The attributes of electroplating as a low-cost, simple, scalable and manufacturable semiconductor deposition technique for the fabrication of large-area and nanotechnology-based device applications are discussed. These strengths of electrodeposition are buttressed experimentally using techniques such as X-ray diffraction, Ultraviolet-visible spectroscopy, Scanning electron microscopy, Atomic force microscopy, Energy-dispersive X-ray spectroscopy and photoelectrochemical cell studies. Based on the structural, morphological, compositional optical, and electronic properties evaluated results, it is evident that electroplating possesses the capabilities of producing high-quality semiconductors usable in producing excellent devices. In this paper, we will describe the progress of electroplating technique mainly for the deposition of semiconductor thin film materials, their treatment processes and fabrication of solar cells.

The performance in terms of minimal Bayes’ error probability for detection of a high-dimensional random tensor is a fundamental under-studied difficult problem. In this work, we consider two Signal to Noise Ratio (SNR)-based detection problems of interest. Under the alternative hypothesis, i.e., for a non-zero SNR, the observed signals are either a noisy rank-R tensor admitting a Q-order Canonical Polyadic Decomposition (CPD) with large factors of size Nq  R, i.e, for 1  q  Q, where R, Nq ! ¥ with R1/q/Nq converge towards a finite constant or a noisy tensor admitting TucKer Decomposition (TKD) of multilinear (M1, . . . ,MQ)-rank with large factors of size Nq  Mq, i.e, for 1  q  Q, where Nq,Mq ! ¥ with Mq/Nq converge towards a finite constant. The detection of the random entries (coefficients) of the core tensor in the CPD/TKD is hard to study since the exact derivation of the error probability is mathematically intractable. To circumvent this technical difficulty, the Chernoff Upper Bound (CUB) for larger SNR and the Fisher information at low SNR are derived and studied, based on information geometry theory. The tightest CUB is reached for the value minimizing the error exponent, denoted by s?. In general, due to the asymmetry of the s-divergence, the Bhattacharyya Upper Bound (BUB) (that is, the Chernoff Information calculated at s? = 1/2) can not solve this problem effectively. As a consequence, we rely on a costly numerical optimization strategy to find s?. However, thanks to powerful random matrix theory tools, a simple analytical expression of s? is provided with respect to the Signal to Noise Ratio (SNR) in the two schemes considered. A main conclusion of this work is that the BUB is the tightest bound at low SNRs. This property is, however, no longer true for higher SNRs.

With increasing global attention on carbon emission control, the carbon market has become an important tool for carbon emission regulation. As an important part of energy trading, the electricity market is closely related to the carbon market. Given the relative maturity of the EU carbon market and electricity market, this paper identifies key points of market volatility through the Coupling coordination model analysis, and uses the GPT large model analysis to analyze policy factors that have a greater impact on carbon market and electricity market transactions. The research results show that market-regulating policies and expectation management policies have the greatest impact on the degree of coupling between the carbon and electricity markets. The impact of secondary policies such as international cooperation policies and structural adjustment policies is relatively insignificant. Although the development of China's carbon market faces many challenges, reasonable policy interventions are expected to achieve significant results in the domestic carbon market. Finally, this paper also draws on analogy analysis to draw corresponding implications for China from the EU's carbon-electricity coupling policy promulgation.

Securing funding is a critical yet complex task for organizations and individuals. This study presents an innovative chatbot designed to streamline the process using advanced Natural Language Processing (NLP) techniques and, specifically, a Retrieval-Augmented Generation (RAG) pipeline optimized for real-world applications. Our chatbot assists users in identifying suitable public tenders for financial support through natural language queries and a comprehensive public data database. The chatbot operates in a two-stage interaction model, initially providing summarized tender information for exploratory brainstorming, followed by detailed data upon user selection. A custom filtering mechanism ensures that the user interface elements responsible for swapping the interaction stages are consistent with the responses generated by the conversational agent. Human-evaluation tests demonstrated an average accuracy of 90.4% in document retrieval, with an average of 2.11 interactions required to find a specific tender. User satisfaction, rated on a scale of 1 to 5, averaged 3.14 (±1.73), indicating generally positive user experience with room for improvement. This approach addresses challenges of relevance, accuracy, and conversational flow, resulting in a reliable chatbot that simplifies the process of finding funding opportunities.

In the rapidly evolving landscape of AI-supported programming education, there's a growing interest in leveraging such tools to support students helping about what good code makes out. This study investigates the impact of ChatGPT on code quality among part-time undergraduate students enrolled in introductory Java programming courses. With no prior Java experience, students from two separate groups completed identical programming exercises emphasizing coding conventions and code quality. Utilizing static code analysis tools, we assessed adherence to a common coding convention ruleset and calculated cyclomatic and cognitive complexity metrics for submitted code. Our comparative analysis highlights significant improvements in code quality for the ChatGPT-assisted group (treatment group), with marked reductions in rule violations and both cyclomatic and cognitive complexities. Specifically, the treatment group demonstrated greater adherence to coding standards, with fewer violations across several rules, and produced code with lower complexity. These results suggest that ChatGPT can be a valuable tool in programming education, aiding students in writing cleaner, less complex code and better adhering to coding conventions. However, the study's limitations, including the small sample size and the novice status of the participants, necessitate further research with larger, more diverse populations and in different educational contexts.

In the context of rapid urbanization, the spatiotemporal patterns of socio-economic exposed to high seismic hazard in large cities lacks quantitative assessment, thus, it is difficult to understand the evolution of earthquake catastrophe risk there. In this paper, exposure change rate, and absolute and relatively exposure indices were proposed to quantitatively study the changes of spatiotemporal exposure to high seismic hazard in urban areas of large cities (i.e., permanent residents in urban areas more than 1 million) in Chinese mainland, using a set of multi-temporal population, GDP, and built-up datasets and a seismic hazard zoning map. The results show that, 64 out of 78 large cities are entirely or partially located in high seismically hazardous zones. Although the urban areas of large cities in high seismically hazardous zones (HSHUAs) cover only 1.73% (167177 km²) of mainland China, their share of population, GDP, and built-up land amounted to 18.14%, 30.24% and 24.55%, respectively, in 2015. Unprecedented urbanization has led to a dramatic exposure growth in HSHUAs between 1990 and 2015. The population, GDP, and built-up land in HSHUAs increased by 110 million people (with an average annual growth rate of 2.37%), 273.71 trillion USD(average annual growth rate of 11.15%), and 10593 km2 (average annual growth rate of 2.02%), respectively. HSHUAs experienced much faster population and GDP growth than the whole country, while relatively slower growth in built-up land. Exposure growth in HSHUAs also was varied for different types of large cities. Megacities had a higher proportion of increased GDP and population in HSHUAs, induced by industry and population agglomeration. Whereas, Type II large cities had a higher proportion of increased built-up land in HSHUAs, driven mainly by urban sprawl. Our analysis indicates that, for large cities under rapid urbanization, it is an urgent need to strengthen resilient urban planning, renewal and construction.

Today, LLMs are not good at personalization providing recommendation. They advise physicians and financial advisors to ask professionals in respective fields for help, even having user information available. Answering questions of software professionals, LLM needs to deliver in-depth answers with codes or algorithms, whereas for professionals in other fields would need definitions and main concepts. The intent of this chapter is to make LLM answer tailored to the needs of users, taking into account available information about them. To do that, we need to generalize available information about a person like her health record, maintaining the privacy of this person. We rely on meta-learning techniques to design a LLM prompt to produce a personalization prompt to obtain a suitable relevant information. Such “meta-prompt” is produced by generalization operation applied to available documents for the user. These documents need to be de-identified so that they are sufficient for personalization on one hand and will maintain user privacy on the other hand. The second neuro-symbolic technique to support personalization is abductive reasoning, acting in parallel to LLM fine-tuning. Traditional recommendation and personalization techniques as well as modern, deep learning – based are presented, and the comparison is drawn to the proposed approach. We also share the evaluation and comparative analyses of these approaches. We consider an example for how to build personalization LLM systems coming from Langchain platform. We will explore how to construct chains to form a personalization profile for a user and apply it to user search and recommendation requests.

Rheometric investigations facilitate the rheological analysis of cementitious suspensions. However, the choice of the rheometric device and raw data conversion affect the results. Contrary to small gap rheometry, where the flow data shear stress

A text obtained by a Large Language Model (LLM) such as GPT4 usually has issues in terms of incorrectness and hallucinations. We build a fact-checking system 'Truth-O-Meter' which identifies wrong facts, comparing the generation results with the web and other sources of information, and suggests corrections. Text mining and web mining techniques are leveraged to identify correct corresponding sentences; also, the syntactic and semantic generalization procedure adopted to the content improvement task. To handle inconsistent sources while fact-checking, we rely on an argumentation analysis in the form of defeasible logic programming. We compare our fact checking engine with competitive approach based on reinforcement learning on top of LLM or token-based hallucination detection. It is observed that LLM content can be substantially improved for factual correctness and meaningfulness.

Wind energy is a rapidly expanding renewable energy technique. Wind farm developers need to understand the interaction between wind farms and the atmospheric flow over complex terrain. Large-eddy simulations provide valuable data for gaining further insight into the impact of rough topography on wind-farm performance. In this research, we investigate the influence of spatial heterogeneity on wind turbine performance. We conducted numerical simulations of a 12×5 wind-turbine array on various rough topographies. First, we evaluated our LES method through mesh convergence analysis, using mean vertical profiles, vertical friction velocity, and resolved and subgrid-scale kinetic energy. Next, we analyze the effects of surface roughness and dispersive stresses on the performance of fully developed large wind farms. Our results demonstrate that the ground roughness element’s flow resistance boosts large wind-farm power production by almost 68% in fully aerodynamic rough surface compared to flat terrain. Dispersive stress analysis revealed that the primary degree of spatial heterogeneity in the wind farm is in the streamwise direction, which is the “wake-occupied” region, and the relative contribution of dispersive shear stress is almost 45% to the overall drag. We also observed that the power performance of the wind farm in complex terrain outperforms the drag. Our study has implications for improving the design of wind turbines and wind farms in complex terrain to increase their efficiency and energy output.

This article examines the wavelet transforms and the machine learning methods in accelerating large eddy simulations of turbulent flows. An overarching challenge of large eddy simulations is to accurately represent the cascade of kinetic energy across the cut-off length scale. Taylor hypothesis suggests that the energy cascade occurs through the process of vortex stretching. However, Richardson hypothesis assumes the self-amplification of the strain field and a hierarchical break down of large eddies lead the energy cascade. The large eddy simulations typically employs the self-amplification of the strain in formulating subgrid models. However, several studies also proposed subgrid models based on vortex stretching. The wavelet-based coherent vortex simulation of turbulence directly accounts for vortex stretching in overall forward scatter of energy, while allowing local backscatter. The wavelet-based large eddy simulation adapts the grid to capture the creation of small-scale eddies, while adopting subgrid models based on the self-amplification of strain. The advancement of artificial intelligence in turbulence modelling is currently evolving around accelerating the numerical simulations of turbulent flow. However, there is a clear connection between the application of wavelet transfroms and neural networks for directly solving the Navier-Stokes equation, indicating some potential benefits of wavelet methods over the neural networks.

This study investigates the possible mechanisms related to the wide range of relative humidity in cirrus clouds (RHi). Under the closed adiabatic assumption, the impacts of vertical motion and ice crystal deposition/sublimation on RHi are investigated through in situ observations and parcel model simulations. Vertical motion is an active external force that changes the RHi, and ice crystal deposition/sublimation plays a role in mitigating the change in RHi. They are the two most important mechanisms involved in controlling the RHi fluctuation during cirrus evolution and could well explain the wide range of RHi in wave-related cirrus clouds. Furthermore, a comparison of cloud statistical characteristics from both observations and simulations shows that a very low value (e.g., 0.001) for the water vapor ice deposition coefficient is highly unlikely.

In large eddy simulation (LES) of turbulent flows, the most critical dynamical processes to be considered by dynamic subgrid models to account for an average cascade of kinetic energy from the largest to the smallest scales of the flow is not fully clear. Furthermore, evidence of vortex stretching being the primary mechanism of the cascade is not out of the question. In this article, we study some essential statistical characteristics of vortex stretching and its role in dynamic approaches of modeling subgrid-scale turbulence. We have compared the interaction of subgrid stresses with the filtered quantities among four models using invariants of the velocity gradient tensor. This technique is a single unified approach to studying a wide range of length scales in the turbulent flow. In addition, it also provides a rational basis for the statistical characteristics a subgrid model must serve in physical space to ensure an appropriate cascade of kinetic energy. Results indicate that the stretching mechanism extracts energy from the large-scale straining motion and passes it onto small-scale stretched vortices.

