Dual Objective Research Articles

ConVQG: Contrastive Visual Question Generation with Multimodal Guidance

Asking questions about visual environments is a crucial way for intelligent agents to understand rich multi-faceted scenes, raising the importance of Visual Question Generation (VQG) systems. Apart from being grounded to the image, existing VQG systems can use textual constraints, such as expected answers or knowledge triplets, to generate focused questions. These constraints allow VQG systems to specify the question content or leverage external commonsense knowledge that can not be obtained from the image content only. However, generating focused questions using textual constraints while enforcing a high relevance to the image content remains a challenge, as VQG systems often ignore one or both forms of grounding. In this work, we propose Contrastive Visual Question Generation (ConVQG), a method using a dual contrastive objective to discriminate questions generated using both modalities from those based on a single one. Experiments on both knowledge-aware and standard VQG benchmarks demonstrate that ConVQG outperforms the state-of-the-art methods and generates image-grounded, text-guided, and knowledge-rich questions. Our human evaluation results also show preference for ConVQG questions compared to non-contrastive baselines.

Contribution to historical manuscript dating: A hybrid approach employing hand-crafted features with vision transformers

Precisely dating historical manuscripts represents a paramount endeavor in the comprehension and the interpretation of their historical significance as well as in the preservation of our cultural heritage; however, despite the strides made in computer-based dating methodologies, the quest for heightened robustness persists. Recent advancements in vision transformers, renowned for their success across diverse image processing domains, have stimulated our inquiry into their potential applicability in the domain of historical manuscript dating. In our pursuit of achieving efficient refinement of the manuscript and ensuring pristine datasets for subsequent analysis, we initiate a meticulous dataset preprocessing step. This involves employing accurate methodologies for denoising through the Non-Local-Means algorithm, and binarization using the Canny-edge detector. Following these preprocessing steps, we delve into the intricate realm of feature detection using the Harris-corner detector. This detector is employed to extract keypoints from the manuscript, and we subsequently apply clustering to these keypoints using the k-means algorithm. Our dual objective here is to extract significant patches of specific dimensions and engage in a systematic augmentation of our dataset. Through this process, we aim to amplify the depth and diversity of our dataset, thereby empowering our models with an enriched corpus of historical knowledge. The final phase of our proposed system unfolds as we leverage the latent power of the sophisticated deep learning architecture known as vision transformers. The model is finetuned to our specific task and re-learned with the automatically extracted handcrafted features, emerges as a formidable classification framework. As an added layer of refinement, we deploy a majority vote mechanism on image patches, meticulously engineered to heighten system accuracy. Our rigorous testing regimen, carried out on the well-known MPS historical document dataset, has yielded results of remarkable caliber. Our system's prowess is vividly reflected in its performance metrics, boasting an impressive Mean Absolute Error (MAE) of 3.97 for document-level evaluations and a resounding 7.42 MAE for patch-level assessments. The significance of this work lies in its potential to revolutionize historical manuscript dating, not only by enhancing precision but also by providing a versatile framework adaptable to diverse historical contexts and document types. Beyond its immediate applications, this research paves the way for a deeper understanding of historical narratives, cultures, and the invaluable insights that lie within the annals of our past.

Assessing the inhibitory effect and intervention mechanism of food traceability system on reducing hog farmers’ overuse of animal antibiotics in China

