In response to the fire protection design challenges faced by the integrated station-city engineering under the new model of station-city fusion, such as large building volume, diverse traffic types, functional integration, spatial complexity, and inapplicable standards, this paper selects Chongqing East Station as a typical engineering case. The fire safety engineering methods and performance-based fire protection design ideas were used. The paper summarized the fire protection difficulties, such as multifunctional combination construction, large fire protection distance, large fire protection zoning area, long evacuation distance, and design of evacuation (quasi) safety zones. Combining with the actual situation of the engineering, this paper analyzed the basic technical measures taken to address these design challenges. By adopting the method of fire safety engineering, the safety of the design scheme is verified, which can provide a solution approach for the current fire protection design challenges in the integrated station-city engineering.

The decline in assisted reproductive technology (ART) success among older women, attributed to decreased oocyte quantity and quality, poses a significant challenge. Currently, no consensus ex-ists on the optimal ovarian stimulation protocol for older women undergoing IVF. This retrospec-tive registered cohort study aimed to compare cumulative live birth rates (CLBR), time to live birth (TTLB), and cost-effectiveness in women over 35 years using gonadotropin-releasing hor-mone agonist (GnRHa) or clomiphene-primed ovarian stimulation (CPOS). We performed pro-pensity score matching (PSM) on 2,871 IVF cycles in women over 35 who received either GnRHa or CPOS protocols, resulting in 375 cycles in each group to compare treatment outcomes. Addition-ally, a decision tree model was utilized to assess the cost-effectiveness of the two protocols. Fol-lowing PSM, both groups showed similar baseline characteristics. The CPOS protocol exhibited a higher cycle cancellation rate (13.07% vs 8.00%, p=0.032), yet maintained comparable fertilization and embryo quality. While CLBRs per initial cycle (41.07% vs. 45.33%, p=0.269) and delivery out-comes were similar between the groups after a 24-month follow-up, TTLB was longer in the CPOS group. Notably, the average cost per live birth in the CPOS group was 21.27% less than that in the GnRHa group (￥32,301.42 vs ￥39,174.22). In conclusion, for women over 35 undergoing IVF, the CPOS protocol demonstrated similar CLBR to GnRHa but with lower costs, suggesting its potential as a viable and cost-efficient ovarian stimulation option.

Heavy metal pollution poses a significant threat to ecosystems and human health worldwide. In response to this pressing issue, bioremediation has emerged as a promising approach for mitigating contamination. This paper adopts a bibliographic method to explore the key techniques and applications of bioremediation. Furthermore, we delve into the most prominent threats to human environmental health and the corresponding remediation methods. Additionally, we discuss the future trajectory of bioremediation research, with a particular focus on the integration of big data and machine learning technologies. These advanced methodologies hold great potential for enhancing the effectiveness and efficiency of environmental remediation efforts in the face of escalating pollution challenges.

Saliency detection in synthetic aperture radar (SAR) image is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR image. Firstly, we extract four features of SAR image as the input of the MSD model, which include the intensity, orientation, uniqueness and global contrast. Then, the saliency map is generated by the multitask sparsity pursuit (MTSP) which integrates the multiple features collaboratively. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps of the source images, an image fusion method is proposed for the SAR and color optical image fusion. The experimental results of real data show the proposed image fusion method is superior to the presenting methods in terms of several universal quality evaluation indexes, as well as in the visual quality. The salient areas in the SAR image can be highlighted and the spatial and spectral details of color optical image can also be preserved in the fusion result.

This study aimed to overcome the difficulty of conducting the horizontal-well cavity leaching test in the field and to investigate the long-term stability of the horizontal-well salt-cavity natural gas storage. The simulation test design is combined with the similarity theory to study the cavity expansion characteristics and the influence law of cavity leaching parameters. Through the design of a rubber hose connection, an integrated closed test system for multi-stage horizontal-well cavity leaching and brine drainage was built. The test system also realises the repeatable backward movement of the injection well during the test. A similarity simulation of the test design was carried out, and the test platform was constructed to carry out multi-stage horizontal-well leaching tests with a nitrogen cushion. In the horizontal-well leaching tests with a nitrogen cushion, the influence laws of the well spacing, flow rate and liquid level position on cavity expansion were investigated. Based on the morphological characteristics of the horizontal-well cavity, a numerical model of the horizontal-well salt cavity was developed, which reflects the real cavity morphology leached in the test. The long-term stability of the horizontal-well salt-cavity natural gas storage under different internal pressures was investigated through numerical simulation.

Excitotoxicity related to the dysfunction of the N-methyl-d-aspartate receptor (NMDAR) has been indicated to play an integral role in the pathophysiology of multiple disease states, including neurodegenerative disorders such as Parkinson’s disease. There is a notable gap in the market for novel NMDAR antagonists, however current methods to analyze potential antagonists rely on indirect measurements of calcium flux and hazardous radioligand binding assays. Recently, a fluorescent NMDAR ligand, N-adamantan-1-yl-dimethylamino-1-naphthalenesulfonic acid, known as AM-DAN was developed by our group. Additional studies on this ligand is necessary to evaluate its potential as a biological tool in NMDAR research. Therefore, this study was aimed at conducting structural analyses, fluorescence experiments, high-accuracy NMDAR molecular modelling and NMDAR phencyclidine (PCP) site competition binding studies using AM-DAN. Results revealed that AM-DAN has appropriate structural properties, significant fluorescent ability in various solvents and is able to bind selectively and compete for the PCP-binding site of the NMDAR. Therefore, AM-DAN holds promise as a novel fluorescent ligand to measure the affinity of prospective drugs binding at the NMDAR PCP-site and may circumvent the use of radioligands.

The moon always use the same side to face toward the earth, but there is a dead angle in the mainstream theory of explaining this phenomenon. That is, it cannot explain why the moon doesn't rotate around the axis which is a straight line to connect the mass centers of the earth and moon. Because the numerous meteorite impact craters on the lunar surface indicate that the moon is completely possible to obtain external momentums and rotate around this axis. This paper proposes a plain explanation, that is, the universal gravitation between the earth and moon as well as the earth's magnetic field have formed a trinity restraint mechanism on the moon. According to this explanation, the moon's rotation can be locked, and the mechanism of lunar libration has been revealed out, which can also confirm mutually with the natural phenomenon that the moon has sought a balance in the swing. In addition, with the help of all kinds of detection data from the Apollo moon landings and other circumlunar spacecraft, as well as the studies and analysis of lunar soil samples, the conclusion is that as far as a whole for the moon, it belongs to paramagnetic substances, and its relative permeability is between 1.008 and 1.03. Although the magnetic flux density of the earth on the lunar orbit has been dropped below 0.0008125 nT or lower due to the impact of the solar wind, but it can be used as a reason to lock the moon without rotating around the axis which is a straight line to connect the mass centers of the earth and moon. If another main reason to cause the existence of this fact cannot be found, even if the magnetic flux density of the geomagnetism in lunar orbit is very small, it also should not be artificially ignored. In this regard, we can artificially change the intensity of the earth's magnetic field, and carefully observe the lunar libration and in the distance between the earth and the moon, to verify the arguments in this paper.

Sediment transport is highly sensitive to flow conditions, showing significant increase during flood events. Based on this principle, this study set out to rank flood events occurring along river Trotuș (Romania) based on the amount of transported sediment and event duration. The 77 flood events recorded from 2000 to 2017 were ranked into 4 classes: type A (4%); type B (16%), type C (14%) and type D (66%). The sediment transport specific for the 4 types of flood events was related to the flow discharge (sediment rating curve and hysteresis effect), the specific stream power and the energy expenditure of these events. More than 60% of the hysteresis loops typical for flood events were clockwise, thus singling out the channel as the main sediment source. Ca. 74% of the total sediment yield was transported at stream power values higher than the 300 Wm-2 threshold, which was exceeded in less than 1% of the investigated timeframe. The changes occurring in the sediment transport rates after major floods show that these events are significant thresholds in the hydrogeomorphic evolution of river channels.

Biofilm serves as the residence for bacteria, wherein bacteria attach to surfaces and produce extracellular polymeric substances, including proteins, polysaccharides, and extracellular DNA. In recent years, biofilm reactors have gained increasing significance, playing a crucial role, for instance, in immobilizing heavy metals from waterways. This paper employs a bibliographic study to analyze the most important keywords and organizations in the field of biofilm reactors, identifying recent significant papers. Additionally, the paper discusses the opportunities for the future development of biofilm reactors. Through this study, a better understanding of key factors in biofilm reactor research is achieved, providing valuable guidance for future studies and applications in this field.

The current epoch, known as the Anthropocene, is characterized by significant human impact on the global ecological system, including altered species survival and distribution patterns due to global warming resulting from industrialization. Accurately quantifying and assessing the extent of human impact on the environment is of utmost importance. To this end, four primary methods have been developed to measure human impact: (1) ecological footprint, (2) Human appropriation of net primary production (HANPP), (3) Planetary boundary, and (4) Living Planet Index. In this paper, we aim to provide a comprehensive understanding of these methods, including their strengths and weaknesses, to evaluate their efficacy in accurately assessing the impact of human activities on the global ecosystem. Furthermore, we explore the potential of utilizing novel machine learning modelling for future predictions in this area. By analyzing and comparing these methods, we can gain insight into the ways in which human activities impact the environment and use this knowledge to inform future policies and practices to mitigate negative effects.

Both lactic acid and short chain fatty acid (SCFA) play important roles in maintenance of intestinal epithelial structure and function. Trilactic glyceride (TLG) obtains both excellences of lactic acid and SCFA. This study was to investigate the effects of trilactic glyceride on growth performance, blood parameters, liver function, intestinal morphology and intestine function of piglets. Twelve weaned piglets (21±2 d) were randomly allocated to two treatment groups: 1) control group, piglets fed the basal diet; 2) TLG group, piglets fed the basal diet supplemented with 0.5 % TLG. On day 21 of the trial, D-xylose (0.1 g/kg·BW) was orally administrated to all piglets and blood samples were collected 1 h thereafter. Then, all the piglets were sacrificed to examine intestinal mucosal morphology and collect fatty tissue, liver and intestinal mucosa for further analysis. The results showed that: compared with the control group, TLG group decreased blood ALB and GGT on day 10 and 20, TLG group decreased blood TP and increased blood TG on day 20 of the trail (p < 0.05); TLG group decreased blood D-xylose and LDL, increased blood HDL (p < 0.05). These data suggested that supplementing trilactic glyceride had beneficial impacts on promoting nutrients’ metabolism, maintaining intestinal integrity, and alleviating oxidative stress and diarrhoea. Further research of molecular mechanisms showed changing expression levels of related proteins and genes, suggesting that these could be involved in the regulation of the impact. The community composition of the gut microbiota was also found to be altered in several operational taxonomic units within the genus, Prevotella (order Bacteroidales), and the order, Clostridiales.

Periacetabular osteotomy (PAO) is an effective approach for surgical treatment of developmental dysplasia of the hip (DDH). However, due to the complex anatomical structure around the hip and limited field of view (FoV) during the surgery, it is challenging for surgeons to perform a PAO surgery. To solve this challenge, we propose a robot-assisted augmented reality (AR)-guided surgical navigation system for PAO. The system mainly consists of a robot arm, an optical tracker, and a Microsoft HoloLens 2 which is a state-of-the-art (SOTA) optical see-through head-mounted display (OST-HMD). For AR guidance, we propose an optical marker-based virtual-physical registration method to estimate a transformation from the optical tracker coordinate system (COS) to the virtual space COS, such that virtual models can be superimposed on the corresponding physical counterparts. Furthermore, to guide the osteotomy, the developed system automatically aligns the bone saw with osteotomy planes planned in preoperative images, and then provides surgeons with not only robot assistance but also AR guidance. Comprehensive experiments were conducted to evaluate both virtual-physical registration accuracy and osteotomy accuracy of the developed navigation system. The proposed virtual-physical registration method achieved an average mean absolute distance error (mADE) of 1.96 ± 0.43 mm. The developed system achieved an average distance deviation of 0.82 ± 0.17 mm and an average angular deviation of 3.77 ± 0.85 . Experimental results demonstrated high virtual-physical registration accuracy. The sub-millimeter distance deviation demonstrated the osteotomy accuracy of the proposed system.

