Real Applications Research Articles

Polyanionic Electrolyte Ionization Desalination Empowers Continuous Solar Evaporation Performance.

Solar evaporation contributes to sustainable and environmentally friendly production of fresh water from seawater and wastewater. However, poor salt resistance and high degree of corrosion of traditional evaporators in brine make their implementation in real applications scarce. To overcome such deficiency, a polyanionic electrolyte functionalization strategy empowering excellent uniform desalination performance over extended periods of time is exploited. This 3D superhydrophilic graphene oxide solar evaporator design ensures stable water supply by the enhanced self-driving liquid capillarity and absorption at the evaporation interface as well as efficient vapor diffusion. Meanwhile, the polyanionic electrolyte functionalization implemented via layer-by-layer static deposition of polystyrene sodium sulfonate effectively regulates/minimizes the flux of salt ions by exploiting the Donnan equilibrium effect, which eventually hinders local salt crystallization during long-term operation. Stable evaporation rates in line with the literature of up to 1.68kg m-2 h-1 are achieved for up to 10 days in brine (15‰ salinity) and for up to 3 days in seawater from Hangzhou Bay in the East China Sea (9‰ salinity); while, maintaining evaporation efficiencies of ≈90%. This work demonstrates the excellent benefits of polyanionic electrolyte functionalization as salt resistance strategy for the development of high-performance solar powered seawater desalination technology and others.

A Two-Parameter Ridge Estimator for Handling Extreme Multicollinearity Problems in Logistic Regression

This paper introduces a robust two-parameter ridge estimator that is customized for logistic regression models, which tend to be sensitive to extreme multicollinearity problems. Inflated standard errors and unreliability in the results stem from the problem of multicollinearity characterized by high correlations among predictor variables in logistic regression model. Traditional approaches like the Maximum Likelihood Estimator (MLE) and one-parameter ridge-type estimators often perform poorly under these settings, thus calling for the development of more robust approaches. This new proposal is called New Biased Two Parameter (NBTP), which extends the ridge regression framework by introducing additional biasing parameters customized for an extreme multicollinearity problem. The paper combines a theoretical analysis with extensive Monte Carlo simulations and real application to Pena data. It demonstrates that the new estimator, New Biased Two Parameter (NBTP), provides much more stable and accurate parameter estimates than previous methods. The results underscore the importance of using robust estimation methods within logistic regression, especially when multicollinearity may be widespread in fields such as medical research, finance, and the social sciences.

Detecting influential observations in single-index Fréchet regression

Regression with random data objects is becoming increasingly common in modern data analysis. Unfortunately, this novel regression method is not immune to the trouble caused by unusual observations. A metric Cook’s distance extending the original Cook’s distances of Cook (1977) to regression between metric-valued response objects and Euclidean predictors is proposed. The performance of the metric Cook’s distance is demonstrated in regression across four different response spaces in an extensive experimental study. Two real data applications involving the analysis of distributions of COVID-19 transmission in the State of Texas and the analysis of the structural brain connectivity networks are provided to illustrate the utility of the proposed method in practice.

Robotic Badges for Girl Scouts

This three-day computer science (CS) robotics coding exploration targets three Girl Scouts (n.d.) Brownie (2nd/3rd grades) badges. For each badge, a 60-minute session follows the 5E instructional model, which incorporates various robots to provide learners with diverse coding experiences. Throughout the coding exploration, learners engaged with real-life robot applications and used different robots to complete coding challenges. This learning exploration concluded by having learners complete at least one robot coding challenge per session, tailored to their capability, and utilize their expressive language skills to describe and explain their designed artifacts, algorithms, and block-based codes. This coding exploration can be extended to support broader elementary-level CS instruction.

