With a high proportion of distributed photovoltaic and lower fossil energy integrated into the distribution network, it is very difficult to ensure the reliability of power supply. The distributed photovoltaic planning model based on big data is proposed. According to the impact stochastic photovoltaics and loads on reliability planning, the static and dynamic capacity-load ratios are proposed. The big data analysis model of distributed photovoltaic planning is established. The big data multi-scenario generation and reduction algorithm of stochastic distributed photovoltaic and load planning is studied, and a source-load big data scenario matching model is proposed. Ac-cording to the source load big data scenario, the dynamic capacity-load ratio of the distribution network is obtained. The static capacity-load ratio calculation method in distribution network planning is studied to ensure the reliability of power supply. Finally, the IEEE 33-bus system is used as an example. The results show that distributed photovoltaic planning based on big data and multi-scenario methods can improve photovoltaic utilization and power supply reliability.

Photoelectric smoke detectors are the most cost-effective devices for very early fire alarms. However, due to different light intensity response values for different fire smoke and interference from interferential aerosols, they have a high false alarm rate, which limits their popularity in Chinese homes. To address these issues, an embedded spatial-temporal convolutional neural network (EST-CNN) model is proposed for real fire smokes identification and aerosols (fire smokes and interferential aerosol) classification. EST-CNN consists of three modules including information fusion, scattering feature extraction, and aerosol classification. Moreover, a two dimensional spatial-temporal scattering (2D-TS) matrix is designed to fuse the scattered light intensities in different channels and adjacent time slices, which is the output of the information fusion module and the input of the scattering feature extraction module. EST-CNN is trained and tested with experimental data measured on the established fire test platform using the developed dual-wavelength dual-angle photoelectric smoke detector. The optimal network parameters are selected through extensive experiments resulting in an average classification accuracy of 95.6% for different aerosols with only 66 kB network parameters. The experimental results demonstrate the feasibility of the designed EST-CNN model to be directly installed in existing commercial photoelectric smoke detectors to realize aerosol classification.

Constructed rapid infiltration system (CRI) is a new type of sewage biofilm treatment technology, but due to its anaerobic zone lacks of the carbon sources and the condition for nitrate retention, its nitrogen removal perfomance is very poor; However, shortcut nitrification-denitrification process presents distinctive advantages, as it saves oxygen, requires less organic matter and needs less time for denitrification compared to conventional nitrogen removal method. Thus, if the shortcut nitrification-denitrification process could be applied to CRI system properly, the simpler, more economic and efficient nitrogen removal method will be obtained. But, as its reaction process shows that the first and the most important step of achieving shortcut nitrification-denitrification is to achieve shortcut nitrification. Thus, in this study, we explored the feasibility to achieve shortcut nitrification, which produces nitrite as the dominant nitrogen species in effluent, by addition of potassium chlorate (KClO₃) to the influent. In an experimental CRI model system, the effects on nitrogen removal, nitrate inhibition and nitrite accumulation were studied, and the advantages of achieving shortcut nitrification-denitrification were also analysed. The results showed that shortcut nitrification was successfully achieved and maintained in a CRI system by adding 5 mM KClO₃ to the influent at a constant pH of 8.4. Under these conditions nitrite accumulation rate was increased, while a lower concentration of 3 mM KClO₃ had no obvious effect. The addition of 5 mM KClO₃ in influent presumably allowed sufficient activity of ammonia-oxidizing bacteria (AOB) but inhibited nitrite-oxidizing bacteria (NOB) strongly enough to result in a maximum nitrite accumulation rate of up to over 80%. As a result, nitrite became the dominant nitrogen product in the effluent. Moreover, if the shortcut denitrification will be achieved in the subsequent research, it could save 60.27 mg carbon source (CH₃OH) consumption when treatment of per liter sewage in CRI system compared with full denitrification process.

Objective: To use the ancient and modern medical record cloud platform (V2.3.5) to organize and explore the medication patterns of the "Three Causes, Extremity, and One Disease Syndrome Formula" by Chen Wuxuan, and to summarize its therapeutic ideas. Method: Collect and organize the "San Yin Si Tian Fang" in the "San Yin Ji Yi Zheng Fang Lun", and use the modern medical record cloud platform (V2.3.5) to conduct medication frequency statistical analysis, cluster analysis, and complex network analysis. As a result, a total of 16 Chinese herbal medicines were collected in the "Three Causes, Extreme and One Disease Syndrome Formula", involving 75 traditional Chinese medicines. Statistical analysis showed that roasted licorice was the most commonly used, followed by Pinellia ternata, papaya, ginger, and Poria cocos; Medications mainly include warm drugs, sweet and pungent flavors, as well as drugs that promote lung and spleen meridians. Cluster analysis can divide the above-mentioned traditional Chinese medicine into two groups, with specific grouping as follows. Group 1: ginger, white peony, aconite, papaya, and achyranthes bidentata; Group 2: roasted licorice, poria cocos, dried ginger, pinellia ternata, and green peel. Conclusion: The characteristics of the 16 prescriptions are biased towards Xin Gan Wen, emphasizing the regulation of the spleen and stomach, which may be related to the common use of prescription by doctors in the Song Dynasty and the tendency towards warm and dry medication.

Activated carbon (AC) is an effective adsorbent for creatinine removal in hemoperfusion. However, the hemocompatibility and adsorption capacity of AC was required to be improved furthermore. Heparin, a widely used anticoagulant biomacromolecule, could improve the hemocompatibility of the absorbents in different ways based on its molecular weight. Thus, it was necessary to study the surface modification with unfractionated heparin (UFH) or low molecular weight heparin (LMWH) on improvement of hemocompatibility and adsorption. In this study, UFH and LMWH were respectively grafted on AC through polyethyleneimine as an intermediate layer. The modification of AC about morphology, mechanical strength and pore structure was characterized by XPS, SEM, TA and BET. It was found that compared with AC, the morphology and mechanical strength AC-UFH and AC-LMWH could be well maintained, but the BET surface was decreased due to the grafting of macromolecules. Furthermore, AC-UFH and AC-LMWH showed the better hemocompatibility on protein adsorption, APTT and platelet activation compared with AC, in which AC-LMWH had lower fibrinogen adsorption and longer APTT than AC-UFH. Besides, it was found that AC, AC-UFH and AC-LMWH had no significant effect on blood cell composition. Finally, the adsorption capacity of adsorbents for creatinine was evaluated. Although there was no significant difference between AC-UFH and AC-LMWH, it was found that heparin could be interacted with creatinine to enhance the adsorption capacity when compared with AC-PEI. This study deepened the understanding of anticoagulation of heparinized surface and provided a theoretical basis for adsorbents in hemoperfusion.

A 31-year-old male noticed blurred vision in his right eye for five days with no obvious predisposing causes, accompanied by mild dizziness. No obvious nodular lesions were found in the body. The patient’s binocular visual acuity was 20/20. Fundus photography showed the optic nerve swelling and radial superficial retinal hemorrhage of both eyes. Blood panel, urine routine, liver, and kidney function were all normal. Total cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein were all in the normal limits. Head MRI showed a mass in the right temporal lobe, clear boundary, and multiple separations, which thinned and disappeared closer to the skull. The right temporal lobe and lateral ventricle were all compressed, with the midline structure shifted to the left. The patient was then transferred to Neurosurgery. During the operation, we observed the tumor had invaded the skull. The actual size of the tumor was 5.6 cm × 7.5 cm × 10.1 cm. Histology revealed foam cell accumulation in the mucous connective tissue of the right temporal lobe. The immunohistochemistry showed: CD34 (+), CD99 (+), EMA (−), GFAP (−), IDH-1 (−), Ki-67 (+) index about 10%, Oliga-2 (−), PR (−), S-100 (−), Vim (+), β-Catenin (+), CD1a (−), CD68 (+). Three months after the removal of the tumor, the visual acuity of both eyes was 20/20; The visual fields were normal, the optic disc edema and retinal hemorrhages had disappeared. MRI indicated the midline structure was back to normal.

Digital Elevation Models (DEMs) are widely used in geographic and environmental studies. In the current work, the fusion of multi-source DEMs is investigated to improve the overall accuracy of public domain DEMs. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods – the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD) and nonlinear adaptive multi-scale decomposition (N-AMD) - are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D – 30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the ice cloud and land elevation satellite (ICESat) global land surface altimetry data as a reference. The vertical accuracy of the fused DEM shows significant improvements of 29.6% and 19.3% in a mountainous region and 27.4% and 15.5% in a low-relief region, compared to the SRTM-1 and AW3D30 respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM.

In this paper, the lamellar structural evolution and microvoids variations of β-iPP during the processing of two different stretching methods, sequential biaxial stretching and simultaneous biaxial stretching, were investigated in detail. It was found that different stretching methods led to significantly different lamellae deformation modes, and the microporous membranes obtained from the simultaneous biaxial stretching exhibited better mechanical properties. For the sequential biaxial stretching, abundant coarse fibers originated from the tight accumulation of the lamellae parallel to the longitudinal stretching direction, whereas the lamellae perpendicular to the stretching direction were easily deformed and separated. Those coarse fibers were difficult to be separated to form micropores during the subsequent transverse stretching process, resulting in a poor micropores distribution. However, for the simultaneous biaxial stretching, the β crystal had the same deformation mode, that is, the lamellae distributed in different directions were all destroyed, forming abundant microvoids and little coarse fibers formation.

Porins are crucial proteins located in the outer membrane that directly influence antimicrobial resistance mechanisms and virulence in bacteria. In this study, a porin gene (Vp-porin) was cloned in V. parahaemolyticus, and the function of Vp-porin on biological characteristics and virulence was investigated. The results of sequence analysis showed that Vp-porin is highly conserved in Vibrio spp. and the predicted 3D structure showed it could form a transmembrane channel with 20 β barrel. Membrane permeabilization provides evidence that membrane integrity of ∆Vp-porin was damaged and the sensitivity to tetracycline, polymyxin B, rifampicin and cephalothin of ∆Vp-porin obvious increased. In addition, loss of Vp-porin damaged motility due to down-regulated flagellar synthesis. Besides, ∆Vp-porin exhibited attenuated cytotoxicity to Tetra-hymena. The relative survival rate of Tetrahymena infection with ∆Vp-porin was 86%, which is much high than that with WT (49%). Take together, Vp-porin in V. parahaemolyticus plays various roles in biological characteristics in membrane integrity, antimicrobial resistance and motility, and contributes to virulence.

This study aims to enhance the detection and assessment of safety hazards in small-scale reservoir engineering using advanced image processing and deep learning techniques. Given the critical importance of small reservoirs in flood management, water supply, and ecological balance, the effective monitoring of their structural integrity is crucial. This paper developed a fully convolutional semantic segmentation method for hidden danger images of small reservoirs using an encoding-decoding structure, utilizing a deep learning framework of convolutional neural networks (CNNs) to process and analyze high-resolution images captured by UAVs. The method incorporated data augmentation and adaptive learning techniques to improve model accuracy under diverse environmental conditions. Finally, the quantification data of hidden dangers (length, width, area, etc.) was obtained by converting the image pixels to the actual size. Results demonstrate significant improvements in detecting structural deficiencies, such as cracks and seepage areas, with increased precision and recall rates compared to conventional methods, and the HHSN-25 network (Hidden Hazard Segmentation Network with 25 layers)proposed in this paper outperforms other methods. The main evaluation indicator mIoU of HHSN-25 is higher than other methods, reaching 87.00%, and Unet is 85.50%, Unet++is 85.55%. The proposed model achieves reliable real-time performance, allowing for early warning and effective management of potential risks. This study contributes to the development of more efficient monitoring systems for small-scale reservoirs, enhancing their safety and operational sustainability.

