Innovative Concept Research Articles

Characteristic alterations of gut microbiota and serum metabolites in patients with chronic tinnitus: a multi-omics analysis

ABSTRACT Chronic tinnitus is a central nervous system disorder. Currently, the effects of gut microbiota on tinnitus remain unexplored. To explore the connection between gut microbiota and tinnitus, we conducted 16S rRNA sequencing of fecal microbiota and serum metabolomic analysis in a cohort of 70 patients with tinnitus and 30 healthy volunteers. We used the weighted gene co-expression network method to analyze the relationship between the gut microbiota and the serum metabolites. The random forest technique was utilized to select metabolites and gut taxa to construct predictive models. A pronounced gut dysbiosis in the tinnitus group, characterized by reduced bacterial diversity, an increased Firmicutes/Bacteroidetes ratio, and some opportunistic bacteria including Aeromonas and Acinetobacter were enriched. In contrast, some beneficial gut probiotics decreased, including Lactobacillales and Lactobacillaceae. In serum metabolomic analysis, serum metabolic disturbances in tinnitus patients and these differential metabolites were enriched in pathways of neuroinflammation, neurotransmitter activity, and synaptic function. The predictive models exhibited great diagnostic performance, achieving 0.94 (95% CI: 0.85–0.98) and 0.96 (95% CI: 0.86–0.99) in the test set. Our study suggests that changes in gut microbiota could potentially influence the occurrence and chronicity of tinnitus, and exert regulatory effects through changes in serum metabolites. Overall, this research provides new perceptions into the potential role of gut microbiota and serum metabolite in the pathogenesis of tinnitus, and proposes the “gut-brain-ear” concept as a pathomechanism underlying tinnitus, with significant clinical diagnostic implications and therapeutic potential. IMPORTANCE Tinnitus affects millions of people worldwide. Severe cases may lead to sleep disorders, anxiety, and depression, subsequently impacting patients’ lives and increasing societal healthcare expenditures. However, tinnitus mechanisms are poorly understood, and effective therapeutic interventions are currently lacking. We discovered the gut microbiota and serum metabolomics changes in patients with tinnitus, and provided the potential pathological mechanisms of dysregulated gut flora in chronic tinnitus. We proposed the innovative concept of the “gut-brain-ear axis,” which underscores the exploration of gut microbiota impact on susceptibility to chronic tinnitus through serum metabolic profile modulation. We also reveal novel biomarkers associated with chronic tinnitus, offering a new conceptual framework for further investigations into the susceptibility of patients, potential treatment targets for tinnitus, and assessing patient prognosis. Subsequently, gut microbiota and serum metabolites can be used as molecular markers to assess the susceptibility and prognosis of tinnitus.Furthermore, fecal transplantation may be used to treat tinnitus.

Strategic Innovation: Integrating System Dynamics into Management Control

This article explores the concept of strategic innovation, with a focus on its integration with system dynamics in the field of management control. By examining the synergies between these two domains, the article proposes innovative perspectives to enhance decision-making processes and overall business management. The authors highlight the benefits of adopting a systemic approach in management control, emphasizing how it can foster a deeper understanding of complex interactions within organizations. Practical case studies illustrate how this integration can lead to significant innovations, providing managers with valuable tools to navigate in an ever-evolving business environment. Ultimately, the article underscores the strategic importance of merging systems thinking with management control to promote more effective, resilient, and future-oriented business management.

