Modeling Strategy Research Articles

Plasma-activated media selectively induces apoptotic death via an orchestrated oxidative stress pathway in high-grade serous ovarian cancer cells.

High-grade serous ovarian cancer (HGSOC) is the most common and aggressive type of ovarian cancer. Due to a lack of an early detection testand overt symptoms, many patients are diagnosed at a late stage where metastasis makes treatment very challenging. Furthermore, the current standard treatment for HGSOC patients, consisting of debulking surgery and platinum-taxane chemotherapy, reduces quality of life due to debilitating side-effects. Sadly, 80-90% of patients diagnosed with advanced stage ovarian cancer will die due to treatment resistance. As such, novel therapeutic strategies for HGSOC that are both more effective and less toxic are urgently required. Here we describe the assessment of cold atmospheric pressure (CAP) gas discharge technology as a novel treatment strategy in pre-clinical models of HGSOC. Plasma-activated media (PAM) was generated using cell growth media. HGSOC cell lines, patient ascites cells and primary tissue explants were tested for their response to PAM via analysis of cell viability, cell death and oxidative stress assays. Our data show that PAM treatment can be more effective than standard carboplatin chemotherapy at selectively targeting ovarian cancer cells in primary patient samples. Further, we also observed PAM to induce apoptosis in HGSOC cancer cell lines via induction of oxidative stress and mitochondrial-mediated apoptosis. These findings suggest that PAM is a viable therapeutic strategy to test in in vivo models of ovarian cancer, with a view to develop an intraperitoneal PAM-based therapy for HGSOC patients. Our studies validate the ability of PAM to selectively target tumour tissue and ascites cells. This work supports the development of PAM towards in vivo validation and translation into clinical practice.

Probing the heating of the neutral atomic interstellar medium in the Dwarf Galaxy Survey through infrared cooling lines

Star formation in galaxies is regulated by dynamical and thermal processes. In the Milky Way and star-forming galaxies with similar metallicity, the photoelectric effect on small dust grains usually dominates the heating of the neutral atomic gas, which constitutes the main star-forming gas reservoir. In more metal-poor galaxies, the lower dust-to-gas mass ratio together with the higher occurrence and luminosity of X-ray sources suggest that other heating mechanisms may be at play. We aim to determine the contribution of the photoelectric effect, photoionization by UV and X-ray photons, and ionization by cosmic rays to the total heating of the neutral gas in a sample of 37 low-metallicity galaxies. In particular, we wish to assess whether X-ray sources can be a significant source of heating. We also attempt to recover the intrinsic X-ray fluxes and compare them with observations when available. We used the statistical code MULTIGRIS together with a photoionization grid of Cloudy models propagating radiation from stellar clusters and potential X-ray sources to the ionized and neutral gas. This grid includes physical parameters such as metallicity, gas density, ionization parameter, and radiative source properties. We describe a galaxy as a combination of many 1D components linked by a few physical hyperparameters. We used infrared cooling lines as constraints to evaluate the most likely combinations and parameters. We constrained the heating fractions for the main mechanisms for the first time in a low-metallicity galaxy sample. We show that for the higher metallicity galaxies, the photoelectric effect dominates the neutral gas heating. At metallicities below $1/8$ the Milky Way value, cosmic rays and photoionization can become predominant. We computed an observational proxy for the photoelectric effect heating efficiency on polycyclic aromatic hydrocarbons (PAHs) using the total cooling traced by We show that this proxy can match theoretical expectations when accounting for the fraction of the heating due to the photoelectric effect according to our models. Finally, we show that it is possible to predict the X-ray fluxes reasonably well in the $0.3-8$\,keV band from the gas cooling lines for most of the galaxies observed in this band. With the current grid and assumptions, determining the exact heating fraction due to cosmic rays remains difficult, but we speculate that heating from X-ray sources is more important. As expected from the low abundance of dust and PAHs in metal-poor galaxies, heating mechanisms other than the photoelectric effect heating must be accounted for. Bright X-ray sources may deposit their energy on large scales in such transparent, dust-poor interstellar medium, and thus they represent promising avenues to understand the physical properties of the main star-forming gas reservoir in galaxies. The modeling strategy adopted here makes it possible to recover the global intrinsic radiation field properties when X-ray observations are unavailable, such as in early universe galaxies.

