Innovative Method Research Articles

Optimization and validation of a micro–QuEChERS method for phthalates detection in small samples of cetacean blubber

In this study, an innovative method was developed to detect and quantify phthalates in fresh cetacean blubber. An adaptation of the ammonium formate QuEChERS method was used and adapted as a micro-extraction for small quantities of samples. Significantly, this technique utilized minimal quantities of reagents and salts, with the additional implementation of rigorous Quality Assurance/Quality Control protocols to further reduce background contamination. To ensure the reliability of this method, comprehensive validation procedures were conducted, with a specific focus on two widely studied cetacean species: the common bottlenose dolphin (Tursiops truncatus) and the short-finned pilot whale (Globicephala macrorhynchus). Determination coefficients (R2) for matrix-matched calibration were >0.93 with limits of quantifications (LOQ) of the method in the range of 5-10 ng/g. Mean recovery values were between 40 and 100%. This novel methodology holds particular relevance for environmental research studies, offering the capability to detect emerging contaminants with minimal sample requirements. This aspect is particularly valuable in investigations that involve free-ranging animals and rely on biopsy sampling. It allows for the assessment of contaminant levels in healthy individuals within wild populations, enhancing our understanding of ecological impacts and potential conservation measures.• A micro-extraction adaptation of the ammonium formate QuEChERS method was developed and applied to a small quantity of fresh cetacean blubber to detect phthalates.

Multi-dichotomous sequencing method MDSM for ordering the importance of variants' properties in multi-criteria decision-making.

There are plenty of Multi-Criteria Decision-Making (MCDM) methods that help to choose the most suitable solution assessed by several criteria (e.g. Saaty 1990; Simos 1990; Pamučar etal. 2018). They are applied in cases where several scales of different units describe the variants or the variants' properties are represented by linguistic, non-numbered terms. The inherent part of the MCDM algorithms is calculating the weights of the variants' properties, necessary for ordering the variants. If - in a certain problem - there are several properties to consider, sequencing their importance becomes a problem itself. The innovative method of sequencing is proposed in the article based on dichotomous splitting of the properties' importance. If made several times, it leads to the coherent - internally and with the decision-maker's intention - order of the properties' importance. Then the weights of the properties can be calculated with the use of different MCDM methods. The description of the method can be shortened as follows:•Divide the full set of features into two dichotomous subsets of lower and higher importance•Continue dichotomous divisions until there are only the subsets containing one element or subsets containing elements of equal importance.

A novel heat integrated double extractive divided-wall column avoiding vapor recompression system in water purification from petrochemical

The chemical plant produces wastewater with tetrahydrofuran (THF), methanol, and water, which form two energy-intensive binary azeotropes. With this aim, this research introduces a novel extractive divided-wall column (DWC) to efficiently separate water from the THF-methanol mixture. The study utilizes a combination of a solvent and steam, leading to the creation of a double extractive DWC (DEDWC) as the foundational model. A key innovation of this study is the recovery and reuse of waste heat from the column's top vapor through vapor recompression, which is used for further separation. While this method requires a significant initial compression ratio and, by extension, a considerable capital investment, the study introduces a heat integration system to mitigate these costs. This system raises the vapor temperature by combining it with the heated solvent (bottom product) and strategically employing intermediate steam-driven reboilers. This inventive method, known as the heat-integrated DEDWC (HiDEDWC), notably increases the vapor temperature by 15 K beyond the reboiler liquid. Impressively, HiDEDWC demonstrates a remarkable improvement in energy and cost efficiency over the standard DEDWC and the version with vapor recompression. Specifically, HiDEDWC achieves an impressive energy saving of 52.81% and reduces the total annual cost by 52.11% compared to DEDWC.

Augmented Reality and Artificial Intelligence Medical Waste Classification System and Method

There are four categories of medical waste that cannot be mixed as this will cause serious problems suchas environmental pollution or infection. In the past, the classification of medical waste often involved a lot ofhuman and material resources to process, with workers at risk of exposure to infectious substances. Therefore, an augmented reality (AR) and artificial intelligence (AI) medical waste classification system and method were developed. This innovative medical waste classification system and method combines AR and AI identification technology to reduce the risk of manual judgment errors by clinical staff when handling medical waste.