With the development of organizational biology and forced from the EU Cosmetic Regulation (EU/1223/2009) with the complete ban of animal testing for cosmetic purposes, 3D skin models have been widely developed due to their similar physiological structure and metabolic function to native human tissue. 3D skin models for in vitro cosmetic evaluation are still promising alternative methods for evaluating phototoxicity, corrosivity and irritation. These models have been used for early screening and later validation of toxicity and efficacy evaluation by many well-known cosmetic companies. 3D skin models have become an important tool in cosmetic evaluation research. This article reviews the development of 3D skin models and their application in cosmetic evaluation. An outlook on the current application and future technological development directions in the cosmetic field is also presented.

The present study investigates the failure mechanisms of industrial fiber materials, using a custom designed fiber cutting performance test bench. The fracture morphologies of single PA6 fibers are examined by scanning electron microscopy. The analysis reveals that fiber cutting can be distinguished according to four distinct stages of fiber failure represented by shearing, cutting, brittle fracture, and tensile failure, which are the result of different mechanisms active during the processes of crack initiation, extension and fracture. The results of fractographic analysis are further verified by an analysis of the blade assembly speed with respect to time over the entire fracture failure process based on high-speed camera data. The results of fractographic analysis and blade assembly speed are fully consistent.

COVID-19 patients can recover with a median SARS-CoV-2 clearance of 20 days post initial symptoms (PIS). However, we observed some COVID-19 patients with existing SARS-CoV-2 for more than 50 days PIS. This study aimed to investigate the cause of viral clearance delay and the infectivity in these patients. Demographic data and clinical characteristics of 22 long-term COVID-19 patients were collected. SARS-CoV-2 nucleic acid, peripheral lymphocyte count, and functionality were assessed. SARS-CoV-2-specific and neutralization antibodies were detected, followed by virus isolation and genome sequencing. The median age of the studied cohort was 59.83±12.94 years. All patients were clinically cured after long-term SARS-CoV-2 infection ranging from 53 to 112 days PIS. Peripheral lymphocytes counts were normal. Interferon gamma (IFN-ƴ)-generated CD4+ and CD8+ cells were normal as 24.68±9.60% and 66.41±14.87%. However, the number of IFN-ƴ-generated NK cells diminished (58.03±11.78%). All patients presented detectable IgG, which positively correlated with mild neutralizing activity (ID50=157.2, P=0.05). SARS-CoV-2 was not isolated, and a cytopathic effect was lacking. Only three synonymous variants were identified in spike protein coding regions. In conclusion, decreased IFN-γ production by NK cells and low neutralizing antibodies might favor SARS-CoV-2 long-term existence. Further, low viral load and weak viral pathogenicity was observed in COVID-19 patients with long-term SARS-CoV-2 infection.

In South Sudan, three case-control studies found a positive association between Nodding Syn-drome (NS) and a Mansonella perstans infection. To investigate whether M. perstans could play the primary and sole causal role in the development of NS, we examined blood films of persons with and without epilepsy in rural villages affected by NS in Mahenge, Tanzania, to identify M. perstans infections. Study participants were also examined for onchocercal nodules and skin lesions and tested for the presence of anti-O. volvulus antibodies (Ov16 ELISA). One hundred and thirteen epilepsy cases and 132 controls were enrolled, with a mean age of 28.3 years (interquartile range 22 – 34 years); 125 (51%) were females. Of the persons with epilepsy, 43 (38.1%) met the probable NS criteria. M. perstans infection was absent in all participants, but Ov16 prevalence and oncho-cerciasis-associated skin manifestations were positively associated with epilepsy, including probable NS. Onchocercal nodules in the mothers of cases were also positively associated with epilepsy. In conclusion, in contrast with O. volvulus, M. perstans is likely not endemic in Mahenge and therefore cannot be a co-factor for NS in the area. Hence, this filaria cannot be the primary and sole causal factor in the development of NS.

In recent years, ESG (environmental, social, governance) has emerged as a critical investment concept. Its goal is to create value for both shareholders and society, encouraging companies to optimize social value. However, the exploration and research into "the proportion of firms ex-porting and the pathways through which the environmental, social, and governance activities of carbon-intensive firms influence firms' financial performance" remains largely unexplored. This study establishes a research framework within this context, utilizing listed Chinese manufactur-ing companies as the research subjects. Employing a fixed-time, fixed-industry, two-way fixed-effects model methodology, it delves into the relationship between ESG ratings, the pro-portion of firms' exports, and firms' financial performance through panel regression modeling. This study emphasizes the moderating role of carbon-intensive firms in the relationship. Find-ings indicate that both enterprise export proportions and carbon-intensive firms significantly affect the correlation between corporate ESG activities and financial performance. This has im-plications for investors, company management, policymakers, and regulators. Based on these findings, we propose policy recommendations at corporate and governmental levels to enhance ESG significance, strengthen corporate governance, and promote continuous ESG advancement. The study provides micro-level evidence of the interaction among ESG ratings, export ratios, carbon-intensive enterprises, and corporate performance, empowering investors to make in-formed decisions.

Administration of crude polysaccharides extract from natural product is a promising gut microbiota-targeted approach to preventing obesity and associated metabolic disorders. Dietary restrictions can change the type and number of gut bacteria, which is an important factor in delaying the onset and burden of diseases. This study aimed to investigate the effects of high-yield crude polysaccharides from Cordyceps militaris (CMP) on high-fat diet (HFD) mice model and the gut microbiota community assembly, and to identify whether selenium (Se) addition would improve CMP action mode during cultivation. We found that the CMP treatment ameliorated adipose and liver pathologic morphology and fat accumulation in obese mice, while, SeCMP intervention was not superior than CMP in body mass gain, but notably decreasing serum triglyceride level increased by HFD. The upregulated expression of gene Cyp7a1 in liver and protein UCP1 in brown adipose tissue (BAT) preliminary indicated that the effect might relate to bile acids (BAs) metabolism pathway and thermogenesis. In addition, CMP showed a drastic decrease in the gut microbes which positively correlated with dyslipidemia parameters. Our result reveals the potential of CMP to be used as functional food in the prevention of diet-induced adipose and liver steatosis, so does SeCMP has outstanding capacity of improving dyslipidemia.

Global warming may increase the potential evapotranspiration (Etp), thereby affecting the amount of clean water resources available in a region. In this study, the systematic biases in temperature outputs of 24 global climate models (GCMs) under 3 emission scenarios are corrected by using 8 bias correction methods. The trend of Etp in Yangzi River Delta in the 21st century is projected by using 4 Etp calculation formulas. The uncertainty of the projections is estimated and decomposed by using multi-way analysis of variance. The influence of uncertainty on the projected change signal is quantified by using the signal-to-noise ratio. The results show that, relative to the reference period (1971~2000), the annual mean daily Etp in Yangzi River Delta will increase by 0.14~0.17mm/d during the period 2021~2050 and by 0.21~0.41mm/d during the period 2061~2090, respectively. During 2021~2050, the uncertainty of Etp increase projections is dominantly contributed by the main effects of GCM (63%) and Etp calculation formula (24%). During 2061~2090, it is mainly contributed by the main effect of GCM (36%), followed by the main effects of emission scenario (34%) and Etp calculation formula (18%). The increase projections of Etp are generally reliable and robust during the two projection periods.

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.

The article discusses early diagnosis and intervention for developmental delays in children, emphasizing the role of specialized services and personnel in supporting recovery and development. It presents findings from a pilot project initiated in Bucharest, aiming to assess the incidence of developmental delays in children aged 1-3 years in selected kindergartens, including ASD. The study involved children, parents, and specialists, utilizing systematic behavior observation (SBO) and semi-structured interviews. Results indicate that out of 613 evaluated children, 81 received specialized control recommendations, with 4 cases showing potential risk of ASD. Additionally, 8 children were diagnosed with autism. The incidence of autism in children aged 1-3 years was 1 in 51. Although exploratory, these findings suggest a model of good practice for future early diagnosis campaigns nationally. Conclusively, the study underscores the importance of early diagnosis and intervention for developmental delays, advocating for a responsive community and emphasizing the significance of national-level efforts to address these issues effectively.

Monoclonal antibodies (mAbs) have become an increasingly important therapeutic modality in recent years, with applications in oncology, hematology, and immunology. This review discusses the production of therapeutic mAbs, including the various types of mAbs (murine, chimeric, humanized, and fully human), their key applications, and the techniques used for their production, including hybridoma technology, single B cell antibody technology, and phage display technology. The review then proposes advanced techniques to overcome them. These include microfluidic sorting, alternative expression systems (including plants, bacteria, and yeast, and cell-free production systems), and the development of complex antibody formats like bi-specific monoclonal antibodies, antibody fragments, antibody-drug conjugates, and nanobody development. These advanced techniques promise to enhance the efficiency, cost-effectiveness, and scalability of mAb production, paving the way for more effective therapeutic applications. This review highlights the theoretical significance of integrating innovative technologies to overcome existing challenges in mAb production, ultimately advancing the field of precision medicine.

Methane is the main cause of sewer explosion accidents, and the available monitoring methods mainly use portable gas sensors for on-site detection. However, these methods cannot grasp the dynamic changes in methane in a sewer in real time or make better predictions of methane explosion accidents. Here, we present a low-power online monitoring system for sewer methane in order to ensure the safety of urban sewers. A low-power ultrasonic gas sensor was used to monitor the methane content in biogas. Due to the impacts of changes in CO2, temperature, and humidity in the sewer on the ultrasonic methane measurement results, a corresponding compensation method was proposed. The effectiveness of the compensation method was verified through experiments, which measured the methane concentration under different influences, and the methane values measured before and after compensation were compared. The power consumption was only 1.28 mW if the system worked in intermittent operation mode, and it could operate for about 1.5 years by selecting a lithium battery with a capacity of 5 A · H. The experimental results showed that the gas detection accuracy could meet the commercial requirements and had a high compensation for the impacts of carbon dioxide, temperature, and humidity.

In recent years, many algorithms based on end-to-end deep networks have been proposed to deal with the target detection problem of videos. However, the deep network models usually consume a lot of computing resources during the procedure of analysis of videos with complex dynamic backgrounds. In this paper, a new method of object detection based on information theory is presented. Firstly, each frame in a video is converted into an effective information map by using the Harris corner detection method. Secondly, the sensitive areas in the frame are extracted by using the context information and the effective information maps of the consecutive video frames. The sensitive areas in the video frame are the candidate areas where the target objects would be appeared at high probabilities. Thirdly, the information entropy features of each sensitive area are extracted to form the feature matrix, based on which, an SVM model is trained for selecting the target areas from the sensitive areas. Finally, the locations of the objects are detected based on the target areas in the video with a complex dynamic background. As a lightweight video detection framework, the method presented in this paper can save a lot of computing resources. Experimental results show that this method can achieve good results in the benchmark of CDnet 2014.

The rapid urbanization has exerted tremendous pressure on natural systems in mountains. As a measure of sustainable use of natural resources, ecological footprint is an important basis for judging whether the development of a country or region is within the biocapacity. Taking Dali Bai Autonomous Prefecture as an example, this study comprehensively analyzes the impact of human activities on mountain resources and environment from the three aspects of urbanization, land use and ecological carrying capacity. The results show that Dali Prefecture with the urbanization rate of 33% is still in the accelerated stage of urbanization. The urban space presents the core-periphery feature, and the central city is the focus of human existence and living activities. The per capita ecological footprint is 1.14 hm2/person higher than the ecological carrying capacity, meaning Dali Prefecture is in an ecological deficit state. This indicates that there is an uncoordinated state between urbanization and environment. Arable land is the main source of per capita ecological footprint in the prefecture. However, the urban expansion overly occupies the arable land in the plain sub-region, leading the arable land to an ecological deficit state. In the future, the development of the mountainous area should focus on the protection of arable land and choose a new sustainable path.

Incident rates of neurodegenerative diseases have steadily increased globally, but there is no therapeutic access available. We newly prescribed medicinal herbal remedy including five different herbal plants called, Chen-Ma-Dan-Sam-Ga-Mi-Bang (CMST), purposed to prove for pharmacological properties and corresponded actions on hippocampus neuronal cell injury by hypoxia-induced mice model. Mice were adapted to normoxia or hypoxia with or without CMST for 5 days. We gathered pharmacological effects of CMST on cell injury by enhancement of dihydroethidium and 4-hydroxynonenal signals which were correlated with abnormal redox status in the protein or gene expression levels (abnormal elevations of nitric oxide, reactive oxygen species, lipid peroxidation and deteriorations of total glutathione, total antioxidant capacity, and activities of superoxide dismutase and catalase) due to hypoxia. CMST also notably exerted to attenuates molecules for neuronal cell injury markers such as p-tau, cleaved caspase-3 due to DNA oxidations (53bp1and phosphor-histone H2AX), inflammatory cytokines, and hemeoxigenase-1. We further figured out the underlying actions of CMST by in vitro experiment through inactivation of microglial cell which can mediate neuronal cell injury. Collectively, CMST prevented from hippocampal neuronal cells via inactivation of microglial cell with normalization of redox status on hypoxia-induced hippocampus neuronal cell injury.