The cardio-oncologic burden of breast cancer: molecular mechanisms and importance of preclinical models.

Breast cancer, the most prevalent cancer affecting women worldwide, poses a significant cardio-oncological burden. Despite advancements in novel therapeutic strategies, anthracyclines, HER2 antagonists, and radiation remain the cornerstones of oncological treatment. However, each carries a risk of cardiotoxicity, though the molecular mechanisms underlying these adverse effects differ. Common mechanisms include DNA damage response, increased reactive oxygen species, and mitochondrial dysfunction, which are key areas of ongoing research for potential cardioprotective strategies. Since these mechanisms are also essential for effective tumor cytotoxicity, we explore tumor-specific effects, particularly in hereditary breast cancer linked to BRCA1 and BRCA2 mutations. These genetic variants impair DNA repair mechanisms, increase the risk of tumorigenesis and possibly for cardiotoxicity from treatments such as anthracyclines and HER2 antagonists. Novel therapies, including immune checkpoint inhibitors, are used in the clinic for triple-negative breast cancer and improve the oncological outcomes of breast cancer patients. This review discusses the molecular mechanisms underlying BRCA dysfunction and the associated pathological pathways. It gives an overview of preclinical models of breast cancer, such as genetically engineered mouse models, syngeneic murine models, humanized mouse models, and various in vitro and ex vivo systems and models to study cardiovascular side effects of breast cancer therapies. Understanding the underlying mechanism of cardiotoxicity and developing cardioprotective strategies in preclinical models are essential for improving treatment outcomes and reducing long-term cardiovascular risks in breast cancer patients.

In vitro screening of UGT2B10 in silico prioritized putative ligands from drugs used in the pediatric hematopoietic stem cell transplantation setting.

UGT2B10 is a phase II drug metabolizing enzyme with limited information on its role in the metabolism of drugs, especially in the pediatric hematopoietic stem cell transplantation setting. Previously, we investigated UGT2B10's role through in silico analyses and prioritized acetaminophen (APAP), lorazepam (LOR), mycophenolic acid (MPA), and voriconazole N-oxide (VCZ N-oxide) for invitro investigations. In this report, we present invitro screening of these candidates and of voriconazole (VCZ) to assess their potential to be substrates and/or inhibitors of UGT2B10. Enzyme kinetics experiments included recombinant UGT2B10 and analytical methods based on ultra high-performance liquid chromatography coupled to mass spectrometry (UHPLC-MS). To determine potential substrates, candidates were incubated at various therapeutically observed concentrations with recombinant UGT2B10 to identify the corresponding glucuronide metabolite. Inhibition capacity was tested using the selective probe cotinine for its glucuronidation to cotinine N-ß-d-glucuronide. IC50 was determined for compounds exhibiting inhibition. Among the tested compounds, LOR (IC50 = 0.01 μM, R2 = 0.9257) and MPA (IC50 = 0.38 mM, R2 = 0.9212) exhibited inhibition potential for UGT2B10. None of the other tested compounds featured inhibition potential and none of the compounds tested exhibited metabolism through UGT2B10. Further exploration on the clinical relevance of this inhibition using modeling strategies, overlapping nature with other UGT isoforms, and screening other molecules for their inhibition potential on UGT2B10 is warranted.

