Future Models Research Articles

Employing microalgae cultivation on fruits and vegetable peel waste to produce biofuel, lutein, and biochar concurrently with an "Agro to Agro" algae biorefinery approach.

The aim of the current investigation is to explore the novel application of pumpkin, papaya, and orange peels as growth substrates for microalgae cultivation, with the overarching goal of advancing a sustainable "Agro to Agro" biorefinery paradigm. The research evaluates the integration of waste management practices into microalgal production, optimizing growth parameters to maximize output. Optimal concentrations of 2.8mg L-1 for orange peels, 35.5mg L-1 for papaya peels, and 35.5mg L-1 for pumpkin peels were identified, alongside a light intensity of 163.7µmolm-2s-1 and a nitrogen concentration of 0.8g L-1. Under these conditions, Chlorella sorokiniana demonstrated peak biomass production of 3.16g L-1, lipid productivity of 1.55g L-1, and carotenoid productivity of 9.18mg L-1, additionally, yielding significant amounts of palmitic acid (47.9%) and lutein. The study further explored the conversion of residual microalgae into biochar, with optimal pyrolysis conducted at 350°C. The as-synthesized biochar was utilized effectively as a soil amendment for cultivating Vigna radiata. The present study underscores the viability of a closed-loop biorefinery approach, demonstrating the recycling of pumpkin, papaya, and orange peels as effective substrates for microalgae cultivation and subsequent biochar conversion for potential industrial applications. The promising results of the study advocate to the feasibility of this integrated model for sustainable future.

Integrating Scientific and Stakeholder-Based Knowledge to Simulate Future Urban Growth Scenarios: Findings from Kurunegala and Galle, Sri Lanka

The promotion of sustainability and resilience within urban environments is widely recognized as an essential approach to educating urban communities through innovative strategies and tools. This paper presents a process for integrating stakeholders into urban growth simulation, thereby enhancing sustainable decision-making. Currently, most urban growth models fail to incorporate the perspectives of diverse stakeholders, leading to reduced equitable participation in the decision-making process. To achieve long-term sustainability, it is imperative to include the input and viewpoints of stakeholders. This study follows a four-step approach: identifying relevant stakeholders, developing the framework, evaluating its effectiveness, and documenting lessons learned. The framework involves key steps, including initial participatory modeling, analysis of development pressures and suitability with stakeholders, and technical urban growth modeling. A unique combination of modeling tools and an innovative approach was employed, incorporating the default FUTURES (GRASS-GIS) model alongside the CA-Markov Chain, Agent-Based Modeling (ABM) (NetLogo), the Cellular-Automata-based Python model, and MOLUSCE-QGIS. This integrated approach facilitates the inclusion of stakeholder-based knowledge into conventional urban growth modeling, providing novel local lessons in science, technology, and innovation initiatives. Validation was conducted through both technical and stakeholder mechanisms, confirming the effectiveness of the proposed framework.

Improving prediction accuracy in agricultural markets through the CIMA-AttGRU model.

In the Chinese futures market, agricultural product futures play a crucial role. While previous studies have primarily relied on historical price data and fundamental financial indicators of agricultural product futures, there is a growing recognition of the value that lies within the vast amounts of textual data generated in the financial domain. Our study specifically focuses on the limitations of existing methods in capturing the complex relationships and rich semantic information embedded in these textual sources. This article designs a CIMA AttGRU (CIMA-AttGRU) model for soybean futures, which is a forecasting method for the agricultural product market. This model uniquely integrates Collective Intrinsic Mode Analysis (CIMA) with an Attention-Gated Recurrent Unit (AttGRU), leveraging the strengths of both techniques to enhance predictive accuracy and adaptability. The rationale behind employing the CIMA-AttGRU model lies in its ability to effectively tackle the inherent challenges of financial market analysis. By incorporating CIMA, the model adeptly filters out market noise, directly addressing the challenge of high volatility. Additionally, with its attention mechanism, the CIMA-AttGRU targets the issue of non-linear patterns by allowing dynamic adjustment to temporal dependencies, offering differential learning capabilities crucial for capturing the nuanced fluctuations in futures prices. Complementing the CIMA and AttGRU, the integration of Class-wise Adversarial Domain Adaptation (CADA) further refines the model's robustness, addressing the critical challenge of domain adaptivity. This aspect is particularly vital for edamame futures, where price determinants can vary significantly over time and across regions. Our empirical results demonstrate a significant improvement in forecasting precision, with the CIMA-AttGRU model achieving a Mean Absolute Error (MAE) reduction of 15% and a Mean Squared Error (MSE) reduction of 20% compared to conventional models. This superior performance, especially in terms of prediction accuracy and handling market fluctuations, highlights the improve of the model compared to existing methods and has made significant explorations in agricultural market forecasting.