Engine strip and build lab: a practical approach to learning gas turbine engines

Abstract The world is currently undergoing a technological transformation with numerous innovative concepts emerging. This shift is driven by remarkable advancements in artificial intelligence and the urgent need for decarbonisation. With this comes a growing demand for skilled engineers who can actively contribute at any stage within the life cycle of a product. This can be the generation of new concepts at low Technology Readiness Levels or contributing actively to their development and operational safety. This paper explores the integration of a 1-day practical activity to reinforce theoretical concepts learned within a classroom-based environment. Small groups of students were given the opportunity of engaging with a small helicopter engine (Rolls-Royce Gnome engine) through the disassembly and reassembly of the exhaust and power turbine section while following the manufacturer’s manual and ensuring industrial norms for safe practice. This hands-on activity included an introduction to tooling, a Gnome familiarisation activity, and an introduction to inspection techniques. Based on the feedback recorded, the students experienced a notable improvement in their basic understanding by effectively reinforcing knowledge acquired within the classroom through active engagement with an actual gas turbine engine.

Pain chronification risk assessment: advanced phenotyping and scoring for prediction and treatments tailored to individualized patient profile

AbstractAcute pain is a physiologic, protective life-important warning neurological signal indicating multi-level tissue modulations caused by a broad spectrum of health adverse events such as stress overload, mechanical trauma, ischemia–reperfusion, sterile and infection-triggered inflammation, single- and multi-organ damage, acute and chronic wounds, tissue remodeling and degeneration, amongst others. On the other hand, pain chronification results in a pathologic transformation from the protective pain signaling into persistent debilitative medical condition with severe consequences including but not restricted to phenotype-specific behavioral patterns, reduced quality of life, and cognitive and mood disorders. Who is predisposed to an increased vs. decreased pain sensitivity and to the pain chronification? The motivation of personalized medicine that “same size does not fit all” is getting obvious also for an advanced approach in algesiology. Consequently, an in-depth patient stratification is essential for the paradigm change in overall pain management from currently applied reactive medical services to the cost-effective predictive, preventive, and personalized medicine (PPPM/3PM) in primary (reversible damage to health and targeted protection against health-to-disease transition) and secondary (personalized protection against disease progression) care. To this end, specifically innovative concepts of phenotyping elaborated in this study play a crucial role in patient stratification for predicting pain-associated outcomes, evidence-based targeted prevention of the pain chronification, and creation of treatment algorithms tailored to individualized patient profiles.

Complementary activity of Trifludimoxazin and Saflufenacil when used in combination for postemergence and residual weed control

Abstract Trifludimoxazin is a new herbicide that inhibits protoporphyrinogen oxidase and is targeted for commercial market introduction in North America, South America, and Asia. It will be available both as a standalone product and in a 1:2 mixture with saflufenacil. The herbicide is intended for use in preplant burndown (PPBD) and preemergence applications in cereal, corn (Zea mays L.), soybean [Glycine max (L.) Merr.], and pulse crops to control a variety of annual broadleaf and grass weed species. Additionally, it is planned to be used in tree crops, oil-palm (Elaeis quineensis Jacq.), and non-crop areas. In this study, we meticulously evaluated the performance and effectiveness of both the standalone herbicide and the innovative mixture concept in combating prevalent weeds commonly encountered in corn and soybean fields. Our findings revealed that both products exhibited exceptional efficacy, significantly reducing the presence of these troublesome weeds. Furthermore, the mixture concept not only demonstrated commendable soil mobility but also showcased impressive residual activity, positioning it as a powerful tool for sustainable weed control. These promising effects are further substantiated by our comprehensive ADME (Adsorption-Distribution-Metabolism-Extraction) studies, which provide insight into the behavior and longevity of the herbicides in the agricultural ecosystem.

A Conceptual Design Method Based on Concept–Knowledge Theory and Large Language Models

Abstract To obtain innovative concepts in the design, designers often need to retrieve and use interdisciplinary knowledge. Concept–knowledge (C–K) theory emphasizes the role of knowledge and introduces the knowledge (K) space and concept (C) space, employing operators to transform the contents between these spaces. Some studies, based on this theory, have successfully provided designers with different forms of knowledge to stimulate concept generation. However, the amount of knowledge provided in these studies is limited, and they fail to offer convenient methods for knowledge retrieval and reasoning, making it challenging to meet the needs of conceptual design across different fields. This paper proposes an enhanced C–K method leveraging large language models (LLMs) to help designers retrieve knowledge and uncover potentially new concepts. Our method redefines the C space and K space within the context of LLMs, dividing the properties of concept into function, appearance, and technology, and requiring the knowledge to correspond to these properties, thereby facilitating a structured connection between concepts and knowledge. Based on this definition, we achieved flexible knowledge retrieval and concept ideation leveraging LLMs. We also conducted a case study on wearable devices to validate our method. The results showed that our method helped designers to retrieve professional knowledge and inspired them to create feasible and innovative concepts.

