Applications of two neuro-based metaheuristic techniques in evaluating ground vibration resulting from tunnel blasting

Peak particle velocity (PPV) caused by blasting is an unfavorable environmental issue that can damage neighboring structures or equipment. Hence, a reliable prediction and minimization of PPV are essential for a blasting site. To estimate PPV caused by tunnel blasting, this paper proposes two neuro-based metaheuristic models: neuro-imperialism and neuro-swarm. The prediction was made based on extensive observation and data collecting from a tunnelling project that was concerned about the presence of a temple near the blasting operations and tunnel site. A detailed modeling procedure was conducted to estimate PPV values using both empirical methods and intelligence techniques. As a fair comparison, a base model considered a benchmark in intelligent modeling, artificial neural network (ANN), was also built to predict the same output. The developed models were evaluated using several calculated statistical indices, such as variance account for (VAF) and a-20 index. The empirical equation findings revealed that there is still room for improvement by implementing other techniques. This paper demonstrated this improvement by proposing the neuro-swarm, neuro-imperialism, and ANN models. The neuro-swarm model outperforms the others in terms of accuracy. VAF values of 90.318% and 90.606% and a-20 index values of 0.374 and 0.355 for training and testing sets, respectively, were obtained for the neuro-swarm model to predict PPV induced by blasting. The proposed neuro-based metaheuristic models in this investigation can be utilized to predict PPV values with an acceptable level of accuracy within the site conditions and input ranges used in this study

Layer-wise Representation Fusion for Compositional Generalization

Despite successes across a broad range of applications, sequence-to-sequence models' construct of solutions are argued to be less compositional than human-like generalization. There is mounting evidence that one of the reasons hindering compositional generalization is representations of the encoder and decoder uppermost layer are entangled. In other words, the syntactic and semantic representations of sequences are twisted inappropriately. However, most previous studies mainly concentrate on enhancing token-level semantic information to alleviate the representations entanglement problem, rather than composing and using the syntactic and semantic representations of sequences appropriately as humans do. In addition, we explain why the entanglement problem exists from the perspective of recent studies about training deeper Transformer, mainly owing to the ``shallow'' residual connections and its simple, one-step operations, which fails to fuse previous layers' information effectively. Starting from this finding and inspired by humans' strategies, we propose \textsc{FuSion} (\textbf{Fu}sing \textbf{S}yntactic and Semant\textbf{i}c Representati\textbf{on}s), an extension to sequence-to-sequence models to learn to fuse previous layers' information back into the encoding and decoding process appropriately through introducing a \emph{fuse-attention module} at each encoder and decoder layer. \textsc{FuSion} achieves competitive and even \textbf{state-of-the-art} results on two realistic benchmarks, which empirically demonstrates the effectiveness of our proposal.Comment: work in progress. arXiv admin note: substantial text overlap with arXiv:2305.1216

Gut microbiome dysbiosis in men who have sex with men increases HIV infection risk through immunity homeostasis alteration

ObjectivesRecent studies pointed out that gut microbiome dysbiosis in HIV infection was possibly confounded in men who have sex with men (MSM), but there is a lack of evidence. It also remained unclear how MSM-associated gut microbiome dysbiosis affected human health. This study aimed to compare the differences in gut microbiome changes between HIV and MSM and reveal the potential impacts of MSM-associated gut microbiome dysbiosis on the immune system.MethodsWe searched available studies based on the PubMed database, and all gut microbiome changes associated with HIV infection and MSM were extracted from the enrolled studies. The gutMgene database was used to identify the target genes and metabolites of the gut microbiome. Bioinformatic technology and single-cell RNA sequencing data analysis were utilized to explore the impacts of these gut microbiome changes on human immunity.ResultsThe results showed significant overlaps between the gut microbiome associated with HIV and that of MSM. Moreover, bioinformatic analysis revealed that gut microbiome dysbiosis in MSM had an impact on several pathways related to immunity, including the IL-17 signaling pathway and Th17 cell differentiation. Additionally, target genes of MSM-associated gut microbiome were found to be highly expressed in monocytes and lymphocytes, suggesting their potential regulatory role in immune cells. Furthermore, we found that MSM-associated gut microbiome could produce acetate and butyrate which were reported to increase the level of inflammatory factors.ConclusionIn conclusion, this study highlighted that MSM-associated gut microbiome dysbiosis might increase the risk of HIV acquisition by activating the immune system. Further studies are expected to elucidate the mechanism by which gut microbiome dysbiosis in MSM modulates HIV susceptibility

Protection Effect of Zhen-Wu-Tang on Adriamycin-Induced Nephrotic Syndrome via Inhibiting Oxidative Lesions and Inflammation Damage

