To this day, use of oily eye drops and non-invasive retinal delivery remains a major challenge. Oily eye drops usually cause ocular irritation and interfere with normal function of the eye, while ocular injections for retinal drug delivery cause significant adverse effects and a high burden on the healthcare system. Here the authors report a novel topical Non-Invasive Ocular Delivery Platform (NIODP) through the periorbital skin for high efficiency anterior and posterior ocular delivery in a non-human primate model (NHP). A single dose of about 7mg JV-MD2 (omega-3 DHA) via the NIODP reached the retina at Cmax of 111µg/g and the cornea at Cmax of 66µg/g. NIODP also delivered JV-DE1, an anti-inflammatory agent in development for dry eye diseases, as efficiently as eye drops did to anterior segments of NHP. The topical NIODP seems to transport drug candidates through the cornea pathway to the anterior and via the conjunctiva/sclera pathway to the posterior segments of the eye. The novel NIODP method has the potential to reshape the landscape of ocular drug delivery, especially for oily eye drops and retinal delivery, where the success of treatment lies in the ocular tolerability and bioavailability of drugs in the target tissue.

The twisted Wang transform distribution family, defined as the composition of parameter shifted inverse CDF function with an original CDF function, is found to be most suitable for matching low shape factor distributions, characterizing hard to fit or to simulate reinsurance portfolio losses for some perils from our previous study. Among them, the best form for matching a hard-to-fit empirical loss distribution for a specific peril, is the Exponential Fractional Extra Power 0 Distribution in (0,1) with CDF:.The simplest yet still a good form of this family is the Transformed Hyperbolic Tangent Distribution with CDF:,which has analytical formulas for the moments. The twisted Wang transform distribution family is compared and confirmed to be superior to all other well-known distribution families through extensive numerical optimization practice, distribution forms guesses, and computer-aided exploration experiments.

This study investigated the relationship between wrist flexion and the dynamics of the swing of a croquet mallet. Twenty seven subjects participated in a study which used 3D motion capture equipment and high speed and high-definition video to determine if there is a correlation between the lateral twist of a croquet player’s swing and the flexion of the wrist during that swing. The study found a significant correlation between the amount of flexion of the wrist from the start of the stroke to the top of the backswing and the twist of the mallet head at the top of the backswing (r=0.330, p<0.01). The methodology and findings are relevant to all sports where minimising wrist flexion is favourable for improving consistency of stroke making. Additionally, reducing wrist motion in stroke-making may reduce the incidence of wrist pain and injury in croquet, further supporting recommendations to reduce the amount of wrist flexion during the croquet swing.

Film therapy, the therapeutic use of movies in psychological therapies, is a growing area of interest to therapists and researchers. This paper presents the MOVIE model of film therapy, a widely applicable, trauma-informed framework for working with film imagery and themes in individual or group therapy, counsellor education and self-help. Film or cinema therapy is a highly accessible and relatable method for many diverse client and student groups with benefits that include, catharsis and emotional processing, greater ease of dialogue around issues, increased empathy, and interpersonal learning, engaging with new perspectives and learning skills. Following the model clients mindfully observe and voice their emotional and psychological responses to movie watching, identify how the imagery, characters, themes, and narrative symbolise personal experience and consider any new possibilities that emerged through re-authoring personal narratives. The model is informed by a practice focused review of literature.

COVID-19 pandemic has affected the public educational sectors in terms of adjustment in educational modalities of instructional delivery, school operations, and policies. With this emerging paradigm shift, teachers' crisis self-efficacy and work commitment are relevant for research. This study's main objective was to determine the significant influence of crisis self-efficacy on the work commitment of public school teachers in Region XI (Davao Region), Philippines, during the COVID-19 pandemic. The sample consisted of 1,340 public school teachers across the Davao Region. The researchers collected the data through adapted questionnaires contextualized to the local setting and administered through online Google forms with appended consent. Mean, standard deviation, Pearson r, and regression analysis were used to analyze data. Results revealed that crisis self-efficacy significantly influences the work commitment of public school teachers during the COVID-19 pandemic. Uncertainty management during this crisis, in particular, best predicts teachers’ work commitment. Data also showed a high level of crisis self-efficacy in terms of action, preventive, achievement and uncertainty management, and high level of teachers’ work commitment in terms of commitment to school, commitment to students, commitment to teaching, and commitment to profession. Correlation results also showed a link between crisis self-efficacy and the work commitment of teachers amid pandemic. Finally, the study concluded with practical recommendations and directions for future research.

Orthobunyaviruses (order Bunyavirales, family Peribunyaviridae) in the Simbu serogroup have been responsible for widespread epidemics of congenital disease in ruminants. Australia has a national program to monitor arboviruses of veterinary importance. While monitoring for Akabane virus, a novel orthobunyavirus was detected. To inform the priority that should be given to this detection, a scoping review was undertaken to 1) Characterise the associated disease presentations and establish which of the Simbu group viruses are of veterinary importance; 2) Examine the diagnostic assays that have undergone development and validation for this group of viruses, and 3) Describe the methods used to monitor the distribution of these viruses. Two search strategies identified 224 peer-reviewed publications for 33 viruses in the serogroup. Viruses in this group may cause severe animal health impacts, but only those phylogenetically arranged in clade B are associated with animal disease. Six viruses (Akabane, Schmallenberg, Aino, Shuni, Peaton, and Shamonda) were associated with congenital malformations, neurological signs, and reproductive disease. Diagnostic test interpretation is complicated by cross-reactivity, the timing of foetal immunocompetence and sample type. Serological testing in surveys remains a mainstay of the methods used to monitor the distribution of SGVs. Due to significant differences in survey designs, only broad mean seroprevalence estimates could be provided. Further research is required to characterise the SGV detected and determine the disease risk posed by novel orthobunyaviruses and how they could challenge current diagnostic and surveillance capabilities.

The use of artificial intelligence (AI) applications in education has the potential to revolutionize the learning experience for international students, who face unique challenges when studying in a foreign country. This paper explores various examples of AI applications in education and their potential impact on international students. AI applications such as personalized learning experiences, adaptive testing, predictive analytics, and chatbots for learning and research are examined for their potential to improve learning efficiency and provide customized education support. It also explores the significant risks and limitations associated with AI technologies, such as privacy, cultural differences, language proficiency, and ethical implications. To maximize the potential benefits of AI applications in higher education, it is crucial to implement appropriate safeguards and regulations. This paper provides a starting point for research on the potential impact of artificial intelligence on international students’ educational experiences and how AI may be integrated into educational administration and learning processes.

This article explores the relationship between Wang Yangming's philosophy and the new quality productivity, revealing how the new quality productivity, while inheriting the core ideas of Yangming's philosophy, achieves a higher level through innovation and development. The new quality productivity emphasizes the integration and innovation of knowledge, technology, and information, characterized by knowledge-driven innovation, information technology support, sustainable green development, and global collaborative development. The fusion of Yangming's philosophy and the new quality productivity holds significant theoretical and practical implications.

Recurrent Pregnancy Loss (RPL) affects 1-2% of women, and its triggering factors are unclear. Several studies have shown the vaginal, endometrial, and gut microbiota may play a role in RPL. A decrease in the quantity of Lactobacillus crispatus in local microbiota has been associated with an increase in local (vaginal and endometrial) inflammatory response and immune cell activation that leads to pregnancy loss. The inflammatory response may be triggered by gram-negative bacteria, lipopolysaccharides (LPS), viral infections, mycosis, or atypia (tumor growth). Bacterial structures and metabolites produced by microbiota could be involved in immune cell modulation and may be responsible for immune cell activation and molecular mimicry. Gut microbiota metabolic products may increase the amount of circulating pro-inflammatory lymphocytes, which, in turn, will migrate into vaginal or endometrial tissues. Local proinflammatory Th1 and Th17 subpopulations along with a decrease in Treg and tolerogenic NK cells are accountable for the increase in pregnancy loss. Local microbiota may modulate the local inflammatory response increasing pregnancy success. Analyzing local and gut microbiota may be necessary to characterize some RPL patients. Although oral supplementation of probiotics has not been shown to modify vaginal or endometrial microbiota, the metabolites produced by it may benefit patients. Lactobacillus crispatus transplantation into the vagina may enhance the required immune tolerogenic response to achieve a normal pregnancy. The effect of hormone stimulation and progesterone to maintain early pregnancy on microbiota has not been adequately studied, and more research is needed in this area. Well-designed clinical trials are needed to ascertain the benefit of microbiota modulation in RPL.

Ferric oxide containing waterworks sludge can be used to reduce formation of hydrogen sulfide during anaerobic digestion. The ferric compound is reduced biochemically in the digester and forms insoluble pyrite in digester sludge. Often virgin ferric chloride is used to solve the hydrogen sulfide problem. Since 2013, Sydvatten AB has supplied a growing number of digestion plants in Sweden with ferric containing dewatered waterworks sludge from the drinking water treatment plant Ringsjöverket to limit the formation of hydrogen sulfide. At the waterworks, ferric chloride is added to enhance coagulation of organic matter from the source water. The sludge formed in this process is dewatered and landfilled, but also recycled in the biogas production in order to reduce the hydrogen sulfide concentration. In this study, the use of sludge, for hydrogen sulfide removal in digesters, was technically and economically evaluated from cases studies from 13 full-scale digesters in Sweden. Compared with use of fresh ferric chloride, the operational costs are reduced up to 50 % by using sludge. The quality of the sludge is high and its content on metals is low or very low, especially when compared with requirements from different certification standards on biosolid reuse applied in Sweden. An addition of waterworks sludge containing iron to a digester for removal of dissolved hydrogen sulfide is a technically and economic good alternative when producing biogas. It is also one step closer to a circular economy, when replacing use of virgin chemicals with the by-product waterworks sludge which saves energy, material and reduces the carbon foot-print of the waterworks.

Over the past decade, the One Strain Many Compounds (OSMAC) approach has been established for silent gene cluster activation and elicitation of secondary metabolite production, but so far the full secondary metabolome of a biosynthetically promising bacterium has not been elucidated yet. Here, we investigate the ability of seven categories of OSMAC conditions to enhance the diversity of new mass features from bacterial strains with little literature coverage but high biosynthetic potential. The strains Bacillus. amyloliquefaciens DSM7, Corallococcus. coralloides DSM2259, Pyxidicoccus. fallax HKI727, Rhodococcus. jostii DSM44719, and Streptomyces. griseochromogenes DSM40499 were selected after genome mining with antiSMASH. After cultivation under OSMAC conditions, the generated extracts were subjected to LC-MS and MZmine analysis to determine new mass features and evaluate the tested culture conditions. 4 predicted compounds, bacillibactin, desferrioxamine B, myxochelin A, and surfactin, were identified and up to 147 new mass features were detected in the generated extracts, which greatly surpasses the number of predicted gene clusters. Among the new mass features are bioactive compounds that were so far unreported for the strains such as cyclo-(Tyr-Pro) from DSM7 and nocardamin from DSM2259. Furthermore, the tested culture conditions were evaluated regarding their suitability for the generation of new mass features from the selected strains and promising new starting points for further screenings are postulated. Especially culture conditions with little prior literature coverage are responsible for the activation of predicted gene clusters

Dermal injections of hyaluronic acid gel for aesthetic skin rejuvenation are increasingly popular nowadays. Although these products are classified as medical devices, the regulations on their administration by licensed practitioners are still weak, whereas their manufacturers increasingly highlight and advertise the cellular effects that underpin the efficacy of these injections. In this re-view, we discuss all current knowledge on the mode of action of dermally injected hyaluronic acid and the potential toxicological implications especially from crosslinked gels in conjunction with the current global regulations. We conclude that dermal injections of hyaluronic acid have several therapeutic implications that warrant further research, and that strict regulations must be applied on their manufacture/quality control and the required qualifications of licensed aesthetic injectors.

