Current Limitations Research Articles

Two-Stage Transient Control for VSG Considering Fault Current Limitation and Transient Angle Stability

Two-Stage Transient Control for VSG Considering Fault Current Limitation and Transient Angle Stability

A Review of In Vitro and In Silico Swallowing Simulators: Design and Applications.

Swallowing is a primary and complex behaviour that transports food and drink from the oral cavity, through the pharynx and oesophagus, into the stomach at an appropriate rate and speed. To understand this sophisticated behaviour, a tremendous amount of research has been carried out by utilising the in vivo approach, which is often challenging to perform, poses a risk to the subjects if interventions are undertaken and are seldom able to control for confounding factors. In contrast, in silico (computational) and in vitro (instrumental) methods offer an alternate insight into the process of the human swallowing system. However, the appropriateness of the design and application of these methods have not been formally evaluated. The purpose of this review is to investigate and evaluate the state of the art of in vitro and in silico swallowing simulators, focusing on the evaluation of their mechanical or computational designs in comparison to the corresponding swallowing mechanisms during various phases of swallowing (oral phase, pharyngeal phase and esophageal phase). Additionally, the potential of the simulators is also discussed in various areas of applications, including the study of swallowing impairments, swallowing medications, food process design and dysphagia management. We also address current limitations and recommendations for the future development of existing simulators.

Advancing Tumor Therapy: Development and Utilization of Protein-Based Nanoparticles

Protein-based nanoparticles (PNPs) in tumor therapy hold immense potential, combining targeted delivery, minimal toxicity, and customizable properties, thus paving the way for innovative approaches to cancer treatment. Understanding the various methods available for their production is crucial for researchers and scientists aiming to harness these nanoparticles for diverse applications, including tumor therapy, drug delivery, imaging, and tissue engineering. This review delves into the existing techniques for producing PNPs and PNP/drug complexes, while also exploring alternative novel approaches. The methods outlined in this study were divided into three key categories based on their shared procedural steps: solubility change, solvent substitution, and thin flow methods. This classification simplifies the understanding of the underlying mechanisms by offering a clear framework, providing several advantages over other categorizations. The review discusses the principles underlying each method, highlighting the factors influencing the nanoparticle size, morphology, stability, and functionality. It also addresses the challenges and considerations associated with each method, including the scalability, reproducibility, and biocompatibility. Future perspectives and emerging trends in PNPs’ production are discussed, emphasizing the potential for innovative strategies to overcome current limitations, which will propel the field forward for biomedical and therapeutic applications.

Research progress of photoacoustic imaging technology in brain diseases

Photoacoustic imaging (PAI) technology, characterized by its high resolution, minimal biological impact, and high sensitivity, has become a cornerstone in biomedical research. Its application spans various domains, showing significant promise for disease diagnosis. Currently, the majority of PAI research is conducted using animal models, with human clinical applications still in early development. This paper reviews the fundamental principles of PAI and explores its use in animal brain imaging studies. It addresses the current challenges and limitations of the technology and evaluates the potential for extending these techniques to human cerebral imaging. PAI offers substantial benefits for diagnosing neurological disorders, and its adaptation for human brain studies is crucial for advancing our understanding of neuropathogenesis, improving early disease detection, and monitoring treatment effectiveness. Continued advancements in PAI are expected to not only augment its role in neuroscience research but also establish it as a valuable tool in clinical diagnostics.

Advances in laser and stem cell treatment: current technologies, limitations, and future prospects

Advances in laser and stem cell treatment: current technologies, limitations, and future prospects

Enhancing Higher-Order Thinking Skills in Early Childhood Through APE Puzzle Hopscotch: A Quasi-Experimental Study

This study examines the influence of APE puzzle hopscotch on higher-order thinking skills (HOTS) in children aged 4-6 years at PAUD IT ADAR Kalasan DIY. Employing a quasi-experimental design, the research included an experimental group receiving the APE puzzle hopscotch intervention and a control group without any intervention. Over four months, pretest and posttest assessments were conducted to evaluate HOTS in both groups. The research instruments measured indicators across analysis (C4), evaluation (C5), and creation (C6). Results revealed significant enhancements in the experimental group’s HOTS, with N-Gain scores showing moderate effectiveness. Statistical tests confirmed the intervention's impact, demonstrating significant differences in post-test scores between the experimental and control groups (p < 0.05). The findings suggest that APE puzzle hopscotch effectively stimulates cognitive development in early childhood. Despite limitations such as a small sample size and short study duration, the study underscores the potential of interactive educational tools in enhancing critical thinking and problem-solving skills. Implications for educational practice include the integration of engaging tools into curricula to foster HOTS. Future research should address the current study's limitations, validate the findings across different settings, and explore the effects on other developmental areas. Additionally, longitudinal studies could provide deeper insights into the long-term benefits of such educational tools. This research contributes to the ongoing discourse on the importance of innovative teaching strategies in early childhood education, emphasizing the role of interactive play in cognitive growth.