Appraisal of Green Communication Technology Deployment in Nigeria

The idea behind “green” processes and technologies is the use of upgraded, ecologically friendly processes and technologies in a way that preserves natural resources and doesn’t disturb the environment. Clean technology and environmental technology are other names for green technology. These days, most governments take steps to promote these technologies because of their value. Governments therefore suggested a number of financial incentives that produce electricity using renewable resources. As its primary objective, green technologies seek to address societal demands while minimizing the use of natural resources and causing no harm to them. The idea is to create materials that are entirely recyclable or reusable. Green technology generates energy through novel and inventive methods. The goal of green communication is to explore sustainability in relation to energy efficiency, the environment, and communication goals. Green communications have an obligation to promote ecological network equipment and systems and to fortify corporate environmental responsibility. The paper presents Nigeria renewable green energy sources, green electronics, recycling, communication technology areas of application. The paper also presents a careful consideration of materials and methods to ensure the chosen deployment are environmentally sustainable. Despite the challenges, utilizing green ICT methods in Nigeria is not just a choice, it is essential for a number of advantageous causes as people throughout the world become more environmentally aware, implementing green ICT practices can improve Nigeria’s standing internationally. It will demonstrate the nation’s dedication to sustainability.

Exploring systemic design practices in public sector innovation

Many societal challenges need to be resolved through innovative solutions at system level. Previous scholars have argued that it may be wise to combine design practice and system theory to form a management method for public sector innovation. However, it appears that few scholars have developed innovation management methods based on that combination. The aim of this article is therefore to contribute with new knowledge about how design practice and system theory can be combined in order to drive public sector innovation. The article proposes the development of a previously proposed systemic design management model and presents a toolbox with twelve innovation management tools. Although there is a logic in using the tools in a certain order, this article emphasizes the need for a flexible use of the tools and to be open to moving back and forth between different tools. The results also indicate that an initial key tool is to ensure that the right people become involved in the different phases of the innovation process. It is more important to involve the right people in different phases than to follow a specific pre-designed process model. In order to successfully create and implement innovative solutions at system level, the innovation processes need to engage different human resources for different tools. Different resources that understand the needs can contribute with new perspectives and also possess the mandate to make systemic changes.

Management of Cardiovascular Diseases by Short-Chain Fatty Acid Postbiotics.

Global health concerns persist in the realm of cardiovascular diseases (CVDs), necessitating innovative strategies for both prevention and treatment. This narrative review aims to explore the potential of short-chain fatty acids (SCFAs)-namely, acetate, propionate, and butyrate-as agents in the realm of postbiotics for the management of CVDs. We commence our discussion by elucidating the concept of postbiotics and their pivotal significance in mitigating various aspects of cardiovascular diseases. This review centers on a comprehensive examination of diverse SCFAs and their associated receptors, notably GPR41, GPR43, and GPR109a. In addition, we delve into the intricate cellular and pharmacological mechanisms through which these receptors operate, providing insights into their specific roles in managing cardiovascular conditions such as hypertension, atherosclerosis, heart failure, and stroke. The integration of current information in our analysis highlights the potential of both SCFAs and their receptors as a promising path for innovative therapeutic approaches in the field of cardiovascular health. The idea of postbiotics arises as an optimistic and inventive method, presenting new opportunities for preventing and treating cardiovascular diseases.

The Application of Bridge Monitoring Projects Exploiting Time Series Data of Structural Health Monitoring

Since the first attempts of Structural Health Monitoring the Vienna Consulting Engineers (VCE) have been involved to advance the BRIMOS technology which offers a wide range of services to engineers and operators of infrastructure networks. Over the last decade the dynamic behavior of hundreds of structures has been measured and evaluated. However, the current practice in Structural Health Monitoring is still a case to case approach which needs a lot of experience for proper interpretation and assessment of the investigated objects. The integration of new technologies is wanted to support and automate the evaluation process as well as increase the objectivity within the assessment routines. This paper shall indicate the need for innovate methods and tools from a user’s point of view and gives a short insight to the workflow and infrastructure of BRIMOS. The efforts which are undertaken within the BRIMOS infrastructure show the approach towards the application of data mining instruments to benefit for future and also from past monitoring projects. Datamining, Time Series Analysis, Knowledge Base, Structural Health Monitoring