Diffuse large B cell lymphoma (DLBCL), the most frequent type of non-Hodgkin lymphoma in adulthood, remains challenging clinical issues. Despite enhanced remission rates can be achieved, there are one-third of patients who will not respond to current treatment or will relapse with resistant disease, necessitating ongoing efforts on effective treatment strategies and agents. The vacuole H+-ATPase inhibitor bafilomycin A1 is broadly used to block late stage of autophagy flux at high concentration. In this study, we show that, to our surprise, bafilomycin A1 effectively inhibited and killed DLBCL cells at nanomolar concentrations (5nM). Bafilomycin A1 targeted cell cycle regulators cyclin D1 and cyclin E2 to induce cell cycle arrest in G0/G1 phase. Meanwhile, it induced caspase-dependent apoptosis with concomitant cleaved caspase-3 and Parp. Furthermore, we found that bafilomycin A1 inhibited autophagy flux at both early and late stages of the autophagy flux through activating ERK and mammalian target of rapamycin signaling, as well as by inhibiting the degradation of autolysosomes. We speculated that bafilomycin A1 as autophagy inhibitor might enhance the effect of DLBCL chemotherapeutic drug rituximab. Accordingly, our results provided evidence that the combination of bafilomycin A1 with rituximab enhanced the inhibition of DLBCL cells notably. Taken together, our data suggest that bafilomycin A1 may be a promising candidate drug in the therapy of diffuse large B cell lymphoma.

Huge filaments with scales from several hundred megaparsecs to gigaparsecs are detected in the distribution of galaxies and clusters, quasars, gamma-bursters. The hypothesis on the nature of the huge filaments as regions of space-time deformation is proposed. An anisotropic deformation of the local region is described by the strain tensor, it depends on the velocities of matter. Galaxies get an extra velocity in the region, which leads to the formation of filamentary structures. The class of exact solution of the GR equations is constructed by introducing the special definition of the Christoffel symbols as function of the velocity of matter. With a definition of these symbols, the motion matter equation turns into identity. For the sake of simplicity, an ideal fluid is considered.

In this work a modified version of the well-known Simple Water Balance (SWB) model, comprising here three parameters instead of one, was used. Although simple, the model was tested in large-scale river basins in east-central Greece, upstream two hydrometric stations. The available historic runoff records comprised 19 hydrologic years each, on a monthly basis. Thirteen among them were used for calibrating the model, whereas the six subsequent, for validating it. Two different efficiency criteria were used as a measure of performance of the modified model. Their values, calculated for both calibration and validation stages, were close and relatively high. Thus, keeping in mind both the size and complexity of the river basins studied, one can conclude that the modified model, despite its simplistic concept and lumped form, fits satisfactorily the historic runoff series.

Text CAPTCHAs are crucial security measures employed on global websites to deter unauthorized intrusions. The presence of anti-attack features incorporated into text CAPTCHAs limits the effectiveness of breaking them, despite CAPTCHA recognition being an effective method for assessing their security. This study introduces a novel color augmentation technique called Variational Color Shift (VCS) to boost the recognition accuracy of different networks. VCS generates a color shift range of every input image and then resamples the image within that range to generate a new image, thus expanding the number of samples of the original dataset to improve training effectiveness. In contrast to Random Color Shift (RCS) which treats the color offsets as hyperparameters, VCS uses the estimated offsets to reparametrize the points sampling from the unit uniform distribution to reconstruct new image pixels, which makes the offsets learnable. To reduce the computational effort of VCS, we also propose two variants of VCS: Sim-VCS and Dilated-VCS. In addition, to solve the overfitting problem caused by many disturbances in CAPTCHAs, we propose an Auto Encoder (AE) based on a Large Separable Kernel Attention (AE-LSKA) to replace the convolutional module in the text CAPTCHA recognizer. This new module employs an AE) to compress the interference while expanding the receptive field using Large Separable Kernel Attention (LSKA), which reduces the impact of local interference on the model training and improves the overall perception of characters. The experimental results show that the recognition rate of the model after integrating the AE-LSKA module is improved by at least 15 percentage points on both M-CAPTCHA and P-CAPTCHA datasets. In addition, experimental results demonstrate that color augmentation using VCS is more effective in enhancing recognition.

Accurate stock market predictions following earnings reports are crucial for investors. Traditional methods, particularly classical machine learning models, struggle with these predictions because they cannot effectively process and interpret extensive textual data contained in earnings reports and often overlook nuances that influence market movements. This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates ‘base factors’, such as financial metric growth and earnings transcripts, with ‘external factors’, including recent market indices performances and analyst grades, to create a rich, supervised dataset. This comprehensive dataset enables our models to achieve superior predictive performance in terms of accuracy, weighted F1, and Matthews correlation coefficient (MCC), especially evident in the comparison with benchmarks such as GPT-4. We specifically highlight the efficacy of the llama-3-8b-Instruct-4bit model, which showcases significant improvements over baseline models. The paper also discusses the potential of expanding the output capabilities to include a ‘Hold’ option and extending the prediction horizon, aiming to accommodate various investment styles and time frames. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools.

In an era where the proliferation of large language models blurs the lines between human and machine-generated content, discerning text authenticity is paramount. This study investigates transformer-based language models—BERT, RoBERTa, and DistilBERT—in distinguishing human-written from machine-generated text. By leveraging a comprehensive corpus, including human-written text from sources such as Wikipedia, WikiHow, various news articles in different languages, and texts generated by OpenAI's GPT-2, we conduct rigorous comparative experiments. Our findings highlight the superior effectiveness of ensemble learning models over single classifiers in this critical task. This research underscores the versatility and efficacy of transformer-based methodologies for a wide range of natural language processing applications, significantly advancing text authenticity detection systems. The results demonstrate competitive performance, with the transformer-based method achieving an F-score score of 0.83 with RoBERTa-large (monolingual) and 0.70 with DistilBERT-base-uncased (multilingual).

FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method Let-Net(Large kernel and attention mechanism Network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to broaden the receptive field and incorporates depthwise convolution with residual connections to reduce parameter count. Secondly, Let-Net integrates an attention mechanism to enhance the information flows in channels and spaces for more comprehensive and distinctive FV feature extraction. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications.

Background: Although cerebral aneurysm (CA) is a defining complication of COL4A1/2-related vasculopathy, the specific factors influencing its onset remain uncertain. This study aimed to identify and analyze these factors. Methods: We described a family presenting with a novel variant in the COL4A1 gene complicated with CA. Concurrently, an exhaustive review of previously documented patients with COL4A1/2-related vasculopathy was conducted by sourcing data from PubMed, Web of Science, Google Scholar, and Ichushi databases. We compared the variant types and locations between patients with CA (positive group) and those without CA (negative group). Results: This investigation encompassed 53 COL4A1/2 variants in 76 patients. Except for one start codon variant, all the identified variants in CA were missense variants. Otherwise, CA was not associated with other clinical manifestations, such as small-vessel disease or other large-vessel abnormalities. A higher frequency of missense variants (95.5% vs 58.1%, p = 0.0035) was identified in the CA positive group. Conclusions: CA development appears to necessitate qualitative alterations in COL4A1/2, and the underlying mechanism seems independent of small vessel disease or other large vessel anomalies. Our findings suggest that a meticulous evaluation of CA is necessary when missense variants in COL4A1/2 are identified.

Semiconductors play a very important role in modern society. The improvement in performance of semiconductors is what has enabled the world to grow at such a fast pace. For decades scaling has been done through Moore’s law i.e performance improvements have been obtained due to transistor scaling where the number of transistors per unit area increase is what has contributed to the increase in performance. However, with transistor scaling reaching its limitations we need to find alternate methods to enhance silicon design to suit the computational need of modern society. To address this problem we will be looking into a few methods that enable performance scaling with a focus on analysing the most popular implementation of each in the industry.

As smoke toxicity is unregulated outside of the mass transport industry, there is no set methodology for assessing smoke toxicity of construction products, like OSB, on a large-scale. This research aims to assess the novel design of a modified ISO 9705 room corner test designed for smoke toxicity quantification. Three tests conditions will be assessed to determine if restriction of ventilation is enough to force a fire to transition to under-ventilated flaming, or if the fuel loading is much more important in affecting the fire severity and fire condition. The research found that fuel loading and test geometry has more significant impact on the fire condition than restricting the ventilation. While imposing restrictions on the test rooms ventilation did increase the equivalence ratio, it was not sufficient enough to force the transition into under-ventilated flaming. The addition of a door to the test room did not force the fire to transition to under-ventilated flaming. When doubling the fuel loading, there was sufficient fuel for the fire to transition to under-ventilated flaming. This research has provided the experimental methodologies to assess smoke toxicity at a range of ventilation conditions on a large-scale.

In this study, the generality and prediction accuracy of a generalised series model for large eddy simulation of premixed and non-premixed turbulent combustion is explored. The model is based on Taylor series expansion of the chemical source term in scalar space and implemented into OpenFOAM. The mathematical model does not depend on the combustion regimes, and has the correct limiting behaviour. The numerical error sources are outlined and analysed. The model is first applied to a piloted methane/air non-premixed jet flame (Sandia Flame D). The statistical (time-averaged and RMS) results agree well with experimental measurements, particularly with regard to the mixture fraction, velocity, temperature, and concentrations of major species CH4, CO2, H2O, and O2. The concentrations of intermediates CO and H2 are over-predicted, due to the limitations of the reduced reaction mechanism employed. Then, a Bunsen piloted flame is simulated. Most of the statistical properties of both reactive species and progress variable are well reproduced. The only major discrepancy evident is in the temperature, which is attributed to the experimental uncertainties of temperature fields in the pilot stream. These findings have demonstrated the model`s generality for both premixed and non-premixed combustion simulation, as well as the accuracy of prediction of reactive species distribution.

Diffuse large B-cell lymphoma (DLBCL) is an aggressive and biologically heterogeneous disease. Approximately 40% of patients with DLBCL will experience disease relapse or will be refractory to first line chemoimmunotherapy. In recent years there have been several new therapeutic agents approved for the treatment of relapsed/refractory (R/R) DLBCL. These agents include anti-CD19 chimeric antigen receptor T-cell therapies (CAR T-cells) and monoclonal antibodies as polatuzumab and tafasitamab. Nevertheless, despite the high efficacy of all these new therapies, there are still patients who do not respond or relapse, representing an unmet clinical need. This review describes new promising therapies that are under investigation to treat R/R DLBCL.

To address the challenges of climate change and food security, the establishment of smart farm complexes is necessary. While there have been numerous studies on the productivity and environmental control of individual greenhouses, research on greenhouse complexes is considerably limited. Conducting environmental studies during the design phase of these complexes poses financial constraints and practical limitations in terms of on-site experiments. To identify potential issues that may arise when developing large-scale greenhouse complexes, it is possible to utilize modeling techniques using Computational Fluid Dynamics (CFD) to assess environmental concerns and location issues before constructing the facilities. Consequently, simulating large-scale CFD models that incorporate multiple greenhouses and atmospheric conditions simultaneously presents significant numerical challenges. The objective of this study was to develop a guideline for verifying CFD models for a large-scale Venlo greenhouse, where acquiring field data before construction is not feasible for designing a greenhouse complex. The verification processes of the CFD models were conducted using 2D and 3D iterative simulations of a 2-hectare greenhouse model, using the improved Grid Independence Test (GIT) and wall Y+ approaches. Subsequently, the aerodynamic characteristics were analyzed in a 3D greenhouse model to access its performance when the wind direction was 90° in summer season. The findings revealed that a grid resolution of 0.8 meters and a first layer height of 0.04 meters were suitable for developing large-scale greenhouse models, resulting in a low Root Mean Square Error (RMSE) of 3.9% and a high coefficient of determination (R2) of 0.968. This process led to a significant reduction of 38% in the number of grid cells. These results will serve as design standards for large-scale greenhouses.

