An atomic-scale approach is employed to simulate the formation of precipitates with different lattice misfits in the early stages of aging of supersaturated aluminum alloys. Simulation results reveal that the increase of lattice misfit could significantly promote the nucleation rate of precipitates, which results in a larger number and smaller size of the precipitates. The morphologies of the precipitates also vary with the degree of lattice misfit. Moreover, the higher the lattice misfit, the earlier the nucleation of the second phase occurs, which can substantially inhibit the movement of dislocations. The research on the lattice misfit of precipitation can provide theoretical guidance for the design of high-strength aluminum alloy.

Vibro-fluidized dry coal beneficiation involves the unavoidable mixing of -1 mm coal dust into the fluidized bed, which compromises the stability and homogeneity of the bed density and reduces the effectiveness of fine-grained coal separation. In order to understand the features of coal powder separation and mixing (-1 mm) in a vibrating fluidized bed, as well as to achieve a uniform and stable bed density, a wide-grained dense medium consisting of -1 mm coal dust and 0.3-0.15 mm magnetite powder was homogeneously mixed in this work. The degree of density separation in the entire bed was examined, as well as the local mixing properties of the Wide-grained Dense Medium. It was discovered that when low vibrational energy was introduced at the same gas velocity, particle separation was enhanced in comparison to the usual fluidized bed. Little bubbles were found to ensnare fine coal dust and migrate upward, increasing density separation. The introduction of high vibrational energy leads to particle disorder within the bed and an increase in the level of particle mixing. The beneficial effects of vibration on the bed are diminished when the gas velocity is gradually increased under specified vibration circumstances. This causes big bubbles to develop more frequently, which increases particle mixing. Among them, the 1-0.5 mm fine-grained coal exhibits the best mixing state; it is less impacted by variations in air velocity and vibration levels, whereas the -0.5 mm coal dust is more affected by each of these variables. Furthermore, a wide range of air velocity regulation without a significant segregation state change was discovered to be achievable by the particle system under vibration circumstances of f=25Hz A=1mm and f=25Hz A=2mm.

Spontaneity and its connections to mental health and wellbeing are a central issue in both theory and practice of psychodrama, and the specialized literature sometimes even associates the presence of pathology to a lack of spontaneity. This paper describes spontaneity in Moreno's theory, its assessment, its association with other measures and concepts, and scientific advances in the field. Specifically, we present results obtained with the English and Portuguese-language versions concerning its assessment. The main purpose of this work was to study the spontaneity measure (SAI-R) in a 3 first-order factors version for a Portuguese sample of 719 subjects, divided into two independent samples. The participants were aged 18 to 69. The mean age of participants was 28.5 years (SD = 7.51), and ages ranged between 18 and 69 years. Data was collected through an online platform of a Portuguese market research company. The results revealed that the 3-factor model has acceptable validity when items 1 (“Creative”) and 7 (“Euphoric”), confirming the composite reliability, the convergent validity, but not the discriminant validity. Through multigroup analysis, the model showed to be stable. Notwithstanding the need for complementary studies, including clinical samples, the SAI-R is a short and valid instrument in clinical and non-clinical contexts when evaluating spontaneity.

As the adoption of deep learning models continues to surge across various applications, the need for efficient deployment architectures becomes increasingly critical. This paper presents a novel approach to enhance the deployment of deep learning models by leveraging serverless architecture. Serverless computing has been popular for its auto-scaling, cost-effectiveness, and simplified management characteristics. However, the intense resource demands of deep learning models pose challenges in maintaining low response times and effective load balancing within serverless environments. The proposed architecture addresses these challenges by integrating principles from both deep learning model optimization and serverless computing. Through systematic experimentation and analysis, we demonstrate that by appropriately designing and tuning the deployment architecture, significant improvements in response time, performance, resource utilization, and load distribution can be achieved.

The retinal pigment epithelium (RPE) is an important monolayer of cells present in the outer retina forming a major part of the blood-retina-barrier (BRB). It performs many essential tasks for the maintenance of retinal integrity and function. With increasing knowledge of the retina, it is getting clearer that both common retinal disorders, like age related macular degeneration, and rare genetic disorders originate in the RPE. This calls for a better understanding of the functions of various proteins within the RPE. In this regard, mice enabling an RPE-specific gene deletion are a powerful tool to study the role of a particular protein within the RPE cells in their native environment, simultaneously negating any potential influences of systemic changes. Moreover, since the RPE cells interact closely with the adjacent photoreceptors, these mice also provide an excellent avenue to study the importance of a particular gene function within the RPE to the retina as a whole. In this review, we outline and compare the features of various Cre mice created for this purpose, allowing researchers a well-informed decision on the choice of Cre mouse to use in relation to their research needs.

Reducing the use of stabilizers is one of the main challenges in food emulsions, especially for beverages. This work aimed to produce oleogel structured nanoemulsions (NEs) without additional surfactants. Lecithin-stearic acid (LSa) and lecithin-sorbitan tristearate (LSt) oleogels formed stable NEs under optimized sonication conditions. Microscopy and rheometry revealed that the presence of self-assembled fibrous networks (SAFiNs) in both dispersed and continuous phases provided steric stabilization to NEs. Lecithin acted as crystal habit modifier of SAFiNs and facilitated their phase partitioning. Notably, short fibers of LSt showed better emulsifying efficiency than the long fibers of LSa. Curcumin release studies under simulated gastrointestinal conditions demonstrated that SAFiNs affect the release capabilities of NEs. Polydispersity index, zeta potential and oil syneresis data showed that emulsions are stable for six months. Moreover, NEs showed thermal stability upon curcumin release at 25 and 50 °C. These results suggested that the developed oleogel-based NEs are suitable for delivery of bioactive agents for beverages and other food applications.

In this work we study the recently developed parametrized partition function formulation and show how we can infer the thermodynamic properties of fermions based on numerical simulation of bosons and distinguishable particles at various temperatures. In particular, we show that in the three dimensional space defined by energy, temperature and the parameter characterizing parametrized partition function, we can map the energies of bosons and distinguishable particles to fermionic energies through constant-energy contours. We apply this idea to both noninteracting and interacting Fermi systems and show it is possible to infer the fermionic energies at all temperatures, thus providing a practical and efficient approach to obtain thermodynamic properties of large fermion systems with numerical simulation. As an example, we present energies for up to 50 noninteracting fermions and up to 20 interacting fermions at all temperatures and show good agreement with the analytical result for noninteracting case.

Selection is a fundamental interaction element in Virtual Reality (VR) and 3D user interfaces (UIs). Raycasting, one of the most common object selection techniques, has been known to have difficulties in selecting small or distant objects. Meanwhile, recent advancements in computer vision technology have enabled seamless vision-based hand tracking in consumer VR headsets, enhancing accessibility to freehand mid-air interaction and highlighting the need for further research in this area. This study proposed a new technique called TouchView which utilizes a virtual panel with a modern adaptation of the Through-the-lens metaphor to improve freehand selection for VR UI. TouchView enables faster and less demanding target selection by allowing direct touch interaction with the magnified object proxies reflected on the panel view. Repeated-measures ANOVA on the results of a follow-up user experiment on multitarget selection with 23 participants showed that TouchView outperformed the current market-dominating freehand raycasting technique, Hybrid Ray in terms of task performance, perceived workload, and preference. The user behavior was also analyzed to understand the underlying reasons for these improvements. The proposed technique can be used in VR UI applications to enhance the selection of distant objects, especially for cases with frequent view shifts.

Educational institutions worldwide have adopted e-learning due to the COVID-19 pandemic. While this shift to online learning (OL) has presented challenges for students and teachers, it has also sparked innovative educational approaches. This study investigates college students' perceptions regarding OL, specifically focusing on gender differences in the experience with OL A quantitative survey gathered information from distinct college students in Gulf Cooperation Council (GCC) nations who experienced OL during the pandemic. The survey included questions related to satisfaction with OL and specific aspects such as course content delivery, interaction with instructors, behavioral changes, and challenges with OL. Male and female student OL experience was compared statistically. The results conclude that male students are more satisfied than female students. Students struggled with internet connection, OL adaptation, focus, and workload during OL. Understanding these gender differences in students' satisfaction with OL is crucial for educators and institutions as they strive to optimize the effectiveness of e-learning strategies. By recognizing and addressing male and female students' unique needs and challenges, educational institutions can enhance the overall educational experience during times of crisis and beyond.

In this paper, we present the definition, some properties, and solve a problem on special primes. These properties help in providing us with a better understanding of the problem posed related to special primes on the open problem garden website. The problem involves finding all the primes q, given a prime p such that q≡1(mod p) and 2^((q−1)/p)≡1(mod q). We prove that a prime number q is a special prime of p if and only if the order of 2 in U(q) divides q−1p. Also, we prove that a prime number q is not a special prime for any prime number if 2 is a generator of the group U(q) and that there exist infinitely many special primes for any given prime number.

The holy town of Joshimath, gateway to the India-China border and religious places like Badrinath, Hemkunt Sahab, Valley of flowers in the Uttarakhand state of India experienced cracks in many of the residential and commercial buildings in the month of November-December 2022 which created panic and relocation of people. The reason(s) for the subsidence is still not known but using the InSAR technology and data from Sentinel-1, SAR data for the region was processed using HyP3 and MintPy for January-December 2022 to understand when the phenomenon started and how much uplift/subsidence the area has underwent in the last one year, as no such study is available till today. The entire town can be classified into two zones with respect to the annual subsidence values. Severe subsidence has been observed in the North and East regions whereas most of the southern region experienced moderate to low subsidence. Since the town is built on the debris of an old landslide, the subsidence may be attributed to the change in the course of the underground water channels due to heavy and continuous construction happening in the region and also due to drop in the water table. The results shows that the subsidence in the region was ongoing since June (from January till May, almost negligible vertical movement was recorded) which accelerated after September (when an uplift was recorded which lasted till October), while the subsidence peaked in the month of December with recorded subsidence of as much as 10 cm in some areas located around the Joshimath town, where most of the residential and commercial buildings are located.

With the development of big data analysis, blockchain and other technologies, the supply chain of enterprises is transforming to lean and intelligent. As an important link in the enterprise supply chain, the intelligent transformation of procurement plays an important role in the improvement of the supply chain efficiency, therefore, the construction of a common method supporting the intelligent upgrade of the enterprise procurement business has become a key concern for enterprise managers. Based on the balanced scorecard theory and the supply chain maturity model, this study combines the actual situation of procurement management in Chinese energy enterprises and constructs a procurement benchmarking system that balances the development direction of the industry and the actual needs of enterprises. Meanwhile, based on the grounded theory, three major themes of the intelligent procurement system (digital business module, procurement synergy mechanism and procurement ecosystem) are extracted to provide a methodological reference for the construction of intelligent procurement systems of energy enterprises. The study concludes with a case study of China National Energy Group Materials Company to demonstrate the application of the intelligent procurement system built in this paper, with a view to providing methodological reference for the intelligent procurement management in energy enterprises.

This paper seeks to model the crack propagation in of AAs (aluminum-alloys) 2524-T3 and 7050-T7452 subjected to fatigue loading at low temperature. Fatigue crack growth tests were performed on AAs 2524-T3 and 7050-T7452 subjected to constant-amplitude and actual random-spectra loading histories at room temperature of about 25 °C and at cryogenic temperature of -70 °C, respectively, to determine their crack growth properties and residual lives. The interaction mechanisms between cryogenic temperature and the fatigue load were deduced on the basis of the results of fractographic analysis. Temperature-dependent residual lives under actual random-spectrum load history were modeled based on a modified accumulation damage rule by accounting for the load interaction. Good correlation was achieved between the predictions and actual experiments, demonstrating the practical and effective use of the proposed method.