TG-SPRED: Temporal Graph for Sensorial Data PREDiction

This study introduces an innovative method aimed at reducing energy consumption in sensor networks by predicting sensor data, thereby extending the network’s operational lifespan. Our model, Temporal Graph Sensor Prediction (TG-SPRED), predicts readings for a subset of sensors designated to enter sleep mode in each time slot, based on a non-scheduling-dependent approach. This flexibility allows for extended sensor inactivity periods without compromising data accuracy. TG-SPRED addresses the complexities of event-based sensing—a domain that has been somewhat overlooked in existing literature—by recognizing and leveraging the inherent temporal and spatial correlations among events. It combines the strengths of Gated Recurrent Units and Graph Convolutional Networks to analyze temporal data and spatial relationships within the sensor network graph, where connections are defined by sensor proximities. An adversarial training mechanism, featuring a critic network employing the Wasserstein distance for performance measurement, further refines the predictive accuracy. Comparative analysis against six leading solutions using four critical metrics—F-score, energy consumption, network lifetime, and computational efficiency—showcases our approach’s superior performance in both accuracy and energy efficiency.

Elevating metaverse virtual reality experiences through network‐integrated neuro‐fuzzy emotion recognition and adaptive content generation algorithms

AbstractInteractions between individuals and digital material have completely changed with the advent of the Metaverse. Due to this, there is an immediate need to construct cutting‐edge technology that can recognize the emotions of users and continuously provide material that is relevant to their psychological states, improving their overall experience. An inventive method that combines natural language processing adaptive content generation algorithms and neuro‐fuzzy‐based support vector machines natural language processing (SVM‐NLP) is proposed by researchers to meet this demand. With this merging, the Metaverse will be able to offer highly tailored and engaging experiences. Initially, a neuro‐fuzzy algorithm was developed to identify people's emotional moods from their physiological reactions and other biometric information. Fuzzy Logic and Support Vector Machine work together to manage the inherent ambiguity and unpredictability, which results in a more exact and accurate categorization of emotions. A key component of the ACGA is NLP technology, which uses real‐time emotional data to dynamically modify and personalize characters, stories, and interactive features in the Metaverse. The novelty of the proposed approach lies in the innovative integration of neuro‐fuzzy‐based SVM‐NLP algorithms to accurately recognize and adapt to users' emotional states, enhancing the Metaverse experience across various applications. The proposed method is implemented using Python software. This adaptive approach significantly enhances users' immersion, emotional involvement, and overall satisfaction within the augmented reality environment by tailoring information to their responses. The findings show that the SVM‐NLP emotion identification algorithm based on neuro‐fuzzy, has a high degree of accuracy in recognizing emotional states, which holds promise for creating a Metaverse that is more emotionally compelling and immersive. Stronger human–computer interactions and a wider range of applications, including virtual therapy, educational resources, entertainment, and social media networking, might be made possible by integrating SVM‐NLP. These sophisticated systems are around 92% accurate in interpreting the emotions.

Innovative learning methods of Islamic education subject in Indonesia: a meta-analysis

This study aimed to evaluate the effects of innovative learning methods in the Islamic education (ISE) subject on learning motivation, learning activity, and learning achievement of students in the elementary and secondary schools in Indonesia. This study employed the meta-analysis method which is among the quantitative research approach. There were 47 literatures that eligible to be included in the database. Results revealed that, for the learning motivation variable, effect size value in the form of odds ratio was significantly higher than 1.0 (average 3.008 [2.020-4.480]; P<0.001). For the learning activity variable, effect size value was significantly higher than 1.0 (average 2.905 [2.113-3.994]; P<0.001). For the learning achievement variable, effect size value was significantly higher than 1.0 (average 4.941 [4.136-5.903]; P<0.001). These indicated that innovative learning methods were able to elevate learning motivation, activity and achievement. There were no significant differences among the categories within most of the sub-groups, except for the sub-group of cycle, in which cycle 2 showed higher learning achievement than that of cycle 1 (P<0.05). It can be concluded that innovative learning methods are able to elevate learning motivation, learning activity, and learning achievement of students at elementary and secondary schools to study the Islamic education subject. Future work should address the interaction between each particular innovative learning method and the ISE specific topic.