A high-precision automatic diagnosis method of maize developmental stage based on ensemble deep learning with IoT devices

Accurately determining the stage of crop development holds significant importance for field crop management. With the advancement of smart agriculture, an increasing number of Internet of Things (IoT) devices are being integrated into agricultural production, enabling more efficient acquisition of high-precision crop images. Currently, research on detecting crop growth stages based on IoT device images remains relatively scarce. Most existing studies rely on a single network model for detection, often encountering issues such as low accuracy and overfitting. Therefore, in this study, we collected maize images using IoT devices and constructed an integrated deep learning model by utilizing four convolutional neural networks (CNNs) to detect the growth period of maize in real time. Additionally, we implemented several improvements on these four CNNs and subsequently tested the performance of the ensemble model on the maize dataset. Regarding the ensemble strategy for the ensemble model, we proposed a dynamic weighted voting method, building upon the original voting approach, which can mitigate model training fluctuations and expedite model convergence. Ultimately, we manually simulated various lighting conditions to assess their impact on the ensemble model. Experimental results demonstrate that the ensemble deep model proposed in this paper represents a robust method for detecting maize growth stages, achieving an accuracy rate of 0.976 on the maize dataset, effectively facilitating high-precision detection of maize growth stages in complex backgrounds.

LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication

LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication

On the hysteretic bending behavior of slack metallic cables

Short and slack stranded metallic cables are encountered in several technical applications to connect different structures or different parts of the same structural system. A couple of relevant examples are provided by flexible-bus conductors used in high-voltage electrical substations or passive damping devices installed on overhead electrical lines, such as Bretelle dampers. Bending behavior of metallic cables is nonlinear and non-holonomic due to the onset and propagation of relative sliding phenomena between the wires. While taut suspended cables have been widely studied in the literature under the classic assumptions of small sag and perfect flexibility, whenever dealing with short and slack cables, the aforementioned hypotheses typically cease to be valid, and the bending stiffness contribution plays a significative role in the determination of the overall structural response. In the present paper, two different modeling strategies for the description of the bending behavior of short and slack stranded cables are presented. The first (discrete bending model) describes the cable as a composite structural element made of wires, which are individually modeled as curved elastic thin rods interacting through normal and tangential contact forces. The second (phenomenological bending model), instead, relies on a description of the cable bending process based on an application of the well-known Bouc-Wen hysteresis model. Applications of the proposed models are presented both at the cross-sectional and at the structural levels. The systematic comparison between theoretical and experimental results allows to assess the performance of the two proposed bending models when used with “nominal” values of the model parameters, which are estimated only on the base of the knowledge of the material and geometric properties of the strand. Moreover, a back-analysis of the model parameters is carried out to get an insight on internal mechanical parameters of the cable and on the overall cross-sectional bending response (e.g. bending stiffness values).

Advancing mechanistic understanding of cohesive sediment transport: Integrating flume experiments, field measurements, and modelling approaches in a gravel-bed river

Gravel-bed rivers draining mountainous forested headwater regions are critically important for drinking water supply and ecological integrity. These rivers, however, have been increasingly impacted by intensifying anthropogenic and natural (especially climate change exacerbated) landscape disturbances that commonly increase hillslope/channel connectivity and the delivery of cohesive sediment (<63 μm) and associated pollutants. Despite the known deleterious threats of excess cohesive sediments, there is still limited understanding of their transport and intra-gravel storage due to the complexities of such processes. Accordingly, the objectives of this study were to: i) calibrate and validate a semi-empirical cohesive sediment transport model (RIVFLOC) using the observations from flume experiments; ii) estimate the intra-gravel storage capacity for cohesive sediment with the calibrated model based on the field dataset (collected in two field campaigns between 2019 and 2021), and; iii) investigate mechanisms of cohesive sediment transport dynamics in this gravel-bed river, identifying knowledge gaps and areas for future research. Our results showed that despite the increased floc settling velocity, deposition was hindered by turbulent flow fields. The model predicted that ~60 % of upstream cohesive sediment would ingress within the 10 km study reach due to the flow interaction with the gravel-bed. Despite the agreement between flume and field observations on ingress rates and preferential ingress of coarser (~100 μm) flocs, notable differences were observed between modelled and field datasets, highlighting unknowns regarding cohesive sediment exfiltration without framework mobilization. This study uniquely integrates field measurements, flume experiments, and modelling strategies to evaluate the transport and fate of cohesive sediment in a gravel-bed river. Accordingly, our findings advance current knowledge on the mechanistic understanding of cohesive sediment transport and highlight future research directions.