Workplace exercise programs are imperative for improving employee health and wellbeing. Employee cognition and productivity can also benefit from such programs; however, the practical application of interventions to influence these outcomes is poorly understood. This research aimed to assess the suitability of a university Staff Fitness Program for improving employee productivity and cognition. Forty-seven participants were included in the study, comprising 27 Staff Fitness participants and 20 control group participants. A two (groups) x three (time-points) between-subjects/within-subjects design was employed over 14 weeks, comprising ten weeks of once-weekly exercise intervention for Staff Fitness participants. All participants completed the Victoria Stroop Colour and Word Test and the Health and Work Questionnaire at each time-point, to assess cognition and productivity, respectively. Participants also completed the International Physical Activity Questionnaire (short form) at time-point one, three and during training weeks (excluding week one) to measure physical activity. Appropriate T-tests and non-parametric equivalents were used to assess group differences in outcome measures and across time-points. There were no significant between group or longitudinal differences indicative of improved productivity or cognition. The Staff Fitness group demonstrated a non-significant improvement (Z = 73.00, p = .055) in physical activity from time-point one (M = 1540.93 MET-min-week, SD = 1083.74) to three (M = 2077.32 MET-min-week, SD = 1401.48) with a large effect size (d = 0.97), while control group physical activity significantly decreased (Z = 10.00, p = .013). The present Staff Fitness Program benefited employees by improving physical activity, despite no improvements in productivity or cognition. Future research should further explore the application of workplace exercise programs and the manipulation of exercise parameters to show improvements in employee productivity and cognition.

Over the last few decades, many countries, especially Caribbean island ones, have been challenged by the devastating consequences of natural disasters, which pose a significant threat to human health and safety. Timely information related to the distribution of vulnerable population and critical infrastructure are key for an effective disaster relief. OpenStreetMap (OSM) has repeatedly been shown to be highly suitable for disaster mapping and management. However, large portions of the world, including countries exposed to natural disasters, remain unmapped. In this study, we propose a methodology that relies on remotely sensed measurements (e.g. VIIRS, Sentinel-2 and Sentinel-1) and derived classification schemes (e.g. forest and built-up land cover) to predict the completeness of OSM building footprints in three small island states (Haiti, Dominica and St. Lucia). We find that the combinatorial effects of these predictors explain up to 94% of the variation of the completeness of OSM building footprints. Our study extends the existing literature by demonstrating how remotely sensed measurements could be leveraged to evaluate the completeness of OSM database, especially in countries at high risk of natural disasters. Identifying areas that lack coverage of OSM features could help prioritize mapping efforts, especially in areas vulnerable to natural hazards and where current data gaps pose an obstacle to timely and evidence-based disaster risk management actions.

Recurrent pregnancy loss (RPL) affects around 2 % of women of reproductive age. Primary RPL is defined by ≥2 pregnancy losses and no normal birth delivery. In secondary RPL, the losses are after a normal pregnancy and delivery. Most cases have no clear aetiology, although primary cases are the most complex. Several gene polymorphisms have been associated with RPL. Some polymorphisms are increased in women of Asian or Caucasian race. In admixed RPL populations, the information on possible genetic links is scarce and contradictory. The study aimed to assess the frequency of two SNP in different enzymes involved in medical conditions observed during pregnancy. Microsomal epoxy hydrolase (mEPH) is involved in detoxifying xenobiotics. It is present in ovaries, and it is hormonally regulated. The endothelial nitric oxide synthase forms nitric oxide from arginine and is involved in vascular tone. Two SNPs, rs1051740 and rs1799983, were assessed. The study included 26 controls and 63 primary RPL patients. Significant differences were observed in the frequencies of both SNP (p<0.05). The frequency of mutated alleles was higher in patients. Double mutated homozygotes were encountered only in RLP patients (p<0.05). Genetic polymorphisms rs1051740 and rs1799983 may be involved in primary RPL in the Venezuelan admix population. Genetic studies could provide crucial information on the aetiology of primary RPL.

The immune response is crucial in Pregnancy Loss (RPL), which affects 1-2% of women. In RPL, peripheral blood lymphocytes and local immune cells are activated with a concomitant decrease in the leukocyte tolerogenic response in the uterus and decidua. IL-17 plays an essential role in T cell activation, weakening tolerogenic responses of the T cells. The report aims to analyze the IL-17 genetic polymorphism, circulating IL-17 lymphocyte populations, the reaction upon lymphocyte priming, the amount of cytokine in serum, and the number of T-reg cells in a group of 50 RPL and 50 normal women from the admixed Venezuelan population. No differences were observed in the genetic polymorphisms rs2275913 and rs763780. A significant decrease (P&lt;0.001) was observed in RPL patients compared to controls in the IL-17+ cell populations CD3+, CD8+, CD56+, and CD4CD25 FoxP3, but not in the CD4IL17+ cells. The response upon cell priming was significantly decreased (P&lt;0.001) in all subpopulations of RPL as compared to controls. It is concluded that despite the increase in IL-17A in the serum of patients, the number of circulating IL-17 cells and the lymphocyte response to stimulation was significantly decreased (p&lt;0.001). No differences in the genetic polymorphism compared to controls suggest that biological factors influence IL-17 levels in RPL patients, which must be carefully studied.

Phenylketonuria (PKU) is an inherited disorder of protein metabolism. It is generally treated using dietary management with limited intake of phenylalanine (Phe). Partial breastfeeding (BF) is encouraged among mothers of infants with PKU, together with a Phe-free mixture of synthetic amino acids. We aimed to describe our current BF rates and complementary feeding practices, as well as examining parental experiences of infant feeding. The aim was to better understand the challenges faced by families so improvements can be made to clinical care. A chart review was carried out on 39 PKU patients, examining the BF rate and duration, use of second stage synthetic protein (SP) and average complementary feeding age. A parental questionnaire on complementary feeding and BF experience was designed. 26% of babies were partially breastfed at three months. 70% of mums would like to have breastfed for longer and cited PKU as a reason for stop-ping. 52% parents reported challenges during the complementary feeding process including food refusal, protein calculation and anxiety around maintaining good Phe levels. Suggestions to improve BF continuation and duration include active promotion of the benefits and suitability, access to lactation consultant and peer support. The delay in introducing a second stage SP may contribute to long-term bottle use for SP. Improved patient education, written resources and sup-port is necessary to improve food choices and long term acceptance of SP.

Intelligent systems are increasingly present in various areas of society, gaining special relevance in healthcare environments. In this work, grounded on previous developments proposed by the authors, the evolution and improvement of a novel intelligent system focused on the diagnosis of obstructive sleep apnea (OSA) is addressed. To do this, starting from two packages of information of a heterogeneous nature, and deploying a set of Machine Learning classification algorithms, focused each of them on different levels of the apnea-hypopnea index (AHI), as well as a set of cascaded expert systems, it is possible to obtain a series of risk metric pairs (Statistical Risk, Symbolic Risk) for each AHI level, understood as a warning forecast associated with the early detection of the pathology. The risk indicators of each pair are subsequently aggregated by means of a symbolic inference system, whose knowledge base was determined using the automatic rule generation approach proposed by Wang and Mendel. At the output of each inference system, a risk indicator is obtained for each AHI level, the Apnea Risk, whose interpretation makes it possible to discriminate between patients who could suffer OSA and those who do not, as well as to generate the appropriate recommendations. For the initial tests of the system, the design and development of a specific software artifact was carried out using data from 4,400 patients from the Álvaro Cunqueiro Hospital in Vigo, obtaining when applied to the test set AUC values within the range 0.74–0.88, which invites optimism and points to the benefits expected from the architecture of the proposed intelligent system.

Plants of the genus Monochoria have long been utilized in food, cosmetics, and traditional herbal treatment. Thailand has the highest species diversity of this genus and a new member, Monochoria angustifolia (G. X. Wang) Boonkerd & Tungmunnithum has been recently described. This plant is called “Siam Violet Pearl” as a common name or “ไข่มุกสีม่วงแห่งสยาม” as its vernacular name in the same meaning in Thai language. Despite their importance, few researches on Monochoria species have been conducted. This study, thus, provided the results to fill in this gap by: i) determining flavonoids phytochemical profiles of 25 natural populations of M. angustifolia covering the whole floristic regions in Thailand, and ii) determining antioxidant activity using various antioxidant assays to investigate the probable mechanism. The results revealed that M. angustifolia presented a higher flavonoid content than the outgroup, M. hastata. Our results also revealed that flavonoids might be used to investigate Monochoria evolutionary connections and for botanical authentication. The various antioxidant assays revealed that M. angustifolia extracts preferentially act through a hydrogen atom transfer antioxidant mechanism. Pearson correlation analysis indicated significant correlations emphasizing that the antioxidant capacity is most probably the result of a complex phytochemical combinations rather than of a single molecule. Altogether, these results showed that this new species provide an attractive alternative starting material with phytochemical variety and antioxidant potential for the phytopharmaceutical industry.

With the emergence of artificial intelligence (AI), many aspects of our lives, from how we work to how we interact with each other and the world around us is showing dramatic changes. Education is one of the key areas that may be impacted by the rise of AI. Although a large number of studies have been conducted in recent years to shed light on how AI may influence various dimensions of education, a very little work has been carried out to consolidate and synthesis empirical studies on the application of AI in higher education. The present study reviewed empirical studies published between 2013 and 2022 to 1) examine the characteristics of published research in the field, and 2) to present thorough insights on the promises and challenges of this dramatic technology in higher and professional education. This review included 44 empirical studies published as peer-reviewed journal articles. The results indicated that there is rapid increase in the publications focusing on AI in higher education in last a few years. However, a big proportion of these publications are technically theoretical and conceptual proposals for AI intervention. The areas of AI applications in higher education that are supported by evidence based research are presented. Imperative implications are also highlighted for future research and implementation of AI based interventions in higher education.

Paving blocks are concrete pieces placed in exposed places to the weather, which are sub-jected to loads and wear. Hence, quality control in the manufacture of paving blocks is es-sential to guarantee the properties and durability of the product in construction projects. In Ecuador, the requirements are described in the Ecuadorian technical standard "NTE INEN 3040", and tensile splitting strength is a fundamental requirement to guarantee product quality. It is analyzed using quality control measurements such as dimensions, the weight of the fresh paving block in the vibro-compacted process, and the percentage of water absorption in order to know how the variables influence and manage to predict the tensile splitting strength to avoid product non-conformity in advance, having a timely and better control of the manufacturing process. The data was obtained from a company that can produce 30 000 units per day of rectangular paving blocks with 6 cm thickness. Mul-tivariate models such as multiple linear regression, regression trees, random forests and neural networks are performed to predict the tensile splitting strength variable through two groups of predictors; the first group is the thickness mm, width mm, length mm, mass of fresh paving block g and percentage of water absorption %. The second group of pre-dictor variables is the density of the fresh paving block kg/m3 and the percentage of water absorption %. It is concluded that the multiple linear regression method performs better in predicting the first group of predictor variables with a mean square error (MSE) of 0.110086, followed by the neural network without hidden layers resulting in an MSE of 0.112198. The best method for the second set of predictors was the neural network with-out hidden layers with a mean square error ( MSE ) of 0.112402, closely followed by the multiple linear regression model with an MSE of 0.115044.

At-home rapid antigen test (RAT) kits for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are valuable public health tools during the present coronavirus disease (COVID-19) pandemic. They provide fast identification of coronavirus infection, which can help to reduce the transmission rates and burden on the healthcare system. However, they have lower sensitivity when compared with the reverse transcription polymerase chain reaction (RT-PCR) tests. One of the reasons for the lower sensitivity is due to the RAT color indicators being indistinct or invisible to the naked eye after the measurements. For this reason, we propose a systematic approach, through which we investigated anonymously provided at-home RAT kit results by using our in-house open source image processing scripts developed for affordable Raspberry Pi computer and Raspberry Pi HQ camera systems (available at https://github.com/kmiikki/ratcv). Therefore, we aimed at minimizing the human-related analysis errors for such kits. We believe that our framework can contribute to reduced the delayed quarantines of infected individuals and spreading of the current infectious disease.

COVID-19 emerged in Wuhan and is later declared as a pandemic by World Health Organization. Different-aged people have varying gender-wise immunity control properties that necessitates understanding COVID-19 impact on age and gender which does not exist, currently. In this paper, COVID-19 surveillance variables are extensively studied along with hospitalization, tests-performed, and recovery data. Dataset is curated from three sources; however, age and gender data belong to Belgium, particularly. Visualizations, frequencies, Pearson’s and polyserial correlation, student’s t-test, and Cramer’s V are used for enhanced analysis. Results show higher mortality rate in males and need of more ventilators to combat severe symptoms.