Portable smart attendance system on Jetson Nano

The masked face recognition-based attendance management system is an important biometric-based attendance tracking solution, especially in light of the COVID-19 pandemic. Despite the use of various methods and techniques for face detection and recognition, there currently needs to be a system that can accurately recognize individuals while they are wearing a mask. This system has been designed to overcome the challenges of widespread mask use, impacting the effectiveness of traditional face recognition-based attendance systems. The proposed system uses an innovative method that recognizes individuals even while wearing a mask without the need for removal. With its high compatibility and real-time operation, it can be easily integrated into schools and workplaces through an embedded system like the Jetson Nano or conventional computers executing attendance applications. This innovative approach and its compatibility make it a desirable solution for organizations looking to improve their attendance-tracking process. The Experimental results indicates using maximum resources possible the execution time needed on Jetson Nano is 15 to 22 seconds and 14 to 18 seconds respectively and the average frame capture if there are at least one face detected on Jetson Nano is 3-4 frames.

Exploring the synergy between instructional design models and learning theories: A systematic literature review

To enhance education, we conducted a comprehensive investigation into integrating instructional design models (IDMs) and learning theories in this systematic literature review. We methodically selected and analyzed 25 publications from a pool of 1,102 documents using the preferred reportinfg items for systematic reviews and meta-analyses framework to guarantee a rigorous and systematic approach to literature selection. Our results demonstrate the worldwide span of study on this topic, including contributions from prestigious academic institutions and scholarly journals. This examination explores both the benefits and drawbacks of combining IDMs with learning theories. Noteworthy positives include increased student motivation, support for innovative teaching methods, and the development of complex and diverse learning environments. However, several shortcomings were observed. most notably relating to accessibility problems, evaluation difficulties, and questions about the adaptability of such integrated techniques. Our findings have implications for a broad range of stakeholders, including educators, instructional designers, and students functioning in a variety of educational contexts. The increase of learner motivation, the creation of novel pedagogical tools, the refining of teacher training programs, and the promotion of interdisciplinary learning methods are significant areas of focus. In addition, our evaluation uncovered a number of gaps in the current literature, indicating intriguing possibilities for future research. The examination of holistic learning environments, the untapped potential of integrated systems, the incorporation of educational robots into pedagogical tactics, and the refining of schema assessment approaches are notable research fields. By providing these insights, this systematic review not only adds to the current body of knowledge, but also has the potential to shape the future trajectory of educational practices, so acting as a significant resource for boosting learning outcomes in a variety of educational environments.

Assessing an innovative method to promote learning from patient narratives: Findings from a field experiment in ambulatory care.

To assess whether an online interactive report designed to facilitate interpretation of patients' narrative feedback produces change in ambulatory staff learning, behavior at the individual staff and practice level, and patient experience survey scores. We studied 22 ambulatory practice sites within an academic medical center using three primary data sources: 333 staff surveys; 20 in-depth interviews with practice leaders and staff; and 9551 modified CG-CAHPS patient experience surveys augmented by open-ended narrative elicitation questions. We conducted a cluster quasi-experimental study, comparing 12 intervention and 10 control sites. At control sites, narratives were delivered free-form to site administrators via email; at intervention sites, narratives were delivered online with interactive tools for interpretation, accompanied by user training. We assessed control-versus-intervention site differences in learning, behavior, and patient experience scores. Staff surveys and interviews were completed at intervention and control sites, 9 months after intervention launch. Patient surveys were collected beginning 4 months pre-launch through 9 months post-launch. We used control-versus-intervention and difference-in-difference analyses for survey data and thematic analysis for interview data. Interviews suggested that the interface facilitated narrative interpretation and use for improvement. Staff survey analyses indicated enhanced learning from narratives at intervention sites (29% over control sites' mean of 3.19 out of 5 across eight domains, p < 0.001) and greater behavior change at staff and practice levels (31% and 21% over control sites' means of 3.35 and 3.39, p < 0.001, respectively). Patient experience scores for interactions with office staff and wait time information increased significantly at intervention sites, compared to control sites (3.7% and 8.2%, respectively); however, provider listening scores declined 3.3%. Patient narratives presented through structured feedback reporting methods can catalyze positive changes in staff learning, promote behavior change, and increase patient experience scores in domains of non-clinical interaction.