Predicting the long-term effects of electronic cigarette use on population health: a systematic review of modelling studies

ObjectiveTo systematically review and synthesise the findings of modelling studies on the population impacts of e-cigarette use and to identify potential gaps requiring future investigation.Data source and study selectionFour databases...

Developing modelling competencies in pre-service mathematics teachers through reflective thinking skills

Background: Ghana’s commitment to quality education has been reflected in its goal of providing equal access to high-quality education, leading to the reforms of the New Common Core Mathematics Curriculum for Basic Schools. The study explored the integration of mathematical modelling using reflective thinking skills, which are not currently core competencies in Ghanaian basic school curriculum.Aim: The study examined the modelling proficiency of pre-service mathematics teachers by assessing their reflective thinking abilities in a modelling laboratory context.Setting: The study focussed on pre-service mathematics teachers from two education colleges in Ghana.Methods: Using purposive sampling to select participants, a quasi-experimental design with pre- and post-test interventions was employed. Data were analysed through content and inferential analysis, supplemented by interviews.Findings: The findings indicated that the comparison group lacked prior knowledge of modelling problems and struggled with comprehension tasks. In contrast, the experimental group successfully translated real-world problems into mathematical models.Conclusion: Providing pre-service mathematics teachers access to a modelling laboratory and modelling-eliciting activities was essential for developing future modellers. This approach would enhance their effectiveness in teaching foundational mathematics in Ghanaian education.Contribution: This study advocated for re-orienting the mathematics curriculum at both Basic schools and Colleges of Education in Ghana to include mathematical modelling and reflective thinking skills as core components.

Energy transition in the oil and gas sector: Business models for a sustainable future

The global energy landscape is undergoing a significant transformation as industries shift towards more sustainable practices, driven by environmental regulations, societal pressure, and technological advancements. The oil and gas sector, traditionally dependent on fossil fuels, is at the forefront of this energy transition. This review explores the evolving business models that facilitate the industry's transition to a low-carbon economy, focusing on strategies for achieving sustainability while maintaining profitability. Key approaches include diversifying energy portfolios by investing in renewable energy sources such as wind, solar, and hydrogen, as well as enhancing operational efficiency through digital technologies like artificial intelligence and the Internet of Things (IoT). The transition is also marked by an increased focus on carbon capture, utilization, and storage (CCUS) technologies, which mitigate the environmental impact of traditional oil and gas operations. Business models are being reshaped to incorporate circular economy principles, where waste and emissions are minimized through innovation and resource optimization. Furthermore, partnerships between oil and gas companies and renewable energy firms are emerging as a critical pathway for mutual growth and sustainability. This energy transition also involves rethinking the workforce and reskilling employees for emerging sectors, emphasizing the need for adaptability in business models. Regulatory frameworks play a pivotal role in shaping these models, as policies increasingly favor green investments and low-carbon initiatives. As oil and gas companies diversify their portfolios, they are creating new revenue streams that align with global decarbonization goals while ensuring financial resilience. This study highlights how these innovative business models are setting the foundation for a sustainable future in the oil and gas sector, ensuring that the industry remains relevant in a rapidly changing energy ecosystem. Keywords: Energy Transition, Oil and Gas, Business Models, Sustainability, Renewable Energy, Carbon Capture, Circular Economy, Digital Transformation, Decarbonization, Low-Carbon Economy.

Histological and morphometrical evaluation of the urethral wall after bioresorbable stent implantation in male New Zealand White Rabbits: A preliminary study.