LCK, a member of the Scr kinase family, is a non-receptor tyrosine kinase involved in immune cell activation, antigen recognition, tumor growth, and cytotoxic response. The enzyme has usually been linked to T lymphocyte activation upon antigen recognition. Lck activation is central to CD4, CD8, and NK activation. However, recently, it has become clearer that activating the enzyme in CD8 cells can be independent of antigen presentation and enhance cytotoxic response. The role of Lck in NK cytotoxic function has been controversial in a similar fashion as the role of the enzyme in CAR T cells. Inhibition of Lck has been mainly to treat hematologic malignancies with a high rate of success. They may be useful in treating other tumor types, and they may be useful to prevent cell exhaustion. New, more selective inhibitors have been documented, and they have shown inter-esting activities not only in tumor growth but in the treatment of autoimmune diseases, asthma, and graft vs host disease. Drug repurposing and bioinformatics can aid in solving several unsolved is-sues on the role of Lck in cancer. In summary, the role of Lck in immune response and tumor growth is not a simple event and requires more research.

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, support quantative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation and multi-year daily use for public health and clinical support decision-making of a particle filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian Sequential Monte Carlo algorithm of Particle Filtering allows the model to be re-grounded daily and adapt to new trends within daily incoming data – including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of dynamic model state, support probabilistic model projection of epidemiology and health system capacity utilization and service demand and probabilistically evaluate trade-offs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision making across Canada, such methods offer strong support for evidence-based public health decision making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

The research investigates the role the environmental manager plays to ensure a successful (or not) implementation of environmental performance within an organization. It is based on interviews of 5-7 actors per company within a sample of 7 companies (42 interviews). We build upon bias of perception of the various actors interviewed within each company to define 4 paradoxes related to the roles and mission of the environmental manager that hinder proper efficiency of environmental management at company level. Paradox 1 is that no one takes ownership of environmental performance within the organization. Paradox 2 is that the environmental manager is in an awkward situation vis-à-vis his boss. Paradox 3 is that the role of the environmental manager is ambiguous vis-à-vis employees. Paradox 4 is that corporate and product approaches are decoupled. We suggest that these paradoxes interact and form a vicious cycle that may in part be responsible for the environmental decoupling phenomenon – the fact that companies often adopt a sustainability policy symbolically without implementing it substantively. Our research suggests that, by leveraging the leadership of the environmental manager through organizational and motivational measures, the vicious cycle can be transformed into a virtuous cycle and the human motivation can become a driver for green change within corporations. We proposed the SEA (Shaping Environmental Action) model based of 4 pillars: information, motivation, organization and strategy.

It has been suggested that parasitic infections, common in Latin American populations, may amplify the inflammatory response of the airways. There are several reports in atopic and asthmatic patients, but few reports of parasitic infection in COPD patients. The study aimed to determine the prevalence of parasitic infections in COPD patients as compared to atopic and asthmatic patients attending the outpatient clinics of the Institute of Immunology and the pneumonology service of the University hospital. A case-control study was conducted: 100 patients with bronchial asthma, 100 patients with COPD, 100 individuals with atopy without respiratory symptoms, and 100 healthy individuals. Serum specific IgG antibody against the parasites Ascaris lumbricoides (Al), Strongyloides stercolaris (Ss) and Toxocara canis (Tc) were measured: by ELISA. IgE levels were used as an indirect indicator of atopy. Positive IgG for Al was observed in all groups, predominantly in atopic cohort, Ss positiveness was recorded only in 4 COPD patients and Tc positiveness was observed in all groups except in controls. There are significant correlations between the values of Al and IgE in controls, atopic and asthmatic patients not with COPD. No correlation was found for Tc. IgE levels and FEV1 correlate only in atopic and asthmatic patients. Parasitic infections are common in atopic patients and in moderate and severe asthmatic and COPD patients. Anti-inflammatory treatment may be responsible for the increase frequency of infection.

In the present work, cost-effective strain gauges were fabricated by using inkjet printing and photonic curing on flexible and recyclable PET substrates. Ohmic resistance (a.k.a. DC resistance) (R0) and complex electrical impedance (Z) as a function of test frequency were characterized, respectively, with the state-of-the-art electronic testing equipments. For the fabrication process, commercially available silver nanoparticle (AgNP) inks and printing substrates were used in order to eliminate any apriori ink processing. In order to validate the in-house cantilever beam measurement setup and devices, first, commercially available metallic foil strain gauges (with the provided gauge factor GF=2 by the manufacturer) were tested at different locations. Thereafter, the printed strain gauges were investigated with several repetitions at different measurement locations. The measurement results demonstrated an affordable, rapid and tailorable design and repeatable fabrication approach for strain gauges with GFavg~6.6, which has potential applications in remote sensing and structural monitoring applications.

Tack is one of the most important factors affecting the operability of prepregs, but it is still very difficult to obtain the change of prepreg tack with time at different temperatures. In this paper, a newly designed T-peel device is used to test the tack decay rate of prepregs at different temperatures. The obtained decay rate deviates less than 8.9% from the manufacturer's data. The maximum deviation of the ratio of tack decay speeds calculated by the Arrhenius equation from the experiments is not higher than -9.7%. It is proved that the present method can obtain the decay rate of tack of prepregs at different temperatures. This method is also applicable to the other two types of prepregs. The article proposes the handling life unit, an operational life measurement unit for prepregs, and establishes that the relationship between tack and remaining shelf life reduces prepreg management costs.

Two histamine receptor subtypes (HR), namely H1R and H4R, as key components, are involved in the transmission of histamine-induced itch. Although exact downstream signaling mechanisms are still elusive, transient receptor potential (TRP) ion channels play important roles in the sensation of histaminergic and non-histaminergic itch. Aim of this study was to investigate the involvement of TRPV1 and TRPA1 channels in the transmission of histaminergic itch. The potential of TRPV1 and TRPA1 inhibitors to modulate H1R- and H4R-induced signal transmission was tested in a scratching assay in mice in vivo and in vitro via Ca2+ imaging of murine sensory dorsal root ganglia (DRG) neurons. The TRPV1 inhibition led to a reduction of H1R- and H4R- induced itch and reduced Ca2+ influx into the neurons. The TRPA1 inhibitor reduced H4R-induced itch and both H1R- and H4R-induced Ca2+ influx. In conclusion, these results indicate that both channels, TRPV1 and TRPA1 are involved in the transmission of histamine-induced pruritus.

In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this research is the use of innovative adversarial prompts designed to rigorously test the content moderation capabilities of these AI systems. The study introduces new types of adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses. Additionally, the research spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation. The empirical results from this investigation are pivotal for assessing the current robustness and security of AI models. They highlight the necessity for ongoing development and meticulous testing to bolster AI defenses against an array of adversarial challenges. Importantly, the study also delves into the ethical and societal implications associated with employing advanced 'jailbreak' techniques in AI testing. The findings are significant for understanding AI vulnerabilities and formulating strategies to enhance the reliability and ethical soundness of AI technologies, paving the way for safer and more secure AI applications.

Actively controlling the camber angle to improve energy efficiency has recently gained interest due to the importance of reducing energy consumption and the driveline electrification trend that makes cost-efficient implementation of actuators possible. To analyse how much energy that can be saved with camber control, the effect of changeing the camber angles on the forces and moments of the tyre under different driving conditions should be considered. In this paper, Magic Formula tyre models for combined slip and camber are used for simulation of energy analysis. The components of power loss during cornering are formulated and used to explain the influence that camber angles have on the power loss. For the studied driving paths and the assumed driver model, the simulation results show that active camber control can have considerable influence on power loss during cornering. Different combinations of camber angles are simulated, and a camber control algorithm is proposed and verified in simulation. The results show that the camber controller has very promising application prospects for energy-efficient cornering.

Innovations in industrial automation, information and communication technology (ICT), renewable energy, monitoring and sensing fields have been paving the way for smart devices, which can acquire and convey information to the internet, in every aspect of our lives. Since there is ever-increasing demand for large yet affordable production volumes for such devices, printed electronics has been attracting great attention in both industrial and academic research. In order to understand the potential and future prospects of the printed electronics, the present paper summarizes the basic principles and conventional approaches while providing the recent progresses in the fabrication and material technologies, applications and environmental impacts.

Essential oils oregano (Origanum vulgare), rosemary (Salvia rosmarinus), clove (Syzygium aromaticum), thyme (Thymus), cinnamon (Cinnamomum verum), basil (Ocimum basilicum), have antifungal properties. This study evaluated its ability to inhibit growth of isolated fungi and purified obtained from the rot of the banana peel, for the purpose of combating or inhibiting fungal growth in bananas (Musa paradisiaca). The methodology consists in preparing the dilutions to inoculate on PDA medium (Potato Dextrose Agar) amended with chloramphenicol to re-inoculate until obtaining the isolated and purified fungi of Trichoderma spp., Aspergillus spp., Penicillium spp. and Fusarium spp., which are characterized macroscopically and microscopically, subsequently, the growth is analyzed in vivo and the inhibition percentage in vitro. The results of the analysis in vivo report that the fungi in order of severity from greatest to least, was Penicillium spp., Trichoderma spp, Fusarium spp. y Aspergillus spp., while that the analysis in vivo reports that the rosemary and the basil does not inhibit fungal growth, while that the clove inhibit a 800 ppm, the cinnamon to 400 ppm and the oregano to 200 ppm to the four fungi. -2077601433541Citation: To be added by editorial staff during production. Academic Editor: Firstname Lastname Received: date Revised: date Accepted: date Published: date Copyright: © 2024 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).00Citation: To be added by editorial staff during production. Academic Editor: Firstname Lastname Received: date Revised: date Accepted: date Published: date Copyright: © 2024 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

This study investigates the implementation of equipment to obtain pyroligneous acid as an alternative to control aphids in mandarin cultivation on the El Arrayanal farm, Monte Olivo parish, Carchi province, Ecuador. Derived from mandarin pruning biomass using a dry distillation pilot oven, pyroligneous acid is an ecological option compared to synthetic phytosanitary products, with less impact on the environment and human health. Using a DBCA design, different concentrations of pyroligneous acid (250 ml, 500 ml, 750 ml, and 1000 ml) applied to immature shoots affected by aphids were analyzed. The 1000 ml concentration achieved 98% control of the pest. Analysis by Gas Chromatography and Mass Spectrometry (GC-MSD) and Total Polyphenols confirmed the effectiveness of pyroligneous acid. The results of the study, carried out in the research and linkage laboratories of the State University of Bolívar, Guaranda, Ecuador, demonstrated significant differences between the doses applied (p-value <0.005), with greater control in the treatment of 1000 ml of acid pyroleño compared to the 250 ml, 500 ml and 750 ml treatments. This pyroligneous acid has multiple uses in organic agriculture as a bioherbicide, biofungicide, biorepellent and foliar and root biostimulant, representing an alternative to obtain sustainable organic products. Despite operational limitations, local pyrolysis technology can be optimized to increase production, positioning pyroligneous acid as a viable and sustainable alternative in organic agriculture, promoting efficient waste management and reducing the use of chemicals.

The scarcely investigated myxobacterium Corallococcus coralloides holds a large genome containing many uncharacterized biosynthetic gene clusters (BGCs) that potentially encode the synthesis of entirely new natural products. Despite its promising genomic potential, suitable cultivation conditions have not yet been found to activate the synthesis of new secondary metabolites (SMs). Finding the right cultivation conditions to activate BGCs in the genome remained a major bottleneck and its full biosynthetic potential was so far not retrieved. Here, we therefore applied a bivariate OSMAC approach, using a combination of two elicitor changes at once for activation of BGCs and concomitant SM production by C. coralloides. The bivariate OSMAC screening was carried out in 24-well System Duetz-plates, applying univariate and bivariate OSMAC conditions. We combined biotic additives and organic solvents with minimal media and complex growth medium. The success of the method was evaluated by the number of new mass features detected in the respective extracts. We found synergistic effects in bivariate OSMAC designs. The number of new mass features detected in bivariate OSMAC exceeded the sum of new mass features found in the respective univariate OSMAC with only one elicitor. Overall, the bivariate OSMAC screening led to 26 new mass features, which were not detected in the univariate OSMAC design. Hence, the presence of multiple elicitors in the bivariate OSMAC designs successfully activated the biosynthetic potential in C. coralloides. We propose the bivariate OSMAC designs with a complex combination of elicitors as a straightforward strategy to robustly expand the SM space of microorganisms with large genomes.