Beyond the Dusty Fog: Local Eye Drop Therapy and Potentially New Treatment Alternatives in Pseudoexfoliative Glaucoma.

Pseudoexfoliative glaucoma (PEG) is a type of secondary open-angle glaucoma characterized by the accumulation of whitish-gray material on the trabecular meshwork and lens, leading to an increase in intraocular pressure (IOP) and optic nerve damage. Local eye drop therapy is one of the first-line treatments for PEG, which include prostaglandin analogues, beta-blockers, and alpha-adrenergic agonists to lower IOP. New treatments beyond conventional techniques, however, are constantly being developed. One potential treatment proposed for PEG is based on magnetic phage display, which involves using magnetic nanoparticles conjugated to specific peptides or proteins selected using phage display techniques to remove aggregates in the anterior chamber of the eye or inflammatory cells and cytokines that contribute to PEG pathogenesis. Other potential treatments include microRNAs (miRNAs) that are involved in the regulation of gene expression at the post-transcription stages. Gene therapies, nanotechnology, immunotherapy and methods based on stem cells can also be potentially used to target and treat specific tissues and cells responsible for regulating IOP. In addition, photobiomodulation therapy (PBMT), a non-invasive procedure that utilizes low-level laser therapy to improve cellular function and promote tissue repair, can prove an interesting alternative in treating PEG. The aim of our mini-review is to provide a brief overview of these innovative methods that appear to offer potentially promising treatment options for PEG.

Unveiling Business Activity Patterns of Digital Transformation through K-Means Clustering with Universal Sentence Encoder in Transport and Logistics Sectors

Digital transformation brings several key benefits to transportation and logistics firms, such as operational efficiency, improved customer experience, and supply chain visibility. This study seeks to identify and categorize business activities within the transportation and logistics sector, focusing on themes related to digital transformation. Additionally, it analyses the impact of firm size and sales performance. Data were sourced from 24 annual reports of four firms listed in the SET, covering the study period of 2017-2022. The study used context-free grammar-based verb patterns and a custom-made dictionary to extract business activities. The study used the Universal Sentence Encoder to represent the text and relied on the Dunn-Index and Silhouette score to determine the optimal number of clusters (both 8 clusters). Subsequently, K-means clustering was performed using these 8 clusters, leading to the categorization of eight digital transformation themes. Among these themes, profit management and regulation compliance are interesting discoveries, as they are rarely mentioned in previous studies. A total of 104 business activities were extracted as a result. The study also found that Online-to-Offline activity is an effective method for business model innovation, as well as a contemporary marketing tool.

Exploring real estate valuation practices in an informal market

This study investigates the intricate realm of real estate valuation in the Palestinian neighborhoods of East Jerusalem. Drawing on 27 interviews with real estate appraisers and an analysis of betterment tax valuations, arbitration reports, and court minutes spanning the years 1991–2023, we unpack the distinct valuation practices in this informal urban setting. We discuss the unique challenges involved in valuations in informal real estate markets and how appraisers address them, suggesting that appraisers in East Jerusalem grapple with structural, top-down challenges—arising from inconsistent Israeli planning regulations, geopolitical instability, and an underdeveloped mortgage market—and bottom-up obstacles stemming from limited transaction reporting by local Palestinian residents. To navigate these challenges, appraisers employ creative solutions, such as a 'copy & paste' strategy, referencing older arbitration verdicts, and creating complementary databases. They also rely on local knowledge and their familiarity with the field. Our research sheds light on the innovative methods appraisers utilize to assess land value in the complex and dynamic environment of East Jerusalem, and in informal areas more broadly.

