Classical Techniques Research Articles

In vivo comparison of mesh fixation solutions in open and laparoscopic procedures for inguinal hernia repair: A meta-analysis

BackgroundAbdominal hernia repair surgeries involve the fixation of a surgical mesh to the abdominal wall with different means such as suture, tacks, and glues. Currently, the most effective mesh fixation system is still debated. This review compares outcomes of mesh fixation in different surgical procedures, aiding surgeons in identifying the optimal technique. MethodsA meta-analysis was conducted according to PRISMA guidelines. Articles published between January 2003 and January 2023 were searched in electronic databases. Randomized controlled trials (RCTs) comparing mesh fixation with cyanoacrylate-based or fibrin glues with classical fixation techniques (sutures, tacks) in open and laparoscopic procedures were included. Results17 RCTs were identified; the cumulative study population included 3919 patients and a total of 3976 inguinal hernias. Cyanoacrylate-based and fibrin glues were used in 1639 different defects, suture and tacks in 1912 defects, self-gripping mesh in 404 cases, and no mesh fixation in 21 defects. Glue fixation resulted in lower early postoperative pain, and chronic pain occurred less frequently. The incidence of hematoma was lower with glue fixation than with mechanical fixation. Recurrence rate, seroma formation, operative and hospitalization time showed no significant differences; but significantly, a higher number of people in the glue group returned to work by 15- and 30-days after surgery when compared to the tacker and suture groups in the same time frame. ConclusionCyanoacrylate and fibrin glue may be effective in reducing early and chronic pain and hematoma incidence without increasing the recurrence rate, the seroma formation, or the operative and hospitalization time.

From programming-to-modeling-to-prompts smart ubiquitous applications

Since its introduction by Mark Weiser, ubiquitous computing has received increased interest in the dawn of technological advancement. Supported by wireless technology advancement, embedded systems, miniaturization, and the integration of various intelligent and communicative devise, context-aware ubiquitous applications actively and intelligently use rich contextual information to assist their users. However, their designs are subject to continuous changes imposed by external factors. Nowadays, software engineering, particularly in the fields of Model-Driven Engineering, displays a strong tendency towards developing applications for pervasive computing. This trend is also fueled by the rise of generative artificial intelligence, paving the way for a new generation of no-code development tools and models specifically trained on open-source code repositories to generate applications from their descriptions. The specificities of our approach lies in starting with a graphical model expressed using a domain-specific language (DSL) composed of symbols and formal notations. This allows for graphically instantiating and editing applications, guiding and assisting experts from various engineering fields in defining ubiquitous applications that are eventually transformed into peculiar models. We believe that creating intelligent models is the best way to promote software development efficiency. We have used and evaluated recurrent neural networks, leveraging the recurrence of processing the same contextual information collected within this model, and enabling iterative adaptation to future evolutions in ubiquitous systems. We propose a prototype instantiated by our meta-model which tracks the movements of individuals who were positive for COVID-19 and confirmed to be contagious. Different deep learning models and classical machine learning techniques are considered and compared for the task of detection/classification of COVID-19. Results obtained from all techniques were evaluated with confusion matrices, accuracy, precision, recall and F1-score. In summary, most of the results are very impressive. Our deep learning approach used a RNN architecture produced up to 92.1% accuracy. With the recent development of OpenAI Codex, optimized for programming languages, we provided the same requirements to the Codex model and asked it to generate the source code for the COVID-19 application, comparing it with the application generated by our workshop.

Improving multilayer perceptron neural network using two enhanced moth-flame optimizers to forecast iron ore prices