Currently, large cell neuroendocrine carcinoma of the lung (LCNEC) is classified as a rare lung cancer subtype, but given the high incidence of lung cancer, the overall number of cases is considerable. The pathologic diagnosis is based on the microscopic appearance of the tumor cells, the amount of intra-tumoral necrosis, the mitotic rate, and the presence of positive neuroendocrine markers identified by immunohistochemistry. Recently, a subdivision into two main categories was proposed based on mutation signatures involving the RB1, TP53, KRAS, and STK11/LKB1 genes, into SCLC-like (small cell lung cancer-like) and NSCLC-like (non-small cell lung cancer-like) LCNEC. In terms of treatment, surgery remains the best option for resectable, stage I–IIIA cases. Chemotherapy and radiotherapy have conflicting evidence. Etoposide/platinum remains the standard chemotherapy regimen. However, based on the newly proposed LCNEC subtypes, some retrospective series report better outcomes using a pathology-driven chemotherapy approach. Encouraging outcomes have also been reported for immunotherapy and targeted therapy, but the real impact of these strategies is still being determined in the absence of adequate prospective clinical trials. The current paper scrutinizes the epidemiology, reviews the reliability of pathologic diagnosis, discusses the need for molecular subtyping, and reviews the heterogeneity of treatment algorithms in LCNEC.

When there are multiple key strata in the overburden of deep coal seam and the surface subsid-ence coefficient after mining is small, it indicates that the overlying key strata fail to break com-pletely after mining. On this occasion, the stress concentration on the working face occurs easily, which in turn leads to the occurrence of dynamic disasters such as rock burst. This study adopted a comprehensive analysis method of field monitoring and numerical simulation to explore the in-fluence of key stratum on the evolution law of mining-induced stress in the working face. Dis-tributed optical fiber sensor (DOFS) and surface subsidence GNSS monitoring system were re-spectively arranged inside and at the mouth of the ground observation borehole. According to the monitoring results of strain obtained from DOFS, the height of broken stratum inside the overlying strata was obtained; according to the monitoring results of surface subsidence, the sur-face subsidence coefficient was proved to be less than 0.1, indicating that the high key stratum does not break completely, but enters a state of bending subsidence instead. In order to reveal the influence of key stratum on the mining-induced stress of working face, two 3DEC numerical models with and without key stratum were established for comparative analysis. As the numeri-cal simulation results show, when there are multiple key strata in the overburden, the stress in-fluence range and stress concentration coefficient of coal seam after mining are relatively large. The study revealed the working mechanism of rock burst accidents after large-scale mining and predicted the potential area of rock burst risk after the mining of the working face, which has been verified by field investigation. The research results are of great guiding significance for the revelation of the working mechanism of rock burst in deep mining condition and its prevention and control.

When undertaking wind assessment around buildings using large eddy simulation (LES), the implementation of the integral length scale at the inlet for inflow generation is controversial, as real atmospheric length scales require huge computational domains. While length scales significantly influence inflow generation in the domain, their effect on the downstream flow field has not yet, been investigated. In this paper, we validate the effectiveness and accuracy of implementing a reduced turbulence integral length scale for inflow generation in LES results at the rooftop of low-rise buildings and develop a technique to estimate the real local length scales using simulation results. We measure the wind locally and calculate the turbulence length scales from the energy spectrum of the wind data and simulation data. According to these results, there is an excellent agreement between the length scale from simulation and measurement when they are scaled with their corresponding freestream/inlet value. These results indicate that a reduced integral length scale can be safely used for LES to provide a reliable prediction of the energy spectrum as well as the length scales around complex geometries. The simulation results were confidently employed to obtain the best location for a wind turbine installation on low-rise buildings.

Large ruminant production in developing countries is inefficient with low growth rates and likely high greenhouse gas emissions (GHGe) per unit of meat or milk produced. Trials conducted in Lao PDR from 2017-2020, studied ad-libitum supplementation for 12 weeks with 20kg high quality molasses nutrient blocks (MNB; Four Seasons Pty Ltd, Brisbane, Australia), that were either: non-medicated (NMB); fenbendazole-medicated (FMB5, Panacur100®, Coopers Australia, @ 5g/kg); triclabendazole-medicated (TMB5 or TMB10, Fasinex®, Novartis Australia, @ 5g/kg or @ 10g/kg, respectively); or formulated with urea (UMB8 or UMB10 @ 8% or 10% urea, respectively). Average daily gains (ADG) were determined for access to all MNB’s and compared with access to NMB’s, no supplementation, or previously determined free-grazing baseline ADG’s (55–84g in cattle; 92–106g in buffalo). ADGs were significantly improved following access to all MNBs for periods up to 12wks. ADGs following access to UMB8 or NMB were calculated for three age cohorts of cattle: young calves <8m (238-298g), growing cattle (143-214g) and lactating cows (179-191g). Modelling using IPCC Inventory software model V 2.69 of published data demonstrated a conservative net abatement of 350kg CO2e was achievable over a 200 day feeding period. An additional trial of Emissions control blocks (EMB’s, n = 200) distributed to farmers (n = 60) and two educational institutions, was conducted. Consumption rates (156g/day) and farmer and institutional acceptance of EMB’s were similar to published findings with other MNB’s, confirming all MNB’s and EMB’s improved animal productivity and body condition score, with healthier animals that were easier to manage. Modelling of changes in GHGe intensity identified an abatement of 470kg CO2e per EMB consumed, delivering a total project emissions abatement of 94t CO2e. Provision of MNBs and EMBs in smallholder large ruminant production systems is likely to achieve impressive abatement of GHGe due to improved efficiency of rumen fermentation and productivity.

Electron-hole recombination and narrow range utilization of sunlight limit the photocatalytic efficiency of TiO2. We synthesized a carbon dots (CDs) modified TiO2 nanoparticles (NPs) with flower-like mesoporous structure, i.e., porous TiO2/CDs nanoflowers. Among such hybrid parti-cles, CDs worked as photosensitizers for the mesoporous TiO2 and enabled the resultant TiO2/CDs nanoflowers with a wide-range light absorption. Rhodamine B (Rh-B) was employed as a model organic pollutant to investigate the photocatalytic activity of the TiO2/CDs nanoflowers. The results demonstrated that the decoration of CDs on both TiO2 nanoflowers and P25 NPs enabled a significant improvement of the photocatalytic degradation efficiency compared with the pristine TiO2. TiO2/CDs nanoflowers with porous structure and larger surface areas than P25 showed a higher efficiency owing to prevent local aggregation of carbon materials. All the results revealed that the introduced CDs and the unique mesoporous structure, large surface areas and loads of pore channels of the prepared TiO2 NPs played important roles in the enhancement of the photocatalytic efficiency of the TiO2/CDs hybrid nanoflowers. Such TiO2/CDs composite NPs also opens a door for photodegradation, photocatalytic water splitting and enhanced solar sun-light as light source.

The late universe contains a wealth of information about fundamental physics and gravity, wrapped up in non-Gaussian fields. To make use of as much information as possible it is necessary to go beyond two-point statistics. Rather than going to higher order N-point correlation functions, we demonstrate that the probability distribution function (PDF) of spheres in the matter field (a one-point function) already contains a significant amount of this non-Gaussian information. The matter PDF dissects different density environments which are lumped together in two-point statistics, making it particularly useful for probing modifications of gravity or expansion history. Our approach in Cataneo et. al. 2021 extends the success of Large Deviation Theory for predicting the matter PDF in ΛCDM in these “extended” cosmologies. A Fisher forecast demonstrates the information content in the matter PDF via constraints for a Euclid-like survey volume combining the 3D matter PDF with the 3D matter power spectrum. Adding the matter PDF halves the uncertainties on parameters in an evolving dark energy model, relative to the power spectrum alone. Additionally, the matter PDF contains enough non-linear information to substantially increase the detection significance of departures from General Relativity, with improvements up to six times the power spectrum alone. This analysis demonstrates that the matter PDF is a promising non-Gaussian statistic for extracting cosmological information, particularly for beyond ΛCDM models.

In the early 1980s it was well established that Leray solutions of the unforced Navier-Stokes equations in Rn decay in energy norm for large time. With the works of T. Miyakawa, M. Schonbek and others it is now known that the energy decay rate cannot in general be any faster than t^-(n+2)/4 and is typically much slower. In contrast, we show in this note that, given an arbitrary Leray solution u(.,t), the difference of any two Stokes approximations to the Navier-Stokes flow u(.,t) will always decay at least as fast as t^-(n+2)/4, no matter how slow the decay of || u(.,t) ||_L2 might happen to be.

Risk assessment management have been a hot topic for the researchers since a very long time. Software risk management is an important part of project management as it contains the identification, analysis, estimation and monitoring of different risks present in the system. This helps developers in decision making while assessing the problems that could arise in the software systems. Risk management is very complex in large scale system as these systems have very complex development. The paper describes risk management techniques for large scale system. Furthermore we have provided a detailed comparative analysis of these techniques with commonly identified risks in software systems and have provided a systematic order for risk management process to ensure risk mitigation.

The technique of Faraday tomography is a key tool for the study of magnetised plasmas in the new era of broadband radio polarisation observations. In particular, observations at metre-wavelengths provide significantly better Faraday depth accuracies compared to traditional cm-wavelength observations. However, the effect of Faraday depolarisation makes the polarised signal very challenging to detect at metre wavelengths (MHz frequencies). In this work, Faraday tomography is used to characterise the Faraday rotation properties of polarised sources found in data from the LOFAR Two-Metre Sky Survey (LoTSS). Of the 76 extragalactic polarised sources analysed here, we find that all host a radio-loud AGN. The majority of the sources (∼64%) are large FRII radio galaxies with a median projected linear size of 710 kpc and median radio luminosity at 144 MHz of 4 × 10^26 W/Hz. In several cases, both hotspots are detected in polarisation at an angular resolution of ∼20". One such case allowed a study of intergalactic magnetic fields on scales of 3.4 Mpc. Other detected source types include an FRI radio galaxy and at least 8 blazars. Most sources display simple Faraday spectra, however, we highlight one blazar that displays a complex Faraday spectrum, with two close peaks in the Faraday dispersion function.

To date, several different approaches are available to study sediment dynamics at reach or watershed scale, based on very different hypothesis. One of such assumptions, the so-called “morphodynamic equilibrium hypothesis” is becoming little unpopular for its embedded simplifications. The aim of this work is to demonstrate how this approach proves yet effective in modelling landscape morphodynamics at the watershed scale, for what concerns the longitudinal profile of a river and the sedimentary aspects. The application of a 1-D model based on the equilibrium hypothesis has been implemented for several large rivers worldwide. Geomorphological parameters have been analysed, which describe the evolution of longitudinal profile (concavity) and sediments characteristics (aggrading and fining), and the results show a reasonably good correspondence with qualitative estimation of the same parameters. At the scale of analysis and for the chosen systems, which show high inertia to geomorphological changes likely owing to their longitudinal extension, the model can detect where the present conditions reflect a big disturbance to the “natural equilibrium” thus allowing water managers to identify present issues to be addressed.

Biologists often set out to find relevant data in an ever changing landscape of interesting databases. While leading journals publish descriptions of databases, they are usually not current and do not frequently update the list to discard defunct or poor quality database. These indices usually include databases that are proactively requested to be included by their authors. The challenge for individual biologists then is to discover, explore and select databases of interest from a large unorganized collection and effectively use them in analysis without too large an investment. The FAIR data principle being advocated to improve searching, finding, accessing, and inter-operating among these diverse information sources to increase usability is proving to be a difficult proposition and consequently, a large number of data sources are not FAIR compliant. Since linked open data does not guarantee FAIRness, biologists are now left on a solo hunt for information on the open network. In this paper, we propose SoDa, for intelligent data foraging on the internet by biologists. SoDa helps biologists discover resources based on analysis requirements, generate resource access plans, and stores cleaned data and knowledge for community use. A secondary search index is also supported for community members to find archived information in a convenient way.

Numerical simulations of the sub-critical flow past a circular cylinder at the Reynolds number, Re=130,000 are performed using two numerical platforms, commercial, Ansys Fluent and open source, OpenFoam (finite volume method and large eddy simulation based on a differential equation for the sub-grid kinetic energy). An overview of the available experimental data and similar large eddy simulation studies is presented. A detailed analysis of all accumulated data demonstrates satisfactory agreement between them with a dispersion of the order of 10% (for the main integral flow parameters). A detailed comparison of the results obtained by single- and double-precision numerical methods using Ansys Fluent did not reveal any noticeable discrepancies for the integral and local flow parameters, as well as spectral characteristics. It is shown that the behavior of the dynamic system of the fluid dynamic equations computed with single precision is stable by Lyapunov and does not lead to any loss of accuracy. The reconstructed attractors of the dynamic systems in the phase space are limited by an ellipsoid.

While Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs), it also presents challenges that can affect model accuracy and performance. Practical applications show that RAG can mask the intrinsic capabilities of LLMs. Firstly, LLMs may become overly dependent on external retrieval, underutilizing their own knowledge and inference abilities, which can reduce responsiveness. Secondly, RAG techniques might introduce irrelevant or low-quality information, adding noise to the LLM. This can disrupt the normal generation process, leading to inefficient and low-quality content, especially when dealing with complex problems. This paper proposes a RAG framework that uses reflective tags to control retrieval. This framework evaluates retrieved documents in parallel and incorporates the Chain of Thought (CoT) technique for step-by-step content generation. The model selects the highest quality and most accurate content for final generation. The main contributions include: 1) Reducing the hallucination problem by selectively utilizing high-scoring document, 2) Enhancing real-time performance through timely external database retrieval, and 3) Minimizing negative impacts by filtering out irrelevant or unreliable information through parallel content generation and reflective tagging. These advancements aim to optimize the integration of retrieval mechanisms with LLMs, ensuring high-quality and reliable outputs.

The study objective is to evaluate the reliability of the Göttingen Mini-pig (G-MP) as a large animal model for preclinical studies addressing articular cartilage treatment procedures. Second this work compared the G-MP to the domestic pig (DP) in terms of surgical anatomy, postoperative management, and follow-up period possibilities and challenges. Six G-MP and four DP underwent a two-stage surgical procedure including cartilage harvesting using a superolateral approach followed by cartilage implantation using a medial parapatellar tendon approach. The superolateral approach exposed 11% (SD ± 5) of the entire trochlea in G-MP and 20% in the DP model. The medial parapatellar tendon approach in G-MP exposed 63% (SD ± 4) of the trochlear surface and exposed 34% (SD ± 13) of the medial femoral condyle, allowing 4 trochlear lesions of 6mm of diameter and one to the medial condyle in 4 out of 6 G-MP and in all DPs. Cartilage thickness was under 1mm in G-MP compared to more than 2mm in DP. Weight-progression was + 4 kg/week in DP and + 0.2 kg/week for G-MP. The G-MP is a feasible model for cartilage research, with adequate access to the joint via the dual approach. Thus enabling 4-5 lesions of 6mm, however a thinner cartilage in the G-MP needs to be considered.

This paper integrates carrier manifolds from control theory and hybrid systems modeling to develop a novel mathematical framework for Artificial General Intelligence (AGI). Carrier manifolds represent the state space of complex AGI systems, while hybrid systems model discrete and continuous dynamics together. The proposed architecture embeds carrier manifold into GPTProX, a hybrid AI solver combining conversational AI, logic programming, and optimization.

To conduct high-precision and high-resolution numerical simulation of complex flow structures in turbomachinery, a high-order finite volume weighted essentially non-oscillatory (WENO) scheme for the large eddy simulation (LES) is improved and embedded into the three-dimensional viscous unsteady CFD solver NUAA-Turbo. Firstly, the spectral characteristics and unsteady convergence of the improved WENO scheme are studied. Compared with the classical WENO scheme, the improved WENO scheme has better dissipation and dispersion characteristics and faster convergence speed. Then, the coefficient CW of the Wall-Adapting Local Eddy-viscosity (WALE) model is calibrated by decaying homogeneous isotropic turbulence (DHIT) test case. Finally, the scheme is applied to conduct high-precision LES calculations of turbulent boundary layer flow, supersonic compression corner flow, and low-pressure turbine cascade separation flow. The numerical simulation results show that this WENO scheme has excellent shock wave capture ability and turbulence resolution and can capture more flow field details with less mesh size, greatly reducing the computational cost and laying a foundation for large-scale engineering application of LES.

Diffuse large B-cell lymphoma (DLBCL) requires a complete staging at diagnosis that may have prognostic and therapeutic implications. The role of bone marrow (BM) biopsy (BMB) is contro-versial in the era of nuclear-imaging techniques. We performed a comparative review of 25 studies focused on BM evaluation at DLBCL diagnosis, including at least two of the following techniques: BMB, flow cytometry, and positron emission tomography (PET-FDG). The report about BM involvement (BMi), diagnostic accuracy and prognostic significance was collected and compared among techniques. A concordance analysis between BMB, FCM and PET was also per-formed, and we deeply evaluated the implications of the different types of BMi: concordant by LBCL or discordant by low-grade B-cell lymphoma for both BMB and FCM, and focal or diffuse uptake pattern for PET. As main conclusion, BMB, FCM and PET are complementary tools which provide different and clinically relevant information in the assessment of BMi in newly DLBCL.

Primary bone lymphoma of the scapula is a rare tumor that usually causes local pain. Presented patient suffered for two years from paresthesia, tingling, numbness, and edema of the little and ring finger. The 45-year-old man underwent several radiological and neurological assessments of the palm, elbow and neck prior to radiographs revealing the tumor of the left shoulder. Once diffuse large B cell lymphoma was confirmed, immunochemotherapy with rituximab, cyclophosphamide, doxorubicin, vincristine, and methylprednisolone (R-CHOP) started. The treatment was accompanied by antiviral treatment with lamivudine due to positive hepatitis B virus serology and with bisphosphonate treatment for the prevention of bone resorption. Once immunochemotherapy was finished, the treatment was supplemented by radiotherapy of the shoulder. After more than three years of accomplishing remission, the patient had an ischemic stroke manifesting with right-sided hemiparesis. Following the physical therapy, the patient is currently in the process of evaluation for thrombophilia, as well as further cardiac assessment due to the positive transcranial Doppler bubble test setting high suspicion for the presence of patent foramen ovale.

We review the literature on medical discourse and attempt to build a computational model of it. Medical discourse sheds a light on communication structure of patient-doctor and other communication scenarios in healthcare and should be leveraged to facilitate and automate this communication when it is possible and practical. We propose a unified framework to represent communication discourse at the meta-level, where the subject of the communication is expressed in a language object. So far, the broad range of work on medical discourse is detached from computational discourse analysis, and we explore the possibilities of filling this gap and computationally treat the peculiarities of how information is passed between the agents in a hospital setting. We select the domain of question answering (QA) against a corpus of medical documents of diverse nature to evaluate our computational model of medical discourse. It turns out that applying specific structures obtained in medical discourse studies improves the relevance and efficiency of question answering.

Endovascular therapy (EVT) has revolutionized the management of acute ischaemic strokes with large vessel occlusion, with emerging evidence suggesting its benefit also in large infarct core volume strokes. In the last 2 years, 4 randomised controlled trials have been published on this topic - RES-CUE-Japan-LIMIT, ANGEL-ASPECT, SELECT2 and TENSION, with overall results showing that EVT improves functional and neurological outcomes compared to medical management alone. This review seeks to summarise the recent evidence presented by these 4 trials and highlight some of the limitations of our current understanding on this topic.

Evaluating of long-term satellite-derived ocean color products using in-situ measurements is essential to ensure data quality and integrity. In this study, the Visible Infrared Imaging Radiometer Suite (VIIRS) remote sensing reflectance (Rrs) products were evaluated and analyzed by using long-term OC-CCI in-situ data from 2012 to 2021. The results demonstrate that the root mean square difference of VIIRS Rrs product accuracy in most bands is better than 0.002 (sr-1) when considering its average life span of 12 years. However, the VIIRS Rrs products in shorter wavelength bands (e.g., at 412 nm) had significantly lower accuracy in general and a long-term bias over the recent years. The annual precision of VIIRS Rrs products showed a declining trend especially in coastal or eutrophic waters. This accuracy deterioration of the VIIRS Rrs products suggests a strong need for continuous monitoring of its performance and further efforts on improving the atmospheric correction algorithm to specifically deal with satellite records at high SZAs and OZAs.

Pigs are susceptible to cold stress due to the absence of brown fat caused by the partial deletion of uncoupling protein 1 during their evolution. Some local pig breeds in China, exhibit potential cold adaptability, but research has primarily focused on fat and intestinal tissues. Skeletal muscle plays a key role in adaptive thermogenesis in mammals, yet the molecular mechanism of cold adaptation in porcine skeletal muscle remains poorly understood. This study investigated the cold adaptability of two pig breeds, Mashen pigs (MS) and Large White pigs (LW), in a 4-day cold (4°C) or room temperature (25°C) environment. We recorded phenotypic changes and collected blood, and longissimus dorsi muscle for transcriptome sequencing. Finally, a candidate gene was randomly selected for functional exploration in porcine skeletal muscle satellite cells. A decrease in body temperature and body weight in both LW and MS pigs under cold stress, accompanied by increased shivering frequency and respiratory frequency. However, MS pigs demonstrated stable physiological homeostasis, indicating a certain level of cold adaptability. LW pigs primarily responded to cold stress by regulating heat production and Glycolipid energy metabolism. MS pigs exhibited a distinct response to cold stress, involving regulation of heat production, energy metabolism pathways, robust fatty acid oxidation ability, and stronger immune response. Furthermore, the functional exploration of PRSS8 in porcine skeletal muscle satellite cells revealed it affected cellular energy metabolism and thermogenesis by regulating ERK phosphorylation. These findings shed light on the diverse transcriptional responses of skeletal muscle in LW and MS pigs under cold stress, offering valuable insights into the molecular mechanisms underlying cold adaptation in pigs.

The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding practices and have gradually replaced ear tagging and other manual tracking techniques. Although sheep face datasets have been introduced in previous studies, they have often involved pose or background restrictions (e.g., fixing of the subject’s head, cleaning of the face), which restrict data collection and have limited the size of available sample sets. As a result, a comprehensive benchmark designed exclusively for the evaluation of individual sheep recognition algorithms is lacking. To address this issue, this study develops a large-scale benchmark dataset, Sheepface-107, comprised of 5,350 images acquired from 107 different subjects. Images were collected from each sheep at multiple angles, including front and back views, in a diverse collection that provides a more comprehensive representation of facial features. In addition to the dataset, an assessment protocol is developed by applying multiple evaluation metrics to the results produced by three different deep learning models: VGG16, GoogLeNet, and ResNet50, which achieved F1-scores of 83.79%, 89.11%, and 93.44%, respectively. A statistical analysis of each algorithm suggested that accuracy and the number of parameters were the most informative metrics for use in evaluating recognition performance.

The papaya mealybug Paracoccus marginatus is an invasive, polyphagous pest and poses a quarantine threat to tropical and subtropical countries. Infested commodity internationally should be undergoing phytosanitary treatment, while irradiation is recommended to replace methyl bromide fumigation. Dose-response tests were conducted on the 2-, 4-, and 6-d-old eggs and gravid females of P. marginatus using the X-ray radiation doses of 15–105 Gy with an interval of 15 Gy, respectively. Radiotolerance was compared using fiducial overlapping and lethal dose ratio (LDR) test, resulting in insignificant difference except for LDR test at LD90 (leading 90% mortality at 95% confidence level (CL)); the estimated dose for 99.9968% mortality at 95% CL was 176.5–185.2 Gy, which was validated in the confirmatory tests using gamma irradiation because gammy and X-rays have equal biological effect on pests. Therefore, a total of estimating 60,386 gravid females of papaya mealybug irradiated with 146.8–185.0 Gy resulted in no F1 nymphs developing. Eggs can therefore be used as an alternative to gravid females for dose-response tests; the recommended minimum dose for phytosanitary treatment of P. marginatus on infested commodity is 185 Gy, with treatment efficacy ≥ 99.9950% at 95% CL.

Traditional numerical techniques such as sliding mesh, dynamic grid mesh, and others, have many limitations in dealing with flow simulation with large-scale movement of solid boundaries; which is the case for complex-shaped hydraulic turbomachinery such as propellers, pumps and turbines. The immersed boundary (IB) method provides a new approach to solve the above limitations. Therefore, this study proposes an sharp-interface IB method based on the level-set function that is suitable for simulating the flow through turbomachinery with complex geometries. This method is applied to actual three-dimensional numerical simulations of high-Reynolds number propellers using an in-house computational fluid dynamics solver. The results show that the proposed method can provide comparatively accurate predictions of unsteady load coefficients within the propeller flow passage, and capture the correct propeller wake characteristics as well as the interaction between the propeller wake and free surface. This study aims at providing a theoretical basis and engineering reference for the application of the IB method in engineering numerical simulations.