Wearable exoskeleton solutions for upper limb rehabilitation or assistance, particularly for the hand area, have become increasingly attractive to researchers, proving to be effective over time in treating hand movement impairments following various neurological diseases. In the present work, the development of a wearable exoskeleton-type device for active hand rehabilitation has been addressed, based on fluid actuators made of elastomeric material and on myoelectric control by capturing myoelectric signals from the forearm area. The flexion movement of the fingers (phalanges) is actively assisted by pneumatic actuation of the built-in actuators and the extension movement is assisted by a depressurization control of the actuator. The device has been designed to be as easy to use as possible, light in weight, and to provide a high degree of comfort when used by the patient for rehabilitation training or daily life activities (ADL), which can be performed in the comfort of one's own home.

Under the dual background of underemployment and health inequality, this paper empirically analyzes the impact of education level on underemployed workers’ health based on CLDS2016 data. The results show that underemployment is significantly related to the decline of self-rated health, increased depression tendency, and morbidity in a certain period. The results indicate that underemployment can significantly reduce the health level of workers in the low education level group and the high education level group. However, it has no significant impact on workers’ health in the middle education level group; even if we change the measurement method of indicators and consider endogeneity, the research conclusion is still robust. Moreover, this kind of health inequality mainly comes from the difference in economic effect and leisure effect of underemployment to workers with different educational levels. This paper provides empirical support for increasing the labor protection mechanism of underemployed people and reducing the health inequality caused by educational level differences.

This study aimed to understand the status of knowledge, attitudes, and practices (KAP) of norovirus infection among primary and junior middle school students in Chizhou City, explore its influencing factors. A multistage stratified random sampling method was used to randomly select 1200 students from 8 primary and junior middle schools for the effective questionnaire survey in Chizhou City. A multivariate logistic regression analysis was used to analyse the possible influencing factors. Of 1176 participants, the average knowledge score of norovirus infection was (9.8 ± 3.6), and the scoring rate was 65.3%. The average attitude score was (11.6 ± 1.2), and the scoring rate was 96.3%. The average practice score was (10.4 ± 1.5), and the scoring rate was 86.9%. The difference in the average scoring rate among the three was statistically significant (P < 0.001). The four independent variables of county, education level, sex and age group statistically correlated with the knowledge score. Primary and junior middle school students in Chizhou City had a good attitude and practice compliance in the prevention and control of norovirus infection; however, their professional knowledge still needed further improvement.

This paper explores the multifaceted impact of biofilms, bacterial communities enveloped in extracellular polymeric substance (EPS). Biofilms play a dual role, acting as environmental champions and health threats. They interact with heavy metals, either causing pollution or aiding environmental remediation. Beneficial biofilms generate electricity and repair concrete cracks, offering sustainable solutions. Conversely, they contribute to infections, posing health risks. The study emphasizes the need for in-depth research, utilizing bibliographic methods to analyze global biofilm research. By identifying keywords, core countries, and pivotal organizations, the paper informs future investigations, offering insights into biofilm research evolution and practical applications.

This paper explores the intricate world of microbial life, focusing on biofilm as a dynamic consortium of bacteria forming complex networks within a self-produced matrix of extracellular polymeric substances (EPS). Analogous to a bacterial house, these structures provide both shelter and support, playing a pivotal role in the microbial realm with dual effects on human and environmental contexts. Biofilm's significance extends beyond its microcosmic architecture, positively contributing to heavy metal removal, electricity generation through microbial fuel cells, and structural remediation, while also posing threats with detrimental formations implicated in conditions from lung to urinary tract infections. Control of biofilm becomes imperative to unlock its positive attributes and mitigate potential negatives, serving as a cornerstone for environmental remediation, sustainable energy, and infrastructure maintenance. Employing a comprehensive bibliographic approach, this study identifies keywords, countries, and organizations influencing biofilm research, proposing innovative avenues like machine learning for future exploration. The aim is not only to contribute to existing knowledge but to pioneer cutting-edge strategies for biofilm optimization and challenge mitigation, shaping its trajectory towards sustainable applications across diverse fields.

This study explores the pivotal biofilm life cycle, essential for comprehending intricate processes in biofilm formation and dispersal. With profound implications for medicine, environmental science, and industrial applications, the life cycle involves initial attachment, irreversible attachment, maturation, and dispersion. Employing bibliographic analysis, the paper unravels critical aspects, unveiling key keywords, prominent research countries/regions, and significant research organizations. This comprehensive approach provides insights into the current biofilm research landscape, identifying gaps for future exploration. Biofilm's influence extends to medical conditions, environmental ecosystems, and industrial challenges, accentuating the urgency of understanding its life cycle. The study also delves into global research distribution and influential organizations, guiding collaborative efforts. As a forward-looking initiative, it proposes future research opportunities, aiming to steer advancements and address challenges in biofilm science. Ultimately, this analysis contributes to the ongoing progress of biofilm research, fostering interdisciplinary collaboration and inspiring future initiatives.

This paper delves into the critical issue of concrete cracks, a common consequence of environmental factors and external forces, and their potential impact on the structural integrity of buildings. Recognizing the urgency of addressing this issue, the concept of self-healing concrete has emerged, aiming to revolutionize the construction industry by autonomously repairing cracks and minimizing the need for labor-intensive and time-consuming manual interventions. The paper employs a bibliographic analysis methodology to comprehensively explore the most significant technologies associated with self-healing concrete, identifying key research countries/regions and prominent research institutions in this field. Through an examination of scholarly articles, the analysis seeks to uncover key themes, technologies, and contributors within the self-healing concrete domain. Insights into the geographical distribution of research efforts and the roles of influential research institutions are provided, shedding light on collaborative networks and knowledge-sharing dynamics that drive innovation in this evolving field.

Heavy metal pollution poses a significant threat to global populations, particularly impacting human health. Developing countries, characterized by chemical engineering and mining industrial activities, often contribute to diverse heavy metal contaminants. Consumption of water contaminated with heavy metals, prevalent in such regions, can lead to severe health risks, including gastric cancer. Addressing the urgent issue of heavy metal pollution, this paper employs a bibliographic approach to explore the latest literature on heavy metal pollution. The study discusses recent research findings and potential methods for the treatment and mitigation of heavy metal pollution, emphasizing the pressing need for effective solutions to safeguard public health.

Biofilm matrix is akin to bacterial abodes, primarily composed of proteins, polysaccharides, extracellular DNA, and lipids, providing essential structural support to bacteria. While some biofilm matrix offers significant benefits, such as engineered biofilm reactors that assist in water pollutant removal, others pose risks to human health, potentially leading to conditions like lung infections. Realizing the potential of advantageous biofilm matrix and mitigating the harmful effects of detrimental ones necessitates a comprehensive and systematic exploration of this intricate domain. To facilitate this examination, our study leveraged a dataset of the 1000 most pivotal papers in the field and conducted an exhaustive bibliographic review. This paper serves as a valuable resource for understanding the current progress in biofilm matrix research and delving into vital avenues for future exploration in this dynamic field.

Biofilm is a complex community of microorganisms that are attached to surfaces and encased in a self-produced extracellular matrix. Machine learning (ML) techniques have been applied to various aspects of biofilm research, such as predicting biofilm formation, identifying key genes, and designing new therapeutic strategies. In this study, we conducted a bibliometric analysis of machine learning in biofilm research to provide a comprehensive overview of the current state of the field. We searched the Web of Science database for articles published included "machine learning biofilm". A total of 126 articles were identified and analysed. Our results showed that the number of publications on machine learning in biofilm has been increasing rapidly over the past decade, indicating a growing interest in the application of ML techniques to biofilm research. The analysis also revealed that the most common research topics in this area were related to biofilm formation, prediction, and control. Furthermore, the most frequently used ML techniques in biofilm research were artificial neural networks and support vector machines. Overall, our study provides valuable insights into the current trends and future directions of machine learning in biofilm research. It also highlights the importance of interdisciplinary collaboration between biofilm researchers and ML experts to drive innovation in this field.

The experiments of Project 8 have been excellent, but the expected goals still difficult to achieve. So much so that some of the results at your fingertips were also missing. In view of this, the focus of this article is to clarify several easy confused concepts. Only in this way, we can reasonably explain the experimental data. The main points are as follows: 1. The value c of the light speed in vacuum and a particle with zero static mass, both of which do not exist in the reality. That is to say, the so-called a photon's static mass is equal to zero but has energy, which is a paradox that confuses two different definitional domains. 2. In the reality, photons are high-speed particles generated by electromagnetic radiation. They must have the characteristics of (static) mass, energy and wave, in order to describe the main body to aim at photons from different angles. 3. After any main body comes into being electromagnetic radiation, its static mass will inevitably decrease accordingly. 4. The charge-mass ratio of an electron is a physical constant, which is the ratio of its charge to the amount of matter, and is not affected by relativistic effects and electromagnetic radiation. 5. The uncertainty of moving electrons is caused by random electromagnetic radiations. Finally, it is pointed out that if the cyclotron frequency of a single electron is measured, and at the same time, its de Broglie wavelength or frequency can also be measured, then its static mass can be obtained. Even so, the expected goals are still difficult to achieve, because random electromagnetic radiations are always taking away continually the matter composition of the single electron being measured. However, this was precisely a result of the research obtained by Project 8, and had universality, which should be reflect on.

Background: Exposure to inorganic arsenic (iAs) remains a global public health problem. The liver is the main target organ of arsenic, leading to arsenic-induced liver fibrosis. Autophagy is involved. Phosphatase and tensin homology deleted on chromosome ten (PTEN) may participate in arsenic-induced liver fibrosis by regulating autophagy, but the exact mechanisms remain unclear. We established a mouse model of arsenic poisoning through the drinking water, and a fibrosis model using the huma stellate cell (HSC) line LX-2, which was exposed to NaAsO₂ for 24h. HE and Masson staining was adopted to observe the degree of liver fibrosis. The cells were transfected using PTEN overexpression plasmid. Western blot and qRT-PCR were used to determine the levels of protein/mRNA expression. The in vivo results were confirmed in HSCs exposed to NaAsO₂, with changes suggesting fibrosis as seen in mice. NaAsO₂ upregulated the expression of the autophagic markers microtubule-associated protein light chain A/B (LC3), recombinant human autophagy effector protein (Beclin-1), hairy and enhancer of split homolog-1 (HES1), but downregulated PTEN. α-smooth muscle actin (α-SMA) expression was significantly upregulated in all NaAsO₂ groups. PTEN overexpression altered NaAsO₂-induced autophagy which LC3, Beclin-1 were downregulated. Notch1, HES1, α-SMA, and collagenⅠexpression were all downregulated in the NaAsO₂ groups. In conclusion, PTEN overexpression might decrease autophagy and inhibit fibrosis progression caused by this toxin. The NOTCH1/HES1 pathway is likely to be involved in this process. Most previous studies did not investigate PTEN and arsenic-induced liver fibrosis specifically. The present study highlights the importance of targeting PTEN for the management of arsenic exposure.

Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of the Driving Scenario Knowledge Graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable Driving Scenario Knowledge Graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.

