Through the literature summary, a list of key factors affecting construction and demolition (C&D) waste recycling and resource governance was identified from three levels: government governance and support, social public participation, and related enterprise participation. By building a collaborative driving model of C&D waste recovery and recycling governance, and based on the system dynamics of C&D waste recovery and recycling governance causal loop diagram, it shows the synergy between various influencing factors of the C&D waste recovery and recycling management. Conclusion: suggestions are come up with promote the development of C&D waste recovery and recycling management by increasing policy guarantees and financial support, encouraging public participation, stimulating the participation of relevant enterprises, and improving the effects of C&D waste recovery and recycling.
In this paper, we estimate the uplink performance of large-scale multi-user multiple-input multiple-output (MIMO) networks. By applying minimum-mean-square-error (MMSE) detection, a novel statistical distribution of the signal-to-interference-plus-noise ratio (SINR) for any user is derived, for path loss, shadowing and Rayleigh fading. Suppose that the channel state information is perfectly known at the base station. Then, we derive the analytical expressions for the pairwise error probability (PEP) of the massive multiuser MMSE–MIMO systems, based on which we further obtain the upper bound of the bit error rate (BER). The analytical results are validated successfully through simulations for all cases.
BACKGROUND
This case report presents a patient with pyogenic spondylitis (PS) associated with lactation-related osteoporosis during pregnancy. The 34-year-old female patient experienced low back pain for one month, beginning one month postpartum, with no history of trauma or fever. Dual-energy X-ray absorptiometry of the lumbar spine revealed a Z-score of -2.45, leading to a diagnosis of pregnancy and lactation-associated osteoporosis (PLO). The patient was advised to cease breastfeeding and take oral calcium and active vitamin D. Despite these interventions, her symptoms worsened, and she had difficulty walking one week later, prompting her to revisit our hospital.
CASE SUMMARY
Lumbar magnetic resonance imaging (MRI) scans showed abnormal signals in the L4 and L5 vertebral bodies and intervertebral space, while an enhancement scan displayed abnormal enhanced high signals around the L4/5 intervertebral disc, suggesting a lumbar infection. A needle biopsy was performed for bacterial culture and pathological examination, culminating in a final diagnosis of pregnancy and lactation-related osteoporosis with PS. Following treatment with anti-osteoporotic medications and antibiotics, the patient’s pain gradually subsided, and she returned to normal life within five months. PLO is a rare condition that has garnered increasing attention in recent years. Spinal infections during lactation in pregnancy are also relatively uncommon.
CONCLUSION
Both conditions primarily manifest as low back pain but require distinct treatments. In clinical practice, when diagnosing patients with pregnancy and lactation-associated osteoporosis, the possibility of spinal infection should be considered. A lumbar MRI should be conducted as needed to prevent delays in diagnosis and treatment.
2D-to-3D human pose lifting is fundamental for 3D human pose estimation (HPE). Graph Convolutional Network (GCN) has been proven inherently suitable to model the human skeletal topology. However, current GCN-based 3D HPE methods update the node features by aggregating their neighbors' information without considering the interaction of joints in different motion patterns. Although some studies import limb information to learn the movement patterns, the latent synergies among joints, such as maintaining balance in the motion are seldom investigated. We propose a hop-wise GraphFormer with intragroup joint refinement (HopFIR) to tackle the 3D HPE problem. The HopFIR mainly consists of a novel Hop-wise GraphFormer(HGF) module and an Intragroup Joint Refinement(IJR) module which leverages the prior limb information for peripheral joints refinement. The HGF module groups the joints by k-hop neighbors and utilizes a hop-wise transformer-like attention mechanism among these groups to discover latent joint synergy. Extensive experimental results show that HopFIR outperforms the SOTA methods with a large margin (on the Human3.6M dataset, the mean per joint position error (MPJPE) is 32.67mm). Furthermore, it is also demonstrated that previous SOTA GCN-based methods can benefit from the proposed hop-wise attention mechanism efficiently with significant performance promotion, such as SemGCN [42] and MGCN [49] are improved by 8.9% and 4.5%, respectively.
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