Research on Teaching Idea Innovation of University Physical Education Teachers

This study takes the teaching reform concept of university PE teachers in the new era as the research object, and uses the literature method, logical analysis method and inductive deduction method to deeply analyze the development opportunity and realistic bottleneck of the teaching innovation concept of university PE teachers. The research shows that the teaching innovation concept of university PE teachers faces new challenges, and the generalization of education consciousness and the motivation of concept innovation of university PE teachers are insufficient. "Cultivating morality and cultivating people" as the basic task of education, the author puts forward the concept of "moral education first" in the innovation of teaching concept of physical education teachers in universities. Construct the evaluation system of "cultivating virtue" and "cultivating body"; To construct a new model of post-service education for physical education teachers in universities, and establish a new standard for the evaluation of professional titles of physical education teachers in universities; The new model of cultivating multi-disciplinary high quality sports talents and the concept of PE teachers' education should be integrated with aesthetic education. The aim is to provide theoretical reference for the timely updating of the teaching concept of university PE teachers under the background of the new era and optimizing the classified development of university PE teachers.

Programmable Nanomodulators for Precision Therapy, Engineering Tumor Metabolism to Enhance Therapeutic Efficacy.

Tumor metabolism is crucial in the continuous advancement and complex growth of cancer. The emerging field of nanotechnology has made significant strides in enhancing the understanding of the complex metabolic intricacies inherent to tumors, offering potential avenues for their strategic manipulation to achieve therapeutic goals. This comprehensive review delves into the interplay between tumor metabolism and various facets of cancer, encompassing its origins, progression, and the formidable challenges posed by metastasis. Simultaneously, it underscores the classification of programmable nanomodulators and their transformative impact on enhancing cancer treatment, particularly when integrated with modalities such as chemotherapy, radiotherapy, and immunotherapy. This review also encapsulates the mechanisms by which nanomodulators modulate tumor metabolism, including the delivery of metabolic inhibitors, regulation of oxidative stress, pH value modulation, nanoenzyme catalysis, nutrient deprivation, and RNA interference technology, among others. Additionally, the review delves into the prospects and challenges of nanomodulators in clinical applications. Finally, the innovative concept of using nanomodulators to reprogram metabolic pathways is introduced, aiming to transform cancer cells back into normal cells. This review underscores the profound impact that tailored nanomodulators can have on tumor metabolic, charting a path toward pioneering precision therapies for cancer.

Asymmetric Large Kernel Distillation Network for efficient single image super-resolution

Recently, significant advancements have been made in the field of efficient single-image super-resolution, primarily driven by the innovative concept of information distillation. This method adeptly leverages multi-level features to facilitate high-resolution image reconstruction, allowing for enhanced detail and clarity. However, many existing approaches predominantly emphasize the enhancement of distilled features, often overlooking the critical aspect of improving the feature extraction capabilities of the distillation module itself. In this paper, we address this limitation by introducing an asymmetric large-kernel convolution design. By increasing the size of the convolution kernel, we expand the receptive field, which enables the model to more effectively capture long-range dependencies among image pixels. This enhancement significantly improves the model's perceptual ability, leading to more accurate reconstructions. To maintain a manageable level of model complexity, we adopt a lightweight architecture that employs asymmetric convolution techniques. Building on this foundation, we propose the Lightweight Asymmetric Large Kernel Distillation Network (ALKDNet). Comprehensive experiments conducted on five widely recognized benchmark datasets-Set5, Set14, BSD100, Urban100, and Manga109-indicate that ALKDNet not only preserves efficiency but also demonstrates performance enhancements relative to existing super-resolution methods. The average PSNR and SSIM values show improvements of 0.10 dB and 0.0013, respectively, thereby achieving state-of-the art performance.

