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Data scarcity in developing countries often poses significant challenges to the use of analytics in addressing development issues. In transportation research, digitized road data is one of the most fundamental data structures, and a poorly digitized road network significantly reduces the ability to optimize trade of micro-enterprises (SDG 8) and placement of hospitals (SDG 3). Unfortunately, current methods to enhance or create digital road networks are not well-adapted to regions with sparse geospatial data, often resulting in poor digital representations of road networks in less-developed regions such as rural areas of developing countries. We present a novel projection-based incremental insertion method that is well-suited to either enhance large existing road networks or combine multiple sources of road networks in regions with sparse geospatial data. In collaboration with PemPem and the World Bank, we perform two case studies that demonstrate the effectiveness of the proposed method. Together with PemPem, we show that our method significantly improves the digital road network for smallholder farmers in Indonesia, where only 40% of the origin–destination pairs in our dataset were previously digitized. Moreover, in a case study of optimizing geospatial accessibility to healthcare in Timor-Leste (World Bank), the improved digital road network detects an additional 5% of people to be in the vicinity of a hospital.

Summary: Implementing grasping tasks under color and multi scene promotion conditions is a key technology. This study proposes a recognition and grasping technique based on the crystal butterfly algorithm and adaptive imitation synthesis. Firstly, inspired by the movement trajectory of butterflies, a dynamic node tracking method called ''Butterfly Trajectory'' was designed. It can complete dynamic trajectory tracking under geometric constraints and achieve route memory. The second color dynamic recognition technology (CDR) has been proposed. It can quickly extract brightness, transparency, and color saturation obtained from multiple angles. Improve the feature extraction speed of Region CNN (R-CNN) instead of traditional methods (HOG). In addition, an adaptive imitation synthesis technique (AISP) is used to achieve the multi scenario promotion of grasping technology. Finally, simulation and physical testing were provided to verify the effectiveness of the design scheme in this article.

Summary: Under the background of the accelerating speed of urban and rural construction, the geographical environment of overhead transmission lines has also changed greatly. Using unmanned aerial vehicle (UAV) to realize intelligent line inspection can significantly shorten inspection time and improve inspection efficiency. In this paper, the intelligent power inspection of UAVs is studied from two levels: path planning and UAV control, and the insulator is identified through actual image recognition. At the path planning level, the improved swarm intelligence algorithm is used to conduct simulation experiments on the UAV flight path to find a safe and effective route. Insulator identification and defect location of overhead transmission lines are trained on the insulator dataset collected by deep learning technology to achieve accurate insulator identification and improve the efficiency of UAV inspection, which has great application prospects in engineering.

The security of images is one of the predominant pivotal aspects in the mammoth and still expanding digital domain. Due to chaotic system properties i.e. randomness and unpredictability is very appropriate to encrypt the images. In this research article, we construct an encryption model via 6D hyperchaotic map and a symmetric matrix for both color and grayscale images. We utilize the 6D hyperchaotic map in the confusion stage to change the pixel location and the symmetric matrix is used for changing the pixel value in the diffusion step for each RGB channel extraction from plain or original image. The image encryption model is checked over differential attacks (NPCR and UACI). Histogram analysis, correlation coefficients, and entropy analysis are also performed as statistical attacks. In conclusion, the image pixels are uniformly distributed, and the average entropy value are 7.9992 and 7.9973 for color and grayscale images, subsequently. The average NPCR and UACI for color images are 99.5956 and 33.4061, correspondingly, while the values for grayscale images are 99.5934 and 33.3054, respectively. These values are in the vicinity of optimal ranges. The suggested scheme's great efficiency and the proposed algorithm's resilience to a wide range of cryptanalytic attacks are implied by experimental results, statistical analysis, and differential attacks.

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

Summary: The quantum denoising technology efficiently removes noise from images; however, the existing algorithms are only effective for additive noise and cannot remove multiplicative noise, such as speckle noise in synthetic aperture radar (SAR) images. In this paper, based on the grayscale morphology method, a quantum SAR image denoising algorithm is proposed, which performs morphological operations on all pixels simultaneously to remove the noise in the SAR image. In addition, we design a feasible quantum adder to perform cyclic shift operations. Then, quantum circuits for dilation and erosion are designed, and the complete quantum circuit is then constructed. For a 2n×2n quantum SAR image with q grayscale levels, the complexity of our algorithm is O (n+q). Compared with classical algorithms, it achieves exponential improvement and also has polynomial-level improvements than existing quantum algorithms. Finally, the feasibility of our algorithm is validated on IBM Q.

Summary: While artificial intelligence drives remarkable progress in natural sciences, its broader societal implications are mostly disregarded. In this study, we evaluate environmental impacts of deep learning in materials science through extensive benchmarking. In particular, a set of diverse neural networks is trained for a given supervised learning task to assess greenhouse gas (GHG) emissions during training and inference phases. A chronological perspective showed diminishing returns, manifesting themselves as a 28% decrease in mean absolute error and nearly a 15,000% increase in the carbon footprint of model training in 2016–2022. By means of up-to-date graphics processing units, it is possible to partially offset the immense growth of GHG emissions. Nonetheless, the practice of employing energy-efficient hardware is overlooked by the materials informatics community, as follows from a literature analysis in the field. On the basis of our findings, we encourage researchers to report GHG emissions together with standard performance metrics.

Purpose: This study aimed to investigate the performance of deep learning algorithms in the opportunistic screening for primary angle-closure disease (PACD) using combined anterior segment parameters. Methods: This was an observational, cross-sectional hospital-based study. Patients with PACD and healthy controls who underwent comprehensive eye examinations, including gonioscopy and anterior segment optical coherence tomography (ASOCT) examinations under both light and dark conditions, were consecutively enrolled from the Department of Ophthalmology at the Beijing Tongren Hospital between November 2020 and June 2022. The anterior chamber, anterior chamber angle, iris, and lens parameters were assessed using ASOCT. To build the prediction models, backward logistic regression was utilized to select the variables to discriminate patients with PACD from normal participants, and the area under the receiver operating characteristic curve was used to evaluate the efficacy of the opportunistic screening. Results: The data from 199 patients (199 eyes) were included in the final analysis and divided into two groups: PACD (109 eyes) and controls (90 eyes). Angle opening distance at 500 μm, anterior chamber area, and iris curvature measured in the light condition were included in the final prediction models. The area under the receiver operating characteristic curve was 0.968, with a sensitivity of 91.74 % and a specificity of 91.11 %. Conclusion: ASOCT-based algorithms showed excellent diagnostic performance in the opportunistic screening for PACD. These results provide a promising basis for future research on the development of an angle-closure probability scoring system for PACD screening.

Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture.

Objective: To externally validate the dynamic prediction model for prediction of upper limb (UL) function 6 months after stroke. The dynamic prediction model has been developed and cross-validated on data from 4 Dutch studies. Design: Data from a prospective Danish cohort study were used to assess prediction accuracy. Setting: A Danish neurorehabilitation hospital. Participants: In this external validation study, follow-up data for 80 patients in the subacute phase after stroke (N=80), mean age 64 (SD11), 43% women, could be obtained. They were assessed at 2 weeks, 3 months, and 6 months after stroke with the Action Research Arm Test (ARAT), Fugl-Meyer Motor Assessment upper limb (FMA), and Shoulder Abduction (SA) Finger Extension (FE), (SAFE) test. Intervention: Not applicable. Main Outcome Measures: Prediction accuracy at 6 months was examined for 3 categories of ARAT (0-57 points): mild (48-57), moderate (23-47), and severe (0-22). Two individual predictions of ARAT scores at ±6 months post-stroke were computed based on, respectively, baseline (2 weeks) and 3 months ARAT, FE, SA values. The absolute individual differences between observed and predicted ARAT scores were summarized. Results: The prediction model performed best for patients with relatively good UL motor function, with an absolute error median (IQR) of 3 (2-9), and worst for patients with severe UL impairment, with a median (IQR) of 30 (3-39) at baseline. In general, prediction accuracy substantially improved when data obtained 3 months after stroke was included compared with baseline at 2 weeks after stroke. Conclusion: We found limited clinical usability due to the lack of prediction accuracy 2 weeks after stroke and for patients with severe UL impairments. The dynamic prediction model could probably be refined with data from biomarkers.

The high expenses of electrical energy needed for heating water can be significantly decreased by using flat plate collectors. Therefore, the design of flat plate collectors having optimal performance has received a lot of attention in recent research projects. However, due to the existence of uncertainties in material properties, geometry and environmental conditions, it is imperative to incorporate uncertainty analysis into the design optimization to obtain reliable results. In this work, a multi-criteria robust design based on multi-objective optimization of the flat plate collector system is developed. Firstly, a determinist optimization (non-consideration of uncertain design parameters in this optimization strategy) of flat plate collector design is conducted. The obtained results showed that the determinist optimization leads to a flat plate collector design with 30% efficiency sensitivity and 27% total cost sensitivity to design parameters uncertainty. As an alternative, the multi-criteria robust design based on multi-objective optimization is developed. For this goal, a combined multi-objective colonial competitive algorithm and polynomial chaos expansion method is developed and used. This multi-criteria robust optimization considers simultaneously four objectives functions including the flat plate collector efficiency, the flat plate collector total cost, and their sensitivities to uncertain design parameters. It is noted that the multi-criteria robust design based on multi-objective optimization offers a robust flat plate collector with performances sensitivity to design parameters uncertainty less than 4.5%. Furthermore, when compared to the literature results, the performances sensitivities of flat plate collector design, obtained by the multi-criteria robust design based on multi-objective optimization, were reduced until 64%.

Today, technology and sustainability are two strategic axes for the development of any industry. Art is no exception and embodies both principles. Artificial intelligence (AI) is driving the art world forwards with its applications and algorithms. Additionally, the circular economy (CE) is concerned with resources and the environment in this context. The objective of the present work is to provide an overview of the current state of research on the application of AI in the art world and an analysis of how CE principles are being incorporated, considering the interactions between AI and the CE. To this end, a systematic review of the literature is carried out in which 60 articles related to the subject are selected, analysed, and classified, highlighting the lines of research addressed. The assessment of the current state of research on the subject concludes with the four main axes of classification of works. The first line is related to AI generative content in art, addressing issues of content creation, image and painting, video, and theatre. The second line is related to AI applications for art industry production, considering the sustainability of the supply chain. The third line focuses on how the CE is being applied to art, while the fourth line focuses on other relevant aspects analysed, such as training and design. The topic is still incipient, mandating further research to study the full potential of AI and the CE in the world of art.

All viruses, including SARS-CoV-2, the virus responsible for COVID-19, continue to evolve, which can lead to new variants. The objective of this study is to assess the agreement between real-world clinical data and an algorithm that utilizes laboratory markers and age to predict the progression of disease severity in COVID-19 patients during the pre-Omicron and Omicron variant periods. The study evaluated the performance of a deep learning (DL) algorithm in predicting disease severity scores for COVID-19 patients using data from the USA, Spain, and Turkey (Ankara City Hospital (ACH) data set). The algorithm was developed and validated using pre-Omicron era data and was tested on both pre-Omicron and Omicron-era data. The predictions were compared to the actual clinical outcomes using a multidisciplinary approach. The concordance index values for all datasets ranged from 0.71 to 0.81. In the ACH cohort, a negative predictive value (NPV) of 0.78 or higher was observed for severe patients in both the pre-Omicron and Omicron eras, which is consistent with the algorithm's performance in the development cohort.

Summary: With the development of e-commerce, the importance of recommendation algorithms has significantly increased. However, traditional recommendation systems struggle to address issues such as data sparsity and cold start. This article proposes an optimization method for a recommendation system based on spectral clustering (SC) and gated recurrent unit (GRU), named the GRU-KSC algorithm. Firstly, this paper improves the original spectral clustering algorithm by introducing Kmc2, proposing a novel spectral clustering recommendation algorithm (K-means++ SC, KSC) based on the existing SC algorithm. Secondly, building upon the original GRU model, the paper presents a hybrid recommendation algorithm (Hybrid GRU, HGRU) capable of capturing long-term user interests for a more personalized recommendation. Experiments conducted on real datasets demonstrate that our method outperforms existing benchmark methods in terms of accuracy and robustness.

