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Creativity is often needed in requirements elicitation, i.e., requirement idea generation; and techniques to enhance creativity are believed to be useful. This paper describes two controlled experiments to compare the requirements-elicitation effectiveness of three creativity enhancement techniques (CET): (1) full EPMcreate; (2) Power-Only EPMcreate, an optimization of full EPMcreate; and (3) traditional brainstorming. In each experiment, one team of university students applied one of the two or three CETs under study in the experiment to generate ideas for requirements for enhancing a high school’s public Web site. The results of the first experiment indicate that Power-Only EPMcreate is more effective, by the quantity and quality of the ideas generated, than the full EPMcreate, which is, in turn, more effective than brainstorming. The results of the second experiment confirm that Power-Only EPMcreate is more effective, by the same measures, than full EPMcreate. In each experiment, for the sake of uniform, reproducible evaluation, a requirement idea is considered high quality if it is both new and useful.

Speech enhancement has substantial interest in the utilization of speaker identification, video-conference, speech transmission through communication channels, speech-based biometric system, mobile phones, hearing aids, microphones, voice conversion etc. Pattern mining methods have a vital step in the growth of speech enhancement schemes. To design a successful speech enhancement system consideration to the background noise processing is needed. A substantial number of methods from traditional techniques and machine learning have been utilized to process and remove the additive noise from a speech signal. With the advancement of machine learning and deep learning, classification of speech has become more significant. Methods of speech enhancement consist of different stages, such as feature extraction of the input speech signal, feature selection, feature selection followed by classification. Deep learning techniques are also an emerging field in the classification domain, which is discussed in this review. The intention of this paper is to provide a state-of-the-art summary and present approaches for using the widely used machine learning and deep learning methods to detect the challenges along with future research directions of speech enhancement systems.

Currently, there is great interest in producing thermal energy (heat) from renewable sources and storing this energy in a suitable system. The use of a latent heat storage (LHS) system using a phase change material (PCM) is a very efficient storage means (medium) and offers the advantages of high volumetric energy storage capacity and the quasi-isothermal nature of the storage process. In recent years, phase change materials (PCMs) have become an interesting research area due to their advantages especially in thermal energy storage (TES). Indeed, there are a large number of PCMs that melt and solidify over a wide temperature range, making them interesting thermal energy storage media in several applications. In the literature, research on PCMs and their associated applications has attracted and still attracts great interest from various researchers and scientists. Most of the research studies on phase change materials (PCMs) have been generally devoted to the development of PCM-based energy storage technologies, the promotion of PCM-based renewable energy sources, and the encouragement of sustainable/profitable (economic) use of PCM-based energy. In order to get an overview of current progress and trends, to highlight research and to identify gaps, from the literature reviews undertaken on this research topic, it is useful to review the major research studies conducted in this field. Our analysis showed that the literature lacks many comprehensive analyses and studies on the applications of PCMs, the phase transition processes (melting and solidification) of PCMs and the factors that influence these transitions, and in particular the calculation models of the thermal performance parameters of a PCM performing a phase transition and the thermal performance parameters of a PCM-based TES system (referred to as LHS unit). To address these questions, we have presented in this review article a detailed overview of the literature on (a) relevant practical applications of PCMs, (b) characteristics and performances of phase transition processes, (c) major factors influencing PCM transition processes such as geometric design of the PCM tank and its orientation, imposed boundary and operating conditions, thermophysical properties of the material (PCM), and (d) models for calculating thermal performance parameters for a PCM performing a phase transition and for an LHS unit. In addition, several techniques aimed at improving heat transfer in PCMs have been introduced and discussed. The findings indicate that there are three types of PCMs: eutectic, inorganic, and organic. Numerous other industries also use PCMs, such as solar energy (including thermal energy storage through the use of photovoltaic and latent heat systems); buildings; HVAC systems; textiles; the biomedical, food, and agricultural industries; the automotive sector; and desalination. Besides PCMs classification and use, it was found that during phase transitions of PCMs heat transfer is dominated by conduction and natural convection. During melting, conduction heat transfer is dominant in the early stages, and as the PCM melts, natural convection dominates. Unlike melting, solidification is dominated by conductive heat transfer. On the other hand, boundary conditions, material properties, and enclosure configuration and orientation all found having an impact on melting and solidification. In this context, by increasing, for example, thermal conductivity, viscosity, wall-imposed temperature, and PCM initial temperature, as well as by decreasing PCM latent heat of melting, PCM melting point, and PCM system orientation, the melting process rate increases. However, by increasing thermal conductivity, viscosity, melting point, and PCM system orientation, as well as by lowering the latent heat of melting, the initial PCM temperature, and the imposed wall temperature, the solidification process rate increases. Lastly, introducing external fields and adding high thermal conductivity additives like fins, metal foam, and nanoparticles can greatly increase the rate at which PCM melts and solidifies.