In an era where dietary habits significantly impact health, technological interventions can offer personalized and accessible food choices. This paper introduces Chef Dalle, a recipe recommen-dation system that leverages multimodal human-computer interaction (HCI) techniques to pro-vide personalized cooking guidance. The application integrates voice-to-text conversion via Whisper, ingredient image recognition through GPT-Vision, and employs TF-IDF vectorization alongside cosine similarity for personalized recipe recommendations. These methods enable users to interact with the system using voice, text, or images, accommodating various dietary re-strictions and preferences. Furthermore, the utilization of DALL-E 3 for generating recipe images enhances user engagement. User feedback mechanisms allow for the refinement of future rec-ommendations, demonstrating the system's adaptability. Chef Dalle showcases potential appli-cations ranging from home kitchens to grocery stores and restaurant menu customization, ad-dresses accessibility, promoting healthier eating habits. This paper underscores the significance of multimodal HCI in enhancing culinary experiences, setting a precedent for future developments in the field.

Frequently Asked Questions (FAQs) systems rely on semantic symmetry similarity measuring between two sentences. To address the challenges of insufficient training data and limited domain-specific understanding in low-resource FAQs, this paper proposes a general framework, PDAMF-FAQ, to solve these issues. Firstly, we propose a paraphrasing-based data augmentation model that integrates syntactic information and edit vectors. Using a rule-based approach, it retrieves template sentences from a corpus and masks relevant words with special characters. Edit vectors between the original and paraphrase sentences are added to a pre-trained model's encoding layer to enhance the model's ability to learn the differences between the original and reference paraphrase sentences. Additionally, this paper presents a mixed-feature semantic matching model based on SimBERT. The model extracts keyword features from the text, replacing these keywords with special characters to construct intent features. These intent features, along with the user question and keyword features, are then concatenated to form the model's input. Experiments were conducted respectively on the paraphrasing-based data augmentation model, mixed-feature semantic matching model and their comprehensive application in a low-resource domain-specific FAQ. The experimental results show that the proposed framework effectively improve the performance of domain-specific FAQ system.

This paper presents an innovative testing framework, testFAILS, designed for the rigorous evaluation of AI Linguistic Systems, with a particular emphasis on various iterations of ChatGPT. Leveraging orthogonal array coverage, this framework provides a robust mechanism for assessing AI systems, addressing the critical question, "How should we evaluate AI?" While the Turing test has traditionally been the benchmark for AI evaluation, we argue that current publicly available chatbots, despite their rapid advancements, have yet to meet this standard. However, the pace of progress suggests that achieving Turing test-level performance may be imminent. In the interim, the need for effective AI evaluation and testing methodologies remains paramount. Our research, which is ongoing, has already validated several versions of ChatGPT, and we are currently conducting comprehensive testing on the latest models, including ChatGPT-4, Bard and Bing Bot, and the LLaMA model. The testFAILS framework is designed to be adaptable, ready to evaluate new bot versions as they are released. Additionally, we have tested available chatbot APIs and developed our own application, AIDoctor, utilizing the ChatGPT-4 model and Microsoft Azure AI technologies

Hydrogen is now recognized as the primary alternative to fossil fuels due to its renewable, safe, high energy density and environmentally friendly properties. Efficient hydrogen production through water splitting has laid the foundation for sustainable energy technologies. However, when hydrogen production is scaled up to industrial levels, operating at high current densities introduces unique challenges. It is necessary to design advanced electrocatalysts for hydrogen evolution reaction (HER) under high current densities. This review will briefly introduce chal-lenges posed by high current densities on electrocatalyst, including catalystic activity, mass dif-fusion and catalyst stability. In an attempt to address these issues, various electrocatalyst design strategies are summarized in detail. In the end, our insights into future challenges for efficient large-scale industrial hydrogen production from water splitting are presented. This review is expected to guide the rational design of efficient high current density water electrolysis electro-catalysts and promote the research progress of sustainable energy.

In the process of oil and gas development and production, when the flow rate in the wellbore is large, the materials underground will be eroded, causing the operation to be unable to proceed smoothly and reducing the efficiency of oil and gas production. Therefore, optimizing the anti-erosion coating under high-speed flow conditions in wellbore is a very interesting thing. It can greatly improve operational efficiency. In this paper, laser cladding was used to prepare the new coating used in the PDC drill bit. Then, the effects of tungsten carbide and multi-layer graphene on the microstructure and properties of the tungsten carbide coating were studied. The research results indicate that, for new coatings, 60% tungsten carbide and 0.3% graphene are the optimal ratios. After adding WC, the hardness has significantly improved. However, when the WC content further increases more than 30%, the increase in hardness is relatively small. In addition, when the content of graphene is more than 0.3%, the branched structure became thicker, and not only the segregation of Cr, Si and W became very obvious again, but also the segregation of Fe occurred at the Ni enrichment site. The research results contribute to improving oil and gas production and operational efficiency under high-speed fluid flow conditions in the wellbore.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

This paper aims to build up a preference function to evaluate the public benefits of the type of agricultural farming, biodiversity, water provisions, land use type, ecotourism modes, and a monetary attribute (environmental trust fund and willingness to contribute) associated with an ecosystem service and land use program in a forest park. This study used the choice experiments to build a random utility model, analyze the average preference for the above land use attributes based on the conditional logit and used a latent class model to test the resident’s heterogeneous preferences for land use planning in the forest park. We also estimated the welfare derived from various land use programs. The empirical result had shown that: (1) increasing organic farming area, maintaining the status quo of species biodiversity, increasing the surface water provision, increasing the area of custom flora, increasing the wetland area, and setting up an integrated framework for ecotourism increase the public’s preference for the land use program; (2) we found that farmer and non-farmer haven’t the same land use preferences; (3) the ecotourism development program incorporating biodiversity, organic farming, ethnobotany, and wetland area with integrated ecotourism are more preferred than other land use program scenarios.

This paper proposes an optimization method for the scheduling of a multi-energy virtual power plant supply-demand balance in the power market environment of Jiangxi Province. The objective of this method is to enhance the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method comprehensively considers the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Additionally, real-time market price feedback is integrated to achieve accurate allocation of power supply and demand. Through a case study of a multi-energy virtual power plant in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply-demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%.

The esterification of propylene glycol methanol (PM) and acetic acid (AA) is an important reaction for the production of propylene glycol methyl ether acetate (PMA). Herein, we used macroporous ion exchange resin Amberlyst-35 as a catalyst to explore the effects of reaction conditions on the reaction rate and equilibrium yield of PMA. Under the optimized conditions of reaction temperature of 353 K, initial reactant PM/AA molar ratio of 1:3, and catalyst loading of 10 wt%, PMA equilibrium yield reached 78%, which is the highest equilibrium yield so far. The reaction equilibrium constants and activity coefficients were estimated to obtain reaction thermodynamic properties, indicating the exothermicity of the reaction. Furthermore, Pseudo-homogeneous (PH), Eley-Rideal (ER), and Langmuir-Hinshelwood-Hogen-Watson (LHHW) kinetic models were fitted based on experimental reaction kinetic data. The results demonstrate that the LHHW model is the most consistent with experimental data, indicating surface reaction controlled process and exhibiting an apparent activation energy of 62.0 kJ/mol. This work represents a valuable example of calculating the reaction thermodynamic and kinetics, which are particularly essential for promising industrial reactor design.

Meat serves as a vital component of the human diet, offering a rich source of protein and essential trace elements. China stands as both a significant producer and consumer of meat on a global scale, leading the world in terms of total meat production and consumption. As the standard of living within the country continues to rise, there is a growing emphasis on the nutritional value and safety of meat consumption. The tenderness of meat is a critical factor influencing its overall quality. Traditionally, tenderness has been assessed through shear force testing, a method that is inherently destructive, inefficient, and results in considerable sample wastage. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into the application of five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations aimed at guiding the future direction of non-destructive, AI-enhanced meat tenderness detection, thereby contributing to the ongoing improvement of the meat industry in China and beyond.

The use of chimeric antigen receptor (CAR) in natural killer (NK) cells for cancer therapy is gaining momentum, marking a significant shift in cancer treatment. This review aims to explore the potential of CAR-NK cell therapy in cancer immunotherapy, providing a fresh perspective. It discusses the innovative approaches in CAR-NK cell design and engineering, particularly targeting refractory or recurrent cancers. By comparing CAR-NK cells with traditional therapies, the review highlights their unique ability to tackle tumor heterogeneity and immune system suppression. Additionally, it explains how novel cytokines and receptors can enhance CAR-NK cell efficacy, specificity, and functionality. The review underscores the advantages of CAR-NK cells, including reduced toxicity, lower cost, and broader accessibility compared to CAR-T cells, along with their potential in treating both blood cancers and solid tumors.

This study simulated the effects of direct predatory pressure, indirect predatory pressure, and conspecific injury signals on the sea cucumber (Apostichopus japonicus) to determine changes in the activity of immune defense enzymes (lysozyme, acid phosphatase, alkaline phosphatase) and antioxidant stress enzymes (catalase, superoxide dismutase, malondialdehyde). Samples of sea cucumber juveniles were collected at 3 hours, 12 hours, 72 hours, and 96 hours post-predatory stress, and six enzymes related to immune defense and antioxidant stress were selected for activity assays, namely lysozyme (LZM), acid phosphatase (ACP), alkaline phosphatase (AKP), superoxide dismutase (SOD), catalase (CAT), and malondialdehyde (MDA). The results indicated that under direct predatory pressure, the activity of catalase in sea cucumbers was significantly higher than that of the control group at 3 hours (P < 0.05), while the activities of acid phosphatase, alkaline phosphatase, and catalase were significantly lower at 72 hours (P < 0.05). Under indirect predatory pressure, the activity of malondialdehyde in sea cucumbers was significantly higher than that of the control group at 12 hours (P < 0.05), the activity of alkaline phosphatase was significantly higher at 72 and 96 hours (P < 0.05), catalase was significantly lower at 72 hours, and the activity of superoxide dismutase was significantly higher at 72 hours (P < 0.05). Under the influence of conspecific injury signals, the activity of malondialdehyde in sea cucumbers was significantly higher than that of the control group at 12 hours (P < 0.05), and the activity of superoxide dismutase was significantly higher at 96 hours (P < 0.05). The sea cucumber enhanced its antioxidant capacity 3 hours after facing the predator, while its immune defense mechanism was suppressed at 72 hours. When facing indirect predatory pressure, the sea cucumber may have made immune and antioxidant preparations for the arrival of unknown risks. The experimental results show that predatory pressure has a significant impact on the immune and antioxidant functions of sea cucumbers, which may be related to the physiological state and environmental adaptability of the sea cucumber. This study provides a new perspective for understanding how sea cucumbers cope with predatory pressure in the natural environment and offers theoretical support for the cultivation management of sea cucumbers.

Silicon carbide particle-reinforced aluminum matrix (SiCp/Al) composites are significant lightweight metal matrix composites extensively utilized in precision instruments and aerospace sectors. Nevertheless, the inclusion of rigid SiC particles exacerbates tool wear in mechanical machining, resulting in a decline in the quality of surface finish. This work undertakes a comprehensive investigation into the problem of tool wear in SiCp/Al composite materials throughout the machining process. Initially, a comprehensive investigation was conducted to analyze the effects of cutting velocity vc, feed per tooth fz, milling depth ap, and milling width ae on tool wear during high-speed milling under SCCO2-MQL (Supercritical Carbon Dioxide Minimum Quantity Lubrication) ultrasonic vibration conditions. Furthermore, a prediction model for tool wear was developed by employing the orthogonal test method and multiple linear regression. The model's relevance and accuracy were confirmed using F-tests and t-tests. Ultimately, through the utilization of a genetic algorithm, the milling parameters were optimized, resulting in the identification of the most optimal combination of parameters. Empirical findings suggest that the careful selection of milling parameters can significantly mitigate tool wear, extend the lifespan of the tool, and enhance the quality of the surface. This work serves as a significant point of reference for the processing of SiCp/Al composite materials.