The use of animal antibiotics is a double-edged sword. It can be used for disease treatment, health protection, and growth promoters. It also creates antibiotic residues and resistance, poses risks, and damages food safety, ecosystems, and public health. Under the pressure of disease risk and expected losses, farmers' overuse of animal antibiotics exacerbates this dual objective incompatibility. In this study, we employ the endogenous switching regression (ESR) model and the mediating effect method to empirically analyze the inhibitory effect and intervention mechanism of the food traceability system (FTS) on reducing farmers’ overuse of antibiotics by using the survey data of hog farmers from China. This paper measures the “dose” unit of antibiotics farmers use regarding cost and the active ingredients of antibiotics per kilogram of treated infected hogs. The study finds evidence that the FTS exerts a significant inhibitory effect on the overuse of antibiotics by farmers. The counterfactual hypothesis unveils that non-participation in the FTS by the involved farmers will increase the cost of antibiotics overuse by 0.080 yuan/kg, while if the farmers currently not participating are to engage with the FTS, the price will decrease by 0.126 yuan/kg. This effect persists across control variables controlled in turn and the unit doses of active ingredients of tetracycline, sulfonamides, β-lactam, chloramphenicol, and macrolide antibiotics. Moreover, our results reveal thatthe inhibitory effects of the FTS on over-the-counter and broad-spectrum antibiotics are more significant than on prescription and narrow-spectrum antibiotics. The results show that the inhibition effect of the FTS on the overuse of antibiotics by free-range, professional, and large-scale farmers exhibits an approximately inverse-U curvilinear relationship. Besides, the intervention mechanism of the FTS on the overuse of antibiotics by farmers mainly consists of social reputation maintenance, liability traceability for antibiotic residues, and biosafety enhancement, and the proportion of their mediating effects in the total impact are 24.22%, 21.84%, and 10.87%, respectively. Our empirical study has yielded several implications, such as strengthening the FTS construction, improving farmers’ antibiotic use skills, promoting standardized breeding levels, and increasing the market premium for food products that adhere to antibiotic residue standards. These outcomes not only contribute to the improvement of livestock production safety but also reduce the problem of antibiotic overuse.

Carbon neutrality in wastewater treatment plants: An integrated biotechnological-based solution for nutrients recovery, odour abatement and CO2 conversion in alternative energy drivers

Wastewater treatment plants play a crucial role in water security and sanitation, ensuring ecosystems balance and avoiding significant negative effects on humans and environment. However, they determine also negative pressures, including greenhouse gas and odourous emissions, which should be minimized to mitigate climate changes besides avoiding complaints. The research has been focused on the validation of an innovative integrated biological system for the sustainable treatment of complex gaseous emissions from wastewater treatment plants. The proposed system consists of a moving bed biofilm reactor coupled with an algal photobioreactor, with the dual objective of: i) reducing the inlet concentration of the odourous contaminants (in this case, hydrogen sulphide, toluene and p-xylene); ii) capturing and converting the carbon dioxide emissions produced by the degradation process into exploitable algal biomass. The first reactor promoted the degradation of chemical compounds up to 99.57% for an inlet load (IL) of 22.97 g m−3 d−1 while the second allowed the capture of the CO2 resulting from the degradation of gaseous compounds, with biofixation rate up to 81.55%. The absorbed CO2 was converted in valuable feedstocks, with a maximum algal biomass productivity in aPBR of 0.22 g L−1 d−1. Dairy wastewater has been used as alternative nutrient source for both reactors, with a view of reusing wastewater while cultivating biomass, framing the proposed technology in a context of a biorefinery within a circular economy perspective. The biomass produced in the algal photobioreactor was indeed characterized by a high lipid content, with a maximum percentage of lipids per dry weight biomass of 35%. The biomass can therefore be exploited for the production of alternative and clean energy carrier. The proposed biotechnology represents an effective tool for shifiting the conventional plants in carbon neutral platform for implementing principles of ecological transition while achieving high levels of environmental protection.

Unraveling athletic performance: Transcriptomics and external load monitoring in handball competition.

