using a single-resolution input causes information loss. Therefore, we propose an unsupervised auto-encoder model with multi-resolution inputs and outputs. We provide a theoretical understanding of the effectiveness of reconstruction error and the improvement of self-supervised learning for anomaly detection. Our experiments on two widely-used retinal imaging datasets show that the proposed methods are superior to other methods, and further experiments verify the validity of each part of the proposed method. [ABSTRACT FROM AUTHOR]

To achieve a mostly nutrients recovery from wastewater to biomass production, a protein-rich Chlorella sp. HL-1 was isolated from piggery anaerobic digestion effluent (PADE), and then a series of experiments were conducted in PADE to optimize culture conditions. The optimal cultivation conditions were initial inoculation of 0.61 g·L−1, pH 8.5, the salinity of 1.5‰, and the dilution rate of 1:2 (PADE: freshwater was 1:2, v/v, 587.67 mg·L−1 TIN). Based on the above conditions, construct a continuous protein-rich Chlorella sp. HL-1 production system in a six-day production interval in undiluted PADE. In the system, at the end of every cycle, discharge 1/3 volume of the culture broth to separate Chlorella. Meanwhile, add the same volume of PADE to start new cyclic cultivation. After five consecutive production cycles, the system could obtain 10.50 ± 0.34 g·L−1 biomass production, and heavy metals such as Cd and Pb in Chlorella sp. HL-1 were within the standards (EC No 1881/2006). Furthermore, the protein content of Chlorella sp. HL-1 was stable at 51.14 ± 0.86% DW, making it possible to be additional nutrients for animals. Meanwhile, the system could achieve 67.00 ± 2.21% %TIN and 71.07 ± 1.09% total phosphorus (TP) removal. Such results demonstrated the feasibility of the continuous production system in converting nutrients from PADE to biomass production of Chlorella sp. HL-1 at a safe mode, and the system might be a better pathway for resource utilization and nutrient removal pretreatment of PADE. [ABSTRACT FROM AUTHOR]

The rapid development and widespread use of ZnO nanoparticles (nZnO) in various industries have raised concerns about their potential environmental impact. Therefore, understanding the fate and role of nZnO in the natural environment is crucial for mitigating their hazardous effects on the environment and human safety. The purpose of the present study was to provide scientific support for understanding and eliminating the joint risk of nanoparticle and heavy metal pollution in the soil environment by revealing the co-transport characteristics of Cd(II) and ZnO nanoparticles (nZnO) in soil under different ionic strength (IS) and pH. The impacts of different IS and pH on the co-transport of Cd(II) and nZnO in a 20 cm long with an inner diameter of 2.5 cm acrylic column packed with 10 cm high soil samples were investigated in the present study. In the above system, a 500 μg L−1 Cd(II) loaded nZnO suspension pulse with varying IS or pH was introduced into the soil column for leaching over 5 PVs, followed up by 5 PVs background solutions without nZnO. The IS was 1, 10, or 50 mM NaCl, with pH6, or the pH was 6, 7 or 8 with 1 mM NaCl. Meanwhile, Sedimentation experiments for nZnO, adsorption of Cd(II) on soil, and nZnO, DLVO theory calculation for the same background condition were conducted. The presence of nZnO significantly increased the mobility of Cd(II) as a result of its strong adsorption capacity for nZnO-associated Cd(II). However, with the increase of IS, the co-transport of nZnO and Cd(II) was decreased and the retention of nZnO in the soil column due to more nZnO attended to aggregate and sediment during the transport and the decrease in the adsorption capacity of nZnO for Cd(II) by competition of Na+. When pH was 6, 7, and 8, the co-transport of nZnO and Cd(II) increased with higher pH due to the lower electrostatic attraction between nZnO and soil under higher pH. Meanwhile, the DLVO theory was fitted to describe the above co-transport process of nZnO and Cd(II). More attention should be paid to the presence of nZnO on the migration of Cd(II) in the natural soil to control the potential risk of nanoparticles and heavy metals to the environment. The risk of co-transport of nZnO and Cd(II) might be controlled by adjusting IS and pH in the soil solution. [ABSTRACT FROM AUTHOR]