Elevated salinity is one of the major environmental limitation factors of plant growth and development and salinity stress compromises the production and survival of plantation and urban forests and agricultural crops in the arid, semi-arid, and intertidal zones. Ulmus pumila, a salt- indigenous tree species in Asia and is widely deployed in salt-affected areas in China, and U.pumila is promising for multi-varietal forestry in plantation and urban forests. The comprehensive mechanism of the intraspecific salt tolerance is still not clear yet. Here, we investigated the physiological responses of the salinity stress based on the antioxidant enzyme activities, osmotic adjustments, and gas exchange among salt-tolerant U. pumila genotypes for 100 days under five different NaCl levels (0%, 0.3%, 0.5%, 0.7%, and 0.9% w/v) with natural surroundings and rain shade at age-2. Salt stress decreased height (HR), ground diameter (DR), and dry weight (biomass) were significantly different among genotypes. HR and performance indices were positively correlated with photosynthesis rate (Pn), apparent mesophyll conductance (AMC), and chlorophyll (CHLL) with (r= 0.7 - 0.8 ***), but were negatively related to the free proline, sugar, and protein accumulation (r=-0.5 ~ -0.7***). We found that high accumulation of sugars and more activities of SOD enzyme in leaf tissue contribute to the osmotic adjustment and ROS scavenging system under salinity treatment; the sugar content and SOD activity play key roles in U. pumila’s tolerance to salt stress, and are promising indicators for U. pumila species ex vitro selections. The ex vitro selection results align with the previous in vitro studies [37] and is promising for the MVF development.

This work presents the mathematical framework of the Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “2nd-CASAM-L”), which enables the most efficient computation of exactly obtained mathematical expressions of first- and second-order sensitivities of a generic system response with respect to functions (“features”) of model parameters. Subsequently, the first- and second-order sensitivities with respect to the model’s uncertain parameters, boundaries, and internal interfaces are obtained analytically and exactly, without needing large-scale computations. Within the 2nd-FASAM-L methodology, the number of large-scale computations is proportional to the number of model features (defined as functions of model parameters), as opposed to being proportional to the number of model parameters, which are considerably more than the number of features, being incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities (of any order) with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces. The application of the 2nd-CASAM-L methodology is illustrated using a simplified energy-dependent neutron transport model of fundamental significance in nuclear reactor physics.

EGFR tyrosine kinase inhibitors (TKIs) are the preferred initial treatment for non-small cell lung cancer (NSCLC) patients harboring EGFR mutations. However, remission is transient, and no further effective treatment options are available for EGFR-TKI-advanced EGFR-mutant NSCLCs. Immunotherapy with immune checkpoint inhibitors (ICIs) induces sustained cancer remission in a subset of NSCLCs. However, ICI therapy exhibits limited activity in most EGFR-mutant NSCLCs. Mechanistically, the strong oncogenic EGFR signaling in EGFR-mutant NSCLCs contributes to the non-inflamed tumor immune microenvironment (TIME), characterized by a limited number of CD8+ T cell infiltration, a high number of regulatory CD4+ T cells and a high number of inactivated infiltrated T cells. Besides, EGFR-mutant NSCLC patients are generally non-smokers with low levels of PD-L1 expression and tumor mutation burden. However, current understanding only partially explains why a small population of EGFR-mutant NSCLCs durably respond to ICI therapy, resulting in many researchers actively working in this field. This review reviews the hope seen from pre-clinical studies and clinical trials which may be adopted to improve the outcome of ICI therapy in EGFR-mutant NSCLCs. Besides, the underlying mechanisms leading to the inferior clinical outcome of ICI therapy in EGFR-mutant NSCLCs are discussed.

In intermediary metabolism, enzymes are mainly for the specific provision of one small molecule from another, B from A, for its further use. But there are also decades of work relating to another evolved purpose, which is to prevent untoward effects of odd compounds of various origins, and of ordinary intermediates, whose improper concentrations may be avoided by hydrolases and other activities of metabolic correction.

Effective discharge, which represents the flow, or range of flows, that transport the most sediment over long term, was determined based on the mean daily flow discharge and mean daily suspended sediment discharge recorded between 1994 and 2014 at four gauging stations along the Trotuș River. This study proposes an efficient method for the estimation of effective discharge based on observed values of the suspended sediment load. By employing this method the suspended sediment load is no longer either under- or overestimated as in the cases when the assessment is based on sediment rating curves. The assessment on effective discharge was performed at two distinct levels: for the entire data series during the investigated time spans and, subsequently, for flows less than the bankfull discharge. The effectiveness curves of the suspended sediment transport characteristics revealed highly multimodal characteristics with many peaks, indicating ample ranges for the effective discharges. The main effective discharge corresponded to large flood events, which are typical for the upper end of the discharge range, whereas the secondary effective discharges corresponded to sub-bankfull flows, which are more frequent. The changes that occurred in the channel bed are reflected by the temporal variations in the effective discharge.

Fully mechanized mining face production involves complex geological conditions; mining pressure; gas, water, fire and other mining environments. The working face needs a shearer, hydraulic support, a scraper conveyor and other equipment for coordination. The deep integration of intelligent technology with factors such as "humans, machines, the environment, and management" in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for coal mining robot technology systems, including single robots, robotized equipment, robot clusters and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve customized research and development and standardized production of various types of robots. A specific analysis was conducted on the research progress of common key technologies such as explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mine robot research and development is proposed. Moreover, robotic mining systems should be promoted to enhance the overall intelligence level and mining efficiency of mining equipment. To develop human‒machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots. The digital twinning of the entire mine robot system should be accelerated, the normalized operation level of coal mine robots should be improved, research on coal mining robots, support robots, and transportation robots should be carried out, intelligence should be achieved in fully mechanized mining faces, and equipment support for fewer and unmanned fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization has been summarized, and the technical and engineering feasibility of the coal mining machine population has been verified.

Gesture recognition technology has been quickly developed in the field of human-computer interaction. The multiple-input multiple-output (MIMO) radar has been widely adopted in gesture recognition because of its notable spatial resolution. In this work, a highly accurate MIMO radar-based hand gesture recognition algorithm with very low complexity is proposed. To make the proposed system applicable in the industry and work well even when the size of the training data is limited, we applied several low-complexity adaptive signal processing methods to extract features and reduce the noise effect. First, spectrum analysis is applied to range-Doppler maps (RDMs) and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness to noise. Afterward, the features related to the distance, speed, direction, and the elevation angle of the moving object are determined by the proposed adaptive signal analysis techniques and the random forest is applied for classification. The proposed system has the capability to precisely distinguish and identify eight motions, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anticlockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.

Work on callings has burgeoned in the past 20 years, yet recent reviews exposed a lack of conceptual clarity and disagreements around its definition, components and measures. One lingering point of contention revolves around the element of prosociality: Is a calling orientation primarily motivated by self-interest, prosocially orientated, or a mix of both? This conceptual paper reviews and examines the pro-self and prosocial component of a calling outlook, by examining and comparing the ways in which they feature in different calling subtypes: Classic, neoclassic, and modern callings. Our analysis suggests that these subtypes vary in where they are located on a pro-self – prosocial continuum: Classic callings are located on the prosocial side of the axis, modern callings are located on pro-self side of the axis, and neoclassic callings can be situated in the middle of the con-tinuum, integrating self-orientated and other-orientated motivations. Our analysis further suggests that these calling subtypes draw on divergent value systems: Classic callings are propelled by self-transcendent values, modern callings are driven by self-actualization motivations, and neoclassic callings integrate both value systems. We therefore argue that the subjective experiences of pursuing a calling within each subtype pathway differ, alt-hough they may culminate in similar outcomes. The paper offers a novel framework for analyzing people’s calling that draws on their values.

This paper utilizes dynamic panel threshold technology to conduct a nonlinear test on the direct effect between environmental regulation and economic fluctuations and the effect of industrial structure upgrading by taking 35 industrial sectors in China from 2003 to 2016 . The research has found that there is an inverted "U" relationship between environmental regulation and economic fluctuations, with the enhancement of environmental regulations, the economic fluctuation increases first and then decreases. The cross-terms of environmental regulation and industrial structure rationalization or industrial structure upgrading are significantly negative, which indicates that the enhancement of environmental regulation is conducive to promoting industrial structure upgrading and reducing the economic fluctuations. While the rationalization factors of industrial structure and advanced industrial structure are significantly negative, indicating that both forms of industrial structure upgrading are conducive to reducing the economic fluctuations. Environmental regulation has technical innovation thresholds for industrial structure upgrading and economic fluctuations, but there are no human capital or FDI thresholds. In the rationalization model of industrial structure, there is a nonlinear "U" relationship between environmental regulation and economic fluctuations when the proportion of scientific research expenditure is more than 1.35%. With the enhancement of environmental regulation, the economic fluctuation reduces first and then increases, and the corresponding inflection point value is 2.398% of the environmental regulation level. At the same time, the environmental regulation can indirectly reduce economic fluctuations by pushing down the industrial structure upgrades. In the advanced model of industrial structure, there is a “U” relationship between environmental regulation and economic fluctuation when the proportion of scientific research expenditure is greater than 1.26%. With continuous enhancement of environmental regulation, the economic fluctuation reduces first and then increases. The corresponding inflection point value is 1.78% of the environmental regulation level, and environmental regulation can indirectly reduce economic fluctuations by promoting the industrial structure at the same time.

Kluyveromyces marxianus (K. marxianus) is a newly emerging industrially relevant yeast. It is known to possess a highly efficient Non-Homologous End Joining (NHEJ) pathway that promotes random integration of non-homologous DNA fragments into its genome. The nature of the integration events was traditionally analyzed by Southern blot hybridization. However, the precise DNA sequence at the insertion sites were not fully explored. We transformed a PCR product of the Saccharomyces cerevisiae URA3 gene (ScURA3) into an uracil auxotroph K. marxianus wildtype strain and picked 24 stable Ura+ transformants for sequencing analysis. We took advantage of rapid advances in DNA sequencing technologies and developed a method using a combination of Illumina MiSeq and Oxford Nanopore sequencing. This approach enables us to uncover the Gross Chromosomal Rearrangements (GCRs) that are associated with the ScURA3 random integration. Moreover, it will shine a light on understanding DNA repair mechanisms in Eukaryotes, which could potentially provide insights for cancer research.

Solanum nigrum fruits have been conventionally available as a material of beverage due to its nutritional substances such as minerals, vitamins, amino acids, proteins, sugars, polyphenols and anthocyanins. Here has rarely reported on the characterizaton of the components and the regulatory mechanism of Anthocyanins in S. nigrum. In this study, we determined that the peel and flesh of S. nigrum fruits shared the similar HPLC profiles, but the different contents and total antioxidant activities for Anthocyanins. After an efficient purification method mainly including extraction with pH 1.0 distilled water and then desorption with pH 1.0 95% ethanol after a DM-130 resin adsorption step to obtain more pure anthocyanins extracts, the purity of anthocyanins extract from S. nigrum fruits reached to 56.1%. Moreover, eight anthocyanins from S. nigrum fruit were identified with HPLC-MS/MS for the first time. A typical R2R3-MYB transcription factor gene, SnMYB, was also cloned for the first time by RACE-PCR from S. nigrum. Moreover, the contents of anthocyanins was shown a good correlation (r = 0.93) with the expression levels of SnMYB during the fruits developmental stages. Most significantly, SnMYB successfully produced high anthocyanins contents (1.03 mg/g) when SnMYB was transiently expressed in tobacco leaves. Taken together, S. nigrum fruits are a promising resource for anthocyanins extraction and SnMYB is an activator that positively regulates anthocyanins biosynthesis in S. nigrum.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating Photoplethysmogram (PPG) and Electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls. Feature selection techniques were used to systematically select key features which were identified for their clinical relevance and significance in capturing cardiovascular dynamics and to reduce computational complexity and to decrease the chance of overfitting the ML algorithms. These features are then utilized to train and evaluate machine learning algorithms, resulting in a model with an impressive accuracy of 98%, sensitivity of 97.60%, specificity of 96.90%, and precision of 97.20%. The integrated approach outperforms single-signal strategies, showcas-ng its potential for early, precise, and non-invasive heart failure diagnosis. Furthermore, the study underscores the significance of continuous monitoring through wearables, emphasizing the benefits of integrating multiple signals for a comprehensive evaluation of cardiovascular health. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

Virtual reality (VR) technology gains theoretical support from rehabilitation and pedagogical theories and offers a variety of capabilities in educational and interventional contexts with affordable products. VR is attracting increasing attention in the medical and healthcare industry as it provides fully interactive three-dimensional simulations of real-world settings and social situations, which are particularly suitable for cognitive and performance training including social and interaction skills. The worldwide rising trend in the prevalence of autism spectrum disorder calls for innovative and efficacious techniques for assessment and treatment. The article offers a summary of current perspectives and evidence-based applications of VR technology as an educational and intervention tool for individuals with autism spectrum disorder, with a primary focus on social communication including social functioning, emotion recognition, and speech and language. Technology- and design-related limitations as well as the disputes over the application of virtual reality to autism research and therapy are discussed and future directions of this emerging field are highlighted with regards to application expansion and improvement, technology enhancement, and the development of brain-based research and theoretical models.