The genetic code evolved around the reading of the tRNA anticodon on the primitive ribosome, and tRNA-34 wobble and tRNA-37 modifications coevolved with the code. We posit that EF-Tu, the closing mechanism of the 30S ribosomal subunit, methylation of wobble U34 at the 5-carbon and suppression of wobbling at the tRNA-36 position were partly redundant and overlapping functions that coevolved to establish the code. The genetic code devolved in evolution of mitochondria to reduce the size of the tRNAome (all of the tRNAs of an organism or organelle). “Superwobbling” or four-way wobbling describes a major mechanism for shrinking the mitochondrial tRNAome. In superwobbling, unmodified wobble tRNA-U34 can recognize all four codon wobble bases (A, G, C and U), allowing a single unmodified tRNA-U34 to read a 4-codon box. During code evolution, to suppress superwobbling in 2-codon sectors, U34 modification by methylation at the 5-carbon position appears essential. As expected, at the base of code evolution, tRNA-37 modifications mostly related to the identity of the adjacent tRNA-36 base. TRNA-37 modifications help maintain the translation frame during elongation.

We consider partition functions, in the form of state sums, and associated probabilistic measures for aperiodic substrates described by model sets and their associated tiling spaces. We propose model set tiling spaces as microscopic models for small scales in the context of quantum gravity. Model sets possess special self-similarity properties that allow us to consider implications on large and observable scales from the underlying (non-ergodic) dynamics. In particular we consider the implication of the underlying aperiodic substrate for the well known problem of time in quantum gravity, and propose a correspondence between small and large scales, the so-called ergodic correspondence, that addresses the emergence of matter properties and spacetime structure. In the process we find a possible bound in the mass spectrum of fundamental particles.

The genetic code evolved by parallel tracks of chaotic and ordered processes. Liquid-liquid phase separation (hydrogels), a chaotic process, constructs diverse membraneless compartments within cells, resulting in regulated hydration and sequestration and concentration of reaction components. Hydrogels relate to chaotic amyloid fiber production. We propose that polyglycine and related hydrogels (i.e. GADV; G is glycine), phase separations, membraneless droplets and amyloid accretions organized protocell domains to drive the earliest evolution of the genetic code and the pre-life to cellular life transition. By contrast, evolution of tRNA, tRNAomes, aminoacyl-tRNA synthetases and translation systems followed highly ordered and systematic pathways, described by well-defined mechanisms and rules. The pathway of evolution of aminoacyl-tRNA synthetases, which tracked evolution of the genetic code, is clarified. Hydrogels and amyloids form a chaotic component, therefore, that complemented otherwise systematic processes. We describe with detail a pre-life world in which hydrogels and amyloids provided the selections of the first life.

There are no theorems (proven theories) in the biological sciences. We propose that the 3 31 nt minihelix tRNA evolution theorem be universally accepted as one. The 3 31 nt minihelix theorem completely describes evolution of type I and type II tRNAs from ordered precursors (RNA repeats and inverted repeats). Despite diversification of tRNAome sequences, statistical tests overwhelmingly support the theorem. Furthermore, the theorem relates the dominant pathway for the origin of life on Earth, specifically, how tRNAomes and the genetic code may have coevolved. Alternate models for tRNA evolution (i.e., 2 minihelix, convergent and accretion models) are falsified. In the context of the pre-life world, tRNA was a molecule that via mutation could modify anticodon sequences and teach itself to code. We relate the clearest history to date of the chemical evolution of life on Earth. From analysis of tRNA evolution, ribozyme-mediated RNA ligation was a primary driving force in evolution of complexity during the pre-life to life transition on Earth.

The 3 31 nucleotide minihelix tRNA theorem describes evolution of type I and type II tRNAs to the last nucleotide. In databases, type I and type II tRNA V loops (V for variable) were improperly aligned, but alignment based on the theorem is accurate. Type II tRNA V arms were a 3’-acceptor stem (initially CCGCCGC) ligated to a 5’-acceptor stem (initially GCGGCGG). The type II V arm evolved to form a stem-loop-stem. In Archaea, tRNALeu and tRNASer are type II. In Bacteria, tRNALeu, tRNASer and tRNATyr are type II. The positioning of the type II V arm is determined by the number of unpaired bases just 5’ of the Levitt base (Vmax). For Archaea, tRNALeu has 2 unpaired bases, and tRNASer has 1 unpaired base. For Bacteria, tRNATyr has 2 unpaired bases, tRNALeu has 1 unpaired base and tRNASer has 0 unpaired bases. Thus, the number of type II tRNAs is limited by the possible set points of the arm. From analysis of aminoacyl-tRNA synthetase structures, contacts to type II V arms appear to adjust allosteric tension communicated primarily via tRNA to aminoacylating and editing active sites. To enhance allostery, it appears that type II V arm end loop contacts may tend to evolve to V arm stem contacts.

Evolutionary, sequence and structural analyses of tRNA variable (V) loops provides a new understanding. Type I tRNA V loops have a primordial length of 5 nt, and type II tRNA V loops have a primordial length of 14 nt. Sequence-based alignments of type I and type II V loops gave deceptive results. Type II V loops are characterized by the trajectory of the V arm, the size and sequence of the loop and the first and last V loop nucleotides and their known contacts. Depending on the V arm trajectory, type II V stems and loops can be interaction sites for aminoacyl-tRNA synthetases. So, type II V arms for tRNALeu, tRNASer and tRNATyr (found in Bacteria) were coevolved and selected to distinguish determinants to support cognate tRNA charging. The alignment of type I and type II V loops was adjusted based on tRNA evolution. Results are consistent with Archaea being an older and simpler life form than Bacteria. TRNAome sequences were derived from radiation of an ordered tRNAPri (Pri for primordial) sequence comprised of known RNA repeats and inverted repeats. V loop and tRNA sequences, therefore, convey a history of the primary successful pathway in the origin of life.

It is shown how the secondary structure of proteins, musical forms and verses of poems are approximately ruled by universal laws relying on graph coverings. In this direction, one explores the group structure of a variant of the SARS-Cov-2 spike protein and the group structure of apolipoprotein-H, passing from the primary code with amino acids to the secondary structures organizing the foldings. Then one look at the musical forms employed in the classical and contemporary periods. Finally, one investigates in much detail the group structure of a small poem in prose by Charles Baudelaire and that of the Bateau Ivre by Arthur Rimbaud.

Every protein consists of a linear sequence over an alphabet of $20$ letters/amino acids. The sequence unfolds in the $3$-dimensional space through secondary (local foldings), tertiary (bonds) and quaternary (disjoint multiple) structures. The mere existence of the genetic code for the $20$ letters of the linear chain could be predicted with the (informationally complete) irreducible characters of the finite group $G_n:=\mathbb{Z}_n \rtimes 2O$ (with $n=5$ or $7$ and $2O$ the binary octahedral group) in our previous two papers. It turns out that some quaternary structures of protein complexes display $n$-fold symmetries. We propose an approach of secondary structures based on free group theory. Our results are compared to other approaches of predicting secondary structures of proteins in terms of $\alpha$ helices, $\beta$ sheets and coils, or more refined techniques. It is shown that the secondary structure of proteins shows similarities to the structure of some hyperbolic $3$-manifolds. The hyperbolic $3$-manifold of smallest volume --Gieseking manifold--, some other $3$ manifolds and the oriented hypercartographic group are singled out as tentative models of such secondary structures. For the quaternary structure, there are links to the Kummer surface.

Taking a DNA-sequence, a word with letters/bases A, T, G and C, as the relation between the generators of an infinite group $\pi$, one can discriminate two important families: (i) the cardinality structure for conjugacy classes of subgroups of $\pi$ is that of a free group on $1$ to $4$ bases and the DNA word, viewed as a substitution sequence, is aperiodic. (ii) The cardinality structure for conjugacy classes of subgroups of $\pi$ is not that of a free group, the sequence is generally not aperiodic and topological properties of $\pi$ have to be determined differently. The two cases rely on DNA conformations such as A-DNA, B-DNA, Z-DNA, G-quadruplexes, etc. We found a few salient results: Z-DNA, when involved in transcription, replication and regulation in a healthy situation implies (i). The sequence of telomeric repeats comprising $3$ distinct bases, most of the time, satisfies (i). For $2$-base sequences in the free case (i) or non free case (ii), the topology of $\pi$ may be found in terms of the $SL(2,\mathbb{C})$ character variety of $\pi$ and the attached algebraic surfaces. The linking of two unknotted curves --the Hopf link-- may occur in the topology of $\pi$ in cases of biological importance, in telomeres, G-quadruplexes, hairpins and junctions, a feature we already found in the context of models of topological quantum computing. For $3$- and $4$ base sequences, other knotting configurations are noticed and a building block of the topology is the $4$-punctured sphere. Our methods have the potential to discriminate potential diseases associated to the sequences.

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer(CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

Our recent work has focused on the application of infinite group theory and related algebraic geometric tools in the context of transcription factors and microRNAs. We were able to differentiate between “healthy" nucleotide sequences and disrupted sequences that may be associated with various diseases. In this paper, we extend our efforts to the study of messenger RNA metabolism, showcasing the power of our approach. We investigate (i) mRNA translation in prokaryotes and eukaryotes, (ii) polyadenylation in eukaryotes, which is crucial for nuclear export, translation, stability, and splicing of mRNA, (iii) miRNAs involved in RNA silencing and post-transcriptional regulation of gene expression, and (iv) the identification of disrupted sequences that could lead to potential illnesses. To achieve this, we employ (a) infinite (finitely generated) groups fp, with generators representing the r+1 distinct nucleotides and a relation between them [e.g., the consensus sequence in the mRNA translation (i), the poly(A) tail in item (ii), and the miRNA seed in item (iii)], (b) aperiodicity theory, which connects “healthy groups" fp to free groups Fr of rank r and their profinite completion F^r, and (c) the representation theory of groups fp over the space-time-spin group SL2(C), highlighting the role of surfaces with isolated singularities in the character variety. Our approach could potentially contribute to the understanding of the molecular mechanisms underlying various diseases and help develop new diagnostic or therapeutic strategies.

This work continues the review and illustrative application to energy systems of the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, in-cluding fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters, in the combined phase-space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21976 im-precisely known parameters. This benchmark is representative of “large-scale computations” such as those involved in the modeling of energy systems. The result (“response”) of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for “high-precision” parameters (2% standard deviations) and “medium-precision” parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st- through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems mod-eled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, MC). At least second-order sensitivities should be retained in order to obtain reliable predictions.

The intestinal microbial community of aquatic animals is closely related to the environment and thus affects the health of aquatic animals. Here we investigated annual variation of intestinal bacteria community structures of Ruditapes philippinarum cultured using two different methods, bottom sown and raft hanging. Bacterial diversity of seawater and sediment in different seasons was examined as well. Correlation between R. philippinarum intestinal bacteria community structures and environmental factors was analyzed. The results showed that in the two aquculture areas where R. philippinarum was differentially cultured, bacteria community structure of seawater and sediment both altered with seasons. However, neither seasonal changes nor difference in culture methods could significantly affect the intestinal bacteria community structures of R. philippinarum. Additionally, in terms of the alpha diversity of intestinal bacteria communities, there was no significant difference in R. philippinarum cultured using the two methods. For benthic cultured clams, alpha diversity of the intestinal bacterial communities was often higher than that of the environmental bacterial communities. For raft hanging cultured calms, Alpha diversity of the intestinal bacterial communities was normally comparable with that of the environmental counterparts. Beta diversity analysis showed that the intestinal bacterial community structure of R. philippinarum was mainly determined by both deterministic and stochastic processes. Correlation analysis showed that the intestinal bacterial community structures of R. philippinarum were determined by a variety of environmental factors. For clams collected from the benthic culture area, the intestinal bacterial structure was more correlated with the physicochemical properties of sediment in comparing with those of seawater. For clams collected from the raft-culture area, the intestinal bacterial structure was mainly determined by the phsicochemical characteristics of seawater. This study helps us to better understand the bacterial community structure in complex aquaculture ecosystems, providing a reference for healthy shellfish aquaculture and ecosystem protection in aquaculture area.