Multimodal Machine Learning for Prognosis and Survival Prediction in Renal Cell Carcinoma Patients: A Two-Stage Framework with Model Fusion and Interpretability Analysis

Current medical limitations in predicting cancer survival status and time necessitate advancements beyond traditional methods and physical indicators. This research introduces a novel two-stage prognostic framework for renal cell carcinoma, addressing the inadequacies of existing diagnostic approaches. In the first stage, the framework accurately predicts the survival status (alive or deceased) with metrics Accuracy, Precision, Recall, and F1 score to evaluate the effects of the classification results, while the second stage focuses on forecasting the future survival time of deceased patients with Root Mean Square Error and Mean Absolute Error to evaluate the regression results. Leveraging popular machine learning models, such as Adaptive Boosting, Extra Trees, Gradient Boosting, Random Forest, and Extreme Gradient Boosting, along with fusion models like Voting, Stacking, and Blending, our approach significantly improves prognostic accuracy as shown in our experiments. The novelty of our research lies in the integration of a logistic regression meta-model for interpreting the blending model’s predictions, enhancing transparency. By the SHapley Additive exPlanations’ interpretability, we provide insights into variable contributions, aiding understanding at both global and local levels. Through modal segmentation and multimodal fusion applied to raw data from the Surveillance, Epidemiology, and End Results program, we enhance the precision of renal cell carcinoma prognosis. Our proposed model provides an interpretable analysis of model predictions, highlighting key variables influencing classification and regression decisions in the two-stage renal cell carcinoma prognosis framework. By addressing the black-box problem inherent in machine learning, our proposed model helps healthcare practitioners with a more reliable and transparent basis for applying machine learning in cancer prognostication.

Sustainability of the Housing Market: Post-COVID-19 Effects on Housing Preferences in Cyprus

After the announcement of the outbreak of the COVID-19 pandemic, people around the world had to adopt online education, working from home and social-distancing policies in an effort to stop the spread of COVID-19. The lockdowns and home isolation brought to the surface the current limitations of the existing housing stock as people needed to re-organize their home space to host various daytime functionalities. Therefore, one could question whether the priorities of potential homebuyers have been changed due to technology, health, and socialization alteration? This study explores the preferences for housing attributes and trends in Cyprus post-COVID-19 pandemic and compares these against the views of real estate professionals who represent the Cypriot residential demand prior the pandemic. Data were gathered via qualitative surveys from 117 potential homebuyers and 36 real estate professionals from across Cyprus. A survey methodology with questionnaires was used to analyze the changes in the real estate demand between the pre- and post-COVID-19 periods. Descriptive statistics were used to determine the mean importance of the housing attributes and to compare the results from the pre- and post-COVID-19 pandemic periods. The analysis found a shift toward houses rather than apartments, and the results suggest that new housing should contain more capacity for private use spaces, and at the same time, it should be designed with flexible and adaptive layouts to accommodate more activities. This study discusses whether these shifts are likely to be temporary or permanent and the implications for real estate professionals.

Animated designs revisited – a case study under EU design law

In view of the ongoing extensive review of EU design law and given the expected abandoning of the current seven-view limit, revisiting protection of animated designs under EU law and practice (and limits to such protection), particularly against the famous-infamous Candy Crush Graphical User Interface (GUI) dispute, allows for some fascinating conclusions with respect to a proper distinction between ‘partial designs’ and ‘parts of a design’, the correct identification of the relevant prior art, the determination of the subject matter of a design, and the finding of infringement. Overall, ‘less’ is apparently ‘more’.

Perils and opportunities in using large language models in psychological research.

The emergence of large language models (LLMs) has sparked considerable interest in their potential application in psychological research, mainly as a model of the human psyche or as a general text-analysis tool. However, the trend of using LLMs without sufficient attention to their limitations and risks, which we rhetorically refer to as "GPTology", can be detrimental given the easy access to models such as ChatGPT. Beyond existing general guidelines, we investigate the current limitations, ethical implications, and potential of LLMs specifically for psychological research, and show their concrete impact in various empirical studies. Our results highlight the importance of recognizing global psychological diversity, cautioning against treating LLMs (especially in zero-shot settings) as universal solutions for text analysis, and developing transparent, open methods to address LLMs' opaque nature for reliable, reproducible, and robust inference from AI-generated data. Acknowledging LLMs' utility for task automation, such as text annotation, or to expand our understanding of human psychology, we argue for diversifying human samples and expanding psychology's methodological toolbox to promote an inclusive, generalizable science, countering homogenization, and over-reliance on LLMs.