Iron deficiency is commonly referred to as anaemia which is a general public health problem that normally occurs as a result of a reduction in red blood cells which is common in developing countries such as Africa. In this study, machine learning algorithms such as CNN, k-NN, Naïve Bayes, Decision Tree and SVM were utilized for the study to detect anaemia in children using conjunctiva images. The images were segmented into their various CIELAB colour space components and the ROI from each image was retrieved. The dataset was split randomly into 70:10:20, which were then used to train, validate, and test the models, as appropriate. The CNN achieved the highest accuracy (98.45 %). The findings of this study demonstrate that non-invasive techniques are essential for detecting anaemia in children. This study deploys a cost-effective mechanism, and result-orientated, to detect anaemia in developing communities where health facilities, resources, and personnel are scarce.

Feature selection is a critical component of machine learning and data mining which addresses challenges like irrelevance, noise, redundancy in large-scale data etc., which often result in the curse of dimensionality. This study employs a K-nearest neighbour wrapper to implement feature selection using six nature-inspired algorithms, derived from human behaviour and mammal-inspired techniques. Evaluated on six real-world datasets, the study aims to compare the performance of these algorithms in terms of accuracy, feature count, fitness, convergence and computational cost. The findings underscore the efficacy of the Human Learning Optimization, Poor and Rich Optimization and Grey Wolf Optimizer algorithms across multiple performance metrics. For instance, for mean fitness, Human Learning Optimization outperforms the others, followed by Poor and Rich Optimization and Harmony Search. The study suggests the potential of human-inspired algorithms, particularly Poor and Rich Optimization, in robust feature selection without compromising classification accuracy.

Anemia is a public health issue with serious ramifications for human health globally. Anemia particularly affects pregnant women and children from 6 to 59 months old even though every individual is at risk. Anemia occurs when the Hb level is below its normal threshold or when the red blood cells are weakened or destroyed. To discover medical remedies on time, early detection or diagnosis of anemia assist patients to understand their condition.The invasive approach for anemia detection is costive and time-consuming as compared to the non-invasive approach which is reliable and suitable for developing communities where medical resources and personnel are inadequate. This study uses palpable palm images (dataset) collected from 710 participants in selected hospitals in Ghana. The images were extracted, segmented and converted into RGB percentile to train, validate and tested the machine learning models. A hybrid model was developed with the application of ensemble learning models using the R programming language on the R Studio platform. Stacking, voting, boosting and bagging ensemble model techniques were used to build the hybrid models, the stacking ensemble model achieved an accuracy of 99.73 ​%. The study justifies that ensemble models are efficient for medical disease diagnosis or detection such as anemia.

Social media platforms are often accused of disproportionally exposing their users to like-minded opinions, thereby fueling political polarization. However, empirical evidence of this causal relationship is inconsistent at best. One reason could be that many previous studies were unable to separate the effects caused by individual exposure to like-minded content from the effects caused by the algorithms themselves. This study presents results from two quasi-experiments in which participants were exposed either to algorithmically selected or randomly selected arguments that were either in line or in contrast with their attitudes on two different topics. The results reveal that exposure to like-minded arguments increased participants’ attitude polarization and affective polarization more intensely than exposure to opposing arguments. Yet, contrary to popular expectations, these effects were not amplified by algorithmic selection. Still, for one topic, exposure to algorithmically selected arguments led to slightly stronger attitude polarization than randomly selected arguments.

Summary: Latent factor models, like principal component analysis (PCA), provide a statistical framework to infer low-rank representation in various biological contexts. However, feature selection is challenging when this low-rank structure manifests from a sparse subspace. We introduce SuSiE PCA, a scalable sparse latent factor approach that evaluates uncertainty in contributing variables through posterior inclusion probabilities. We validate our model in extensive simulations and demonstrate that SuSiE PCA outperforms other approaches in signal detection and model robustness. We apply SuSiE PCA to multi-tissue expression quantitative trait loci (eQTLs) data from GTEx v8 and identify tissue-specific factors and their contributing eGenes. We further investigate its performance on the large-scale perturbation data and find that SuSiE PCA identifies modules with a higher enrichment of ribosome-related genes than sparse PCA (false discovery rate [FDR] =9.2×10−82 vs. 1.4×10−33), while being ∼ 18x faster. Overall, SuSiE PCA provides an efficient tool to identify relevant features in high-dimensional biological data.

Responsible Innovation (RI) aims to enable research and innovation to take a more systematic approach to anticipating potential risks and consequences of planned research/innovative outputs. The Anticipation, Reflection, Engagement and Action (AREA) framework provides a flexible mechanism for organisations and researchers to align research and innovation with societal values, needs and challenges. However, its actual application may prove challenging for practitioners. In this paper, we describe how the AREA framework has been embedded in an interdisciplinary research project in the UK over the period of 2019–2021 through dedicated monthly RI workshops. To gain insights from this practice, we have collected qualitative data from discussions throughout the project and performed a systematic analysis of the logging practice itself. We discuss benefits and challenges associated with embedding an RI framework within an interdisciplinary research team, and present recommendations for future adoption of the AREA framework in a cross-disciplinary research setting.

With its unique advantages in artificial intelligence, data analysis, interpolation and numerical extrapolation, etc. ML has recently been quickly developed for the discovery of advanced energy materials. In particular, many algorithms have been developed to predict material properties. Herein, we first introduce the ML algorithms used in material science and the structure of each algorithm. Then we examine the algorithms that have been used recently in functional materials, especially in solar cells, batteries, and phase-change materials. Finally, advantages and disadvantages of each algorithm are compared to aid readers in choosing a suitable algorithm for specific applications.

In recent times, few studies on program comprehension have incorporated the use of psycho-physiological devices and self-report tools to measure participants’ emotions and cognitive workload. The use of self-report scales is beneficial in settings where there is no access to biosensors or a need for a general understanding of programmers’ emotions and workload during learning or in an assessment of a large group. Hence, we designed a study that would use self-report tools — the Self-Assessment Manikin (SAM) test and NASA TLX (Task Load Index) to obtain the emotions and workload of the participants on two types of programming tasks (logical and conceptual). Our aim was to identify the relationship between emotions and expertise, and workload and expertise on conceptual and logical questions in timed and untimed conditions. Our findings indicate that participants showed Low Valence-High Arousal (LVHA) in timed conditions and High Valence-Low Arousal (HVLA) in untimed conditions. Findings further indicate that high performers showed High Valence-Low Arousal (HVLA) on conceptual questions and High Valence-High Arousal (HVHA) on logical questions, whereas low performers showed High Valence-Low Arousal (HVLA) on conceptual questions and Low Valence-High Arousal (LVHA) on logical questions. We found that participants had a higher workload in timed conditions than untimed conditions and had a higher workload on logical questions than conceptual questions. Furthermore, we employed machine learning algorithms to predict expertise using emotions and workload and the best classifier produced an accuracy of 71.4%, with 72% weighted precision, 71.5% weighted recall, and 71.11% weighted F-score.