Transcription factors (TFs) are proteins that recognize specific DNA fragments in order to decode the genome and ensure its optimal functioning. TFs work at the local and global scales by specifying cell type, cell growth and death, cell migration, organization and timely tasks. We investigate the structure of DNA-binding motifs with the theory of finitely generated groups. The DNA ‘word’ in the binding domain -the motif- may be seen as the generator of a finitely generated group Fdna on four letters, the bases A, T, G and C. It is shown that, most of the time, the DNA-binding motifs have subgroup structure close to free groups of rank three or less, a property that we call ‘syntactical freedom’. Such a property is associated to the aperiodicity of the motif when it is seen as a substitution sequence. Examples are provided for the major families of TFs such as leucine zipper factors, zinc finger factors, homeo-domain factors, etc. We also discuss the exceptions to the existence of such a DNA syntactical rule and their functional role. This includes the TATA box in the promoter region of some genes, the single nucleotide markers (SNP) and the motifs of some genes of ubiquitous role in transcription and regulation.

It is shown that the representation theory of some finitely presented groups thanks to their $SL_2(\mathbb{C})$ character variety is related to algebraic surfaces. We make use of the Enriques-Kodaira classification of algebraic surfaces and the related topological tools to make such surfaces explicit. We study the connection of $SL_2(\mathbb{C})$ character varieties to topological quantum computing (TQC) as an alternative to the concept of anyons. The Hopf link $H$, whose character variety is a Del Pezzo surface $f_H$ (the trace of the commutator), is the kernel of our view of TQC. Qutrit and two-qubit magic state computing, derived from the trefoil knot in our previous work, may be seen as TQC from the Hopf link. The character variety of some two-generator Bianchi groups as well as that of the fundamental group for the singular fibers $\tilde{E}_6$ and $\tilde{D}_4$ contain $f_H$. A surface birationally equivalent to a $K_3$ surface is another compound of their character varieties.

Glioblastoma (GBM) is the most common malignant brain tumor in adults. Despite advancements in treatment, the prognosis for patients with GBM remains poor due to its aggressive nature and resistance to therapy. CRISPR-based genetic screening has emerged as a powerful tool for identifying genes crucial for tumor progression and treatment resistance, offering promising targets for tumor therapy. In this review, we provide an overview of the recent advancements in CRISPR-based genetic screening approaches and their applications in GBM. We highlight how these approaches have been used to uncover genetic determinants of GBM progression and susceptibility to various therapies. Furthermore, we discuss ongoing challenges and future directions of CRISPR-based screening methods in advancing GBM research.

The Kummer surface was constructed in 1864. It corresponds to the desingularisation of the quotient of a 4-torus by 16 complex double points. Kummer surface is kwown to play a role in some models of quantum gravity. Following our recent model of the DNA genetic code based on the irreducible characters of the finite group G5:=(240,105)≅Z5⋊2O (with 2O the binary octahedral group), we now find that groups G6:=(288,69)≅Z6⋊2O and G7:=(336,118)≅Z7⋊2O can be used as models of the symmetries in hexamer and heptamer proteins playing a vital role for some biological functions. Groups G6 and G7 are found to involve the Kummer surface in the structure of their character table. An analogy between quantum gravity and DNA/RNA packings is suggested.

This paper investigates the dynamic properties of artificial neural networks using differential equations and explores the influence of parameters on stability and neural oscillations. By analyzing the equilibrium point of the differential equations, we identify conditions for asymptotic stability and criteria for oscillation in artificial neural networks. Furthermore, we demonstrate how adjusting synaptic weights between neurons can effectively control stability and oscillation. The proposed model offers potential insights into the malfunctioning mechanisms of biological neural networks implicated in neurological disorders like Parkinson's disease tremors and epilepsy seizures, which are characterized by abnormal oscillations.

With the rapid development of wind power generations, the inertial response of wind turbines (WTs) are widely concerned recently, which is important for grid frequency dynamic and stability. This paper recognizes and understands the inertial response of type-3 and type-4 WTs from the view of equivalent internal voltage, in analogy with typical synchronous generators (SGs). Due to the dynamic of the equivalent inertial voltage different from SGs, the electromechanical inertia of WTs is completely hidden. The rapid power control loop and synchronization control loop is the main reasons that the WT's inertial response is disenabled. On the basis of the equivalent internal voltage's dynamic, the existing inertia control method for WTs are reviewed and summarized as three approaches from the view of WT's control, i.e. optimizing the power control or synchronization control or both. At last, the main challenges and issues of these inertia controls are attempted to explain and address.

In light of the self-simulation hypothesis, a simple form implementation of the principle of efficient language is discussed in a self-referential geometric quasicrystalline state sum model in three dimensions. Emergence is discussed in context of geometric state sum models.

The advancement in science and technology has led to luffa sponge (LS) being widely used as a natural material in industrial application as its polyporous structure and light texture. In order to enhance the utility of LS fibers as the reinforcement of lightweight composite materials, this study investigate its water absorption, mechanical properties, anatomical characteristic and thermal performance. Hence, moisture regain, tensile properties of LS fiber bundles were measured in accordance with standards and the structural characteristics were investigated via microscopic observation. Scanning electron microscopy (SEM) was used to observe the surface morphology and fracture surface of fiber bundles. Test results shows that the special structure where the phloem tissues degenerate to cavities had a significant influence on the mechanical properties of LS fiber bundles. Additionally, the transverse sectional area occupied by fibers in a fiber bundle (SF), wall thickness and ratio of wall to lumen of fiber cell, and crystallinity of cellulose had an impact on the mechanical properties of LS fiber bundles. Furthermore, the fiber bundles density of LS varies range of 385.46-468.70 kg/m³, much less than that of jute (1360.40 kg/m³) and Arenga engleri (950.20 kg/m³) while LS fiber bundles has superior specific modulus.

Since the implementation of the Materials Genome Project by the Obama administration in the United States, the development of various computational materials databases has fundamentally expanded the choices of industries such as materials and energy. In the field of thermoelectric materials, the thermoelectric figure of merit ZT quantifies the performance of the material. From the viewpoint of calculations for vast materials, the ZT values are not easily obtained due to their computational complexity. Here, we show how to build a database of thermoelectric materials based on first-principles calculations for the electronic and heat transport of materials. Firstly, the initial structures are classified according to the values of bandgap and other basic properties using the clustering algorithm K-means in machine learning, and high-throughput first principles calculations are carried out for narrow-bandgap semiconductors which exhibiting potential thermoelectric application. The present framework of calculations mainly includes deformation potential module, electrical transport performance module, mechanical and thermodynamic properties module. We have also set up a search webpage for the calculated database of thermoelectric materials, providing searching and viewing the related physical properties of materials. Our work may inspire the construction of more computational databases of first-principle thermoelectric materials and accelerate research progress in the field of thermoelectrics.

Cultivated land plays an important role in water and soil loss in the earth-rocky mountainous region, northern China, however, its responses to soil control measures and rainfall characteristics are still not fully understood. In this study, 85 erosive rainfall events in 2011-2019 were grouped into three types, and the responses of runoff and soil loss on five cultivated plots with different slopes in the upstream catchment of the Miyun Reservoir to soil conservation measures and rainfall regimes were evaluated. Results found that event-averaged runoff depths and soil loss rates on the five plots ranged from 7.05 mm to 0.03 mm and from 300.51 t km-2 to 0.37 t km-2 respectively, depending on rainfall regimes, soil conservation measures, and slope gradients. The high occurring frequency (i.e., 72.94%) rainfall regime A with short rainfall duration (RD), low rainfall amount (P), and high mean rainfall intensity (Im) yielded lower runoff depth and higher soil loss rate. Rainfall regime B with longer RD, and higher P and Im, however, produced higher rainfall depth and lower soil loss rate. Terraced plot had the highest runoff and soil loss reduction efficiencies of over 96.03%. Contour tillage had comparable sediment reduction efficiency to that of the terraced plot on gentle slopes (gradient less than 11.0%), while its runoff reduction efficiency was less than 13.11%. This study implies that in the Miyun Reservoir catchment and similar regions in the world, contour tillage should be promoted on gentle slopes, and terrace construction should be given enough attention since it can greatly reduce water quantity and cause water shortage in downstream catchments.

Tourism Geographers in Anglo-American geographical departments migrate to teach and research tourism in applied schools and departments such as business, resource and environment, and even cultural and creative industry. Under national research assessment and funding pressures, tourism geographers are not encouraged to keep on undertaking serious theoretical researches related to their mother discipline and transferred to research applied researches and publish in hybrid journals. Such changes in tourism geographers` research behavior have not been tested and reported in Asian academic contexts, especially Taiwan while frequently being discussed internationally. This research aims to investigate the differences in the research behavior of tourism geographers between those who are staffed in geography departments and those who migrate to applies departments such as tourism and recreation, resource and environment, and others in terms of publishing journals, paradigmatic approaches, research types(qualitative, quantitative, and mixed), research methods and geographical scales conducted based on content analysis of articles published by tourism geographers in geography and tourism and recreation departments. This study revealed that there are significant differences in the research behavior of tourism geographers between different department. Finally, this paper explains the differences in terms of Taiwan changing social and economic conditions, academic traditions and politics of geography and applied departments, research habits and preferences of tourism geographers among departments and other aspects. This study can help international tourism geography academic circles to understand the complexity and diversity of tourism geography academic landscape of the Chinese language.

Excess nitrogen in agricultural drainage poses a serious threat to the water quality safety of the Yellow River basin. Utilizing aquatic plants to modify the rhizosphere microbial community structure and facilitate nitrogen transformation is a crucial strategy for mitigating regional water eutrophication. Our results revealed that the denitrification rate (DR), dissimilatory nitrate reduction to ammonium rate (DNRAR) and nitrogen fixation rate (NFR) of rhizosphere sediments exhibited a significant increase in artificially cultivated ditch. Compared with those in natural ditches, the richness of the bacterial community and the relative abundances of Bacteroidota, Verrucomicrobiota, Firmicutes, Anaeromyxobacter and Geobacter, which are involved mainly in the nitrogen cycle, in the rhizosphere sediments of artificially cultivated ditch were significantly greater due to improvements in environmental conditions. In artificially cultivated ditch, the dominant functional genes affecting DRNARs in the rhizosphere sediments of P. australis were nrfC and nrfA, while DRs were driven mainly by norB and napA, which were influenced by the nitrogen and carbon levels; and the dominant functional genes affecting NFRs in the rhizosphere sediments of T. orientalis were nifD, nifK and nifH. Our results provide a scientific basis for the use of aquatic plants for mitigating excess nitrogen levels in agricultural drainage.

We find that the degeneracies and many peculiarities of the DNA genetic code may be described thanks to two closely related (fivefold symmetric) finite groups. The first group has signature $G=\mathbb{Z}_5 \rtimes H$ where $H=\mathbb{Z}_2 . S_4\cong 2O$ is isomorphic to the binary octahedral group $2O$ and $S_4$ is the symmetric group on four letters/bases. The second group has signature $G=\mathbb{Z}_5 \rtimes GL(2,3)$ and points out a threefold symmetry of base pairings. For those groups, the representations for the $22$ conjugacy classes of $G$ are in one-to-one correspondence with the multiplets encoding the proteinogenic amino acids. Additionally, most of the $22$ characters of $G$ attached to those representations are informationally complete. The biological meaning of these coincidences is discussed.

In China, environmental pollution generated via industrialization as well as the profound changes in the social structure and gradual maturation of the social hierarchy have jointly contributed to the Chinese people's increased environmental consciousness and appeals for environmental justice (EJ). Because of the absence of an EJ theory and a lack of empirical research focused on China, this paper proposes a ‘Pyramid Model’ for China’s EJ research that involves the following three factors: basic demographic and socioeconomic factors, U.S.-based EJ principles, and Chinese characteristics. A nationwide pilot analysis of environmental inequity at the prefecture level is conducted by empirically studying the association of demographics and socioeconomic status with sources of industrial pollution in China. The prefecture-based results are shown to be robust and indicate that areas inhabited by ethnic minorities and western regions in China carry disproportionate environmental burdens. However, a different picture for migrants is presented, revealing that Chinese migrants are not currently exposed to greater levels of industrial pollution, and relevant interpretations of these findings are provided. The results also show that environmental inequality associated with income level, which is observed in the U.S., does not occur in China.

