Effective Solution Research Articles

Articulatory phonetics in the market: combining public engagement with ultrasound data collection

Abstract This paper is a methodological contribution outlining an approach we have developed to recording ultrasound tongue imaging and audio research data as part of public engagement work. The paper is based on our experience of recording in East Lancashire, UK, at two such events as well as building on the work of other colleagues who have conducted similar projects. We have successfully managed to collect relevant articulatory research data while providing an interesting and enjoyable event for the public. In a context of stretched research budgets and researcher time constraints, this combination allows a time-effective combination of tasks, whilst democratizing academic work and engaging local communities. Our paper has two aims: (i) to describe the logistical and ethical considerations for organizing an event combining articulatory research and public engagement, and (ii) to provide methodological reflection on data collection and eventual data quality obtained including assessment of background noise. We hope to provide inspiration and suggestions for colleagues wishing to pursue research in this area and also acknowledge where some of our methods would benefit from more effective solutions. Sample documents for ethics, publicity, risk assessments, staff planning, timelines, and budgeting are included in our supplementary material toolkit available at https://osf.io/ky3cz/.

Application Of Variation Translation Theory In The Translation Of Culture-Loaded Words

This paper explores the application of variation translation theory in the translation of culture-loaded words. It analyzes the challenges faced in translating such words and demonstrates how variation translation theory provides effective solutions. Through a comprehensive literature review, detailed methodology, analysis of results, and conclusive discussions, this study highlights the significance and relevance of applying variation translation theory in enhancing cross-cultural communication.

Land layout optimisation for virtual land sales in the metaverse: two-dimensional assortment problem

With the growing popularity of the metaverse, the virtual real estate in the metaverse has generated immense investment enthusiasm. Considering the virtual land sale mode allowing the potential buyers to request the purchase of rectangular land parcels of a specified size, this study investigates how to lay out the requested land parcels for minimising the size of the required rectangular open land, which can be formulated completely as the two-dimensional assortment problem (2DAP). Due to the strong NP-hardness of the 2DAP, an effective and efficient heuristic solution approach named binary adjoining algorithm (BAA) is presented for tackling the 2DAPs in large scales. The conducted computational experiments show that the BAA can outperform the state-of-the-art piecewise-linearisation mixed integer linear programming model as well as four existing advanced metaheuristic techniques designed for the 2DAP, in both solution quality and computational time, on the small-size instances. The superiority of the BAA over either foregoing reference method on the large-size instances with up to 60 requested land parcels is also demonstrated.

Computer-assisted total bilateral knee arthroplasty for a patient with severe hyperextension in residual poliomyelitis: a case report

Background. Total knee arthroplasty in patients in the residual stage of poliomyelitis is a technically difficult job. The reasons for this are anatomical features and therefore difficulties in an implant choice and conflicting results. In the present case report, we describe a unique method of a computer-navigated installation of a rotating-hinge implant in a non-standard position for a patient with residual poliomyelitis. The aim of the study is to demonstrate a method of a computer-navigated installation of rotating-hinge knee implants in a patient with severe hyperextension and valgus deformity in the residual stage of poliomyelitis. Case description. The clinical case presents a 55-year-old man with lower peripheral paraparesis as a consequence of poliomyelitis and bilateral valgus knee arthritis with severe hyperextension. The patient moved on crutches at a distance no more than 300 meters. Two-stage total knee arthroplasty was performed using a navigation system and second-generation rotating-hinge implants. At the 4-year follow-up period, we note a good clinical result, and the patient is satisfied with the total knee arthroplasty outcomes. Conclusion. The clinical case demonstrates good results of performing total knee arthroplasty in a patient with bilateral severe hyperextension and knee valgus deformity in residual poliomyelitis. Having analyzed available topic-related literature, we developed an original method of a computer-navigated installation of a second-generation rotating-hinge implant. The method can be an effective treatment solution for patients with knee arthritis deformity in the residual stage of poliomyelitis.