The aim of our study is to utilise longitudinal and representative national data to explore the extent that the association between the fast food environment and overweight in adolescents is confounded by neighbourhood deprivation. Longitudinal data from the Millennium Cohort Study for England were obtained for waves 5 (ages 11/12; 2011/12; n=13,469) and 6 (ages 14/15; 2014/15; n=11,884). Our outcome variable was overweight/obesity defined using age and sex-specific International Obesity Task Force cut points. Individuals were linked, based on their residential location, to data on the density of fast food outlets and neighbourhood deprivation. Structural Equation Models were used to model associations at both ages and explicitly test for confounding. While we found some evidence for an association between the number of fast food outlets and overweight, any associations disappeared following accounting for the confounding nature of neighbourhood deprivation. Neighbourhood deprivation was consistently associated to overweight, with adolescents who resided in deprived areas more likely to be overweight. Results were largely consistent depending on different methodological decisions. Our findings suggest that policy efforts should prioritise focusing on tackling the social determinants of excess body mass which will be more effective than interventions aimed at the built environment.

This study presents the development of a number of finite element (FE) models of the pelvis using different continuum and structural modelling approaches. Four FE models were developed using different modelling approaches: continuum isotropic, continuum orthotropic, hybrid isotropic and hybrid orthotropic. The models were subjected to an iterative adaptation process based on the Mechanostat principle. Each model was adapted to a number of common daily living activities (walking, stair ascent, stair descent, sit-to-stand and stand-to-sit) by applying onto it joint and muscle loads derived using a musculoskeletal modelling framework. The resulting models, along with a structural model previously developed by the authors, were compared visually in terms of bone architecture, and their response to a single load case was compared to a continuum FE model derived from CT imaging data. The main findings of this study were that the continuum orthotropic model was the closest to the CT derived model in terms of load response albeit having less total bone volume, suggesting that the role of material directionality in influencing the maximum orthotropic Young's modulus should be included in continuum bone adaptation models. In addition, the hybrid models, where trabecular and cortical bone were distinguished, had similar outcomes, suggesting that the approach to modelling trabecular bone is less influential when the cortex is modelled separately.

This comprehensive review explores the principles, applications, and future directions of human-powered energy generation technologies. It begins by providing a detailed examination of viable human energy sources, elucidating the physical principles behind various energy harvesting devices. The review then compiles and assesses existing products in the field of human power generation, while also outlining the opportunities and challenges it presents. Finally, an innovative concept of an eco-village system is introduced to demonstrate a future potential application scenario for human-generated electricity. It outlines two developmental stages for this system and underscores its significance in the construction of the harmonious and sustainable communities. This forward-thinking approach could pave the way for broader discussions on sustainable living and eco-friendly energy generation in a world increasingly focused on environmental concerns and social stability.

Strategic intelligence covers the examination of what impacts and influences the business and the related SWOT analysis, (strengths, weaknesses, opportunities, and threats mapping). Through this research, leaders will be able to properly allocate and re-route resources, identify areas of improvement, and ensure that strategic initiatives are aligned with the organization's mission. Strategic intelligence is, therefore, effective through the use of critical thought and data-driven decisions and seeing the outcomes of different positioned alternatives. It must also form and support a culture of cooperation and open communication within the organizations, stimulating employees' inputs and learning curves. The article's subject is the necessity for strategic intelligence, a crucial competence for leaders, managers, and companies striving to remain competitive, to be utilized and best leveraged in today's volatile and dynamic business contexts. It helps the leadership turn proactive, foresee trends, and make correct, agile decisions that enforce companies' long-term growth and success. This study follows a research design that includes a thorough literature review and bibliometric research, executed via VoSViewer, illustrating the concept of strategic intelligence from a leadership point of view as a driver of competitive advantage. Further, the article points out that the limitations are acknowledged because the literature on the matter is limited, as strategic intelligence is relatively new to the business sector; moreover, more research is required to fully understand and use the potential of strategic intelligence in business growth. By overcoming these limitations and continuing to study this concept, businesses can gain a competitive edge and make informed decisions that fuel their success in an ever-evolving market. As a result, this study contributes to developing strategic intelligence in leadership and business and paves the road for further research and applications of strategic intelligence theories in organizational contexts.

Conflict between agricultural water resources and socio-economy development is a global problem. Accurate evaluation of coordinated development of agricultural water resources and socio-economy and risk mitigation is necessary for sustainable development. An evaluation method, including selection of criteria, data collection, determination of weight, evaluation of coordinated development, prediction of parameters, and judgment of coordinated development state, has been proposed to study coordinated development degree. To deal with uncertainties, Monte Carlo method and fuzzy set method were used. The method is demonstrated to solve a real-world evaluation problem in Shanxi Province in the middle of China. Results show that coordinated development degrees were (0.7, 0.8) for most of the cities of Shanxi in 2015, indicating that coordinate development state was intermediate coordinate. To achieve balanced development, more attention should be put on socio-economic development in Taiyuan and Yanquan, and agricultural water resources utilization in Jinzhong, Yuncheng and Xinzhou. The average coordinated development degree is 0.758, and coordinate development state was intermediate coordinate from 2006 to 2015. Coordinated development degree has a trend of decreasing markedly, coordinate development state will be barely coordinated, and agricultural water resources utilization lags behind socio-economic development in 2020. The study demonstrates the practicability of the improved method, by evaluating coordinated development degree under uncertainty and forecasting future risks, which will conduce to promote sustainable development of agricultural water resources and socio-economy.

To solve the benefit allocation problem between the subjects of the supply chain of general contracting projects. The Cobb-Douglas function is applied to establish the benefit distribution model when the two cooperate in the supply chain system, analyze the optimal benefit situation of one-time cooperation between the two parties under two modes of egoistic decision and collectivistic decision as well as the benefit distribution problem of multiple cooperation, and conduct numerical simulation to calculate the functional relationship between each decision variable and the contribution degree of the general contractor under different effort cost coefficients. The results show that：In the case of one-time cooperation between general contractors and subcontractors, regardless of the decision mode, the degree of effort of both parties is positively related to their own contributions and negatively related to each other's contributions; the overall benefit of the supply chain system is positively proportional to the degree of contribution of the general contractor and inversely proportional to the share of benefit distribution of the subcontractor; in multiple cooperation, the equilibrium result of both parties achieving Pareto optimality at each stage is that both parties choose to cooperate.

In this paper, industrial structure distortion, two-way FDI and carbon emission intensity are brought into a unified research framework, and based on China's panel data from 2011 to 2020, empirical tests are conducted employing Exploratory Spatial Data Analysis (ESDA), spatial econometric model and intermediary effect test. The results show the following. Firstly, China's industrial structure distortion index shows a downward trend. The industrial structure distortion index is the highest in the west, followed by the middle, and the lowest in the East. Secondly, the relationship between carbon emission intensity and economic development shows a "decoupling" effect and keeps decreasing year by year. The spatial disparity is remarkable, showing the pattern of "the east leading, the middle catching up and the west lagging ". At the provincial level, except in Xinjiang province, the carbon emission intensity of other provinces showed different degrees of decline. In terms of spatial distribution, the polarization characteristics of carbon emission intensity are significant, and the traditional spatial distribution pattern has been broken. Thirdly, there is a positive spatial correlation between China's industrial structure distortion, two-way FDI and carbon emission intensity. The distortion of industrial structure will not only lead to the increase of local carbon emission intensity but also produce reverse spillover to adjacent areas. IFDI and OFDI provide a strong driving force for the decline of carbon emission intensity. IFDI promotes the decline of carbon emission intensity in adjacent areas, while OFDI will increase the carbon emission intensity in surrounding areas. The interaction of IFDI and OFDI can significantly reduce the carbon emission intensity of local and adjacent areas. Fourthly, the results of intermediary effect analysis show that two-way FDI is the two channels of industrial structure distortion affecting carbon emission intensity. Industrial structure distortion can affect the transmission mechanism of carbon emission intensity by affecting two-way FDI.

As the lifespan of porous asphalt pavement extends, it invariably experiences performance degradation and an increase in tire/pavement noise compared with the original surface when it is newly built. To understand the medium-term performance of porous asphalt pavement during service and reveal the influence of different pavement performance indicators on tire/pavement noise generation, a seven-year continuous observation and data analysis study was conducted. Porous asphalt with different designed material parameters was taken into account, including thin-layer porous asphalt overlay with a maximum nominal aggregate size of 10mm (PUC-10), porous asphalt with a maximum nominal aggregate size of 13mm (PAC-13), and a two-layer porous asphalt incorporating both PUC-10 and PAC-13 (PUC-10+PAC-13). Using automated pavement technology testing equipment, key performance indexes, including damage rate (DR), rut depth (RD), international roughness index (IRI), and sideway force coefficient (SFC), were measured and calculated. The on-board sound intensity (OBSI) method was used to test noise levels in three types of porous asphalt pavement structures and on a dense graded easy compact asphalt concrete (ECA) for comparison. The progressions of the medium-term performances were analyzed according to the measured results. The correlations between tire/pavement noise levels and testing speed, testing lane, pavement structure, and road condition indicators were investigated by regression. The research results revealed that the DR and SFC of the three types of porous asphalt pavement displayed more significant attenuation in the service period, while the IRI and RD showed small changes. Among them, the SFC demonstrated significant attenuation over the first 4 years, decreasing between 13.8% and 17.0%. From the 3-7 years of operation, the transverse crack disease in porous asphalt pavement progressed rapidly. The three porous asphalt pavement structures tested exhibited commendable noise reduction capabilities. Compared to ECA, the OBSI noise levels of these structures were lower by 2.09 dB, 1.53 dB, and 2.88 dB, respectively. Compared to other pavement condition indicators, RD had a more significant impact on the OBSI value of the PUC-10 porous asphalt pavement. For the PUC-10+PAC-13 porous asphalt pavement, there was a significant linear relationship between the OBSI value and SFC, mainly due to polishing of the coarse aggregate surface. It leads to a decrease of the micro-texture, which lies in increasing tire/pavement noise at high-frequency ranges and decreasing SFC. This study will have guiding significance for the research of the attenuated law and durability improvement of porous asphalt tire/pavement noise.

Affective prosody is an indispensable cognitive cue that moderates social activities, and has become a prevailing research topic in psychology-related disciplines. The present study conducts the first bibliometrics-based visualization analysis concerning affective prosody to evaluate the influential cases, including countries/regions, institutions, publication venues, academic articles, and disciplinary contributions, and the diachronic changes of publication trends and research hotspots. With the combination of statistical results and a qualitative literature inspection, limitations of extant studies and promising research directions were also proposed. The present study extracted the bibliographic data of 1,624 articles retrieved from the Web of Science Core Collection, which were published over the past 25 years (1997-2021). Statistical results revealed four leading powers (the U.S., Germany, England, and Canada) and four emerging fronts (China, France, Netherlands, and Switzerland), and identified three primary research themes in this field, including clinical implication, measurable index, and modality-specific issues. Literature inspection demonstrated current limitations in individual characteristics control and experiment-related influential factors, and proposed two prosperous research directions. Findings of the present study could facilitate academic retrieval of affective prosody research, help concerned researchers identify thematic hotspots and seek appropriate collaboration, and provide convenience for research policy and management in this field.

Being able to forecast events is of great importance in many fields, from brain behavior to earthquakes or stock markets. Because each dynamical system has intrinsic features, different statistical tools have to be used for each system. Here we study the time series of the output intensity of a fiber laser with an ordinal patterns analysis, and we look for temporal correlations in order to statistically forecast the most intense events. We set two thresholds, a low one and a high one, to distinguish between low intensity versus high intensity events. We find that when the time series is performing events below the low threshold it shows some preferred temporal patterns before performing events above a high threshold.