Bounded Multivariate Contaminated Normal Mixture Model with an Application in Skin Cancer Detection

Introduction: In real-world datasets, outliers are a common occurrence that can have a significant impact on the accuracy and reliability of statistical analyses. Detecting these outliers and developing robust models to handle their presence is a crucial challenge in data analysis. For instance, natural images may have complex distributions of values due to environmental factors like noise and illumination, resulting in objects with overlapping regions and non-trivial contours that cannot be accurately described by Gaussian mixture models. In many real life applications, observed data always fall in bounded support regions. This leads to the idea of bounded support mixture models. Motivated by the aforementioned observations, we introduce a bounded multivariate cntaminated normal distribution for fitting data with non-Gaussian distributions, asymmetry, and bounded support which makes finite mixture models more robust to fitting, since rare observations are given less importance in calculations. Methods: A family of finite mixtures of bounded multivariate contaminated normal distributions is introduced. The model is well-suited for computer vision and pattern recognition problems due to its heavily-tailed and bounded nature, providing flexibility in modeling data in the presence of outliers. A feasible expectation- maximization algorithm is developed to compute the maximum likelihood estimates of the model parameters using a selection mechanism. Results: The proposed methodology is validated by conducting experiments on both simulated data and two real natural skin cancer images. We estimate the parameters by the proposed expectation-maximization algorithm. The obtained results shown that the proposed model has successfully enhanced accuracy in segmenting skin lesions. Conclusion: The reliable model-based clustering using finite mixtures of bounded multivariate contaminated normal distributions is introduced. An expectation-maximization algorithm was created to estimate parameters, with closed-form expressions utilized at the E-step. Practical tests on images for skin cancer detection showed enhanced accuracy in delineating skin lesions.

Printable and Tunable Bioresin with Strategically Decorated Molecular Structures.

As personalized medicine rapidly evolves, there is a critical demand for advanced biocompatible materials surpassing current additive manufacturing capabilities. This study presents a novel printable bioresin engineered with tunable mechanical, thermal, and biocompatibility properties through strategic molecular modifications. The study introduces a new bioresin comprising methyl methacrylate (MMA), ethylene glycol dimethacrylate (EGDMA), and a photoinitiator, which is further enhanced by incorporating high molecular weight polymethyl methacrylate (PMMA) to improve biostability and mechanical performance. The integration of printable PMMA presents several synthesis and processing challenges, necessitating substantial modifications to the 3D printing process. Additionally, the bioresin is functionalized with antibacterial silver oxide and bone-growth-promoting hydroxyapatite at various weight ratios to extend its application further. The results demonstrate the agile printability of the novel bioresin and its potential for transformative impact in biomedical applications, offering a versatile material platform for additive manufacturing-enabled personalized medicine. This work highlights the adaptability of the novel printable bioresin for real-life applications and its capacity for multiscale structural tailoring, potentially achieving properties comparable to native tissues and extending beyond conventional additive manufacturing techniques.

Industry 4.0 technology implementation in manufacturing: a selection method and real case applications

The pressing necessity for digitalisation in industrial plants, driven by Industry 4.0 national initiatives and heightened global competition, underscores the urgency for companies to initiate digital transformation projects. Despite this urgency, the academic literature lacks comprehensive guidance on models specifically dedicated to the selection of digital technologies. This article addresses this gap by proposing a multi-criteria decision-making model, grounded in a methodological framework, for the systematic selection of digital technologies in the manufacturing sector. The proposed model combines fuzzy logic and the analytic hierarchy process (AHP) and incorporates a well-established classification of digital technologies. The model is able to select the single best candidate technology as well as the best candidate group of technologies that share the same purpose. In this way, the model tries to capture the interconnection element that is at the core of the digitalisation concept. To test its validity, the model was applied in two manufacturing companies operating in distinct production sectors. One of these companies was undergoing a digitalisation process in its plants, providing an additional basis for comparing the results of the proposed model.