Local adaptation, plasticity, and evolved resistance to hypoxic cold stress in high-altitude deer mice.

A fundamental question in evolutionary biology concerns the relative contributions of phenotypic plasticity vs. local adaptation (genotypic specialization) in enabling wide-ranging species to inhabit diverse environmental conditions. Here we conduct a long-term hypoxia acclimation experiment to assess the relative roles of local adaptation and plasticity in enabling highland and lowland deer mice (Peromyscus maniculatus) to sustain aerobic thermogenesis at progressively increasing elevations. We assessed the relative physiological performance capacities of highland and lowland natives as they were exposed to progressive, stepwise increases in hypoxia, simulating the gradual ascent from sea level to an elevation of 6000 m. The final elevation of 6000 m far exceeds the highest attainable elevations within the species' range, and therefore tests the animals' ability to tolerate levels of hypoxia that surpass the prevailing conditions within their current distributional limits. Our results demonstrate that highland natives exhibit superior thermogenic capacities at the most severe levels of hypoxia, suggesting that the species' broad fundamental niche and its ability to inhabit such a broad range of elevational zones is attributable to a combination of genetically based local adaptation and plasticity. Transcriptomic and physiological measurements identify evolved changes in the acclimation response to hypoxia that contribute to the enhanced thermogenic capacity of highland natives.

An ADP framework for flexibility and cost aggregation: Guarantees and open problems

With the increasing amount of Distributed Energy Resources (DERs), coordination of Distribution Grid Operators (DSOs) and Transmission Grid Operators (TSOs) is of paramount importance. Managing a large number of DERs at the TSO level is, however, challenging. To address this problem, flexibility aggregation is a topic of frequent research activities. Aggregation means to describe the combined flexibility of the DERs at the vertical grid coupling between DSO and TSO. Existing works are often limited with respect to guaranteeing feasibility, with respect to efficient numerical implementation, and in terms of quantification of the cost of DER usage. In the present paper, we investigate aggregation based on Approximate Dynamic Programming (ADP). We propose efficient numerical aggregation schemes using tools from computational geometry thus avoiding the need to solve multiple OPF problems. We rely on different variants of the DistFlow model for radial grids, which are computationally efficient. This allows to model of current and voltage limits and enables the consideration of voltage dependencies in the aggregation. Furthermore, we propose a method for cost aggregation and identify open problems of flexibility aggregation.

Evaluation of airborne asbestos concentrations associated with the maintenance of brakes on an industrial overhead crane

Objectives To evaluate potential airborne asbestos exposures during brake maintenance and repair activities on a P&H overhead crane, and during subsequent handling of the mechanic’s clothing. Methods Personal (n = 27) and area (n = 61) airborne fiber concentrations were measured during brake tests, removal, hand sanding, compressed air use, removal and reattachment of chrysotile-containing brake linings, and reinstallation of the brake linings. The mechanic’s clothing was used to measure potential exposure during clothes handling. Results All brake linings contained between 19.9% to 52.4% chrysotile asbestos. No amphibole fibers were detected in any bulk or airborne samples. The average full-shift airborne chrysotile concentration was 0.035 f/cc (PCM-equivalent asbestos-specific fibers, or PCME). Average task-based personal air samples collected during brake maintenance, sanding, compressed air use, and brake lining removal tasks ranged from 0 to 0.48 f/cc (PCME). The calculated 30-minute time-weighted average (TWA) airborne chrysotile concentration associated with 5-15 minutes of clothes handling was 0–0.035 f/cc PCME. Conclusion The results indicated that personal and area TWA fiber concentrations measured during all crane brake maintenance and clothes handling tasks were below the current OSHA 8-h TWA Permissible Exposure Limit for asbestos of 0.1 f/cc. Further, no airborne asbestos fibers were measured during routine brake maintenance tasks following the manufacturer’s maintenance manual procedures. All short-term airborne chrysotile concentrations measured during non-routine tasks were below the current 30-minute OSHA excursion limit for asbestos of 1 f/cc. This study adds to the available data regarding chrysotile exposure potential during maintenance on overhead cranes.