Medical imaging is essential in modern health care, allowing accurate diagnosis and effective treatment planning. These images, however, often demonstrate low contrast, noise, and brightness distortion that reduce their diagnostic reliability. This review presents a structured and comprehensive analysis of advanced histogram equalization (HE)-based techniques for medical image enhancement. Our review methodology encompasses: (1) classical HE approaches and related limitations in medical domains; (2) adaptive schemes like Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogrma Equalization (CLAHE) and their advance variants; (3) brightness-preserving schemes like BBHE and MMBEBHE and related algorithms; (4) dynamic and recursive histogram equalization methods incorporating DHE and RMSHE; (5) fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images; and (6) hybrid optimization methodologies through the application of metaheuristic algorithms (World Cup Optimization, Particle Swarm Optimization, Genetic Algorithms, along with histogram-based methodologies.) There is also a comparative discussion given based on contrast improvement, image brightness preservation, noise management, and computational efficiency. Such advancements have better capabilities of improving image quality, which is more important for improved diagnosis and image analysis.

Input enhancement techniques, such as frequency of occurrence and typographic enhancement, have been reported beneficial to the learning of L2 collocations. This study compared the effects of three times of frequency and bolding on the learning of L2 collocations by EFL learners so as to find a more effective technique for instructors and students to acquire collocations through reading activities. Four classes of English majors (N = 90) participated in the study and were randomly assigned to a frequency group (FG), a typographic enhancement group (TEG), and a control group (CG). The learning of collocations was measured through form recall and form recognition tasks. Major findings indicated that: (1) the FG performed significantly better than the TEG and the CG on both form recall and form recognition in the immediate posttest. No significant differences were observed between the TEG and the CG, (2) significant differences were reported between the FG and the other two groups in the delayed posttest, and (3) the effect of frequency of occurrence could not be retained after 2 weeks given the significant difference discovered between the immediate and delayed posttests. More studies are needed to explore the effects and mechanisms of frequency of occurrence and typographic enhancement on the learning of L2 collocations.

•MALDI MSP/I can detect and map blood in enhanced marks over paint.•The application of MALDI MSP/I does not prevent subsequent DNA typing.•An alternative forensic workflow for suspected blood marks on painted walls integrating MALDI MSP/I is suggested.•MALDI MSP can detect blood in fingermarks concealed under a coat of paint. [Display omitted] Matrix Assisted Laser Desorption Ionization Mass Spectrometry Profiling and Imaging (MALDI MSP and MALDI MSI), in combination with bottom up proteomics, have proven to successfully detect and map blood-derived peptide signatures in blood fingermarks, with high specificity and compatibility with a number of blood enhancement techniques (BET). In the present study, the application of MALDI MSP and MSI to blood marks has been investigated further. In particular, the MALDI based detection and visualisation of blood has been explored in tandem with DNA typing. This investigation has been undertaken in a scenario simulating blood fingermarks on painted walls. In the present study, two sets of marks were analysed with each set comprising of a depletion series of four marks deposited on a surface treated to simulate painted walls: Set I - developed with Ninhydrin (NIN) and Set II- developed with Acid Black-1 (AB-1). For both sets, the application of MALDI MSP was successful in detecting haem and human specific haemoglobin peptide markers. MALDI MSI also provided molecular images by visualising haem on the ridge pattern enhanced by BET. The feasibility of successful and subsequent DNA profiling from the recovered fingermarks was also assessed for marks that had undergone enzymatic in situ digestion and MALDI MSI; it was observed that in 73% of the samples analysed, a DNA profile suitable for comparison was obtained. Based on these results, a possible operational workflow has been proposed incorporating the use of a MALDI MS based approach as a confirmatory test for human blood enabling subsequent DNA typing.

This paper presents a comprehensive review of symmetrically shaped antennas in terms of antenna size, dielectric materials, resonating band, peak gain, radiation pattern, simulating tools, and their applications. In this article, flower shape, leaf shape, tree shape, fan shape, Pi shape, butterfly shape, bat shape, wearable, multiband, monopole, and fractal antennas are discussed. Further, a survey of previously reported bandwidth enhancement techniques of microstrip patch antenna like introduction of thick and lower permittivity substrate, multilayer substrate, parasitic elements, slots and notches, shorting wall, shorting pin, defected ground structure, metamaterial-based split ring resonator structure, fractal geometry, and composite right-hand/left-handed transmission line approach is presented. The physics of these techniques has been discussed in detail which is supported by circuit theory model approach.