In order to make full use of the complementarity of existing navigation systems to improve the navigation and positioning accuracy, this paper investigates the visual/INS integrated navigation system based on the factor graph optimization (FGO) algorithm. In the traditional visual/INS integrated navigation system, there is often the problem of image blurring due to the rapid change of light and shadow captured by the vision sensor and the attitude change of the carrier, which leads to the decrease of the positioning accuracy of the combined navigation system. Therefore, this paper firstly introduces Retinex algorithm and BID algorithm to carry out the design of image de-blurring method; then implements the visual/INS integrated navigation algorithm based on the factor graph optimization method; finally, the experimental scheme is designed, comparative experiments are carried out, and the researched method is verified by using the public dataset. The experimental results show that the combined navigation algorithm realized in this paper has high accuracy, and its positioning accuracy is improved by 40% compared with visual positioning.

Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly within social media platforms like X (formerly Twitter) and in addressing cyberbullying present on them. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of Transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Leveraging state-of-the-art LLMs such as ChatGPT-4o, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and quantized for multilabel classification (detecting both biases and cyberbullying). The study's outcomes include a prototype of a hybrid application that combines a Bias Data Detector and a Bias Data Generator, validated through extensive testing.

Since surface relief transmission gratings have very strict requirements on operators and use environment, according to the semiconductor laser external cavity spectral beam combining system, this paper proposes a design scheme of semiconductor laser array spectral beam combining system based on grating-external cavity. The finite element approach was used to create a wideband, high-efficiency fill-in multilayer dielectric transmission grating structure for a high-power spectrum beam combining system. The incidence angle, ridge height, duty cycle, and sidewall inclination angle of the transmission grating were tuned and evaluated, and a link between the transmission grating's diffraction efficiency and grating characteristics was discovered. The calculated design of the high-power fused silica transmission grating has a negative first-order peak diffraction efficiency of 99.5% in the 800nm range. In the spectral region of 765-872 nm, the transmission grating's diffraction effectiveness exceeds 92%. The filled ultra-high diffraction efficiency multilayer dielectric transmission grating design addresses the issue of resistance to high-power lasers under complicated operating settings, it’s intended to maintain a high diffraction efficiency even after several cleaning cycles, and it’s an ideal component for high-power spectrum beam combining systems.

Hyperspectral images (HSI) contain abundant spectral information. Efficient extraction and utilization of this information for image classification remain prominent research topics. Previously, hyperspectral classification techniques primarily relied on statistical attributes and mathematical models of spectral data. Deep learning classification techniques have recently been extensively utilized for hyperspectral data classification, yielding promising outcomes. This study proposes a deep learning approach that uses polarization feature maps for classification. Initially, the polar coordinate transformation method is employed to convert the spectral information of all pixels in the image into spectral feature maps. Subsequently, the proposed Deep Context Feature Fusion Network (DCFF-NET) is utilized to classify these feature maps. The model is validated using three open-source hyperspectral datasets: Indian Pines, Pavia University, and Salinas. The experimental results indicated that DCFF-NET achieves excellent classification performance. Experimental results on three public HSI datasets demonstrate that the proposed method accurately recognizes different objects with an overall accuracy (OA) of 86.68%, 94.73%, and 95.14% based on the pixel method, and 98.15%, 99.86%, and 99.98% based on the pixel-patch method.

The migration from MATLAB to Python (M-to-PY) has gained significant traction in recent computational research. While MATLAB has long served as a linchpin in myriad scientific endeavors, there's an emerging trend to rejuvenate these projects using Python's extensive AI tools and libraries. This study presents a semi-automated process for M-to-PY conversion, using a detailed case study of an image skeletonization project comprising fifteen MATLAB files and a 1404-image dataset. Skeletonization is foundational for ongoing 3D motion detection research using AI transformers, predominantly developed in Python. The utilization of ChatGPT-4, acting as an AI co-programmer, is pivotal in this conversion. By leveraging the public OpenAI API, we developed an M-to-PY converter prototype, evaluated its efficacy using test cases from the Bard bot, and subsequently employed the converted code in an AI application. The dual contributions encompass a well-tested M-to-PY converter and a Skeleton App capable of sketching and skeletonizing any given image, enriching the AI toolset. This study accentuates how AI resources, like ChatGPT-4, can simplify code transitions, opening doors for innovative AI implementations using primarily MATLAB-coded scientific research.

Andean blueberries are wild berries grown and consumed in Ecuador which contain high values of bioactive compounds, mainly anthocyanins, with powerful antioxidant activity. The aim of this study was to evaluate the profile and contents of (poly)phenols and carotenoids in Andean blueberry by HPLC-DAD-MSn and determine a wide range of its biological activities. The antioxidant capacity of this fruit was evaluated in vitro by three different methods and in vivo using the zebrafish animal model, also the toxicity effect was determined by the zebrafish embryogenesis test. Besides, the antimicrobial activity and the capacity of Andean blueberry to produce hemagglutination in blood cells were evaluated. Finally, the bioaccessibility of (poly)phenols and related antioxidant capacity were determined in the different phases of an in vitro digestion. The global results indicated no toxicity of Andean blueberry, weakly bacteriostatic activity, and high contents of anthocyanins and antioxidant capacity, which were partially bioaccesible in vitro (~ 50 % at the final intestinal step), contributing to the knowledge of its health benefits for consumers and its potential use in the food and pharmaceutical industry as functional ingredient.

This study investigates cloud cavitation suppression around a model scale NACA66 hydrofoil using active water injection and explores the effect of multiple injection parameters. Numerical simulations and a mixed-level orthogonal test method are employed to systematically analyze the impact of jet angle αjet, jet location Ljet, and jet velocity Ujet on cavitation suppression efficiency and hydrofoil energy performance. The study reveals that jet location has the greatest influence on cavitation suppression, while jet angle has the greatest influence on hydrofoil energy performance. The optimal parameter combination (Ljet = 0.30C, αjet = +60 degrees, Ujet = 3.25 m/s) effectively balances energy performance and cavitation suppression, reducing cavitation volume by 49.34% and improving lift-drag ratio by 8.55%. The study found that the jet's introduction not only enhances vapor condensation and reduces the intensity of the vapor-liquid exchange process but also disrupts the internal structure of cavitation clouds and elevates pressure on the hydrofoil suction surface, thereby effectively suppressing cavitation. Further analysis shows that positive-going horizontal jet components enhance the lift-drag ratio, while negative-going components have a detrimental effect. Jet arrangements near the trailing edge negatively impact both cavitation suppression and energy performance. These findings provide valuable reference for selecting optimal injection parameters to achieve a balance between cavitation suppression and energy performance in hydrodynamic systems.

Antifungal properties are found in essential oil extracts of oregano (Origanum vulgare), rosemary (Salvia rosmarinus), clove (Syzygium aromaticum), thyme (Thymus), cinnamon (Cinnamomum verum), basil (Ocimum basilicum), therefore, the ability to prevent or inhibit the growth of Cladosporium spp., Lasiodiplodia spp., Colletotrichum spp., Fusarium spp., and Aspergillus spp. was studied. Cladosporium spp., Lasiodiplodia spp. Colletotrichum spp. Fusarium spp. y Aspergillus spp. fungi that were isolated and purified from the rot found on the banana peels (Musa paradisiaca). To perform a visual (macro-scopic) analysis of the Petri dish containing the pure fungus and a more detailed (microscopic) analysis of the fungal spores to classify it by genus, then, dilutions are prepared to inoculate onto amended PDA medium with chloramphenicol. Weekly re-inoculations are performed until iso-lated and purified fungi are obtained, which are then evaluated for in vitro inhibition and in vivo growth, according to the results of the in vivo analysis, the fungi were classified from higher to lower severity in: Colletotrichum spp., Lasiodiplodia spp., Aspergillus spp., Fusarium spp. and Cladosporium spp and based on fungal growth observations, it was found that basil and rosemary do not inhibit growth at the maximum concentration. Cinnamon inhibits at 400 ppm, rosemary halts fungal growth at 600 ppm, clove at 1000 ppm, and oregano at 800 ppm.

In modern warfare, the diversified tasks can sometimes be challenging to fulfill with a single projectile, necessitating the incorporation of cooperation constraints in trajectory planning. Limited by the stringent requirements of the launch platform and lower costs, the maneuverability and ability to adjust the flight time range of projectiles are weak. Therefore, the rationality of performance indicators and flight time coordination strategies is especially critical. To fully exploit the control capabilities of projectiles, a battlefield area segmentation method is proposed, differentiated performance indicators and a cooperative trajectory planning model that considers the entire range of projectiles are established. Considering the limitations of existing flight time coordination strategies in applications, targeted improvements have been made in different cooperation scenarios, and a bi-level adaptive cooperation strategy(BACS) with both optimality and flexibility is proposed. To rapidly determine feasible conditions, a simple and universal method for obtaining the capability ranges of cooperation is introduced. The multi-phase optimal control problems are transformed into nonlinear programming problems using the Radau pseudo-spectral method, and simulation results show that the proposed methods are capable of handling trajectory planning tasks in different cooperation scenarios. The improved strategies effectively enhance the planning success rate under relatively harsh conditions and BACS demonstrates good compatibility with the problems studied.

Due to constraints in launch platform and cost, the maneuverability of gliding-guided projectiles is limited, necessitating a rational design of their trajectory schemes. To reduce the sensitivity of trajectory schemes to uncertainties while ensuring compatibility between flight schemes and guidance control systems, and fully exploiting the control capability of the projectile, a closed-loop robust trajectory planning method is proposed. Models of major uncertain factors and states deviation at the control-start point are established. Based on the NIPCE method, the stochastic dynamic model is transformed into a high-dimensional deterministic model with PCE coefficients as state variables, and the uncertainty propagation law is obtained. A PID algorithm is employed to design a tracking guidance law based on position error feedback, and open-loop and closed-loop robust trajectory planning models are established accordingly. The optimal control problem is solved by transforming it into a nonlinear programming problem using the direct shooting method. Simulation results indicate that the NIPCE method can significantly improve the computational efficiency of uncertainty propagation while ensuring accuracy. Open-loop robust planning can effectively mitigate the sensitivity of gliding trajectories to uncertainties but cannot completely eliminate terminal dispersion. Closed-loop robust planning effectively improves control effort consumption on the basis of open-loop planning.

In this paper, we extend the KdVSKR equations in (1+1)-dimension and (2+1)-dimension to fractional KdVSKR equations with modified Riemann-Liouville derivative. The (2+1)-dimensional KdVSKR equation, which is a recent extension of (1+1)-dimensional KdVSKR equation, can model the resonances of solitons in shallow water. By means of the Hirota bilinear method, finite symmetry group method and consistent Riccati expansion method, many new interaction solutions have been derived. Soliton-cnoidal interaction solution of the (1+1)-dimensional fractional KdVSKR equation has been derived for the first time. For the (2+1)-dimensional frac-tional KdVSKR equation, two-wave interaction solutions and three-wave interaction solutions, including dark-soliton-sine interaction solution, bright-soliton-elliptic interaction solution, and lump-hyperbolic-sine interaction solution, have been derived. Impact of the fractional order on the shapes of these solutions has been illustrated by figures. Three-wave interaction solutions of fractional systems have not been reported in the existing references. The research idea in this paper can be applied to other fractional differential equations.

To achieve n-type doping in diamond, extensive investigations employing first principles have been conducted on various models of phosphorus doping and boron-phosphorus co-doping. The primary focus of this study is to comprehensively analyze the formation energy, band structure, density of states, and ionization energy of these structures. It is observed that within a diamond structure solely composed of phosphorus atoms, the formation energy of an individual carbon atom is excessively high. However, the substitution of two carbon atoms leads to the conversion of diamond into a p-type semiconductor. Upon examining the P-B co-doping structure, it is revealed that the doped impurities exhibit a tendency to form more stable cluster configurations. As the separation between the freely doped atoms and the cluster impurity structure increases, the overall stability of the structure diminishes, consequently resulting in an elevation of the ionization energy. Analysis of the electronic density of states demonstrates that the electron orbit of the B atom makes negligible contributions to the impurity levels during P-B doping.

