Shivasubramanian Gopinath, Joseph Rosen, Vijayakumar Anand
Fresnel incoherent correlation holography (FINCH) is a self-interference-based incoherent digital holography method. In FINCH, light from an object point is split into two beams, modulated differently using two lenses with different focal distances, and creates a self-interference hologram. At least three phase-shifted holograms are recorded and synthesized into a complex hologram, which reconstructs the object image without twin image and bias noises. Compared with conventional imaging, FINCH exhibits a longer depth of focus (DOF) and higher lateral resolution. In this study, we propose and demonstrate a new method termed post-engineering of axial resolution in FINCH (PEAR-FINCH), which enables post-recording DOF engineering for the first time. In PEAR-FINCH, a library of FINCH holograms catalogued with unique axial characteristics, DOF, and focus location is recorded by changing the focal distance of one of the diffractive lenses. Selected holograms from this library are combined to engineer new axial characteristics not achievable in FINCH. A two-step reconstruction, involving numerical back-propagation and deconvolution with a point spread hologram, is implemented. Experiments with multiplane objects having large axial separations confirm that PEAR-FINCH achieves a substantially extended DOF compared with direct imaging and FINCH. PEAR-FINCH will be promising for applications in biomedical imaging, holography, and fluorescence microscopy.
Balkar Yildirim, Alan Albert Coley, Diego Fernando López
We investigate whether black holes can persist through the bounce with a minimal scale factor in a non-singular cosmology, whereby black holes from a previous contracting phase survive into the current expanding one. We do so by studying a generalized McVittie spacetime which embeds a spherically symmetric black hole in a positive spatial curvature bouncing FLRW cosmological background within the modified theory of teleparallel new general relativity. There are no further assumptions on the spacetime (e.g., on the form of the scale factor) initially, and the local evolution is derived from the field equations of the theory, utilizing a perturbative scheme which is valid ``near the bounce". To leading order we obtain a simple bounce solution similar to that in general relativity for a closed FLRW model with a positive cosmological constant, but in which the curvature term in the Friedmann equation is re-normalized within new general relativity. Qualitatively the minimum of the bounce at $t=0$ changes, but near the bounce the evolution remains symmetric. The central inhomogeneity evolves at higher perturbative orders, where the details depend on the arbitrary constants of the perturbative solution. Hence the evolution of the local horizon during the bounce changes qualitatively, where the effects depend on the signs of the perturbation, and the symmetry across the bounce is disrupted due to a linear term.
Bearing in mind the expression, “<i>The art challenges the technology, and the technology inspires the art</i>”, we say that oral surgery is changing rapidly due to the introduction of new technologies that improve the way surgical treatments are planned and performed [...]
ABSTRACT The authors theoretically report a plasmonic photodetector based on graphene‐black phosphorus heterostructure which is capable of operating from visible to mid‐infrared (MIR) wavelengths. The combination of plasmonic nanostructure and graphene‐black phosphorus heterostructure can significantly enhance the absorption, simulation results show that the proposed photodetector is capable of working from 400 nm to 3 μm with ultrahigh responsivity all exceeding 14,000 AW−1 and large modulation bandwidth all over 327 GHz. In addition, by utilising the evident anisotropic characteristics of black phosphorus, the broadband plasmonic photodetector exhibits the angle‐dependent responsivity which can be used to design the customised plasmonic photodetector. The authors believe that the proposed photodetector would provide the new approach to design the broadband and angle‐dependent optoelectronic devices based on 2D materials.