This study aims to comprehend the impact of handball practice on sub-elite athletes by investigating transcriptomic changes that occur during a match. The primary focus encompasses a dual objective: firstly, to identify and characterize these transcriptomic alterations, and secondly, to establish correlations between internal factors (gene expression), and external loads measured through Electronic Performance and Tracking Systems (EPTS variables). Ultimately, this comprehensive analysis seeks to evaluate both acute and chronic responses to exercise within the context of handball training. The study included sixteen elite male athletes from the FC Barcelona handball second team. Blood samples were extracted at three different time points: before the match at baseline levels (T1), immediately upon completion (T2), and 24 hours after completion (T3). Differential gene expression, Gene Ontology Term and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted in two comparisons: Comparison 1 (T1 vs T2) and Comparison 2 (T1 vs T3). Further, the correlation between gene expression levels and training variables (external load) was conducted. In T1 vs T2, 3717 of the 14632 genes detected were differentially expressed (adjusted p-value < 0.05), and enrichment of terms related to the immune system, mitochondria, and metabolic processes was found. Further, significant linear correlations were obtained between High-Speed running (HSR) and high-intensity variables such as acceleration ACC and deceleration DEC values with amino acids, and inflammatory and oxidative environment-related pathways, both in chronic and acute response. This research highlights the effects of external workload on elite athletes during a handball match and throughout the season. The study identifies deregulation in the immune system, mitochondrial functions, and various metabolic pathways during the match. Additionally, it establishes correlations between the external load and pathways associated with amino acids, inflammation, oxidative environment, and regulation. These findings offer insights into the immediate and chronic responses of athletes to physical effort.

Message Framing in CSR Advertising on Social Media during the COVID-19 Pandemic

In response to the environmental and social challenges posed by the COVID-19 pandemic, businesses are actively seeking ways to generate social benefits while also boosting their financial performance. Corporations are embracing corporate social responsibility (CSR) practices as a means to achieve this dual objective. This study aims to investigate how global corporations have responded to the pandemic by promoting their CSR commitments through CSR advertising. A quantitative content analysis was conducted to examine how leading global corporations promote their CSR practices on social media during the COVID-19 pandemic. Specifically, the study examined CSR advertising posts from twelve global corporations’ Instagram accounts across the years 2019, 2020, and 2021, totaling 886 posts. The findings show that corporations immediately responded to the COVID-19 pandemic by adopting CSR practices in myriad areas of social concern and strategically framing those efforts through CSR advertising. This study contributes to the CSR literature by providing an instrument for scholars and practitioners to reevaluate the role of business and CSR initiatives during uncertain times. Furthermore, it highlights opportunities for corporations to prioritize CSR initiatives, not only during the pandemic but also in the long term, to achieve a balance between social, environmental, and economic goals

Utility of Stochastic Ground Motion Models in Conditional Spectrum-Based Selection Consistent with the Causal Earthquakes

ABSTRACT Conditional Spectrum (CS), characterized by a conditional mean and conditional standard deviation, serves as a target spectrum linking seismic hazard information to ground motion selection for seismic demand analysis. Current Ground Motion Selection and Modification (GMSM) methods aim to align with this target spectrum but face challenges due to the limited availability of recorded ground motions and potential modifications, leading to difficulties in ensuring selected ground motions accurately reflect site characteristics. Addressing this issue, the study presents an algorithm to select hazard-consistent CS-based records utilizing a stochastic ground motion model with a dual objective: (1) alleviate the need for scaling during the selection process, and (2) select records consistent with the target hazard and the contributing causal scenarios. To achieve both objectives, the study generates a database of hazard-targeted ground motions utilizing the kriging surrogate with scenarios sampled from the disaggregation matrix. Then, a postprocessing step explicitly considers causative parameters in the selection process. The algorithm’s potential to select site-specific ground motions is demonstrated using sites in the Western United States, providing insights into the computational cost and accuracy. Additionally, statistical comparisons are conducted to explore circumstances where similar hazard consistency can be achieved without the postprocessing step, reducing the overall computational cost. Finally, the selected ground motions are favorably compared to those selected through traditional conditional spectrum-based approaches based on their spectral shape and ground motion intensity measures.