This study examines the differential impact of proactive and reactive job crafting behaviors on job well-being, drawing from the perspective of boundary theory. The increasing popularity of work connectivity behavior after-hours (WCBA) after work has attracted widespread attention from scholars on the relationship between WCBA and employee well-being. One view suggests that the impact of WCBA is negative, while another view suggests it is positive. Obviously, the impact of (WCBA) on well-being at work is still contradictory. To clarify the complexity of the impact of WCBA on well-being at work, based on boundary theory, we divided WCBA into proactive WCBA (PC) and reactive WCBA (RC), and examined the double-edged sword effect of WCBA on well-being at work, as well as the mediating mechanisms of job control and work-to-home conflict, and the moderated effects of boundary segmentation preferences. This study uses empirical sampling method to collect data from 125 employees for a period of five days for quantitative research. The results show that, first, PC has an inverted U-shaped effect on job control, and job control is negatively related to well-being at work. Thus the mediating effect of job control is significant. Second, RC has a negative impact on job control, and there is also a negative relationship between job control and well-being at work. Therefore, the mediating effect of job control is significant. Third, PC and RC are positively correlated with work-to-home conflict, and work-to-home conflict has a significant positive impact on well-being at work. Therefore, the mediating effect of work-family conflict is significant. Fourth, the study also found that integration preference moderates the relationship between work-to-home conflict and PC on well-being at work, that is, the mediating effect of work-to-home conflict is stronger for employees with low integration preference. This study enriches our understanding of WCBA, PC and RC from the perspective of proactive and passive employee behaviors. The study also provides a new interpretation of the impact of WCBA on well-being at work and offers valuable insights for the sustainable development in digital social transformation and the application of boundary theory and the theory of empowerment-subjugation in achieving the United Nations Sustainable Development Goals. In addition, the study deepens our understanding of the heterogeneous regulatory role played by work-family integration preferences in influencing well-being at work under different types of WCBA.

This paper is an attempt to present a COVID-19 prediction model for India. Lockdown plays an important role in the arrest of community spread of the disease. This was evident from the study of other countries such as Russia, Belgium and Germany, where peak cases were recorded within a month of the imposition of lockdown, that it showed an immediate positive effect. However, in India, even after 65 days of lockdown, there is no decrease in the number of daily new cases reported. There were many models prepared for India and almost all of them were proven wrong by the increase in the number of cases. The model in this paper is prepared using the COVID-19 trend in other countries, population density and the pandemic bell curve. Based on the available data until 24^th May 2020, two scenarios have been presented. In one, the peak shall be obtained when the number of daily new cases per million reaches 190 and in the second when the daily new cases per million reach 724. One model predicts the number of cases to reach 1 million by mid-July 2020. The other model predicts the number of cases to peak by mid-July with the total cases reaching 20 million. The predicted cases were compared with the actual cases recorded for the period 25^th May to 11^th June 2020. It was observed that the actual values matched quite reasonably with the predicted values.

Tao and Ming (name) or Tao and Yan (language or speech) is an inseparable pair of important concepts in traditional Chinese language philosophy. In LaoTze’s opinion, Tao could not be spoken out, and language or speech could not reach Tao’s essence. Yan and Tao are separated from each other, and they are paradoxical. All the confusion derives from Yan regardless of how delicate they are. Tao can be expressed in words, but it will never be expressed through common languages. In this article, Tao is regarded as a human being’s thoughts or intentions and is even considered to be the origin of the real world. Language can express people’s thought, although it has some limitations. There exist some contradictions between the real word and the world expressed in words. To enable people to better understand the existence of the real world from multiple perspectives at the language or linguistic level without being constrained by words, this article studies LaoTze’s linguistic philosophy reflected in his opinion on the relationship between Tao and Yan and makes a comparative analysis of modern Western language philosophers’ stand on language (or speech) and existence.

At present, there are still no specific therapeutic drugs and appropriate vaccines for Dengue. Therefore, it is very important to explore distinct clinical diagnostic indicators. In this study, we combined differentially expressed genes (DEGs) analysis and weighted co-expression network analysis (WGCNA) to screen a stable and robust biomarker which can be used to distinguish three clinical stages of Dengue and severity of Dengue. CD38 can distinguish excellently Early Acute, Late Acute, Convalescent stages for Dengue patients, and ZNF595 can discriminate DHF from DF in whole acute stages. We also found that three clinical stages can be discriminated based on the fractions of Plasma cells, activated memory CD4+ T cells, and Monocytes. In different clinical stages different immune cells function positively. Negative inhibition of viral replication based on Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene set enrichment analysis (GSEA), up-regulated autophagy genes and impairing immune system are potential reasons resulting in dengue hemorrhagic fever (DHF).

We consider the conjecture of the relationship between the entanglement entropy of spacetime, matter and the action in Einstein's general relativity. Our analysis suggests the possibility of regarding the entanglement entropy of spacetime and matter as the dimensionless action in general relativity by using Planck units. In this case, the action principle originates from the maximization of the entanglement entropy. We also show that the fundamental property of entanglement entropy leads to attractive characteristic of gravitational force for classical particles.

We consider the quantum thermodynamic origin of the gravitational force of matter by applying the spacetime entanglement entropy and the Unruh effect originating from vacuum quantum fluctuations. By analyzing both the local thermal equilibrium and quasi-static processes of a system, we may get both the magnitude and direction of Newton's gravitational force in our theoretical model. Our work shows the possibility that the elusive Unruh effect has already shown its manifestation through gravitational force.

The apple leaf-curling midge, Dasineura mali Kieffer (Diptera: Cecidomyiidae), is an invasive pest of apple. To provide crucial information for its pest risk analysis, forecast and management, we investigated the effects of temperatures (5, 10, 15, 20 and 25°C) and daylengths (10, 11, 12, 13, 14 and 15 hrs) on its development and survival. The midge could not complete its development at 10°C or lower. After transferred from 20°C to 10°C, mature larvae could survive for about two months and develop to adults. Pupation and emergence rates were significantly higher at 20°C than at 10°C and 25°C. Daylength had no effect on these parameters. The low temperature threshold and thermal requirement to complete development from eggs to adults were 3.7°C and 627 degree-days, respectively. The midge had significantly lower thermal requirement for completion of its lifecycle at 20°C (614.5 degree-days) than at 15°C (650.1 degree-days) and 25°C (634.8 degree-days). The thermal model developed in this study provided accurate predictions of the number of D. mali generations and adult emergence time in each generation in different regions of New Zealand. We suggest that the model could be used to predict population dynamics of this pest in other parts of the world.

Oral mucositis (OM) is inflammation of the mouth caused by damage to the mucous membranes that line the mouth and throat. It is a side effect of cancer treatment, particularly in patients with head and neck squamous cell carcinoma (HNSCC) who undergo radiotherapy, chemotherapy, and/or immunotherapy with immune checkpoint inhibitors. The etiology and pathogenic mechanisms of OM is complex and multifaceted, involving cytotoxicity (cell death), inflammation, infection, change in microbiome, and immune-mediated cytotoxicity. We summarize the literature about attempts to use various omics methodologies (genomics, transcriptomics, microbiomics and metabolomics) to elucidate the biological pathways associated with the development or the severity of OM. Integrating different omics into multi-omics approaches carries the potential to discover links among host factors (genomics), host responses (transcriptomics, metabolomics), and local environment (microbiomics).

The individual and combined effects of extracellular products (ECP) from Arthrobotrys oligospora (Ao) and A. musiformis (Am) growth in liquid media against Haemonchus contortus L3 (HcL3) were assessed. The following methodological steps were established: isolation, morphological (MI) and molecular identification (Mol-I), assessment of nematocidal activity (NA) of fungal liq-uid culture filtrates (LCF) produced in two liquid media alone and in combination and finally, the myco-compound profile identification (MCP). The MI of both isolates suggested that the fungi corresponded to the species Ao and Am. This result was confirmed by PCR analysis followed by sequencing, alignment and obtaining coverage and similarity with respect to isolates reported in the NCBI database. Likewise, the highest Hc mortalities were 91.4% with individual LCF of Am and 86.2% with those of Ao at the highest concentration (100 mg/mL) in Czapek-Dox Broth. The combination of both LCF resulted in a similarly high larval mortality with no statistical differ-ences in relation to individual activity (P>0.05). The MCP showed the presence of alkaloids in both fungi. Coumarins, triterpenes and saponins were found only in Ao. Both fungi produced ECP with a high NA that could be identified and assessed in future studies as potential natural an-thelmintic compounds.

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as the Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we both study the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard, we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators; the multiple point Maximum A-Posteriori (MAP) estimators; an efficient class of multiple-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators; and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.

In this study, a new method for synthesizing Au-NaYF4:Yb3+/Er3+-DSPE-PEG2K nanocomposites was introduced. Using a hydrothermal method, the synthesized Yb3+- and Er3+-codoped NaYF4 upconversion luminescent materials and Au nanoparticles were doped into upconversion nanomaterials and modified with DSPE-PEG2k up-conversion nanomaterials. This material is known as Ag-UCNPs-DSPE-PEG2k, it improves both the luminous intensity because of the doped Au nanoparticles and has low cytotoxicity because of the DSPE-PEG2k modified. Exciting UCNPs with a wavelength of 980nm near-infrared light will emit light with a wavelength of 520nm to further excite gold nanoparticles to convert light energy into heat. Successful synthesized gold nanoparticles was confirmed using transmission electron microscopy (TEM). The morphology of UCNPs was observed using scanning electron microscopy (SEM), and the mapping confirmed the successful doping of Au nanoparticles. Fluorescence spectra were used to compare changes in luminescence intensity before and after doping Au nanoparticles. The cytotoxicity of Au-UCNPs-DSPE-PEG2K was tested via the cell counting kit-8 (CCK-8) method, and its imaging ability was characterized using the Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) method.

Due to the advantages of low latency, low power consumption and high flexibility of FPGA-based acceleration technology, it has been more and more widely studied and applied in the field of computer vision in recent years. An FPGA-based feature extraction and tracking accelerator for real-time visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) is proposed, which can realize the complete acceleration processing capability of the image front-end and directly output the feature point ID and coordinates to the backend. The accelerator consists of image preprocessing, pyramid processing, optical flow processing, and feature extraction and tracking modules. For the first time, it implements a hardware solution that combines features from accelerated segment test (FAST) corners with Gunnar Farneback (GF) dense optical flow, to achieve better feature tracking performance and provide more flexible technical route selection. In order to solve the scale invariance and rotation invariance lacking problem of FAST features, an efficient pyramid module with a five-layer thumbnail structure is designed and implemented. The accelerator is implemented on a modern Xilinx Zynq FPGA. The evaluation result shows that the accelerator can achieve stable tracking of features of violently shaking images, and is consistent with the results of MATLAB code running on PC. When operating at 100MHz, the accelerator can process 108 frames per second for 720P images and 48 frames per second for 1080P images. Compared to PC CPUs that consume seconds of time, the processing latency is greatly reduced to the order of milliseconds, making GF dense optical flow an efficient and practical technical solution on the edge side.

Carboxypeptidase A4 (CPA4) has shown the potential possibility as a biomarker in the early diagnosis for certain cancers. However, no previous research has linked CPA4 to therapeutic or prognostic significance in bladder cancer. Using data from The Cancer Genome Atlas (TCGA) database, we set out to determine the full extent of the link between CPA4 and BLCA. We further analyzed the interacting proteins of CPA4 and infiltrated immune cells via TIMER2，STRING and GEPIA2 databases. The expression of CPA4 in tumor and normal tissues was compared using the TCGA+GETx database. The connection between CPA44 expression and clinicopathologic characteristics and overall survival (OS) was investigated using multivariate methods and Kaplan-Meier survival curves. The potential functions and pathways were investigated via gene set enrichment analysis. Furthermore, we analyze the associations between CPA4 expression and infiltrated immune cells with their respective gene marker sets using the ssGSEA, TIMER2, and GEPIA2 databases. Compared to matching normal tissues, human CPA4 was found to be substantially expressed. We confirmed that overexpression of CPA4 is linked with shorter OS, DSF, PFI, and increased diagnostic potential using Kaplan-Meier and ROC analysis. The expression of CPA4 is related to T-bet, IL12RB2, CTLA4, and LAG3, among which T-bet and IL12RB2 are Th1 marker genes, while CTLA4 and LAG3 are related to T cell exhaustion, which may be used to guide the application of checkpoint blockade and the adoption of T cell transfer therapy.

Transverse momentum spectra of +, p, Λ, Ξ or ¯Ξ+, Ω or ¯Ω+ and deuteron (d) in different centrality intervals in Gold-Gold (Au-Au) and Lead-Lead (Pb-Pb) collisions at 62.4 GeV and 2.76 TeV respectively, are analyzed by the blast wave model with Boltzmann Gibbs statistics. We extracted Kinetic freeze out temperature,transverse flow velocity and kinetic freezeout volume from the transverse momentum spectra of the particles. It is observed that the non-strange and strange (multi-strange) particles freezeout separately due to different reaction cross-sections. While the freezeout volume and transverse flow velocity are mass dependent which decrease with the rest mass of the particles. The present work reveals the scenario of double kinetic freezeout in collisions at STAR and ALICE Collaborations. Furthermore, the kinetic freezeout temperature and freezeout volume are larger in central collisions than the peripheral collisions and they decrease from central to periphery. However the transverse flow velocity remains almost unchanged from central to peripheral collisions.

