Initial Model Research Articles

The Mountain Model for Evidence-Based Practice Quality Improvement Initiatives.

This article introduces the Mountain Model, the first conceptual model for evidence-based practice quality improvement (EBPQI) initiatives. The Mountain Model merges modern evidence-based practice (EBP) and quality improvement (QI) paradigm principles into a unified conceptual framework with the goal of disseminating and sustaining EBPQI projects across health care and related settings. The model was developed within the nursing discipline, but is designed for transdisciplinary implementation through interprofessional teams.

Predictive modeling of initiation and delayed mental health contact for depression

BackgroundDepression is prevalent among Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) Veterans, yet rates of Veteran mental health care utilization remain modest. The current study examined: factors in electronic health records (EHR) associated with lack of treatment initiation and treatment delay; the accuracy of regression and machine learning models to predict initiation of treatment.MethodsWe obtained data from the VA Corporate Data Warehouse (CDW). EHR data were extracted for 127,423 Veterans who deployed to Iraq/Afghanistan after 9/11 with a positive depression screen and a first depression diagnosis between 2001 and 2021. We also obtained 12-month pre-diagnosis and post-diagnosis patient data. Retrospective cohort analysis was employed to test if predictors can reliably differentiate patients who initiated, delayed, or received no mental health treatment associated with their depression diagnosis.Results108,457 Veterans with depression, initiated depression-related care (55,492 Veterans delayed treatment beyond one month). Those who were male, without VA disability benefits, with a mild depression diagnosis, and had a history of psychotherapy were less likely to initiate treatment. Among those who initiated care, those with single and mild depression episodes at baseline, with either PTSD or who lacked comorbidities were more likely to delay treatment for depression. A history of mental health treatment, of an anxiety disorder, and a positive depression screen were each related to faster treatment initiation. Classification of patients was modest (ROC AUC = 0.59 95%CI = 0.586–0.602; machine learning F-measure = 0.46).ConclusionsHaving VA disability benefits was the strongest predictor of treatment initiation after a depression diagnosis and a history of mental health treatment was the strongest predictor of delayed initiation of treatment. The complexity of the relationship between VA benefits and history of mental health care with treatment initiation after a depression diagnosis is further discussed. Modest classification accuracy with currently known predictors suggests the need to identify additional predictors of successful depression management.

Evaluation of the Rural Interprofessional Behavioral Health Scholars Program to Expand Public Health Workforce Capacity During COVID-19

ABSTRACT Mental health and substance use are growing nationwide public health concerns being exacerbated by the COVID-19 pandemic. At the same time, there is a dire shortage in the public health workforce. This paper shares the design and mixed-method outcome evaluation of a Behavioral Health Workforce Education and Training (BHWET)-funded rural, interprofessional, behavioral health training program (RIBHS) delivered during COVID-19 at one southeastern US public university. Twenty-six advanced year MSW students completed the RIBHS program. Data were collected from trainees via pre/post surveys and two focus groups. Trainees’ self-reported statistically significant increases in teamwork, behavioral health, and rural practice competencies. Trainees also described how COVID-19 shaped their experience in the RIBHS program. The RIBHS program prepared trainees with the competencies needed to address the growing behavioral health crisis in the US and can serve as a training model for other social work and related public health workforce initiatives.

Johnson–Cook yield functions for cyclotetramethylene-tetranitramine (HMX) and cyclotrimethylene-trinitramine (RDX) derived from single crystal plasticity models