This research was to investigate beneficial impact and molecular mechanism of B. coagulans on piglets intestine. Twenty-four 21 days old weaned piglets were allotted to three treatments: control group (basal diet), B6 group (basal diet + 2×10⁶ CFU/g B. coagulans), B7 group (basal diet + 2×10⁷ CFU/g B. coagulans). The results showed that compared with control group, B6 and B7 group significantly decreased diarrhea rate and the concent of CHOL, GGT and DAO in plasma; decreased villus height and increase crypt depth in jejunum and ileum; increased the activities of SOD and CAT and decreased the concent of MDA and H₂O₂ in intestine. These data suggested that supplementing B. coagulans had beneficial impacts on promoting nutrients metabolism, maintaining intestinal integrity and alleviating oxidative stress and diarrhea. Futher research of molecular mechanisms showed that, these beneficial impacts were regulated by changing expression levels of related proteins (including HSP70, Caspase-3, Bax, Villin and Occludin), and genes (including RPL4, IFN-α, IFN-β, IFN-γ, MX1, MX2, OAS1, IL-1β, IL-4, CXCL-9, CCL-2, AQP3, SGLT-1, LPL, INSR and b^0,+AT), and altering community composition of gut microbiota (particularly family Clostridiaceae, Enterobacteriaceae, and Veillonellaceae and genus Prevotella, Turicibacter, and Lactobacillus).

Artificial intelligence (AI) systems are poised to become deeply integrated into society. If developed responsibly, AI has potential to benefit humanity immensely. However, it also poses a range of risks, including risks of catastrophic accidents. It is crucial that we develop oversight mechanisms that prevent harm. This article outlines a framework for evaluating and auditing AI to provide assurance of responsible development and deployment, focusing on catastrophic risks. We argue that responsible AI development requires comprehensive auditing that is proportional to AI systems’ capabilities and available affordances. This framework offers recommendations toward that goal and may be useful in the design of AI auditing and governance regimes.

The mechanism underlying microbiologically induced carbonate precipitation have not been thoroughly characterized, although numerous scholars and experts have specifically investigated questions regarding minerals induced by bacteria. The study of the precipitation of carbonate minerals induced by halophilic bacteria has aroused wide concern. The present study aimed to investigate the characterization and process of biomineralization in high salt systems by a halophilic bacterium, Chromohalobacter israelensis strain LD532 (GenBank: KX766026), which was isolated from the Yinjiashan Saltern in China. Carbonate minerals induced by LD532 were investigated in several sets of comparative experiments that employed magnesium sulfate and magnesium chloride as Mg resources. Magnesium calcite and aragonite were induced by LD532 bacteria, whereas these minerals did not appear in the control group. The mineral phases, micromorphologies, and crystal structures were analysed using X-ray powder diffraction, scanning electron microscopy, and energy dispersive X-ray detection. The carbonic anhydrase and urease secreted by strain LD532 through metabolism increased the pH value of the liquid medium and promoted the process of carbonate precipitation. Further study using high resolution transmission electron microscopy, energy dispersive X-ray detection and analysis of ultrathin slices showed that the nucleation sites of carbonate minerals were located on extracellular polymeric substances and the membranes of intracellular vesicles of LD532 bacteria, which provided favourable conditions for the growth of carbonate mineral crystals. The morphologies and compositions of minerals formed in solutions of MgSO₄ and MgCl₂ display significant differences, indicating that different sources of Mg²⁺ may also affect the physiological and biochemical activities of microorganisms and thus mineral deposition. This study will be of some interest for the interpretation of carbonate biomineralization in natural salt environments and has some value as a reference in understanding sedimentary carbonates in ancient marine environments, such as tidal flats.

Poplar is a globalized commercial tree species that supports humanity's economy, energy, and ecology. To evaluate the twelve hybrid Populus euramericana genotypes developed in China, a total of six locations were selected for the test, comprising four climatic types and three soil kinds. The objective of this study is to characterize the early stages of Populus euramericana growth and test locations; to identify good genotypes for stable and high yield that may be encouraged; and to offer practical experience and technical assistance for further breeding of Populus euramericana. Main research methods include the statistical description of tree heights and diameter at breast heights[DBH], the establishment of a mixed effect model to analyze the genotype and environmental interaction effect [G×E], the use of best linear unbiased prediction[BLUP] values as GGE biplots to achieve visual screening, and the calculation of genetic parameters. Results show that the genotype effect [G], the environmental effect [E], and the G×E is significant; the BLUP value has a strong correspondence with the observed value; the goodness of fit of all biplots can explain more than 85% of the variation; broad-sense heritability of tree height and DBH is 0.13 and 0.3, type-B correlation is 0.36 and 0.65; G5, G7, G4 and G9 are excellent genotypes with high yield and stability; using these four genotypes tree height and DBH can get 3.35% and 0.81% genetic gains.The study concludes as follows: Rank-change interaction and scale-effect interaction were distinctly occurred. The G, E, and G×E all had a significant effect on the growth of poplar trees during their early stage. G4, G5, G7, and G9 genotypes have favorable development characteristics. N146 is a great source of paternal genetics.

As artificial intelligence takes an increasingly prominent role in healthcare, a growing body of research is being dedicated to its application in the investigation of communication sciences and disorders (CSD). This study conducts a bibliometric analysis of AI-based research in the field of CSD published up to December 2023. Utilizing the Web of Science and Scopus databases, we identified 15,035 publications, with 4,375 meeting our inclusion criteria. From 1985 onwards, there has been a consistent annual increase in publications, averaging at 16.51%, notably surging from 2012 to 2023. The primary communication disorders studied include autism, dysarthria, dementia, Parkinson’s, and aphasia. Noteworthy AI techniques employed in CSD research encompass machine learning, automatic speech recognition, support vector machine, and deep learning. Our analysis provides a comprehensive overview, serving as a valuable resource for researchers, developers, and professionals seeking to comprehend the evolving landscape of AI in CSD research. Moving forward, collaborative efforts need to prioritize addressing ethical concerns, ensuring data privacy, enhancing methodological enhancements, and considering ecological validity in the application of AI in studies related to communication disorders.

This work presents a new scheme for autonomous control and obstacle detection of a catamaran unmanned surface vessel. Based on the Newtonian mechanics, hydrodynamics, and maneuvering modelling group modelling principles in the attached coordinate system, a three-degree-of-freedom motion model of the catamaran unmanned surface vessel is constructed. The fuzzy inference method is used to overcome the drawback that the traditional PID control cannot tune the PID parameters online, and the trajectory controller is designed. Meanwhile, 3D LIDAR is used to achieve obstacle detection. The results of the trajectory tracking simulations and experiments verify the accuracy of the model design and the effectiveness of the autonomous control design.

Cancer is a major concern for people, and accurately predicting the probability of cancer incidence and mortality is an important research topic. With the development of big data and artificial intelligence technology, a new machine learning model has emerged. Using 72,591 pieces of data, including age, case count, population size, race, gender, site of onset, and year of discovery, we built a machine learning model. Through calculations, we found that the decision tree, random forest, logistic regression, support vector machine, and neural network achieved testing accuracies of 62.11%, 61.68%, 54.53%, 55.72%, and 63.10%, respectively. With the help of this model, scientists and policymakers can accurately predict future cancer incidence and mortality rates through databases, allowing them to make relevant policies (such as timely allocating doctors and medical resources) to better serve the people.

This paper examines the use of business maturity models as a tool for achieving business maturity and improving organizational performance in a rapidly changing business environment. Specifi-cally, the paper evaluates existing maturity models and methodologies, analyzes their effective-ness, and develops a novel and enhanced model tailored to the oil and gas sector. The authors highlight the importance of considering fundamental factors that underlie the implementation of maturity models and the need to evaluate their fit with organizational goals and values. The pa-per concludes by emphasizing the ongoing nature of business maturity and the effectiveness of theproposed Business Maturity Framework (BMF) model in enhancing operational excellence, competitiveness, and sustainable growth in the oil and gas industry. The paper makes a notable contribution to the specialized literature precisely through the differ-ent approaches to the business maturity process and through the proposed business maturity model itself as a base of strategy formulation, which includes a new dimension. This dimension analyzes whether the market and customers are ready for the level of digitization that the com-pany wants to achieve in a future state.

B lymphocyte development has two DNA recombination processes: V(D)J recombination of the immunoglobulin (Igh) gene variable region and class switching of the Igh constant regions from IgM to IgG, IgA, or IgE. V(D)J recombination is required for successful maturation of B cells from pro-B to pre-B to immature-B and then to mature B cells in the bone marrow. CSR occurs outside of the bone marrow when mature B cells migrate to peripheral lymphoid organs, such as spleen and lymph nodes. Both V(D)J recombination and CSR depend on an “open chromatin” state that makes DNA accessible to specific enzymes, recombination activating gene (RAG), and activation-induced cytidine deaminase (AID). Acetyltransferases GCN5 and PCAF possess redundant functions acetylating histone H3 lysine 9 (H3K9). Here, we generated a mouse model that lacks both GCN5 and PCAF in B cells. We found that double-deficient mice possess low levels of mature B cells in the bone marrow and peripheral organs, an accumulation of pro-B cells in bone marrow, and reduced CSR levels. We conclude that both GCN5 and PCAF are required for B cell development in vivo.

Orogenic antimony-gold deposits contribute significantly to the global antimony resource base. China's orogenic antimony-gold deposits are primarily in southern Tibet. Investigations indicate that antimony combines and migrates with sulfur hydroxides in ore-forming fluids. Previous research on fluid inclusions in orogenic antimony-gold ores with sedimentary rock accommodation revealed the presence of organic inclusions, including liquid hydrocarbons, alongside common components such as CO2, H2O, CH4, and NaCl. However, the impact of liquid hydrocarbons on antimony migration and mineralization is still de-batable. To investigate the transportability of antimony by liquid hydrocarbons in orogenic antimony ores, we selected n-dodecanethiol and n-dodecane as the subjects. We measured the solubility and occurrence form of antimony in these compounds at various temperatures and durations. The results indicate that after 5 and 10 days of reaction at 100 °C, the antimony concentrations in the n-dodecanethiol and n-dodecane groups were 67.44±7.62 ppm, 75.15±16.74 ppm, 1.40±1.02 ppm, and 3.02±3.09 ppm, respectively. At 150 °C for 5 and 10 days, the respective concentrations were 50.58±5.39 ppm, 77.26±45.20 ppm, 2.66±3.08 ppm, and 2.41±2.03 ppm. At 200 °C for 5 and 10 days, the corresponding concentrations were 339.76±71.94 ppm, 218.97±25.03 ppm and 6.53±7.17 ppm, 2.27±0.82 ppm (n=3). The measured solubility of antimony in the n-dodecanethiol group increased gradually with rising temperature. The solubility of antimony in the n-dodecane group was low and notably inferior to that observed in the n-dodecanethiol group. X-ray photoelectron spectroscopy (XPS) analysis demonstrated a distinct thiol (R-SH) peak at 163.31 eV and compound peaks of antimony reacting with thiols at 162.06 and 160.87 eV. This suggests that antimony predominantly forms complexes with thiols for migration. Our findings suggest that specific liquid hy-drocarbon components, predominantly thiols, can interact with antimony at metallogenic temperatures and persist in ore-forming fluids, facilitating migration and mineral enrichment. Earlier experimental studies on gold and crude oil have indicated that liquid hydrocarbons also play an essential role in the transportation and enrichment of gold during the formation of gold deposits, thus indicating that liquid hydrocarbons possess the considerable potential to act as an ore-forming fluid during orogenic antimony-gold deposit formation in southern Tibet.

In the paper, a fully discrete compact difference scheme with $O(\tau^{2}+h^{4})$ precision is established by considering the numerical approximation of the one-dimensional Allen-Cahn equation. The numerical solutions satisfy discrete maximum principle under reasonable step ratio and time step constraint is proved. And the energy stability for the fully discrete scheme is investigated. An example is finally presented to show the effectiveness of scheme.

Background: Ultraviolet (UV) radiation from the sunlight can harm skin cells, causing short-term effects, such as sunburns and long-term effects, such as pigmentation changes, changes in the skin's immune responses, and increased risk of skin cancer. These conditions can be largely prevented with proper sun protection measures and behavioural changes. This study aims to assess the knowledge on sun protection behaviour and baseline knowledge of medical students from the “Carol Davila” University of Medicine and Pharmacy, Bucharest, Romania. Methods: Data in this cross-sectional study were collected through a Google Forms questionnaire, which was distributed from November 2023 to December 2023 via digital communication channels to students across all academic years. STROBE guidelines were consulted for data synthesis; Results: A total of 668 stu-dents responded. The student cohort demonstrated a commendable level of awareness of the risks associated with sun exposure and the advantages of employing sun protection factor (SPF); how-ever, the incidence of sunburns remained notably elevated (72.3%). Further analysis revealed cor-relations among gender, academic year, presence of photosensitizing conditions, and adherence to SPF application. Additionally, there is a need to improve understanding of the appropriate appli-cation techniques for SPF, as only 11.5% of the students exhibited proficiency in this regard; Con-clusions: As future healthcare providers, medical students hold significance in promoting healthy behaviours in the broader community. This study indicates that while their knowledge about sun protection is satisfactory, there is scope for enhancement.