Coal sludge, a fine-grained by-product of hard coal benefit, comprises a mixture of coal particles and mineral and organic matter. Generated during sedimentation and dewatering processes in preparation plants, it is typically recovered as a semi-solid filter cake. The material has potential applications in energy production and, with appropriate processing and stabilization, could be utilized in geotechnical facilities. The strength properties defined by the internal friction angle and cohesion, as well as the deformation properties expressed by compressibility, are among the most important mechanical characteristics of soil. This article presents tests of coal sludge, for which the internal friction angle, cohesion, and oedometric primary and secondary moduli were determined. The material was prepared at its optimum moisture content and maximum dry density prior to testing. In the direct shear test, using a shear box of 6 × 6 cm, each sample was consolidated for 24 h under the applied vertical stress, under which it was subsequently sheared. The shear rate was constant at 0.01 mm/min, and the test was conducted up to 10% horizontal deformation. The vertical stresses applied ranged from 50 to 200 kPa. In the oedometer test, samples were prepared to fit the dimensions of the oedometer ring, and each subsequent load stage was applied after 24 h. The range of vertical stresses in this test was from 12.5 to 400 kPa. The results of the direct shear test (φ = 24°, c = 28 kPa) are similar to the strength parameters typically obtained for medium-cohesive soils, such as sandy silt (φ = 22°, c = 25 kPa. The results of the compressibility tests (0.89 MPa < M<sub>0</sub> < 6.35 MPa) correspond to values characteristic of organic soils, for example, organic silts (0.5 MPa < M<sub>0</sub> < 5 MPa). Moreover, analysis of the consolidation curves showed that up to a vertical stress of 100 kPa, coal sludge does not exhibit rheological behavior. The obtained results indicate that coal sludge, when compacted up to its optimum moisture content and to an adequate dry density, can be effectively utilized for geotechnical applications, such as the construction of isolation barriers, as a component of geotechnical mixtures, or as a sealing material for the reclamation of post-mining areas.
This study investigates the efficiency of electrocoagulation (EC) in removing Tartrazine Yellow (TY) azo dye from synthetic wastewater using aluminium electrodes. The effects of current density, <i>i</i> (0.008–0.024 A cm<sup>−2</sup>), initial solution pH (3.0–7.0), and treatment time, <i>t</i> (10–50 min) on key process parameters, including pH, temperature (<i>T</i>), TY dye concentration (<i>c</i>) and removal efficiency (<i>R</i>), anode consumption, and sludge characterisation were studied. The experiments were conducted in a batch reactor according to the experimental plan developed in Design-Expert software, which was also used for the evaluation of the obtained results. As the EC process progresses, the removal efficiency of the TY dye increases, while the removal dynamics and the final value of <i>R</i> (ranging from about 28% to 99%) depend on the experimental conditions (<i>i</i>, initial pH, and <i>t</i>). A high <i>R</i>-value is reached faster with the application of higher current densities and lower initial pH. This is associated with a higher proportion of carbon and sulphur in the sludge (from the TY dye) after the EC process. Additionally, a mathematical model was developed to predict the experimental data. A numerical optimisation method using response surface methodology (RSM) was applied to determine the optimal operating conditions for TY dye removal. This resulted in the following conditions: pH = 3.37, <i>t</i> = 18.74 min, and <i>i</i> = 0.016 A cm<sup>−2</sup>, achieving a removal efficiency of ≈70%.
Chakrit Suvanjumrat, Jetsadaporn Priyadumkol, Kunthakorn Khaothong
et al.
Enhancing natural convection heat transfer in heated electrical devices, particularly those with curved geometries and limited space for cooling systems is a crucial area of research. This study experimentally evaluated the performance of a corona wind generator—an electrohydrodynamic (EHD) system—employing needle-to-cylinder configurations to improve natural convection around a heated cylinder. Three configurations were investigated: a single vertical wire electrode, a single lateral wire electrode, and two lateral wire electrodes, positioned perpendicular to the cylindrical surface at varying distances. Voltages ranging from 0 to 9000 V were applied to produce a corona wind jet. The findings revealed that lateral wire electrode configurations significantly enhanced natural convection heat transfer, achieving an average Nusselt number improvement exceeding 51.17 % at 8000 V compared to natural convection alone. Among these, the single lateral electrode configuration demonstrated superior performance, yielding a 13.87 % higher average Nusselt number than the vertical electrode configuration. It was observed that the corona wind jet initially impinged on the heated cylinder; however, increasing the distance between the electrode tip and the cylinder caused the jet to rise due to buoyancy, reducing its cooling effectiveness. Despite this limitation, the lateral electrode configurations effectively enhanced natural convection. The experimental results were utilized to develop a practical Nusselt number correlation that integrates voltage, electrode tip distance, distance of two electrodes, and cylinder diameter. The proposed model demonstrated high accuracy, with R2 values ranging from 0.81 to 0.94, offering a valuable tool for designing efficient cooling systems for electrical devices.
The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.
Sharon Guardado, Risha Parveen, Zheying Zhang
et al.
The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.
Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey
et al.
Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.
Alexandra Mazak-Huemer, Christian Huemer, Michael Vierhauser
et al.