Solidification cracking is a major obstacle when joining dissimilar alloys using additive manufacturing. In this work, location-specific solidification cracking susceptibility has been investigated using an integrated computational materials engineering (ICME) approach for a graded alloy formed by mixing P91 steel and Inconel 740H superalloy. An alloy derived from a mixture of 26 wt.% P91 steel and 74 wt.% Inconel 740H, with high configurational and total entropy, was fabricated using wire-arc additive manufacturing. Microstructure characterization revealed intergranular solidification cracks, which increased in length along with the build height. With inputs from experiments, such as secondary dendrite arm spacing, the DICTRA (diffusion-controlled transformations) module within the Thermo-Calc software was used to model location-specific solidification cracking susceptibility. The top region, with the highest cooling rate, has the highest solidification cracking susceptibility and is in good agreement with the experimentally observed crack length. From Scheil simulations, it was deduced that pronounced segregation of Nb and Cu within the cracks increased the solidification range by suppressing the solidus temperature. The overall solidification cracking susceptibility and freezing range was highest for the 26 wt.% P91 alloy amongst the mixed compositions between P91 steel and 740H superalloy, proving that solidification characteristics play a major role in alloy design for additive manufacturing.

The current study emphasizes on optimizing a suitable ratio of enzymatically hydrolyzed potato powder (EHPP) and whole milk powder (WMP) to produce a quality yogurt by evaluating the physicochemical and rheological properties. The results showed that the addition of EHPP decreased the pH towards acidic conditions which resulted in the high acidity of yoghurt. The proximate composition showed that EHPP increased the ash, protein, water holding capacity (WHC) while fat, synersis, color parameters and total solid were decreased when compared to control yoghurt (CY) sample. Moreover, texture profile (TPA) analysis showed that the addition of EHPP decreased the hardness and cohesiveness while springiness did not show significant difference. Furthermore, rheological properties revealed that EHPP decreased the storage modulus (G’) and loss modulus (G”) when compared with control. In addition to this, sensory analysis revealed that the treatment P4M (1:3) was found as optimum ratio regarding taste, flavor, and aroma. Besides this, scanning electron microscopy (SEM) confirms that the high amount of EHPP resulted in the void holes while CY showed dense gel structure. The prepared yogurt with EHPP provides an excellent flavor, satisfying sweetness and homogeneous texture. These findings suggest the optimum formulation ratio of prepared yogurt was found to be P4M (1:3) for desirable attributes and consumer acceptance. The prepared yogurt from the EHPP presents the potential industrial applications.

Diffuse cavernous hemangioma of the rectum (DCHR) is a rare benign lesion of vascular malformation. According to previous studies, the most reported major symptoms of DCHR are recurrent painless rectal bleeding or iron deficiency anemia[1][2][3][4][5][6]. We present a case of an 8Y11M boy who was diagnosed with DCHR by MRI. The patient underwent Laparoscopic-assisted transanal distal sigmoidectomy and sigmoido-anal anastomosis（Laparoscopic Swenson Procedure）, and recovered well. After the operation, the child had short-term fecal incontinence and loose stools, which returned to normal after half a year. Regardless of age and symptoms, early and correct diagnosis and treatment of DCHR and postoperative restoration of normal anal function are necessary. Laparoscopic Swenson procedure may be a good option for the treatment of DCHR.

We report an all-optical radio-frequency (RF) spectrum analyzer with a bandwidth greater than 5 terahertz (THz), based on a 50-cm long spiral waveguide in a CMOS-compatible high-index doped silica platform. By carefully mapping out the dispersion profile of the waveguides for different thicknesses, we identify the optimal design to achieve near zero dispersion in the C-band. To demonstrate the capability of the RF spectrum analyzer, we measure the optical output of a femtosecond fiber laser with an ultrafast optical RF spectrum in the terahertz regime.

Passive and forced drinking harm was prevalent but less recognized in Chinese adolescents. We educated adolescents on such harm to reduce their intention to drink. Students (n=1244) from 7 secondary schools in Hong Kong participated in a video-based health talk on passive and forced drinking harm. Paired t-test was used to assess their change in knowledge of passive and forced drinking, health and social harm of drinking after the health talk. McNemar's chi-squared test and adjusted multivariable logistic regression (AOR) were used to assess their change in intention to drink and intention to quit. Students were less likely to drink (OR 0.29, 95% CI 0.19-0.42) and more likely to quit drinking (OR 3.50, 1.10-14.6) after the health talk. Increased knowledge of passive drinking was associated with less intention to drink (AOR 0.93, 0.90-0.97), increased knowledge of health harm (adjusted b 0.06, 0.05-0.08), and social harm of drinking (adjusted b 0.12, 0.10-0.16). Similar associations were observed in forced drinking (intention to drink: AOR 0.87, 0.79-0.96; health harm: adjusted b 0.16, 0.12-0.19; social harm: adjusted b 0.36, 0.28-0.43). We showed preliminary evidence that the health talk on passive and forced drinking reduced the intention to drink in adolescents.

Background: Passive drinking is prevalent in adolescents worldwide, but its prevalence and harm are understudied. Methods: Secondary students (n=5840, grades 7-12) from 23 selected schools in Hong Kong participated in the survey from 2015-16. Students reported the harm of passive drinking, perceived health status, Patient Health Questionnaire-2, Perceived Stress Scale-4, perceived happiness, family health, happiness, and harmony in the questionnaire. The associations were analyzed using multivariable logistic regression (odds ratio, OR) and linear regression (unstandardized coefficient, b), adjusted for confounders. Results: 29.1% (95% CI 27.8 to 30.5%) of students experienced passive drinking in the past 30-day. Past 30-day parental passive drinking was associated with a higher level of depressive symptoms (AOR 1.63, 95% CI 1.26 to 2.10), stress (adjusted b 0.76, 0.42 to 1.10), and lower level of perceived happiness (adjusted b -0.52, -0.72 to -0.33). Past 30-day parental passive drinking was associated with a lower level of family health (adjusted b -1.39, 95% CI -1.66 to -1.11), family happiness (adjusted b -1.36, -1.64 to -1.08), and family harmony (adjusted b -1.40, -1.70 to -1.10). Conclusion: Passive drinking was associated with poorer mental health, family wellbeing, and lower level of happiness among Hong Kong Chinese adolescents.

The COVID-19 pandemic is adversely impacting food and nutrition security and requires urgent attention from policymakers. Sustainable intensification of agriculture is one strategy that attempts to increase food production without adversely impacting the environment, by shifting from water-intensive crops to other climate-resistant and nutritious crops. This paper focuses on the Indian state of Andhra Pradesh by studying the impact of shifting 20% of the area under paddy and cotton cultivation to other crops like millets and pulses. Using FAO’s CROPWAT model, along with monsoon forecasts and detailed agricultural data, we simulate the crop water requirements across the study area. We simulate a business-as-usual base case and compare it to multiple crop diversification strategies using various parameters – food, calories, protein production, as well as groundwater and energy consumption. Results from this study indicate that reduced paddy cultivation decreases groundwater and energy consumption by around 9-10%., and a calorie deficit between 4-8% - making up this calorie deficit requires a 20-30% improvement in the yields of millets and pulses. We also propose policy interventions to incentivize the cultivation of nutritious and climate-resistant crops as a sustainable strategy towards strengthening food and nutrition security while lowering the environmental footprint of food production.

The advancement of agricultural technology is intrinsically linked to the evolution of the agricultural industry, with the latter being contingent upon technological progress for its sustained enhancement. In the context of China, a nation of significant agricultural stature, the globalization of agricultural science and technology (Agri-Sci-Tech) has emerged as a pivotal narrative within the country's agricultural growth trajectory. The strategic alignment of China's Agri-Sci-Tech industry with its global counterparts is driven by the ambition to assimilate sophisticated agricultural production techniques and managerial acumen from its Western counterparts, thereby catalyzing the international expansion of the nation's Agri-Sci-Tech endeavors. This scholarly discourse delves into the imperative of interdisciplinary convergence, underscoring the paradigm and intrinsic attributes associated with the cultivation of agricultural expertise. It scrutinizes the prevailing state of foreign language instruction within the academic curriculum, positing strategic interventions and recommendations for the development of a robust agricultural foreign language talent cultivation framework. The treatise accentuates the criticality of cultivating a multifaceted agricultural talent pool proficient in foreign languages, advocating for a "professional+foreign language" model that facilitates international dialog within China's Agri-Sci-Tech industry and bolsters the creation of globally recognized brands. Moreover, the cultivation of a "foreign language+professional" composite talent model is posited as a transformative solution to the existing impasse in foreign language education in China, offering a novel pathway for educational innovation and global competitiveness within the agricultural domain.

Thermal interface material (TIM) that can exist as liquid at the service temperature enables efficient heat transfer across two adjacent surfaces in electronic applications. In this work, the thermal conductivities of different phase regions in the Ga-In system at various compositions and temperatures are measured for the first time. A modified comparative cut bar technique is used for the measurement of the thermal conductivities of In_xGa_1-x (x=0, 0.1, 0.214, 0.3, and 0.9) alloys at 40, 60, 80, and 100^oC that are the temperatures commonly encountered in consumer electronics. The thermal conductivity values for the liquid and semi-liquid (liquid+β) Ga-In alloys are higher than the TIM currently used in consumer electronics. These measured quantities, along with the available experimental data from the literature, served as input for the thermal conductivity parameter optimization using the CALPHAD (CALculation of PHase Diagram) method for the pure elements, solution phase, and two-phase region. A set of self-consistent parameters for the description of the thermal conductivity of the Ga-In system is obtained. There is good agreement between the measured and calculated thermal conductivity values for all the phases. Hence, it can be envisaged that liquid/semi-liquid Ga-In alloys can be considered as a potential TIM in consumer electronics due to its high thermal conductivity.

In this study, a new method for synthesizing Ag-NaYF4:Yb3+/Er3+ @ SiO2 nanocomposites was introduced. Using a hydrothermal method, the synthesized Yb3+- and Er3+-codoped NaYF4 upconversion luminescent materials and Ag nanoparticles were doped into upconversion nanomaterials and coated with SiO2 up-conversion nanomaterials. This material is known as Ag-UCNPs-SiO2’ it improves both the luminous intensity because of the doped Ag nanoparticles and has low cytotoxicity because of the SiO2 coating. The morphology of UCNPs was observed using scanning electron microscopy (SEM), and the mapping confirmed the successful doping of Ag nanoparticles. Successful coating of SiO2 was confirmed using transmission electron microscopy (TEM). Fluorescence spectra were used to compare changes in luminescence intensity before and after doping Ag nanoparticles. The reason for the increase in luminescence intensity after doping with Ag nanoparticles was simulated using first-principles calculations. The cytotoxicity of Ag-UCNPs-SiO2 was tested via the cell counting kit-8 (CCK-8) method, and its imaging ability was characterized using the micro-CT method.

The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth application of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, are progressively integrated into NIRS. In this paper, two discriminant analysis problems including wheat kernels and Yali pears were used as examples, and several representative calibration models to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which is based on gramian angular difference field and coordinate attention convolutional neural networks (G-CACNN). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results of using the original spectrum. The accuracy of G-CACNN in two discrimination tasks is 98.48% and 99.39%, respectively. Finally, This research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method.

Carbon fiber insole (CFI), which is lightweight and stiff to reduce energy loss and help wearers perform better in sports, has recently been introduced. However, there are scarce reports on the effects of CFI on sports performance, muscle fatigue and wearing comfort. This study investigat-ed the short-term effects of CFI on sports performance, lower extremity muscle activity, and sub-jective comfort. Thirty young healthy males performed various sports tasks and treadmill runs with wearable sensors under two experimental insole conditions (benchmark insole as a baseline, CFI). The results showed that compared to the benchmark insole, CFI significantly improved sports performance in terms of power generation and agility. However, it activated more of the Tibialis Anterior and Gastrocnemius Medialis muscles and was perceived stiffer, and less com-fortable. These findings suggested that CFI can improve sports performance, but it could cause more lower extremity muscle fatigue and subjective discomfort.