Anomaly detection is a classical problem of identifying whether a query is an inlier or an outlier, with only inliers available during training. Reconstruction error is regarded to be a good metric for anomaly detection in many approaches, while they ignore the data distribution of inliers. In this study, we present a novel model leveraging data distribution and reconstruction error for anomaly detection. To obtain a model for anomaly detection in an unsupervised manner, we train a generative adversarial network and an autoencoder by minimizing the adversarial loss and reconstruction error, where two modules share the parameters of generator and decoder. Directly combining the autoencoder and generative models faces the challenge that the encoded vectors do not always follow the fixed prior distribution of the input of generator. To address this challenge, we propose a simple strategy that is theoretically guaranteed under proper conditions. Based on the training process, we propose a novel normality score combining the reconstruction error and the latent density for anomaly detection. In several benchmark datasets, the proposed method obtains state-of-the-art results. [ABSTRACT FROM AUTHOR]

In this study, a novel biocontrol bacterium was isolated and identified as Paenibacillus sp. LYX-1 from soils in the peach orchard. Both Cd2+ resistance and biosorption behavior of strain LYX-1 was explored. Meanwhile, the Cd2+ resistance and biosorption mechanisms were further identified by Cd-resistant genes, SEM–EDS, FTIR, XPS, and TEM analysis. The results showed that strain LYX-1 could resist 50 mg/L Cd2+ and had the CzcD gene responsible for Cd2+ efflux. Under pH 8.0 and at a dose of 1.0 g/L sorbent dose, the removal efficiencies of living and dead cells were as high as 90.39% and 75.67% at 20 mg/L Cd2+, respectively. For the adsorption isotherm test, results revealed that both Langmuir (R2 = 0.9704) and Freundlich (R2 = 0.9915) model could describe the Cd2+ biosorption well for living strain LYX-1. The maximum equilibrium biosorption capacities of living and dead biomass were 30.6790 and 24.3752 mg/g, respectively. In the adsorption kinetic test, the adsorption process of both living and dead strain LYX-1 all satisfied the pseudo-second kinetic equation. A desorption study showed that strain LYX-1 sorbents could be recycled and regenerated by eluents efficiently. SEM–EDS analysis reflected that Cd2+ was bound to the cell wall. Besides, the biosorption process was controlled by chemisorption with the participation of the -OH, -NH, -C = O, O = C-O, C-N, S2−, and phosphate functional groups on the cell surface of strain LYX-1, which were identified by FTIR and XPS. Bioaccumulation also made a contribution to the Cd2+ removal during the biosorption process of living sorbent. The above results indicated that strain LYX-1 had higher Cd2+ tolerance and Cd2+ removal capacity. This strain exhibits promising application to the removal of Cd2+ in the Cd-contaminated environment. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to develop and validate a new model that focused on the risk of imminent vertebral fractures in women with osteoporosis.Data from 2,048 patients were extracted from three hospitals, of which 1,720 patients passed the inclusion and exclusion screen. The patients from Nanfang Hospital (NFH) were randomized at a 2:1 ratio to create a training cohort (n = 709) and an internal validation cohort (n = 355), with the patients from the other two hospitals (n = 656) used for external validation. The risk factors included in the imminent osteoporotic vertebral compression fractures (OVCFs) prediction model (labelled TVF) were sorted by the least absolute shrinkage and selection operator and constructed by logistic regression. The area under the receiver operating characteristic curve (AUC), the decision curve, and the clinical impact curves of the optimal model were analyzed to verify the model.There were 138 and 161 fresh fractures in NFH and the other two hospitals, respectively. The lowest BMD T value and the history of vertebral fracture