With the increasing significance of Research, Technology, and Innovation (RTI) policies in recent years, the demand for detailed information about the performance of these sectors has surged. Many of the current tools are limited in their application purpose. To address these issues, we introduce a requirements engineering process to identify stakeholders and elicitate requirements to derive a system architecture, for a web-based interactive and open-access RTI system monitoring tool. Based on several core modules, we introduce a multi-tier software architecture of how such a tool is generally implemented from the perspective of software engineers. A cornerstone of this architecture is the user-facing dashboard module. We describe in detail the requirements for this module and additionally illustrate these requirements with the real example of the Austrian RTI Monitor.
Chun-Wei Huang, Si Ying Yau, Chiao-Ling Kuo
et al.
Study region: The Choushui River Fan, Taiwan. Study focus: Groundwater overdraft has led to not only groundwater depletion but also environmental disasters, such as subsidence and seawater intrusion in the Choushui River Alluvial Fan, Taiwan. The influence of land subsidence is gradually shifting from the coast to the center of the fan and threatening Taiwan high-speed rail. However, it remains a great challenge to manage and model the groundwater aquifer due to numerous unregulated wells. This study maps and locates private wells using deep learning technologies. We trained and validated convolutional-based deep learning neural networks (DNNs), using street view images. We applied the DNNs to a land subsidence area along the Taiwan high-speed rail, termed the Golden Corridor in Taiwan. The results showed that DNNs can recognize pumping wells with at least 90% accuracy. The testing cases showed their capability to recall all the pumping wells in three road segments along the Golden Corridor. Finally, we spatially estimated potential pumping of a subsidence area using the fine-trained DNNs. New hydrological insights for the region: Given the prevalence of unknown private pumping in the Choushui River Fan, our image data-driven computer vision approach not only eases labor-intensive private well investigations but also advances hydrologic understanding for groundwater modeling. We enhance comprehension of unknown sinks and provide their spatial distribution to improve groundwater modeling.
The creation of a Software Requirements Specification (SRS) document is important for any software development project. Given the recent prowess of Large Language Models (LLMs) in answering natural language queries and generating sophisticated textual outputs, our study explores their capability to produce accurate, coherent, and structured drafts of these documents to accelerate the software development lifecycle. We assess the performance of GPT-4 and CodeLlama in drafting an SRS for a university club management system and compare it against human benchmarks using eight distinct criteria. Our results suggest that LLMs can match the output quality of an entry-level software engineer to generate an SRS, delivering complete and consistent drafts. We also evaluate the capabilities of LLMs to identify and rectify problems in a given requirements document. Our experiments indicate that GPT-4 is capable of identifying issues and giving constructive feedback for rectifying them, while CodeLlama's results for validation were not as encouraging. We repeated the generation exercise for four distinct use cases to study the time saved by employing LLMs for SRS generation. The experiment demonstrates that LLMs may facilitate a significant reduction in development time for entry-level software engineers. Hence, we conclude that the LLMs can be gainfully used by software engineers to increase productivity by saving time and effort in generating, validating and rectifying software requirements.
Horia Mărgărit, Amanda Bowman, Krishnageetha Karuppasamy
et al.
In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges encountered in mapping the algorithm to physical hardware, and the software engineering considerations needed to achieve realistic results on today's non-fault-tolerant systems.
Abstract Multi-dimensional heterojunction materials have attracted much attention due to their intriguing properties, such as high efficiency, wide band gap regulation, low dimensional limitation, versatility and scalability. To further improve the performance of materials, researchers have combined materials with various dimensions using a wide variety of techniques. However, research on growth mechanism of such composite materials is still lacking. In this paper, the growth mechanism of multi-dimensional heterojunction composite material is studied using quasi-two-dimensional (quasi-2D) antimonene and quasi-one-dimensional (quasi-1D) antimony sulfide as examples. These are synthesized by a simple thermal injection method. It is observed that the consequent nanorods are oriented along six-fold symmetric directions on the nanoplate, forming ordered quasi-1D/quasi-2D heterostructures. Comprehensive transmission electron microscopy (TEM) characterizations confirm the chemical information and reveal orientational relationship between Sb2S3 nanorods and the Sb nanoplate as substrate. Further density functional theory calculations indicate that interfacial binding energy is the primary deciding factor for the self-assembly of ordered structures. These details may fill the gaps in the research on multi-dimensional composite materials with ordered structures, and promote their future versatile applications. Graphical Abstract