An Operations Research–Based Teaching Unit for Grade 12: The ROAR Experience, Part III

In this paper, we finish describing the project and the experimentation of Ricerca Operativa Applicazioni Reali (ROAR; in English, Real Applications of Operations Research), a three-year project for higher secondary schools. ROAR is composed of three teaching units addressed to grades 10, 11, and 12, respectively. To improve students’ interest, motivation, and skills related to science, technology, engineering, and mathematics disciplines, ROAR integrates the teaching of mathematics and computer science through operations research. Its implementation started in 2021 in a grade 10 class at the scientific high school IIS Antonietti in Iseo (Brescia, Italy). We provided the details of the first two units in previous papers. Here, we focus on the third and last unit, carried out from October 2022 to January 2023, with the same students, then in a grade 12 class. Similarly to the first two units, we describe objectives, prerequisites, topics and methods, the organization of the lectures, digital technologies used, and a challenging final project that, this time, involved the manufacturer company Filtrec S.p.A. with a case. After analyzing the feedback from students, teachers, and practitioners engaged in the experimentation, we reflect on the entire experimentation and provide some insights to replicate a similar experience. Funding: G. Colajanni was partially supported by the research project Programma Ricerca di Ateneo UNICT 2020-22 linea 2, Ottimizzazione di Modelli di Network slIce 5G con UAV, the Italian Ministry of University and Research and the European Union for the Programma Operativo Nazionale project on Research and Innovation 2014-2020, D.M. 1062/2021, and the GNAMPA-INdAM Group. A. Raffaele was partially supported by the National Group for Scientific Computation. E. Taranto was partially supported by the National Group for Algebraic and Geometric Structures, and their Applications. Supplemental Material: The online appendices are available at https://doi.org/10.1287/ited.2023.0065 .

Moisture-Triggered 71000-Fold Stiffness Change Materials Via Crystallization and Hydrogen Bonding.

Stiffness change materials have been widely used in soft robots, intelligent adhesives and biomedical minimally invasive devices which can respond to specific stimuli. Compared with stimuli such as light, electricity, magnetism, temperature, and pressure, moisture is a non-toxic, readily available, and inexhaustible triggered source in the atmosphere. However, it is challenging to achieve a stiffness variable material with a large stiffness change using moisture. Traditional sensitive materials usually swell to expand the volume or dissolve to a liquid-like state when absorbing moisture, which is unfavorable for real applications. Herein, we demonstrate a polyvinylamine (PVAm)/polyethylenimine (PEI) composite film with variable stiffness change by moisture absorption and release. PVAm is a semicrystalline polymer with high stiffness and PEI forms intermolecular hydrogen bonding with primary amine groups on PVAm. The synergistic effect of hydrogen bonding and crystallization was maximized when 15 wt % PEI was added to the composite system, resulting in a large stiffness change of up to 7.1 × 104 (0.022 MPa versus 1560.85 MPa) of PVAm/PEI composite film under 25 °C, 95% RH. The crystallinity structure and hydrogen bonding can be broken and reformed by adjusting humidity. As promising variable stiffness polymers, the developed PVAm/PEI composite film is demonstrated as a reconfigurable multitool for shape memory and locking on demand.

Partially precise instrument measurements-aided deep learning for industrial quality prediction

Material composition is a kind of important quality index in the process industry. Even though instruments for online measuring these compositions have been widely applied, the precision of material composition measurements is suspicious due to corrosion, scaling and other factors. Laboratory values are more convinced, while these instruments are largely idle in real applications. Nevertheless, despite suspicious precision, partially precise trends exist in these measurements, which are also useful for indicating the variation in quality. This means that a wealth of information directly related to quality variables can provide positive guidance for quality prediction. Enlightened by the requirement of information utilization, a long short-term memory network with embedded trend consistency criteria (TCC-LSTM) is proposed for industrial quality prediction through extremely efficient utilization of partially precise quality instrument data. Specifically, based on the property that the trends of the measured values for quality variable are similar to that of the corresponding laboratory values over time, six trend consistency criteria are designed to evaluate the reliability of instrument data, so as to determine the contribution weights of these data in deep learning-based quality prediction. Moreover, in the neural network structure, the space-wise and time-wise attention mechanisms are designed for capturing important variables and time information. Extensive experiments on an actual alumina digestion process demonstrate the efficiency of TCC-LSTM, whose correlation coefficient is averagely improved by 0.2247 and mean absolute error is as low as 0.008079.