Charging and Discharging Optimization of Vehicle Battery Efficiency for Minimizing Company Expenses Considering Regular User Travel Habits

With the increasing popularity and development of electric vehicles, the demand for electric vehicle charging is also constantly increasing. To meet the diverse charging needs of electric vehicle users and improve the efficiency of charging infrastructure, this study proposes an optimization strategy for electric vehicle charging and discharging. This method considers both the user’s travel mode and the operational efficiency of the charging pile. Firstly, a probability model based on travel spatiotemporal variables and Monte Carlo algorithm were used to simulate the travel trajectory of electric vehicles, providing a data foundation for optimizing the charging and discharging schemes of electric vehicles. Then, with the dual objective of minimizing the operating costs of charging piles and user charging costs, a linear programming model was constructed to optimize the charging and discharging strategies of electric vehicles. Finally, the model was validated using an apartment building as an example. The results indicate that, under the normal travel habits of users, with the goal of minimizing company expenses, the annual cost of the company reaches its minimum at a certain number of charging piles. When the cost of electric vehicle users dominates the objective function, they will pay more attention to battery degradation, significantly reducing their willingness to participate in discharge.

An Investigation of the Relationship between Democratic Attitudes and Communication Skills of Drama Educators

Creative drama encompasses two primary learning objectives: the internalization of democratic values and the cultivation of advanced communication skills. To foster these attributes in individuals through creative drama education, it is imperative that the educators themselves possess these skills. This research pursues a dual objective: firstly, it delves into the communication skills and democratic attitudes of creative drama educators, considering various influencing factors. Subsequently, it explores the correlation between communication skills and democratic attitudes. The study included 143 creative drama educators from 18 cities in Turkey. Various statistical analyses, including independent samples t-tests, Analysis of Variance (ANOVA), and Pearson Product Moment Correlation Analysis, were employed. The findings reveal that the gender and years of experience of drama educators are not significantly linked to their democratic attitudes or communication skills. However, the faculty that they are graduated from significantly impacts both their communication skills and democratic attitudes. Finally, the Pearson Product Moment Correlation Analysis demonstrates a significant and positive relationship between the communication skills and democratic attitudes of creative drama educators.

How to Treat a Cyclist's Nodule?-Introduction of a Novel, ICG-Assisted Approach.

Perineal nodular induration (PNI) is a benign proliferation of the soft tissue in the perineal region that is associated with saddle sports, especially road cycling. The etiology has not been conclusively clarified; however, repeated microtrauma to the collagen and subcutaneous fat tissue by pressure, vibration and shear forces is considered a mechanical pathomechanism. In this context, chronic lymphedema resulting in the development of fibrous tissue has been suggested as an etiological pathway of PNI. The primary aim of this study was to introduce and elucidate a novel operative technique regarding PNI that is assisted by indocyanine green (ICG). In order to provide some context for this approach, we conducted a comprehensive review of the existing literature. This dual objective aimed to contribute to the existing body of knowledge while introducing an innovative surgical approach for managing PNI. We reviewed publications relating to PNI published between 1990 and 2023. In addition to the thorough review of the literature, we presented our novel surgical approach. We described how this elaborate approach for extensive cases of PNI involves surgical excision combined with tissue doubling and intraoperative ICG visualization for exact lymphatic vessel obliteration to minimize the risk of recurrence based on the presumed context of lymphatic congestion. The literature research yielded 16 PubMed articles encompassing 23 cases of perineal nodular induration (PNI) or cyclist's nodule. Of these, 9 cases involved females, and 14 involved males. Conservative treatment was documented in 7 cases (30%), while surgical approaches were reported in 16 cases (70%). Notably, a limited number of articles focused on histopathological or radiological characteristics, with a shortage of structured reviews on surgical treatment options. Only two articles provided detailed insights into surgical techniques. Similarly to the two cases of surgical intervention identified in the literature research, the post-operative recovery in our ICG assisted surgical approach was prompt, meaning a return to cycling was possible six weeks after surgery. At the end of the observation period (twelve months after surgery), regular scar formation and no signs of recurrence were seen. We hope that this article draws attention to the condition of PNI in times of increasing popularity of cycling as a sport. We aimed to contribute to the existing body of knowledge through our thorough review of the existing literature while introducing an innovative surgical approach for managing PNI. Due to the successful outcome, the combination of tissue doubling, intraoperative ICG visualization and postoperative negative wound therapy should be considered as a therapeutic strategy in cases of large PNI.