Global warming and climate change have led to extreme changes in climatic conditions in recent years. The Taiwan government designates the construction of the Kinmen County as low carbon islands, to promote the operation of 100 electric motorcycles and battery demonstration. This study combined with island tourism, after boarding the island, visitors can rent electric motorcycles from the passenger service center and coordinate with the island tour map to show the location of the battery exchange points, so as to facilitate the search. During the operation, the amount of electric motorcycle lease is 15,551 times, the total mileage of motor vehicle is 284,404 km, the number of battery exchange is 622 times, the lease income is about NT$900,000. To reduce carbon and economic benefits of the assessment, compared to the motorcycles (50 c.c), electric motorcycles (EM 100) can reduce the carbon emissions by 8,726 kg, reducing energy costs of NT$422,594.

Nowadays, unmanned vehicles are widely used in various fields. However, the safety of unmanned vehicles is directly determined by the autopilot systems, and sensor faults are a very critical factor that affects the normal operation of the autopilot systems. To efficiently and comprehensively test unmanned systems for sensor faults, this paper proposes a chip-level testing platform for unmanned vehicle autopilot systems based on FPGA hardware-in-the-loop simulation. Unlike existing testing platforms, the introduction of FPGA technology allows us to achieve nanosecond real-time simulation frequency, thus realizing chip-level simulation of sensors and black-box automated testing. Firstly, we propose a set of general platform and modeling methods, and the paper presents a detailed application of the four most common necessary sensors in autopilot systems as examples. Secondly, we propose for the first time a chip-level fault testing method for unmanned systems, and build a test platform with a quadrotor vehicle, the PX4 software platform, and the Pixhawk4 hardware platform as examples. Finally, the experimental results verify that the testing platform proposed in this paper can adapt to various autopilot systems and has high simulation test reliability.

A Co–doped porous carbon was successfully fabricated by a facile carbonizing procedure using coal hydrogasification semi-coke (SC) as the carbon and cobalt nitrate as the magnetic precursors, respectively. The microwave absorption (MA) performances were regulated by adjusting the mass ratio of the precursors. The dielectric loss from the carbon framework, the magnetic loss from nano–sized Co particles coupled with multiple scattering from the residual pores synergistically contribute to the favorable MA capabilities. When the initial mass ratio of cobalt nitrate and SC was 1:1, the Co/C composite showed the lowest reflection loss of -33.45 dB at the thickness of 4.0 mm. The effective absorbing bandwidth (EAB) could achieve 3.5 GHz at 2 mm in thickness. This work not only exploits a new avenue for the structural design and facile fabrication of dielectric and magnetic loss combinations, but also opens up another way for the high value–added utilization of SC.

Elevated indoor CO2 levels might have adverse effects on human health. However, the introduction of outdoor air to lower indoor CO2 concentrations results in significant HVAC energy consumption. Aligning with office hours and the natural light cycle, the utilization of photosynthesis in living walls offers an energy-efficient and sustainable solution for the mitigation of high CO2 levels in office spaces. This study experimentally investigates the impacts of the carbon fixation pathways, light intensity, and substrate moisture content on the CO2 removal rate of living walls at the room scale. Furthermore, the fresh air energy-saving effects of living walls under different scenarios are accurately simulated in EnergyPlus. The results demonstrate that choosing C3 plants over CAM plants in living walls yields higher CO2 removal efficiency. In a 30-m2 office room accommodating 2-3 occupants, living walls can reduce the demand for fresh air by 12.3%-27.8% and decrease fresh air energy consumption by 11.2%-28.2%. The city with the highest energy savings has energy savings that are 4.5 times greater than those of the city with the lowest energy savings. The findings of this research promote the application and development of living walls, thus providing a viable solution for improving indoor air quality.

The dust originating from the extinct lake of the Aral Sea poses a considerable threat to the sur-rounding communities and ecosystems. The accurate location of these wind erosion areas is an essential prerequisite for controlling sand and dust activity. However, few relevant indicators re-ported in the current study can accurately describe and measure wind erosion intensity. A novel wind erosion intensity (WEI) of a resolution unit was defined in this paper based on the deformation due to the wind erosion in this resolution unit. We also derived the relationship between WEI and soil InSAR temporal decorrelation (ITD). The ITD is usually caused by the surface change over time, which is very suitable for describing wind erosion. However, within one resolution unit, the ITD signal usually includes soil and vegetation contributions, and few studies are referred to this. Therefore, we proposed an ITD decomposition model (ITDDM) to decompose the ITD signal of a resolution unit. The least-squares method (LSM) based on singular value decomposition (SVD) is used to estimate the ITD of soil (SITD) within a resolution unit. We verified the results qualitatively by the landscape photos, which can reflect the actual conditions of the soil. At last, the WEI of the Aral Sea from June 23, 2020, to July 05, 2020, was mapped. The results confirmed that (1) Based on the ITDDM model, the SITD can be accurately estimated by the LSM method, (2) the Aral Sea is experiencing severe wind erosion, and (3) The middle, northeast, and southeast bare areas of the South Aral Sea are where salt dust storms may occur.

In this perspective article, we ponder topographical enhancements of artificial neural networks. In our recent paper in JMLR, we proved a quasi-equivalence between the network width and depth and also discussed the power of intra-links, which can be viewed as network height. In 1982, Hopfield published a network to model human associative memory, which contains many loops for dynamic evolution toward fixed points. Based on noising-denoising loops, diffusion models are recently developed to enable Bayesian modeling and inference with big data. Furthermore, we envision development of multi-AI-agent systems through “netware” engineering as a quantum leap of software engineering for emergent behaviors and autonomous AI at individual and population levels. We believe that the novel use of links and loops in space and time via multi-scale coupling would catalyze the next-generation neural networks.

Evodiamine is a bioactive alkaloid that is specified as a biomarker for the quality assessment of Evodia rutaecarpa and for traditional Chinese medicines containing this plant. We previously reported that quantitative structure-activity modeling indicated that evodiamine may cause cardiotoxicity. However, previous investigations have indicated that evodiamine has beneficial effects in patients with cardiovascular diseases and there are no previous in vitro or in vivo reports of evodiamine-induced cardiotoxicity. The present study investigated the effects of evodiamine on primary cultured neonatal rat cardiomyocytes in vitro, and on zebrafish in vivo. Cell viability was reduced in vitro, where evodiamine had a 24-h 50% inhibitory concentration of 28.44 µg/mL. Cells exposed to evodiamine also showed increased lactate dehydrogenase release and maleic dialdehyde levels, and reduced superoxide dismutase activity. In vivo, evodiamine had a 10% lethal concentration of 354 ng/mL and induced cardiac malfunction, as evidenced by changes in heart rate and circulation, and pericardial malformations. This study indicated that evodiamine could cause cardiovascular side effects involving oxidative stress. These findings suggest that cardiac function should be monitored in patients receiving preparations containing evodiamine.

As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

Thermal safety of lithium-ion traction batteries is a highly concerned issue in the field of electric transportation. The large amount of gas emission during the thermal runaway process of batteries has highly safety hazards such as fire and explosion. Quantitative analysis of emission is one of the important challenges for testing and evaluating battery safety. Focus on quantifying the gas emission through large-scale thermal propagation in battery modules and packs, based on the idea of cell result multiplication, this article conducts thermal runaway emission analysis on single cell and modules, and compares the behavior of thermal runaway and gas emission of the cell and module from the aspects of temperature, pressure, gas composition and battery morphology. The feasibility of the cell result multiplication method was verified from the perspective of experimental data.

Decades of research identified numerous gene biomarkers of cardiac diseases whose restored sequence or/and expression level was hoped to recover the normal cardiac function. However, each human has unique and dynamic pathophysiological characteristics resulting from the unrepeatable combination of favoring factors such are: race, sex, age, medical history, diet, stress, exposure to toxins, habits etc. As such, no treatment fits everybody and finding personalized solutions is a top priority for medicine of 21st century. The Genomic Fabric Paradigm (GFP) provides the most theoretically possible comprehensive characterization of the transcriptome, its alterations in disease and recovery following a treatment. By attaching to each gene the independent average expression level, expression variation and expression coordination with each other gene, GFP delivers thousands times more information than the traditional analysis. This report presents the theoretical bases of the GFP and some applications to our microarray data from mouse models of post ischemic, and constant and intermittent hypoxia-induced heart failure. The GFP analyses revealed novel transcriptomic aspects of the gene expression control and networking under ischemic conditions. Through all-inclusive characterization of the transcriptome and the unrepeatable gene hierarchy in each condition, GFP is an essential avenue towards development of a truly personalized cardiogenomic therapy.

The intricate relationship between physical and social environments within organizations plays a pivotal role in shaping sustainable innovation endeavors. This paper presents a comprehensive analysis of two key dynamics that have garnered substantial scholarly attention for promoting sustainable innovation: the physical environment and the organizational climate. To elucidate the intricate interplay between these dynamics, we propose a novel three-dimensional framework that guides the modeling of the intricate processes through which the physical environment and innovation climate synergistically influence innovation outcomes. Building on this framework, we delve into an in-depth literature review concerning physical environments that exert a significant impact on innovation. Through this review, we identify and elaborate on four pivotal elements of the physical environment: communality, personalization, comfort, and healthiness. Leveraging insights gleaned from this review, we outline two promising avenues for future research in the realm of the physical environment's interaction with the innovation climate. Furthermore, we underscore the critical importance of adopting an interdisciplinary approach that seamlessly integrates insights from both the physical and social domains to comprehensively understand the sustainable innovation landscape.

Consciousness pervades our daily experiences, yet it remains largely unaccounted for in contemporary physics and chemistry theories. Several existing theories, such as the Integrated Information Theory (IIT), Global Workspace Theory (GWT), Electromagnetic Field Theory (EMF Theory of Consciousness), and Orchestrated Objective Reduction Theory (Orch-OR), attempt to clarify the essence of consciousness. Yet, they often encounter significant challenges. These challenges arise due to the intricate nature of our neural systems and the limitations of current measurement and computational technologies, which often prevent these theories from being rigorously mathematically described or quantitatively tested. Here we introduce a novel theory that hypothesizes consciousness as an inherent property of certain particle configurations. Specifically, when a group of particles align in a particular state, they exhibit consciousness. This relationship between particle states and conscious perceptions is governed by what we term the "universal consciousness code". And we propose a possible practical mathematical method to decipher the complex relationship between neural activities and consciousness and to test our theory using the latest artificial intelligence technologies.

The mangrove plant Acanthus ilicifolius and its relative, A. mollis, have been previously proved to possess diverse pharmacological effects. Therefore, evaluating the differentially expressed proteins of these species under tidal flooding stress is essential to fully exploit and benefit from their medicinal values. The roots of A. ilicifolius and A. mollis were exposed to 6 h of flooding stress per day for 10 days. The dry weight, hydrogen peroxide (H2O2) content, anatomical characteristics, carbon and energy levels, and two-dimensional electrophoresis coupled with MALDI-TOF/TOF MS technology were used to reveal the divergent flooding resistant strategies. A. ilicifolius performed better under tidal flooding stress, which was reflected in the integrity of the morphological structure, more efficient use of carbon and energy, and a higher percentage of up-regulated proteins associated with carbon and energy metabolism. A. mollis could not survive in flooding conditions for a long time, as revealed by incomplete cell structures of the roots, less efficient use of carbon and energy, and a higher percentage of down-regulated proteins associated with carbon and energy metabolism. Energy provision and flux balance played a role in the flooding tolerance of A. ilicifolius and A. mollis.

A macro network loading Model for multi-flow lines, time-varying and pedestrian congestion is proposed. The station hub is abstracted as a network of different types of nodes, and the flow of passengers at each node is calculated in real time for the purpose of simulating the hub's collection and distribution process. For correct transmission of passenger flow on heterogeneous networks, three types of indexes are proposed to distinguish the nodes, and then match the corresponding fundamental diagrams. This paper divides the update process of the dynamic network loading model into multiple processes by flow lines, and improves computational speed of DNL model. The proposed model is applied to the simulation of passenger flow collection and distribution in an actual hub station with multi-flow lines. The analysis results illustrate that the model can accurately reflect the realistic congestion facilities and explain the formation process of high-density areas. A rolling passenger flow control model based on optimal control theory is proposed. The effectiveness of the control model is verified based on simulation data.