In order to perform multi-degree-of-freedom motion measurements of marine machinery such as ship-borne mechanical platform in an absolute environment without a reference, absolute measurement methods using acceleration sensors and tilt gyroscopes are typically employed. However, due to the influence of wave forces on ship-borne mechanical platform, the coupling between different degrees of freedom can cause mutual interference, resulting in significant sensor measurement disturbances that make efficient computation and real-time analysis challenging. Specifically, when ship-borne mechanical platform swings, the proof mass of the acceleration sensor produces an undesirable signal output in the vertical direction, which leads to imprecise acceleration integration, thereby affecting accurate motion collection and posture estimation. To address these challenges, by analyzing the influence of the inclination angle of the ship-borne mechanical platform on the sensor measurement, and using the working principle of the acceleration sensor and angle sensor, a correction method for the motion measurement of the ship-borne mechanical platform based on multi-sensor fusion is proposed. In this article, we first analyze the influence of inclination angle on the integral effect in the heave direction. Then the configuration using four groups of acceleration sensors to correct the integral effect is proposed. Finally, the optimal inclination angle is determined through Kalman filtering based on the measured value of angle sensors and estimated values from the acceleration sensor sets. Experiments have proved that the average error of the corrected heave displacement signal is 25.34 mm, which is better than the integral displacement signal of a single acceleration sensor. At the same time, the acceleration sensor is used to calculate the roll angle and pitch angle of the ship-borne mechanical platform, and combined with the angle sensor signal to perform Kalman filtering, which filters out the errors caused by the shaking and instability of the mechanical platform, and can more accurately estimate the true inclination of the platform. Therefore, this method can enhance the precision and accuracy of the ship-borne mechanical platform motion signal acquisition, providing more valuable experimental data for research in marine engineering and related fields.

Wenchuan earthquake that occurred in China in 2008 caused severe damage to a large number of electric substations. In this paper, Kriging interpolation method was used to calculate the impact area of the instrumental seismic intensity in Wenchuan earthquake, and to compare the intensities based on strong motion observation against the instrumental seismic intensities at locations where the observation data is available. The instrumental seismic intensities were calculated for the Wenchuan Earthquake at substations in the national power grid with voltage of 110kV or higher in areas of Mianyang, Deyang, Guangyuan and Chengdu. The cumulative Gaussian distribution function was then used to fit the relationship of the curves of the damage probabilities of high-voltage electrical equipment such as transformers, voltage mutual inductors, current mutual inductors, circuit breakers, isolating switches and lightning arrester with their instrumental seismic intensities. The damage probability density distribution curve of high-voltage electrical equipment based on the instrumental seismic intensities was obtained. The results showed that: (1) In the lower seismic intensity region, the mean instrumental seismic intensity was in good agreement with the traditional seismic intensity, but there was noticeable dispersion; in regions of intensity IX and above, the instrumental intensity was lower than the seismic intensity, but there was a lower degree of dispersion. (2) Among high-voltage electrical equipment, the transformers were most vulnerable to damage and they had some damage even under lower instrumental intensity. More damage would be produced when the instrumental intensity reached VIII or above; the second most vulnerable equipment was the circuit breaker, and the damage was most likely to occur when the instrument intensity was IX or above . (3) The damage rate curves of lightning arresters, current mutual inductors, voltage mutual inductors and isolating switches were relatively close to each other and the damage probability was the highest when the instrumental intensity was about X.

Wenchuan earthquake that occurred in China in 2008 caused severe damage to a large number of electric substations. In this paper, Kriging interpolation method was used to calculate the impact area of the instrumental seismic intensity in Wenchuan earthquake, and to compare the intensities based on strong motion observation against the instrumental seismic intensities at locations where the observation data is available. The instrumental seismic intensities were calculated for the Wenchuan Earthquake at substations in the national power grid with voltage of 110kV or higher in areas of Mianyang, Deyang, Guangyuan and Chengdu. The cumulative Gaussian distribution function was then used to fit the relationship of the curves of the damage probabilities of high-voltage electrical equipment such as transformers, voltage mutual inductors, current mutual inductors, circuit breakers, isolating switches and lightning arrester with their instrumental seismic intensities. The damage probability density distribution curve of high-voltage electrical equipment based on the instrumental seismic intensities was obtained. The results showed that: (1) In the lower seismic intensity region, the mean instrumental seismic intensity was in good agreement with the traditional seismic intensity, but there was noticeable dispersion; in regions of intensity IX and above, the instrumental intensity was lower than the seismic intensity, but there was a lower degree of dispersion. (2) Among high-voltage electrical equipment, the transformers were most vulnerable to damage and they had some damage even under lower instrumental intensity. More damage would be produced when the instrumental intensity reached VIII or above; the second most vulnerable equipment was the circuit breaker, and the damage was most likely to occur when the instrument intensity was IX or above . (3) The damage rate curves of lightning arresters, current mutual inductors, voltage mutual inductors and isolating switches were relatively close to each other and the damage probability was the highest when the instrumental intensity was about X.

Chinese Taihe Black-bone silky fowl (TBsf) is the homology of medicine and food and has high nutritional and medical value all over the world. However, the nutritional compositions and specific metabolite advantages of Taihe silky fowl muscle are still poorly understood. In this study, we investigated the differences of nutritional components between TBsf and another similar breed (Black Feathered chicken and laid green-shelled eggs, BF-gsc). Meanwhile, we also explored the divergences in muscle characteristics of Taihe silky fowl fed with two different diets, that is normal chicken feed (TBsf-ncf) and Broussonetia papyrifera-fermented feed (TBsf-bpf). Firstly, the growth performance and biochemical index of Taihe silky fowl was significantly different compared with black-feathered chicken. Secondly, we identified the metabolic alterations in Taihe silky fowl by performing an un-targeted UHPLC-Q-TOF-MS/MS analysis. Our results suggested that the whole metabonomic characteristics had obvious separation between TBsf-ncf, TBsf-bpf and BF-gsc groups both in the positive and negative ion mode by PCA analysis. Next, OPLS-DA multivariate analysis revealed that 57 metabolites (in positive mode) and 49 metabolites (in negative mode) were identified as differential metabolites between TBsf-ncf and BF-gsc group. These differential metabolites were mainly enriched to ABC transporters, biosynthesis of amino acids and aminoacyl-tRNA biosynthesis. Besides, there were 47 metabolites (in positive) and 13 metabolites (in negative) were differentially regulated between TBsf-ncf and TBsf-bpf group, which were majorly involved in histidine metabolism and linoleic metabolism. Furthermore, the integrated network analysis suggested that DL-arginine, DL-isoleucine, linoleoylcarnitine, stearoylcarnitine (positive) and ricionleic acid, D-proline, uric acid (negative) were the significantly metabolic biomarkers in Taihe silky fowl. Moreover, the metabolites of primaquine, ticlpoidine, riboflavin, acetylcarnitine (positive) and salicylic acid, acetaminophen sulfate, glutamic acid (negative) were markedly changed in the Taihe silky fowl fed with BP-fermented feed. In summary, a global survey of the nutritional components and metabolite differences were performed in muscle tissues of Taihe silky fowl between various breeds and feeds. The comprehensive expression profiles of the metabolites in Taihe silky fowl affected by genetic and environmental factors were acquired. This study provided valuable evidence fo breed and feed-induced putative biomarkers as well as improved the economic value of Taihe silky fowl through targeted metabolite regulation.

Material identification based on R_value (transparency natural logarithm ratio of low-energy to high-energy) of line-scan dual-energy X-ray transmission (DE-XRT) has a good prospect for industrial application. Unfortunately, the DE-XRT signals before attenuation cannot be directly measured, whereas their precision is very important to R_value. Therefore, a context-based signal synthesis method which takes the filtered signals that remove high-frequency noises and retain low-frequency actual fluctuations as the reference value, and takes into account the vertical (forward/column) direction and horizontal (scanning/row) direction anisotropy of line-scan images was proposed. The vertical is a time series with continuity of signal trend; the horizontal is a spatial characteristic with the fluctuation synchronization within the same row signals. The special synthesis evaluations of curve synthesis difference and surface synthesis difference were also proposed. Experimental results show that the tow evaluations are both only about 0.0007, and it only takes 35 ms to complete the surface synthesis of 119×119 pixels on the CPU with 3.4 GHz main frequency. The presented method can achieve high signal synthesis precision and calculation real-time, so as to facilitate DE-XRT material identification. It can be extended to improve the precision of numerical synthesis in other line-scan uncomplete signals.

This paper addresses a new micromechanical model to predict biaxial tensile moduli of plain weave fabric (PWF) composites by considering the interaction between the orthogonal interlacing strands. The two orthogonal yarns in micromechanical unit cell (UC) were idealized as the curved beams with a path depicted by using sinusoidal shape functions. The biaxial tensile moduli of PWF composites were derived by means of the minimum total complementary potential energy principle founded on micromechanics. The biaxial tensile tests were respectively conducted on the RTM-made EW220/5284 PWF composites at five biaxial loading ratios of 0, 1, 2, 3 and ∞ to validate the new model. The predictions from the new model were compared with experimental data and good correlation was achieved between the predictions and actual experiments, demonstrating the practical and effective use of the proposed model. Using the new model, the biaxial tensile moduli of plain weave fabric (PWF) composites could be predicted based only on the properties of basic woven fabric.

(1) Background: Substantial evidence that neural timing deficits are prevalent in developmental disorders, aging, and concussions resulting from a mild Traumatic Brain Injury (mTBI) is presented. We show that if timing deficits are remediated using low-level movement discrimination training, then high-level cognitive skills, including reading, attention, processing speed, and working memory improve substantially. (2) Methods: Two case studies are presented using MEG source imaging on an adult dyslexic, and a healthy older adult observed before and after training on movement discrimination two times/week for 8 weeks for adult dyslexic. (3) Results: We found improvements in reading, attention, processing speed, and working memory on neuropsychological tests. Substantial MEG signal increases in visual Motion Networks (V1, V3, MT, MST), Attention Networks (ACC, dlPFC, vlPFC and precuneous/ PCC areas) and Memory Networks (dlPFC). (4) Conclusions: Improving neural timing deficits before cognitive exercises to improve specific cognitive skills provides a rapid and effective method to improve cognitive skills. Improving the timing and sensitivity of low-level dorsal pathways, improving feedforward and feedback pathways, is essential to improve high-level cognitive skills. This adaptive training with substantial feedback shows cognitive transfer to tasks not trained on, significantly improving a person’s quality of life rapidly and effectively.

Suitable soil structure and nutrient security are important for plant growth and development, characteristics of soil fractal dimension and distribution of physical and chemical properties and their interactions play an important role in studying the stability of soil structure and water and fertilizer cycles. As a sustainable management model, intercropping has positive benefits for erosion control, spatial optimization of resources, as well as improving system productivity. The effects of four intercropping methods on soil fractal dimension and physicochemical properties were investigated by intercropping Salvia miltiorrhiza with forage and S. miltiorrhiza with forest under typical karst rock desertification habitats in Guizhou. The results showed that soil nutrient content of intercropping was significantly higher than that of monoculture, the organic carbon content of soil grown under forest is higher than other treatments, and there was a non-significant change in soil water content of intercropping compared with monoculture. The soil fine-grained matter of intercropping was significantly higher than that of monoculture, while the soil fractal dimension showed a tendency to become larger with the increase of fine-grained matter. The intercropping planting, due to its component types and spatial and temporal configurations, leads to differences in soil water and fertilizer interactions, which can be combined with other ecological restoration measures to optimize the composite model and jointly promote the restoration and development of ecologically fragile areas.