Abstract The quality of the output produced by the multi-layer perceptron neural network depends on the careful selection of its weights and biases. The gradient descent technique is commonly used for choosing MLP’s optimal configuration, but it can suffer from being stuck in local optima and slow convergence toward promising regions in the search space. In this article, we propose two new optimization algorithms based on the moth-flame optimization algorithm (MFO), which mimics moths’ special navigation methods at night. We use these algorithms to enhance the performance of the training process of the MLP neural network. To demonstrate the effectiveness of our approach, we apply it to the problem of predicting iron ore prices, which plays an important role in the continuous development of the steel industry. We use a large number of features to predict the iron ore price, and we select a promising set of features using two feature reduction methods: Pearson’s correlation and a newly proposed categorized correlation. Surprisingly, new features not mentioned in the literature are discovered, and some are discarded. The time series dataset used has been extracted from several sources and pre-processed to fit the proposed model. We compare our two proposed MFO algorithms, the roulette wheel moth-flame optimization algorithm and the global best moth-flame optimization algorithm, against four swarm intelligence algorithms and five classical machine learning techniques when predicting the iron ore price. The results acquired indicate the superior performance of the suggested algorithms concerning prediction accuracy, root-mean-square error, mean-square error, average absolute relative deviation, and mean absolute error. Overall, our work presents a promising approach for improving the performance of MLP neural networks, and it demonstrates its effectiveness in the challenging problem of predicting iron ore prices.

Multi-view Image Fusion Using Ensemble Deep Learning Algorithm For MRI And CT Images

Medical image fusions are crucial elements in image-based health care diagnostics or therapies and generic applications of computer visions. However, the majority of existing methods suffer from noise distortion that affects the overall output. When pictures are distorted by noises, classical fusion techniques perform badly. Hence, fusion techniques that properly maintain information comprehensively from multiple faulty pictures need to be created. This work presents Enhanced Lion Swarm Optimization (ESLO) with Ensemble Deep Learning (EDL) to address the aforementioned issues. The primary steps in this study include image fusions, segmentation, noise reduction, feature extraction, picture classification, and feature selection. Adaptive Median Filters are first used for noise removal in sequence to enhance image quality by eliminating noises. The MRIs and CT images are then segmented using the Region Growing–based k -Means Clustering (RKMC) algorithm to separate the images into their component regions or objects. Images in black and white are divided into image. In the white image, the RKMC algorithm successfully considered the earlier tumour probability. The next step is feature extraction, which is accomplished by using the Modified Principal Component Analysis (MPCA) to draw out the most informative aspects of the images. Then the ELSO algorithm is applied for optimal feature selection, which is computed by best fitness values. After that, multi-view image fusions of multi modal images derive lower-, middle-, and higher-level image contents. It is done by using Deep Convolution Neural Network (DCNN) and the Tissue-Aware Conditional Generative Adversarial Network (TAcGAN) algorithm, which fuses the multi-view features and relevant image features, and it is used for real-time applications. ELSO +EDL algorithm gives better results in terms of accuracy, Peak Signal-To-Noise Ratio (PSNR), and lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) when compared to other existing algorithms.

Predicting dental anxiety in young adults: classical statistical modelling approach versus machine learning approach

ObjectivesTo predict and identify the key demographic and clinical exposure factors associated with dental anxiety among young adults, and to compare if the traditional statistical modelling approach provides similar results to the machine learning (ML) approach in predicting factors for dental anxiety.MethodsA cross-sectional study of Western Illinois University students. Three survey instruments (sociodemographic questionnaire, modified dental anxiety scale (MDAS), and dental concerns assessment tool (DCA)) were distributed via email to the students using survey monkey. The dependent variable was the mean MDAS scores, while the independent variables were the sociodemographic and dental concern assessment variables. Multivariable analysis was done by comparing the classical statistical model and the machine learning model. The classical statistical modelling technique was conducted using the multiple linear regression analysis and the final model was selected based on Akaike information Criteria (AIC) using the backward stepwise technique while the machine learining modelling was performed by comparing two ML models: LASSO regression and extreme gradient boosting machine (XGBOOST) under 5-fold cross-validation using the resampling technique. All statistical analyses were performed using R version 4.1.3.ResultsThe mean MDAS was 13.73 ± 5.51. After careful consideration of all possible fitted models and their interaction terms the classical statistical approach yielded a parsimonious model with 13 predictor variables with Akaike Information Criteria (AIC) of 2376.4. For the ML approach, the Lasso regression model was the best-performing model with a mean RMSE of 0.617, R2 of 0.615, and MAE of 0.483. Comparing the variable selection of ML versus the classical statistical model, both model types identified 12 similar variables (out of 13) as the most important predictors of dental anxiety in this study population.ConclusionThere is a high burden of dental anxiety within this study population. This study contributes to reducing the knowledge gap about the impact of clinical exposure variables on dental anxiety and the role of machine learningin the prediction of dental anxiety. The predictor variables identified can be used to inform public health interventions that are geared towards eliminating the individual clinical exposure triggers of dental anxiety are recommended.