The aim of the study was the histological and morphometrical evaluation of the urethral wall at three time points after bioresorbable stent implantation in male New Zealand White Rabbits. The research was performed on 26 male New Zealand White rabbits aged 3-4 months and weighing 2.1-3.0 kg. Two models of bioresorbable sodium alginate-based stents were developed and implanted into the urethral lumen for one (T1), three (T3), and six weeks (T6). Sections of 5 µm thickness were cut from the urethra at intervals of 2 mm. The sliced sections were stained with hematoxylin-eosin (H&E), Van Gieson's (VG), Von Kossa, and Movat-Russell modified pentachrome (MOVAT) staining methods. The study provided valuable information for future models of urethral stents. The first model of the stent failed to fit the requirements due to inadequate mechanical properties. It curled up on itself losing the ability to adhere to the animals' urethra and was bioresorbed three weeks after implantation. The more rigid no. 2 stent was effective in widening the urethral lumen but did not biodegrade during the experiment. A comprehensive assessment of the second model's properties of biosorption and biointegration requires an extended observation of at least 12 months for an in depth morphological analysis. Stent migration is not likely to be caused solely by the mechanical properties of the urethra or urinary flow but mainly by muscle contraction of the organ wall.

A data-driven framework for enhancing coastal flood resilience in resource-crunched developing nations

A comprehensive Flood Resilient Scenario Model ‘FReSMo’ employs a data-driven, evidence-based approach for assessing climate-induced flood risk and validating the efficacy of mangroves (as context-specific adaptation measure) in reducing residential building damage. Based on an improvised Source-Pathway-Receptor-Consequence-Evidence concept, FReSMo employs a three-step analysis. First, hazard mapping estimates coastal flood extents for various return period under different Shared Socioeconomic Pathways. Second, the model maps the exposure of residential buildings to these flood extents by projecting built-up area for 2050 using the FUTURES model, based on physiographic, socio-demographic, and economic parameters. Finally, a data-driven probabilistic damage model is applied to estimate the built-up area damage for a 100-year coastal flood event (SSP-2.6). The pre-and post-adaptation damage estimate demonstrates the efficacity of NBS (mangroves) in reducing coastal flood risk. A 100 m mangrove patch on the Sagar coastline reduced the building damage cost by 70% for a 48-hr and 75% for a 24-hr flood. Considering a plantation cost for 6.2 km2, the total benefit, despite persistent losses, was a 222% return on investment. FReSMo transcends conventional risk assessment frameworks by offering a comprehensive approach for evaluating the cost-effectiveness of adaptation investments in developing countries, making it an invaluable tool in the face of climate change.

Smart cities in Lima: A transformative model for a sustainable future

Smart cities in Lima: A transformative model for a sustainable future

Ergatic approach to reconstruction and modeling of the spatial future of Ukraine

ABSTRACT The article expresses the idea of ergaticity and the ergatic approach (behavioral model) in decision-making on the spatial development of Ukraine, which considers the state and its space as a ‘system-process’ that is characterized by behavior, the ability to self-organize and self-develop, and the choice of effective behavior in changing realities and situations. Foreign and domestic scientific sources, statistical compilations, project materials, strategies, and programs for the development of Ukraine, its regions, and cities served as source data. The authors use long-term monitoring of processes at different hierarchical levels of the space of the state, their dynamics, and changes. The study resulted in the substantiation of the conceptual provisions of the ergatic approach to planning and modeling the spatial future of Ukraine. The suggested spatial changes relate to functional and urban zoning of the territories of the state; development of connections and understanding of the multidimensional dynamics of processes in the territory; placement of new elements and investments in the existing structure of natural landscape and urban planning conditions; spatial, temporal, structural, and parametric coordination of processes in the space of the state and creation of systemic effects of using the capacity of ergaticity in the spatial development of Ukraine.

Chinese Title Generation for Short Videos: Dataset, Metric and Algorithm.

Previous work for video captioning aims to objectively describe the video content but the captions lack human interest and attractiveness, limiting its practical application scenarios. The intention of video title generation (video titling) is to produce attractive titles, but there is a lack of benchmarks. This work offers CREATE, the first large-scale Chinese shoRt vidEo retrievAl and Title gEneration dataset, to assist research and applications in video titling, video captioning, and video retrieval in Chinese. CREATE comprises a high-quality labeled 210 K dataset and two web-scale 3 M and 10 M pre-training datasets, covering 51 categories, 50K+ tags, 537K+ manually annotated titles and captions, and 10M+ short videos with original video information. This work presents ACTEr, a unique Attractiveness-Consensus-based Title Evaluation, to objectively evaluate the quality of video title generation. This metric measures the semantic correlation between the candidate (model-generated title) and references (manual-labeled titles) and introduces attractive consensus weights to assess the attractiveness and relevance of the video title. Accordingly, this work proposes a novel multi-modal ALignment WIth Generation model, ALWIG, as one strong baseline to aid future model development. With the help of a tag-driven video-text alignment module and a GPT-based generation module, this model achieves video titling, captioning, and retrieval simultaneously. We believe that the release of the CREATE dataset, ACTEr metric, and ALWIG model will encourage in-depth research on the analysis and creation of Chinese short videos.