Brain-stimulation reward, also known as intracranial self-stimulation (ICSS), is a commonly used procedure for studying brain reward function and drug reward. In electrical ICSS, an electrode is surgically implanted into the medial forebrain bundle in the lateral hypothalamus or the ventral tegmental area in the midbrain. Operant lever responding leads to the delivery of electrical pulse stimulation. The alteration in the stimulation frequency-lever response curve is used to evaluate the impact of pharmacological agents on brain reward function. If a test drug induces a leftward or upward shift in the ICSS response curve, it implies a reward-enhancing or abuse-like effect. Conversely, if a drug causes a rightward or downward shift in the functional response curve, it suggests a reward-attenuating or aversive effect. A significant drawback of electrical ICSS is the lack of cellular selectivity in understanding the neural substrates underlying drug reward versus aversion. Excitingly, recent advancements in optical ICSS (oICSS) have facilitated the development of at least three cell type-specific oICSS models – dopamine-, glutamate-, and GABA-dependent oICSS. In these new models, a comparable frequency-rate response curve has been established and employed to study the substrate-specific mechanisms underlying brain reward function and a drug's rewarding versus aversive effects. In this review article, we summarize recent progress in this exciting research area. The findings in these new behavioral models have not only increased our understanding of the neural mechanisms underlying drug reward and addiction but have also introduced a novel experimental approach in preclinical medication development for treating substance use disorders.

In order to foster a more sustainable and eco-friendly trajectory for the construction industry, while concurrently mitigating environmental pollution and energy inefficiency, it is imperative to cultivate an environmentally conscious building and urban environment. Under the background of carbon peak and carbon neutrality, green building evaluation system (GBRS) has become a research hotspot in the field of green building. This paper systematically summarizes the current research progress based on GBRS, focuses on the current weight and indicators setting, analyzes it with Cite Space software, and puts forward its future development direction. The results show that the weight setting method of green building evaluation system is determined by multidimensional and multi-method. From the perspective of environment, society and economy, the scope of indicators setting is expanding. The green building evaluation system will play a guiding role in green building design, and the evaluation process will continue to be optimized and developed in the direction of intelligence and automation.

The challenging terrain and deep ravines characteristic of mountainous regions often result in slower path planning and suboptimal flight paths for Unmanned Aerial Vehicles (UAVs) when traditional meta-heuristic optimization algorithms are employed. This study investigates the application of an Improved Enhanced Snake Optimizer (IESO) for three-dimensional path planning in a simulated rugged mountainous terrain. The initialization process in the enhanced snake optimizer is refined by integrating the Chebyshev chaotic map. Additionally, a non-monotonic factor is introduced to modulate the "temperature," and a boundary condition is incorporated into the dynamic opposition learning mechanism. These modifications collectively reduce the likelihood of population convergence to local optima during optimization. The feasibility of IESO is validated through time complexity and global convergence analyses. Comparative simulation experiments benchmarked IESO against five state-of-the-art optimization algorithms across test functions and path-planning simulated scenarios. The experimental results indicate that the enhanced algorithm achieves superior optimization precision, faster convergence speeds, and improved quality in the planned trajectories by 30%.

Amyotrophic Lateral Sclerosis (ALS) stands as a debilitating neurodegenerative condition characterized by the progressive degeneration of motor neurons. Despite extensive research in various model animals, the cellular signal mechanisms of ALS remain elusive, impeding the development of efficacious treatments. Among these models, a well-characterized and diminutive organism, Caenorhabditis elegans (C. elegans), has arisen as a potent tool for investigating the molecular and cellular dimensions of ALS pathogenesis. This review summarizes the contributions of C. elegans models to our comprehension of ALS, emphasizing pivotal findings pertaining to genetics, protein aggregation, cellular pathways, and potential therapeutic strategies. We analyze both the merits and constraints of C. elegans system in the realm of ALS research, and point towards future investigations that could bridge the chasm between C. elegans foundational discoveries and clinical applications.

Modeling is essential to understand better the generative mechanisms responsible for experimental observations gathered from complex systems. In this work, we are using such an approach to analyze the electrocardiogram (ECG). We present a systematic framework to decompose ECG signals into sums of overlapping lognormal components. We used reinforcement learning to train a deep neural network to estimate the modeling parameters from ECG recorded in babies of 1 to 24 months of age. We demonstrate this model-driven approach by showing how the extracted parameters vary with age. After correction for multiple tests, 10 of 24 modeling parameters showed statistical significance below the 0.01 threshold, with absolute Kendall rank correlation coefficients in the [0.27, 0.51] range. We presented a model-driven approach to the analysis of ECG. The impact of this framework on fundamental science and clinical applications is likely to be increased by further refining the modeling of the physiological mechanisms generating the ECG. By improving the physiological interpretability, this approach can provide a window into latent variables important for understanding the heart-beating process and its control by the autonomous nervous system.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

The volatile fatty acids (VFAs) productions, as well as particulate organics decomposition, soluble chemical oxygen demand (SCOD) yield, and the VFAs production pathways, of mesophilic and thermophilic anaerobic fermentation were investigated. Batch experiments showed that the decomposition rate of volatile suspended solids (VSS), particulate carbohydrate (P-C) and particulate protein (P-P) followed the first-order kinetic model at different temperature. However, the intermediates， accumulated in the process of protein or carbohydrate digestion had a more significant inhibitory effect on the production of VFAs during the mesophilic anaerobic acidification process. Thermophilic fermentation led to more accumulation of VFAs compared to mesophilic fermentation. Then 13C stable isotope labeling experiment demonstrated that, acetic (HAc), propionic (HPr) and butyric (HBu) could be produced from carbohydrate and protein digestion, but valeric (HVa) was only generated from the degradation of protein. Finally, the possible pathway of HVa synthesis was proposed.

An accurate and timely precipitation forecast is essential for water resources management in hydropower, irrigation, and reservoir control. The conventional methods are limited by their inability to capture the high precipitation variability in time and space. In the present work, a wavelet-based deep learning approach is adopted to forecast precipitation using the lagged monthly rainfall, local climate variables, and global teleconnections such as IOD, PDO, NAO, and Nino 3.4 as predictors. The method was tested and validated over the Krishna River Basin in India. Overall, the forecasting accuracy was higher using the wavelet-based hybrid models than the single-scale models. The proposed multi-scale model was then applied to the different climatic regions of the country, and it was shown that the model could forecast the rainfall at reasonable accuracy for different climate zones of the country.

Purpose: Pancreatic cancer (PAAD) is one of the most lethal malignancies. Immunotherapy is largely ineffective in PAAD. T-cell exhaustion contributes to the resistance for immunotherapies. We investigated the prognostic potential of T cell exhaustion-related genes (TEXGs). Methods: Single-cell RNA (scRNA) sequencing dataset from TISCH, and bulk sequencing datasets from TCGA and GTEx were applied to screen differently expressed TEXGs. Kaplan-Meier survival, LASSO regression, and Univariate/Multivariate Cox regression analysis were performed to construct TEXGs risk model. This model was used to predict the prognosis, tumor immune microenvironment, and immunotherapy response. PAAD cohorts from ICGC and GSE71729 datasets were used to validate this risk model. Pan-cancer expression of SPOCK2 was performed in TISCH database. Results: A six-gene (SPOCK2, MT1X, LIPH, RARRES3, EMP1, and MEG3) risk model was constructed. Low-risk PAAD patients had longer survival time in both training dataset (TCGA-PAAD, n=178) and validation datasets (ICGC-PACA-CA, ICGC-PAAD-US, and GSE71729, n=412). Multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic variable for PAAD. High-risk patients correlated with cancer-related KEGG pathway enrichment, higher mutation frequency of KRAS and TP53 genes, and immunosuppressive status. Immunohistochemistry staining confirmed the changes of TEXGs in clinical samples. Moreover, pan-cancer scRNA sequencing datasets from TISCH analysis indicated that SPOCK2 might be a novel marker of exhausted CD8+ T cells. Conclusion: In conclusion, we established and validated a T cell exhaustion-related prognostic signature for PAAD patients. Moreover, our study suggests that SPOCK2 may be a novel marker of exhausted CD8+ T cells.

This paper aims to address the exponential stability and stabilization problems for a class of delayed nonlinear Markov jump systems under randomly occurring Denial-of-Service (DoS) attack and packet loss. Firstly, the stochastic characteristics of DoS attack and packet loss are depicted by the attack success rate and packet loss rate. Secondly, a Period Observation Window (POW) method and a hybrid-input strategy are proposed to compensate for the impact of DoS attack and packet loss on the system. Thirdly, A Dynamic Event-triggered Mechanism (DETM) is introduced to save more network resources and ensure the security and reliability of systems. Then, by constructing a general common Lyapunov functional and combining with the DETM and other inequality analysis techniques, the less conservative stability and stabilization criteria for the underlying systems are derived. In the end, the effectiveness of our result is verified through a numerical example.

This paper presents an innovative approach to dimensionality reduction and feature extraction in high-dimensional datasets, with a specific application focus on wood surface defect detection. The proposed framework integrates sparse modeling techniques, particularly Lasso and proximal gradient methods, into a comprehensive pipeline for efficient and interpretable feature selection. Leveraging pre-trained models such as VGG19 and incorporating anomaly detection methods like Isolation Forest and Local Outlier Factor, our methodology addresses the challenge of extracting meaningful features from complex datasets. Evaluation metrics such as accuracy and F1 score, alongside visualizations, are employed to assess the performance of the sparse modeling techniques. Through this work, we aim to advance the understanding and application of sparse modeling in machine learning, particularly in the context of wood surface defect detection.

Technological advances in tea production are critical to meet global demands for tea consumption. To illuminate differences in the scientific and commercial relevance of research programs between major tea-related research organizations around the world, we conducted a bibliometric analysis comparing the scientific outputs, including peer-reviewed publications and patents, of six major research organizations published between January 01, 2008 and July 11, 2020. For this competitive ranking analysis, we examined a number of papers, research topics, academic influence, capacity for development, capacity for collaboration, and capacity for technological innovation. Among the six research organizations, Anhui Agricultural University (in China) had the most publications, while the Chinese Academy of Agricultural Sciences, Tea Research Institute showed a higher capacity for development. The Council for Scientific and Industrial Research (in India) and the University of Shizuoka (Japan) both showed high academic influence, while the University of Shizuoka, Tea Research Association Tocklai and National Agriculture and Food Research Organization (Japan) showed a high capacity for collaboration. The Chinese Academy of Agricultural Sciences, Tea Research Institute applied for the most patents, while the Council for Scientific and Industrial Research applied for the most international patents.

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.

Focused microwave breast hyperthermia (FMBH) employs a phased antenna array to perform beamforming that can focus microwave energy at targeted breast tumors. Selective heating of the tumor endows the hyperthermia treatment with high accuracy and low side effects. The effect of FMBH is highly dependent on the applied phased antenna array. This work investigates the effect of polarizations of antenna elements on the microwave focusing results by simulations. We explore two kinds of antenna arrays with the same number of elements using different digital realistic human breast phantoms. The first array has all the elements’ polarization in the vertical plane of the breast while the second array has half elements’ polarization in the vertical plane and the other half in the transverse plane, i.e., cross polarization. In total 96 sets of different simulations are performed, and the results show that the second array leads to a better focusing effect in dense breasts than the first array. This work is very meaningful for the potential improvement of the antenna array for FMBH, which is of great significance for the future clinical applications of FMBH. The antenna array with cross polarization can also be applied in micro-wave imaging and sensing for biomedical applications.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that disproportionally accounts for the majority of breast cancer-related deaths due to the lack of specific targets for effective treatments. In this review, we highlight the complexity of the transforming growth factor-beta family (TGF-β) pathway and discuss how the dysregulation of the TGF-β pathway promotes oncogenic attributes in TNBC which negatively affects patient prognosis. Moreover, we discuss recent findings highlighting TGF-β inhibition as a potent method to target mesenchymal (CD44⁺/CD24^-) and epithelial (ALDH^high) cancer stem cell (CSC) populations. CSCs are associated with tumorigenesis, metastasis, relapse, resistance, and diminished patient prognosis; however, due to differential signal pathway enrichment and plasticity, these populations remain difficult to target and persist as a major barrier barring successful therapy. This review highlights the importance of TGF-β as a driver of chemoresistance, radioresistance and reduced patient prognosis in breast cancer and highlights novel treatment strategies which modulate TGF-β, impede cancer progression and reduce the rate of resistance generation via targeting the CSC populations in TNBC and thus reducing tumorigenicity. Potential TGF-β inhibitors targeting based on clinical trials are summarized for further investigation which may lead to the development of novel therapies to improve TNBC patient prognosis.