Evaluating the performance of water indices and mapping the spatial distribution of water-related ecosystems are important for monitoring surface water resources. This is particularly the case for Ethiopia since there is limited information available on water resources development over time despite its relevance for the people and ecosystems. To address this problem, this paper evaluates the performance of seven water indices for country-scale surface water detection based on high spatial and multi-temporal resolution Sentinel-2 data, processed using the Google Earth Engine cloud computing system. Results show that the water index (WI) and automatic water extraction index with shadow (AWEIsh) are the most accurate ones to extract surface water. Comparisons are based on qualitative visual inspections and quantitative accuracy indicators. For the latter, WI and AWEIsh obtained kappa coefficients of 0.96 and 0.95, respectively, and an overall accuracy of 0.98 each. Both indices accounted for similar spatial coverages of surface waters with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia.

Treating relapsed and refractory diffuse large B-cell lymphoma is still challenging for clinicians, but the available CAR-T and bispecific antibodies revolutionized therapy. Autologous stem cell transplantation was the most effective treatment modality previously. The authors report data from a single center over ten years. The retrospective study included 116 patients. There were 53 relapsed, 39 primary refractory cases, 19 had CNS involvement, and 5 received primary consolidation transplants. The median duration of follow-up was 46 months. The median event-free survival was 75 months, and the median overall survival was 105 months for all cases. Five-year overall survival was 59%, and event-free survival was 54%. Pretreatment prognostic factors at diagnosis had no effect on the outcome of transplantation. The authors found no difference between survival in relapsed or refractory cases, and the number of salvage lines or the germinal center / activated B-cell type also did not influence the results. Complete metabolic response before transplant confirmed by 18FDG PET/CT strongly affected survival. The pretransplant creatinine and CRP levels significantly influenced the long-term outcome. The number of stem cells infused did not affect survival, but engraftment within nine days did result in better survival. These data support the finding that the response to salvage therapy did select a better prognostic group who may still benefit from autologous transplantation.

Over the last several decades, large wildfires are increasingly common across the United States causing disproportionate impact on forest health and function, human well-being, and economy. Here, we examine the severity of large wildfires across the Contiguous United States over the past decade (2011-2020) using a wide array of meteorological, vegetational, and topographical features in the Deep Neural Network model. A total of 4,538 wildfire incidents were used in the analysis covering 87,305 square miles of burned area. We observed the highest number of large wildfires in California, Texas, and Idaho, with lightning causing 43 % of these incidents. Importantly, results indicate that the severity of wildfire occurrences is highly correlated with the climatological forcings, land cover, location, and elevation of the ecosystem. Overall, results may serve useful guide in managing landscapes under changing climate and disturbance regimes.

Immune checkpoints are a set of molecules dysregulated in several human and canine cancers and aberrations of the PD-1/PD-L1 axis are often correlated with a worse prognosis. To gain insight into the role of immune checkpoints in canine diffuse large B-cell lymphoma (cDLBCL), we investigated PD-L1, PD-1 and CD8A expression by RNAscope. Results were correlated with several clinico-pathological features, including treatment, Ki67 index and outcome. A total of 33 dogs treated with CHOP (n=12) or CHOP plus APAVAC (n= 21) were included. PD-L1 signal was diffusely distributed among neoplastic cells, whereas PD-1 and CD8A were localized in tumor infiltrating lymphocytes. However, PD-1 mRNA was also retrieved in tumor cells. An association between PD-L1 and PD-1 scores was identified and a higher risk of relapse and lymphoma-related death was found in dogs treated with chemotherapy alone and dogs with higher PD-L1 and PD-1 scores. The correlation between PD-L1 and PD-1 is in line with the mechanism of immune checkpoints in cancers, where neoplastic cells overexpress PD-L1 that, in turn, binds PD-1 receptors in activated TIL. We also found that Ki67 index was significantly increased in dogs with the highest PD-L1 and PD-1 scores, indirectly suggesting a role in promoting tumor proliferation. Finally, even if the biological consequence of PD-1+ tumor cells is unknown, our findings suggest that PD-1 intrinsic expression in cDLBCL might contribute to tumor growth escaping adaptive immunity.

Recent analyses of cosmic microwave background (CMB) maps have been interpreted as demonstrating that the Universe was born without an initial rotation. However, these analyses are based on unrealistic models and do not contain essential ingredients such as quantum effects, the strong, weak and gravitational interactions between the components, their intrinsic spins and magnetic moments, as well as primordial black holes. If the Universe was born spinning these effects would distribute the initial spin angular momentum among its components long before the CMB forms at recombination. A primordial spin would now appear as a nonzero total angular momentum of its components along the direction of the original spin, and a primordial large-scale rotation would no longer be apparent. The existence of a special axis or direction would break a fundamental symmetry assumed in general relativity, cosmic isotropy, and a net angular momentum implies a cosmic parity violation.

This paper proposes a mathematical model to simulate Day-ahead markets of large-scale multi-energy systems with high share of renewable energy. Furthermore, it analyses the importance of including unit commitment when performing such analysis. The results of the case study, which is performed for the North Sea region, show the influence of massive renewable penetration in the energy sector and increasing electrification of the district heating sector towards 2050, and how this impacts the role of other energy sources such as thermal and hydro. The penetration of wind and solar is likely to challenge the need for balancing in the system as well as the profitability of thermal units. The degree of influence of the unit commitment approach is found to be dependent on the configuration of the energy system. Overall, including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of increasing considerably the computational time. Relaxing integer variables reduces significantly the computational time, without highly compromising the accuracy of the results. The proposed model, together with the insights from the study case, can be specially useful for system operators for optimal operational planning.

The energy loss of photons due to the cosmological red shift is interpreted, here, as a periodical process of transferring electromagnetic field energy into the space. The transferred energy portions are independent on photon energy if this transfer occurs with their frequency. The amounts of periodically released energy are so small that the related ultra-long wave length photons have to be understood as perfectly delocalized. Thus, the described point of view bridges the quantum micro cosmos with the macro cosmos. It is proposed to regard this energy exchange as a typical property of universal time arrow and to interpret the “large numbers”, in particular the dimensionless reciprocal of product of Hubbles constant and Planck time as the fundamental parameter describing cosmic evolution.

According to the standard cold dark matter (CDM) cosmology, the structure of dark halos including those of galaxy clusters reflects their mass accretion history. Older clusters tend to be more concentrated than younger clusters. Their structure, represented by the characteristic radius $r_s$ and mass $M_s$ of the Navarro-Frenk-White (NFW) density profile, is related to their formation time. In this study, we show that $r_s$, $M_s$, and the X-ray temperature of the intracluster medium (ICM), $T_X$, form a thin plane in the space of $(\log r_s, \log M_s, \log T_X)$. This tight correlation indicates that the ICM temperature is also determined by the formation time of individual clusters. Numerical simulations show that clusters move along the fundamental plane as they evolve. The plane and the cluster evolution within the plane can be explained by a similarity solution of structure formation of the universe. The angle of the plane shows that clusters have not achieved “virial equilibrium” in the sense that mass/size growth and pressure at the boundaries cannot be ignored. The distribution of clusters on the plane is related to the intrinsic scatter in the halo concentration--mass relation, which is originated from the variety of cluster ages. The well-known mass--temperature relation of clusters ($M_\Delta\propto T_X^{3/2}$) can be explained by the fundamental plane and the mass dependence of the halo concentration without the assumption of virial equilibrium. The fundamental plane can also be used for calibration of cluster masses.

Plasmonic gold nanorods play important roles in nowadays state-of-the-art plasmonic sensing techniques. Most of the previous studies and applications focused on gold nanorods with relatively small aspect ratios, where the plasmon wavelengths are smaller than 900 nm. Gold nanorods with large aspect ratios are predicted to exhibit high refractive-index sensitivity (Langmir 2008, 24, 5233–5237.), which therefore should be promising for developing of high-performance plasmonic chemical- and bio-sensors. In this study, we developed gold nanorods with aspect ratios over 7.9, which exhibit plasmon resonances around 1064 nm. The refractive index (RI) sensitivity of these nanorods have been evaluated by varying their dielectric environment, whereby a sensitivity as high as 473 nm/RIU can be obtained. Furthermore, we have demonstrated the large-aspect-ratio nanorods as efficient substrate for surface enhanced Raman spectroscopy (SERS), where an enhancement factor (EF) as high as 9.47×10⁸ was measured using 4-methylbenzenethiol (4-MBT) as probe molecule. Finally, a type of flexible SERS substrate is developed by conjugating the gold nanorods with the polystyrene (PS) polymer. The results obtained in our study can benefit the development of plasmonic sensing techniques utilized in the near-infrared spectral region.

Presented study demonstrates the application of actual technologies and tools for the development of a large scale axial flux electricity generator. (1) Background: The specifics of its application – run-of-river site – predefine design requirements, close to wind turbine generator. An extensive study of available solutions is used as a start point for further concept development; (2) Methods: A step based methodology is defined for concept parameters assessment and feasibility study. It demonstrates the advantages of virtual prototyping when assessing various design parameters; (3) Results: Several simulations over built different virtual prototypes provide sufficient information to elaborate an improved design concept. Major result is a ready for detailed design concept, with defined major parameters and studied work behavior; (4) Conclusions: The major outcome is the presentation of the application of virtual prototyping in assessment of large structures, where physical prototyping is expensive and time consuming approach. Application of virtual prototyping at very early product development stage is an effective way to undertake efficient solutions towards the concept of the product.

As the performance of large language models is proven in general domains, we can consider applying them to specific tasks to improve the system’s specialization and responsiveness. In this paper, we will develop a lightweight software system based on RAG (Retrieval-Augmented Generation) to test whether this approach can provide effective advice and protection for patients after discharge. All patient data in this study comes from the neurology department. We will select three diseases — Acute Cerebral Infarction, Atherosclerosis, and Hypertension to test our system, and the generated results will be evaluated by professional doctors. Additionally, we will introduce a memory mechanism to enhance the system’s performance and responsiveness. Finally, we will include suggestions for system improvements and extensions. The experimental results indicate that both the foundational capabilities of large language models (such as Llama 3 and GPT-4) and the system architecture (e.g., the integration of RAG and memory mechanisms) significantly influence overall performance. By comparing different models and architectures, it is observed that the GPT-4 system combined with RAG and memory mechanisms outperforms the baseline in terms of total score and improvements, demonstrating the effectiveness of system architecture optimization in enhancing the models’ ability to handle complex tasks. Since the data still contains sensitive information of patients and hospitals after being desensitized, we cannot show all the test data. However, after obtaining the hospital’s permission, through data desensitization, we showed some of the test data. You can click here to get the prototype we developed and some test data.

As motor capacity and rated voltage increase, the demand for motor insulation also increases. Additionally, the electric field distribution at the end of a large-scale hydro generator is extremely non-uniform, and corona discharge readily occurs. This destroys the main insulation, which significantly affects the service life of electrical machinery. Thus, the regulation of the electric field concentration at the end of a large-scale hydro generator needs to be addressed. There are many problems in resistance–capacitance chain model research. Particularly, these models cannot accurately reflect the insulation, corona structure, and electric field distribution in electrical machinery. Additionally, the corner electric field and loss distributions cannot be obtained, leading to a higher calculation error. The electric field distribution at the motor end and the loss distribution were analyzed using the finite-element method. First, by applying the basic principles of electric circuits and electromagnetic field theory, we calculated the electric field distribution at the stator bar end. Second, a 3D model of the stator bar was built using Creo Parametric, yielding a model that is more closely related to the prototype wirebar model. Then, the potential, electric field, and loss distributions around the stator bar were simulated by using COMSOL simulation software. The effects of the circular arc corner, nonlinearity coefficient, anti-corona coating length, and resistivity of the stator bar on the electric field distribution at the motor end were explored.

Wave and tidal energy, recognized as vital renewable resources, harness the ocean's kinetic and potential power. This study aims to provide an in-depth patent analysis of the technological landscape within these sectors. We applied a dual approach: first, a descriptive analysis was conducted to explore patent publication trends, technology lifecycle stages, patent activity by country, top assignees, and IPC classifications. Our analysis provided a detailed overview of the sector's growth and the key players involved. Second, we utilized topic modeling, specifically BERTopic enhanced with large language models (LLMs), to identify and fine-tune key technolog-ical themes within the patent data. In this study, we identified seven distinct clusters each for wave and tidal energy using this approach. This method led to a novel categorization of the pa-tents, revealing latent themes within the patent data. Although our categorization differs from traditional methods, it provides deeper insights into the thematic focus of the patents, highlight-ing emerging trends and areas of innovation within wave and tidal energy technologies to better exploit and optimize ocean energy conversion infrastructure.