High-fidelity constitutive models are critical for accurate meso-scale continuum modeling and prediction of shock initiation of crystalline energetic materials (EMs). While empirically calibrated or atomistic-guided anisotropic elastoplastic models of EM such as cyclotetramethylene-tetranitramine (HMX) and cyclotrimethylene-trinitramine (RDX) capture important micromechanical phenomena (such as dislocation evolution, slip-resistance, and anisotropic elasticity), the computational cost of using anisotropic single-crystal plasticity models can become prohibitive for meso-scale computations of void-collapse and hotspot formation in microstructures. Thermo-mechanically representative, isotropic, pressure, temperature, and rate-dependent material constitutive models are practical alternatives for meso-scale simulations of the shock response of microstructures. To this end, this work constructs physically consistent isotropic plasticity from anisotropic single-crystal plasticity models for HMX and RDX. State-of-the-art crystal plasticity models for HMX and RDX are used to compute the stress states in single crystals oriented in three different directions relative to shocks generated by impact at velocities ranging from 100 to 1000 m/s. Post-shock von Mises stress fields for the three orientations are then used to calibrate the strain-rate hardening coefficient and the reference strain rate for a rate-dependent Johnson–Cook (JC) yield surface model. We compare the pressures and the post-shock von Mises stresses between the JC and the anisotropic models to show that the isotropic computations closely approximate the averaged deformation response of the three different crystal orientations. We then model the interaction of a shock generated by a 500 m/s impact with a 0.5 μm void and show that the pressures and the deviatoric stresses obtained using the isotropic model closely match those computed from anisotropic models for both HMX and RDX. The resulting isotropic J2 plastic flow model for HMX and RDX can be employed to perform meso-scale simulations for energy localization due to shear bands and void collapse in the two materials.

Penyuluhan Pendirian Koperasi Moderasi Beragama Di Desa Kutorojo Kecamatan Kajen Kabupaten Pekalongan

This community service project focuses on organizing workshops to establish moderate religious cooperatives in Kutorojo Village, Kajen District, Pekalongan Regency. The significance of this topic lies in addressing the economic challenges faced by the community while promoting religious moderation and social cohesion. The objective is to empower the community economically through the establishment of cooperatives while fostering interfaith understanding and cooperation. The methodological approach involves collaborative efforts between university students, local community members, and government agencies to organize and deliver informative sessions on cooperative formation, legal foundations, and principles. Additionally, discussions and interactive sessions are conducted to encourage active participation and address any concerns or questions. The results indicate a high level of community engagement and enthusiasm towards establishing cooperatives, as well as an improved understanding of the significance of moderation in religious discourse. The workshops serve as a stepping stone towards achieving economic empowerment and social harmony within Kutorojo Village, serving as a potential model for similar initiatives in other communities.

Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack

Distributed denial-of-service (DDoS) attacks pose a significant risk to the dependability and consistency of network services. The utilization of deep learning (DL) models has displayed encouraging outcomes in the identification of DDoS attacks. Nevertheless, crafting a precise DL model necessitates an extensive volume of labeled data and substantial computational capabilities. Within this piece, we introduce a technique to enhance a pre-trained DL model for the identification of DDoS attacks. Our strategy’s efficacy is showcased on an openly accessible dataset, revealing that the fine-tuned model we propose surpasses both the initial pre-trained model and other cutting-edge approaches in performance. The suggested fine-tuned model attained 95.1% accuracy, surpassing the initial pre-trained model as well as other leading-edge techniques. Please note that the specific evaluation metrics and their values may vary depending on the implementation, hyperparameter settings, number of datasets, or dataset characteristics. The proposed approach has several advantages, including reducing the amount of labeled data required and accelerating the training process. Initiating with a pre-existing ResNet50 model can also enhance the eventual model’s accuracy, given that the pre-trained model has already acquired the ability to extract significant features from unprocessed data.

Mouse models of CLL: in vivo modeling of disease initiation, progression, and transformation to Richter transformation

Chronic lymphocytic leukemia (CLL) is a highly complex disease characterized by the proliferation of CD5+ B cells in lymphoid tissues. Current modern treatments have brought significant clinical benefits to CLL patients. However, there are still unmet needs. Patients relapse on Bruton's tyrosine kinase inhibitors and BCL2 inhibitors and often develop more aggressive diseases including Richter transformation (RT), an incurable complication of up to ∼10% patients. This evidence underscores the need for improved immunotherapies, combination treatment strategies, and predictive biomarkers. A mouse model that can recapitulate human CLL disease and certain components of the tumor immune microenvironment represents a promising preclinical tool for such purposes. In this review, we provide an overview of CRISPR-engineered and xenograft mouse models utilizing either cell lines, or primary CLL cells suitable for studies of key events driving the disease onset, progression and transformation of CLL. We also review how CRISPR/Cas9 established mouse models carrying loss-of-function lesions allow one to study key mutations driving disease progression. Finally, we discuss how next generation humanized mice might improve to generation of faithful xenograft mouse models of human CLL.