We previously showed that feeding mice a diet containing 1% mantle tissue decreased food consumption, leading to death. We also isolated and identified toxic substances in the mantle tissue. In the present study, we investigated the characteristics and stability of mantle tissue toxicity. Treatment of mantle tissue with 1 mM HCl, 1 mM NaOH, 1 mM dithiothreitol, and 1 mM H2O2 and heating did not reduce the toxicity of mantle tissue in mice. These results suggest that mantle toxins are stable in tissues, particularly when exposed to acidic and digestive enzymes. We examined whether mantle tissue exhibited acute toxicity. Diets containing 1% and 20% mantle extract showed similar levels of toxicity, demonstrating that feeding of mantle tissue does not lead to acute toxicity. Finally, we examined the toxicity of the mantle tissue against small intestinal tissue. Chronic feeding of mantle tissue to mice changed the color of the small intestine. Real-time PCR analysis showed that mantle tissue feeding caused changes of inflammation and endoplasmic reticulum stress markers in the small intestine. These results suggest that feeding of mantle tissue causes toxicity after causing initial damage to the small intestinal tissue.

Pathogens, especially invasive species, have caused significant global ecological, economic, and social losses in forests. Plant disease research has traditionally focused on direct interactions between plants and pathogens in an appropriate environment. However, recent research indicates that the microbiome can interact with the plant host and pathogens to modulate plant resistance or pathogen pathogenicity, thereby altering the outcome of plant–pathogen interactions. Thus, this presents new opportunities for studying the microbial management of forest diseases. Compared to parallel studies on human and crop microbiomes, research into the forest tree microbiome and its critical role in forest disease progression has lagged. The rapid development of microbiome sequencing and analysis technologies has resulted in the rapid accumulation of a large body of evidence regarding the association between forest microbiomes and diseases. These data will aid the development of innovative, effective, and environmentally sustainable methods for the microbial management of forest diseases. Herein, we summarize the most recent findings on the dynamic structure and composition of forest tree microbiomes in belowground and aboveground plant tissues (i.e., rhizosphere, endosphere, and phyllosphere), as well as their pleiotropic impact on plant immunity and pathogen pathogenicity, highlighting representative examples of biological control agents used to modulate relevant tree microbiomes. Lastly, we discuss the potential application of forest tree microbiomes in disease control as well as their future prospects and challenges.

Target recognition mainly includes three approaches: optical image-based, echo detection-based, and passive signal analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an "end-to-end" model that cascades a noise estimation network with a recognition network to identify working modes in noise environment. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signal, the recognition network is realized based on Multi-Scale Convolutional Attention Network (MSCANet). Firstly, the deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem that the conventional method is seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environments, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

The portal website of a university or a college usually represents the cultural characteristics and teaching advantages and plays a vital role in higher education, showing the style of the university or college to society and improving the admission rate. Mississippi State (MS) is bounded by the Mississippi River in the west and the Gulf of Mexico in the south, with a population of 3.1 million. It has a rich cultural heritage, and many universities, especially historically black colleges and universities (HBCUS), have strong scientific research strength in agriculture, medicine, petrochemicals and papermaking. Therefore, this article will analyze the design of the portal websites of four HBCUs in Mississippi from two aspects: user experience (UE) and functional architecture (FA), determine their advantages, disadvantages, similarities and differences, and propose some optimization suggestions.

Large language model (LLM) is a representation of a major advancement in AI, and has been used in multiple natural language processing tasks. Nevertheless, in different business scenarios, LLM requires fine-tuning by engineers to achieve satisfactory performance, and the cost of achieving target performance and fine-tuning may not match. Based on the Baidu STI dataset, we study the upper bound of the performance that classical information retrieval methods can achieve under a specific business, and compare it with the cost and performance of the participating team based on LLM. This paper gives an insight into the potential of classical computational linguistics algorithms, and which can help decision-makers make reasonable choices for LLM and low-cost methods in business R&D.

While the language model using the stop sign as an independent token has been widely used to decide when the model should stop, it may lead to the growth of vocabulary dimensions and further problems. Similarly, present research on game algorithms usually estimate stopping point related problems based on the evaluation of the winning rate. However, information redundancy may also exist in such models, thus increasing the training difficulty. Above two types of tasks (and similar autoregressive tasks) show a common problem of stopping point prediction. In this paper, we describe a design of separated model, trying to separate the complexity of stopping point prediction from the main task model, so that the information used for estimating stopping point can be reduced. On this basis, in order to verify the rationality of using separated model, we propose a model-free test method. It judges the separability of transformed data based on point difference and sequence difference metrics. In this way, it can predict the credibility of the separated model inference.

Directed energy deposition (DED) is an efficient method to fabricate functionally graded materials (FGMs) with gradient composition and complex structures, allowing for local tailoring of properties instead of the costly need for extraneous welds and joints. In this study, a FGM from stainless steel to Inconel alloy was successfully fabricated using the powder-based laser DED. A very refined grain structure has been observed in at the composition with 75 wt.% Inconel alloy content, which also exhibits the highest (entropy). For the first time, the post heat treatments, microstructure and aging precipitation behaviors of FGMs were systematically studied via experimental characterization and computation, to elucidate their effects on the gradient smoothing and mechanical properties. The diffusion and segregation of Ni, Nb and Ti elements underly the transformation mechanism between Laves, δ, γ’ and γ’’ phases during precipitation. Homogenization on FGMs not only eliminates the heterogeneity inherited from the AM process, but also provides a practical way to smoothen the gradient on composition and microstructure for the eventual good gradient properties. It has a direct influence on the following precipitation behaviors in the FGM, which highly relies on the diffusion degree of the elements in the matrix and grain boundaries. The high-throughput thermodynamic modeling and kinetic modeling were exploited to evaluate the experimental microstructure and address computational uncertainty using different thermodynamic conditions and databases, which enables an accelerated design through local tailoring of process-structure-property relationships to develop new functional materials.

In this work, cobalt doped ZnO nanorod arrays with anticorrosion function were successfully prepared on FTO substrate by a simple aqueous solution method. XRD and EDS results indicate the doped Co2+ has successfully incorporated into the ZnO crystal lattice. Photocurrent density and open circuit potential (OCP) results indicate the photocathodic protection performance for 316 stainless steel (316 SS) and Q235 carbon steel in 3.5 wt.% NaCl solution under 300 W Xe lamp enhanced with the increase of cobalt concentration, and the photoanode with 15% Co/Zn ratio has the optimal photocathodic protection effect. The mechanism of enhancement may be result from the narrowed band gap, the lower recombination rate of photogenerated electron-holes, the intermediate impurity level and the split of the hypo-outer shell of cobalt ions.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that disproportionally accounts for the majority of breast cancer-related deaths due to the lack of specific targets for effective treatments. While there is immense focus on the development of novel therapies for TNBC treatment, a persistent and critical issue is the rate of heart failure and cardiomyopathy which is a leading cause of mortality and morbidity amongst cancer survivors. In this review, we highlight mechanisms of cardiotoxicity post-chemotherapeutic exposure, assess how this is assessed clinically and highlight the transforming growth factor-beta family (TGF-β) pathway and discuss its role as a mediator of cardiomyopathy. We highlight recent findings demonstrating TGF-β inhibition as a potent method to prevent cardiac re-modeling, fibrosis and cardiomyopathy. We describe how dysregulation of the TGF-β pathway is associated with negative patient outcomes across 32 types of cancer including TNBC. We then highlight how TGF-β modulation may be a potent method to target mesenchymal (CD44+/CD24-) and epithelial (ALDHhigh) cancer stem cell (CSC) populations in TNBC models. CSCs are associated with tumorigenesis, metastasis, relapse, resistance, and diminished patient prognosis; however, due to plasticity and differential regulation these populations remain difficult to target and persist as a major barrier barring successful therapy. TGF-β inhibition represents an intersection of two fields: cardiology and oncology. Through inhibiting cardiomyopathy, cardiac damage and heart failure may be prevented and through CSC targeting, patient prognosis may be improved. Together, both approaches, if successfully implemented would target the two greatest causes of cancer-related morbidity in patients and potentially lead to a breakthrough therapy.

Cell migration is essential for physiological, pathological and biomedical processes such as, in embryogenesis, wound healing, immune response, cancer metastasis, tumour invasion and inflammation. In light of this, quantifying mechanical properties during the process of cell migration is of great interest in experimental sciences, yet few theoretical approaches in this direction have been studied. In this work, we propose a theoretical and computational approach based on the optimal control of geometric partial differential equations to estimate cell membrane forces associated with cell polarisation during migration. Specifically, cell membrane forces are inferred or estimated by fitting a mathematical model to a sequence of images, allowing us to capture dynamics of the cell migration. Our approach offers a robust and potentially accurate framework to compute geometric mechanical membrane forces associated with cell polarisation during migration and also yields geometric information of independent interest, we illustrate one such example that involves quantifying cell proliferation levels which are associated with cell division, cell fusion or cell death.

Lidocaine hydrochloride (HCl) 2% with 1:100,000 epinephrine (LW/E) is widely used to prevent pain during dental procedures and has been associated with injection sting, jittering effects, slow onset, and a bitter aftertaste. Since LW/E’s introduction in 1948, no significant modifications have been proposed. This study aims to design and characterize an improved dental lidocaine HCl injectable formulation without epinephrine (LW/O/E) via buffers, sweeteners, and amino acids. LW/O/E injections were prepared with pH and osmolality values of 6.5 – 7.0 and 590-610 mOsm/kg. Using the electronic tongue (ETongue), the LW/O/E injectable formulations were characterized for viscosity, injectability, and taste analysis. The results were compared with the LW/E control. In vivo efficacy and anesthetic duration of the samples were measured through radiant heat tail-flick latency (RHTFL) and hot plate (HP) tests and local toxicity was assessed after a single intra-oral injection in Sprague Dawley rats (SDR). The viscosity and injectability values of the LW/O/E samples were found to be comparable to the LW/E injection. ETongue taste analysis showed an improvement in bitterness reduction of the LW/O/E samples compared to the LW/E formulation. Toxicity studies of samples in SDR showed minor and transient signs of erythema/eschar and edema. Anesthetic duration via RHTFL and HP paw withdrawal latency time in SDR were found to be comparable for the LW/O/E Sample 3A and the LW/E injection (p<0.05). In conclusion, the buffered, higher osmolality and reduced bitterness developed LW/O/E formulation (Sample 3A) could be considered a promising alternative to the LW/E formulation for dental use.

The minimum dominating tree (MDT) problem consists of finding a minimum weight sub-graph from an undirected graph, such that each vertex not in this sub-graph is adjacent to at least one of the vertices in it, and the sub-graph is connected without any ring structures. This paper presents a Dual Neighborhoods Search (DNS) algorithm for solving the MDT problem, which integrates several distinguishing features, such as two neighborhoods collaboratively working for optimizing the objective function, a fast neighborhood evaluation method to boost the searching effectiveness, and several diversification techniques to help the searching process jump out of the local optimum trap thus obtaining better solutions. DNS improves the previous best-known results for 4 public benchmark instances while providing competitive results for the remaining ones. Several ingredients of DNS are investigated to demonstrate the importance of the proposed ideas and techniques.

Steel products have experienced long-standing problems such as unstable product quality and low product homogeneity. In the continuous casting process, realizing constant temperature pouring is an effective way to improve product homogeneity. Plasma heating can compensate for the temperature drop during casting with a tundish and maintain a stable degree of superheating of the molten steel in the tundish. Plasma heating has a certain impact on the cleanliness of the molten steel and on the tundish covering flux in the tundish while compensating for the temperature drop. This paper uses SEM-EDS, XRD and FactSage to analyze the cleanliness of the molten steel and the characteristics of the tundish covering flux before and after heating. The results show that the number density of inclusions in the tundish is significantly lower after heating, improving the floating removal rate of small-sized inclusions; after heating, the surface morphology of the tundish covering flux sample appears transparent and glassy, with uniform morphology; XRD results show that the tundish covering flux after plasma heating exhibits no crystal precipitation and is amorphous and that there is a certain regularity before and after heating; there are no obvious changes in the composition of the tundish covering flux in the liquid phase area.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

OBJECTIVE A quality improvement study to assess catheter-associated urinary tract infection (CAUTI) rate post-implementation of a bladder catheter with integrated active drain line urine clearance and automated intra-abdominal pressure monitoring in a burn intensive care unit (ICU). DESIGN Eight-year retrospective before and after study (2015–2022). SETTING A single American Burn Association verified Burn Center with fourteen inpatient beds. PATIENTS Patients meeting criteria for admission to a Burn Center METHODS Retrospective cohort study following the implementation of a novel urine output monitoring system with integrated drain line and urine clearance. Data from a 48-month (from January 2015-December 2018) historical control (period 1) were compared to data from a 28-month (from January 2020 to April 2022) post-implementation period (period 2). Pre- and post-implementation CAUTI event incidences were compared. Charts were reviewed to characterize the patients. RESULTS A total of 42 CAUTIs in 2243 patients were identified using the National Health and Safety Network (NHSN) definition during the analyzed period. There were 40 CAUTI events in period 1 and two CAUTIs in period 2. The incidence of CAUTI events pre-implementation was 0.030 (mean of 10 CAUTI events per year) compared to 0.002 (mean of 1 CAUTI event per year) post-implementation of an automatic drain line clearing UO monitoring system showing a significant reduction in CAUTI events (P<0.01, risk ratio novel vs. gravity bladder catheter 0.071, 95% confidence interval: 0.017-0.294). CONCLUSIONS CAUTIs were reduced in the period following the implementation of a novel urinary catheter system with an integrated active drain line and urine clearance in burn patients.