Two new zoanthamine alkaloids, namely 3-acetoxynorzoanthamine (1) and 3-acetoxyzoanthamine (2), have been isolated from the zoantharian Zoanthus cf. pulchellus collected off the coast of the Peninsula of Santa Elena – Ecuador, together with three known alkaloids zoanthamine, norzoanthamine and 3-hydroxynorzoanthamine. The chemical structures of 1 and 2 were determined by interpretation of their 1D and 2D NMR data and comparison with literature data. This is the first report of zoanthamine-type alkaloids from Zoanthus cf. pulchellus collected in the Tropical Eastern Pacific. The neuroinflammatory activity of all the isolated compounds were evaluated in microglia BV-2 cells and high inhibitory effects were observed in ROS and NO generation.

A portable laser photoacoustic sensor based on a Field-Programmable Gate Array (FPGA) is reported for methane detection. A tunable DFB diode laser in the 1654 nm wavelength range is used as an excitation source. The photoacoustic signal processing was implemented by a FPGA device. A small resonant photoacoustic cell is designed. The minimum detection limit (1σ) of 10 ppm for methane (CH₄) is demonstrated.

The greatly diversified flowering plants (angiosperms) are the dominating and defining group in the current earth ecosystem. However, from which group by what way the flowers, especially their gynoecia (key characteristics of angiosperms), are derived have been the questions in botany unanswered over centuries. Such an embarrassing situation can be attributed to the lack of plants with partially enclosed ovules, which are supposed to be a post between gymnosperms and angi-osperms. Here we report a fossil plant that has an apparent coniferous vegetative and reproduc-tive morphologies but has a single seed partially wrapped by the subtending bract. Such a mor-phology suggests that the carpels in angiosperms may be equivalent to bracts enclosing their axil-lary seed(s). Such a non-traditional interpretation of the homology of angiosperm carpels is com-patible with various new progresses made in botany and is in line with Tomlinson’s recent hy-pothesis. Together with other fossil evidence reported recently, it appears that gynoecia in angio-sperms are derived in multiple ways.

Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the CBAM attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images.

The SK model proposed by Sherrington and Kirkpatrick is an important mean field model for spin glasses. The dependence structure of Hamiltonians of the SK model is considered in this paper. We have investigated the covariance matrix of Hamiltonians of the SK model, and establish a relationship between the rank of the corresponding covariance matrix and the dimension of the space spanned by the Hamiltonians. Finally, we also give an upper bound for the rank of the covariance matrix, which might be the best.

The nuclear factor-ĸB (NF-ĸB) transcriptional system is a major effector pathway involved in inflammatory responses. Previous studies found that a Gardenia decoction (GD) inhibited the expression of NF-κB in a lipopolysaccharide (LPS)-stimulated mouse intestinal injury model. Herein, we hypothesized that geniposide (GE), a component of Gardenia jasminoides Ellis, also exerts anti-inflammatory effects and inhibits NF-ĸB activity in LPS-induced intestinal epithelial cells (IEC-6). IEC-6 cells were stimulated with LPS, following which the effects of GE on NF-ĸB signaling in the IEC-6 cells were examined by western blotting to detect IĸB phosphorylation/degradation. The expression of NF-κB was determined by immunofluorescence assay (IFA). Enzyme-linked immunosorbent assay (ELISA) was used to detect the inhibitory effect of GE on the release of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and interleukin-1β (IL-1β) activated by LPS in IEC-6 cells. In addition, the migration ability of IEC-6 cells was observed by the scratch method. These results showed that GE dose-dependently downregulated levels of the proinflammatory cytokines TNF-α, IL-6 and IL-1β that had been upregulated by LPS and suppressed the phosphorylation of IĸB and NF-ĸB induced by LPS. Our findings indicated that GE could reduce LPS-induced NF-ĸB signaling and proinflammatory expression in IEC-6 cells and significantly enhance the migration of IEC-6 cells. Moreover, GE inhibited the expression of NF-κB, nuclear transfer, and transcriptional activity in IEC-6 cells. GE could block the synthesis of inflammatory factors of IEC-6 cells by inhibiting activation of the IĸB/NF-κB signaling pathway induced by LPS.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

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

To enhance the safety of power grid operations, this study proposes a high-precision short-term photovoltaic power prediction method that integrates information from surrounding pho-tovoltaic stations and the Conv-LSTM-ATT model. In the deep learning prediction model, not only is numerical weather prediction (NWP) data from the target photovoltaic station used as input features, but also highly correlated features from nearby photovoltaic stations are incor-porated. The research begins by analyzing the correlation between irradiance and power se-quences, along with distance factors, to calculate a composite similarity index between the target and other regional photovoltaic stations. Stations with high similarity indices are then selected as data sources. Subsequently, Bayesian optimization techniques are employed to find the optimal data fusion ratios. Ultimately, using the selected data, power prediction mod-eling is conducted via the Conv-LSTM-ATT deep neural network. Experimental results con-firm the superiority of the proposed model, which demonstrates higher predictive accuracy compared to three other classical models. The data fusion strategy determined by Bayesian optimization significantly enhances prediction accuracy, reducing the root mean square error (RMSE) of the test set by 20.04%, 28.24%, and 30.94% for three weather types, respectively.

Concrete is more stable than filler as a false roof in a downward approach type infill quarry, but the mechanical model of a concrete false roof is not clear. As a result, there has been limited application. Therefore, it is necessary to establish the mechanical model of a concrete artificial false roof to enhance the stability of the downward approach infill quarry. This paper combines theoretical analysis, model derivation, finite element analysis and field application methods. Firstly, the basic assumptions of thin plate theory and elasticity mechanics are used as the premise to establish the differential equations of the deflection curve of the concrete thin plate false roof. Then, the three types of bending moment generation of the concrete thin plate false roof are explored. Secondly, the relationship between the location of dangerous surface and dangerous point in hard-supported weak-slab structure and soft-supported weak-slab structure is analyzed, and the type of uniform load and calculation model of concrete thin-slab false roof are improved. Finally, according to the established mechanical model of thin slab false roof, finite element analysis and field application were carried out. The results show that there are three types of deflection curve differential equations in the concrete thin slab false roof perpendicular to the strike, and three extreme points exist in the middle section of the thin slab and on both sides of the surrounding rock. The hazardous surface of the hard-supported weak-slab structure is present at the intersection of the encircling rock and the thin-slab fake ceiling, with the perilous point situated on the top of this surface. The hazardous surface of the soft-supported weak-slab structure can be found in the centre section of the thin-slab fake ceiling, with the perilous point being located at the bottom of this surface. Moreover, the damage mode for both structures is that of maximum tensile stress damage. Upon conducting a finite element analysis, the results indicate that the concrete's maximum tensile stress is significantly high.

Increasing rates of infection by antibiotic resistant bacteria have led to a resurgence of interest in bacteriophage (phage) therapy. Several phage therapy studies in animals and humans have been completed over the last two decades. We conducted a systematic review of safety and toxicity data associated with phage therapy in both animals and humans reported in English-language publications from 2008 – 2021. Overall, 69 publications met our eligibility criteria including 20 animal studies, 35 clinical case reports or case series, and 14 clinical trials. After summarizing safety and toxicity data from these publications, we discuss potential approaches to optimizing safety and toxicity monitoring with the therapeutic use of phage moving forward. In our systematic review of the literature, we found few, but no serious, adverse events associated with phage therapy. Comprehensive and standardized reporting of potential toxicities associated with phage therapy has generally been lacking in the published literature. Structured safety and tolerability endpoints are necessary when phages are administered as anti-infective therapeutics.

Wear-resistant steel/carbon steel composite plate not only has the double performance advantage of high strength and wear-resistance, but also can reduce energy consumption and production cost. Based on 50% reduction rate, the wear resistance of BTW1/Q345 composite was studied at different annealing temperatures, and the dry friction and wear tests of BTW1/Q345 composite at different annealing temperatures were carried out by using RETC MFT-5000. The macro-micro morphology and wear mechanism of wear marks at different annealing temperatures were analyzed by using white light interference three-dimensional surface profiler, Scanning electron microscope scanning electron microscope (SEM) and backscatter electron diffraction (EBSD) . The results show that with the increase of annealing temperature, the grain size near the scratch of BTW1 in the wear-resistant layer of the composite plate is refined, and the wear-resistant property is higher than that of the original sample.When the annealing temperature is 860℃,the wear resistance is the best.

With the fast growth of smartphones and digital cameras, massive images are generated every day in the world. They are easily transmitted on the insecure channel-Internet. It has been a hot spot for protecting sensitive images during communication. A new cryptosystem is proposed using a six dimensional (6D) hyperchaotic system and DNA computing. The hash value is obtained with the function of SHA-512. It keeps the encrypted result closely connected with the original image. The initial values of the cryptosystem are produced with the hash value and the secret key. The pixel is divided into four parts, and a large matrix is formed. Scrambling is performed on the new image. DNA coding, modern DNA complementary rules, DNA computing, and DNA decoding are performed on the new image. Diffusion is also executed, and ciphered image is achieved finally. The experimental result reveals the outcome of the proposed algorithm. Security analysis shows that the designed algorithm has a huge secret key space, low correlation, and high sensitivity. It also signifies that the designed algorithm could protect against common attacks and is more secure than some existing methods.

With the large-scale application of Internet of Things (IoT) in the medical field, eHealthcare has attracted wide attention. With the help of IoT devices, eHealthcare allows doctors to monitor patients’ health conditions and make recommendations accordingly. The addition of cloud computing greatly saves Healthcare Center’s (HC) computing resources. In order to provide privacy and fine-grained access control, Attribute-b ased encryption (ABE) is widely favored. HC usually operates multiple medical institutions in different regions, and each medical institution can be regarded as a attribute authority. Therefor, multi-authority (MA) ABE is usually concerned to encrypt the patient data. However for existing MA-ABE algorithms, the ciphertext is long and the efficiency of encryption and decryption is low. These problems will greatly increase the computing burden of patients and seriously affect the efficiency of data transmission. Hence we propose a decentralized key-policy MA-ABE in eHealthcare. The proposed algorithm can achieve decentralization, that is, it does not need central authority. Meanwhile, compared with existing MA-ABE algorithms, the length of ciphertext generated by our algorithm is shorter, and the efficiency of encryption and decryption is significantly improved. With the help of Java Pairing Based Cryptography, we carried out the experiments which show that our algorithm can effectively reduce the time required for encryption and decryption.

The remarkable adsorption capacity of graphene derived materials has prompted their examination in composite materials suitable for deployment in treatment of contaminated waters. In this study, crosslinked calcium alginate – graphene oxide beads were prepared and activated by exposure to pH 4 by using 0.1M HCl. The activated beads were investigated as novel adsorbents for the removal of organic pollutants (Methylene Blue dye and the pharmaceuticals Famotidine and Diclofenac) with a range of physicochemical properties. Effects of initial pollutant concentration, temperature, pH and adsorbent dose were investigated and kinetic models were examined for fit to the data. Maximum adsorption capacities q_max obtained were 1334, 35.50 and 36.35 mg g^-1 for the uptake of Methylene blue, Famotidine and Diclofenac respectively. The equilibrium adsorption had an alignment with Langmuir isotherms while the kinetics were most accurately modelled using a pseudo- first –order and second order models according to the regression analysis. Thermodynamic parameters such as ΔG^◦, ΔH^◦ and ΔS^◦ were calculated and the adsorption process was determined to be exothermic and spontaneous.