Salutogénesis teoría y praxis

Health generation, known as Salutogenesis, understands health as “the ability of individuals or communities to adapt and manage the physical, mental, or social challenges that arise in life.” For over 10 years, salutogenic-focused projects have been carried out in Colombia. The process began with reflections and theories that shaped the initial models, implemented and evaluated in organizations, communities, and the healthcare sector, ranging from low to high complexity. Experiences demonstrate that it is feasible to generate health and well-being by creating capacities that enable individuals and communities to face biological, psychosocial, and cultural challenges, among others, common in the country and its regions. This article represents an exercise in social appropriation of knowledge aiming to disseminate partial results of the project titled “Comprehensive Care Model for Patients with Multimorbidity at Central Fundadores Clinic —Promedan—, in Medellín-Colombia, with emphasis on management (clinical, salutogenic, and risk) between 2021 and 2024: Design, Implementation, and Evaluation”.

Robust joint adaptive multiparameter waveform inversion with attenuation compensation in viscoacoustic media

Full-waveform inversion (FWI) has been proven as an effective method to estimate subsurface parameters by iteratively reducing the data residual between the predictions and the observations. Nevertheless, FWI is greatly dependent on the initial model, and a poor initial model will lead to an incorrect solution. Furthermore, owing to the anelasticity of the earth, seismic waves will attenuate during propagation, which results in an attenuated gradient and makes the convergence rate of FWI even worse in viscoacoustic medium. Commonly, multiparameter (e.g. velocity and Q) waveform inversion can be used to mitigate these problems. Benefiting from the theory of Q-compensated wavefield propagation, we develop a Q-compensated joint multiparameter waveform inversion method to weaken the nonlinearity of the FWI objective function, which enables it to cope with challenges related with attenuation-induced gradient energy loss and cycle skipping simultaneously. We refer to our Q-compensated joint multiparameter FWI scheme as QJMFWI. The main contributions of QJMFWI are as follows: (1) given the difficulty associated with the estimation of velocity and Q simultaneously in viscoacoustic media, QJMFWI provides a straightforward waveform inversion method for velocity and Q model construction, by which we can obtain velocity and Q information with improved accuracy and resolution; and (2) compared with conventional FWI methods, QJMFWI relaxes the requirement for a good initial velocity and Q model, which can avoid trapping into local minima. Numerical and field data examples demonstrate that QJMFWI is an effective method to invert for accurate subsurface parameters in viscoacoustic media.

Abstract 1443: The PI3K-AKT-mTOR axis persists as a therapeutic dependency in KRASG12D-driven non-small cell lung cancer