Background: Bedside clinical pharmacy prevents drug-related problems, but is not feasible in many countries due to limited resources. Hence, clinical rules using structural information in the electronic health record can help identifying potentially inappropriate prescriptions (PIPs). We aimed to develop and implement a risk-based clinical pharmacy service and evaluate its impact on prescribing at the trauma surgery ward. Methods: The proportion of residual PIPs per day, i.e. the number of PIPs that persisted up to 24 h after pharmacist intervention divided by the number of PIPs at T0, was evaluated before and after implementation of the intervention in an interrupted time series analysis. The pre-intervention cohort received usual pharmacy services, i.e. a 0.3 FTE clinical pharmacist trainee. Fifteen clinical rules, targeting antimicrobial, anticoagulant and analgesic therapy were implemented in the post-intervention period. The pre-intervention period was compared to two post-intervention scenarios: A) clinical rule alerts reviewed by a 0.3 FTE clinical pharmacist trainee; and B) clinical rule alerts reviewed daily for approximately 1 h by a clinical pharmacist trainee. Results: Pre-intervention, a median proportion of 67% (range 0%−100%) residual PIPs per day was observed. Scenario A showed an immediate relative reduction of 14% (p = 0.72) and scenario B a significant immediate relative reduction of 85% (p = 0.0015) in residual PIPs per day. In scenario A, recommendations were provided for 19% of clinical rule alerts, of which 67% was accepted by the surgeon within 24 h. In scenario B, recommendations were given for 56% of alerts, of which 84% was accepted. Conclusions: Using clinical rules is an effective approach to organize bedside clinical pharmacy services and improves prescribing at the trauma surgery ward. Advanced training and daily follow-up of the clinical rules are two requirements to be considered.

Summary: A Wheeler graph represents a collection of strings in a way that is particularly easy to index and query. Such a graph is a practical choice for representing a graph-shaped pangenome, and it is the foundation for current graph-based pangenome indexes. However, there are no practical tools to visualize or to check graphs that may have the Wheeler properties. Here, we present Wheelie, an algorithm that combines a renaming heuristic with a permutation solver (Wheelie-PR) or a Satisfiability Modulo Theory (SMT) solver (Wheelie-SMT) to check whether a given graph has the Wheeler properties, a problem that is NP-complete in general. Wheelie can check a variety of random and real-world graphs in far less time than any algorithm proposed to date. It can check a graph with 1,000s of nodes in seconds. We implement these algorithms together with complementary visualization tools in the WGT toolkit, available as open source software at https://github.com/Kuanhao-Chao/Wheeler_Graph_Toolkit.

In the minimal variant of the Vector game, the game is played at a circular table with n seats also called positions consecutively labelled from 0 to n−1. At start, a single token is placed at position 0. The two players, called Magnus and Derek, jointly control the token. If the current position is i, a round consists of Magnus choosing a positive distance l ≤n/2, then Derek deciding if the token moves clockwise or anticlockwise. Magnus’s aim in the game is to minimize the cardinality of the set of all positions occupied in the course of the game (while Derek’s is to maximize it).Let α(n) denotes the eventual size of the occupied set if both players play optimally. The focus of this paper is on a strategy for Derek to achieve at least α(n) visited positions in a small number of rounds (α(n)2 rounds) no matter how Magnus plays.

The current economic scale is bigger and bigger, the social material living standard also along is also getting higher and higher with the rapid economic growth. However, the problems caused by economic development are also increasing, on the one hand, there is the contradiction between supply and demand caused by resource consumption and shortage of resources; on the other hand, there also is contradiction between the great pollution and destruction in the ecological environment and the public's increasingly demanding ecological environment. Especially, the contradiction between the ecological environment and the social environment has become the focus of attention of the Chinese public. Therefore, the ecological environment protection becomes the current consensus either from the national level or the social level, how to manage and protect the ecological environment is also a question of the current social thinking. The common practice of ecological environment protection is to control and protect, at the same time, governance is to restore the damaged environment. There are many means for protection, such as energy conservation, emission reduction, monitoring and so on. Energy saving and emission reduction not only mean that the consumption of resources is reduced, but also mean that the discharge of pollutants and destroy the ecological environment are reduced. And monitoring refers to the existing ecological environment monitoring; the change of ecological environment is detected by real-time observation, so that counter measures are made according to the changes.

Cloud Computing is a robust model that allows users and organizations to purchase required services per their needs. The model offers many services such as storage, platforms for deployment, convenient access to web services, and so on. Load Balancing is a common issue in the cloud that makes it hard to maintain the performance of the applications adjacent to the Quality of Service (QoS) measurement and following the Service Level Agreement (SLA) document as required from the cloud providers to enterprises. Cloud providers struggle to distribute equal workload among the servers. An efficient LB technique should optimize and ensure high user satisfaction by utilizing the resources of VMs efficiently. This paper presents a comprehensive review of various Load Balancing techniques in a static, dynamic, and nature-inspired cloud environment to address the Data Center Response Time and overall performance. An analytical review of the algorithms is provided, and a research gap is concluded for the future research perspective in this domain. This research also provides a graphical representation of reviewed algorithms to highlight the operational flow. Additionally, this review presents a fault-tolerant framework and explores the other existing frameworks in the recent literature.

Summary: The domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene selection is to minimize computing costs and enhance classification precision. Therefore, this article designs a new wrapper gene selection algorithm named artificial bee bare-bone hunger games search (ABHGS), which is the hunger games search (HGS) integrated with an artificial bee strategy and a Gaussian bare-bone structure to address this issue. To evaluate and validate the performance of our proposed method, ABHGS is compared to HGS and a single strategy embedded in HGS, six classic algorithms, and ten advanced algorithms on the CEC 2017 functions. The experimental results demonstrate that the bABHGS outperforms the original HGS. Compared to peers, it increases classification accuracy and decreases the number of selected features, indicating its actual engineering utility in spatial search and feature selection.