Defining Active Adult: A Promising Housing Model for an Active Aging Future

Defining Active Adult: A Promising Housing Model for an Active Aging Future

Research on Grain Futures Price Prediction Based on a Bi-DSConvLSTM-Attention Model

Grain is a commodity related to the livelihood of the nation’s people, and the volatility of its futures price affects risk management, investment decisions, and policy making. Therefore, it is very necessary to establish an accurate and efficient futures price prediction model. Aiming at improving the accuracy and efficiency of the prediction model, so as to support reasonable decision making, this paper proposes a Bi-DSConvLSTM-Attention model for grain futures price prediction, which is based on the combination of a bidirectional long short-term memory neural network (BiLSTM), a depthwise separable convolutional long short-term memory neural network (DSConvLSTM), and an attention mechanism. Firstly, the mutual information is used to evaluate, sort, and select the features for dimension reduction. Secondly, the lightweight depthwise separable convolution (DSConv) is introduced to replace the standard convolution (SConv) in ConvLSTM without sacrificing its performance. Then, the self-attention mechanism is adopted to improve the accuracy. Finally, taking the wheat futures price prediction as an example, the model is trained and its performance is evaluated. Under the Bi-DSConvLSTM-Attention model, the experimental results of selecting the most relevant 1, 2, 3, 4, 5, 6, and 7 features as the inputs showed that the optimal number of features to be selected was 4. When the four best features were selected as the inputs, the RMSE, MAE, MAPE, and R2 of the prediction result of the Bi-DSConvLSTM-Attention model were 5.61, 3.63, 0.55, and 0.9984, respectively, which is a great improvement compared with the existing price-prediction models. Other experimental results demonstrated that the model also possesses a certain degree of generalization and is capable of obtaining positive returns.

Cost prediction of building projects using the novel hybrid RA-ANN model

PurposeAccurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.Design/methodology/approachThe proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.FindingsThe results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.Originality/value(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

Restoration of Doleshwar Mosque: Reviving Craft and Engaging Craftsmen of Lime-surkhi

ABSTRACT The goal of this paper is to present the revival of the long-lost craft of lime-surkhi and demonstrate its use in the restoration of Doleshwar Mosque in Bangladesh. This paper evaluates the current status of the lime-surkhi craft and shows how it is being revived. Doleshwar Mosque, a British colonial mosque that showcases some of the best surviving specimens of lime-surkhi work, is used as a case study. The research here investigates the challenges that the craft and craftsmen encounter and discusses how the restoration effort might serve as a model for a sustainable future for the craft, using key informant interviews and focus group discussions as research methodologies. There are no comparable studies on this topic in the context of Dhaka in Bangladesh. The study offers a three-step process for facilitating the survival of the craft through collaboration and capacity building: identifying the rare craftsmen, giving them the recognition they deserve, and passing on the traditional knowledge by drawing in the next generation. This article has the potential to present the development of new policies pertaining to the revival of crafts, not just for Bangladesh but also for other cities facing similar issues with dwindling crafts and craftsmen.

The Price Trend of Polypropylene

Since the polypropylene (PP) is one of the most widely used plastics, the futures prices of polypropylene are of great significance for studying the Chinese plastic market and related investors. This paper analyzes the factors that influence the changes in PP futures prices during and before the pandemic period by examining the correlation between upstream raw materials, downstream product markets, and PP futures prices. The results show that the futures prices of the raw materials are positively correlated with PP futures prices. Additionally, during the COVID-19 pandemic, due to the reduction in domestic PP demand in China, blocked crude oil imports, and refinery closures caused by control measures, the impact of crude oil futures prices on PP futures prices is relatively diminished. Furthermore, the increased demand for plastic packaging during the pandemic led to abnormal fluctuations in the packaging stock index, making it less valuable for analyzing PP futures prices. Finally, a regression analysis is conducted to establish a generalized predictive model for PP futures prices during non-pandemic periods, which is 9.91469 * crude oil futures prices + (-1.22343) * packaging stock index + 3.96203 * textile stock index + (-0.26201) * medical devices stock index. Its reliability is tested using data tests, residual vs. fitted analysis, and K-fold cross-validation, among other methods.