Clarifying the distribution of temperature and pressure in the wellbore and cavern during hydrogen injection and extraction is crucial for quantitatively assessing cavern stability and wellbore integrity. This paper establishes an integrated flow and heat transfer model for the cavern and wellbore during hydrogen injection and production, analyzing the variations in temperature and pressure in both the wellbore and the cavern. The specific conclusions are as follows. (1) The temperature and pressure parameters of hydrogen and natural gas within the chamber and wellbore were compared. Under identical injection and production conditions, the temperature of hydrogen in the chamber was 10°C higher than that of natural gas, and 16°C higher in the wellbore. The pressure of hydrogen in the chamber was 2.9 MPa greater than that of natural gas, and 2.6 MPa higher in the wellbore. (2) A comparative analysis was conducted on the impact of surrounding rock’s horizontal and numerical distance on temperature during hydrogen and natural gas injection processes. As the distance from the cavity increases, from 5 to 15 meters, the temperature fluctuation in the surrounding rock diminishes progressively, with the temperature effect in the hydrogen storage chamber extending at least 10 meters. (3) The influence of rock thermal conductivity parameters on temperature during the processes of hydrogen injection and natural gas extraction is compared. The better the thermal conductivity, the deeper the thermal effects penetrate the rock layers, with the specific heat capacity having the most significant impact.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4,202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 27 for fiber length (FL), 16 for fiber strength (FS), 24 for micronaire (MC), 17 for fiber uniformity (FU), and 24 for fiber elongation rate (FE). Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosome D1 and D11. Each QTL contributed 1.98%–21.45% to the phenotypic variation (PV), including 13 major effect QTLs that contributed more than 10% toward PV. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18%–21.45% of PV, qFS-A11-1 in F2:3, F2:4 and F2:5 with 6.05%–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

The open burning of sugarcane residue is commonly used as a low-cost and fast method during pre-harvest and post-harvest periods. However, this practice releases various pollutants, including dioxins. This study aims to predict polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs, or dioxins) emissions using the Grey Model (GM (1,1)) and to map the annual flux spatial distribution at the provincial level from 2023 to 2028. Using the activity rate of dry crop residue from national agencies and literature, and following the guidelines set by the United Nations Environment Programme (UNEP), an annual emission inventory was developed at the provincial level. The distribution of emissions from 2016 to 2022 was then mapped. The average PCDD/Fs emission values exhibit significant variation among the provinces, averaging 309 pg TEQ/year. Spatially, in line sugarcane production, South Sumatra and East Jawa consistently show high emissions, often exceeding 400 pg/m². Emissions based on the UNEP emission factor tend to be higher compared to other factors, due to its generic nature and lack of regional specificity. The emission predictions using the Grey Model GM (1,1) indicate that North Sumatra is expected to experience a steady increase in PCDD/Fs emissions, whereas South Sumatra and Lampung are projected to see a slight decline. This forecast assumes there will be any changes in regional intervention strategies. Most regions in Jawa Island show a gradual increase in emissions, except for East Jawa, which is predicted to have a slight decline from 416 pg/year in 2023 to 397 pg/year in 2028. Additionally, regions like Gorontalo and parts of East Jawa are projected to remain "hotspots" with consistently high emissions, suggesting the need for targeted interventions. Future studies should consider developing monthly emissions profiles to account for local agricultural practices and seasonal conditions. The emission data generated in this study, which includes both spatial and temporal distributions, is valuable for air quality modeling studies. Utilizing this data can help assess the impact of current and future emissions on ambient air quality.

This survey of research literature is an overview of the current state of science addressing DSM-5 diagnostic criteria as it relates to identifying Autism Spectrum Disorder (ASD) in young children. Selected sources are of high quality and have been published since 2020 in peer-reviewed journals relevant to children’s health. The narrative is organized from the earliest to the most recent publications and is divided into sections around categories and themes that highlight the main arguments, findings, and controversies identified by researchers. There are indications in the literature that while ASD can be identified and differentiated from other developmental disorders in young children, doing so requires that clinicians who have relied on the DSM-5 should also use multiple measures, including other formalized screening tools as assessments that gather observational input from parents and caregivers, as well as findings from physical examinations, laboratory investigations, and magnetic resonance imagining (MRI). This high level of corroboration points to an emerging diagnosis paradigm through which transdiagnostic approaches to early diagnosis and intervention may exist.

This study investigates the intricate relationship between Venezuelan migration and urban growth in Colombia from 2018 to 2021. The study employs remote sensing data and social network metrics to uncover migration patterns and their impact on urban expansion. The methodology consists of three stages. Firstly, nighttime satellite imagery is used to analyze year-over-year urban growth in Colombia. Secondly, social network data estimates Venezuelan migration, overcoming challenges of underreporting and informal border crossings. Lastly, an econometric analysis explores the quantitative link between Venezuelan migration and urban growth, integrating socioeconomic variables to address endogeneity. The findings reveal the complex interplay of Venezuelan migration, socioeconomic factors, and urban growth. The study outlines remote sensing analysis, introducing the Anthropogenic Footprint Expansion Index (AFEI) to quantify urban growth. Facebook API data estimates migration trends and explores socioeconomic impacts on urban expansion. The analysis uncovers migration, poverty, aging, and urban population proportion as key factors affecting Colombia's urban landscape. Furthermore, the research underscores how Venezuelan migration affected short-term urban expansion pre- and post-COVID-19. Migration had a notable effect before the pandemic, but this influence waned afterward. The study highlights migration's short-term nature and emphasizes age demographics' role in medium-term dynamics.

Currently, in some domestic and foreign mines, the backfill drilling pipeline experience a rupture phenomenon even when the wear degree is low. This results in a delay in production due to the filling becoming 'sick'. This paper presents, for the first time, the damage mechanism from a mechanical perspective and re-derives the anti-extrusion strength model of the backfill drilling pipeline. We investigate the influence of law on the anti-extrusion strength of pipelines from the perspective of stratum and cement ring. We then verify the theoretical and simulation results through engineering examples. The results demonstrate that the Mises stress criterion is a suitable modification principle for the anti-extrusion strength model of the backfill drilling pipeline. The anti-extrusion strength of the pipeline is related to the elastic modulus and Poisson's ratio of the stratum, and the thickness of the cement ring. It is negatively affected by the depth of the stratum. For hard strata, a cement ring with a smaller elastic modulus is suitable, while for soft stratum, a cement ring with a larger elastic modulus is recommended. When the missing angle of the cement ring is less than 60°, the stress concentration factor increases up to 2.2. The stress unloading capacity of the cement ring ranges from 32.7% to 37.8%, and optimal performance of the cement ring is achieved when it has high strength and low rigidity. The backfill filling pipeline of a copper mine abroad was destroyed due to external extrusion force exceeding its anti-extrusion strength value. The modified pipeline anti-extrusion strength model is 18.2% higher than the casing API strength value. This finding can inform the design of the backfill filling pipeline for China's kilometer-deep wells in the future.

To facilitate the computation of the uniaxial compressive strength value of the cemented paste backfill under the condition of high sand-cement ratio, and to analyze the size effect of the cemented paste backfill with high sand-cement ratio under varied aspect ratios, the cemented paste backfill specimens with sand-cement ratios of 8:1-24:1 and aspect ratios of 0.5, 1, and 2 were prepared. Through response surface experiments and finite element analysis, the influence of three aspect ratios on the cemented paste backfill strength was explored, and the failure characteristics and size effect mechanisms of cemented paste backfill with various aspect ratios were analyzed. The model of strength conversion between cemented paste backfill with different aspect ratios was established. The results show that the compressive strength of the cemented paste backfill decreases with the increase of the aspect ratio under the same mass concentration and sand-cement ratio. When the aspect ratio is 0.5 and 1, the splitting failure is the main factor; when the aspect ratio is 2, the shearing failure is the main factor. The three-dimensional stress caused by the end effect of the cemented paste backfill is the main reason for the high strength when the aspect ratio is 0.5. Due to the one-dimensional stress zone generated in the middle of the cemented paste backfill with an aspect ratio of 2 in the laboratory, its strength value is closer to the actual uniaxial compressive strength value of the cemented paste backfill. The strength values of the cemented paste backfill with aspect ratios of 0.5 and 1 were higher than the actual uniaxial compressive strength values in the laboratory. The rationality of the theoretical analysis was verified by the ABAQUS analysis results. According to the experimental results, a strength conversion model between different aspect ratios is established, which can provide a reference for the design of the uniaxial compressive strength of the cemented paste backfill.

Ecuador is the world's fifth largest cocoa producer, generating hundreds of annual tons of residues from this fruit. This research aims to value this residual (cocoa pod husk) by using it as raw material to obtain pectin, which is widely used in the food and pharmaceutical industries. For them, the extraction of three different organic acids with GRAS status (and therefore safe for their use), citric, malic and fumaric acids, were studied. In addition, two other factors, temperature (70-90°C) and extraction time (60-90 min), were explored in a central composite design of experiments. Under the conditions of the experiment, it was obtained that the best yields were reached for citric acid, malic acid and fumaric acid and for ~86 min of extraction time. The temperature did not show a significant influence on the yield. The pectins obtained under optimal conditions were characterised, showing their similarity with commercial pectin. However, the equivalent weight and the esterification degree of the pectin obtained with fumaric acid classify it as having a high equivalent weight and a low degree of esterification. It differs significantly from the other two acids, perhaps due to the limited solubility of fumaric acid.

Respiratory therapist(RT)internet-supervised At-Home Telespirometry(AHT)was validated in pilot 22/95=23% ALS Clinic population. Erect, supine Vital Capacity(eFVC, sFVC)were measured longitudinally using ZephyRx smartphone interface and database recording in-clinic-conventional(Viaire-Vyasis-)and at-home-portable(MIR-Spirobank-Smart-)spirometers. Bland-Altman(BA)analysis supported content validity-precision and 7-day-test-re-test-validity [mean difference=0.15L(conventional–portable;95%CI -0.40L, 0.70L); = 2.7% predicted (conventional-portable;95%CI -12.1%, 17.5%) ]meeting ATS-ERS performance standards. RT Internet-supervised AHT measured sFVC was more frequently obtained[35/52=67.3 %]in-home-vs-in-clinic sFVC[9/21=42.9 %](Chi-Square;P=0.0533). Monthly mean eFVC slope decline for the eFVC > 60 % predicted baseline cohort was – 1.24% predicted per month[95% CI -2.15 ,-0.33% predicted]statistically significantly(P=0.015)less compared with the – 5.86% predicted per month[95% CI -9.44, -2.29 % predicted]decline for the eFVC < 60 % predicted baseline cohort and in this cohort was associated with statistically significant decreased survival(P=0.0219). RT Internet-supervised AHT facilitated initiation of non-invasive ventilation(NIV)in 5/22[23%]patients including 3/4[75%] homebound subjects. RT Internet-supervised AHT can be deployed for ALS patients stratified according to baseline eFVC for earlier detection of eFVC and sFVC decreases between clinic visits. A prospective research study[NCT05106569]is ongoing to further define the capability of online deployment of Respiratory therapist(RT)internet-supervised AHT in the timely initiation of NIV use to improve ALS respiratory care in larger numbers of ALS subjects.

The superconductivity of CaC6 as a function of pressure and of abundant Ca isotopes is revisited using DFT calculations on a 2c – double hexagonal superlattice. The introduction of superlattices is motivated by previous synchrotron absorption and Raman spectroscopy results on other superconductors that show evidence of superlattice vibrations at low (THz) frequencies. A 2c superlattice for CaC6 is illustrative of atomic orbital symmetry and periodicity, including bonding and antibonding s-orbital character implied by cosine modulated electronic bands. Inspection of the cosine band reveals that the cosine function has a small (meV) energy difference between bonding and antibonding regions relative to a midpoint non-bonding energy. Fermi surface nesting is apparent in an appropriately folded Fermi surface using a superlattice construct. Nesting relationships identify phonon vectors for conservation of energy and for phase coherency between coupled electrons at opposite sides of the Fermi surface. Detailed analysis of this Fermi surface nesting provides accurate estimates of the superconducting gaps for CaC6 with change in applied pressure. Recognition of superlattices within a rhombohedral or hexagonal representation provides consistent mechanistic insight on superconductivity and electron−phonon coupling in CaC6.