Exploring Novel Fixed Point Solutions in Boundary Value Problems

Within this manuscript, we present an innovative concept of contraction, building upon the foundation laid by Jleli and Samet. Subsequently, we introduce the concept of θ-contractions. Leveraging these novel ideas, we formulate a series of fresh fixed-point theorems applicable to spaces utilizing the Controlled Branciari metric. Notably, our approach integrates and consolidates diverse fixed-point outcomes, eliminating the necessity for the Hausdorff assumption. To illustrate the practicality of our findings, we provide examples and applications to boundary value problems associated with fourth-order differential equations.

High Entropy Ceramics for Electromagnetic Functional Materials

AbstractMicrowave absorbing materials play an increasingly important role in modern electronic warfare technology for enhancing electromagnetic compatibility and suppressing electromagnetic interference. High‐entropy ceramics (HECs) possess extraordinary physical and chemical properties, and more importantly, the high tunability of multi‐component HECs has brought new opportunities to microwave absorbing materials. Rich crystallographic distortions and multi‐component occupancies enable HECs to have highly efficient microwave absorption properties, excellent mechanical properties, and thermal stability. Therefore, the structural advantages of HECs are integrated from comprehensive perspectives, emphasizing on the role of dielectric and magnetic properties in the absorption phenomenon. Strategies are proposed to improve the microwave absorption capacity of HECs, including composition optimization, microstructure engineering, and post‐treatment technology. Finally, the problems and obstacles associated with high‐entropy materials (HEMs) research are discussed. The innovative design concepts of high‐entropy microwave absorbing ceramics are highlighted.

Multidisciplinary Research Addressing Emerging Global Challenges of Ensuing Decade: A Review

Multidisciplinary research is the hallmark in 21st century and a way forward towards holistic sustainability. Ancient philosophers viz; Aristotle, Socrates, Confucius, Al-Farabi, Sigmund Freud, Jabar-bin-Hayan, Benjamin Bloom, Karl Marx and Friedrich Nietzsche laid foundations of philosophies based on their intellectual multidisciplinary knowledge approach. The promising impact of multi-scientific disciplines demonstrate the importance of diverse academic and research domains. In fact, every discipline is interrelated and interdependent to each other with a minute hairline difference. MDR unites scientists globally from distinct academic backgrounds to address pressing challenges of next decade, but no doubt cooperation is crucial in scientific study as it stimulates innovation, creativity, and systematic conception. Present review emphasizes importance of transdisciplinary research in diverse meta-problems, tackling future challenges and multi-faceted complexities. Emerging technology has made it possible for scientists and scholars from various purviews around the world to collaborate on projects and research without being at the same location. The most significant recommendation of this article highlights the future scope of transdisciplinary approach by inclusion of collaborative research meeting sustainable development goals (SDG’s) through multi research, thereby aligning researchers and scientists towards internationalization.

"Anchoring Capture" Effect Mimicking Proline in Hardy Deep-Sea Fish to Stabilize the Zinc Anode with Lower Operating Temperature.