Background and purpose: Radiotherapy planning considerations were developed for the previous calculation algorithms yielding dose to water-in-water (Dw,w). Advanced algorithms improve accuracy, but their dose values in terms of dose to medium-in-medium (Dm,m) depend on the medium considered. This work aimed to show how mimicking Dw,w planning with Dm,m can introduce new issues. Materials and methods: A head and neck case involving bone and metal heterogeneities outside the CTV was considered. Two different commercial algorithms were used to obtain Dm,m and Dw,w distributions. First, a plan was optimised to irradiate the PTV uniformly and get a homogeneous Dw,w distribution. Second, another plan was optimised to achieve homogeneous Dm,m. Both plans were calculated with Dw,w and Dm,m, and the differences between their dose distributions, clinical impact, and robustness were evaluated. Results: Uniform irradiation produced Dm,m cold spots in bone (-4%) and implants (-10%). Uniform Dm,m compensated them by increasing fluence but, when recalculated in Dw,w, the fluence compensations produced higher doses that affected homogeneity. Additionally, doses were 1% higher for the target, and + 4% for the mandible, thus increasing toxicity risk. Robustness was impaired when increased fluence regions and heterogeneities mismatched. Conclusion: Planning with Dm,m as with Dw,w can impact clinical outcome and impair robustness. In optimisation, uniform irradiation instead of homogeneous Dm,m distributions should be pursued when media with different Dm,m responses are involved. However, this requires adapting evaluation criteria or avoiding medium effects. Regardless of the approach, there can be systematic differences in dose prescription and constraints.

Objectives: To develop a Deep Learning Artificial Intelligence (AI) model that automatically localizes the position of radiographic stent gutta percha (GP) markers in cone beam computed tomography (CBCT) images to identify proposed implant sites within the images, and to test the performance of the newly developed AI model. Materials and Methods: Thirty-four CBCT datasets were used for initial model training, validation and testing. The CBCT datasets were those of patients who had a CBCT examination performed wearing a radiographic stent for implant treatment planning. The datasets were exported in Digital Imaging and Communications in Medicine (DICOM), then imported into the software Horos ®. Each GP marker was manually labelled for object detection and recognition by the deep learning model by drawing rectangles around the GP markers in all axial images, then the labelled images were split into training, validation, and test sets. The axial sections of 30 CBCT datasets were randomly divided into training and validation sets. four CBCT datasets were used for testing the performance of the deep learning model. Descriptive statistics were calculated for the number of GP markers present, number of correct and incorrect identifications of GP markers. Result: The AI model had an 83% true positive rate for identification of the GP markers. Of the areas labelled by the AI model as GP markers, 28 % were not truly GP markers, but the overall false positive rate was 2.8 %. Conclusion: An AI model for localization of GP markers in CBCT images was able to identify most of the GP markers, but 2.8% of the results were false positive and 17% were missed GP markers. Using only axial images for training an AI program is not enough to give an accurate AI model performance.

Background: Anaemia is a global public health challenge that affects children and pregnant women. Anaemia develops when the body's supply of red blood cells declines or when the structure of the cells is weakened. The clinical diagnosis of anaemia has several challenges in practice, including insufficient funding for medical tests, inadequate medical personnel and resources in remote areas, and client reluctance that results in abstinence. Several machine learning techniques for anaemia detection have been developed due to their affordability, simplicity of use, and non-invasive nature as compared to the invasive approach. Methods: We perform a systematic review and examine current trends and concepts of machine learning in healthcare services to identify viable approaches to detect anaemia. We compare the most successful machine learning algorithms currently in use regarding machine learning, evaluation metrics, image augmentation, and the origin and size of the dataset used. Results: The result of this study is a clear indication that non-invasive methods such as the use of machine learning algorithms to detect anaemia are affordable, and provides results on time. Conclusions: This systematic review provide scientific evidence, with the results describing how effective machine learning could make anaemia detection feasible. Machine and deep learning algorithms are introduced and used to make a wide-ranging analysis of images, diagnosis, and clinical analysis in the disciplines of medical fields such as anaemia detection.

ABSTRACT: A wide range of recent studies are focusing on current issues of financial fraud, especially concerning cybercrimes. The reason behind this is even with improved security, a great amount of money loss occurs every year due to credit card fraud. In recent days, ATM fraud has decreased, while credit card fraud has increased. This study examines articles from five foremost databases. The literature review is designed using extraction by database, keywords, year, articles, authors, and performance measures based on data used in previous research, future research directions and purpose of the article. This study identifies the crucial gaps which ultimately allow research opportunities in this fraud detection process by utilizing knowledge from the machine learning domain. Our findings prove that this research area has become most dominant in the last ten years. We accessed both supervised and unsupervised machine learning techniques to detect cybercrime and management techniques which provide evidence for the effectiveness of machine learning techniques to control cybercrime in the credit card industry. Results indicated that there is room for further research to obtain better results than existing ones on the basis of both quantitative and qualitative research analysis.

Algorithmic decision-making implemented through artificial intelligence (AI) projects is augmenting or replacing human decision-making across numerous industries. Although AI systems may impact individuals and society in life-and-death situations, project organizations or plans may ignore the concerns of the passive stakeholders. This research describes the components, lifecycle, and characteristics of AI projects. It employs stakeholder theory and a systematic literature review with thematic analysis to identify and classify individuals, groups, and organizations into six stakeholder project roles. It configures the stakeholder salience model with a harm attribute to identify passive stakeholders—individuals affected by AI systems but powerless to affect the project—and their nexus to the project. The study contributes a novel method for identifying passive stakeholders and highlights the need to engage developers, operators, and representatives of passive stakeholders to achieve moral, ethical, and sustainable development.

In general, people will judge a morally wrong behavior when perpetrated by an artificial intelligence (AI) as still being wrong. But moral judgements are complex, and therefore we explore how moral judgements made about AIs differ from those made about humans in real-world situations. In contrast to much of the current research on the morality of AIs, we examine real-world encounters where an AI commits a moral wrong as reported by participants in previous research. We adapt these to create nearly identical scenarios with human perpetrators. In Study 1, across scenarios, humans are perceived as more wrong, intentional, and blameworthy compared to AIs. In Study 2, we replicate those results and find that showing the participants the contrasting scenario – showing the AI scenario when one is rating the human scenario or vice versa – does not have a significant effect on moral judgements. An exploratory word-frequency analysis and illustrative quotes from participants’ open-ended explanations show that AIs are more often denied agency and perceived as programmed and therefore unintentional in producing the moral outcome. In contrast, humans are perceived as highly agentic and intentional, either fully responsible for the wrongdoing or not morally culpable because the behavior was perceived as a mistake.