Improved learning efficiency of deep Monte-Carlo for complex imperfect-information card games

Deep Reinforcement Learning (DRL) has achieved considerable success in games involving perfect and imperfect information, such as Go, Texas Hold’em, Stratego, and DouDiZhu. Nevertheless, training a state-of-the-art model for complex imperfect-information card games like DouDiZhu and Big2 remains resource and time-intensive. To address this challenge, this paper introduces two innovative methods: the Opponent Model and Optimized Deep Monte-Carlo (ODMC). These methods are designed to improve the training efficiency of Deep Monte-Carlo (DMC) for imperfect-information card games. The Opponent Model predicts hidden information, enhancing the agent’s learning speed in DMC compared to the original training that only utilizes observed information as input features. In ODMC, the Minimum Combination Search (MCS) is a heuristic search algorithm based on dynamic programming. It calculates the minimum combination of actions in the current state, and ODMC uses MCS to filter suboptimal actions in each state. This reduces the action space considered by DMC, resulting in faster training that focuses on evaluating the most promising actions. The effectiveness of the proposed approach is evaluated by examining two complex card games with imperfect information: DouDiZhu and Big2. Ablation experiments are conducted to evaluate both the Opponent Model (D+OM and B+OM) and ODMC (D+ODMC and B+ODMC), along with their combined variants (D+OMODMC and B+OMODMC). Furthermore, D+OMODMC and B+OMODMC are compared with state-of-the-art DouDiZhu and Big2 artificial intelligence (AI) programs, respectively. The experimental results demonstrate that the proposed methods achieve comparable performance to the original DMC, but with only 25.5% of the training time on the same device. These findings are valuable for mitigating both the equipment requirements and training time in complex imperfect-information card games.

Machine Learning Based Predictive Modelling of Vehicle CO2 Emissions: An Extensive Examination of Automobile Features

There is a growing interest in discovering novel and inventive methods to decrease the emission of carbon dioxide (CO2) from diverse sources, mostly due to concerns about safeguarding the environment and mitigating the adverse effects of greenhouse gas emissions, especially in the automotive sector. This study article explores the fascinating potential of predicting the quantity of CO2 generated by autos using machine learning (ML) based methodologies. In order to create a system that can predict carbon dioxide emissions depending on a number of factors, we are investigating machine learning. Significant details about the car, such as its construction, kind, number of cylindrical objects, horsepower, gearbox type, fuel type, and fuel consumption, are included in the dataset that forms the basis of our investigation. This project's main objective is to create accurate and dependable prediction models for calculating vehicle-generated CO2 emissions. Legislators that deal with the environment, corporations, and consumers could find great value in these models. We use a range of machine learning (ML) techniques, such as ensemble techniques, neural networks, decision trees, and regression algorithms, to accomplish this goal. This study assesses the prediction accuracy, adaptability to different situations, and data utilisation of several techniques. To determine which predictive models are most suited for predicting CO2 emissions within the limitations of the given information, we employ a comprehensive evaluation process. Additionally, we look at how different feature subsets affect prediction accuracy, providing an indication of the relative significance of different vehicle parameters in relation to CO2 emissions. Our results advance the field of predictive emissions models and provide insight on the primary drivers of vehicle-related greenhouse gas emissions.

Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration

Contrastive learning has emerged as a prevailing paradigm for high-level vision tasks, which, by introducing properly negative samples, has also been exploited for low-level vision tasks to achieve a compact optimization space to account for their ill-posed nature. However, existing methods rely on manually predefined and task-oriented negatives, which often exhibit pronounced task-specific biases. To address this challenge, our paper introduces an innovative method termed 'learning from history', which dynamically generates negative samples from the target model itself. Our approach, named Model Contrastive Learning for Image Restoration (MCLIR), rejuvenates latency models as negative models, making it compatible with diverse image restoration tasks. We propose the Self-Prior guided Negative loss (SPN) to enable it. This approach significantly enhances existing models when retrained with the proposed model contrastive paradigm. The results show significant improvements in image restoration across various tasks and architectures. For example, models retrained with SPN outperform the original FFANet and DehazeFormer by 3.41 and 0.57 dB on the RESIDE indoor dataset for image dehazing. Similarly, they achieve notable improvements of 0.47 dB on SPA-Data over IDT for image deraining and 0.12 dB on Manga109 for a 4x scale super-resolution over lightweight SwinIR, respectively. Code and retrained models are available at https://github.com/Aitical/MCLIR.