were integrated into the TVF model. The prediction power of TVF was demonstrated by the AUCs of 0.788 (95% confidence interval [CI], 0.728–0.849) in the training cohort and 0.774 (95% CI, 0.705–0.842) in the internal validation cohort, and 0.790 (95% CI, 0.742–0.839) and 0.741 (95% CI, 0.668–0.813) in the external validation cohorts.The TVF model demonstrated good discrimination to stratify the imminent risk of OVCFs. We therefore consider the model as a pertinent commencement in the search for more accurate imminent OVCFs prediction.Methods: This study aimed to develop and validate a new model that focused on the risk of imminent vertebral fractures in women with osteoporosis.Data from 2,048 patients were extracted from three hospitals, of which 1,720 patients passed the inclusion and exclusion screen. The patients from Nanfang Hospital (NFH) were randomized at a 2:1 ratio to create a training cohort (n = 709) and an internal validation cohort (n = 355), with the patients from the other two hospitals (n = 656) used for external validation. The risk factors included in the imminent osteoporotic vertebral compression fractures (OVCFs) prediction model (labelled TVF) were sorted by the least absolute shrinkage and selection operator and constructed by logistic regression. The area under the receiver operating characteristic curve (AUC), the decision curve, and the clinical impact curves of the optimal model were analyzed to verify the model.There were 138 and 161 fresh fractures in NFH and the other two hospitals, respectively. The lowest BMD T value and the history of vertebral fracture were integrated into the TVF model. The prediction power of TVF was demonstrated by the AUCs of 0.788 (95% confidence interval [CI], 0.728–0.849) in the training cohort and 0.774 (95% CI, 0.705–0.842) in the internal validation cohort, and 0.790 (95% CI, 0.742–0.839) and 0.741 (95% CI, 0.668–0.813) in the external validation cohorts.The TVF model demonstrated good discrimination to stratify the imminent risk of OVCFs. We therefore consider the model as a pertinent commencement in the search for more accurate imminent OVCFs prediction.Results: This study aimed to develop and validate a new model that focused on the risk of imminent vertebral fractures in women with osteoporosis.Data from 2,048 patients were extracted from three hospitals, of which 1,720 patients passed the inclusion and exclusion screen. The patients from Nanfang Hospital (NFH) were randomized at a 2:1 ratio to create a training cohort (n = 709) and an internal validation cohort (n = 355), with the patients from the other two hospitals (n = 656) used for external validation. The risk factors included in the imminent osteoporotic vertebral compression fractures (OVCFs) prediction model (labelled TVF) were sorted by the least absolute shrinkage and selection operator and constructed by logistic regression. The area under the receiver operating characteristic curve (AUC), the decision curve, and the clinical impact curves of the optimal model were analyzed to verify the model.There were 138 and 161 fresh fractures in NFH and the other two hospitals, respectively. The lowest BMD T value and the history of vertebral fracture were integrated into the TVF model. The prediction power of TVF was demonstrated by the AUCs of 0.788 (95% confidence interval [CI], 0.728–0.849) in the training cohort and 0.774 (95% CI, 0.705–0.842) in the internal validation cohort, and 0.790 (95% CI, 0.742–0.839) and 0.741 (95% CI, 0.668–0.813) in the external validation cohorts.The TVF model demonstrated good discrimination to stratify the imminent risk of OVCFs. We therefore consider the model as a pertinent commencement in the search for more accurate imminent OVCFs prediction.Conclusion: This study aimed to develop and validate a new model that focused on the risk of imminent vertebral fractures in women with osteoporosis.Data from 2,048 patients were extracted from three hospitals, of which 1,720 patients passed the inclusion and exclusion screen. The patients from Nanfang Hospital (NFH) were randomized at a 2:1 ratio to create a training cohort (n = 709) and an internal validation cohort (n = 355), with the patients from the other two hospitals (n = 656) used