Abstract Direct inhibitors of KRASG12C and more recently KRASG12D are currently under clinical evaluation and may represent the beginning of a major translational breakthrough for non-small cell lung cancer (NSCLC) and RAS addicted cancers in general, targeting the most commonly mutated oncogene in this disease. However early reports of resistance with inactive state-selective KRASG12C inhibitors have highlighted the pressing need for rational combination partners. While preclinical combination strategies to enhance KRASG12D inhibition and combat innate resistance have already been reported for pancreatic and colorectal cancer, the application of KRASG12D inhibitors alongside combination partners in KRASG12D-driven NSCLC has not been investigated. Here we compared KRASG12C- and KRASG12D-initiated lung tumorigenesis using isogenic genetically engineered mouse models and human and mouse cell models of KRASG12C and KRASG12D-driven NSCLC initiation to compare oncopotency and signaling of each subtype. We then examined whether signaling differences observed at initiation persist in established tumors. We identified that KRASG12D is more potent than KRASG12C in inducing tumor formation. This higher potency was accompanied by co-operative activation of the PI3K-AKT-mTOR signaling axis. Disruption of this pathway using either an AKT inhibitor or mTORC1-specific inhibitor (RMC-6272, a preclinical tool compound representative of the clinical candidate RMC-5552) led to a greater reduction in transformation and proliferation in a KRASG12D−driven lung initiation model compared to the KRASG12C counterpart. Although differences between these oncogenic phenotypes become less distinguishable once tumors are established, signaling dependencies and vulnerabilities are still preserved. For instance, KRASG12C lung tumor cells are relatively more susceptible to ERK and MEK inhibition, while KRASG12D lung tumor cells are more susceptible to AKT and mTORC1 inhibition. Inhibiting the PI3K-AKT-mTOR axis alongside KRASG12D inhibition (MRTX1133) induced a robust, synergistic apoptotic response in KRASG12D tumor cell lines of varying sensitivity to KRASG12D inhibition alone, highlighting a potential combinatorial approach to override intrinsic resistance to KRASG12D inhibitors. Our preclinical data identify treatment vulnerabilities and suggest patient selection strategies for combination approaches that should be i) contextualized to individual RAS mutants, and ii) tailored to their downstream signaling programs. Citation Format: William J. McDaid, Leah Wilson, Helen Adderley, Martin Baker, Joshua Searle, Timothy Budden, Amaya Viros, Mihaela Aldea, Arianna Marinello, Jacqueline Aredo, Benjamin Besse, Heather A. Wakelee, Colin R. Lindsay, Angeliki Malliri. The PI3K-AKT-mTOR axis persists as a therapeutic dependency in KRASG12D-driven non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1443.

Telehealth-Delivered Program and Accompanying Patients to Enhance the Clinical Condition of Patients Throughout a Liver Transplant: Protocol for a Mixed Methods Study.

Liver transplantation (LT) is indicated in patients with severe acute or chronic liver failure for which no other therapy is available. With the increasing number of LTs in recent years, liver centers worldwide must manage their patients according to their clinical situation and the expected waiting time for transplantation. The LT clinic at the Centre hospitalier de l'Université de Montréal (CHUM) is developing a new health care model across the entire continuum of pre-, peri-, and posttransplant care that features patient monitoring by an interdisciplinary team, including an accompanying patient; a digital platform to host a clinical plan; a learning program; and data collection from connected objects. This study aims to (1) evaluate the outcomes following the implementation of a patient platform with connected devices and an accompanying patient, (2) identify implementation barriers and facilitators, (3) describe service outcomes in terms of health outcomes and the rates and nature of contact with the accompanying patient, (4) describe patient outcomes, and (5) assess the intervention's cost-effectiveness. Six types of participants will be included in the study: (1) patients who received transplants and reached 1 year after transplantation before September 2023 (historical cohort or control group), (2) patients who will receive an LT between December 2023 and November 2024 (prospective cohort/intervention group), (3) relatives of those patients, (4) accompanying patients who have received an LT and are interested in supporting patients who will receive an LT, (5) health care professionals, and (6) decision makers. To describe the study sample and collect data to achieve all the objectives, a series of validated questionnaires, accompanying patient logbooks, transcripts of interviews and focus groups, and clinical indicators will be collected throughout the study. In total, 5 (steering, education, clinical-technological, nurse prescription, and accompanying patient) working committees have been established for the study. Recruitment of patients is expected to start in November 2023. All questionnaires and technological platforms have been prepared, and the clinicians, stakeholders, and accompanying patient personnel have been recruited. The implementation of this model in the trajectory of LT recipients at the CHUM may allow for better monitoring and health of patients undergoing transplantation, ultimately reducing the average length of hospital stay and promoting better use of medical resources. In the event of positive results, this model could be transposed to all transplant units at the CHUM and across Quebec (potentially affecting 888 patients per year) but could also be applied more widely to the monitoring of patients with other chronic diseases. The lessons learned from this project will be shared with decision makers and will serve as a model for other initiatives involving accompanying patients, connected objects, or digital platforms. PRR1-10.2196/54440.