The low plating/stripping efficiency of zinc anodes, dendrite growth, and high freezing points of aqueous solutions hinder the practical application of aqueous zinc-ion batteries. This paper proposes a zwitterionic permeable network solid-state electrolyte based on the "anchor-capture" effect to address these problems by incorporating proline (Pro, a biological antifreeze agent) into the electrolyte. Extensive validation tests, Quantum Chemistry (QC) calculations, Molecular Dynamics (MD) Simulations, and ab initio molecular dynamics simulations consistently indicate that the amino groups in proline adsorb onto the Zn metal surface, stabilizing the zinc anode-electrolyte interface, suppressing side reactions from water decomposition, and homogenizing zinc-ion flux. This electrolyte demonstrates excellent reversibility in Zn-Mn2O3 cells and Zn-Zn half-cells, achieving a high coulombic efficiency of over 99.4% across 2000 cycles in Zn-Mn2O3 full cells, and delivering a discharge-specific capacity of 175.2 mAhg-1 at -35°C and 1 Ag-1. Additionally, an appropriate concentration of proline lowers the electrolyte's freezing point to -45°C through the network's solid-state effect, ensuring the stable operation of the solid-state battery at -35°C. This innovative concept of network solid-state electrolytes injects new vitality into the development of multifunctional solid-state electrolytes.

Flow and Heat Transfer Behaviors of Impinging Spiral Jet Arrays in a Rectangular Channel Based on Battery Cooling Technique

Impinging spiral jet array cooling, an innovative concept, has been proposed as a way to enhance the thermal management of batteries in electric vehicles. The comprehensive flow and heat transfer behaviors of impinging spiral jet arrays in a rectangular channel with/without crossflow were numerically studied. In this study, the total spiral nozzle length was 290 mm under the helix generated at a slot nozzle thickness of 1.0 mm. The rectangular crossflow channel for the heated zone had length and width ratios of 18 and 12. The jet-to-target spacing was 1.0. The jet Reynolds numbers ranging from 5000 to 20000 were performed via the shear stress transport turbulence model. The results showed that the regional average Nusselt number increased with the increase in jet Reynolds number for all conditions. The average heat transfer rate in the case with crossflow was beyond 7.8% higher than in the case without crossflow due to the cumulative crossflow, the concentration of each impingement jet was deflected and bent downstream.

Research on the Sensing Properties and Vibration Reduction Performance of Self-Sensing Self-Tuning Magnetic Fluid Damper

Abstract The semi-active control damping system has gained popularity due to its quick response time and versatility. However, external sensors are susceptible to environmental interference, affecting system reliability and increasing complexity and maintenance costs, restricting their use. To address this, a self-sensing self-tuning magnetic fluid damper is proposed. The vibration-measuring induction coil is wound on the damper to sense the magnetic fluid vibration information in real time, and the vibration signal is communicated to the self-tuning control circuit. The control circuit calculates and determines the dominant frequency of structural vibration, then outputs the relevant current signal to set the damper's natural frequency to track the excitation frequency, resulting in self-tuning vibration reduction. First, the self-sensing unit's output induced electromotive force model is created, followed by an expression of the damper's natural frequency, indicating that the self-sensing unit can achieve self-tuning vibration reduction by tracking the excitation frequency. The multi-field coupling simulation model of the magnetic fluid damper is generated, and the induction coil coupling mode and damper excitation angle are defined to obtain the maximum induced voltage.
Finally, an experimental platform was developed to assess the damper's self-sensing and self-tuning vibration reduction performance. The experimental results demonstrate that the suggested self-tuning magnetic fluid damper exhibits excellent self-sensing capabilities and effectively reduces vibrations. This offers an innovative research concept for using magnetic fluid in self-sensing technology and vibration reduction domains.

Retracing digital assumptions in social movements: the Italian case of No Tav

This paper aims to shed light on the key role of digital repertoire in shaping participation forms, challenging internet as a flexible, dynamic, and pervasive platform that transcends reality in terms of sense and relationship construction processes. The research design traces the narration and performance patterns in the last decade of the No Tav movement case study. The work examines the concept of innovation as both an evolving repertoire and a set of users’ practices. The empirical analysis is conducted through digital content analysis using quantitative textual analysis techniques. The research questions aim to understand how activists produce and share knowledge, what mobilization patterns emerge and are enabled by digital media, and how organizational assumptions change due to the digital turn.

Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis

Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with ASP.NET framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5Stack platform with ESP32 and MPU-6050 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios.