Real-time estimates of the emergence and dynamics of SARS-CoV-2 variants of concern: A modeling approach

The emergence of SARS-CoV-2 variants of concern (VOCs) punctuated the dynamics of the COVID-19 pandemic in multiple occasions. The stages subsequent to their identification have been particularly challenging due to the hurdles associated with a prompt assessment of transmissibility and immune evasion characteristics of the newly emerged VOC. Here, we retrospectively analyze the performance of a modeling strategy developed to evaluate, in real-time, the risks posed by the Alpha and Omicron VOC soon after their emergence. Our approach utilized multi-strain, stochastic, compartmental models enriched with demographic information, age-specific contact patterns, the influence of non-pharmaceutical interventions, and the trajectory of vaccine distribution. The models’ preliminary assessment about Omicron’s transmissibility and immune evasion closely match later findings. Additionally, analyses based on data collected since our initial assessments demonstrate the retrospective accuracy of our real-time projections in capturing the emergence and subsequent dominance of the Alpha VOC in seven European countries and the Omicron VOC in South Africa. This study shows the value of relatively simple epidemic models in assessing the impact of emerging VOCs in real time, the importance of timely and accurate data, and the need for regular evaluation of these methodologies as we prepare for future global health crises.

Effect of powder and absorptivity assumptions in the high-fidelity modeling of Laser Powder Bed Fusion processes

Laser Powder Bed Fusion (LPBF) technology represents the most promising metal additive manufacturing process to be scaled up to an industrial level for producing full batches of parts within the automotive aerospace and health sectors, among others. Despite this higher technological readiness, imperfections such as warping, porosities or undesired non-equilibrium microstructures can arise unexpectedly due to the complex thermal histories inherent to LPBF. Being able to anticipate to such imperfections is thus of high importance. With this purpose, numerical modeling strategies can be employed to significantly reduce experimental work on the melt pool scale. This allows to quantify the odds and magnitude of these process unwanted effects. More precisely, Computational Fluid Dynamics (CFD) enables high-fidelity models on the laser-matter interaction during LPBF processing. In this work, a modeling approach is presented in which the driving forces for melt formation are included in a CFD workspace, where multiple reflections of the laser on the melt pool Liquid-Gas Interface (LGI) are considered by means of an absorptivity function of the LGI depth. This simplification and the use of adaptive mesh strategies allows the model to be computed within a reasonable time. Results and applicability of the model on the determination of the melting mode (conduction or keyhole) are displayed and compared with experimental measurements of the melt pool width and depth for 316L and Ti64 single tracks produced on a LPBF machine. Likewise, further aspects such as total absorbed power are assessed.

Complex Dynamics and PID Control Strategies for a Fractional Three-Population Model

In recent decades, there have been many studies on Hopf bifurcation and population stability with time delay. However, the stability and Hopf bifurcation of fractional-order population systems with time delay are lower. In this paper, we discuss the dynamic behavior of a fractional-order three-population model with pregnancy delay using Laplace transform of fractional differential equations, stability and bifurcation theory, and MATLAB software. The specific conditions of local asymptotic stability and Hopf bifurcation for fractional-order time-delay systems are determined. A fractional-order proportional–integral–derivative (PID) controller is applied to the three-population food chain system for the first time. The convergent speed and vibration amplitude of the system can be changed by PID control. For example, after fixing the values of the integral control gain ki and the differential control gain kd, the amplitude of the system decreases and the convergence speed changes as the proportional control gain kp decreases. The effectiveness of the PID control strategy in complex ecosystem is proved. The numerical simulation results are in good agreement with the theoretical analysis. The research in this paper has potential application values concerning the management of complex population systems. The bifurcation theory of fractional-order time-delay systems is also enriched.