Lateral-PLIF for spinal arthrodesis: concept, technique, results, complications, and outcomes.

Posterior lumbar interbody fusion (PLIF) surgery represents an effective option to treat degenerative conditions in the lumbar spine. To reduce the drawbacks of the classical technique, we developed a variant, so-called Lateral-PLIF, which we then evaluated through a prospective consecutive series of patients. All adult patients treated at our institute with single or double level Lateral-PLIF for lumbar degenerative disease from January to December 2017 were prospectively collected. Exclusion criteria were patients < 18years of age, traumatic patients, active infection, or malignancy, as well as unavailability of clinical and/or radiological follow-up data. The technique consists of insert the cages bilaterally through the transition zone between the central canal and the intervertebral foramen, just above the lateral recess. Pre- and postoperative (2years) questionnaires and phone interviews (4years) assessed pain and functional outcomes. Data related to the surgical procedure, postoperative complications, and radiological findings (1year) were collected. One hundred four patients were selected for the final analysis. The median age was 58years and primary symptoms were mechanical back pain (100, 96.1%) and/or radicular pain (73, 70.2%). We found a high fusion rate (95%). A statistically significant improvement in functional outcome was also noted (ODI p < 0.001, Roland-Morris score p < 0.001). Walking distance increased from 812m ± 543m to 3443m ± 712m (p < 0.001). Complications included dural tear (6.7%), infection/wound dehiscence (4.8%), and instrument failure (1.9%) but no neurological deterioration. Lateral-PLIF is a safe and effective technique for lumbar interbody fusion and may be considered for further comparative study validation with other techniques before extensive use to treat lumbar degenerative disease.

Modular Circuit Synthesis Oriented Modelling Approach for Non-Isolated DC-DC Converters in DCM

The continued and envisioned large-scale integration of renewable energy sources as a reaction to rising global temperatures and climate change will need a readily available DC grid to increase commissioning and operating efficiency. The effective operation of these grids is predicated on the correct control of its main control points. A plethora of DC-DC converters that find use in DC microgrids act as the main control points. DC-DC converters are non-linear and can operate in different modes with completely unique characteristics. To utilise classical control techniques, laborious equivalent linear models are derived for DC-DC converters using averaging modelling schemes. The application and limitations of these modelling techniques are well captured in the available literature. The most common limitation of the available modelling schemes is that more focus is dedicated to converter attributes like order, functionality and operating mode, even when optimal power flow and voltage regulation within the DC network are of more interest. Structure-based modelling techniques like the use of basic building blocks nullify converter attributes in the modelling process which translates to modelling efficiency. In light of the merits seen with the use of basic building blocks when modelling converters in CCM, the current study extends these merits to converters operation in DCM. Similar to modelling converters in CCM, modelling techniques that are available in the literature continue to consider converter attributes in the modelling process for DCM operation. Moreover, the two modes of operation are treated as unique entities and often modelled in a non-unified manner, which compromise modelling efficiency since the same converter can operate in a different state solely based on loading. The aim is to increase modelling efficiency but also nullify operating mode in the modelling process. The same basic building blocks are now modelled as two-port networks for DCM operation and adopted based on the exact configuration of a specified converter to compute its steady-state and dynamic models. All the advantages seen when modelling converters in CCM using basic building blocks are retained and augmented when considering DCM operation. Thus, any converter with well-defined basic building blocks can be easily modelled solely based on the connection of constituent basic building blocks.