Energy-Efficient Wireless Multimedia Sensor Nodes for Plant Proximal Monitoring

The paper presents a double-radio wireless multimedia sensor node (WMSN) with a camera on board, designed for plant proximal monitoring. Camera sensor nodes represent an effective solution to monitor the crop at the leaf or fruit scale, with details that cannot be retrieved with the same precision through satellites or unnamed aerial vehicles (UAVs). From the technological point of view, WMSNs are characterized by very different requirements, compared to standard wireless sensor nodes; in particular, the network data rate results in higher energy consumption and incompatibility with the usage of battery-powered devices. Avoiding energy harvesters allows for device miniaturization and, consequently, application flexibility, even for small plants. To do this, the proposed node has been implemented with two radios, with different roles. A GPRS modem has been exclusively implemented for image transmission, while all other tasks, including node monitoring and camera control, are performed by a LoRaWAN class A end-node that connects every 10 min. Via the LoRaWAN downlink, it is possible to efficiently control the camera settings; the shooting times and periodicity, according to weather conditions; the eventual farming operations; the crop growth stages and the season. The node energy consumption has been verified in the laboratory and in the field, showing that it is possible to acquire one picture per day for more than eight months without any energy harvester, opening up further possible implementations for disease detection and production optimization.

Recent Advances in Hydrothermal Oxidation Technology for Sludge Treatment

With the rapid development of urbanization and the widespread adoption of wastewater treatment facilities, the volume of sludge produced has steadily increased. Hydrothermal oxidation (HTO) technology offers an effective solution for sludge reduction, harmless disposal, and resource recovery, making it a highly promising method for sludge treatment. In recent years, HTO has attracted significant attention due to its efficiency and environmental benefits. This paper provides a detailed explanation of the fundamental principles of HTO in sludge treatment, with a focus on the removal of organic pollutants, nitrogen transformation, and phosphorus recovery. The influence of key operational parameters, such as reaction temperature, time, initial oxygen pressure, and pH, on the performance of HTO treatment is also explored. In addition, the research status of HTO sludge treatment and an example of product recovery after treatment are also discussed. It examines the challenges associated with scaling up HTO for large-scale sludge treatment, along with potential research directions for future work. Special attention is given to the innovation of catalysts, with the goal of achieving self-catalysis in sludge treatment. Moreover, considering that ammonia nitrogen (NH3-N) is a major intermediate product in HTO, its removal, as well as the prediction and planning of other unintended products, remains a key issue. Further areas of interest include improving sludge dewatering performance and enhancing the production of valuable single carboxylic acids, which can boost resource recovery efficiency. This paper also highlights the diversification of sludge applications after HTO treatment. By providing insights into future development trends, this review offers valuable references for further research and practical applications. The ultimate goal is to support the development of HTO as a sustainable and efficient solution for sludge treatment, addressing environmental concerns while maximizing resource recovery opportunities.

Intelligent process migration in heterogeneous distributed systems

In distributed processing environments, multiple groups of processes are found sharing resources and competing for access. These processes may or may not require synchronization and it is essential to reach a consensus to manage access to resources in a way that establishes a strict order, thus ensuring mutual exclusion. The proposal presented is an innovative and effective solution for the management of shared resources in distributed systems, which allows solving problems related to overload and workload balancing. The evaluation of the state of computational loads and the final comparison using standard deviation are useful tools to detect and correct imbalances in the system. In addition, the possibility of establishing initial configurations of the algorithm for each particular situation allows adapting the solution to the specific needs of each system.

Least angle sparse principal component analysis for ultrahigh dimensional data

AbstractPrincipal component analysis (PCA) has been a widely used technique for dimension reduction while retaining essential information. However, the ordinary PCA lacks interpretability, especially when dealing with large scale data. To address this limitation, sparse PCA (SPCA) has emerged as an interpretable variant of ordinary PCA. However, the ordinary SPCA relies on solving a challenging non-convex discrete optimization problem, which maximizes explained variance while constraining the number of non-zero elements in each principal component. In this paper, we propose an innovative least angle SPCA technique to address the computational complexity associated with SPCA, particularly in ultrahigh dimensional data, by sequentially identifying sparse principal components with minimal angles to their corresponding components extracted through ordinary PCA. This sequential identification enables solving the optimization problem in polynomial time, significantly reducing computational challenges. Despite its efficiency gains, our proposed method also preserves the main attributes of SPCA. Through comprehensive experimental results, we demonstrate advantages of our approach as a viable alternative for dealing with the computational difficulties inherent in ordinary SPCA. Notably, our method emerges as an efficient and effective solution for conducting ultrahigh dimensional data analysis, enabling researchers to extract meaningful insights and streamline data interpretation.