Customer experiences and coping behaviors during crisis situations: The role of service adaptation and service transformation

Understanding how customers react to changes in service encounters during crisis situations, is extremely valuable for companies. Guided by the four fundamental premises of customer experience (Becker and Jaakkola, 2020) and drawing on the Touchpoints-Context-Qualities (TCQ)-framework (De Keyser et al., 2020) and Stimulus-Organism-Response (S-O-R) Theory (Mehrabian and Russell, 1974), this study aims to better understand (1) the customer experience during a crisis, and (2) how outside versus within firm-controlled touchpoints, (3) contextual factors (here, individual and firm/industry) and (4) a firm’s service adaptation (e.g., firms adapt to regulatory measures) and service transformation (e.g., firms transform their business by exploring new service delivery models) strategies shape customer experiences and coping behaviors. Using social media data (here, tweets) in the context of the COVID-19 pandemic, our qualitative and quantitative findings reveal that (1) customers’ affective responses are not restricted to negative experience (here, fear, anger, sadness, and disgust), but also include positive experiences (here, joy, surprise, acceptance, and anticipation), (2) the government as third-party affects the customer experience, (3) service adaptions and service transformations evoke different affective responses, and (4) both positive and negative experiences influence customers’ coping behaviors, but positive experiences exert a greater influence on the inward coping behaviors (here, active coping, planning, and positive reinterpretation), while negative experiences exert a greater influence on the outward coping behaviors (here, seeking social support for instrumental reasons, and venting). Our findings have implications for managers and public policy makers that want to manage the customer experience during crisis situations.

The influence of disks deformation on the stability analysis of an aircraft braking system

Friction-induced vibration emanating from aircraft braking system is a key issue in the design phase, due to the significant damage it can cause to the brake structure. Although the problem of unstable vibrations in aircraft braking systems has been studied by a number of researchers, the suitability of the mechanical modeling strategy for predicting instabilities remains an open problem. The need for relevant numerical models is therefore essential in order to be as predictive as possible during the design phase. Preliminary studies must therefore be carried out to validate or invalidate the modeling hypotheses traditionally used. Indeed the stability analysis of an aircraft braking system is performed in order to study a low-frequency instability. An industrial model is used, hence reducing the number of degrees of freedom (DoF) is of utmost importance in order to have reasonable computation times. When studying low-frequency phenomena, this can be achieved by neglecting the deformations of the disks. However, no current study has shown that this hypothesis is realistic. So the aim of this paper is to assess the effect of the rigidity hypothesis on the results predicted by the stability analysis. In order to do so, the stability analysis results of a model with rigid disks and one with non-rigid disks are compared, with a particular attention on the main instability phenomenon. It is found that considering rigid disks has a very limited influence on the frequency of the low-frequency eigenmodes, but it over-predicts the real part of the unstable eigenmode. Besides, a component mode synthesis (CMS) technique is shown to reduce significantly the size of the non-rigid disks model while ensuring a satisfying precision regarding eigenmodes prediction.

Hierarchical Nearest Neighbor Gaussian Process models for discrete choice: Mode choice in New York City