In this paper, the preparation methods of porous silicon nitride materials with controllable dielectric constant and pore structure were systematically studied. By using microwave sintering technology, porous silicon nitride materials with high closed pore ratio were prepared by adjusting the content of sintering additives and sintering process parameters, and controlling the grain boundary phase and pore structure and size. The effects of sintering conditions on the total porosity, closed porosity, pore structure and dielectric properties of materials were systematically studied.

The root system plays an irreplaceable role in plant growth. Its improvement can increase crop productivity. However, such system is still mysterious for us. The underlying mechanism has not been fully uncovered. The investigation on proteins related to the root system is an important means to complete this task. In the previous time, lack of root-related proteins makes it impossible to adopt machine learning methods for designing efficient models for the discovery of novel root-related proteins. Recently, a public database on root-related proteins was set up and machine learning methods can be applied in this field. In this study, we proposed a machine learning based model, named Graph-Root, for identification of root-related proteins. The features derived from protein sequences and one network were extracted, where the former features were processed by graph convolutional neural network and multi-head attention, and the later features abstracted the linkage between proteins. These features were fed into the fully connected layer to make prediction. The 5-fold cross-validation and independent tests suggested its good performance. It also outperformed the only one previous model, SVM-Root. Furthermore, the importance of each feature type and component in the proposed model was investigated.

Ransomware attacks have rapidly proliferated, inflicting severe financial damages on businesses and individuals. Machine learning approaches to automate ransomware detection have shown promise but grapple with challenges like limited training data. This study introduces a novel deep learning model for few-shot ransomware classification. The model employs entropy features derived directly from malware binaries coupled with a twin neural network architecture utilizing transfer learning. Tests on over 1000 samples across 11 families demonstrate a weighted F1-score of 85.8\%, surpassing existing methods. The approach mitigates biases in limited training data and preserves intricacies lost in image-based features. It exhibits precise classification capabilities even with sparse samples of new ransomware variants. The research highlights the potential of entropy-driven deep learning to equip defenses against emerging zero-day ransomware strains.

Ransomware attacks have risen alarmingly, with encryption techniques becoming more complex. This paper introduces a novel detection model tailored for ransomware's distinctive characteristics. The Intel PIN tool extracts Windows API invocation sequences related to file operations. These sequences are used to construct n-grams, forming feature vectors enhanced by a new Class Feature Weighting (CFW) metric to improve malware detection. Preliminary results demonstrate elevated accuracy and precision versus existing methods. The major contributions are: (1) Introducing an innovative deep learning model for few-shot ransomware classification using entropy features and transfer learning. (2) Achieving high weighted F1-score in classifying ransomware variants into families with limited training data. (3) Demonstrating the potential of entropy-based features to capture intricacies lost in image-based approaches, improving detection of new strains.

This paper explores the application of Large Language Models (LLMs), such as GPT-3 and GPT-4, in generating cybersecurity policies and strategies to mitigate ransomware threats, including data theft ransomware. We discuss the strengths and limitations of LLMs for ransomware defense and provide recommendations for effectively leveraging LLMs while ensuring ethical compliance. The key contributions include a quantitative evaluation of LLM-generated policies, an examination of the legal and ethical implications, and an analysis of how LLMs can enhance ransomware resilience when applied judiciously.

This paper presents ConF, a novel deep learning model designed for accurate and efficient prediction of non-coding RNA families. NcRNAs are essential functional RNA molecules involved in various cellular processes, including replication, transcription, and gene expression. Identifying ncRNA families is crucial for comprehensive RNA research, as ncRNAs within the same family often exhibit similar functionalities. Traditional experimental methods for identifying ncRNA fam-ilies are time-consuming and labor-intensive. Computational approaches relying on annotated secondary structure data face limitations in handling complex structures like pseudoknots and have restricted applicability, resulting in suboptimal prediction performance. To overcome these chal-lenges, ConF integrates mainstream techniques such as residual networks with dilated convolutions and cross multi-head attention mechanisms. By employing a combination of dual-layer convolu-tional networks and BiLSTM, ConF effectively captures intricate features embedded within RNA sequences. This feature extraction process leads to significantly improved prediction accuracy compared to existing methods. Experimental evaluations conducted on a ten-fold publicly available dataset demonstrate the superiority of ConF in terms of accuracy, sensitivity, and other perfor-mance metrics. Overall, ConF represents a promising solution for accurate and efficient ncRNA family prediction, addressing the limitations of traditional experimental and computational methods.

Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive integral sliding mode control method with a feedforward compensation disturbance observer to enhance the capability of a microsatellite attitude control system for gaze tracking of high-speed moving targets. Specifically, first, we present the attitude control system model for microsatellites and the calculation method for the desired attitude of target tracking based on image feedback. Then, an adaptive integral sliding mode attitude control algorithm with a feedforward compensation disturbance observer, which meets the requirements of high-precision tracking control, is designed. The developed algorithm utilizes the disturbance observer to observe the friction torque of the flywheel and compensates for it through feedforward control. It also employs the adaptive integral sliding mode control algorithm to reduce the impact of uncertain disturbances, decrease the steady-state error of the system, and enhance the attitude control precision. Simulation experiments demonstrated that the designed disturbance observer can successfully observe the frictional disturbance torque of the flywheel. The attitude Euler angle control precision for high-speed moving target tracking reached 0.009°, and the angular velocity control precision reached 0.005°/s, validating the effectiveness of the proposed approach.

Understanding the distribution of rock glaciers provides key information for investigating and recognizing the status and changes of the periglacial environment. Deep learning algorithms and red-green-blue (RGB) bands from high-resolution satellite images have been extensively employed to map rock glaciers. However, the near-infrared (NIR) band offers rich spectral information and sharp edge features that could significantly contribute to semantic segmentation tasks, but it is rarely utilized in constructing rock glacier identification models due to the limitation of three input bands for classical semantic segmentation networks, like DeeplabV3+. In this study, a dual-encoder DeeplabV3+ network (DEDNet) was designed to overcome the flaws of classical DeeplabV3+ network (CDNet) when identifying rock glaciers using multispectral remote sensing images by extracting spatial and spectral features from RGB and NIR bands, respectively. This network, trained with manually labeled rock glacier samples from QiLian Mountains, established a model with accuracy, precision, recall, specificity, and mIoU (Mean Intersection over Union) of 0.9131, 0.9130, 0.9270, 0.9195, and 0.8601, respectively. The well-trained model was applied to identify new rock glaciers in a test region, achieving a producer's accuracy of 93.68% and a user's accuracy of 94.18%. Furthermore, the model was employed to two study areas in northern Tien Shan (Kazakhstan) and Daxue Shan (Hengduan Shan, China) with high accuracy, which proved that the DEDNet offers an innovative solution to more accurately map rock glaciers on a larger scale due to its robustness across diverse geographic regions.

Myocardial infarction (MI) is a serious cardiovascular disease that can lead to death. Cardiac troponin I (cTnI) is a biomarker of MI in the circulation system. For detection cTnI protein, many method suffer the same problem, time-consuming. Main drawbacks are time consuming, have tedious operation, require an expensive reader for translating signals, and require non-accessible reagents. Our goal is to create a kit for cTnI protein testing, that the result can easily be read by color difference. The main breakthrough is, the resolution between different concentration can be told by eyes. The result will be future used to create a commercial, potable, convenient, daily-used rapid-test kit. In this study, a cTnI biosensor is proposed that can be read by the naked eye, performs diagnosis based on the pattern color, does not require a reader machine, is easy to operate, and is portable. Our work proposed a device that shortens the diagnosis time, has a quantitative analysis range of 0.32 –200 ng/mL in the human serum matrix, has a 0.32 ng/mL limit of detection (LOD), and shows many advantages comparing to traditional cTnI ELISA plate.

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which yields best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, thus incorporating, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizing the results produced by extant data assimilation and data adjustment procedures. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC). The application of the 4th-BERRU-PM methodology to energy systems is illustrated by means of the above-mentioned OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation involving 21976 imprecisely known parameters, the solution of which is representative of “large-scale computations.” The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

To solve the problem of smooth switching between car-following model and lane-changing model, the Intelligent Driver Model (IDM) for a single lane was used to study the driver's behavior switching mechanism of normally following, generating intentions to change lanes, creating space and speed gains, and performing lane change. In the case of sufficient lane-changing space and speed gains, the ego vehicle's intention to change lane was considered to solve the switching boundary between car-following behavior and lane-changing behavior, which is also the IDM failure point. In the event that there is no lane-changing gains, the IDM was optimized by incorporating the constraint components of the target lane vehicles in conjunction with the actual motion state of the ego vehicle, and the Stepless Switching Intelligent Driver Model (SSIDM) was constructed. Drivers' natural driving information was collected and scenario mining was performed on structured roads. On the basis of the collected data, an elliptic equation was used to fit the behavior switching boundary, and the two component balance coefficients of the front and rear vehicles on the target lane were identified. According to the test set verification results, the Mean Square Error (MSE) of the SSIDM is 2.172, which is 57.98% less than that of the conventional single-lane IDM. The SSIDM can accomplish stepless switching comparable to driver's behavior between the car-following behavior and the lane-changing behavior, with greater precision than IDM.

Types of DNA recombination include homologous recombination and nonhomologous recombination. Homologous DNA recombination is a general term that includes exchange of information between chromatids: (reciprocal) crossing-over, gene conversion, and post-meiotic segregation. Gene conversion is now thought to be a type of non-Mendelian segregation of heterozygous markers near the recombination initiation site. Thus, it includes both gene conversion and post-meiotic segregation previously described. DNA non-HR including transpositional recombination and site-specific recombination. Our understanding of the molecular mechanism by which DNA recombination occurs has significantly increased in the past decades. Currently The synthesis-dependent strand annealing model is now thought to give rise to most or all noncrossovers, with the double-strand-break repair model forming mainly crossovers. The Shapiro model proposed by Dr. J. Shapiro explains the molecular mechanism of transpositional recombination. Site-specific recombination results from another distinct model. We previously proposed a novel theory which can provide a more reasonable and simpler explanation accounting for DNA HR including the 3 classes of recombinogenic events described above. In the new supplementedly molecular model, DNA meiotic recombination can be initiated by a copy choice mechanism, that is, copying part of 1 single-stranded DNA template, followed by DNA polymerase switching to another single-stranded DNA template, and then resuming the following DNA synthesis along the new template. The current review suggests that transpositional recombination and site-specific recombination should be initiated by copy choice during DNA synthesis rather than break/join mechanism. The work indicates that review of DNA nonhomologous recombination are very necessary. The novel theory would challenge earlier models accounting for transpositional recombination and site-specific recombination and would be critical to the understanding of the mechanisms. We hope copy choice initiating DNA nonhomologous recombination will be one of the concepts that are explored. Proper and specific experiments are required to reconstruct the detailed mechanism described here.

In Taiwan, farmers often apply excess compost to ensure adequate crop yield in highly frequent tillage, highly weathered, and lower fertility soils. The potential of biochar (BC) for diminishing soil C mineralization, and improving soil nutrient availability in compost over-fertilized soil is promising, but the study is still under-examined. To test the hypothesis, 434 days in vitro C mineralization kinetics of incubation experiment were conducted. Woody BC 0%, 0.5%, 1.0% and 2.0% (w/w) made of lead tree (Leucaena leucocephala (Lam.) de. Wit) were added to an Oxisols, and two Inceptisols of Taiwan. In each treatment, 5% swine manure compost (2 times recommended amount) was added and served as the over-fertilized soil. The results indicated that soil type strongly influenced the impact of BC addition on soil carbon mineralization potential. Respiration per unit of total organic carbon (total mineralization coefficient, TMC) of three studied soils significantly decreased with BC addition increased. Principal component analysis (PCA) suggested that for retaining more plant nutrients in addition to the effects of carbon sequestration, it is recommended that farmer could use locally produced biochars and composts in highly weathered and highly frequent tillage soil. Adding 0.5%-1% woody BC in soil should be reasonable and appropriate.