Rebleeding following aneurysmal subarachnoid hemorrhage before ‘endovascular first’ treatment: a retrospective case-control study of published scoring systems

BackgroundPre-treatment re-bleeding following aneurysmal subarachnoid hemorrhage (aSAH) affects up to 7.2% of patients even with ultra-early treatment within 24 hours. We retrospectively compared the utility of three published re-bleed prediction...

Diverse perspectives in zebrafish seizure models: An exploration of chemical inducers

Numerous studies have investigated seizures in zebrafish, employing diverse methods to induce and observe epilepsy. Chemical inducers have become indispensable tools for precisely manipulating neural activity in controlled experiments. This research is dedicated to a meticulous comparison and analysis of various seizure-inducing chemicals in zebrafish. The selected chemical inducers include pentylenetetrazole (PTZ), kainic acid (KA), picrotoxin (PTX), pilocarpine, and other substances. The primary mechanism of these inducers involves altering neurotransmitter receptors, particularly GABA and glutamate, at inhibitory or excitatory synapses. This alteration creates an imbalance between inhibitory and excitatory signals in the brain, contributing to epileptogenesis. The resulting hyperactivity in the zebrafish brain disrupts metabolism, elevates reactive oxygen species (ROS), and causes neuronal damage. Chemical induction offers valuable insights into the neurological activity of zebrafish, enabling the observation of behaviors such as whole-body contractions, loss of posture, or freezing. Various parameters can be adjusted to examine the effects of chemical inducers on zebrafish development and to replicate various types of seizures, such as status epilepticus and temporal lobe epilepsy. Despite the array of options available, many chemical inducers remain underutilized in zebrafish studies. To ensure the successful induction of models, monitoring gene expression markers such as c-fos, which represents neuronal activity, becomes crucial. Additionally, a scoring system is employed to analyze seizure severity and identify treatment efficacy. The existing gaps in utilizing chemical inducers present great opportunities for future model development. This chemically-induced seizure model in zebrafish is pivotal for unraveling seizure mechanisms, contributing significantly to advancements in neurological research.

In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models.

In vitro models of Mycobacterium tuberculosis (Mtb) infection are a valuable tool for examining host-pathogen interactions and screening drugs. With the development of more complex in vitro models, there is a need for tools to help analyze and integrate data from these models. To this end, we introduce an agent-based model (ABM) representation of the interactions between immune cells and bacteria in an in vitro setting. This in silico model was used to simulate both traditional and spheroid cell culture models by changing the movement rules and initial spatial layout of the cells in accordance with the respective in vitro models. The traditional and spheroid simulations were calibrated to published experimental data in a paired manner, by using the same parameters in both simulations. Within the calibrated simulations, heterogeneous outputs are seen for bacterial count and T cell infiltration into the macrophage core of the spheroid. The simulations also predict that equivalent numbers of activated macrophages do not necessarily result in similar bacterial reductions; that host immune responses can control bacterial growth in both spheroid structure dependent and independent manners; that STAT1 activation is the limiting step in macrophage activation in spheroids; and that drug screening and macrophage activation studies could have different outcomes depending on the in vitro culture used. Future model iterations will be guided by the limitations of the current model, specifically which parts of the output space were harder to reach. This ABM can be used to represent more in vitro Mtb infection models due to its flexible structure, thereby accelerating in vitro discoveries.

Insect population dynamics under Wolbachia-induced cytoplasmic incompatibility: Puzzle more than buzz in Drosophila suzukii.

In theory, the introduction of individuals infected with an incompatible strain of Wolbachia pipientis into a recipient host population should result in the symbiont invasion and reproductive failures caused by cytoplasmic incompatibility (CI). Modelling studies combining Wolbachia invasion and host population dynamics show that these two processes could interact to cause a transient population decline and, in some conditions, extinction. However, these effects could be sensitive to density dependence, with the Allee effect increasing the probability of extinction, and competition reducing the demographic impact of CI. We tested these predictions with laboratory experiments in the fruit fly Drosophila suzukii and the transinfected Wolbachia strain wTei. Surprisingly, the introduction of wTei into D. suzukii populations at carrying capacity did not result in the expected wTei invasion and transient population decline. In parallel, we found no Allee effect but strong negative density dependence. From these results, we propose that competition interacts in an antagonistic way with Wolbachia-induced cytoplasmic incompatibility on insect population dynamics. If future models and data support this hypothesis, pest management strategies using Wolbachia-induced CI should target populations with negligible competition but a potential Allee effect, for instance at the beginning of the reproductive season.