What are the main findings? The study presents the first systematic evaluation of how spectral enhancement techniques applied to Sentinel-2 imagery influence deep learning models for detecting palaeochannel-related soil and crop marks. Among the tested approaches, the multi-temporal composite (MV) consistently achieved the highest segmentation accuracy. Seasonal variability strongly affects detection performance: early growth and post-harvest periods provide the most favourable conditions, while peak vegetation severely reduces visibility and segmentation accuracy across all enhancement techniques. What are the implications of the main findings? The results demonstrate that incorporating spectral enhancement techniques and seasonally tailored preprocessing strategies significantly improve the robustness and precision of deep learning-based palaeochannel detection workflows. By highlighting the interplay between spectral transformations, seasonal conditions, and model behaviour, this study establishes a new benchmark for integrating enhancement methods into AI-driven prospection pipelines, supporting more accurate, scalable, and season-adaptive applications in archaeological and environmental remote sensing. This study presents the first systematic investigation into the influence of spectral enhancement techniques on the segmentation accuracy of specific soil and vegetation marks associated with palaeochannels. These marks are often subtle and can be seasonally obscured by vegetation dynamics and soil variability. Spectral enhancement methods, such as spectral indices and statistical aggregations, are routinely applied to improve their visual discriminability and interpretability. Despite recent progress in automated detection workflows, no prior research has rigorously quantified the effects of these enhancement techniques on the performance of deep learning–based segmentation models. This gap at the intersection of remote sensing and AI-driven analysis is critical, as addressing it is essential for improving the accuracy, efficiency, and scalability of subsurface feature detection across large and heterogeneous landscapes. In this study, two state-of-the-art deep learning architectures, U-Net and YOLOv8, were trained and tested to assess the influence of these spectral transformations on model performance, using Sentinel-2 imagery acquired across three seasonal windows. Across all experiments, spectral enhancement techniques led to clear improvements in segmentation accuracy compared with raw multispectral inputs. The multi-temporal Median Visualisation (MV) composite provided the most stable performance overall, achieving mean IoU values of 0.22 ± 0.02 in April, 0.07 ± 0.03 in August, and 0.19 ± 0.03 in November for U-Net, outperforming the full 12-band Sentinel-2 stack, which reached only 0.04, 0.02, and 0.03 in the same periods. FCC and VBB also performed competitively, e.g., FCC reached 0.21 ± 0.02 (April) and VBB 0.18 ± 0.03 (April), showing that compact three-band enhancements consistently exceed the segmentation quality obtained from using all spectral bands. Performance varied with environmental conditions, with April yielding the highest accuracy, while August remained challenging across all methods. These results highlight the importance of seasonally informed spectral preprocessing and establish an empirical benchmark for integrating enhancement techniques into AI-based archaeological and geomorphological prospection workflows.

Long-distance, high latency teleoperation tasks are difficult, highly stressful for teleoperators, and prone to over-corrections, which can lead to loss of control. At higher latencies, or when teleoperating at higher vehicle speed, the situation becomes progressively worse. To explore potential solutions, this research work investigates two 2D visual feedback-based assistive interfaces (sliding-only and sliding-and-zooming windows) that apply simple but effective video transformations to enhance teleoperation. A teleoperation simulator that can replicate teleoperation scenarios affected by high and adjustable latency has been developed to explore the effectiveness of the proposed assistive interfaces. Three image comparison metrics have been used to fine-tune and optimise the proposed interfaces. An operator survey was conducted to evaluate and compare performance with and without the assistance. The survey has shown that a 900ms latency increases task completion time by up to 205% for an on-road and 147% for an off-road driving track. Further, the overcorrection-induced oscillations increase by up to 718% with this level of latency. The survey has shown the sliding-only video transformation reduces the task completion time by up to 25.53%, and the sliding-and-zooming transformation reduces the task completion time by up to 21.82%. The sliding-only interface reduces the oscillation count by up to 66.28%, and the sliding-and-zooming interface reduces it by up to 75.58%. The qualitative feedback from the participants also shows that both types of assistive interfaces offer better visual situational awareness, comfort, and controllability, and significantly reduce the impact of latency and intermittency on the teleoperation task.