Surgical simulators are crucial in early craniofacial and plastic surgical training, necessitating synthetic materials that accurately replicate tissue properties. Recent critiques of our lab’s currently deployed silicone surrogate have highlighted numerous areas for improvement. To further refine our models, our group's objective is to find a composition of materials that is closest in fidelity to native oral mucosa during surgical rehearsal by expert craniofacial surgeons. Fifteen platinum silicone-based surrogate samples were constructed with variable hardness and slacker percentages. These samples underwent evaluation of tactile sensation, hardness, needle puncture, cut resistance, suture retention, defect repair, and tensile elasticity. Expert craniofacial surgeon evaluators provided focused qualitative feedback on selected top-performing samples for further assessment and statistical comparisons. Evaluation revealed surrogate characteristics with satisfactory to good performance. Sample 977 exhibited the highest performance, and comparison with the original surrogate (sample 810) demonstrated significant improvements in critical areas, emphasizing the efficacy of the refined composition. The study identified a silicone composition that directly addresses the feedback received by our team’s original silicone surrogate. The study underscores the delicate balance between biofidelity and practicality in surgical simulation. The need for ongoing refinement in surrogate materials is evident to optimize training experiences for early surgical learners.

Surgical simulators are crucial in early craniofacial and plastic surgical training, necessitating synthetic materials that accurately replicate tissue properties. Recent critiques of our lab’s currently deployed silicone surrogate have highlighted numerous areas for improvement. To further refine our models, our group's objective is to find a composition of materials that is closest in fidelity to native oral mucosa during surgical rehearsal by expert craniofacial surgeons. Fifteen platinum silicone-based surrogate samples were constructed with variable hardness and slacker percentages. These samples underwent evaluation of tactile sensation, hardness, needle puncture, cut resistance, suture retention, defect repair, and tensile elasticity. Expert craniofacial surgeon evaluators provided focused qualitative feedback on selected top-performing samples for further assessment and statistical comparisons. Evaluation revealed surrogate characteristics with satisfactory to good performance. Sample 977 exhibited the highest performance, and comparison with the original surrogate (sample 810) demonstrated significant improvements in critical areas, emphasizing the efficacy of the refined composition. The study identified a silicone composition that directly addresses the feedback received by our team’s original silicone surrogate. The study underscores the delicate balance between biofidelity and practicality in surgical simulation. The need for ongoing refinement in surrogate materials is evident to optimize training experiences for early surgical learners.

Three-phase AC electric arc furnace (EAF) is a typical non-linear load, causing many power quality problems. Most of the researches on the voltage problems of EAF mainly focus on the voltage fluctuation, and less on the transient voltage problems caused by EAF short circuit and open circuit. In this paper, the relationship between voltage and current of EAF is obtained by combining hyperbolic function and exponential function, then the white noise and chaotic circuit are added to establish the EAF model which is suitable for the study of voltage fluctuation and transient voltage. This paper analyzes the causes of the transient voltage problem of the EAF, calculates the short-circuit current, reactive impact and the influence on voltage at the point of common coupling (PCC) in the three-phase short-circuit of the EAF, and compares the calculation results with the simulation results to prove the accuracy of this model. The results show that the reactive impact of three-phase short circuit is about twice as much as that of normal operation of EAF, resulting in about 30% voltage sag at the PCC, which is very unfavorable to the power grid. This paper provides reference for transient power quality evaluation and dynamic reactive power compensation of EAF.

Skin cancer is a prevalent and heterogenous disease with several subtypes, such as melanoma, basal cell carcinoma, and squamous cell carcinoma. Among them, melanoma is the most aggressive subtype, with a higher propensity to spread compared to most solid tumors. The application of OMICS approaches has revolutionized the field of melanoma research by providing comprehensive insights into the molecular alterations and biological processes underlying melanoma development and progression. This review aims to offer an overview of melanoma biology, covering its transition from primary to malignant melanoma, as well as the key genes and pathways involved in the initiation and progression of this disease. Utilizing online databases, we extensively explored the general expression profile of genes, identified the most frequently altered genes, and gene mutations, and examined genetic alterations responsible for drug resistance. Additionally, we studied the mechanisms responsible for immune checkpoint inhibitors resistance in melanoma.

We report a photonic-based radio frequency (RF) arbitrary waveform generator (AWG) using a soliton crystal micro-comb source with a free spectral range (FSR) of 48.9 GHz. The comb source provides over 80 wavelengths, or channels, that we use to successfully achieve arbitrary waveform shapes including square waveforms with a tunable duty ratio ranging from 10% to 90%, sawtooth waveforms with a tunable slope ratio of 0.2 to 1, and a symmetric concave quadratic chirp waveform with an instantaneous frequency of sub GHz. We achieve good agreement between theory and experiment, validating the effectiveness of this approach towards realizing high-performance, broad bandwidth, nearly user-defined RF waveform generation.

We report a broadband radio frequency (RF) channelizer with up to 92 channels using a coherent microcomb source. A soliton crystal microcomb, generated by a 49 GHz micro-ring resonator (MRR), is used as a multi-wavelength source. Due to its ultra-low comb spacing, up to 92 wavelengths are available in the C band, yielding a broad operation bandwidth. Another high-Q MRR is employed as a passive optical periodic filter to slice the RF spectrum with a high resolution of 121.4 MHz. We experimentally achieve an instantaneous RF operation bandwidth of 8.08 GHz and verify RF channelization up to 17.55 GHz via thermal tuning. Our approach is a significant step towards the monolithically integrated photonic RF receivers with reduced complexity, size, and unprecedented performance, which is important for wide RF applications ranging from broadband analog signal processing to digital-compatible signal detection.

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diadnosis (CAD) schemes of medical images. This study aims to investigate and compare advantages and potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3,000 digital mammograms is assembled in which 1,496 images depict malignant lesions and 1,054 images depict benign lesions. Two CAD schemes are developed to classify breast lesions. The first scheme is developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme is built based a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes are trained and tested using a 10-fold cross-validation method. Several score fusion methods are also investigated to classify breast lesions. CAD classification performance is evaluated by the area under ROC curve (AUC). Results: ResNet50 model-based CAD scheme yields AUC = 0.85±0.02, which is significantly higher than radiomics feature-based CAD scheme with AUC = 0.77±0.02 (p < 0.01). Additionally, fusion of classification scores generated by two CAD schemes does not further improve classification performance. Conclusion: This study indicates that using deep transfer learning is more efficient to develop CAD schemes and enables to yield higher lesion classification performance than CAD schemes developed using radiomics-based technology.

Current generation witnesses a huge interest in optical waveguide due to their salient feature such as low cost, immune to electromagnetic interference, easy to multiplex, compact size, etc. These features of the optical fibers makes it an useful tool for various sensing applications including medicine, automotive, biotechnology, food quality control, aerospace, physical and chemical monitoring etc. Among all the reported application, the device has been widely exploited to measure the physical and chemical variation in surrounding environment. Optical fiber based temperature sensor plays a crucial role in this decade to detect high fever and tackle COVID like pandemic. Recognizing the major developments in the field of the optical fibers, this article aims to provide recent progress in temperature sensor utilizing several sensing configuration including standard fiber, photonic crystal fiber, and Bragg grating fibers. Additionally, the article also highlights the advantages, limitations, and future possibilities in this area.

Convolutional neural networks (CNNs) face challenges in capturing long-distance text correlations, and Bidirectional Long Short-Term Memory (BiLSTM) networks exhibit limited feature extraction capabilities for text classification of work order. To address the abovementioned problems, this work utilizes an ensemble learning approach to integrate model elements efficiently. This study presents a method for classifying work order texts using a hybrid neural network model called BERT-BiLSTM-CNN. First, use Bert for preprocessing to obtain text vector representations. Then, capture context and process sequence information through BiLSTM. Next, capture local features in the text through CNN. Finally, obtain classification results through Softmax. Through comparative analysis, the method of fusing these three models is superior to other hybrid neural network model architectures in multiple classification tasks. It has a significant effect on work order classification.

Visualization of the seabed terrain is one of the key functions of marine geographic information system. The major challenge here is that the obtained data for seabed terrain usually consists of elevation and sediment only but does not include remote sensing images, which is important for ground terrain visualization, as they can be used as textures to reveal detailed information and achieve realistic visual results. Existing seabed terrain visualizing methods (including annotations, color-mapping, and texture-mapping) have limitations in reality and intuition, as they are inadequate to express the physical characteristics of sediment accurately. This paper presents a novel and advanced 3D visualization method of seabed terrain, which introduces the Physically Based Rendering (PBR) theory into the field of geographic visualization. We analyze the main categories of seabed sediments and their optical features respectively, then develop a procedural method to generate physically based rendering materials for different sediments. Then an enhanced bidirectional reflectance distribution function is employed to achieve accurate and intuitive terrain rendering. We also refine the texture sampling method and propose a procedural seabed objects generation method to construct a more natural and realistic undersea environment. Experimental results reveal our method can make good use of the limited seabed terrain data and get significant improvements in visual effect, which can help users to cognize and analyze the seabed geographic environment more accurately and intuitively.

The objective of the present study is to isolate phages targeting multidrug resistant (MDR), extended spectrum beta lactamase (ESBL) positive clinical isolate of E. coli ( U1007), sequence and analyze the phage genome and use machine learning tools to predict host cell surface receptor and finally evaluate the efficiency of monophage and phage cocktail in vitro and in vivo in a zebrafish model. Phage specific for E. coli U1007 was isolated from Ganges River (designated as U1G), Cuoom River (designated as CR) and Hospital waste water (designated as M phage). The obtained phages were triple purified and enriched. U1G phages had a greater burst size of 124 PFU/cell and a latent time of 25 min.M phage had a burst size of 150 PFU/cell with a shortlatent time of 20min. Similarly CR phage has a short latent 20 min and a burst size of 115 PFU/cell. Based on capsid architecture, U1G phage resembles Podoviridae, CR phage is structurally similar to Myoviridae and M phage has morphology that resembles Siphoviridae. Genome sequencing and analysis revealed that the size of the U1G phage genome is 73275 bp Whereas that of CR phage and M phage are 45236 bp and 45294 bp, respectively. All three genomes were marked by the absence of genes encoding tRNA sequence, antibiotic resistant or virulent genes. A machine learning (ML) based multi-class classification model using algorithms such as Random Forest, Logistic Regression, and Decision Tree were employed to predict the host receptor targeted by all 3 phages and the best performing algorithm Random Forest predicted LPS O antigen , LamB or OmpC for U1G FhuA, OmpC for CR phage and FhuA, LamB, TonB or OmpF for the M phage as the host receptor targeted by the receptor binding protein (RBP) of the phages. In vitro time kill assay showed that treatment with monophages alone and along with colistin resulted in regrowth whereas phage combinations significantly reduced the regrowth and by 24h the phage cocktail along with colistin produced a significant 3 log declinein cell counts relative to the untreated control. In vivo intramuscular infection study in zebrafish showed that phages were non toxic and a cocktail of dual (U1G +M) phage along with colsitin resulted in a significant 3.5 log decline in cell counts whereas triple phage combination along with colistin resulted in 3 log decline in cell counts probably due to host receptor competition. Our study highlights the potential of phage cocktail therapy in mitigating MDR clinical isolate of E. coli in vitro and in vivo.