Near-optimal tensor methods for minimizing the gradient norm of convex functions and accelerated primal–dual tensor methods

Motivated, in particular, by the entropy-regularized optimal transport problem, we consider convex optimization problems with linear equality constraints, where the dual objective has Lipschitz pth order derivatives, and develop two approaches for solving such problems. The first approach is based on the minimization of the norm of the gradient in the dual problem and then the reconstruction of an approximate primal solution. Recently, Grapiglia and Nesterov [Tensor methods for finding approximate stationary points of convex functions, Optim. Methods Softw. (2020), pp. 1–34] showed lower complexity bounds for the problem of minimizing the gradient norm of the function with Lipschitz pth order derivatives. Still, the question of optimal or near-optimal methods remained open as the algorithms presented in [Grapiglia and Nesterov, Tensor methods for finding approximate stationary points of convex functions, Optim. Methods Softw. (2020), pp. 1–34] achieve suboptimal bounds only. We close this gap by proposing two near-optimal (up to logarithmic factors) methods with complexity bounds O ~ ( ε − 2 ( p + 1 ) / ( 3 p + 1 ) ) and O ~ ( ε − 2 / ( 3 p + 1 ) ) with respect to the initial objective residual and the distance between the starting point and solution, respectively. We then apply these results (having independent interest) to our primal–dual setting. As the second approach, we propose a direct accelerated primal–dual tensor method for convex problems with linear equality constraints, where the dual objective has Lipschitz pth order derivatives. For this algorithm, we prove O ~ ( ε − 1 / ( p + 1 ) ) complexity in terms of the duality gap and the residual in the constraints. We illustrate the practical performance of the proposed algorithms in experiments on logistic regression, entropy-regularized optimal transport problem, and the minimal mutual information problem.

Leveraging Swarm Intelligence for Optimal Thermal Camera and Sensor Placement in Industrial Environments

This research addresses the sensor placement optimization problem (SPOP) in the industrial sector, aiming to enhance operational efficiency and safety through the strategic deployment of sensors. The focus is on optimizing the locations of thermal cameras and motion sensors, with a dual objective of maximizing coverage and minimizing redundancy in the production hall. To solve this challenge, a method based on the application of the nature-inspired bat algorithm was employed. The study reveals noteworthy findings, emphasizing the proficiency of the bat algorithm (BA) in optimizing the placement of thermal cameras and motion sensors. Numeric outcomes demonstrate the algorithm’s effectiveness in maximizing machine coverage while minimizing sensor usage within a real-world industrial environment. These results underscore the versatility and reliability of the BA, establishing it as a valuable tool for addressing complex optimization tasks in industrial settings.

Adhesive bonding of glass-fibre thermoplastic composite: process optimisation and sustainability analysis using LCA methodology

In recent years, great attention has been paid to the challenge of integrating more sustainable circular approaches to manufacturing, which involve optimising the entire product cycle, from design to fabrication, assembly and eventual reuse/recycling. In this context, the use of thermoplastic composite materials in combination with adhesive bonding is attracting increasing attention to achieve this dual objective. Therefore, the proposed research focuses on the experimental and statistical optimisation of an adhesive bonding process for glass-fibre–reinforced composite substrates with polypropylene matrix, with the aim of comparing its performance with that of joints made using more conventional configurations, i.e. utilising thermosetting substrates. A low-pressure plasma pre-bonding surface treatment is adopted, and its effectiveness in enhancing joint performance compared to more conventional preparations for plastics is discussed. By varying plasma parameters according to the design of experiments statistical approach, the joint response is studied in terms of tensile shear strength and modelled in accordance with the response surface methodology to identify the optimum condition of process parameters within the established system boundaries. The study found that with optimised pre-treatment, adhesive joints of glass-fibre–reinforced composites with thermoplastic matrices (first scenario) can achieve tensile shear strength (TSS) comparable to that of thermoset composites (second scenario), making them viable options in various applications. Two alternative scenarios—namely, joints with thermoplastic composite or thermoset composite substrates—are then analysed by the life cycle assessment methodology and compared according to their relative environmental impact, demonstrating that glass-fibre thermoplastic composite joints are competitive alternatives to thermoset joints in terms of mechanical static performance achieved, but significantly preferable with regard to environmental sustainability.