Visualisation Design Ideation with AI: A New Framework, Vocabulary, and Tool

This paper introduces an innovative framework for visualisation design ideation, which includes a collection of terms for creative visualisation design, the five-step process, and an implementation called VisAlchemy. Throughout the visualisation ideation process, individuals engage in exploring various concepts, brainstorming, sketching ideas, prototyping, and experimenting with different methods to visually represent data or information. Sometimes, designers feel incapable of sketching, and the ideation process can be quite lengthy. In such cases, generative AI can provide assistance. However, even with AI, it can be difficult to know which vocabulary to use and how to strategically approach the design process. Our strategy prompts imaginative and structured narratives for generative AI use, facilitating the generation and refinement of visualisation design ideas. We aim to inspire fresh and innovative ideas, encouraging creativity and exploring unconventional concepts. VisAlchemy is a five-step framework: a methodical approach to defining, exploring, and refining prompts to enhance the generative AI process. The framework blends design elements and aesthetics with context and application. In addition, we present a vocabulary set of 300 words, underpinned from a corpus of visualisation design and art papers, along with a demonstration tool called VisAlchemy. The interactive interface of the VisAlchemy tool allows users to adhere to the framework and generate innovative visualisation design concepts. It is built using the SDXL Turbo language model. Finally, we demonstrate its use through case studies and examples and show the transformative power of the framework to create inspired and exciting design ideas through refinement, re-ordering, weighting of words and word rephrasing.

Construction and Empirical Research of an Evaluation System for High-Quality Development of Small Towns in Guangxi Under the New Development Concept

The high-quality development of small towns in Guangxi is crucial for regional economic balance and sustainable development. This research conducts an in-depth exploration and systematic construction and validation analysis of the high-quality development of small towns in Guangxi. Guided by the new development concepts of innovation, coordination, green, openness, and sharing, a comprehensive evaluation system is established to assess the development levels of 70 small towns from 2005 to 2022. The research findings are as follows: 1. The analysis revealed significant growth in these dimensions over the study period, highlighting the unique characteristics of different types of small towns. 2. It was observed that small towns in southeastern Guangxi generally demonstrated higher development levels, while those in the northwestern regions exhibited considerable disparities, indicating regional imbalances. 3. The overall gap in the high-quality development level of small towns is gradually narrowing, but the difference is still relatively significant. 4. The findings underscore the advantages of the evaluation system developed, which effectively captures the multidimensional nature of high-quality development and serves as a robust framework for future research.

A novel stability-indicating chromatographic quantification of the antiparkinsonian drug safinamide in its pharmaceutical formulation employing HPTLC densitometry and ion-pair HPLC–DAD

Parkinson's disease (PD) emerges as a notable health concern among the elderly population. Safinamide mesylate (SAF) is a novel and emerging add-on therapy in PD treatment. The stability of innovative drug formulations and the development of appropriate stability-indicating methods are of great importance to modern pharmaceutical analysis. The current work has established novel comprehensive stability-indicating chromatographic approaches, HPTLC coupled with densitometric quantification and HPLC–DAD, for the selective assay of SAF in pharmaceutical formulation along with its synthetic precursor impurity; 4-hydroxy benzaldehyde (4-HBD) in presence of its stress induced degradation products. The stability of SAF was investigated under different stress conditions. It was found that SAF is likely to undergo acid, base hydrolysis, and oxidative degradation. Using mass spectrometry and infrared spectroscopy, the structures of the forced degradation products were confirmed and elucidated. The dissolution behavior of Parkimedine® Tablets was also monitored in the FDA suitable medium. Multiple assessment tools were used to evaluate the environmental sustainability of the proposed methods and the reported one. The greenness tools included Complex-GAPI and AGREE metrics. In addition, the innovative concepts of "blueness" and "whiteness" evaluation were incorporated through the newly introduced BAGI and RGB12 algorithms, respectively.