A recently published article posited the use of polymer matrix composites (PMCs) as a replacement material for low-carbon steel (LCS) in large power transformer (LPT) tanks [1]. PMCs offer potential advantages such as weight reduction, corrosion prevention, and self-healing of the tank material which may not be achievable with traditional LCS. LCS can arrest ballistic projectiles at relatively low velocities, but at higher velocities, penetration can occur leading to a potential major transformer failure. Numerical simulations of steel projectile impacts upon LCS plates and glass and/or carbon fiber polymer composite laminates showed that with sufficient thickness both could prevent projectile penetration. This prevention was especially efficient when the plates were coated with a high strain rate sensitive material such as polyurea (PU). Our simulations show that the addition of a 3 mm coating of PU to a standard 10 mm thick LCS plate used in LPT tanks could prevent penetration of a 400 m/s steel projectile. For PMC plates, the simulations with the same projectile velocity showed three times faster deceleration with a 3 mm PU coating than without the coating. Thus, two realistic approaches to improving the ballistic protection of LPTs are presented. In addition to their other advantages, this research clearly shows that PMCs with the coating could provide significant ballistic protection for the next generation of LPT tanks.

Background P53 gene mutation is a very common event in human neoplasia, and genetic mutations in the P53 gene in a single allele are responsible for a hereditary cancer susceptibility syndrome (Li Fraumeni). These variants encode distinct isoforms of the p53 protein, which may disrupt its transcriptional activity. These point mutant proteins are more stable than the normal protein and the mutant product accumulates at a high level that allows obtaining important information about p53 gene expression in malignant cells, especially in Chronic Lymphocytic Leukemia, (CLL). By Enzyme Linked Immune-Absorbent Assay method, (ELISA), was analyzed the frequency of p53 protein expression to 20 eligible patients diagnosed with CLL for to investigate the relationship of this protein in the stages of the disease and the impact on treatment response and survival. In ELISA technique was used the specific antibodies for isoform p53 protein, PAb 240 antibodies. These antibodies bind specifically to denatured p-53 protein. Species reactivity is for human in conformity with the prospect from Manual, Catalog No. LS-F174, Bio-Rad. Results The average concentrations of p53 proteins in 17 of 20 cases were found 16.76 μg / dl, with CV = 0.5% and the probability index p = 0.034. Very high pathological values in the 3 cases of isoform p53 protein were calculated in 2 men, (PM) in the value of 60 µg / dL, respectively at 40 µg / dL and in the case of females, (PW), in 40 µg / dL value, with the transformation into Diffuse Large Lymphoma, (DLL). Conclusion This ELISA method has proven to be a useful prognostic tool for the application of personalized treatment of on-immune therapy, in cases diagnosed with the type B-CLL.

Abstract: Giant Cell Arteritis (GCA) is an autoimmune/autoinflammatory disease affecting large vessels in patients over 50 years old. The disease presents as an acute inflammatory response with two phenotypes, cranial-GCA and Large Vessel Vasculitis (LV)-GCA, involving the thoracic aor-ta and its branches. 18F-fluorodeoxyglucose positron emission tomography-computed tomogra-phy (18F-FDG PET-CT) is among the imaging techniques contributing to diagnosis of patients with systemic disease. However, its association with soluble inflammatory markers is still elu-sive. This proof-of-concept study aims to identify novel soluble serum biomarkers in PET/CT pos-itive patients with LV-GCA and associate them with active (0 months) and inactive disease (6 months following treatment), in sequential samples. The Most-Disease-Segment Target-to-Background Ratio (TBRMDS) was calculated for 9 LV-GCA patients, while 12 cranial-GCA and 7 Polymyalgia Rheumatica patients with negative initial PET/CT served as disease controls. Serum macrophage-related cytokines were evaluated by Cytometric Bead Array (CBA). Finally, previously published NMR-metabolomics data acquired at the same blood sampling were associated with PET/CT findings. TBRMDS was significantly increased in active versus inactive disease (3.45 vs. 2.55, p=0.008). The analysis identified 6 serum metabolites, as more sensitive to change from the active to inactive state. Among them, choline levels were exclusively altered in the LV-GCA group, but not the disease controls. Cytokine levels were not associated with PET/CT activity. Combining with CRP, ESR, and TBRMDS a composite index was generated to depict well the differences between active and inactive systemic LV-GCA (25.45 vs 11.45, p=0.0039). These preliminary results could pave the way for more extensive studies integrating serum metabolomic parameters with PET/CT imaging data to extract sensitive composite disease indexes useful for the everyday clinical practice.

Interaction with social agents employing natural language have been discussed especially since Weizenbaum (1966) observed the social effects of his chatbot named 'Eliza', which imitated a cli-ent-centered conversation. With Nass and Reeves’ media equation theory and computers as social actors paradigm, a well-tested framework has been introduced to the study of human-computer interaction. In the 1990s, there was a renewed focus on natural language analysis, which has be-come a crucial area of research in machine learning. Recent advancements in data processing and storage capacities have led to the development of AI-generated utterances that closely resemble human utterances. As a result, AI-based systems can now mimic social relationships to a signifi-cant extent. In light of these technological developments, the article explores whether quasi-social relationships should be considered within the field of social psychology and examines the poten-tial advantages and challenges of this approach.

In China, "new quality productive forces" have become an important focus for high-quality development; accelerating this development is an important task for the Chinese government in 2024. Such development relies on the traction of the digital economy. In view of this, developing “new quality productive forces” driven by the Chinese digital economy requires focusing on blocking points, such as the insufficient vitality of data, inadequate algorithmic regulation, the uneven distribution of computing power resources and the immature market environment. In order to dredge these blocking points, it is necessary to establish basic rules on data, strengthening algorithm supervision, building a national integrated computing power network, and optimizing the market environment.

The growing usage of Generative AI tools in scientific writing requires a critical examination of their benefits and challenges. This literature review is aimed at comprehensively analyzing current empirical research articles focused on the application of Generative AI in scientific writing. The Google Scholar database was used to search for the literature. The following keywords were used: "Generative AI" and "academic writing", "LLM" (Large Language Models) and "academic writing", "Generative AI" and "Scientific writing", and "ChatGPT" and "Scientific Writing". The search was restricted to articles published between January 1, 2023, and April 30, 2024. 15 articles were selected as appropriate for the study and analyzed. It was found that, thus far, ChatGPT is the most exploited tool in the studies. AI tools such as Bard (Gemini), Bing, Claude2, and Elicit were also tested. The benefits of Generative AI usage in scientific writing were found to be omnipresent. It can aid in the generation of structured abstracts, titles, introductions, literature reviews, and conclusions of a scientific article. Generative AI also makes writing more efficient and time-saving. Its capabilities in improving language and proofreading are well-established. However, the generation of inaccurate content and references by current commercially available LLMs poses a serious problem. The lack of critical thinking and tendency to produce non-original content are significant drawbacks. Generative AI should be employed with human oversight, serving as an assistant rather than a replacement. Transparency in AI usage in scientific writing is essential, along with the necessity for proper legal regulation.

Large-scale farms are pivotal sites for the development of the agricultural industry, and the quality of the ecological environment is a significant factor affecting the growth of cereal crops. Consequently, assessing the ecological quality of large-scale farms is of great importance. This paper leverages the advantages of remote sensing imagery for long-term, quantitative, and dynamic monitoring of ecological quality over extensive areas. It develops an ecological assessment procedure suitable for agricultural regions based on an Improved Remote Sensing Ecological Index (IRSEI), which considers the coupling of ecosystem component elements. This procedure introduces a Pan-Salinity Index (PSI) tailored to the characteristics of soil salinization in farming areas and incorporates an ecological greenness index based on the Normalized Difference Vegetation Index (NDVI), an ecological humidity index derived from the K-T transform (WET), an ecological dryness index synthesized from the bare soil index and the built-up index (NDBSI), and an ecological heat index represented by the Land Surface Temperature (LST) calculated from atmospheric parameter models. These five indices are integrated into the ecological assessment system. The experimental results visually demonstrate the development and changes of ecological impact factors in the farm research area over the past decade, as well as the spatiotemporal variations in the quality of the farm's ecological environment. The findings indicate that between 2010 and 2019, the overall trend of IRSEI first declined, then rose, and subsequently experienced a slight decrease. Through differential analysis, regions and areas with declining ecological quality were identified, providing a data reference for the formulation and implementation of future ecological protection and management measures for the farm. The proposed IRSEI method offers a rapid and effective new monitoring approach for agricultural planting areas with a tendency towards soil salinization.

Modern language models are designed to generate text so realistic that it could easily be mistaken for human-authored content. Additionally, they can engage in conversations with humans in a manner that appears coherent and reasonable. The epitome of this technological advancement is ChatGPT, a model based on OpenAI's Generative Pretrained Transformer (GPT) language model. With its ability to produce high-quality content in mere seconds, ChatGPT surpasses other chatbots in terms of capabilities, drawing significant interest and excitement from both the business community and academic researchers. This study offers a comprehensive review of current research on ChatGPT, delving into its technological foundation, supportive mechanisms, and findings regarding its benefits across various fields and application areas. We evaluate and discuss the strengths and weaknesses of ChatGPT based on this review and suggest potential avenues for future research.

Weaning is a critical period in the pig’s life. Piglets are confronted with abrupt changes to their physical and social environment, as well as management and nutritional changes. Weaning was always associated with a growth check and was frequently accompanied by post-weaning diarrhea in piglets. However, rapid increases in litter size, in the last decade, has increased within-litter piglet weight variation, with piglets now generally lighter at weaning, making the challenges associated with weaning even greater. Many interventions can be employed during the suckling period to ease the weaning transition for piglets. Pre-weaning strategies such as supervised farrowing (assistance with suckling, oxytocin provision), provision of pain relief to sows around farrowing, split-suckling, early oral supplementation with glucose, bovine colostrum, fecal microbiota transplantation, feed additives, solid and liquid creep feeding (milk and liquid feed) have all been investigated. The objective of these strategies is to stimulate earlier maturation of the digestive tract, improve immunity, reduce latency to the first feed post-weaning and increase early post-weaning feed intake and growth. This review focuses in particular on: 1) pain relief provision to sows around farrowing, 2) split-suckling of piglets, 3) pre-weaning provision of supplementary milk and/or liquid feed, 4) other strategies to stimulate earlier enzyme production (e.g. enzyme supplementation) and 5) other nutritional strategies to promote improved gut structure and function (e.g. L-glutamine supplementation). Correctly implementing these strategies can not only increase post-weaning growth and reduce mortality but also maximize lifetime growth in pigs.

This primer provides an overview of the rapidly evolving field of generative artificial intelligence, specifically focusing on large language models like ChatGPT (OpenAI) and Bard (Google). Large language models have demonstrated unprecedented capabilities in responding to natural language prompts. The aim of this primer is to demystify the underlying theory and architecture of large language models, providing intuitive explanations for a broader audience. Learners seeking to gain insight into the technical underpinnings of large language models must sift through rapidly growing and fragmented literature on the topic. This primer brings all the main concepts into a single digestible document. Topics covered include text tokenization, vocabulary construction, token embedding, context embedding with attention mechanisms, artificial neural networks, and objective functions in model training. The primer also explores state-of-the-art methods in training large language models to generalize on specific applications and to align with human intentions. Finally, an introduction to the concept of prompt engineering highlights the importance of effective human-machine interaction through natural language in harnessing the full potential of artificial intelligence chatbots. This comprehensive yet accessible primer will benefit students and researchers seeking foundational knowledge and a deeper understanding of the inner workings of existing and emerging artificial intelligence models. The author hopes that the primer will encourage further responsible innovation and informed discussions about these increasingly powerful tools.

Large Language Models (LLMs) have revolutionized the field of Artificial Intelligence with their ability to understand and generate natural language discourse. This has led to the use of LLMs in various creative fields such as music, art, and storytelling, which has sparked a debate on the impact of LLMs on human creativity. This article explores the cyclic effect of LLMs on human creativity, where LLMs can generate an ever-evolving cycle of creative output from both humans and machines. The article also highlights the importance of responsible and ethical use of LLMs, including privacy and copyright issues with the training data used by LLMs. The article emphasizes the need for transparency, data security measures, and AI governance policies to protect user data and ensure the safe use of LLMs. It also calls for the development of educational materials and resources to increase public understanding of LLMs and their ethical implications. The article concludes by highlighting the potential of LLMs as powerful tools for communication and education, but also emphasizes the need for ethical considerations to ensure the responsible and beneficial use of LLMs in society.