Research on Pricing and Replenishment Decision of Vegetable Products Based on Optimization Algorithms

With the improvement of people's living standards, people's requirements for the freshness and variety richness of food are also constantly increasing. Due to the short shelf life of fresh vegetable products, reasonable prediction and formulation of pricing and replenishment strategies have a significant impact on supermarket revenue. This article first cleans the data and uses Excel and Grabbs statistics to determine the missing and abnormal values of the data. The missing values are supplemented by 0, and the abnormal values are determined and eliminated through manual intervention. Conduct descriptive statistics on the preprocessed data to obtain partial data features of categories classified by day and month. Analyze numerical features to determine the distribution pattern of data using monthly classification, and then use SPSS PRO to conduct Spearman correlation analysis to obtain the relationship between vegetable categories. Next, this article quantifies six data indicators, including daily sales volume of each category, average unit price during normal sales, and discount degree. Then, MATLAB is used for regression fitting analysis to obtain the relationship expressions between each indicator and the total sales volume. Excel analysis is used to summarize the data to obtain the available variety information of each item of vegetables in the supermarket from June 24th to 30th, And use Python software to establish a time series prediction model to analyze and process the information of supermarket products, and obtain the demand for individual products on July 1st. Using the single product selected by the supermarket as the 0-1 decision variable, and the total operating profit and supply demand relationship of the supermarket as the dual objective variables, a multiple linear regression model is constructed to obtain the final replenishment policy and pricing decision. The data in this paper is from the National College Students Mathematical Modeling Competition C question.

Floating-point histograms for exploratory analysis of large scale real-world data sets

Histograms are among the most popular methods used in exploratory analysis to summarize univariate distributions. In particular, irregular histograms are good non-parametric density estimators that require very few parameters: the number of bins with their lengths and frequencies. Although many approaches have been proposed in the literature to infer these parameters, most existing histogram methods are difficult to exploit for exploratory analysis in the case of real-world data sets, with scalability issues, truncated data, outliers or heavy-tailed distributions. In this paper, we focus on the G-Enum histogram method, which exploits the Minimum Description Length (MDL) principle to build histograms without any user parameter. We then propose to extend this method by exploiting a new modeling space based on floating-point representation, with the objective of building histograms resistant to outliers or heavy-tailed distributions. We also suggest several heuristics and a methodology suitable for the exploratory analysis of large scale real-world data sets, whose underlying patterns are difficult to recover for digitization reasons. Extensive experiments show the benefits of the approach, evaluated with a dual objective: the accuracy of density estimation in the case of outliers or heavy-tailed distributions, and the effectiveness of the approach for exploratory data analysis.

Research On Pricing Strategies Based on Time Series Analysis Model and Greedy Algorithm Model

The issue of replenishment and pricing arrangements for vegetable categories and individual items in future supermarkets is crucial. This article is based on a time series analysis model to analyze the correlation between different categories of single products, and constructs a dual objective greedy algorithm optimization model, constrained by maximizing profits and minimizing operational loss rates. Predict the total replenishment amount and pricing strategy for 50 types of vegetables, including categories such as cauliflower, leaves, chili peppers, eggplants, edible mushrooms, and underwater acoustic root canals. Next week's predicted sales and prices are: cauliflower 0.381 kg, 11.7331 yuan/kg; Leaves 5.9-6.084 kg, 9.031-8.504 yuan/kg; Chili peppers range from 8.327 to 5.8 kilograms, with a price of 15.632 to 14.793 yuan/kilogram; Eggplant 10.962-11.321 kg, 7.843-7.861 yuan/kg; Edible mushrooms 5.5118-5.374 kg, 12.323-11.476 yuan/kg; The underwater sound root canal costs 3.321-3.378 kilograms, 25.619-15.981 yuan/kilogram.