Optimal Re-Materialization Strategies for Heterogeneous Chains: How to Train Deep Neural Networks with Limited Memory

Training in Feed Forward Deep Neural Networks is a memory-intensive operation which is usually performed on GPUs with limited memory capacities. This may force data scientists to limit the depth of the models or the resolution of the input data if data does not fit in the GPU memory. The re-materialization technique, whose idea comes from the checkpointing strategies developed in the Automatic Differentiation literature, allows data scientists to limit the memory requirements related to the storage of intermediate data (activations), at the cost of an increase in the computational cost. This paper introduces a new strategy of re-materialization of activations that significantly reduces memory usage. It consists in selecting which activations are saved and which activations are deleted during the forward phase, and then recomputing the deleted activations when they are needed during the backward phase. We propose an original computation model that combines two types of activation savings: either only storing the layer inputs, or recording the complete history of operations that produced the outputs. This paper focuses on the fully heterogeneous case, where the computation time and the memory requirement of each layer is different. We prove that finding the optimal solution is NP-hard and that classical techniques from Automatic Differentiation literature do not apply. Moreover, the classical assumption of memory persistence of materialized activations, used to simplify the search of optimal solutions, does not hold anymore. Thus, we propose a weak memory persistence property and provide a Dynamic Program to compute the optimal sequence of computations. This algorithm is made available through the Rotor software, a PyTorch plug-in dealing with any network consisting of a sequence of layers, each of them having an arbitrarily complex structure. Through extensive experiments, we show that our implementation consistently outperforms existing re-materialization approaches for a large class of networks, image sizes and batch sizes.

Bayesian Inference for Inverse Power Exponentiated Pareto Distribution Using Progressive Type-II Censoring with Application to Flood-Level Data Analysis

Progressive type-II (Prog-II) censoring schemes are gaining traction in estimating the parameters, and reliability characteristics of lifetime distributions. The focus of this paper is to enhance the accuracy and reliability of such estimations for the inverse power exponentiated Pareto (IPEP) distribution, a flexible extension of the exponentiated Pareto distribution suitable for modeling engineering and medical data. We aim to develop novel statistical inference methods applicable under Prog-II censoring, leading to a deeper understanding of failure time behavior, improved decision-making, and enhanced overall model reliability. Our investigation employs both classical and Bayesian approaches. The classical technique involves constructing maximum likelihood estimators of the model parameters and their bootstrap covariance intervals. Using the Gibbs process constructed by the Metropolis–Hasting sampler technique, the Markov chain Monte Carlo method provides Bayesian estimates of the unknown parameters. In addition, an actual data analysis is carried out to examine the estimation process’s performance under this ideal scheme.

Round Autoprosthesis: Use of Adipodermaglandular Flap in T Scar Mastopexy.

This study focuses on the use of round or anatomically shaped breast autoprosthesis with different volumes prepared from the central and lower poles of the breast. The technical details and surgical outcomes for patients with varying degrees of breast ptosis are discussed. This study involved 42 patients who underwent the Wise-pattern superior pedicle mastopexy procedure using the adipodermaglandular island flap technique. The research spanned from December 2017 to August 2022. The study participants had not previously undergone breast surgery, did not desire breast implants, and exhibited grade 2 and 3 breast ptosis according to the Regnault Classification. Age and preoperative breast measurements of the patients were recorded for subsequent analysis. Measurements, such as the distance from the nipple-areolar complex to the inframammary fold and the distance from the suprasternal notch to the nipple-areolar complex, were taken both before the surgery and one year after. A systematic process was followed to identify acute and subacute complications during the postoperative follow-up period. This study involved 42 patients with a mean age of 33.9 years (range: 23-49 years). These procedures were conducted between December 2017 and August 2022. The average SN-N distance before surgery measured 26.7cm (range: 24-33cm). One year after surgery, the average SN-N distance was 23.1cm (range: 21.3-24.8cm). The follow-up duration for the examined cases ranged from 12 to 18 months on average. Among the observed cases, delayed wound healing was noted in one instance, venous insufficiency of the nipple in another, and fat necrosis in a third case. The overall complication rate in the group was determined to be 7.1%. In our study, the detachment of dermal connections at the level of the inframammary fold (IMF) and the smooth advancement of the flap in the form of an "island flap" made a contribution to upper pole fullness. Furthermore, we hypothesize that the fusion of the medial and lateral ends of the flap will enhance tissue integration during the healing process, promoting compatibility between the autoprosthesis tissue and breast tissue. Regarding the rates of complications described, classic mastopexy techniques have exhibited similar rates in our findings. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Full Characterisation of Heroin Samples Using Infrared Spectroscopy and Multivariate Calibration.