In Taiwan, farmers often apply excess compost to ensure adequate crop yield in highly frequent tillage, highly weathered, and lower fertility soils. The potential of biochar (BC) for diminishing soil C mineralization, and improving soil nutrient availability in compost over-fertilized soil is promising, but the study is still under-examined. To test the hypothesis, 434 days in vitro C mineralization kinetics of incubation experiment were conducted. Woody BC 0%, 0.5%, 1.0% and 2.0% (w/w) made of lead tree (Leucaena leucocephala (Lam.) de. Wit) were added to an Oxisols, and two Inceptisols of Taiwan. In each treatment, 5% swine manure compost (2 times recommended amount) was added and served as the over-fertilized soil. The results indicated that soil type strongly influenced the impact of BC addition on soil carbon mineralization potential. Respiration per unit of total organic carbon (total mineralization coefficient, TMC) of three studied soils significantly decreased with BC addition increased. Principal component analysis (PCA) suggested that for retaining more plant nutrients in addition to the effects of carbon sequestration, it is recommended that farmer could use locally produced biochars and composts in highly weathered and highly frequent tillage soil. Adding 0.5%-1% woody BC in soil should be reasonable and appropriate.

Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an Adaptive Denoising Autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE's noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings.

This research used an experimental study design in which 60 subjects were randomly assigned to either an experimental (image e-book guidance) group or a control (text guidance) group after meeting the admission conditions and agreeing to participate in the study. It was proven that providing image e-book guidance before surgery significantly changed the behavior of patients and promoted relief of urinary catheter discomfort through self-efficacy to reduce urinary catheter pain after surgery. It was found that providing image e-book guidance can shorten the time for health education and provide patients with repeated medical education and familiarity with health guidance, which can help to address the important clinical service demand issue and the shortage of nursing staff.

In this paper, a new axial-type magnetic gear with auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AFS-AMG, it has the higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterpart in RMGs, AFS-AMGs achieve small size. Then, the geometrical parameters of AFS-AMGs are optimized to obtain the better electromagnetic performances, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume.

With the development of society and the economy, the unified planning of architectural style has become a difficult problem in the competition between urban expansion and the protection of tra-ditional buildings in villages and towns. At the same time, it also allows people to re-examine the appearance and quality of life of traditional village buildings. In this paper, the Conditional Gen-erative Adversarial Network (CGAN) is used to construct a method of building facade generation in villages and towns, so as to gradually realize the governance of the style of villages and towns. At the same time, it has also reduced the restoration of the facades of villages and towns and the graphic design of rural tourism products, showing its application value and potential in the field of planning and design. In the research, taking villages and towns in the Wuyishan area of China as an example, the method is used to carry out model training, image generation, and comparison of the derivation results of different assumed building contours and product contours. The research shows that: (1) CGAN can be used to derive and design the facades of conventional civil buildings in villages and towns. (2) In terms of product graphic design, especially the common tourist cultural products fans and water cups, show significant potential. (3) The construction of this method is not only applicable to villages and towns under the World Heritage City, but can be further promoted and used in the future for cities and villages that have a demand for architectural style consistency.

Previous research has paid little attention to the relationship between corporate governance and brand equity in the tourism industry. This study aims to investigate the moderating effect of corporate governance (CG) on the relationship between brand equity (BE) and corporate profit-ability (CP) with ten control variables including five company characteristics and five macroeconomic variables. Financial data is retrieved from the Taiwan Economics Journal Database (TEJ), covering a total of 196 records from 32 listed companies for 16 years. Results indicate that BE has a significantly positive impact on CP in Taiwanese-listed tourism companies, and that CG moderates the relationship between BE and CP. These findings could help management executives enhance profitability by deepening BE and CG in the tourism industry. Managerial implications are also discussed.

Polyacetylene phytochemicals are emerging as potentially responsible for the chemoprotective effects of consuming apiaceous vegetables. There is some evidence suggesting that polyacetylenes impact carcinogenesis by influencing a wide variety of signalling pathways, which are important in regulating inflammation, apoptosis, cell cycle regulation, etc. Studies have shown a correlation between human dietary intake of polyacetylene-rich vegetables with reduced risk of inflammation and cancer. Polyacetylene supplementation can influence cell growth, gene expression, and immunological responses, and reduce tumour number in rat and mouse models. Cancer chemoprevention by dietary polyacetylenes involves several mechanisms, including effects on inflammatory cytokines, the NF-κB pathway, antioxidant response elements, UPR pathway, growth factor signalling, cell cycle progression, and apoptosis. This review summarises the published research on falcarinol-type polyacetylene compounds and their mechanisms of action regarding cancer chemoprevention and treatment, and also identifies some gaps in our current understanding of the health benefits of these polyacetylenes.

As the most active plateau on the Earth, the Qinghai-Tibet Plateau has a complex crust-mantle structure. Knowledge of the distribution of such a structure provides information for understanding the underlying geodynamic processes. We obtains a three-dimensional density model of crustal and upper mantle beneath Qinghai-Tibet plateau and its surrounding areas from the residual geoid anomalies using the Earth Gravitational Model (EGM) 2008. We estimate a refined density model by iterations, using an initial density contrast model. We confirm that the EGM2008 mission products can be used to constrain the crust-mantle density structures. Our major findings are: (1). At 300-400 km depth, high-D anomalies terminate around Jinsha River Suture (JRS) in the central TP, suggesting that the Indian plate has been reached over the Bangong Nujiang Suture (BNS) and almost reach to the JRS. (2). On the eastern TP, low-D anomalies at the depth of 0-300 km together with high-D anomalies at 400-670 km further verified the current eastward subduction of Indian plate. The ongoing subduction provides forces to the occurrences of frequent earthquakes and volcano. (3). At 600 km depth, low-D anomalies inside the TP illustrate the existence of hot weak material beneath there, contributing to the external material inward-thrusting.

In order to help the transportation industry achieve the goal of carbon peak and carbon neutrality, this study is based on the selection of eight variables, such as population size, per ca pita GDP, personal vehicle ownership, passenger and freight turnover, and green space coverage, as the factors influencing the carbon emissions of the transportation industry in Jiangsu Province, and the prediction and analysis of the transportation carbon emission trends from 2000 to 2021, and the prediction and analysis of the carbon emissions of the transportation industry and the time of the peak of the carbon emissions in Jiangsu Province. The trend of transportation carbon emissions from 2000 to 2021 was predicted and analyzed, and the carbon emissions and peak time of carbon emissions in the transportation industry in Jiangsu Province were predicted and analyzed. Comparing the predicted results with other models to verify the accuracy of the results, it was found that the WOA-SVM model had the smallest error among several models. On this basis, targeted measures are proposed to accelerate the process of carbon peak and ensure the smooth achievement of carbon neutrality goals in Jiangsu Province. The results indicate that under the current policy measures, the peak carbon emissions in Jiangsu Province will occur in 2038, with a peak of 48.72 million tons. Jiangsu Province should actively adopt energy-saving and emission-reduction measures, build a green and low-carbon transportation development model, and achieve the carbon peak target ahead of schedule.

Elevated circulating triglyceride levels have been linked to an increased risk of diabetes, although the precise mechanisms remain unclear. This study aimed to investigate whether low-density lipoprotein (LDL) cholesterol, homeostatic model assessment (HOMA) for insulin resistance, and C-reactive protein served as mediators in this association across a sample of 18,435 US adults. Mediation analysis was conducted using the PROCESS Version 4.3 Macro for SPSS. Simple medi-ation analysis revealed that all three potential mediators played a role in mediating the associa-tion. However, in the parallel mediation analysis, where all three mediators were simultaneously included, HOMA for insulin resistance remained a significant mediator (indirect effect coefficient, 0.47; 95% confidence interval [CI], 0.43—0.52; p < 0.05) after adjusting for all tested confounding factors. Conversely, LDL cholesterol (indirect effect coefficient, -0.13; 95% CI, -0.31—0.05; p > 0.05) and C-reactive protein (indirect effect coefficient, 0.01; 95% CI, -0.003—0.02; p > 0.05) ceased to be significant mediators. HOMA for insulin resistance accounted for 49% of the association between triglycerides and diabetes. In conclusion, HOMA for insulin resistance was the dominant mediator underlying the association between triglycerides and diabetes. Therefore, reducing triglyceride levels may hold promise for improving insulin sensitivity in diabetic patients.

At present, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed and treatment results. EMR has been recognized as a valuable resource for large scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation and data reduction. For semi-structured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (Named Entity Recognition) and RE (Relation Extraction). In this paper, we introduce the process of EMR processing, including data collection, data preprocessing, data mining, evaluation and knowledge application, analyze the current status of the key technologies, such as data preprocessing and data mining, and provide an overview of the application domains and prospects of EMR mining technologies. Finally, we summarize the existing problems in the research of EMR mining, and review the development trends.

Management of chronic inflammation and wounds has always been a key issue in the pharmaceutical and healthcare sector. Curcumin (CCM) is an active ingredient extracted from turmeric rhizomes that has antioxidant, anti-inflammatory, and antibacterial activities, thus showing significant effectiveness toward wound healing. However, its shortcomings such as poor water solubility, poor chemical stability and fast metabolic rate limit its bioavailability and long-term use. In this context, hydrogels appear to be a versatile matrix for carrying and stabilizing drugs due to the biomimetic structure, soft porous microarchitecture, and pleasant biomechanical properties. The drug loading/releasing efficiencies can also be controlled by use of highly crystalline and porous metal organic frameworks (MOFs). Here, a flexible hydrogel composed of sodium alginate (SA) matrix and CCM-loaded MOFs was constructed for long-term drug release and antibacterial activity. The morphology and physicochemical properties of composite hydrogels were analyzed by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), ultraviolet visible spectroscopy (UV-Vis), Raman spectroscopy and mechanical property tests. The results showed that the composite hydrogel was highly twistable and bendable to mechanically comply with human skin. The as-prepared hydrogel could capture efficient CCM for slow drug release as well as effective killing of bacteria. Therefore, such composite hydrogel is expected to provide a new management system for chronic wound dressings.

The prototype misfit layer compound (SnS)1.17NbS2 consists of alternatively a metallic triatomic NbS2 layer in which Nb atoms are sandwiched by S atoms and an insulating SnS double-layer of NaCl-type structure. Here we investigate the effect of lattice misfit on the stability and chemical bonding in the misfit layer compound using a first-principles density-functional theory approach. The calculations show that for the (SnS)1+xNbS2 approximants, the most stable one has x = 0.167, close to the experimental observations. Charge analysis finds a moderate charge transfer from the SnS to NbS2. Sn or S vacancies in the SnS part affect the electronic properties and interlayer interactions. The obtained information here helps understand the mechanism of formation and stability of the misfit layer compounds and ferecrystals, and further to design of novel multilayer compounds and emerging van der Waals heterostructures.

To address the issue of intelligent optimization algorithms being prone to local optima, resulting in insufficient feature extraction and low fault type recognition rates when optimizing Variational Mode Decomposition (VMD) and Support Vector Machine (SVM) parameters, this paper proposes a fault diagnosis method based on an improved Artificial Gorilla Troops Optimization (GTO) algorithm. The GTO algorithm is enhanced using Logistic chaotic mapping, a linear decreasing weight factor, the global exploration strategy of the Osprey Optimization Algorithm, and the Levy flight strategy, improving its ability to escape local optima, adaptability, and convergence accuracy. This algorithm is used to optimize the parameters of VMD and SVM for fault diagnosis. Experiments on fault diagnosis with two datasets of different sample sizes show that the proposed method achieves a diagnostic accuracy of no less than 98% for samples of varying sizes, with stable and reliable results.

China's cotton production has shown a concentration to the advantageous regions in recent years. Based on the relevant data of cotton production in China, the study measures the cotton production agglomeration levels of 16 major cotton-producing provinces from 1978 to 2020, summarizes the spatiotemporal evolution characteristics of cotton production agglomeration, and analyzes the factors influencing cotton production agglomeration using spatial econometric models. The results indicate that: Firstly, since the opening-up and reform, China's cotton production agglomeration levels have shown an overall upward trend, exhibiting periodic dynamic changes over time, and the spatial pattern reveals a dynamic regional gradient. Secondly, there is spatial correlation among the production scales of cotton in different provinces, with a trend resembling a "U" shape, and the agglomeration form shows a low-level convergence. Thirdly, technological level, policies, substitute industrial products and foreign trade are identified as important factors. The cotton production in each province is spatially correlated through these factors, and there is also a presence of spillover effects. In conclusion, the government should prioritize considerations of various influencing factors to formulate tailored support policies, promote large-scale production to enhance technological levels, and focus on policies aimed at improving the quality of cotton.