A modified and weighted Gower distance-based clustering analysis for mixed type data: a simulation and empirical analyses

BackgroundTraditional clustering techniques are typically restricted to either continuous or categorical variables. However, most real-world clinical data are mixed type. This study aims to introduce a clustering technique specifically designed for datasets containing both continuous and categorical variables to offer better clustering compatibility, adaptability, and interpretability than other mixed type techniques.MethodsThis paper proposed a modified Gower distance incorporating feature importance as weights to maintain equal contributions between continuous and categorical features. The algorithm (DAFI) was evaluated using five simulated datasets with varying proportions of important features and real-world datasets from the 2011–2014 National Health and Nutrition Examination Survey (NHANES). Effectiveness was demonstrated through comparisons with 13 clustering techniques. Clustering performance was assessed using the adjusted Rand index (ARI) for accuracy in simulation studies and the silhouette score for cohesion and separation in NHANES. Additionally, multivariable logistic regression estimated the association between periodontitis (PD) and cardiovascular diseases (CVDs), adjusting for clusters in NHANES.ResultsIn simulation studies, the DAFI-Gower algorithm consistently performs better than baseline methods according to the adjusted Rand index in settings investigated, especially on datasets with more redundant features. In NHANES, 3,760 people were analyzed. DAFI-Gower achieves the highest silhouette score (0.79). Four distinct clusters with diverse health profiles were identified. By incorporating feature importance, we found that cluster formations were more strongly influenced by CVD-related factors. The association between periodontitis and cardiovascular diseases, after adjusting for clusters, reveals significant insights (adjusted OR 1.95, 95% CI 1.50 to 2.55, p = 0.012), highlighting severe periodontitis as a potential risk factor for cardiovascular diseases.ConclusionsDAFI performed better than classic clustering baselines on both simulated and real-world datasets. It effectively captures cluster characteristics by considering feature importance, which is crucial in clinical settings where many variables may be similar or irrelevant. We envisage that DAFI offers an effective solution for mixed type clustering.

Implementasi Sistem Pendataan Transaksi dan Pengelolaan Stok Menggunakan OutSystems Berbasis Website

Transaction recording, stock management, and monthly transaction reporting are critical activities that support a company’s operational workflow. At PT. Bintang Permata Semesta, these activities are still conducted manually, leading to difficulties in accessing real-time data, as well as increasing the potential for operational errors. With technological advancements, web-based systems have emerged as effective solutions to streamline these processes. This research focuses on the development of a web-based transaction recording and stock management system using OutSystems, a low-code visual development platform designed for rapid application creation. The system developed includes features such as transaction recording, automatic stock calculations, data searching and filtering, low-stock level notifications, and automatic monthly transaction report generation. Additionally, the website is synchronized with cloud services, enabling access anytime and anywhere via desktop or mobile devices. The research utilizes a qualitative method, gathering primary data through structured interviews, direct observations, and literature reviews. The software development method used is Personal Extreme Programming (PXP). The resulting system aims to assist employees in both the main and branch offices with more efficient transaction recording, real-time stock monitoring, and report generation. This implementation highlights the benefits of using OutSystems to create a dynamic and user-friendly web-based system, ultimately improving operational efficiency within the company.

Domain Ontology-Driven Knowledge Graph Generation from Text

A knowledge graph serves as a unified and standardized representation for extracting and representing textual information. In the field of knowledge extraction and representation research, named entity recognition and relation extraction provide effective solutions for knowledge graph generation tasks. However, it is a challenge that lies in extracting domain-specific knowledge from the rich and general textual corpora and generating corresponding domain knowledge graphs to support domain-specific reasoning, question-answering, and decision-making tasks. The hierarchical domain knowledge representation model (i.e. domain ontology) provides a solution for this problem. Therefore, we propose an end-to-end approach based on domain ontology embedding and pre-trained language models for domain knowledge graph generation from text, which incorporates domain node recognition and domain relation extraction phases. We evaluated our domain ontology-driven model on the Wikidata-TekGen dataset and the DBpedia-WebNLG dataset, and the results indicate that our approach based on the pre-trained language models with fewer parameters compared with the baseline models has significantly contributed to the domain knowledge graph generation without prompts.

Stability Analysis of DC Microgrids: Insights for Enhancing Renewable Energy Integration, Efficiency and Power Quality

In the current context of smart grids, microgrids have proven to be an effective solution to meet the energy needs of neighborhoods and collective buildings. This study investigates the voltage behavior and other critical parameters within a direct current (DC) microgrid to enhance system efficiency, stability, and reliability. The dynamic performance of a DC microgrid is analyzed under varying load and generation conditions, with particular emphasis on the voltage response and load-sharing mechanisms required to ensure stable operation. The findings indicate that specific control strategies, particularly droop methods, are effective in mitigating voltage fluctuations, enhancing power quality, and ensuring proper load distribution across multiple sources. This study also addresses significant challenges, including voltage regulation and fault resilience, to provide guidelines for designing robust and efficient DC microgrids. These insights are essential to inspire further advancements in control strategies and facilitate the practical deployment of DC microgrids as a sustainable solution for distributed energy systems, especially in scenarios prioritizing high DC load penetration and renewable energy integration.