The analysis of heroin samples, before use in the protected environment of user centra, could be a supplementary service in the context of harm reduction. Infrared spectroscopy hyphenated with multivariate calibration could be a valuable asset in this context, and therefore 125 heroin samples were collected directly from users and analysed with classical chromatographic techniques. Further, Mid-Infrared spectra were collected for all samples, to be used in Partial Least Squares (PLS) modelling, in order to obtain qualitative and quantitative models based on real live samples. The approach showed that it was possible to identify and quantify heroin in the samples based on the collected spectral data and PLS modelling. These models were able to identify heroin correctly for 96% of the samples of the external test set with precision, specificity and sensitivity values of 100.0, 75.0 and 95.5%, respectively. For regression, a root mean squared error of prediction (RMSEP) of 0.04 was obtained, pointing at good predictive properties. Furthermore, during mass spectrometric screening, 10 different adulterants and impurities were encountered. Using the spectral data to model the presence of each of these resulted in performant models for seven of them. All models showed promising correct-classification rates (between 92 and 96%) and good values for sensitivity, specificity and precision. For codeine and morphine, the models were not satisfactory, probably due to the low concentration of these impurities as a consequence of acetylation. For methacetin, the approach failed.

Central Chile comprises a previously unknown nonbreeding area for the migratory population of Turkey Vulture (Cathartes aura ruficollis) breeding in the northwestern Argentine Patagonia

ABSTRACT Bird migration in the Neotropics is complex and highly diverse. Indeed, for many taxa, basic questions such as where they go are still relevant. Satellite-tracking studies of Turkey Vulture (Cathartes aura) migration have revealed extensive variability in their movement strategies, contributing to their widespread distribution. However, South American migratory populations are still among the least explored. By integrating five years of satellite-tracking data, citizen science databases and classical raptor monitoring techniques, we present a case study reporting a hitherto unknown nonbreeding area for the Turkey Vulture subspecies ruficollis. Specifically, we document that a minor proportion of the population that breeds in northwestern Argentine Patagonia migrates northwards to central Chile at the beginning of the austral autumn. Our findings improve our basic knowledge of the Turkey Vulture migration within the Neotropics and establish a baseline to study the movement strategies of the migratory ruficollis populations breeding in high latitudes of South America.

Vision based process monitoring in wire arc additive manufacturing (WAAM)

AbstractA stable welding process is crucial to obtain high quality parts in wire arc additive manufacturing. The complexity of the process makes it inherently unstable, which can cause various defects, resulting in poor geometric accuracy and material properties. This demands for in-process monitoring and control mechanisms to industrialize the technology. In this work, process monitoring algorithms based on welding camera image analysis are presented. A neural network for semantic segmentation of the welding wire is used to monitor the working distance as well as the horizontal position of the wire during welding and classic image processing techniques are applied to capture spatter formation. Using these algorithms, the process stability is evaluated in real time and the analysis results enable the direction independent closed-loop-control of the manufacturing process. This significantly improves geometric fidelity as well as mechanical properties of the fabricated part and allows the automated production of parts with complex deposition paths including weld bead crossings, curvatures and overhang structures.