Purpose: Early glottic cancer is a highly curable malignancy. Disease control is achieved by resec-tion or external beam radiation with similar long-term results according survival. Surgery is predominantly performed by transoral endoscopic approach, due to reduced complication rates when compared to open cordectomy. Open cordectomy might still be warranted in cases of inad-equate surgical accessibility. Aim of the present study was to compare endoscopic versus open approach in relation to complications and outcome in an up-to-date patient group. Methods: 219 patients underwent primary tumor resection in early glottic cancer (UICC stages I-II). 168 opera-tions were performed endoscopically, 51 operations were open transcervical cordectomies. We compared survival data with non-parametric methods and functional outcome according to need of tracheostomy, feeding tube and voice functional outcome following ELS protocol. Results: There were no significant differences in overall survival (OS). 2 years OS after endoscopic surgery was 90.5% vs 95.3% after transcervical resection. As expected, OS in T1b and T2 tumors was worse than in T1a tumors. We found a statistically significant difference in disease free survival (DFS) within the T1a and T1b/T2 group (Log rank test: p=0.006) but no relevant difference be-tween the two surgical modalities, even when the data was stratified by T-categories performing a Cox regression (hazard ratio 1.21, p=0.712). Patients who underwent open surgery had a sig-nificantly worse peri- and postoperative course with longer hospital stay, higher frequency of tracheotomies and longer need for nasogastric tubes. Functional results were comparable within the two patient groups while higher tumor size / stage was an unfavorable predictor for voice re-habilitation. Conclusion: Open cordectomy leads to more complications but offers comparable oncological and functional outcome when compared to transoral endoscopic approach in early glottic cancer. Open surgery therefore continues to be a valid treatment option when endoscopic resection is not possible.

Driving factors of smart city construction are exploratively studied by grounded theory method based on text sources from journal papers in SCI and CSSCI databases. Initial scale of driving factors about smart city construction is obtained on the basis of above analyses. This paper modifies measuring items of the initial scale with a small sample pretest and reliability test, then forms final items by exploratory factor analysis. According to the above scale, questionnaires are designed to obtain empirical data, and confirmatory factor analysis is used to verify further validity and reliability of the scale. The results show that driving factors of smart city construction include three main dimensions: problem-oriented factors, business- or technology-driven factors and endogenous development requirements.

Intense climate change and rapid urbanization have increased the risk of urban flooding, seriously affecting urban economic and social stability. Enhancing the urban flood resilience (UFR) has required a new solution to cope with urban flood disasters. In this study, taking Yingtan city as an example, a system of indicators for evaluating UFR was constructed, with 17 representative indicators, comprising three subsystems: socio-economic, ecological and infrastructural. A hybrid (FAHP-EWM)-TOPSIS model was applied, to develop an index-based measurement to compare and evaluate UFR, and Gray Relational Analysis (GRA) was used to discover the main factors affecting UFR. In addition, the natural discontinuous method was innovatively used to divide the UFR grade interval into levels, and the grade change was evaluated based on the TOPSIS method. The results showed that (1) from 2010 to 2022, the UFR in Yingtan City increased by 80.69%, and the factors affecting UFR are highly correlated with urban infrastructure development. However, the ecological resilience in the subsystem showed a fluctuating downward trend, because of the influence of the surface area of lakes and rivers. (2) The grades of UFR for Yingtan City increased from Level III (2010 and 2016) to Level IV (2022), with local financial expenditures and the age structure of the population being the main factors currently limiting the development of UFR. The study provides a theoretical basis for the construction of an indicator system for assessing the UFR of Yingtan and proposes practical improvement directions for UFR.

There are rich proper noun derivative semantic relationships in geographic entity vector data. The identification of traditional proper noun derivative semantic relationships usually requires toponym researchers to examine the source of derived toponyms by consulting a large number of toponym documents. This method cannot adapt to the mining of proper noun derivative semantic relationships for large-scale geographical entity. In this regard, this paper proposes a method of identifying the proper noun derivative semantic relationships for geographical entity by combining prompt learning and XGBoost model. This method transforms the extraction problem of proper noun derivative semantic relationships into a binary classification problem by analyzing the characteristics of the derivation relationship of toponyms in the form of toponyms and the spatial and temporal distribution of geography. Firstly, prompt learning and gaussian mixture model are used to extract and cluster the derivation words of toponyms, so as to realize the recognition of derived toponyms collections. Then, enumerate the possible proper noun derivations in the toponym set, and then the XGBoost model is used to identify the semantic relations derived from the proper noun of geographical entities. In the experiment, the recognition method of proper noun derivative semantic relationships based on prompt learning and XGBoost proposed in this paper has achieved good relationship recognition effect in the experiment. This method has certain practical value in the fields of toponym management and toponym translation.

We suppose that a Cooper pair (CP) will experience a damping force exerted by the condensed matter. A Langevin equation of a CP in two dimensional condensed matter is established. Following a method similar to Nelson's stochastic mechanics, generalized Schr\"{o}dinger equation of a CP in condensed matter is derived. If the CPs move with a constant velocity, then the corresponding direct current (DC) electrical conductivity can be calculated. Therefore, a Drude like formula of resistivity of CPs is derived. We suppose that the damping coefficient of CPs in two dimensional cuprate superconductors is a linear function of temperature. Then the resistivity and scattering rate of CPs turn out to be also linear-in-temperature. The origin of linear-in-temperature resistivity and Planckian dissipation in cuprate superconductors may be the linear temperature dependence of the damping coefficient of CPs.

Carbon dioxide (CO2)miscible fracturing huff and puff technology plays a pivotal role in enhancing the recovery rate of crude oil, particularly in reservoirs with challenging physical properties, strong water sensitivity, high injection pressure, and complex water injection dynamics. In this study, the oil-increasing mechanism and huff and puff effect of CO2 miscible fracturing fluid are investigated through a comprehensive experimental approach. Specifically, experiments on PVT gas injection expansion, minimum miscible pressure, and CO2 miscible fracturing huff and puff are conducted on the G fault block reservoir in J Oilfield. The findings demonstrate that injecting CO2 into reservoirs leads to oil volume expansion, viscosity reduction, and saturation pressure increase. Additionally, the inclusion of solubilizers and viscosity reducers further enhances the efficiency of crude oil extraction. Solubilizers not only improve oil recovery but also reduce the minimum miscible pressure required for effective CO2 dispersion. Furthermore, the shut-in time, permeability, and huff and puff method all have considerable impacts on huff and puff recovery rate. This study offers valuable technical insights, supporting the application of CO2 miscible fracturing huff and puff technology to enhance oil recovery in low-permeability reservoirs.

Autoimmune kidney diseases arise from a loss of tolerance to antigens, often with unclear triggers. In this review we explore the role of the gut microbiome and how disease, diet and therapy can alter the gut microbiota consortium. Perturbations in the gut microbiota may induce the translocation of microbiota-derived inflammatory molecules such as liposaccharide (LPS) and other toxins systemically by penetrating the gut epithelial barrier. Once in the blood stream these pro-inflammatory mediators activate immune cells which release pro-inflammatory molecules many of which are antigens in autoimmune diseases. The ratio of gut bacteria Bacteroidetes:Firmicutes is associated with worse outcomes in multiple autoimmune kidney diseases including Lupus nephritis, MPO-ANCA vasculitis and Goodpasture’s syndrome. Therapies which enhance short chain fatty acid (SCFA)-producing bacteria in the gut have powerful therapeutic potential. Dietary fibre is fermented by gut bacteria which in turn release SCFAs which protect the gut barrier as well as modulate immune responses towards a tolerogenic anti-inflammatory state. Herein, we describe where the current field of research is and the strategies to harness the gut microbiome as potential therapy.

An eight-sided prism sample, obtained from a hot-rolled AZ31 magnesium alloy sheet, was compressed at room temperature along the transverse direction to investigate the influence of local strain on twinning behavior using electron backscatter diffraction (EBSD) measurements. The octagonal surface of the sample was divided into distinct regions based on hardness distribution and metallographic observations. The EBSD maps of different areas show significant variations, suggesting that these areas experienced different local strain conditions. The EBSD measurements provided detailed data from three distinct regions, with the strain state known in two regions and uncertain in the third. The combined analysis of the Schmid factor (SF) and the strain compatibility factor (m') was employed to study twin variant selection. The comparison of twinning behavior in different regions showed that the strain compatibility factor offered a more comprehensive explanation for the observed twinning pattern in the region with local shear strain.

This study introduces a novel predictive model for atomization droplet size, developed through comprehensive data collected under elevated temperature and pressure conditions using a twin swirl airblast nozzle. The model, grounded in flow instability theory, has been meticulously parameterized using the Particle Swarm Optimization (PSO) algorithm. Through rigorous analysis, including analysis of variance (ANOVA), the model has demonstrated robust reliability and precision, with a maximum relative error of 19.3% and an average relative error of 6.8%. Compared to the classical atomization model by Rizkalla and Lefebvre, this model leverages theoretical insights and incorporates a range of interacting variables, enhancing its applicability and accuracy. Spearman correlation analysis reveals that air pressure and the air pressure drop ratio significantly negatively impact droplet size, whereas the fuel-air ratio (FAR) shows a positive correlation. Experimental validation at ambient conditions shows that the model is applicable with a reliability threshold of ≥0.13 and highlight the predominance of the pressure swirl mechanism over aerodynamic atomization at higher fuel flow rates (q > 1.25 kg/h). This research effectively bridges theoretical and practical perspectives, offering critical insights for the optimization of airblast nozzle design.

It is imperative to conduct experimental studies on the seepage behavior of gas-bearing coal under cyclic dynamic loading conditions. This paper focuses on the evolution of coal permeability under the combined effects of dynamic loading, static loading, and gas adsorption. The principal conclusions are as follows. (1) As the frequency and amplitude of dynamic loading increase, the development of pore and fissure structures within the coal body becomes increasingly pronounced during dynamic loading cycles, resulting in a gradual rise in permeability. Notably, as the coal approaches its yielding stage, the permeability can increase by up to 47%. (2) The permeability curve is divided into four regions: the compaction reduction zone, the oscillation zone, the gradual recovery zone, and the abrupt increase zone of failure. Ultimately, in the failure phase, the permeability surges dramatically, potentially reaching 4 to 5 times the initial permeability. (3) When the static load stage is constant, the rate of change in permeability of the coal under dynamic loading decreases with increasing adsorption amount. When the adsorption amount is constant, the rate of change in permeability of the coal under dynamic loading increases with the increase in static load stress stage, with the maximum increase reaching 75.2%. It can be concluded from the rate of change in permeability and the dynamic loading sensitivity coefficient that the permeability of the coal is highly sensitive to cyclic dynamic loading, with increased sensitivity associated with larger static load stages and decreased sensitivity with greater adsorption amounts.

Color image binarization plays a pivotal role in image preprocessing work, significantly impacting subsequent tasks, particularly in text recognition. This paper concentrates on Document Image Binarization (DIB), aiming to separate a image into foreground (text) and background (non-text content). Through a thorough analysis of conventional and deep learning-based approaches, we conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields pre- and post-network training to underscore the Transformer model's advantages. Subsequently, we introduce a lightweight model based on the U-Net structure, enhanced with the Mobile ViT module to better capture global information features in document images. Given its adeptness at learning both local and global features, our proposed model exhibits superior performance on two standard datasets (DIBCO2012 and DIBCO2017) compared to state-of-the-art methods. Notably, our proposed DIB method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing. Moreover, its parameter count is less than a quarter of the HIP'23 model, which achives best results on three datasets(DIBCO2012, DIBCO2017 and DIBCO2018). Finally, two sets of ablation experiments were conducted to verify the effectiveness of the proposed binarization model.

We introduce derangetropy, a novel functional measure designed to characterize the dynamics of information within probability distributions. Unlike scalar measures such as Shannon entropy, derangetropy offers a functional representation that captures the dispersion of information across the entire support of a distribution. By incorporating self-referential and periodic properties, it provides deeper insights into information dynamics governed by differential equations and equilibrium states. Through combinatorial justifications and empirical analysis, we demonstrate the utility of derangetropy in depicting distribution behavior and evolution, providing a new tool for analyzing complex and hierarchical systems in information theory.

The carbonyl iron particles were dispersed in a polychloroprene rubber (CR) matrix under an external magnetic field for practical application as microwave absorption composites film. The film prepared under external magnetic field with a thickness of only 0.54 mm showed least reflection loss of -15.98 dB and the reflection loss value less than -10.0 dB over the frequency range of 11.4~14.8 GHz. In comparison with the microwave absorption properties of calculation by transmission line theory based on the tested relative complex permittivity and permeability and film prepared by general route without external magnetic field, the film made with external magnetic field exhibited more excellent microwave absorption properties, strongly depending on the increment of anisotropy and rearrangement of magnetic particles. The results indicated the composite film made under external magnetic field have excellent microwave absorption properties, which suggest that the composites thin film could be used as a thinner and lighter microwave absorber.