NetQDA: Local Network-Guided High-Dimensioanl Quadratic Discriminant Analysis

Quadratic Discriminant Analysis (QDA) is a well-known and flexible classification method that considers differences between groups based on both mean and covariance structures. However, the connection structures of high-dimensional predictors are usually not explicitly incorporated into modeling. In this work, we propose a local network-guided QDA method that integrates the local connection structures of high-dimensional predictors. In the context of gene expression research, our method can identify genes that show differential expression levels as well as gene networks that exhibit different connection patterns between various biological state groups, thereby enhancing our understanding of underlying biological mechanisms. Extensive simulations and real data applications demonstrate its superior performance in both feature selection and outcome classification compared to commonly used discriminant analysis methods.

Full-parametric and joint inversion of multimode surface wave data for identifying glacial ice thickness and freezing extent in subglacial sediments via the hunger games search algorithm

Determining glacier ice thickness and the extent of freezing in subglacial sediments are crucial in glaciological studies. Noninvasive geophysical methods, such as multichannel analysis of surface waves, are typically used for these tasks. In this study, we introduce a novel metaheuristic called hunger games search (HGS), which simulates the hunger-driven instincts and behavioral decisions of animals for the full-parametric inversion of seismic surface waves. We apply HGS to determine layer thicknesses, densities, S-wave velocities, and primary wave velocities of different layers through the joint inversion of multimode Rayleigh wave dispersion curves (RWDCs). This marks the first study using HGS for the inversion of dispersion data. Sensitivity studies of model parameters prior to the inversion indicate the necessity of postinversion uncertainty evaluations to mitigate the effects of varying sensitivity levels. In addition, a parameter tuning study is carried out to maximize the performance of HGS. Compared with some swarm intelligence-based optimizers (particle swarm optimization, cuckoo search, gray wolf optimization, sparrow search optimization, and whale optimization algorithm), HGS outperforms in the inversion of synthetic multimode RWDCs with 10% uncertainties with respect to a simulated glacier structure. In real data applications, a data set acquired at Midtdalsbreen, an outlet of the Norwegian Hardangerjøkulen ice cap, is inverted using the tailored HGS metaheuristic. The results obtained are consistent with previous geophysical studies. Furthermore, our analysis reveals that the performance of HGS when dealing with real applications is not highly sensitive to the selection of layers within the range of five to eight. The accuracy of HGS is undoubtedly contaminated by a larger model space; however, additional depth information derived from colocated ground-penetrating radar data can be directly integrated into HGS to obtain satisfactory results again.

G-Transmuted Pareto Distribution for Modeling Failure Time Data

In this paper, we investigate flexible and skewed families of transmuted distributions derived by introducing one or more additional parameters to a baseline distribution. Specifically, we propose a novel three-parameter G-Transmuted Pareto distribution. A comprehensive analysis of its mathematical properties is provided, alongside an evaluation of its reliability characteristics. Using real-world data, we demonstrate the effectiveness of the proposed distribution in modeling and highlight its utility as a robust statistical tool for a real life practical application.