This paper proposes a compact planar Wi-Fi antenna with optimized radiation patterns for small uncrewed aerial vehicle (UAV) applications in both urban and open areas. It is suitable for mounting on the outermost side of the non-metallic wing of small UAVs. It has small dimensions of 16.5 mm (L) by 30.3 mm (W) by 1.6 mm (h), and the measured results of its prototype are in agreement with simulated data. Its impedance bandwidths over the two frequency ranges are 2.11 to 2.58 GHz and 5.06 to 7.5 GHz ( ). At 5.8 GHz, it has stronger radiation below the small UAV to reduce interference from rare-use directions. Its maximum radiations, the directions of the maximum radiation in each elevation plane, are below the UAV and between 14° and 29° from the horizontal plane. At 2.4 GHz, it has quasi-omnidirectional radiation to ensure a stable link in all directions, and its maximum radiations are near the horizontal plane. The optimized radiation patterns at 5.8 and 2.4 GHz can provide more antenna gain when the small UAV flies farther in urban and open areas, respectively. In addition, it has good vertically polarized radiation for long-distance applications.

The variable-length arbitrary Lagrange-Euler (ALE)-ANCF finite element, which employ nonrational interpolating polynomials, cannot exactly describe the rational cubic Bezier curves such as conic and circular curves. The rational absolute nodal coordinate formulation (RANCF) finite element, whose reference length (undeformed length) is constant, can exactly represent the rational cubic Bezier curves. A new variable-length finite element called the ALE-RANCF finite element, which is capable of accurately describe the rational cubic Bezier curves, is proposed by combining the desirable features of the ALE-ANCF and RANCF finite element. In order to control the reference length of ALE-RANCF element within a suitable range, element segmentation and merging schemes are proposed. It is demonstrated that exact geometry and mechanic is maintained after the ALE-RANCF element is divided into two shorter ones, and compared with the ALE-ANCF elements, there are smaller deviations and oscillations after two ALE-RANCF elements are merged into a longer one. Numerical examples are presented and the feasibility and advantages of the ALE-RANCF finite element are demonstrated.

Carotid atherosclerotic lesions are a major cause of cerebrovascular disease (CVD). Identification and quantification of carotid plaques are important for categorizing the vulnerability of plaques for rupture and assessing the impact of treatments. The irregularity of plaque surface is associated with previous plaque rupture and plays an important role in the risk of stroke. Thus, the aim of this study is to develop and validate novel vulnerability biomarkers from three-dimensional ultrasound (3DUS) images by analyzing the surface morphological characterization of carotid plaque using fractal geometry features. 3D box-counting and 3D blanket are the two types of 3D fractal dimension that were employed to describe the smoothness of plaques. This fractal dimension analysis tool was used to evaluate the effect of atorvastatin using 3DUS carotid images, which were acquired from 6 patients treated with atorvastatin with 80 mg daily and 5 patients with placebo. The Student's T Test results showed that those two fractal features were effective for detecting the statin-related changes in carotid atherosclerosis with p<0.0068 and p<0.015 respectively, suggesting that 3D fractal dimension measurements can be used effectively to analyze the surface characteristics of carotid plaques, especially for evaluating the impact of the treatment.

China is in the climbing phase of the "Inverted U" environmental Kuznets curve, from excessive environmental consumption to the development stage of governance and protection, and green innovation. It is the most complex period of environmental quality. Environmental and policy regulation are very important. Select panel data of 29 provinces of China (excluding Hong Kong, Macau, Taiwan, Hainan and Tibet) from 2008 to 2017, construct a nonlinear two-threshold regression model, empirically analyse the impact of environmental tax and government innovation subsidies to green processes innovation, and attempt to explore whether the implementation of these two policies at the same time can make enterprises more inclined to green process innovation and whether there is an optimal space for policy implementation. It is proposed that the synergy effect of environmental tax and government innovation subsidy policies on enterprise green process innovation should be fully utilized, and the enterprise green process innovation environment and incentive mechanism should be improved to promote the green transformation and sustainable development of enterprises.

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.

Hydrogen sulfide (H₂S) is a novel gasotransmitter, sucrose (SUC) is both cellular energy and signaling molecule. However, their signaling interaction in maize thermotolerance is poorly known. In this study, using maize seedlings as materials, metabolic and functional interaction of H₂S and SUC in maize thermotolerance was investigated. Data show that under heat stress survival rate and tissue viability were increased by exogenous SUC, while malondialdehyde content and electrolyte leakage were decreased by SUC, indicating SUC could upraise maize thermotolerance. Also, SUC-upraised thermotolerance was enhanced by H₂S, while weakened by inhibitor (propargylglycine) and scavenger (hypotaurine) of H₂S and SUC-transport inhibitor (N-ethylmaleimide). To figure out underlying mechanism of H₂S-SUC interaction-upraised thermotolerance, redox parameters in mesocotyls of maize seedlings were measured before and after heat stress. The data display that activity and gene expression of H₂S-metabolizing enzymes were up-regulated by SUC, whereas H₂S had no significant effect on activity and gene expression of SUC-metabolizing enzymes. Besides, activity and gene expression of catalase, glutathione reductase, ascorbate peroxidase, peroxidase, dehydroascorbate reductase, monodehydroascorbate reductase, and superoxide dismutase were reinforced by H₂S, SUC, and their combination under non-heat and heat conditions in varying degrees. Similarly, content of ascorbic acid, flavone, carotenoid, and polyphenol was increased by H₂S, SUC, and their combination, whereas production of superoxide radical and hydrogen peroxide level were impaired by these treatments to different extent. These results imply that the metabolic and functional interaction of H₂S and sucrose exists in the formation of maize thermotolerance through redox homeodynamics.

Lipid nanoparticles (LNPs) are promising carriers for constructing drug delivery systems (DDSs) in disease treatments. Previous studies have suggested that lipid composition and biophysical properties of LNPs can significantly impact their interaction with cells and tissues, allowing for the development of suitable LNPs for precise drug delivery. Our previous study proposed the concept and facile preparation of reconstituted lipid nanoparticles (rLNPs), which not only have the advantages of traditional LNPs but also contain the lipids of mother cell/tissue. In this study, we have found that brain-derived rLNPs (B-rLNPs) can have much better accumulation to the ischemic area of the ischemic stroke (IS) model than liver-derived rLNPs (L-rLNPs). This homing effect hopefully makes rLNPs a useful tool for developing highly accessible devices with homologous targeting ability for precise drug delivery.

The best business relationships are built as relationships between people and organizations that work together to achieve common goals. In the current digital era, B2B collaboration relationships are essential for business success. The response time to customer requests can be a key factor in maintaining these relationships and obtaining a competitive advantage. The use of VBA automation for repetitive tasks can help speed up this process, but it is important that computer operators have medium knowledge about using VBA macros. This work deals with issues related to improving response times to requests received by email, using a macro program for repetitive tasks. The empirical study was conducted on an average of 50 daily emails for order confirmation, with an average writing time of 68 s per email, resulting in a potential reduction in working time for the operator of approximately 1 h/day, only for order confirmation. The results show a significant improvement in working time for computer operators, using VBA buttons for repetitive tasks.

Supervised machine learning (SML) is a powerful method for predicting a small number of well-defined output groups (e.g., potential buyers of a certain product) by taking as input a large number of known well-defined measurements (e.g., past purchases, income, ethnicity, gender, credit record, age, favorite color, favorite chewing gum). SML is predicated upon the existence of a training dataset in which the correspondence between the input and output is known to be true. SML has had enormous success in the world of commerce, and this success may have prompted a few scientists to employ it in the study of molecular and genome evolution. Here, we list the properties of SML that make it an unsuitable tool in certain evolutionary studies. In particular, we argue that SML cannot be used in an evolutionary exploratory context for the simple reason that training datasets that are known to be a priori true do not exist. As a case study, we use an SML study in which it was concluded that most human genomes evolve by positive selection through soft selective sweeps (Schrider and Kern 2017). We show that in the absence of legitimate training datasets, Schrider and Kern (2017) used (1) simulations that employ many manipulatable variables and (2) a system of cherry-picking data that would put to shame most modern evangelical exegeses of the Bible. These two factors, in addition to the lack of methodological detail and negative controls, lead us to conclude that all evolutionary inferences derived from so-called SML algorithms (e.g., S-HIC) should be taken with a huge shovel of salt.

In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose factorising the final task of audio transcription into multiple intermediate tasks in order to improve the training performance when dealing with this kind of low-resource datasets. We evaluate three data-efficient approaches of training a stacked convolutional and recurrent neural network for the intermediate tasks. Our results show that different methods of training have different advantages and disadvantages.

MIGAL – Galilee Research Institute is a regional R&amp;D center in the northeast peripheral region of Israel. An internationally recognized applied research institute, MIGAL specializes in biotechnology and computational sciences, plant sciences, precision agriculture and environmental sciences, as well as food, nutrition and health. Most of MIGAL’s researchers serve as the core faculty at Tel Hai Academic College (TH). Despite the country’s small surface area, socioeconomic inequality in Israel is high by OECD standards, with wage differences between rich and poor regions reaching up to 400%. The aims of this study are to identify possible socioeconomic impacts of MIGAL–TH on the peripheral northeast’s development. We discuss the effects of academic research and institutions of higher education on mitigating differences between the center and periphery of the country. Data for MIGAL, TH and the northeastern peripheral region were collected from the yearly reports of the two institutions and the Israel Central Bureau of Statistics. MIGAL was found to serve as a link between research, academic teaching and socioeconomic development in the northeast periphery. Several variables related to this link and describing MIGAL–TH and northeastern periphery development were analyzed over time: MIGAL’s budget, total number of employees and number of employees with PhDs; number of TH graduate students; socioeconomic index (SEI) of the northeastern periphery and its position on the Israeli list of regional SEIs. The signs and significance levels of their trends indicate a potential socioeconomic impact of academic research and higher education on peripheral development in the northeast of the country. Research budgets and the creation of jobs for academics living in the region are just a few examples of this impact.

Tulou dwellings in Southeast China have captivated global interest due to their distinctive appearance, sustainable construction materials and technologies, and their defensive and collective housing functions. Despite several recognized as World Cultural Heritage sites, the vast majority of tulou buildings are undergoing irreversible decline and destruction, necessitating a comprehensive and systematic study. Taking 83 Tulou buildings in Raoping County, Chaozhou City, Guangdong Province as the research object, this study reconstructs the historical scenes and systematically reveals the emergence, popularity, and consolidation process of tulou dwellings as integrated defensive and residential buildings for ordinary people by conducting a comprehensive analysis of historical documents and local chronicles. Based on an extensive field investigation, the study systematically analyzes the geospatial distribution and the spatial characteristics of Raoping tulou and its residential unit. The results demonstrate the adaptability and flexibility of tulou dwellings, showcasing their developmental process and revealing the inclusive nature of these traditional residences, as well as the initiative of those who reside within them. The research findings contribute to a more dynamic, comprehensive, and authentic understanding of tulou and Chinese traditional residences, providing valuable references for the preservation and sustainable development of tulou architectural heritage.

Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition, which remains challenging for traditional methods due to the complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include: 1) The convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; 2) A large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and 3) Experimental results demonstrate that HDMF is super capable of copping with the AMC problem, and achieves much better performance when compared with the independent network. The source code and the database will be publically available.

Aspartame is the methyl-ester of the aspartate-phenylalanine dipeptide. Over time it became a very popular artificial sweetener. However, since its approval by the main food safety agencies, several concerns have been raised related to neuropsychiatric effects and neurotoxicity, due to its ability to activate glutamate receptors, as well as carcinogenic risks by increased production of reactive oxygen species. Within this review we critically evaluated reports concerning the safety of aspartame. Some studies evidenced subtle mood and behavioral changes upon daily high-dose intake below the admitted limit. Epidemiology studies also evidenced associations between daily aspartame intake and higher predisposition for malignant diseases like non-Hodgkin lymphomas and multiple myelomas, particularly in males, but association by chance still could not be excluded. While the debate over the carcinogenic risk of aspartame is ongoing, it is clear that its use may pose some dangers in peculiar cases, such as patients with seizures or other neurological diseases; it should be totally forbidden for patients with phenylketonuria, and reduced doses or complete avoidance are advisable during pregnancy. It would be also highly desirable for every product containing aspartame to clearly indicate on the label the exact amount of the substance and some risk warnings.