for external validation. The risk factors included in the imminent osteoporotic vertebral compression fractures (OVCFs) prediction model (labelled TVF) were sorted by the least absolute shrinkage and selection operator and constructed by logistic regression. The area under the receiver operating characteristic curve (AUC), the decision curve, and the clinical impact curves of the optimal model were analyzed to verify the model.There were 138 and 161 fresh fractures in NFH and the other two hospitals, respectively. The lowest BMD T value and the history of vertebral fracture were integrated into the TVF model. The prediction power of TVF was demonstrated by the AUCs of 0.788 (95% confidence interval [CI], 0.728–0.849) in the training cohort and 0.774 (95% CI, 0.705–0.842) in the internal validation cohort, and 0.790 (95% CI, 0.742–0.839) and 0.741 (95% CI, 0.668–0.813) in the external validation cohorts.The TVF model demonstrated good discrimination to stratify the imminent risk of OVCFs. We therefore consider the model as a pertinent commencement in the search for more accurate imminent OVCFs prediction. [ABSTRACT FROM AUTHOR]

Cross-coupling reactions have developed into powerful approaches for carbon–carbon bond formation. In this work, a Ni-catalyzed migratory Suzuki–Miyaura cross-coupling featuring high benzylic or allylic selectivity has been developed. With this method, unactivated alkyl electrophiles and aryl or vinyl boronic acids can be efficiently transferred to diarylalkane or allylbenzene derivatives under mild conditions. Importantly, unactivated alkyl chlorides can also be successfully used as the coupling partners. To demonstrate the applicability of this method, we showcase that this strategy can serve as a platform for the synthesis of terminal, partially deuterium-labeled molecules from readily accessible starting materials. Experimental studies suggest that migratory cross-coupling products are generated from Ni(0/II) catalytic cycle. Theoretical calculations indicate that the chain-walking occurs at a neutral nickel complex rather than a cationic one. In addition, the original-site cross-coupling products can be obtained by alternating the ligand, wherein the formation of the products has been rationalized by a radical chain process. Migratory cross-coupling reactions are powerful tools to form bonds at predictable positions. Here the authors report a nickel-catalyzed migratory Suzuki–Miyaura cross-coupling of unactivated alkyl electrophiles with aryl and vinyl boron reagents and provide experimental and computational mechanistic evidence. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the association of parathyroid hormone (PTH) gene Bst BI polymorphism, calciotropic hormone levels, and dental fluorosis of children. A case-control study was conducted in two counties (Kaifeng and Tongxu) in Henan Province, China in 2005-2006. Two hundred and twenty-five children were recruited and divided into three groups including dental fluorosis group (DFG), non-dental fluorosis group (NDFG) from high fluoride areas, and control group (CG). Urine fluoride content was determined using fluoride ion selective electrode; PTH Bst BI were genotyped using PCR-RFLP; osteocalcin (OC) and calcitonin (CT) levels in serum were detected using radioimmunoassay. Genotype distributions were BB 85.3% (58/68), Bb 14.7% (10/68) for DFG; BB 77.6% (52/67), Bb 22.4% (15/67) for NDFG; and BB 73.3% (66/90), Bb 27.7% (24/90) for CG. No significant difference of Bst BI genotypes was observed among three groups ( P > 0.05). Serum OC and urine fluoride of children were both significantly higher in DFG and NDFG than in CG ( P < 0.05, respectively), while a similar situation was not observed between DFG and NDFG in high fluoride areas ( P > 0.05). Serum OC level of children with BB genotype was significantly higher compared to those with Bb genotype in high fluoride areas ( P < 0.05). However, no significant difference of serum CT or calcium (Ca) was observed. In conclusion, there is no correlation between dental fluorosis and PTH Bst BI polymorphism. Serum OC might be a more sensitive biomarker for detecting early stages of dental fluorosis, and further studies are needed. [ABSTRACT FROM AUTHOR]