A Priority based Self-Organised MAC Protocol for Real Time Wireless Sensor Network Applications

Wireless Sensor Networks (WSNs) are expressively utilized in various real-time control and monitoring applications. WSNs have been expanded considering the necessities in industrial time-bounded applications to support the dependable and time-bound delivery of data. Recently, Machine Learning (ML) algorithms have been used to address various WSN-related issues. The use of ML techniques supports dynamically modifying MAC settings based on traffic patterns and network conditions. In WSNs to control the communication between a large numbers of tiny, low-power sensor nodes while preserving energy and reducing latency, effective MAC protocols are essential. This paper addresses the ML centered priority-based self-organized MAC (ML-MAC) protocol to provide a priority-based transmission system to ensure the timely delivery of critical data packets. In this research, depending upon the predictions of the ML model, the MAC parameters are dynamically adjusted to find priority-based channel access and the optimal routing path to meet the deadline of critical data packets. From the result analysis, the average throughput and delay of the proposed ML-MAC algorithm outperforms the existing I-MAC protocol.

Pristine NiMOF Sandwiched between 1D and 3D Engineered Au Particles and Dendrites for Ultraswift Folic Acid Sensing in Cellular Microenvironment.

Catalytic metal-organic frameworks (MOFs)-based sensor matrices can act synergistically with Au metallic nanostructures to generate amplified signal readouts by causing the electro-oxidation of the target analyte. Folic acid (FA), an essential water-soluble vitamin and a precursor for enzymes, requires timely and precise monitoring in the serum of individuals with varying clinical diagnoses. An attempt has been made in this direction through our work, where the rapid detection of FA through its oxidation at metal centers from hybrid nanomaterials is deployed for signal generation. A nonenzymatic, nonimmunometric approach involving a sandwich model, comprising NiMOF layered between gold nanoparticles (AuNPs) and gold nanodendrites (AuNDs) incorporated within a sensor matrix, has been deployed for this purpose. The probe displayed great analytical performance with a linear dynamic range (LDR) from 1 × 10-11 M to 1 × 10-3 M and a limit of detection (LOD) of 0.43 × 10-11 M. The probe's average response time with respect to changes in FA concentration was recorded as less than 2.1 s, making it a rapid sensing platform for FA detection. The real-life applicability of the developed sensor was tested in serum, followed by analysis in a breast cancer cellular microenvironment, which yielded a current recovery between 95.11 and 98.17%. The in vitro analysis was further validated through live-cell imaging using the standard method of fluorescence. The shorter fabrication time of the developed sensor compared to existing ones makes it a facile and efficient sensing platform for FA detection in clinical settings. This study represents the first report on the conjunction of 1D, 2D, and 3D materials as a sensing matrix for molecular detection applications.

Fuzzy Self-tuning Bees Algorithm for designing optimal product lines

The Product Line Design (PLD) problem is an NP-hard combinatorial optimization problem in marketing that aims at determining an optimal product line through which a firm can optimize a desired objective, like its profits or market share. Since the PLD problem has been proved to have high complexity in real-life applications, high-quality solutions have been detected by researchers who develop various optimization methods and test their performance. The Bees Algorithm (BA) is a successful swarm intelligent optimization algorithm which is based on the behavior of bees. The aim of this research is to develop and assess BA in the optimal PLD problem. In this effort, a set of fuzzy rules has been developed to autonomously compute parameters for each individual solution throughout the optimization process. The performance of two BA variants is compared with those of popular previous approaches, using both real and simulated data of customer preferences. The findings reveal that BA constitutes an enhanced alternative approach for designing optimal product lines.

Empirical Likelihood Ratio Based K-Nearest Neighbours Regression

Regression models play a pivotal role in real-life applications by enabling the analysis and prediction of continuous outcomes. Among these, the k-Nearest Neighbours (KNN) model stands out as a significant advancement in machine learning. KNN's ability to make predictions based on the proximity of data points has found wide-ranging applications in various fields. However, the traditional KNN regression model has its limitations, including sensitivity to noise and an uneven distribution of neighbours. In response, this paper introduces a novel approach: an empirical likelihood ratio (ELR) based regression algorithm. The ELR technique offers distinct advantages over distance-based nearest neighbour computations, particularly in handling skewed data distributions and minimizing the impact of outliers. The proposed ELRbased KNN regression model is rigorously assessed through both simulation studies and real-life scenarios. The results explicitly demonstrate the enhanced performance of the ELR-based approach over the conventional KNN model. This research contributes to a deeper understanding of regression techniques and underscores the practical significance of leveraging empirical likelihood ratios in refining predictive models for real-world applications.. KEYWORDS :Regression model, k-Nearest neighbours, Empirical likelihood ratio, Distance measures, Data distributions.