In-situ Constructed Core-shell Catalyst Enabling Subzero Capacity Unlocking of Cost-effective and Long-life Vanadium Flow Batteries.

Vanadium flow battery (VFB) promises a route for achieving grid-scale power storage by harnessing renewable energy sources. However, the sluggish reaction kinetics of vanadium redox couples and serious hydrogen evolution reaction (HER) still restrict the further development of VFB. Addressing these challenges requires not only effective solutions but also ones that are cost-efficient and scalable to meet the demands of affordable energy storage. Here, we present an in-situ constructed Cu@Cu6Sn5 core-shell catalyst by incorporating metal ions into the electrolyte. The Cu core, encapsulated by the Cu6Sn5 shell, forms an excellent conductive pathway to the graphite felt electrode. Charge transfer between Cu and Sn within Cu6Sn5 shell accelerates the reaction kinetics of V2+/V3+ redox couple and selectively inhibits HER, as confirmed through in-situ weak measurement imaging method. The Cu@Cu6Sn5 battery achieves a peak power density of 1119.1 mW cm-2 at 1350 mA cm-2, operates stably for 1200 cycles without catalyst failure, and is available over a wide-temperature range. Furthermore, we identify a demand of subzero capacity unlocking. Achieving a 23.4% theoretical capacity unlocking at -10 °C with a cut-off voltage up to 1.75 V, bespeaking a crucial breakthrough toward cost-effective VFB.

Remote Sensing Techniques for Water Quality Monitoring: A Review

Freshwater resources are facing increasing challenges to water quality, due to factors such as population growth, human activities, climate change, and various human-made pressures. While on-site methods, as specified in the USGS water quality sampling handbook, are usually precise, they require more time, are costly, and provide data at specific points, which lacks the essential comprehensive geographic and temporal detail for water body assessment and management. Hence, conventional on-site monitoring methods are unable to provide a complete representation of freshwater systems. To address concerns regarding geographic and time-based coverage, remote sensing has developed into an effective solution, taking advantage of recent advancements in sensor technology and methodologies. The combination of GPS and GIS technologies, along with remotely sensed data, provides an efficient resource for continual monitoring and evaluation of water bodies. The use of remotely sensed data helps to establish a reliable geospatial database, serving as a standard for subsequent evaluations. The review emphasizes the contribution of remote sensing to water monitoring. It starts by exploring various space-borne and airborne sensors utilized for this purpose. Subsequently, the review explores remote sensing applications for water quality. Lastly, the review discusses the overall benefits and challenges related to remote sensing in water monitoring.

In‐situ Constructed Core‐shell Catalyst Enabling Subzero Capacity Unlocking of Cost‐effective and Long‐life Vanadium Flow Batteries

Vanadium flow battery (VFB) promises a route for achieving grid‐scale power storage by harnessing renewable energy sources. However, the sluggish reaction kinetics of vanadium redox couples and serious hydrogen evolution reaction (HER) still restrict the further development of VFB. Addressing these challenges requires not only effective solutions but also ones that are cost‐efficient and scalable to meet the demands of affordable energy storage. Here, we present an in‐situ constructed Cu@Cu6Sn5 core‐shell catalyst by incorporating metal ions into the electrolyte. The Cu core, encapsulated by the Cu6Sn5 shell, forms an excellent conductive pathway to the graphite felt electrode. Charge transfer between Cu and Sn within Cu6Sn5 shell accelerates the reaction kinetics of V2+/V3+ redox couple and selectively inhibits HER, as confirmed through in‐situ weak measurement imaging method. The Cu@Cu6Sn5 battery achieves a peak power density of 1119.1 mW cm‐2 at 1350 mA cm‐2, operates stably for 1200 cycles without catalyst failure, and is available over a wide‐temperature range. Furthermore, we identify a demand of subzero capacity unlocking. Achieving a 23.4% theoretical capacity unlocking at ‐10 °C with a cut‐off voltage up to 1.75 V, bespeaking a crucial breakthrough toward cost‐effective VFB.