Digitized counter-diabatic (CD) optimization algorithms have been proposed and extensively studied to enhance performance in quantum computing by accelerating adiabatic processes while minimizing energy transitions. While adding approximate counter-diabatic terms can initially introduce adiabatic errors that decrease over time, Trotter errors from decomposition approximation persist. On the other hand, increasing the high-order nested commutators for CD terms may improve adiabatic errors but could also introduce additional Trotter errors. In this article, we examine the two-qubit model to explore the interplay between approximate CD, adiabatic errors, Trotter errors, coefficients and commutators. Through these analysis, we aim to gain insights into optimizing these factors for better fidelity, shallower circuit depth, and reduced gate number in near-term gate-based quantum computing.

Disparities between fold amplitude (A) and intrusion thickness (Hsill) are critical in identifying elastic or inelastic deformation in a forced fold. However, accurate measurement of these two parameters is challenging because of the limit in separability and detectability for the seismic data. In the TZ-47 exploring area from the Tarim Basin, Northwest China, we combined wireline data and 3-D seismic data, to accurately constrain the fold amplitude and total thickness of sills that inducing the roof uplift. The measurement results show that the forced fold amplitude is 155 m and the total sill thickness is 148.4 m. When using a magma density of 2.7 g/cm3, and solid rock density of 3 g/cm3, the molten magma thickness at the time of intrusion would be 153.8 m, which is almost no difference from the forced fold amplitude. Therefore, the TZ-47 fold is a pure elastic forced fold induced by emplacement of multiple sills. Measurement solely based on seismic data may not be able to detect some thin interlayers and may result in large errors.

Network Embedding (NE) is an important method to learn the representations of network via a low-dimensional space. Conventional NE models focus on capturing the structure information and semantic information of vertices while neglecting such information for edges. In this work, we propose a novel NE model named BimoNet to capture both the structure and semantic information of edges. BimoNet is composed of two parts, i.e., the bi-mode embedding part and the deep neural network part. For bi-mode embedding part, the first mode named add-mode is used to express the entity-shared features of edges and the second mode named subtract-mode is employed to represent the entity-specific features of edges. These features actually reflect the semantic information. For deep neural network part, we firstly regard the edges in a network as nodes, and the vertices as links, which will not change the overall structure of the whole network. Then we take the nodes' adjacent matrix as the input of the deep neural network as it can obtain similar representations for nodes with similar structure. Afterwards, by jointly optimizing the objective function of these two parts, BimoNet could preserve both the semantic and structure information of edges. In experiments, we evaluate BimoNet on three real-world datasets and task of relation extraction, and BimoNet is demonstrated to outperform state-of-the-art baseline models consistently and significantly.

Cotton fiber, a crucial and sustainable resource for global textile production, undergoes a complex five-stage developmental process, encompassing initiation, elongation, transition, secondary cell wall biosynthesis, and maturation. These elongated single-cell fibers originate from the outer ovule epidermis. The development of cotton fibers involves intricate changes in gene expression and physiological processes, resulting in a nearly pure cellulose product vital for the global cotton industry. Decoding genes associated with fiber development enhances our understanding of cotton fiber mechanisms and facilitates the cultivation of varieties with enhanced quality. In recent decades, advanced omics approaches, including genomics, transcriptomics, and proteomics, have played a pivotal role in identifying genes and gene products linked to cotton fiber development, including the MYB transcription factor family which coordinates cotton fiber development. Molecular studies have revealed transcription factors like MYB, WRKY, Homeodomain Leucine Zipper (HD-ZIP) and basic Helix-Loop-Helix (bHLH) influencing fiber initiation and elongation. The intricate interplay of phytohormones like auxin, gibberellic acid (GA), brassinosteroid (BR), jasmonic acid (JA), ethylene, abscisic acid (ABA), and cytokinin is explored, providing a comprehensive perspective on shaping cotton fibers. Numerous candidate genes and cellular processes affecting various aspects of fiber development hold promise for genetic engineering or marker-assisted breeding to improve fiber quality. This review presents a comprehensive overview of key achievements in cotton molecular biology, with a specific emphasis on recent advancements in understanding transcription factors and phytohormones involved in cotton fiber initiation and elongation.

This study explores the implementation of an interdisciplinary curriculum in product and media design education and its impact on knowledge innovation and competency development. The curriculum is based on scaffolding theory, incorporating design proposals, workshops, digital design, marketing tests, and marketing activities guided by teachers and mentors from a branded ceramic workshop. The research findings demonstrate that the interdisciplinary curriculum facilitates knowledge innovation and fosters the development of students' professional skills, creativity, and problem-solving skills. The study also highlights the importance of stable scaffolding, including artefact, peer, and teacher support, which significantly contributes to cultivating transformational competencies, as outlined in the 2030 OECD Learning Compass. Additionally, the collaboration with Shanshing Four Seasons Celadon Studio on a patented ceramic product, "Funny Monkey," is a tangible example of the journey towards a triple-helix knowledge economy. This research underscores the importance of interdisciplinary curricula in promoting knowledge innovation and integrating transformational competencies in education. Scaffolding theory provides practical guidance for student learning and teaching strategies, presenting a sustainable roadmap for developing interdisciplinary curricula and offering a concrete and transferable pedagogical prototype for educational innovation.

Arid- and semi-arid Central Asia is particularly sensitive to climate change. The changes in extreme precipitation in Central Asia stemming from climate warming are the subject of intense debate within the scientific community. This study employed a Morlet wavelet analysis to examine the annual occurrence number of extreme precipitation in Central Asia from May to September during the period of 1951–2005. Their modulating planetary-scale climate modes were identified by using a linear regression analysis. Two major scales of the temporal variability were derived: 2–3.9 yr and 4–6 yr. The dominant variability was 2–3.9-yr scale and was associated with the negative phase of the Polar/Eurasia (POL) pattern. The 4–6-yr scale provided a secondary contribution and was closely linked to the negative phase of the North Atlantic Oscillation (NAO). These planetary climate modes acted as precursors of extreme precipitation over Central Asia. The negative phase of POL directly contributed to a negative height anomaly over Central Asia, which was intimately related to extreme precipitation. In contrast, the negative NAO phase possibly manifested as a Rossby wave source, which was subsequently exported to Central Asia through a negative–positive–negative Rossby wave train.

To address the insufficient mechanical properties of calcined phosphogypsum, this research aims to investigate the utilization of basalt fiber and calcium carbonate whiskers as reinforcing agents. The study examines their impact on the flexural and compressive strength, toughness, water resistance, and tensile strength of calcined phosphogypsum. In the individual tests, basalt fibers with different lengths (3 mm, 6 mm, 9 mm, and 18 mm) were added at dosages of 0%, 0.5%, 1.0%, and 1.5% respectively. The findings demonstrate that basalt fiber effectively enhances the flexural and compressive strength, toughness, and tensile strength of calcined phosphogypsum, albeit potentially compromising water resistance. Among the various fiber lengths, the 6 mm fibers exhibit the most advantageous influence on the performance of calcined phosphogypsum. A subsequent test was conducted to investigate the impact of cross-scale fibers on the properties of calcined phosphogypsum by mixing 6 mm basalt fibers and calcium carbonate whiskers. The results illustrate that calcium carbonate whisker refines the pores, thereby improving the flexural strength and toughness of calcined phosphogypsum. Furthermore, it compensates for the water resistance limitations associated with the sole utilization of basalt fiber while further augmenting the tensile strength and strain capacity. However, it should be noted that increasing the dosage of both calcium carbonate whiskers and basalt fibers concurrently leads to increased porosity of phosphogypsum and reduced compressive strength.

In the construction of shield crossing existing mined tunnel without load, it is imperative to develop corresponding design standards that reflect actual engineering force characteristics to ensure the successful completion of tunnel construction. This research uses The MIDAS-GTS finite element software to facilitate the creation of a numerical model of the shield structure for an air-push-over mine tunnel project in Changsha, China, investigating the stress field’s evolution during shield construction, and calculating the maximum positive and negative bending moments and maximum axial forces for different structures and other force states under various construction conditions. This study's findings informed the design and construction optimisation of the shield tunnelling empty-push method. The outcomes of this numerical simulation led to several key findings: (1) The soil density exerted a significantly greater impact on the internal forces of the initial support structure than both the tunnel depth and soil Poisson's ratio. Additionally, a sudden shift in internal forces occurred within the 300-350 mm range when the lining thickness was altered. (2) Factors such as the tunnel depth, soil density, soil Poisson's ratio μ, and lining thickness have a similarly influenced internal forces of the segment and the initial support. Notably, the backfill layer thickness significantly affected the segment’s maximum axial force causing an abrupt change of approximately 300 mm. (3) Under the shield machine equipment’s weight constraint, it is essential to control the guide rail’s thickness, to prevent it from becoming overly large.

Background: The coronavirus disease-19 (COVID-19) has spread globally with more than 80,000 people infected, and nearly 3000 patients died. Currently, we are in an urgent need for effective treatment strategy to control the clinical deterioration of COVID-19 patients. Methods: The clinical data of 10 COVID-19 patients receiving short-term moderate-dose corticosteroid (160mg/d) plus immunoglobulin (20g/d) were studied in the North Yard of The First Hospital of Changsha, Hunan from January 17^th to February 27^th, 2020. Epidemiological, clinical, laboratory, radiological findings were analyzed. Results: After treatment with combination of low-dose corticosteroid (40-80mg/d) and immunoglobulin (10g/d), patients’ lymphocyte count (0.88±0.34 vs 0.59±0.18, P<0.05), oxygenation index including SPO₂ (94.90±2.51 vs 90.50±5.91, P<0.05) and PaO₂/FiO₂ (321.36±136.91 vs 129.30±64.97, P<0.05) were significantly lower than pre-treatment, and CT showed that the pulmonary lesion deteriorated in all patients. While after treatment of short-term moderate-dose corticosteroid plus immunoglobulin, patients’ APACHE Ⅱ score (9.10±6.15 vs 5.50±9.01, P<0.05), body temperature (37.59±1.16 vs 36.46±0.25, P<0.05), lymphocyte count (0.59±0.18 vs 1.36±0.51, P<0.05), Lactate dehydrogenase (419.24±251.31 vs 257.40±177.88, P<0.05), and C-reactive protein (49.94±26.21 vs 14.58±15.25, P<0.05) significantly improved compared with post-treatment with low-dose therapy. In addition, oxygenation index including SPO₂ (90.50±5.91 vs 97.50±1.18, P<0.05), PaO₂ (60.47±14.53 vs 99.07±34.31, P<0.05), and PaO₂/FiO₂ (129.30±64.97 vs 340.86±146.72, P<0.05) significant improved. Furthermore, CT showed that pulmonary lesions obviously improved in 7 patients. After systematic therapy, 4 out of 10 COVID-19 patients recovered and discharged. Conclusions: Short-term moderate-dose corticosteroid plus immunoglobulin is effective for reversing the continued deterioration of COVID-19 patients who failed to respond to the low-dose therapy. Funding: This work was supported by the Innovative Major Emergency Project Funding against the New Coronavirus Pneumonia in Hunan Province (Dr. Ji-Yang Liu, number 2020SK3014; Dr. Yuan-Lin Xie, number 2020SK3013).

Magnetometric Resistivity(MMR) Method, which can use the power supply method of the traditional apparent resistivity method to measure the magnetic field. At present, the application research abroad is relatively extensive, but the domestic(China) research on the application of the MMR method is very few, and it is not even well-known. This paper is based on the MMR theoretical method under the point source DC condition, combined with the abnormal potential method and the modified Biot-Savart law, and using the three-dimensional numerical calculation method of the finite difference method to calculate the abnormal potential field, electric field, and magnetic field on the matlab2018a platform. Calculate, realize the multi-physics simulation in the electromagnetic field through Matlab platform programming, and verify the correctness of the algorithm by the spherical anomaly model with an analytical solution. Through the visual simulation of the three-dimensional data volume of the multi-physics field in the electromagnetic field, we can better understand the response mechanism of the electromagnetic field under DC conditions and grasp their three-dimensional spatial distribution rules. It is hoped that the research in this article can help the research of MMR Personnel better use this method for exploration.