The present paper proposes a fractal analysis of the Covid-19 dynamics in 45 European countries. We introduce a new idea of using the box-counting dimension of the epidemiologic curves as a means of classifying the Covid-19 pandemic in the countries taken into consideration. The classification can be a useful tool in deciding upon the quality and accuracy of the data available. We also investigated the reproduction rate, which proves to have significant fractal features, thus enabling another perspective on this epidemic characteristic. Moreover, we studied the correlation between two meteorological parameters: global radiation and daily mean temperature and two Covid-19 indicators: daily new cases and reproduction rate. The fractal dimension differences between the analysed time series graphs could represent a preliminary analysis criterion, increasing research efficiency. Daily global radiation was found to be stronger linked with Covid-19 new cases than air temperature (with a greater correlation coefficient -0.386, as compared with -0.318), and consequently it is recommended as the first-choice meteorological variable for prediction models.

Background and Objectives Neurofibromatosis 1 (NF1 MIM # 162200), also known as von Recklinghausen disease, is among the most common autosomal dominantly transmitted disorders characterized mainly by neurocutaneous manifestations such as café au lait spots, intertriginous freckling, various types of neurofibromas (cutaneous neurofibromas or plexiform tumors), neurological manifestations, and bone abnormalities. It occurs with an incidence of 1:3000–1:4000 cases. Cone-rod dystrophies belong to the large group of retinal dystrophies, a highly heterogeneous group of disorders clinically manifested mainly by damage to cone and rod cells and have a broad spectrum of clinical manifestations. Many genes are involved in their etiology, with more than 100 currently identified, including the CRX gene responsible for the autosomal dominant form of cone-rod dystrophy (ad CORD). Materials and Methods: The authors present an intriguing case of a patient diagnosed with Neurofibromatosis type 1 (NF1) as an infant and ocular involvement (myopia and astigmatism onset at age 15), which yielded a surprising molecular testing result. Results: Molecular DNA analysis identified a rare occurrence of two mutational variants, including a pathogenic variant in the NF1 gene, c.3871-2A, and a likely pathogenic variant in the CRX gene c.119G>A. Conclusions: The authors present this case to highlight the surprises offered by molecular testing in a condition where the diagnosis is usually based on clinical criteria, thus providing patients with competent genetic advice.

Dirofilariosis is an infectious disease caused by species of the Dirofilaria genus. It is manifested by the appearance of a subcutaneous swelling, especially in the eye region. We present the case of a 29-year-old patient that accused facial asymmetry in the right genian region. Following clinical and paraclinical evaluations, the diagnosis of parasitic cyst was established in the context of dirofilariosis with Dirofilaria repens (D. repens). Treatment consisted of surgical excision of the formation associated with prophylactic antibiotic medication. Macroscopic analysis of the excision piece revealed a structure that contains a cystic cavity and a filamentous form with a length of approximately 10 mM and a diameter of 1 mM. This is the first case of dirofilariosis located in the genian region reported in Romania. The overview of this pathology is important to raise awareness among physicians about its presence and its clinical variations.

In this essay, I revisit Neural Networks (NNs) through the lens of evolutionary dynamics. From the two most important features of NNs, I recover the two most general equations of evolutionary dynamics. These two equations may thus serve as a connection between NNs and a body of knowledge that has been built over multiple centuries and has found application across a vast range of disciplines. This connection may therefore also help explain why NNs with the two features in question are applicable across a much broader range of domains than initially envisioned.

While modeling data in reality, uncertainties in both data (aleatoric) and model (epistemic) are not necessarily Gaussian. The uncertainties could appear with multimodality or any other particular forms which need to be captured. Gaussian mixture models (GMMs) are powerful tools that allow us to capture those features from a unknown density with a peculiar shape. Inspired by Fourier expansion, we propose a GMM model structure to decompose arbitrary unknown densities and prove the property of convergence. A simple learning method is introduced to learn GMMs through sampled datasets. We applied GMMs as well as our learning method to two classic neural network applications. The first one is learning encoder output of autoencoder as GMMs for handwritten digits images generation. The another one is learning gram matrices as GMMs for style-transfer algorithm. Comparing with the classic Expectation maximization (EM) algorithm, our method does not involve complex formulations and is capable of achieving applicable accuracy.

Purpose: Pitch plays an important role in auditory perception of music and language. This study provides a systematic review with meta-analysis to investigate whether individuals with autism spectrum disorder (ASD) have enhanced pitch processing ability and identify the potential factors associated with processing differences between ASD and neurotypicals. Method: We conducted a systematic search through six major electronic databases focusing on the studies that used nonspeech stimuli to provide a qualitative and quantitative assessment across existing studies on pitch perception in autism. We identified potential participant- and methodology-related moderators and conducted meta-regression analyses using mixed-effects models. Results: On the basis of 22 studies with a total of 464 participants with ASD, we obtained a small-to-medium positive effect size (0.26) in support of enhanced pitch perception in ASD.Moreover, the mean age and non-verbal IQ of participants were found to significantly moderate the between-studies heterogeneity. Conclusion: Our study provides the first meta-analysis on auditory pitch perception in ASD and demonstrates the existence of different developmental trajectories between individuals with ASD and neurotypicals. Non-verbal ability can be a significant contributor to the lower-level/ local processing bias in ASD. We highlight the need for further investigation of pitch perception in ASD under challenging listening conditions. Future neurophysiological and brain imaging research studies with a longitudinal design are also needed to better understand the nature of the atypical processing in ASD to obtain new insights into the underlying neural mechanisms and to help guide auditory-based interventions for improving language and social functioning.

This paper presents an original method of additive manufacturing of cylindrical parts with variable circumference thickness, which allows the control of the deposition of molten material using an algorithm for decomposing the part geometry into volumetric elements with known dimensional configuration. In the absence of a post-processor capable of controlling additive manufacturing on a 5-axis numerical control machine, control of the deposition of molten material is done using parameterized programs, which can control both the feed speeds of the machine tool axes and the specific functions of the printing equipment. Additive manufacturing can make a positive contribution to sustainable development compared to traditional manufacturing technologies, thus making a positive contribution for a sustainable future. The aim of the work is to make it possible to 3D print parts with variable wall thickness using a CNC machining centre. To obtain the variable thickness layer we have implemented an original method of deposition with molten material (FDM), the coordination of the system composed of physical elements respectively programmable elements, is realized through control functions materialized in parameterized part programs, the generated outputs being the variable speed of the machine axes on a circular trajectory, the angular positioning, the filament advance.

The digital orthophoto is an image with geometric accuracy and no distortion. It is acquired through a top view of the scene and finds widespread applications in map creation, planning, and related fields.This paper classifies the algorithms for digital orthophoto generation into two groups: explicit methods and implicit methods. Explicit methods rely on traditional geometric methods, obtaining geometric structure presented by explicit parameters with Multi-View Stereo (MVS) theories, as seen in our proposed Top view constrained Dense Matching (TDM). Implicit methods rely on neural rendering, obtaining implicit neural representation of scenes through the training of neural networks, as exemplified by Neural Radiance Fields (NeRF). Both of them obtain digital orthophoto by rendering from a top view perspective. In addition, this paper conducts an in-depth comparative study between explicit and implicit methods. The experiments demonstrate that both algorithms meet the measurement accuracy requirements and exhibit a similar level of quality in terms of generated results. Importantly, the explicit method shows a significant advantage in terms of efficiency, with a time consumption reduction of two orders of magnitude under our latest CUDA version TDM algorithm. Although explicit and implicit methods differ significantly in their representation forms, they share commonalities in the implementation across algorithmic stages. These findings highlight the potential advantages of explicit methods in orthophoto generation, while also providing beneficial references and practical guidance for the fast digital orthophoto generation using implicit methods.

Leucine-rich repeat receptor-like kinases (LRR-RLKs) have been reported to play important roles in plant growth, development and stress responses. However, no comprehensive analysis of this family has been performed in Gossypium, which are important economic crop and suffer various stresses in growth and development. Here we conducted a comprehensive analysis of LRR-RLK family in four Gossypium species (G. arboreum, G. barbadense, G. hirsutum and G. raimondii). A total of 1641 LRR-RLK genes were identified in the four Gossypium species involved in our study. Maximum-likelihood phylogenetic tree revealed that all the LRR-RLK genes were divided into 21 subgroups. Exon-intron organization structure of LRR-RLK genes kept relative conserved in subfamilies and between Arabidopsis and Gossypium. Subfamilies XI and XII were found dramatically expanded in Gossypium. Tandem duplication acted as an important mechanism in expansion of Gossypium LRR-RLK gene family. Function analysis suggested that plant hormone signaling and plant-pathogen interaction pathway were enriched in Gossypium LRR-RLK genes. Promoters analysis and expression profiles analysis revealed that Gossypium LRR-RLK genes were extensively regulated by TFs, phytohormone and various environmental stimuli, and play key roles in stress defense and diverse development processes. Our study provided valuable information for further function study of Gossypium LRR-RLK genes.

The superfamily Lauxanioidea is a significant dipteran clade including over 2500 known species in three families: Lauxaniidae, Celyphidae and Chamaemyiidae. We sequenced the first five (three complete and two partial) lauxanioid mitochondrial (mt) genomes, and used them to reconstruct the phylogeny of this group. The lauxanioid mt genomes are typical of the Diptera, containing all 37 genes usually present in bilaterian animals. A total of three conserved intergenic sequences have been reported across the Cyclorrhapha. The inferred secondary structure of 22 tRNAs suggested five substitution patterns among the Cyclorrhapha. The control region in the Lauxanioidea has apparently evolved very fast, but four conserved structural elements were detected in all three complete mt genome sequences. Phylogenetic relationships based on the mt genome data were inferred by Maximum Likelihood and Bayesian methods. The traditional relationships between families within the Lauxanioidea, (Chamaemyiidae + (Lauxaniidae + Celyphidae)), was corroborated, however, the higher level relationships between cyclorrhaphan superfamilies are mostly poorly supported.

At different heat treatment temperatures, the hardness and flexural strength of M2 high-speed steel selective laser melting (SLM) parts show mixed trends. When the heat treatment temperature is 260°C, the hardness and flexural strength of the M2 high-speed steel SLM part are decreased, but the hardness difference between the upper and lower surfaces of the M2 high-speed steel SLM part is also reduced. When the heat treatment temperature is 560°C, the hardness and flexural strength of the M2 high-speed steel SLM part are almost close to that of the original M2 high-speed steel SLM part, and the performance gradient in the sample is improved, and the overall structure is uniform. When the subsequent heat treatment temperature is 860 °C, the hardness of the SLM parts reaches a minimum, with an average value of 24 HRC. However, the flexural strength exceeds that of the original SLM parts. Moreover, the microstructure of the sample is uniform, which significantly improves the anisotropy of performance.

In this work, an novel flow-electric field coupling configuration for enhanced Zn-Cd cementation was designed and utilized. A series of tests were conducted to explore the optimization of Zn-Cd cementation process and its mechanism.Firstly, the various characterisations of the cadmium sponge at different locations in the device were compared and it was concluded that the purity of the cadmium sponge was optimal from the anode surface. The generation and stripping of cadmium sponge was revealed for the first time by cross-sectional electron microscopy. The four stages of the apparent reaction in the system were analysed in relation to the pH, cadmium concentration and cadmium sponge flaking at each flow rate. It was proved that the separation of cadmium sponge mainly occurred in the third phase. Secondly, by comparing the morphology and specific surface area of cadmium sheep at different flow rates, the optimum flow field velocity was identified as 30 ml/s. Six flow field configurations were compared and preferred. The results demonstrate that the LCAH modulation results in a more sparse structure of the cadmium sponge, which is more easily exfoliated from the zinc anode surface by fluid impact. This was considered to be the most beneficial flow field configuration for improving the cadmium cementation rate and reducing the cost of the reaction. Further, the reaction steps of the system were analysed. The diffusion process under different flow field configurations was confirmed by calculations to follow first order kinetics. The diffusion coefficient of LACH proved to be the highest in the comparative tests and this result was also supported by the apparent experimental results.