Assessing the economic feasibility of voluntary carbon markets in land use management scenarios for Scottish saltmarshes

Saltmarshes have been valued for their multiple ecosystem services, including agriculture, recreation, habitats for fish and birds, coastal protection and carbon sequestration and long-term storage. However, because of the many threats faced, including rising sea levels, limited sediment supplies and anthropogenic impacts, saltmarshes require careful restoration and protection management to continue providing these benefits. In this study, we examined the feasibility of land abandonment caused by reinstated tidal flow in Scottish saltmarshes, based on the differential gains in soil carbon stock between natural saltmarshes and those manged for agricultural uses, and suggested considerations for a voluntary carbon market plan. The study's results showed that the proportion of Scottish saltmarshes used for agricultural activities was not very large (2.56 %, equivalent to 179.73 ha). The average net benefit (i.e., after removing the opportunity cost of land) of the new land use is GBP £ 5368.71/ha after a 67-year post-abandonment management program, showing that this model is economically viable to implement a carbon market mechanism. A voluntary carbon market scheme may be easier to implement than a compliance scheme, although the price of carbon credits under the voluntary carbon market is not high to fully compensate for the opportunity costs of land in all the Scottish regions. In effect, the carbon price at the break-even point (e.g., that balances the opportunity cost of land) in the Scottish regions varied widely (range £ 1.06–59.83/tC), which means that for regions with higher opportunity costs there is a need to consider compensating farmers through a blended private-public mechanism. Despite these difficulties, implementing saltmarshes management models for carbon market initiatives can benefit society in a broader sense, not just mitigate climate change.

Optimization of Sustainable Production Processes in C45 Steel Machining Using a Confocal Chromatic Sensor

Metal machining production faces a myriad of demands encompassing ecology, automation, product control, and cost reduction. Within this framework, an exploration into employing a direct inspection of the machined area within the work zone of a given machine through a confocal chromatic sensor was undertaken. In the turning process, parameters including cutting speed (A), feed (B), depth of cut (C), workpiece length from clamping (D), and cutting edge radius (E) were designated as input variables. Roundness deviation (Rd) and tool face wear (KM) parameters were identified as output factors for assessing process performance. The experimental phase adhered to the Taguchi Orthogonal Array L27. Confirmatory tests revealed that optimizing process parameters according to the Taguchi method could enhance the turning performance of C45 steel. ANOVA results underscored the significant impact of cutting speed (A), feed (B), depth of cut (C), and workpiece length from clamping (D) on turning performance concerning Rd and KM. Furthermore, initial regression models were formulated to forecast roundness variation and tool face wear. The proposed parameters were found to not only influence the machined surface but also affect confocal sensor measurements. Consequently, we advocate for the adoption of these optimal cutting conditions in product production to bolster turning performance when machining C45 steel.

Low cycle fatigue behavior and life prediction of a directionally solidified alloy

Alloys used in engines are subjected to challenging environments characterized by thermal and mechanical cyclic loadings during start-up and shut-down processes. These conditions can significantly increase the occurrence of fatigue failure mechanisms. Therefore, this study focuses on investigating the low cycle fatigue (LCF) behavior of directionally-solidified alloy at two distinct temperatures, namely 600 °C and 800 °C. Strain-controlled LCF tests were conducted at the specified temperatures, utilizing constant total strain amplitudes of 0.4%, 0.6%, 0.8%, and 1% under a totally reversed loading ratio (R = -1). The Coffin-Manson model, based on plastic deformation, along with a hysteresis energy-based criterion model, were employed to predict and evaluate fatigue life and LCF behavior. Notably, the hysteresis energy and Coffin-Manson models exhibited superior capability in predicting LCF life at 800 °C compared to 600 °C.   REFERENCES Salehnasab, J. Marzbanrad, E. 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Optimizing Screening for Intrastriatal Interventions in Huntington's Disease Using Predictive Models.