Identifying periods impacted by sewer inflow and infiltration using time series anomaly detection

Accurate diagnosis of sewer inflow and infiltration (I/I) is crucial for ensuring the safe transportation of sewage and the stability of wastewater treatment processes. Identifying periods impacted by I/I is essential for I/I diagnosis, but current methods lack a standard criterion and require adaptation to specific conditions, resulting in low accuracy, complexity, and limited generalizability. This paper proposes a novel approach to distinguish I/I periods from time series of sewer measurements based on anomaly detection theory through an iterative use of a time-series reconstruction model. This method eliminates the need for external data such as rainfalls and avoids intensive manual data analysis. Operating directly on in-sewer data, it enhances accuracy compared to existing approaches and is applicable to various external factors such as rainfall, snowmelt, and seawater intrusion. The method can be applicable to a broad range of monitoring data, including flow rate, temperature, and conductivity. Validated through simulation studies and demonstrated via real-life applications, this method offers an efficient solution for I/I detection, facilitating further I/I diagnosis, including I/I quantification and location identification.

Highly sensitive and Durable crack structures on Flexible, Friction-Resistant substrates

Flexible sensors using crack-sensitive structures have garnered significant interest due to their exceptional sensitivity stemming from crack disconnection and reconnection mechanisms. However, studies on the sensor’s crack size is imperative, as cracks are typically regarded as defective or harmful in real-life applications. In this study, a new multi-layer configuration (PDMS/Ecoflex)/CNT/PET/(PDMS/Ecoflex) is employed to create piezoresistive pressure sensors with reduced crack size labelled crack free, demonstrating extensive linearity and exceptional sensor responsiveness. The crack size on the conductive layer was significantly reduced using Cetyl Trimethyl Ammonium Bromide cationic surfactant. PDMS/Ecoflex (PE) blend matrix was shown to have an outstanding mechanical and tribological properties compared to PDMS and Ecoflex alone achieving flexibility of up to 500 %, decreasing friction by 56 %, and enhancing wear resistance. The crack-free sensors exhibited a linearity of 0.99, high sensitivity (GF = 132), and low response time (19 ms). Furthermore, crack-free pressure sensors exhibit distinct characteristics such as low detection limits, rapid response/recovery, negligible hysteresis, excellent dynamic response (over 1000 cycles), and exceptional long-term durability. the cracked-free sensor was compared to a standard crack-based sensor to analyze the crack appearance and mechanism on the overall performance and application including health monitoring and various body movement detection.

Effect of long-term nanofluid usage on horizontal ground source heat pump performance

Nanofluids in thermal systems such as heat pumps are one of the innovative approaches due to their high thermal conductivity. However, nanofluids suffer from effects such as agglomeration and settling. Gravitational sedimentation occurs in the absence of circulation or mean flow conditions; this is a common problem in real-life engineering applications. The current experimental study focuses on how the system performance will be affected in this long standby situation. The study investigated the effect of nanofluid on the system performance in a Nanofluid-Assisted Ground Source Heat Pump (NAGSHP) system, which was examined experimentally long term. The findings show a loss of up to 3% in the performance of the Ground Heat Exchanger (GHE) and a 2.5% decrease in the coefficient of performance (COP) of the system. These values are even lower than the results obtained from experiments with ethylene glycol-water (without nanofluid) base fluid in the previous study. These results show that nanofluids cause performance degradation in long standby conditions. Further studies can investigate the interaction between surfactants and nanoparticles that reduce the sedimentation rate, considering different flow conditions, and show the implications of these results in engineering applications.