Zhen-wu-tang (ZWT), a well-known formula in China, is widely used to treat chronic kidney diseases. However, very little information on ZWT’s mechanism of action is currently available. In this study, we investigated the possible protective role and underlying mechanism of ZWT on nephrotic syndrome (NS) induced by Adriamycin (intravenous injection, 6.0 mg/kg) in rats using biochemical and histopathological approaches. ZWT decreased urine protein excretion and the serum levels of total cholesterol, triglycerides, blood urea nitrogen, and creatinine significantly in diseased rats. A decrease in plasma levels of total protein and albumin was also recorded in nephropathic rats. Pathological results show an improved pathological state and recovering glomerular structure in ZWT treatment groups. ZWT decreased renal IL-8 level but increased renal IL-4 level. In addition, rats subjected to ZWT exhibited less IgG deposition in glomerulus compared with model group. RT-PCR results showed that ZWT decreased the mRNA expression of NF-κB p65 and increased the mRNA expression of IκB. Furthermore, ZWT reduced the level of MDA and increased SOD activity. These results demonstrated that ZWT ameliorated Adriamycin-induced NS in rats possibly by inhibiting Adriamycin-induced inflammation damage, enhancing body’s antioxidant capacity, thereby protecting glomerulus from injury

A deep dive into tunnel blasting studies between 2000 and 2023-A systematic review

Tunnel blasting is a common practice used to excavate rock formations. Many academic research articles have emerged and burgeoned in the field of tunnel blasting. These articles are dedicated to investigating objectives such as blasting vibration, rock damage, and vibration energy individually. However, no systematic analysis is conducted to consolidate and analyze the findings from the literature related to tunnel blasting. This study addresses this by offering a systematic review to explore the state of tunnel blasting research. A science mapping approach using bibliometric analysis is employed to examine 144 peer-reviewed journal articles. The review identified the most influential journals, institutions, researchers, and articles on tunnel blasting research, and it also summarizes the research hotspots of tunnel blasting according to the cluster analysis of research keywords. Findings in this review revealed the contribution of two leading journals, three leading institutions, and three leading researchers on the research of tunnel blasting. Moreover, four research keywords, i.e., blasting vibration, numerical simulation, rock damage, and overbreak, were identified as the research hotspots in 2018–2023. Finally, this review also speculated the future research directions/avenues of tunnel blasting, aiming to bring to light the deficiencies in the currently existing research and provide paths for future research

Curcumin’s Metabolites, Tetrahydrocurcumin and Octahydrocurcumin, Possess Superior Anti-inflammatory Effects in vivo Through Suppression of TAK1-NF-κB Pathway

Curcumin (CUR), a promising naturally occurring dietary compound, is commonly recognized as the potential anti-inflammatory agent. While the application of CUR was hampered by its low stability and poor systemic bioavailability, it has been suggested that the biological activities of CUR are intimately related to its metabolites. In the current investigation, we aimed to comparatively explore the anti-inflammatory effects of tetrahydrocurcumin (THC), octahydrocurcumin (OHC), and CUR, and to elucidate the underlying action mechanisms on experimental mice models of acute inflammation, i.e., xylene-induced ear edema, acetic acid-induced vascular permeability, and carrageenan-induced paw edema. The results showed that THC and OHC exerted significant and dose-dependent inhibitions on the formation of ear edema induced by xylene and paw edema provoked by carrageenan and inhibited the Evans blue dye leakage in peritoneal cavity elicited by acetic acid. Moreover, THC and OHC treatments were more effective than CUR in selectively inhibiting the expression of cyclooxygenase 2 (COX-2) and suppressing nuclear factor-κB (NF-κB) pathways via transforming growth factor β activated kinase-1 (TAK1) inactivation in the carrageenan-induced mouse paw edema model

Applications of Two Neuro-Based Metaheuristic Techniques in Evaluating Ground Vibration Resulting from Tunnel Blasting

Peak particle velocity (PPV) caused by blasting is an unfavorable environmental issue that can damage neighboring structures or equipment. Hence, a reliable prediction and minimization of PPV are essential for a blasting site. To estimate PPV caused by tunnel blasting, this paper proposes two neuro-based metaheuristic models: neuro-imperialism and neuro-swarm. The prediction was made based on extensive observation and data collecting from a tunnelling project that was concerned about the presence of a temple near the blasting operations and tunnel site. A detailed modeling procedure was conducted to estimate PPV values using both empirical methods and intelligence techniques. As a fair comparison, a base model considered a benchmark in intelligent modeling, artificial neural network (ANN), was also built to predict the same output. The developed models were evaluated using several calculated statistical indices, such as variance account for (VAF) and a-20 index. The empirical equation findings revealed that there is still room for improvement by implementing other techniques. This paper demonstrated this improvement by proposing the neuroswarm, neuro-imperialism, and ANN models. The neuro-swarm model outperforms the others in terms of accuracy. VAF values of 90.318% and 90.606% and a-20 index values of 0.374 and 0.355 for training and testing sets, respectively, were obtained for the neuro-swarm model to predict PPV induced by blasting. The proposed neuro-based metaheuristic models in this investigation can be utilized to predict PPV values with an acceptable level of accuracy within the site conditions and input ranges used in this study