Standard Discrete Choice Models (DCMs) assume that unobserved effects that influence decision-making are independently and identically distributed among individuals. When unobserved effects are spatially correlated, the independence assumption does not hold, leading to biased standard errors and potentially biased parameter estimates. This paper proposes an interpretable Hierarchical Nearest Neighbor Gaussian Process (HNNGP) model to account for spatially correlated unobservables in discrete choice analysis. Gaussian Processes (GPs) are often regarded as lacking interpretability due to their non-parametric nature. However, we demonstrate how to incorporate GPs directly into the latent utility specification to flexibly model spatially correlated unobserved effects without sacrificing structural economic interpretation. To empirically test our proposed HNNGP models, we analyze binary and multinomial mode choices for commuting to work in New York City. For the multinomial case, we formulate and estimate HNNGPs with and without independence from irrelevant alternatives (IIA). Building on the interpretability of our modeling strategy, we provide both point estimates and credible intervals for the value of travel time savings in NYC. Finally, we compare the results from all proposed specifications with those derived from a standard logit model and a probit model with spatially autocorrelated errors (SAE) to showcase how accounting for different sources of spatial correlation in discrete choice can significantly impact inference. We also show that the HNNGP models attain better out-of-sample prediction performance when compared to the logit and probit SAE models, especially in the multinomial case.

Practical methodology for quantifying the structural robustness of RC building structures

Several structural robustness requirements have been included in building codes over the past two decades. These mainly include measures to prevent collapse after an initial component failure by ensuring a minimum level of continuity within the structural system so that loads supported by the failed component can be redistributed to the rest of the structural system. However, there is still a general lack of consensus on how to evaluate the effectiveness of these measures. The present study proposes a practical methodology to evaluate the structural robustness of RC building structures by combining an efficient modelling strategy and suitable robustness indexes. The proposed computational modelling strategy for RC buildings accounts for relevant dynamic and nonlinear phenomena, including compressive arching, catenary action, and membrane effects. This strategy can be implemented at a reasonable computational expense in widely used commercial structural analysis software and has been validated by comparisons to selected experimental tests from the literature. The modelling strategy is then employed to compute four different robustness indexes proposed in the literature for three hypothetical building designs, allowing the indexes to be systematically compared. It was found that evaluating a system using multiple robustness indexes can provide a more holistic view of system performance compared to relying solely on a single index. Ultimately, the potential usefulness of the proposed methodology is demonstrated through two practical applications involving the estimation of component damage levels and the evaluation of different design and retrofitting solutions. Such a methodology can be useful for practising engineers to optimise the design of new buildings or to support decisions on the assessment and retrofitting of existing ones.

A Multi-Model Approach to Legal Judgment Prediction Using Advanced Knowledge Integration Techniques

This paper presents a multi-model approach to legal judgment prediction, emphasizing the integration of advanced knowledge techniques to enhance predictive accuracy and interpretability. As the volume of legal data continues to grow, traditional prediction methods often fall short in capturing the complexities of legal reasoning and case outcomes. By combining various modeling strategies, including rule-based systems and machine learning algorithms, this research demonstrates how a multi-model framework can leverage the strengths of different methodologies to provide more reliable predictions. Furthermore, the incorporation of knowledge integration techniques, such as knowledge graphs and Large Language Models (LLM), enriches the predictive models by offering contextual insights and improving feature selection. The findings indicate that this innovative approach not only improves the accuracy of legal predictions but also fosters transparency and trust among legal practitioners. Ultimately, this study lays the groundwork for future research in legal judgment prediction, advocating for the continued exploration of hybrid models and the application of emerging technologies to address the evolving challenges within the legal landscape.

A Review of Adversarial Attacks and Defense Techniques in Text Processing Models

Abstract. With the rise of neural networks, the need for accuracy, robustness, and security has increased. Research has shown that small, carefully crafted perturbations, known as adversarial examples, can deceive models and lead to incorrect predictions. Current research focuses on the image domain, while there is a notable lack of exploration in the text domain, due to its discrete nature. This paper reviews adversarial attack techniques and defense strategies in text-based neural network models, aiming to improve the security and resilience of these models in practical applications. Adversarial examples, which can deceive models with small perturbations, expose vulnerabilities in their robustness and security. Techniques such as TextFooler focus on synonym replacement for generating adversarial examples, while Text Random Smooth (Text-RS) enhances defense through adaptive noise strategies. The research of search space aims to explore the feature of that, proposing search space for Imperceptibility (SSIP) and Search Space for Effectiveness (SSET) to estimate the different attack methods. Furthermore, the Chinese Variation Graph Integration (CHANGE) method improves the resilience of Chinese language models by leveraging variation graphs. These advancements highlight the importance of developing effective generation and defense mechanisms for adversarial examples in text processing models. Future research should enhance adversarial example techniques, explore efficient defense strategies, and investigate transferability to improve the security and robustness of text processing models.