The Beauty of Clay: Exploring contemporary Ceramic Art as an Aesthetic Medium in Education

The development of ceramic art as an aesthetic medium in Chinese education is significant aspect of contemporary fine arts education system. This study explored the status of contemporary status of ceramic arts in education and its linkage with aesthetic pleasure within the context of China. The dimensions considered in this study included integration of ceramic arts in the curriculum of Chinese universities, edu-communicative function of ceramic arts, the aesthetic characteristics of ceramic art and role of modern digital tools in spreading the art and craft associated with clay. To fulfill the aims of study and to reach an efficient conclusion, this study employed a qualitative approach. Using this approach, an overview of Chinese ceramic education was presented using both primary and secondary data, where the primary data was derived from interviews with informants from relevant universities. The findings of the study highlight that ceramic art is a significant aspect of contemporary Chinese arts education in its universities, which has the dual objective of develop artistic skills of students and also enhance the edu-communicative function of the ceramic art representing the Chinese culture. The findings revealed that ceramic art not only caried aesthetic elements but it was also a prime source of creating aesthetic pleasure as it played the role of aesthetic medium in education. It was evident that due to its aesthetic purpose, ceramic art demonstrated the notions of ‘Art for Art’s sake’. It has also been found that digital tools play a significant role in bringing innovation in ceramic art education and spread the skills of clay-art. The findings of this paper have numerous implications for the personnel and practitioners of Chinese fine arts institutions especially those who have curriculum related to ceramic art.

Extended maternity leave and children's long‐term development

AbstractCountries around the world are increasingly expanding legal maternity leaves, with the dual objective of protecting mothers' jobs during their recovery after childbirth and enhancing child development. Using exhaustive census data, we find that a three‐year paid leave in France had zero average effects on children's long‐term schooling achievement, and no detectable impact heterogeneity. The lack of positive effects on children adds to the case against a policy that has strong adverse effects on mothers' careers.

A Novel Virtual Arrival Optimization Method for Traffic Organization Scenarios

The International Maritime Organization (IMO) has been progressively implementing stricter regulations on ship carbon emissions, leading to many vessels adopting the virtual arrival (VA) method to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through virtual arrival while simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions.

Computational analysis of dystopian elements in the partition fiction: A machine learning approach to the indian English novels

Despite the growth of digital humanities, the specific application of computational methods to analyse literary themes and elements remains underexplored. This study aims to use machine learning algorithms for text classification on seven novels about the partition of India. The article has a dual objective: firstly, to develop and implement an innovative opinion mining framework that leverages machine learning techniques to identify and classify dystopian elements in Partition novels; and secondly, to evaluate and compare the efficacy of the model in accurately classifying dystopian sentiments within selected literary texts. The proposed framework includes six phases of data collection, test-pre-processing, text extraction, text exploration, modelling or classification using machine leaning, and performance evaluation. Machine learning approaches such as Logistic Regression, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor are adopted to classify the text based on compiled dictionary. First, the study revealed that the main components and characteristics of dystopian elements found in selected narratives are ‘fear’, ‘suffer’, ‘oppression’ and ‘violence’. Second, the analysis showed that SVM with CountVectorizer, using imbalanced dataset and random oversampling dataset, outperforms other classifiers in classifying dystopian types in the selected novels. The results also suggest that CountVectorizer works better for the dataset compared to TF-IDF.