As pivotal markers of chromatin accessibility, DNase I hypersensitive sites (DHSs) intimately link to fundamental biological processes encompassing gene expression regulation and disease pathogenesis. Developing efficient and precise algorithms for DHSs identification holds paramount importance for unraveling genome functionality and elucidating disease mechanisms. This study innovatively presents iDHS-RGME, an Extremely Randomized Trees (Extra-Trees)-based algorithm that integrates unique feature extraction techniques for enhanced DHSs prediction. Specifically, iDHS-RGME utilizes two feature extraction approaches: Reverse Complementary Kmer (RCKmer) and Geary Spatial Autocorrelation (GSA), which comprehensively capture sequence attributes from diverse angles, bolstering information richness and accuracy. To address data imbalance, Borderline-SMOTE is employed, followed by Maximum Information Coefficient (MIC) for meticulous feature selection. Comparative evaluations underscored the superiority of the Extra-Trees classifier, which was subsequently adopted for model prediction. Through rigorous five-fold cross-validation, iDHS-RGME achieved remarkable accuracies of 94.71 % and 95.07 % on two independent datasets, outperforming previous models in terms of both precision and effectiveness., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The COVID-19 pandemic has altered the circulation of non-SARS-CoV-2 respiratory viruses. In this study, we carried out wastewater surveillance of SARS-CoV-2 and influenza A virus (IAV) in three key port cities in China through real-time quantitative PCR (RT-qPCR). Next, a novel machine learning algorithm (MLA) based on Gaussian model and random forest model was used to predict the epidemic trajectories of SARS-CoV-2 and IAV. The results showed that from February 2023 to January 2024, three port cities experienced two waves of SARS-CoV-2 infection, which peaked in late-May and late-August 2023, respectively. Two waves of IAV were observed in the spring and winter of 2023, respectively with considerable variations in terms of onset/offset date and duration. Furthermore, we employed MLA to extract the key features of epidemic trajectories of SARS-CoV-2 and IAV from February 3rd, to October 15th, 2023, and thereby predicted the epidemic trends of SARS-CoV-2 and IAV from October 16th, 2023 to April 22nd, 2024, which showed high consistency with the observed values. These collective findings offer an important understanding of SARS-CoV-2 and IAV epidemics, suggesting that wastewater surveillance together with MLA emerges as a powerful tool for risk assessment of respiratory viral diseases and improving public health preparedness., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The potential for machine learning to answer questions of environmental science, monitoring, and regulatory enforcement is evident, but there is cause for concern regarding potential embedded bias: algorithms can codify discrimination and exacerbate systematic gaps. This paper, organized into two halves, underscores the importance of vetting algorithms for bias when used for questions of environmental science and justice. In the first half, we present a case study of using machine learning for environmental justice-motivated research: prediction of drinking water quality. While performance varied across models and contaminants, some performed well. Multiple models had overall accuracy rates at or above 90 % and F2 scores above 0.60 on their respective test sets. In the second half, we dissect this algorithmic approach to examine how modeling decisions affect modeling outcomes - and not only how these decisions change whether the model is correct or incorrect, but for whom. We find that multiple decision points in the modeling process can lead to different predictive outcomes. More importantly, we find that these choices can result in significant differences in demographic characteristics of false negatives. We conclude by proposing a set of practices for researchers and policy makers to follow (and improve upon) when applying machine learning to questions of environmental science, management, and justice., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Effective pathogen inactivation is highly desired in public health but limited by existing methods each capable of assessing pathogen inactivation effectiveness (PIE) only in a specific condition. We therefore developed a novel method maxPIE designed to identify maximal PIEs across inactivation conditions by leveraging the power of massive array technologies. maxPIE implements a three-step algorithm to quickly identify maximal PIEs of inactivation treatments: (1) dilute pathogens into different initial titers each stored in an array well, (2) submit one sorted array to one treatment, (3) scan the treated array to find the maximum. maxPIE outperformed the conventional methods in (a) inactivating S. aureus using ultraviolet light of different wavelengths with different durations; (b) antibiotic treatment of S. aureus, E. coli, and multidrug-resistant E. coli; (c) inactivating S. aureus in plasma using ultraviolet light in different wavelengths with and without riboflavin. maxPIE was easy to understand and interpret and was robust in situations where conventional PIE methods would suffer. Hence, maxPIE can serve as an innovative and high throughput approach that can be widely used to enhance pathogen inactivation practices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Colorectal cancer is one of the most diagnosed types of cancer in developed countries. Current diagnostic methods are partly dependent on pathologist experience and laboratories instrumentation. In this study, we used Fourier Transform Infrared (FTIR) spectroscopy in transflection mode, combined with Principal Components Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares - Discriminant Analysis (PLS-DA), to build a classification algorithm to diagnose colon cancer in cell samples, based on absorption spectra measured in two spectral ranges of the mid-infrared spectrum. In particular, PCA technique highlights small biochemical differences between healthy and cancerous cells: these are related to the larger lipid content in the former compared with the latter and to the larger relative amount of protein and nucleic acid components in the cancerous cells compared with the healthy ones. Comparison of the classification accuracy of PCA-LDA and PLS-DA methods applied to FTIR spectra measured in the 1000-1800 cm -1 (low wavenumber range, LWR) and 2700-3700 cm -1 (high wavenumber range, HWR) remarks that both algorithms are able to classify hidden class FTIR spectra with excellent accuracy (100 %) in both spectral regions. This is a hopeful result for clinical translation of infrared spectroscopy: in fact, it makes reliable the predictions obtained using FTIR measurements carried out only in the HWR, in which the glass slides used in clinical laboratories are transparent to IR radiation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background & Aims: Early identification of worsening HF enables timely adjustments to prevent hospitalization. Recent studies show the HeartLogic™ algorithm detects congestion and reduces HF events. However, it is unclear which patients benefit most. Therefore, this study aims to identify and characterize HF patients who benefit most from CIED-based remote monitoring with HeartLogic™., Methods: In this multicenter retrospective study, patients with a CIED and HeartLogic™ algorithm under structured follow-up were included. Patients were classified as having "substantial benefit" or "no benefit" from monitoring., Results: In total, 242 patients were included (male n = 190, 79%, median age 61 years [IQR 61-77]). Median follow-up was 1.2 years [IQR 1.1-2.7]. Among 378 alerts, 266 were true positive (70%) and 112 false positive (30%). Of the 242 patients, 69 (29%) were classified as having "substantial benefit", while 173 (71%) had "no benefit" from HeartLogic™ monitoring. Univariate and multivariate analysis showed that patients with "substantial benefit" had higher NYHA functional class (OR 2.64, P = 0.004), higher NT-ProBNP (OR 1.02, P = 0.003), higher serum creatinine (OR 1.10, P < 0.001), lower LVEF (OR 1.19, P = 0.004), more severe mitral regurgitation (OR 2.16, P = 0.006), higher right ventricular end diastolic volume (OR 1.05, P = 0.040), higher pulmonary artery pressures (OR 1.19, P = 0.003), and were more likely to use loop diuretics (OR 2.79, P = 0.001). Among patients with "substantial benefit," the positive predictive value (PPV) of HeartLogic™ to detect congestion was 92%., Conclusion: The utilization of CIED-based HeartLogic™ driven HF care demonstrated pronounced efficacy, predominantly in patients exhibiting characteristics of HF at a more advanced disease stage., Competing Interests: Declaration of competing interest The department of Cardiology of the LUMC reports receiving unrestricted research and educational grants from Boston Scientific Corporation, Medtronic, and Biotronik. The department of cardiology of OLV Aalst reports receiving research and educational grants from Boston Scientific, Medtronic, Biotronik, Abbott St Jude Medical, and Microport and an educational and research grant from Boston Scientific with reference number ISRRM11793. The funders were not involved in study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. ADE receives consultancy and speaker fees from Boston Scientific Corporation and Medtronic. SLMAB receives speaker fees from Boston Scientific Corporation and Medtronic. The other authors have no conflict of interest to declare., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The sesquiterpene α-farnesene and its corresponding oxidation products, namely conjugated trienols (CTols) is well known to be correlated with the development of superficial scald, a typical physiological disorder after a long term of cold storage in pear fruit. In this work, hyperspectral imaging (HSI) technology was used for nondestructive predicting of α-farnesene and CTols [CT258, CT281 and CT(281-290)] content in 'Yali' pear. In order to obtain the best performance of calibration model and simplify the calibration model further, various preprocessing methods together with their combinations and different wavelength selection algorithms, including successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE), were investigated and compared based on linear partial least square regression (PLSR) and nonlinear least square support vector machine (LS-SVM) models, respectively. In conclusion, compared to the PLSR models, the results of LS-SVM models based on original and preprocessing methods performed better for the prediction of α-farnesene and CTols, while the performance of LS-SVM models based on the selected characteristic wavelengths were worse. For α-farnesene, the best result was obtained by LS-SVM model based on MSC-FD pretreatment with the RPD value of 2.6, R p  = 0.925 and RMSEP = 4.387 nmol cm -2 . And for CTols, CT281 performed better compared with CT258 and CT(281-290), achieving the result with RPD = 2.4, R p  = 0.913 and RMSEP = 2.734 nmol cm -2 based on LS-SVM model combined with SD pretreatment. The overall results illustrated HSI technology could be used for rapid and nondestructive prediction of α-farnesene and CTols in 'Yali' pear, which would be helpful for supporting postharvest decision systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Electrostatic Repulsion-Hydrophilic Interaction Chromatography (ERLIC) is one of the legacy separation tools developed by Dr. Andrew Alpert and has been used for developing unique separation methods of hydrophilic compounds, including peptides. In the past it has been studied using designed peptide libraries to elucidate major features of its separation mechanism, while comprehensive peptide retention modeling for ERLIC is still lacking. In this work we employed a proteomics-derived ∼170,000 peptide retention datasets to evaluate major ERLIC retention features using the framework of our Sequence-Specific Retention Calculator model. The separation conditions were adjusted to obtain a wider proteome coverage, particularly for non-modified peptides, resulting in a superior separation orthogonality for a 2D LC combination with reversed-phase C18 LC-MS in the second dimension. The SSRCalc ERLIC model presents a consistent theme with the existing ERLIC retention mechanism, reflecting a dependence on peptide orientation and the position of charged and hydrophilic residues across the peptide backbone. R 2 values of 0.935 and 0.955 accuracy were demonstrated for the standard interpretable SSRCalc model and machine learning algorithm, respectively. The effects of various PTMs on peptide retention were evaluated in this study, covering spontaneous (oxidation, deamidation) and enzymatic (N-terminal acetylation, phosphorylation, glycosylation) modifications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Analytical solution to ideal chromatography model has been established for binary Langmuir isotherm and rectangular injections. However, retention time of the less adsorbed species, which is of great theoretical and practical significance, cannot be given in a closed form and is conventionally solved by numerical integration with a floating boundary. A simplified approach is provided in this article. A 4th order algebraic equation was derived and used to solve the maximum concentration that can be further used to explicitly calculate retention time. Under most practical conditions, reliable initial guess can be easily acquired, allowing for the application of Newton-Raphson method for rapid determination of the root of the 4th order equation. In addition, derivatives of retention time with respective to isotherm parameters can be given in analytical forms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Peptide separation selectivity was evaluated for hydrophilic interaction liquid chromatography (HILIC) ZIC-HILIC, ZIC-cHILIC, and XBridge Amide sorbents using formic acid as eluent additive (pH 2.7). Sequence-specific retention prediction algorithms were trained using retention datasets of ∼30,000 peptides for each column. Our retention models were able to attain ∼0.98 R 2 -value and yielded retention coefficients that can be probed to understand peptide-stationary phase interaction. Overall, the hydrophilicity for these columns decreased when the mobile phase changed pH from 4.5 to 2.7, when using 0.1 % formic acid in the mobile phase. The acidic residues became protonated, and the resultant hydrophilic interaction is dampened at the lower pH, leaving only the basic residues as the primary hydrophilic interactors. Hence, peptides of increasing charge have higher retention. In this comparison between the three columns, ZIC-HILIC has the highest chromatographic resolution between groups of peptides of different charge. From the position-dependent retention coefficients for ZIC-HILIC at pH 2.7, we found that the amino acids at the terminal positions of the peptide modulate the basicity of the N-terminal amino group or the C-terminal Arg/Lys for tryptic peptides. With respect to the separation orthogonality between HILIC and acidic pH RPLC for two dimensional separations, the orthogonality values were lower at pH 2.7 than operating HILIC at pH 4.5 for the first dimension. We also demonstrate that ZIC-HILIC was able to distinguish citrullinated and deamidated peptides based on predicted retention values., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Oligonucleotides (ONs) are acquiring clinical relevance and their demand is expected to grow. However, the ON production capacity is currently limited by high manufacturing costs. Since the purification of the target ON sequence from molecularly similar variants represents a major bottleneck, this work presents a resource-effective strategy for the optimization of their preparative reversed-phase chromatographic purification. First, a model based on the equilibrium-dispersive theory was introduced to describe the chromatographic operation. Considering a deoxyribose nucleic acid with 20 nucleobases as case study, a genetic algorithm was developed to efficiently determine the adsorption isotherm and mass transfer parameters for the target ON and impurities. After the estimation of these parameters, a strategy for the in-silico optimization of the operation was established. The product collection window, gradient duration, and resin loading were considered as process variables and their influence on yield and productivity was investigated after setting a purity specification of 99.0%. The optimal process parameters identified through this analysis were experimentally verified, confirming the reliability of the model, calibrated with only 5 experimental runs. In addition, this optimal setpoint was exploited to design the multicolumn countercurrent solvent gradient purification (MCSGP) of this ON mixture, which allowed to boost the yield of the process and to work at cyclic steady state, while respecting the purity constraint. This study confirmed the potential of this in-silico optimization strategy in both improving the performance of the traditional single-column operations and in the rapid development of multicolumn processes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