Non-modal stability analysis of magneto-hydrodynamic flow in a single pipe

A complete understanding of the stability of fluid flows under varying magnetic field profiles is imperative for achieving control of plasma and operating fluids in the blankets of future fusion reactors. In this context, the primary objective of this study is to investigate the influence of varying magnetic profiles on the flow regime of a generic fluid, which is representative of both thermonuclear plasma and conductive fluids within a nuclear fusion reactor. To this aim in this work non-modal stability theory is adopted to perform stability analysis of a magneto-hydrodynamic (MHD) flow in an infinite circular pipe in order to study the effects of the magnetic field on the fluid dynamics of the pipe flow. In particular, the effects on the general stability of two magnetic field profiles are compared with the reference case of a pipe Poiseuille flow without magnetic field. Firstly, the classic modal stability technique is employed to study asymptotical stability. Then, non-modal stability analysis is applied to magneto-hydrodynamic pipe flow to study the system's response for a finite time immediately after a perturbation. Fourier–Chebyshev Petrov–Galerkin spectral method is used to compute the eigenvalues and pseudospectra of the weak formulation associated with the linearised system. Investigations on the dependence of spectra and transient growths on the specific magnetic profiles are conducted for different values of perturbation wave numbers. The obtained results show that in general the magnetic field has an effect of stabilization on the system, which depends on the specific magnetic profile considered. In addition, the non-modal stability analysis reveals that the inclusion of the magnetic field mitigates the effects of perturbations also in the short term, a phenomenon that cannot be seen using only modal stability analysis.

Isolation and Identification of Some Bacteria Associated with Biogas Production from Food Waste and Rumen Content

The goal of this study was to investigate the bacterial community associated with biogas production from food waste and rumen content. Anaerobic mono-digestion and co-digestion were performed using mixture design within Design Expert, resulting in 100 experimental runs. Parameters such as food waste and rumen content, water content, temperature, pH, number of digester agitation per day and retention time were varied during the anaerobic digestion processes. Classical microbiological techniques were used to isolate and identify strict anaerobic and facultative anaerobic bacteria from the food waste and rumen content before and after anaerobic digestion. Sixteen bacterial species belonging to 12 different generae were isolated and identified from the food waste, rumen content and composite digestates obtained from the 100 bio-digesters. These generae included Acetobacterium, Bacteroides, Clostridium, Enterobacter, Escherichia, Lactobacillus, Pseudomonas, Ruminococcus, Staphylococcus, Streptococcus, Syntrophomonas and Syntrophobacter. With the exception of Escherichia coli and Pseudomonas sp., all other bacterial species identified in the substrates were also found in samples of the composite digestates, suggesting that they may have played important roles in the anaerobic digestion process inside the 100 bio-digesters. Overall, this study provides valuable insights into the microbial community structure and function during biogas production from food waste and rumen content. The results could contribute to the development of more efficient and sustainable biogas production processes.

Linear time periodic system approximation based on Floquet and Fourier transformations for operational modal analysis and damage detection of wind turbine

Operational Modal Analysis (OMA) identifies modal properties of mechanical structures from vibration data collected from a few sensors under operation conditions. These methods are widely used to monitor civil engineering structures that are modeled as time-invariant systems. However, when dealing with wind turbines and rotating machines, many dedicated OMA techniques were developed to deal with the time-periodic behavior. Existing methods pre-process the data to adapt them to classical identification techniques. Yet, these methods present limitations as either requiring a high number of measured rotation periods, assuming the assumption of an isotropic rotor (i.e. undamaged), or the knowledge of the rotational speed. This work proposes to lift these difficulties by proving that the application of the classical system identification methods can produce meaningful estimates for anisotropy monitoring. This observation is based on directly approximating the time-periodic dynamic behavior of the wind turbine as a properly defined time-invariant system under periodic inputs. It results in the possibility of using classical identification methods without modification to retrieve the system matrices of the approximated time-invariant system. This approach does not require knowing the rotor speed, requiring relatively fewer measurements and it is not restricted to isotropic rotors. The identified modes can be used reliably for the monitoring of operating wind turbines and more especially for fault detection, at the expense of losing a part of the complete description of the periodic system. The resulting anisotropy monitoring approach and its capacities are illustrated in two cases. Firstly, on an academic model of a wind turbine and then on an aero-servo-elastic numerical model of a rotating 10 MW wind turbine to demonstrate the ability of the method to deal with realistic and representative data. It is shown for the latest case, from sensors located on the blades of the wind turbine, the approach is able to identify correctly the system properties (frequencies and mode shapes) and is also able to detect efficiently a fault on a blade.