This paper presents ROSGPT, an innovative concept that harnesses the capabilities of large language models (LLMs) to significantly advance human-robot interaction. We develop ROSGPT as a ROS2 package that seamlessly integrates ChatGPT with ROS2-based robotic systems. The core idea is to leverage prompt engineering with LLMs, specifically ChatGPT, utilizing its unique properties such as ability eliciting, chain-of-thought, and instruction tuning. The concept employs ontology development to convert unstructured natural language commands into structured robotic instructions specific to the application context through prompt engineering. We capitalize on LLMs' zero-shots and few-shots learning capabilities by eliciting \textit{structured} robotic commands from \textit{unstructured} human language inputs. To demonstrate the feasibility of this concept, we implemented a proof-of-concept that integrates ChatGPT with ROS2, showcasing the transformation of human language instructions into spatial navigation commands for a ROS2-enabled robot. This versatile concept can be easily adapted to various other robotic missions. ROSGPT serves as a new stride towards Artificial General Intelligence (AGI) and paves the way for the robotics and natural language processing communities to collaborate in creating novel, intuitive human-robot interactions. The open-source implementation of ROSGPT on ROS 2 is available on GitHub.

Diffuse large B-cell lymphoma (DLBCL) is the most common haematological malignancy in humans and dogs. Several studies disclosed some similarities between the two species, including the constitutive activation of NF- κB pathway as a fundamental underlying pathogenetic mecha-nism. In humans, downregulation of IL-1R8 is implicated in DLBCL development, but its role in dogs has not been explored so far. To gain insight into the pathogenesis of this tumor in dogs, we evaluated the mRNA and protein expression of IL-1R8 in 12 hyperplastic lymph nodes ob-tained from dogs not bearing tumors and from 50 dogs with DLBCL. Moreover, we analysed through qRT-PCR the expression of TLR7, TLR9, MYC, and p52 genes that are known to be in-volved in the IL-1R8 regulatory network. IL-1R8 and p52 were downregulated in DLBCLs com-pared to control lymph nodes (p<0.001), while a higher expression of TLR7, TLR9 and MYC was observed in tumors (p<0.01). Immunohistochemistry confirmed gene expression results, reveal-ing a significantly lower IL-1R8 staining score in DLBCLs compared to control lymph nodes (p<0.0001). Taken together, these results suggest that IL-1R8 downregulation may represent one of the mechanisms driving DLBCL pathogenesis in dogs, mainly through dysregulation of the Toll-like/Interleukin receptors signalling cascade and the aberrant activation of classical NF-κB pathway.

Pipeline integrity is an important area of concern for the oil and gas, refining, chemical, hydrogen, carbon sequestration, and electric-power industries, due to the safety risks associated with pipeline failures. Regular monitoring, inspection, and maintenance of these facilities is therefore required for safe operation. Large standoff magnetometry (LSM) is a non-intrusive, passive magnetometer-based mea- surement technology that has shown promise in detecting defects (anomalies) in regions of elevated mechanical stresses. However, analyzing the noisy multi-sensor LSM data to clearly identify regions of anomalies is a significant challenge. This is mainly due to the high frequency of the data collection, mis-alignment between consecutive inspections and sensors, as well as the number of sensor measurements recorded. In this paper we present LSM defect identification approach based on ma- chine learning (ML). We show that this ML approach is able to successfully detect anomalous readings using a series of methods with increasing model complexity and capacity. The methods start from unsupervised learning with "point" methods and eventually increase complexity to supervised learning with sequence methods and multi-output predictions. We observe data leakage issues for some methods with randomized train/test splitting and resolve them by specific non-randomized splitting of training and validation data. We also achieve a 200x acceleration of support-vector classifier (SVC) method by porting computations from CPU to GPU leveraging the cuML RAPIDS AI library. For sequence methods, we develop a customized Convolutional Neural Network (CNN) architecture based on 1D convolutional filters to identify and characterize multiple properties of these defects. In the end, we report the scalability of the best-performing methods and compare them, for viability in field trials.

The local climate effects of utility-scale photovoltaic (PV) plants have also attracted more and more attention with the rapid development of PV industry. Turbulent fluxes and intensity characteristics for the PV plant and the adjacent reference site are investigated in the gobi area in Wujianqu in Xinjiang. Results indicate that near surface boundary layer is more unstable during the daytime while is more stable at the night in the PV plant than the reference site. The average roughness is 0.089m and 0.041m for the PV plant and the reference site respectively. The turbulence intensity in the vertical direction (I_w) of the PV plant is higher than the reference site especially during the daytime. The turbulent kinetic energy of the PV plant during the daytime is lower than the reference site.The momentum in the PV plant is higher than the reference site, especially during the daytime. Compared to the reference site, the PV plant has higher sensible heat flux and less latent heat flux. Site specific turbulent intensity relationships were developed for the PV plant and the reference site by analyzing wind components as a function of stability parameter (z/L). The turbulent components of wind followed 1/3 power law in the unstable conditions and stable conditions in the PV plant and the reference site.

The primary purpose of the study was the determination of the spatial day-to-day variability of extreme temperatures in Poland and the dependency of large temperature changes on atmospheric circulation in accordance with the Grosswetterlagen (GWL) classification. The goal was implemented based on data from 1966 to 2015, made available by the Institute of Meteorology and Water Management – National Research Institute. Day-to-day changes in maximum and minimum temperatures were designated with a rate of ≥ 6 °C (large) and with a rate of ≥ 12 °C (very large) and their spatial distribution was presented. Finally, the analysis of the dependency of considerable temperature changes on atmospheric circulation in accordance with the Grosswetterlagen (GWL) classification was conducted. The obtained results showed that in Poland in the period 1966-2015 the number of large changes in both Tmax and Tmin slightly increased, although the fluctuations show spatial variability. Large changes in Tmax occur more frequently in spring, and in Tmin in winter. Large changes in Tmax and Tmin are mainly recorded during cyclonic circulation, however, the anticyclonic circulation types favour especially large decreases in Tmin.

There is an increasing trend that Unmanned Combat Air Vehicles (UCAVs) are employed to complete different combat missions in modern wars. This paper investigates a UCAV routing problem, which simultaneously considers the decisions for the configuration of weapons carried by the UCAV subject to its capacity and the allocation of weapons to the targets subject to their destroying requirements. An integer linear programming model is developed to formulate the problem. An adaptive large neighborhood search (ALNS) heuristic is proposed to solve the problem, in which seven neighborhood structures are designed and employed. Randomly generated instances covering the small, medium and large sizes are used to test the proposed ALNS algorithm. CPLEX is also utilized to solve the small-size instances, whose results are compared with that obtained by the ALNS algorithm. And the extensive experimental results confirm the effectiveness and superiority of the proposed ALNS algorithm.

The ecological background condition of the semi-arid steppe region (SASR) is extremely fragile. It is recognized that the development of coal and electricity power is a kind of strong human interference behavior for regional landscape ecology. Landscape ecological classification (LEC) is the premise of landscape ecology research of the mining area. The current research on the SASR and grassland LEC of coal-power base is relatively less, but still remains uncertainty concerning how to stratify and classify urban mining landscapes into units of ecological significance at spatial scales appropriate for management. This study is based on hierarchy theory, scale theory, landscape process, the patch-corridor-matrix model, the network, the theory of multiple planning integration and the principle of remote sensing. According to the comprehensive principle, principles of the combining of structure and function, principle of the combining human-ominated and natural landscape, principle of emphasis, and principle of combining qualitative analysis with quantitative research of LEC in large-scale coal-power base(LSCPB). On the basis of occurrence method land classification, fully consider the ecological attributes of the land, integration pattern, processes and function theory of the landscape ecology, the LEC system of the LSCPB in the SASR has been constructed by using top-down decomposition classification method. Empirical research of the Victory and Mindong No.1 mining areas of Shenhua Group shows that the classification system constructed in this paper can meet the requirements of LEC and fully reflect the status of landscape ecology of LSCPB in SASR. This study can provide theoretical guidance for the landscape ecology of LSCPB, while also supporting a theoretical reference for the LEC research.

Scaling the operation is the single greatest challenge in every enterprise. The more complex the enterprise, the greater the challenge. Scaling in healthcare has been a consistent challenge, with the need to meet clinical endpoints with less staff, increased costs of resources, regulatory oversight, and the goal of improving access to care. The article explores the emerging paradigm of multi-agent AI systems in healthcare, representing a significant leap beyond traditional Large Language Models. We comprehensively analyze the potential of these systems to revolutionize patient care, streamline administrative processes, and support complex clinical decision-making. The paper describes a hypothetical sepsis management system comprising seven specialized AI agents, each handling specific aspects of patient care, from data collection and diagnosis to treatment recommendations and resource management. We also examine applications in chronic disease management and hospital patient flow optimization. The technical implementation of these systems is discussed, including the use of advanced Large Language Models, inter-agent quality control measures, implementing guardrails, self-reflection, integration with Electronic Health Records, and the importance of explainable AI for decision transparency. In addition to describing the promising potential benefits, such as enhanced diagnostic accuracy and personalized treatment plans, we address significant challenges, including data quality assurance, workflow integration, and ethical considerations. We conclude the article by highlighting future directions, such as integrating IoT devices and developing more sophisticated natural language interfaces. Our work underscores the transformative potential of multi-agent AI systems in healthcare while emphasizing the need for rigorous validation, ethical oversight, and a patient-centered approach in their development and implementation.

Three-dimensional (3D) geological modeling technology offers intricate geological information services and technical support for the research of urban geological conditions and their evolutionary mechanisms. It further facilitates the analysis of environmental geological problems, encompassing their distinguishing features and genetic mechanisms. The precision of such three-dimensional models is predominantly influenced by the caliber of modeling data, the chosen modeling methodologies, and the intricacy of the modeled geological objects. This research, exemplified by the three-dimensional geological structure modeling of the Wuhan metropolitan development area, aims to explore a comprehensive three-dimensional modeling framework for urban geology, particularly in scenarios with large-scale and complex geological conditions. This study holds significant technical value in enhancing the application of 3D geological modeling, as well as practical importance for urban geological investigations and research.

Artificial intelligence methods, especially artificial neural networks (ANNs), have increasingly been utilized for the mathematical description of physical phenomena in (metallic) material processing. Traditional methods often fall short in explaining the complex, real-world data ob-served in production. While ANN models, typically functioning as "black boxes," improve pro-duction efficiency, a deeper understanding of the phenomena, akin to that provided by explicit mathematical formulas, could enhance this efficiency further. This article proposes a general framework that leverages (CAE) ANNs to explain predicted results alongside their graphical presentation, marking a significant improvement over previous approaches and those relied on expert assessments. Unlike existing Explainable AI (XAI) methods, the proposed framework mimics the standard scientific methodology, utilizing minimal parameters for the mathematical representation of physical phenomena and their derivatives. Additionally, it analyzes the relia-bility and accuracy of the predictions using well-known statistical metrics, transitioning from deterministic to probabilistic descriptions for better handling of real-world phenomena. The proposed approach addresses both aleatory and epistemic uncertainties inherent in the data. The concept is demonstrated through the hot extrusion of aluminum alloy 6082, where CAE ANN models and predicts key parameters, and ChatGPT explains the results, enabling researchers and/or engineers to better understand the phenomena and outcomes obtained by ANNs.

Given the widespread use of AI language models such as ChatGPT, there is an urgent need 1 to understand their political biases in different languages. This study aims to uncover such biases. To 2 investigate this issue, we applied IDRLabs’ Political Coordinates Test to ChatGPT based on GPT-4 3 in four languages - English, French, German, and Italian. The results confirm previous studies that 4 identified a left-libertarian bias in ChatGPT. There was remarkable consistency in political orientation 5 across all four languages, although minor variations were found. Differences in ChatGPT response 6 distributions across languages could indicate culturally determined differences in interpretation 7 of political questions. The results highlight the need to diversify and optimize data collection and 8 model training methods to ensure greater fairness and neutrality and minimize the influence of bias. 9 Our study highlights the importance of cultural differences in the processing and interpretation of 10 political information by AI models.

Land cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. In this article, we focus on the land cover segmentation tasks and complete the following research work: Firstly, to address the issues of uneven distribution of foreground and background and significant differences in target scales in remote sensing images, we propose a decoder called MDCFD based on multi-dilation rate convolution fusion. The decoder utilizes dilated convolution to expand the receptive field, enhancing the model's ability to capture global features and thus improving the model's ability to distinguish between foreground and background. Meanwhile, we design a multi-dilation rate convolution fusion module (MDCFM), which fuses the outputs of different dilation rate convolution layers. Secondly, aiming at the problems of diverse scenes, significant differences between categories, and many background interferences in remote sensing images, we propose a hybrid attention module called LKSHAM based on large kernel convolution. This module combines spatial attention and channel attention mechanisms and combines the two attention modules in series. By introducing large kernel convolution, the model's ability to extract contextual information is improved. At the same time, we adopt a strategy of decomposing large kernel convolution into multiple depthwise convolutions to reduce computational complexity. The improved network models designed by this paper can achieve an improvement of over 1.1% in the mIoU metric on segmentation tasks on the Potsdam and Vaihingen datasets.