We investigate an efficient computational tool to suggest useful treatment regimens for people infected with the human immunodeficiency virus (HIV). Structured treatment interruption (STI) is a regimen in which therapeutic drugs are periodically administered and withdrawn to give patients relief from an arduous drug therapy. Numerous studies have been conducted to find better STI treatment strategies using various computational tools with mathematical models of HIV infection. In this paper, we leverage a modified version of the double deep Q network with prioritized experience replay to improve the performance of classic deep learning algorithms. Numerical simulation results show that our methodology produces significantly more optimal cost values for shorter treatment periods compared to other recent studies. Furthermore, our proposed algorithm performs well in one-day segment scenarios, whereas previous studies only reported results for five-day segment scenarios., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hee-Dae Kwon reports financial support was provided by National Research Foundation of Korea. Myungjoo Kang reports financial support was provided by National Research Foundation of Korea and Institute for Information and Communications Technology Promotion of Korea. Changyeon Yoon reports financial support was provided by Korea Ministry of Trade Industry and Energy. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In recent years, multi-cellular models, where cells are represented as individual interacting entities, are becoming ever popular. This has led to a proliferation of novel methods and simulation tools. The first aim of this paper is to review the numerical methods utilised by multi-cellular modelling tools and to demonstrate which numerical methods are appropriate for simulations of tissue and organ development, maintenance, and disease. The second aim is to introduce an adaptive time-stepping algorithm and to demonstrate it's efficiency and accuracy. We focus on off-lattice, mechanics based, models where cell movement is defined by a series of first order ordinary differential equations, derived by assuming over-damped motion and balancing forces. We see that many numerical methods have been used, ranging from simple Forward Euler approaches through to higher order single-step methods like Runge-Kutta 4 and multi-step methods like Adams-Bashforth 2. Through a series of exemplar multi-cellular simulations, we see that if: care is taken to have events (births deaths and re-meshing/re-arrangements) occur on common time-steps; and boundaries are imposed on all sub-steps of numerical methods or implemented using forces, then all numerical methods can converge with the correct order. We introduce an adaptive time-stepping method and demonstrate that the best compromise between L ∞ error and run-time is to use Runge-Kutta 4 with an increased time-step and moderate adaptivity. We see that a judicious choice of numerical method can speed the simulation up by a factor of 10-60 from the Forward Euler methods seen in Osborne et al. (2017), and a further speed up by a factor of 4 can be achieved by using an adaptive time-step., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: James M. Osborne reports financial support was provided by Australian Research Council. The funders had no influence in the construction or findings of this study., (Copyright © 2024 The Author. Published by Elsevier Ltd.. All rights reserved.)