Efficiency serves as a crucial metric in assessing the advancement of scientific research for universities. Reasonable inputs of human resources, finances, materials, and other resources, along with high-quality research output, are important guarantees for the research efficiency of Chinese universities. This study delves into the patterns of changes of research efficiency among universities across the 31 provinces and municipalities of China from 2012 to 2022. By scrutinizing input-output perspectives and utilizing data sourced from the China Statistical Yearbook, the analysis unveils prevailing scenarios of input redundancy and output insufficiency in research efficiency across universities. The static and dynamic trends in research efficiency were further investigated by employing a static non-parametric Data Envelopment Analysis (DEA)-BCC model and a dynamic Malmquist index. Key findings from the analysis from 2012 to 2022 include: (1) notable growth in investments in teaching, research equipment assets, and research and development funds among nationwide universities; (2) a steady increase in the mean value of technical efficiency (TE) from 0.97 to 0.99, indicating continuous enhancement in overall efficiency in research input resource allocation across the provinces; (3) the mean Malmquist productivity index of 1.285, with an average annual increase of 28.5%, signifying a consistent improvement in university research efficiency throughout the study period; and (4) the provinces experiencing varying degrees of input redundancy or output insufficiency were selected to suggest the need for tailored strategies in resource allocation. The study underscores the importance of a holistic evaluation system involving stakeholders from government, universities, and society to optimize research efficiency and promote scientific innovation and social development.

With the rapid development of network science, Turing pattern on complex networks has attracted extensive attention from researchers. In this paper, we focus on spatial patterns in multiplex ER (Erdös-Rényi) random networks, taking the predator-prey model with Allee effect and hyperbolic mortality as an example. In theory, the threshold condition for generating Turing pattern is given using the Turing instability theory of multiplex networks. Numerically, we design relevant experiments to explore the impact of network topology on Turing pattern. The factors considered include model parameters, diffusion rate, average degree of the network, and differences in the average degree of different layers. The results indicate that the importance of diffusion rate and network average degree for Turing pattern is affirmed on the single-layer network. For multiplex networks, the differentiation of average degrees in different layers controls the generation of Turing pattern, which is not affected by the diffusion rates of the two populations. More interestingly, we observe the switching of Turing pattern and spatiotemporal pattern. We believe that these findings contribute to a better understanding of self-organization on complex networks.

To address the issue of reduced orbit estimation accuracy resulting from discrepancies between in-orbit measured geomagnetic field values and IGRF model values in autonomous orbit estimation of microsatellites based on geomagnetic field measurements, this study introduces an adaptive cubature H-infinity filtering algorithm. Firstly, it presents the low-altitude orbital dynamics model and geomagnetic field measurement model for microsatellites. Subsequently, within the adaptive cubature H-infinity filtering algorithm, a spherical simplex rule and a second-order Gaussian-Legendre quadrature criterion are employed to compute the spherical surface integral and radial integral. Additionally, a spherical simplex-radial quadrature rule is proposed to enhance the approximation accuracy of nonlinear Gaussian weighted integrals. Furthermore, by integrating extended H-infinity filtering based on game theory with the cubature quadrature rule, an inverse proportional relationship between constraint level and filtering innovation is established. This allows for adaptive adjustment of the constraint level to enhance robustness against model errors. Finally, through semi-physical simulation experiments, it is demonstrated that compared with traditional algorithms, the proposed algorithm improves autonomous orbit estimation position accuracy of microsatellites by 7.49%, while also enhancing velocity accuracy by 3.1%, thus validating its effectiveness.

In GNSS/IMU integrated navigation systems, factors such as satellite occlusion and non-line-of-sight conditions can lead to degradation of satellite positioning results, Subsequently affecting the overall accuracy of the integrated navigation system. To address this issue and ef-fectively utilize historical pseudorange information from satellites, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints are con-structed using satellite ‘s position from previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint can serve as an alternative solution for individual satellites in case of signal anomalies, ensuring the integrity and continuity of the graph optimi-zation model. Additionally, this paper conducts an analysis of the graph optimization model constructed using these virtual constraints, comparing it with traditional integrated navigation graph optimization model, and analyzes and reconstructs the marginalization process based on these virtual constraints. The experimental results, compared with tightly coupled Kalman fil-tering and the original graph optimization method on a set of real-world data, demonstrate that the introduction of virtual pseudoranges maintains high positioning accuracy. The RMSE error between it and the original graph optimization remains within 5%, affirming the high feasibility of this approach.

Polymethyl methacrylate (PMMA) polymer is widely used in various fields today. In order to thoroughly reveal the structural impact performance of PMMA materials, this paper is based on the comprehensive Johnson-Cook constitutive model and damage failure model, and accurately confirm the J-C constitutive and damage failure model parameters of PMMA through material test data, The dynamic process of steel bullet impacting PMMA plate structure is analyzed by finite element software ABAQUS. The calculation results show that the numerical simulation results in this paper are in good agreement with the experimental test data. Therefore, the feasibility and accuracy of the impact analysis of PMMA structures based on J-C constitutive and damage failure models in this paper are studied. Finally, based on the J-C constitutive model and damage failure model, the variation of the residual velocity of the bullet with the PMMA plate thickness is analyzed in depth, that is, the results show that the residual velocity of the bullet has a certain linear relationship with the plate thickness.

Sign language recognition is essential in hearing-impaired people’s communication. Sign language recognition is an important concern in computer vision and has been developed with rapid progress in image recognition technology. However, sign language recognition using a general monocular camera has problems with occlusion and recognition accuracy in sign language recognition. In this research, we aim to improve accuracy by using a 2-axis bending sensor as an aid in addition to image recognition. We aim to achieve higher recognition accuracy by acquiring hand keypoint information of sign language actions captured by a monocular RGB camera and adding sensor assist. To improve sign language recognition, we need to propose new AI models. In addition, the amount of dataset is small because it uses the original data set of our laboratory. To learn using sensor data and image data, we used MediaPipe, CNN, and BiLSTM to perform sign language recognition. MediaPipe is a method for estimating the skeleton of the hand and face provided by Google. In addition, CNN is a method that can learn spatial information, and BiLSTM can learn time series data. Combining the CNN and BiLSTM methods yields higher recognition accuracy. We will use these techniques to learn hand skeletal information and sensor data. Additionally, the 2-axis Bending sensor glove data support training AI model. Using these methods, we aim to improve the recognition accuracy of sign language recognition by combining sensor data and hand skeleton data. Our method performed better than using skeletal information, achieving 96.5% accuracy in Top-1.

Land use is an embodiment of human socioeconomic activities and represents a bridge between these activities and natural systems. Through social activities, humans transform land use to promote social and economic development and improve production, living conditions, and eco-logical functions of land. Rural residential land represents a space for rural residents to reside in and exhibits spatial characteristics that evolve over time, which is proof of rural socioeconomic development. This paper observes rural residential land in 21 cities on the Grand Canal and analyses its spatial differentiation. Then, it explores the driving factors of this land using spatial grid analysis and the geographic detector model. Lastly, it proposes three different forms of rural residential land based on the results. The study found that: (1) the change of rural residential land in the northern part of the Grand Canal was more volatile than that in the southern part. The change of rural residential land from 1990 to 2020 conformed to the pattern of cultivated land - rural residential land - urban construction land; (2) Based on the driving factors of rural residen-tial land, the land is divided into one-dimensional cities, two-dimensional cities and three-dimensional cities. Circular, linear, and scattered cities of different sizes were affected by socioeconomic factors, transportation accessibility, and the natural environment, respectively; and (3) Finally, according to the spatial differentiation characteristics and the driving factors of rural residential land, the study proposes the construction of three types of villages through the strate-gies of constructing large-scale villages, relocating and reconstructing new villages, and con-structing high-quality villages, respectively. Enhancing the scientific planning of rural residential land and its efficiency can offer the protection of agricultural land and the integration of urban and rural areas in the new era.

Failure characteristics induced by unloading disturbance and the corresponding mechanical mechanism of the coal seam floor are important theoretical bases for water-bursting prevention from the floor of the coal seam and rock burst alarm in deep mining. However, the existing two-dimensional ground-pressure-control theory based on shallow mining cannot sufficiently guide deep-mining practices. In this study, the redistribution of mining-induced stress field in rocks surrounding the longwall face and mechanical behaviors of strata in deep mining are investigated through a combination of numerical simulation, physical simulation, and field measurement. Results demonstrate that mining-induced stress fields in the floor of the longwall face differ in space and time. Vertical stress unloading from top to bottom of the floor and horizontal stress unloading are relatively low. A concentration zone of high horizontal stress exists at stope boundaries. The critical yield load of rock stratum in the floor is determined through thin plate yield theory. Under the combined effect of concentrated high horizontal and vertical resilience stresses, strata in the floor fracture from seam to seam if the load increases to the minimum critical buckling value. Fractured strata slide along the fracture surface, which leads to floor heave. The stope floor shows evident time-delay progressive failure characteristics. The stress shell in the stope floor in deep mining is found to be a sensitive mechanical parameter that produces three-dimensional ground-pressure behavior in the floor. This ground-pressure behavior in the stope floor is controlled by the existence of the corresponding stress shell and effects induced by its space–time evolution. This study provides theoretical basis for the dynamic control of a hazard-inducing environment in engineering and minimizing or altering disaster-occurrence conditions during the construction engineering of the coal seam floor.

Impact of land use and land cover (LULC) change on soil erosion is still imperfectly understood, especially in northeastern China (NEC). Based on the Revised Universal Loss Equation (RUSLE), the variability of soil erosion at different spatial scales following land use changes in1980, 1990, 2000, 2010, and 2017 was analyzed. The regionally spatial patterns of soil loss coincided with the topography, rainfall erosivity, soil erodibility, and use patterns, and around 45% soil loss came from arable land. Regionally, soil erosion rates increased from 1980 to 2010 and decreased from 2010 to 2017, ranging from 3.91 to 4.45 t ha-1 yr-1 with an average of 4.22 t ha-1 yr-1 in 1980-2017. The rates of soil erosion less than 1.41 t ha-1 yr-1 decreased from 1980 to 2010, and increased from 2010 to 2017, and opposite changing patterns occurred in higher erosion classes (i.e., above 5 t ha-1 yr-1). At a provincial scale, Liaoning Province experienced the highest soil erosion rate of 9.43 t ha-1 yr-1, followed by Jilin Province, the east Inner Mongolia, and Heilongjing Province. Arable land continuously increased at the expense of forest in the high-elevation and steep-slope areas from 1980 to 2010, and decreased from 2010 to 2017, resulting in increased areas with erosion rates higher than 7.05 t ha-1 yr-1. At a county scale, around 75% of the countries had soil erosion rate higher than its tolerance level. The county numbers with higher erosion rate increased in 1980-2010 and decreased in 2010- 2017, resulting from the sprawl and withdrawal of arable land. The results indicate that appropriate policies can control soil loss through limiting arable land sprawl in areas of unfavorable regions in the NEC.

Energy resources are important sources of social and economic development, human survival and security. However, energy resources are objective and scarce, especially the fossil energy, such as coal, oil, natural gas, etc. Human survival environment is increasingly damaged, and the condition of energy is also gradually becoming deficient, so it is necessary to develop new energy resources to meet the increasing demand of material civilization. Solar energy can meet the needs of human development in the future as a kind of clean resource and it has been concerned by all countries in the world. We analyzed the effectiveness of subsidies to the PV industry of both the price and R&D by the spatial econometric model. In some region, the production capacity of photovoltaic industry has shown a significant spatial agglomeration situation in China since 2012. At the same time, benchmarking price and R&D subsidies played a positive incentive to the photovoltaic industry production capacity. It is a certain degree of inverse correlation between the scale of policy subsidies and the resource endowment of solar resources. Therefore, the countermeasures should be considered from the aspects of institutions, industry, technology and public financial policies.