Surface finishing is challenging for additively manufactured components with complex geometries. Magnetic abrasive finishing (MAF) is a promising surface finishing technology that can refine the surface quality of components with complex shapes produced by additive manufacturing. However, there is insufficient study regarding the impact of MAF on microstructure-property relationships for additively manufactured builds, which is critical for evaluating the mechanical performance. Furthermore, although MAF is usually used as the final step of post-processing, it remains unclear whether adjusting the sequence between MAF and other processes, e.g., heat treatment, can potentially improve the mechanical performance. In this work, the effects of MAF on the microstructure and mechanical property evolution of Inconel 718 superalloys made by laser powder bed fusion (LPBF) were studied. The application of MAF was found to significantly reduce the surface roughness of alloys and refine the grain size of aged samples. Moreover, MAF is able to increase the elongation of materials, which can be further influenced by the sequence of MAF and different heat treatments. The highest elongation can be achieved when MAF is performed between homogenization and aging processes. This work demonstrated a promising solution to improving the performance of LPBF Inconel 718 by combining MAF and heat treatment, which provides new perspectives on the post-processing development of additively manufactured alloys for advanced mechanical properties.

Following our previous success in identifying new steroid-based anticancer agents, we herein disclosed the structural requirements for retaining high potency against cancer cells and associated modes of action. The structurally novel steroidal dimer by001 inhibited growth of different esophageal cancer cells and colony formation at low micromolar levels, elevated cellular ROS levels and caused mitochondrial dysfunction. Mechanistic studies showed that by001 induced cell death through the mitochondria and death receptor-mediated apoptotic pathways and autophagy induction, as well as inhibited migration.

A comprehensive review on symmetry and conditional symmetry is made from the core conception of symmetry and conditional symmetry. For a dynamical system, the structure of symmetry means its robustness against the polarity change of some of the system variables. Symmetric systems typically show symmetrical dynamics and even when the symmetry is broken, symmetric pairs of coexisting attractors are born annotating the symmetry in another way. The polarity balance can be recovered by the combinations of the polarity reverse of system variables, and furthermore, it can also be restored by the offset boosting of some of the system variables if the variables lead to the polarity reverse from their functions. In this case, conditional symmetry is constructed giving a chance for a dynamical system outputting coexisting attractors. Symmetric strange attractors typically represent the flexible polarity reverse of some of the system variables, which brings more alternatives of chaotic signal and more convenience for chaos application. Symmetric and conditionally symmetric coexisting attractors can also be found in memristive systems and circuits. Therefore, symmetric chaotic system and those of conditional symmetry provide sufficient system options for chao-based applications.

The development of photocatalysts with a wide spectral response and effective carrier separation capability is essential for the green degradation of tetracycline. In this work, a magnetic recyclable Z-scheme ZnO/ZnFe2O4 heterojunction (ZZF) was successfully constructed by the solid phase method using MIL-88A(Fe)@Zn as the precursor. Appropriate band gap width and Z-scheme charge transfer mechanism make ZZF have excellent visible light absorption performance, efficient charge separation and strong redox ability. Under visible light irradiation, the degradation efficiency of TCH for optimal sample ZZF2 can reach 86.3 % within 75 min in deionized water, and achieve 92.9 % within 60 min in tap water, which exhibits superior stability and reusability after five cycles. Moreover, the catalyst in the water can be conveniently recovered by magnetic force. After visible light irradiation for 70 minutes, the temperature of the reaction system increased by 21.9 °C. Its degradation constant (35.53×10-3 min-1) increases 5.1 times that at room temperature (6.95×10-3 min-1). Thermal energy perfects the kinetic driving force of the reactants and promotes carrier migration to produce more •O2- and •OH. This work provides a potential candidate and inspires the design of photocatalyst for tetracycline hydrochloride degradation.

High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is of great significance for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a Park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the Filter algorithm, this article proposed a triangular grid filter based on Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground points extraction, this method has good accuracy, stability and achieves good results in separating ground points from non-ground points. At first, using Slope Filter to remove some non-ground points, to reduce the error of taking ground points as non-ground points; Secondly, established a triangular grid based on the triangular relationship between each point, the violation-triangle can determin through the grid, and then the corresponding violation points were found in the violation-triangle; Thirdly, according to the three-point collinear method to extract the regular points, used these points to extract the regular landmarks by KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm; Finally, removed disperse points and irregular landmarks by Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 sample data provided by ISPRS, and obtained an average error of 3. 46%. The results showed that the algorithm in this article have a high processing efficiency and accuracy, which can greatly improve the processing efficiency of point cloud data in practical applications.

Frontal Polymerization (FP) is recognized as a polymer synthesis method capable of generating a highly localized and self-propagating exothermic front, considered a more rapid and energy-efficient manufacturing scheme for polymer-based fiber-reinforced composite materials. This study conducts a numerical investigation based on the Finite Element Method into the initiation and propagation of frontal polymerization of acrylamide. Utilizing Differential Scanning Calorimetry (DSC) experiments, the study phenomenologically tests the curing kinetics reactions of acrylamide (AM) in deep eutectic solvent (DES)-based polymerizable systems, thereby establishing a curing kinetics model for the autocatalytic reaction of polyacrylamide. By coupling the heat conduction diffusion equation of the frontal polymerization reaction, the Finite Element Method is employed to solve the reaction-diffusion model, aiming to explore the evolution of temperature and degree of curing during the manufacturing process. The results demonstrate the reliability and accuracy of the nth-order autocatalytic model in the study of curing kinetics for DES-based synthesis, indicating that the front propagation speed of the AM-based FP reaction is influenced by the activation temperature, duration of activation temperature, and initial temperature of the solution. Experimental validation of the polyacrylamide manufacturing through frontal polymerization corroborates the accuracy and reliability of the model predictions, providing a reference for the numerical model study and temperature control in the synthesis of acrylamide composite hydrogels via FP.

Different kinds of freshwater fish soups show a diverse range of health functions, due to their different nutritional substances and corresponding bioactivities. Crucian carp soup and snakehead soup have different dietotherapy functions, crucian carp soup is suitable for lactating women and snakehead soup is suitable for postoperative patients. In the current study, the changes of nutrient profiles in the different fish soups, such as chemical composition, free amino acids, mineral and fatty acid contents, were investigated. The antioxidant activities of the fish soups were evaluated by using the DPPH radical scavenging activity, the ferrous ion chelating activity, the hydroxyl radical-scavenging activity and the reducing power effect. In order to learn the theoretical basis of the potential role fish soup plays in diet therapy functions after being digested by the human body, the nutrient profiling and bioactivities of the fish soup samples after simulated gastrointestinal digestion were also explored. The intensive profiles of nutritional composition and antioxidant activities of these two kinds of fish soups were expected to partly provide the theoretical basis of therapeutic effects.

Micro-combs [1-4] - optical frequency combs generated by integrated micro-cavity resonators – offer the full potential of their bulk counterparts [5,6], but in an integrated footprint. The discovery of temporal soliton states (DKS – dissipative Kerr solitons) [4,7-11] as a means of mode-locking micro-combs has enabled breakthroughs in many fields including spectroscopy [12,13], microwave photonics [14], frequency synthesis [15], optical ranging [16,17], quantum sources [18,19], metrology [20,21] and more. One of their most promising applications has been optical fibre communications where they have enabled massively parallel ultrahigh capacity multiplexed data transmission [22,23]. Here, by using a new and powerful class of micro-comb called “soliton crystals” [11], we achieve unprecedented data transmission over standard optical fibre using a single integrated chip source. We demonstrate a line rate of 44.2 Terabits per second (Tb/s) using the telecommunications C-band at 1550nm with a spectral efficiency – a critically important performance metric - of 10.4 bits/s/Hz. Soliton crystals exhibit robust and stable generation and operation as well as a high intrinsic efficiency that, together with a low soliton micro-comb spacing of 48.9 GHz enable the use of a very high coherent data modulation format of 64 QAM (quadrature amplitude modulated). We demonstrate error free transmission over 75 km of standard optical fibre in the laboratory as well as in a field trial over an installed metropolitan optical fibre network. These experiments were greatly aided by the ability of the soliton crystals to operate without stabilization or feedback control. This work demonstrates the capability of optical soliton crystal micro-combs to perform in demanding and practical optical communications networks.

We demonstrate a photonic RF integrator based on an integrated soliton crystal micro-comb source. By multicasting and progressively delaying the input RF signal using a transversal structure, the input RF signal is integrated discretely. Up to 81 wavelengths are provided by the microcomb source, which enable a large integration time window of ~6.8 ns, together with a time resolution as fast as ~84 ps. We perform signal integration of a diverse range of input RF signals including Gaussian pulses with varying time widths, dual pulses with varying time intervals and a square waveform. The experimental results show good agreement with theory. These results verify our microcomb-based integrator as a competitive approach for RF signal integration with high performance and potentially lower cost and footprint.

This research investigates the fatigue crack propagation on the lower surface of rocket deflector troughs of offshore rocket launch platforms. Initially, a numerical model of the offshore rocket launch platform is established using ABAQUS based on the Extended Finite Element Method (XFEM). Subsequently, two variable parameters—namely, the initial crack length and initial tilt angle—are introduced. This research systematically analyzes the impact of these parameters on fatigue crack propagation patterns in both the maximum stress and maximum deformation regions of the deflector channels under the combined conditions of high temperature and impact. Finally, the research indicates that the propagation length of surface cracks in the deflector trough exhibits a trend of initial increase followed by a subsequent decrease with the increase in the preset inclination angle. Notably, the stable propagation rate of the crack in the region of maximum deformation surpasses that observed in the region of maximum stress. Through meticulous comparative analysis, it is evident that temperature loading significantly fosters the initiation and propagation of cracks, particularly in the upper region of the deflector channel's lower surface.

Kiwifruit is a perennial horticultural crop species with high nutritional and economic value. However, various pathogens stress brings a serious threat to kiwifruit. Pentatricopeptide repeat proteins (PPRs) are characterized by tandem repetitions of 35 amino acid motifs, and have been found to function in plant RNA editing. However, the roles of PPRs in plant development and disease resistance remain unclear. In this study, we performed a genome-wide identification and characterization of PPR gene family in two kiwifruit species (Actinidia chinensis (Ach), and Actinidia eriantha (Ace)) with markedly different disease resistance. A total of 497 and 499 PPRs were identified in Ach and Ace, respectively. All the kiwifruit PPRs could be phylogenetically divided into four subfamilies. There were about 40.68% PPRs predicted to be localized to mitochondria or chloroplasts. Synteny analysis showed that the expansion of kiwifruit PPRs mainly came from segmental duplication. Based on RNA-seq data from the fruit during 12 periods of development and maturity, weighted correlation network analysis suggested that two PPRs Actinidia20495.t1 and Actinidia15159.t1 were involved in the fruit development and maturation. In addition, we observed different gene expression of PPRs and chloroplast RNA editing profiles between resistant and susceptible kiwifruits after pathogen infection, indicating the roles of PPRs in stress response by modulating the editing extend of mRNA. The differentially expressed upstream transcription factors of PPRs were further identified, they may regulate resistance adaption by modulating the PPRs expression. These results provide a solid foundation for further analyses of the functions and molecular evolution of PPRs, in particular, for clarifying the resistance mechanisms in kiwifruits and breeding new cultivars with high resistance.

Chemical composition analysis of açaí extracts revealed higher levels of total polyphenol content in purple açaí samples for both commercial (4.3 – 44.7 gallic acid equivalents mg/g) and non-commercial samples (30.2 – 42.0 mg/g) compared to white (8.2 – 11.9 mg/g) and oil samples (0.8 – 4.6 mg/g). The major anthocyanin compounds found in purple açaí samples were cyanidin-3-glucoside and cyanidin-3-rutinoside with total concentrations in the range of 3.6 – 14.3 cyanidin-3-glucoside equivalents mg/g. The oligomeric proanthocyanidins were quantified in the range of 1.5 – 6.1 procyanidin B1 equivalents mg/g. Moreover, açaí presented significant levels of calcium, magnesium, manganese, iron, zinc and copper, essential minor and trace elements, in comparison with other berries. All of the açaí extracts at 50 μg/mL potently inhibited the release of reactive oxygen species in lipopolysaccharide-stimulated RAW 264.7 macrophage cells, but none inhibited the release of nitric oxide. Furthermore, all the açaí samples demonstrated potential as wound healing agents due to the high levels of migration activity in human fibroblast cells.