Powdery Scab of Potato: A Diagnostic Guide

Powdery scab, caused by Spongospora subterranea, is a soil-and tuber-borne disease of potato. The fungus-like protozoan pathogen can be found around the world and in almost every large potato-producing state across the United States. Initial signs of infection in the eyes and lenticels of tubers are slightly sunken, purple-brown lesions, and when a tuber lesion reaches maturity, the periderm bursts, releasing masses of tiny, powdery balls. The objective of this diagnostic guide is to provide detailed information on the isolation, sampling, pathogenicity testing, and detection of the pathogen in the soil and tubers using classical and molecular techniques to facilitate the identification of the disease in the laboratory and field.

A multi-objective approach for communication reduction in federated learning under devices heterogeneity constraints

Federated learning is a paradigm that proposes protecting data privacy by sharing local models instead of raw data during each iteration of model training. However, these models can be large, with many parameters, provoking a substantial communication cost and having a notable environmental impact. Reducing communication overhead is paramount but conflictual to maintaining the model’s accuracy. Most research has dealt with the different factors influencing communication reduction separately without addressing their correlations. Moreover, most of them do not consider the heterogeneity of clients’ hardware. Finding the optimal configuration to fulfil all these training aspects can become intractable for classical techniques. This work explores the add-in that multi-objective evolutionary algorithms can provide for solving the communication overhead problem while achieving high accuracy. We do this by 1) realistically modelling and formulating this task as a multi-objective problem by considering the devices’ heterogeneity, 2) including all the communication-triggering aspects, and 3) applying a multi-objective evolutionary algorithm with an intensification operator to solve the problem. A simulated client–server architecture of four devices with four different processing speeds is studied. Both fully connected and convolutional neural network models are investigated with 33,400 and 887,530 weights, respectively. The experiments are performed using the MNIST and Fashion-MNIST datasets. A comparison is made between three approaches using an extensive set of metrics. Results prove that our approach obtains solutions with better accuracy than the full-communication setting and other methods while getting reductions in communications by around 1,000 times in most cases and up to 10,000 times in some cases compared to the maximum communication setting.

Implementing the Flipped Classroom Model in Science Lessons for Junior High School Students

The present study refers to the idea of the flipped classroom. The flipped or otherwise called inverted classroom is a modern teaching concept, which essentially reverses the terms of the traditional learning method. The students study the lesson beforehand, outside the classroom using new technologies, while the time inside the classroom is mainly devoted to discussions and solving questions by the educator, hands-on activities that stimulate the students’ interest and connect the acquired knowledge with everyday problems. Reversing the traditional teaching process appears to be paying off for students, as since utilizing classical teaching techniques, their performance seemed to decline over time. For this purpose, the flipped classroom method was applied in a junior high school class in Athens, Greece, for Physics lessons during the 2022–2023 school year. At the end of the school year, the students that participated in the flipped classroom completed a survey that was developed jointly with the cooperation of the respective teachers who taught the course in order to establish the degree of the students’ acceptance of the specific method. The results of the survey and a correlation of the students’ performance who participated in the flipped classroom with the performance achieved by the rest of the students that followed traditional teaching methods, as well as with the previous year’s students’ performance who also followed the classical teaching model, are analyzed and discussed.

On the reliable estimation of sequential Monod kinetic parameters

While dozens of studies have attempted to estimate the Monod kinetic parameters of microbial reductive dechlorination, published values in the literature vary by 2–6 orders of magnitude. This lack of consensus can be attributed in part to limitations of both experimental design and parameter estimation techniques. To address these issues, Hamiltonian Monte Carlo was used to produce more than one million sets of realistic simulated microcosm data under a variety of experimental conditions. These data were then employed in model fitting experiments using a number of parameter estimation algorithms for determining Monod kinetic parameters. Analysis of data from conventional triplicate microcosms yielded parameter estimates characterized by high collinearity, resulting in poor estimation accuracy and precision. Additionally, confidence intervals computed by commonly used classical regression analysis techniques contained true parameter values much less frequently than their nominal confidence levels. Use of an alternative experimental design, requiring the same number of analyses as conventional experiments but comprised of microcosms with varying initial chlorinated ethene concentrations, is shown to result in order-of-magnitude decreases in parameter uncertainty. A Metropolis algorithm which can be run on a typical personal computer is demonstrated to return more reliable parameter interval estimates.