Analysis and solution of streak effect in high dynamic range CMOS image sensors

Analysis and solution of streak effect in high dynamic range CMOS image sensors

Architecture for Multiagent Systems with UNS Middleware and mediator agent using object-centric process mining

The increasing complexity of industrial processes demands innovative solutions for the integration and management of multi-agent systems. This work proposes a hierarchical multi-agent system architecture to unify these systems, aiming for efficiency and autonomy in complex and connected production environments. The proposal focuses on the integration of multiple tasks and layers, promoting operational freedom without compromising security and agility. To optimize the use and understanding of data flows, the system uses Object-Centric Process Mining techniques at runtime, an innovative approach for processes with autonomous agents, given its ability to manage event- and object-oriented data. The architecture adopts the OCEL 2.0 log standard, which provides a structured and interoperable basis for the administration of large volumes of production data, facilitating detailed analyses and quick, accurate decisions. The proposed structure is aligned with the requirements of Industry 4.0, especially the RAMI 4.0 model, and relies on the mediation of a unified system core (UNS), which integrates subsystems such as SCADA, intelligent devices, and Process Mediators. Performance tests have demonstrated that the architecture is robust, scalable, and agile, with the OCPM Mediator accelerating secure responses to agents and promoting a resilient, autonomous, and data-driven decision-making process. This work highlights that the proposed architecture offers an effective solution for multi-agent systems in advanced manufacturing, enabling the industry to address the challenges of digital transformation.

A Survey on the Barriers Faced by School Students in the Learning Process

This research paper discusses the barriers faced by school students in the learning process. Several factors contribute to these barriers, including a demotivating environment at school and home, distractions from gadgets, phobias of certain subjects, distractions in the study environment, unattractive teaching methods, and fear of examinations, talkative behavior, and more. Sometimes, students lack motivation from both school and parents, which decreases their ability to learn. The objective of this research paper is to identify the barriers to learning faced by school students and provide effective solutions for a smoother learning process. The researcher suggests that meditation is an effective way to prevent mental instability and alleviate these challenges. Additionally, unattractive teaching methods can contribute to these barriers; therefore, teachers should adopt engaging teaching methods such as PPTs, group discussions, and educational games. Data was collected from school students, specifically those in grades 5 through 12, and the researcher found that most students face difficulties in mathematics

Evaluating the sustainability of biohydrogen, biogas, and biohythane generation from agricultural, industrial, and municipal waste sources

AbstractBiofuel production from waste materials offers an effective solution for generating renewable energy in biohydrogen, biogas, and biohythane while reducing waste and recycling valuable nutrients. This review examines recent advances in biohydrogen and biogas production from renewable resources, focusing on the environmental impacts of different production techniques. It emphasizes life cycle analysis (LCA) as an important tool for assessing these impacts and discusses key production pathways and processes. The variability in LCA methodologies – including differences in software, impact categories, system boundaries, functional units, and analytical approaches – poses challenges for direct comparisons of studies. Despite these challenges, fermentation‐based production methods show substantial environmental advantages, such as reduced CO2 emissions, diminished ozone depletion potential, improved human health outcomes, lower ecotoxicity, and reduced fossil fuel use. Combining biohydrogen production with anaerobic digestion for biohythane generation demonstrates even greater energy efficiency and greenhouse gas reduction. Adopting waste‐to‐energy strategies grounded in life cycle principles could advance renewable energy system implementation and sustainability significantly.

A COMPREHENSIVE IMPLEMENTATION ALGORITHM 2D TRANSFORMATION IN GRAPHIC WEB-EDITORS BASED ON THE METHODS OF AFFINE TRANSFORMATIONS

This article presents a complex algorithm for the optimized implementation of 2D transformations in graphic web editors based on the methods of affine transformations. The object of this study is the algorithms and methods of affine transformations for the implementation of 2D transformations in graphic web editors. The research is aimed at applying the principles of these transformations and their impact on graphic objects, as well as at finding effective solutions for their implementation. An analysis of the main existing algorithms and methods for implementing 2D transformations, such as the Bresenham and Wu algorithms for visualizing straight and curved lines, algorithms for filling areas using affine transformation methods, was carried out. Therefore, the issue of improving and simplifying algorithms and methods of affine transformations for implementing 2D transformations in graphic web editors is relevant and important for modern web design and development. The work considers the main algorithms of affine transformations that ensure the implementation of scaling, rotation, shift, skew functions and their interaction with graphic objects. The proposed algorithm includes the main advantages of implementing the above functions and allows you to create more efficient and high-quality graphic applications, providing at the same time greater functionality and productivity. Applying such an approach creates new opportunities for the development and use of new types of web editors in the field of web design. Based on the proposed algorithm, a web application has been developed that allows you to create various transformations and animations of figures on the plane. The results of the conducted testing confirm the effectiveness and possibility of applying the proposed approach in real projects for the development of graphic web editors for working with 2D transformations.