Crustal Characterization of the Hengill Geothermal Fields: Insights From Isotropic and Anisotropic Seismic Noise Imaging Using a 500‐Node Array

AbstractThe Hengill volcano and its associated geothermal fields represent Iceland's most productive harnessed high‐temperature geothermal fields, where resources are fueled by cooling magmatic intrusions connected to three volcanic systems. The crustal structure in this area is highly heterogeneous and shaped by the intricate interplay between tectonic forces and magmatic/hydrothermal activities. This complexity makes detailed subsurface characterization challenging. In this study, we aim to push the current resolution limits using a 500‐node temporary seismic array and perform an isotropic and, for the first time, radially‐anisotropic velocity model of the area. The high‐resolution isotropic velocity model reveals the characteristic N30ºE fissure swarm that crosses the area within the top 500 m and outlines a deep‐seated low‐velocity body composed of cooling magmatic intrusions at 5 km depth. This deeper body is located near the eastern part of the three volcanic centers and connected to a shallower body at 2–3 km depth that strikes westward toward Hengill volcano. Additionally, our study discovered that non‐induced earthquakes deeper than 2 km align with velocity contrasts that reflect structural variability, indicating the potential to identify deep permeable pathways using dense array imaging. The anisotropic model indicates that the shallow crust of Hengill within the top 2 km is dominated by vertical fractures or cracks, likely attributed to overall divergent deformation from rifting in the study area. This characteristic is diminished at depths greater than 2–3 km, replaced by a layering pattern where the lava flows and/or subhorizontal intrusions become the primary factors influencing the observed anisotropy.

Recent advances in the therapeutic potential of cathelicidins.

The alarming increase in antimicrobial resistance in the last decades has prompted the search for alternatives to control infectious diseases. Antimicrobial peptides (AMPs) represent a heterogeneous class of molecules with ample antibacterial, antiviral, and antifungal effects. They can be found in many organisms, including all classes of vertebrates, providing a valuable source of new antimicrobial agents. The unique properties of AMPs make it harder for microbes develop resistance, while their immunomodulatory properties and target diversity reinforce their translational use in multiple diseases, from autoimmune disorders to different types of cancer. The latest years have witnessed a vast number of studies evaluating the use of AMPs in therapy, with many progressing to clinical trials. The present review explores the recent developments in the medicinal properties of cathelicidins, a vast family of AMPs with potent antimicrobial and immunomodulatory effects. Cathelicidins from several organisms have been tested in disease models of viral and bacterial infections, inflammatory diseases, and tumors, with encouraging results. Combining nanomaterials with active, natural antimicrobial peptides, including LL-37 and synthetic analogs like ceragenins, leads to the creation of innovative nanoagents with significant clinical promise. However, there are still important limitations, such as the toxicity of many cathelicidins to healthy host cells and low stability in vivo. The recent advances in nanomaterials and synthetic biology may help overcome the current limitations, enabling the use of cathelicidins in future therapeutics. Furthermore, a better understanding of the mechanisms of cathelicidin action in vivo and their synergy with other host molecules will contribute to the development of safer, highly effective therapies.

The Phosphorus-Iron Nexus: Decoding the Nutrients Interaction in Soil and Plant.

Phosphorus (P) and iron (Fe) are two essential mineral nutrients in plant growth. It is widely observed that interactions of P and Fe could influence their availability in soils and affect their homeostasis in plants, which has received significant attention in recent years. This review presents a summary of latest advances in the activation of insoluble Fe-P complexes by soil properties, microorganisms, and plants. Furthermore, we elucidate the physiological and molecular mechanisms underlying how plants adapt to Fe-P interactions. This review also discusses the current limitations and presents potential avenues for promoting sustainable agriculture through the optimization of P and Fe utilization efficiency in crops.

Procedurally generated AI compound media for expanding audial creations, broadening immersion and perception experience

Recently, the world has been gaining vastly increasing access to more and more advanced artificial intelligence tools. This phenomenon does not bypass the world of sound and visual art, and both of these worlds can benefit in ways yet unexplored, drawing them closer to one another. Recent breakthroughs open possibilities to utilize AI driven tools for creating generative art and using it as a compound of other multimedia. The aim of this paper is to present an original concept of using AI to create a visual compound material to existing audio source. This is a way of broadening accessibility thus appealing to different human senses using source media, expanding its initial form. This research utilizes a novel method of enhancing fundamental material consisting of text audio or text source (script) and sound layer (audio play) by adding an extra layer of multimedia experience – a visual one, generated procedurally. A set of images generated by AI tools, creating a story-telling animation as a new way to immerse into the experience of sound perception and focus on the initial audial material. The main idea of the paper consists of creating a pipeline, form of a blueprint for the process of procedural image generation based on the source context (audial or textual) transformed into text prompts and providing tools to automate it by programming a set of code instructions. This process allows creation of coherent and cohesive (to a certain extent) visual cues accompanying audial experience levering it to multimodal piece of art. Using nowadays technologies, creators can enhance audial forms procedurally, providing them with visual context. The paper refers to current possibilities, use cases, limitations and biases giving presented tools and solutions.