The attitude dynamics and orbit dynamics of large rigid space structures are coupled with each other under gravity gradient, which may affect the stability of the large rigid space structures. In this paper, the gravity gradient stability of a large rigid space structure is studied under both small and large disturbances. Based on the rigid body dynamics and orbit dynamics, an accurate dynamic model without any linearization of the large rigid space structure is established via the natural coordinate formulation (NCF), which is able to describe the large overall motions of the structures. By using the generalized-α algorithm, the gravity gradient stability of the large rigid space structure is simulated and analyzed via various examples, including the influence of large disturbance angles, the positions at the stabilization and unstabilization regions. Finally, the relationship between spinning stability and gravity gradient stability is also investigated via a large spinning space structure.

Unmanned aerial vehicle (UAV), or drone is recognized for its potential to improve efficiency and address last-mile delivery issues. As a result, there has been a lot of activity in recent years in the field of drone scheduling and routing. Unlike the vehicle routing problem, drone route design is difficult due to several operational characteristics, such as speed optimization, multitrip operation, and energy consumption estimation. On the one hand, drone energy consumption is a complex nonlinear function of both speed and payload in practice. On the other hand, the high operating speed of drones can significantly curtail the drone range, thereby limiting the efficiency of drone delivery systems. Most of the existing drone delivery models either assume constant drone speed or do not consider the effect of drone speed and parcel weight on energy consumption, leading to costly or energy-infeasible routes. This paper addresses the trade-off between speed and flight range in a multi-trip drone routing problem with variable flight speeds (DRP-VFS), in which a team of homogeneous drones is employed for delivery services. We propose a new model to particularly consider energy constraints using a nonlinear energy consumption model and treat drone speeds as decision variables so that various drone speeds can be adopted in applications. The DRP-VFS is initially formulated as mixed-integer linear programming (MILP) to minimize total energy consumption. To solve large-scale instances, we propose a three-phase adaptive large neighborhood search (ALNS) algorithm. The experimental results demonstrate that suboptimal solutions can be found effectively in practical scenarios using the proposed method. Furthermore, results indicate that operating drones at variable speeds leads to about 21% of energy savings compared to fixed speeds, boasting advantages in cost-savings and range extensions.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma and a heterogeneous disease characterized by mature B-cell neoplasms. The standard immunochemotherapy for DLBCL is R-CHOP, which includes rituximab, cyclophosphamide, doxoru-bicin, vincristine, and prednisone. Up to 30-40% of patients with DLBCL either exhibit primary refractory disease to standard treatment or experience relapse after the initial response. A 72-year-old man diagnosed with DLBCL involving chromosomal translocations t(1;22)(q21;q11.2) and t(6;18)(p25;q21) showed primary refractory disease after the fourth cycle of R-CHOP. The patient ultimately experienced cardiac involvement by the lymphoma and received salvage chemotherapy. He passed away about 15 months away after the diagnosis of DLBCL. We conducted fluorescence in situ hybridization (FISH) for further analysis of the chromosomal translocations. The breakpoint of chromosome 1q21 was located at a distance of around 151 Mb from the telomeric end of chromosome 1p. The breakpoint in chromosome 22q11 contains the immunoglobulin lambda locus. Furthermore, the breakpoint of chromosome 6p was in the telomeric region of chromosome 6p21. The breakpoint of chromosome 18q21 contains BCL2. This case report presents the first documented co-occurrence of chromosomal translocations t(1;22)(q21;q11.2) and t(6;18)(p25;q21) in a patient with DLBCL. These chromosomal translocations may indicate a worse clinical outcome.

Febrile neutropenia (FN) is a major concern in patients undergoing chemotherapy for diffuse large B-cell lymphoma (DLBCL); however, the overall risk of FN is difficult to assess. This study aimed to develop a model for predicting the occurrence of FN in patients with DLBCL. In this multicenter, retrospective, observational analysis, a multivariate logistic regression model was used to analyze the association between FN incidence and pretreatment clinical factors. We included adult inpatients and outpatients (aged ≥ 18 years) diagnosed with DLBCL who were treated with chemotherapy. The study examined 246 patients. Considering FN occurring during the first cycle of chemotherapy as the primary outcome, a predictive model with a total score of 5 points was constructed as follows: 1 point each for viral hepatitis, extranodal involvement, and a high level of soluble interleukin-2 receptor and 2 points for lymphopenia. The area under the receiver operating characteristic curve of this model was 0.844 (95% confidence interval: 0.777–0.911). Our predictive model can assess the risk of FN before patients with DLBCL start chemotherapy, leading to better outcomes.

A novel approach was developed that combined LP-based row generation with optimization-based sorting to tackle computational challenges posed by budget allocation problems with combinatorial constraints. The proposed approach dynamically generated constraints using row generation and prioritized them using optimization-based sorting to ensure a high-quality solution. Computational experiments and case studies revealed that as the problem size increased, the proposed approach outperformed simplex solutions in terms of solution search time. Specifically, for a problem with 50 projects (N = 50) and 2,251,799,813,685,250 constraints, the proposed approach found a solution in just 1.4 seconds, while LP failed due to the problem size. The proposed approach demonstrated enhanced computational efficiency and solution quality compared to traditional LP methods.

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multi-scene and multi-size, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.

Hydrogels, soft materials with 3D polymer networks in aqueous solution, have been developed for engineering and bio-related fields. However, these conventional hydrogels are weak and brittle due to lack of energy dissipation mechanisms. Recently, dual-network hydrogels have been proposed, combining rigid and flexible networks and exhibiting high strength, stretchability, and toughness. This paper explores the rheological properties of dual-network hydrogels based on acrylamide and sodium alginate under large deformations. This dual network is a combination of a covalently crosslinked polyacrylamide network and a supramolecular crosslinked sodium alginate network at the presence of divalent calcium cations. Small and large amplitude oscillatory shear methods with Fourier transform rheology, stress decomposition method, and Chebyshev polynomial analysis of large amplitude oscillatory shear (LAOS) data were employed to evaluate non-linearity limit, toughness, and network rigidity. The concentration of calcium ions affects (concentrations 0-80 mg/ml) the nonlinear transition and limit points, and all gel samples exhibit strain hardening, shear thickening, and shear densification.

In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education and research. One such technology is ChatGPT, a powerful large language model developed by OpenAI. This technology offers exciting opportunities for students and educators, including personalized feedback, increased accessibility, interactive conversations, lesson preparation, evaluation, and new ways to teach complex concepts. However, ChatGPT poses different threats to the traditional education and research system, including the possibility of cheating on online exams, human-like text generation, diminished critical thinking skills, and difficulties in evaluating information generated by ChatGPT. This study explores potential opportunities and threats that ChatGPT poses to overall education from the perspective of students and educators. Furthermore, for programming learning, we explore how ChatGPT helps students improve their programming skills. To demonstrate this, we conducted different coding-related experiments with ChatGPT, including code generation from problem descriptions, pseudocode generation of algorithms from texts, and code correction. We also verified the generated codes with an online judge system to evaluate their accuracy.

We propose a class of stochastic models for a dynamics of limit order book with different type of liquidities. Within this class of models we study the one where a spread decreases uniformly, belonging to the class of processes known as a population processes with uniform catastrophes. The law of large numbers (LLN), central limit theorem (CLT) and large deviations (LD) are proved for our model with uniform catastrophes. Our results allow us to satisfactorily explain the volatility and local trends in the prices, relevant empirical characteristics that are observed in this type of markets. Furthermore, it shows us how these local trends and volatility are determined by the typical values of the bid-ask spread. In addition, we use our model to show how large deviations occur in the spread and prices, such as those observed in flash crashes.

Traditional travel agencies and tour operators are recognized as relevant stakeholders in the tourism distribution chain, even though their role as transforming agents in the achievement of more sustainable tourism is beginning to be accepted. This document collects the main topics and the most recent approaches from the academic literature in its analysis of Corporate Social Responsibility (CSR) practices developed by these intermediaries. The work has been structured around six recurring themes in most of the studies carried out: the new intermediaries that operate on the Internet; the role of travel agencies and tour operators in tourism sustainability; the influence of the size of the company; CSR as a disruptive innovation in this sector; the potential of the sustainable tourism supply chain; and the motivations and attitudes of the managers of these businesses when they engage with responsibility. This last topic is developed in greater detail since it is closely linked to the previous ones and dictates in most cases the meaning and function of the sustainable performance.

The most common type of non-Hodgkin lymphoma in adults is diffuse large B-cell (DLBCL). There is a historical unmet need for more effective therapies in the 2nd and 3rd line setting. Emerging immunochemotherapies have shown activity in small studies of heavily pre-treated patients with prolonged remissions achieved in some patients. Anti-CD19 CAR (chimeric antigen receptor) T cells are potentially curative in the 3rd line and beyond setting and are under investigation in earlier lines of therapy.1 Antibody-drug conjugates (ADC’s) such as polatuzumab vedotin targeting the pan-B-cell marker CD79b has proven effectiveness in multiply-relapsed DLBCL patients. Tafasitamab (MOR208) is an anti-CD19 monoclonal antibody producing prolonged remissions when combined with Lenalidomide in patients who were not candidates for salvage chemotherapy or autologous stem cell transplant. Selinexor, an oral, small-molecule selective inhibitor of XPO1-mediated nuclear export (SINE), demonstrated prolonged activity against heavily-pretreated DLBCL without cumulative toxicity and is being investigated as part of an oral, chemotherapy-free regimen for relapsed aggressive lymphoma. This article reviews current strategies and novel therapies for relapsed/refractory DLBCL.

River regulations ultimately degrade fluvial forms and morphodynamics and simplify riparian and aquatic habitats. For several decades, river restoration actions have been performed to recover geomorphic processes and diversify these habitats to enhance both river biodiversity and ecosystem services. The objective of this study is to provide quantitative feedback on the experimental restoration of a large regulated and by-passed river (the Upper Rhine downstream of the Kembs Dam, France/Germany). This restoration consisted of the construction of two transverse groynes and the removal of bank protection. A monitoring framework composed of topo-bathymetric surveys as well as flow velocity and grain size measurements was established to assess the channel morphodynamic responses and evaluate their effects on habitat suitability for five native fish species using habitat models. A riverscape approach was used to evaluate the landscape changes in terms of both the configuration and the composition, which cannot be considered with classic approaches (e.g., WUA). Our results show that the two transverse groynes and, to a lesser extent, bank erosion, which was locally enhanced by the two groynes, increased habitat diversity due to the creation of new macroforms (e.g., pools and mid-bars) and fining of the bed grain size. Using a riverscape approach, our findings highlight that the restoration improved lentic fish habitats (eel and juvenile nase species) due to slowing of the local current and the deposition of fine sediments downstream of both groynes. As a consequence, the restoration improved the habitat suitability of the studied reach for more fish species compared with the pre-restoration conditions. This study also demonstrates that the salmon habitats downstream of the restored reach were improved due to fining of the bed grain size. This finding highlights that for restorations aimed at fish habitats, the grain size conditions must be taken into consideration along with the flow conditions. Furthermore, the implementation of groynes, while not a panacea, can be a strategy for improving fish habitats on highly regulated rivers, but only when more functional and natural options are impossible due to major constraints.

The akimotoite--majorite--bridgmanite (Ak--Mj--Bm) triple point in MgSiO3 has been measured in large-volume press (LVP; COMPRES 8/3 assembly) and laser-heated diamond anvil cell (LHDAC). For the LVP data, we calculated pressures from the calibration by Leinenweber (2012). For the LHDAC data, we conducted \textit{in situ} determination of pressure at high temperature using the Pt scale by Dorogokupets and Dewaele (2007) at synchrotron. The measured temperatures of the triple point are in good agreement between LVP and LHDAC at 1990-2000 K. However, the pressure for the triple point determined from the LVP is 3.9+/-0.6~GPa lower than that from the LHDAC dataset. The triple point determined through these experiments will provide an important reference point in the pressure-temperature space for future high-pressure experiments and allow mineral physicists to compare the pressure--temperature conditions measured in these two different experimental methods.