AI and ML-based risk assessment of chemicals: predicting carcinogenic risk from chemical-induced genomic instability

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, highlighting successful applications and case studies across diverse sectors. From predicting genotoxicity and mutagenicity to elucidating molecular pathways underlying carcinogenesis, integrative approaches offer a comprehensive framework for understanding chemical exposures and mitigating associated health risks. Future perspectives for advancing chemical risk assessment and cancer prevention through data integration, advanced machine learning techniques, translational research, and policy implementation are discussed. By implementing the predictive capabilities of AI/ML and omics technologies, researchers and policymakers can enhance public health protection, inform regulatory decisions, and promote sustainable development for a healthier future.

An Optimized Method for BMI in Environmental Projects Based on the Value-Oriented AHP

Effective rural solid waste management (RSWM) is crucial for sustainable rural development, particularly in developing countries, which face dual challenges from economic growth and environmental protection. To build a more sustainable business model for RSWM, this study employs a value proposition analysis approach to systematically analyze the multi-level requirements of various stakeholders involved in the current models of RSWM. It then proposes a novel optimizing approach for RSWM models from the perspective of business model innovation (BMI) by integrating the value proposition (VP) theory with the algorithm of the Analytic Hierarchy Process (AHP) to fill the research gap. In this study, an AHP-based evaluating algorithm is firstly proposed based on the viewpoints of multiple stakeholders’ value propositions. Using this method, four typical pilot RSWM models across China are assessed and ranked, followed by a comprehensive analysis of the results and the incorporation of hierarchical criteria from multiple value dimensions. Building on the analysis of the results, optimization strategies for a novel RSWM model are proposed by constructing a conceptual framework of the business model. In addition, the analysis also indicates that both phases of sorting and collection and transportation are the main factors for fulfilling the overall satisfaction of the RSWM models. Lastly, this paper concludes by summarizing the relevant theoretical and managerial implementations of the proposed approach, providing a foundation for the scientific development of appropriate RSWM models by providing a new idea for BMI especially for environmental management projects that include multiple stakeholders.

Mathematical Modelling and Simulation Strategies for Controlling Damage to Forest Resources Due to Illegal Logging

Forests are one of the natural resources that provide many benefits for the welfare of living things.The dense population causes people to depend more on forest resources. One of them is illegal logging.Various strategies to control forest damage due to illegal logging have been carried out, namely by directhandling to improve damaged conditions while preventing the recurrence of forest damage. The purposeof this research is to build a mathematical model of forest resource damage control strategies due toillegal logging, determine assumptions, formulate the model, and conduct analysis and problem solvingincluding: determining the equilibrium point, determining the stability analysis of the equilibrium point,and conducting numerical simulations of the equilibrium point. The last step is to interpret the results ofthe analysis obtained and make conclusions. Based on the research and simulation results of the model,it can be concluded that taking into account the variable of forest resource damage control strategydue to illegal logging, the result shows that if the density of forest resources has been affected by thedisturbance of population density around the forest, it is necessary to have a forest resource damagecontrol strategy in order to compensate for the people around the forest who do a lot of illegal logging.In order to maintain the forest so that the forest does not quickly become extinct and can overcomedrought, prevent flooding, maintain groundwater quality, protect animals, reduce air pollution, climatecontrol, reduce dust particles, prevent the greenhouse effect, supply natural fertilizers, prevent erosion,and maintain springs.