An improved system design method for cell-based energy storage systems: A combination of a constraint satisfaction problem with an extended Ragone plot

Over the past three decades, advancements in electrical energy storage technologies have substantially enhanced performance and reduced costs, rendering them viable for diverse applications. Cell-based energy storage systems, such as batteries and supercapacitors, are particularly notable for their adaptability and scalable interconnection capabilities. However, the design process for such systems lacks uniform standards, presenting a gap in research for the conceptual system design. This paper introduces an improved system design method (SDM), addressing critical limitations of existing approaches: (a) Shifting from current-based to power-based requirements; (b) Enabling flexible adaptation of operational design points beyond fixed datasheet specifications; (c) Ensuring harmonization of energy storage design with other system components, notably power electronics. Central to our SDM is the novel “extended Ragone plot” (ERP), which maps cell-specific energy–power relations and allows for flexible adjustment of operational limits. Since it can be determined experimentally, the subsequent design method is model-free and can efficiently be executed for different cell types. In the SDM, this ERP is coupled with a constraint satisfaction problem (CSP) to compute a comprehensive design solution space that accommodates all application-specific requirements and component interrelations. It is implemented in a holistic, interactive tool, facilitating mathematical optimization and enabling visual analysis of energy storage system designs. As a proof-of-concept, two case studies on the design of lithium-ion battery systems for stationary grid supply demonstrate the method’s superiority over conventional approaches. While in the first case, an optimal design can be determined immediately, in the second case, there are no valid design solutions based on the cell’s initial operating point. Utilizing the ERP, a practical design solution is achieved by reducing the maximum cell power by approximately 9%, thereby modifying the cell’s available voltage and current limits. This approach provides a possible solution for scenarios where a system design based on data sheets alone is not feasible. A comparative analysis and graphical synthesis of both case studies validate this hypothesis, illustrating the novel system design method’s ability to precalculate and evaluate diverse design scenarios in advance. The source code and data used in this study are openly accessible, offering the SDM as an interactive tool for analyzing and designing energy storage systems.

Volatile organic compounds: A promising new frontier for cancer screening

The late onset of cancer symptoms can cause a significant delay in diagnosis, impacting patients’ prognosis and quality of life, thus prompting a need for alternative screening and detection methods. Neoplastic processes cause distinct and immediate changes to the body’s metabolism, creating unique patterns in the volatile organic compounds (VOCs) produced and released through exhaled breath. For this reason, VOC profiles have emerged as diagnostic indicators for several types of malignancies, facilitating early cancer detection. Both non-invasive and accessible, the analysis of breath VOCs for cancer screening and detection has gained recognition as a new frontier in cancer diagnostics. Using exhaled breath instead of gold-standard cancer detection and screening tools that are traditionally invasive and uncomfortable for the patient could be revolutionary in improving patient compliance. Further, compared to the gold-standard tools, breath testing is relatively inexpensive, and the method of analysis, storage, and transporting the samples is simplified. Several studies have demonstrated the accuracy of VOC analysis in detecting various types of cancer, including breast cancer, colon cancer, prostate cancer, gastric cancer, and melanoma. This article summarizes the evidence supporting VOC analysis for cancer screening and detection. It reviews the clinical utility, current limitations, and necessity for standardization across all VOC screening tools to ensure the standardization and reliability of measurements. The evidence supporting breath tests to detect cancer accurately is strong, demonstrating that VOC sampling improves patient outcomes and decreases the global burden of malignant conditions by detecting cancer earlier.

Thickness Measurements with EMAT Based on Fuzzy Logic.

Metal thickness measurements are essential in various industrial applications, yet current non-contact ultrasonic methods face limitations in range and accuracy, hindering the widespread adoption of electromagnetic ultrasonics. This study introduces a novel combined thickness measurement method employing fuzzy logic, with the aim of broadening the applicational scope of the EMAT. Leveraging minimal hardware, this method utilizes the short pulse time-of-flight (TOF) technique for initial thickness estimation, followed by secondary measurements guided by fuzzy logic principles. The integration of measurements from the resonance, short pulse echo, and linear frequency modulation echo extends the measurement range while enhancing accuracy. Rigorous experimental validation validates the method's effectiveness, demonstrating a measurement range of 0.3-1000.0 mm with a median error within ±0.5 mm. Outperforming traditional methods like short pulse echoes, this approach holds significant industrial potential.