In this study, we utilized an innovative quantitative read-across (RA) structure-activity relationship (q-RASAR) approach to predict the bioconcentration factor (BCF) values of a diverse range of organic compounds, based on a dataset of 575 compounds tested using Organisation for Economic Co-operation and Development Test Guideline 305 for bioaccumulation in fish. Initially, we constructed the q-RASAR model using the partial least squares regression method, yielding promising statistical results for the training set (R 2 =0.71, Q 2 LOO =0.68, mean absolute error [MAE] training =0.54). The model was further validated using the test set (Q 2 F1 =0.51). Subsequently, we explored the q-RASAR method using other regression-based supervised machine-learning algorithms, demonstrating favourable results for the training and test sets. All models exhibited R 2 F2 =0.75, MAE test =0.51). Subsequently, we explored the q-RASAR method using other regression-based supervised machine-learning algorithms, demonstrating favourable results for the training and test sets. All models exhibited R 2 and Q 2 F1 values exceeding 0.7, Q 2 LOO values greater than 0.6, and low MAE values, indicating high model quality and predictive capability for new, unidentified chemical substances. These findings represent the significance of the RASAR method in enhancing predictivity for new unknown chemicals due to the incorporation of similarity functions in the RASAR descriptors, independent of a specific algorithm., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This study utilized hyperspectral imaging technology combined with mathematical modeling methods to predict the protein content of rice grains. Firstly, the Kjeldahl method was used to determine the protein content of rice grains, and different preprocessing techniques were applied to the spectral information. Then, a prediction model for rice grain protein content was developed by combining the spectral data with the protein content. After performing multiplicative scatter correction (MSC) preprocessing and selecting feature wavelengths based on successive projections algorithm (SPA), the multivariate linear regression (MLR) model showed the best prediction performance, with a calibration set R 2 C of 0.9393, a validation set R 2 V of 0.8998, an RMSEV of 0.1725, and an RPD of 3.16. Finally, the quantitative protein content model was mapped pixel by pixel to visualize the distribution of rice protein, providing possibilities for non-destructive protein content detection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)