Intrastriatal delivery of potential therapeutics in Huntington's disease (HD) requires sufficient caudate and putamen volumes. Currently, volumetric magnetic resonance imaging is rarely done in clinical practice, and these data are not available in large research cohorts such as Enroll-HD. The objective of this study was to investigate whether predictive models can accurately classify HD patients who exceed caudate and putamen volume thresholds required for intrastriatal therapeutic interventions. We obtained and merged data for 1374 individuals across three HD cohorts: IMAGE-HD, PREDICT-HD, and TRACK-HD/TRACK-ON. We imputed missing data for clinical variables with >72% non-missing values and used the model-building algorithm BORUTA to identify the 10 most important variables. A random forest algorithm was applied to build a predictive model for putamen volume >2500 mm3 and caudate volume >2000 mm3 bilaterally. Using the same 10 predictors, we constructed a logistic regression model with predictors significant at P < 0.05. The random forest model with 1000 trees and minimal terminal node size of 5 resulted in 83% area under the curve (AUC). The logistic regression model retaining age, CAG repeat size, and symbol digit modalities test-correct had 85.1% AUC. A probability cutoff of 0.8 resulted in 5.4% false positive and 66.7% false negative rates. Using easily obtainable clinical data and machine learning-identified initial predictor variables, random forest, and logistic regression models can successfully identify people with sufficient striatal volumes for inclusion cutoffs. Adopting these models in prescreening could accelerate clinical trial enrollment in HD and other neurodegenerative disorders when volume cutoffs are necessary enrollment criteria. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Estimation of Temperature and Salinity from Marine Seismic Data—A Two-Step Approach

Ocean-water temperature and salinity are two vital properties that are required for weather-, climate-, and marine biology-related research. These properties are usually measured using disposable instruments at sparse locations, typically from tens to hundreds of kilometers apart. Laterally interpolating these sparse measurements provides smooth temperature and salinity distributions within the oceans, although they may not be very accurate. Marine seismic data, on the other hand, show visible reflections within the water-column which are primarily controlled by subtle sound-speed variations. Because these variations are functions of the temperature, salinity, and pressure, estimating sound-speed from marine seismic data and relating them to temperature and salinity have been attempted in the past. These seismically derived properties are of much higher lateral resolution (less than 25 m) than the sparse measurements and can be potentially used for climate and marine biology research. Estimating sound-speeds from seismic data, however, requires running iterative seismic inversions, which need a good initial model. Currently practiced ways to generate this initial model are computationally challenging, labor-intensive, and subject to human error and bias. In this research, we outline an automated method to generate the initial model which is neither computational and labor-intensive nor prone to human errors and biases. We also use a two-step process of, first, estimating the sound-speed from seismic inversion data and then estimating the salinity and temperature. Furthermore, by applying this method to real seismic data, we demonstrate the feasibility of our approach and discuss how the use of machine learning can further improve the computational efficiency of the method and make an impact on the future of climate modeling, weather prediction, and marine biology research.

Analyse and Predict the Detection of the Cyber - Attack Process by Using a Machine-Learning Approach

Crimes committed online rank among the most critical global concerns. Daily, they cause country and citizen economies to suffer massive financial losses. With the proliferation of cyber-attacks, cybercrime has also been on the rise. To effectively combat cybercrime, it is essential to identify its perpetrators and understand their methods. Identifying and preventing cyber-attacks are difficult tasks. To combat these concerns, however, new research has produced safety models and forecast tools grounded on artificial intelligence. Numerous methods for predicting criminal behaviour are available in the literature. While they may not be perfect, they may help in cybercrime and cyber-attack tactic prediction. To find out whether an attack happened and, if so, who was responsible, one way to look at this problem is by using real-world data. There is data about the crime, the perpetrator's demographics, the amount of property damaged, and the entry points for the assault. Potentially, by submitting applications to forensics teams, victims of cyber-attacks may get information. This study uses ML methods to analyse cyber-crime consuming two patterns and to forecast how the specified characteristics will furnish to the detection of the cyber-attack methodology and perpetrator. Based on the comparison of eight distinct machine-learning methods, one can say that their accuracy was quite comparable. The Support Vector Machine (SVM) Linear outperformed all other cyber-attack tactics in terms of accuracy. The initial model gave us a decent notion of the assaults that the victims would face. The most successful technique for detecting malevolent actors was logistic regression, according to the success rate. To anticipate who the perpetrator and victim would be, the second model compared their traits.  A person’s chances of being a victim of a cyber-attack decrease as their income and level of education rise. The proposed idea is expected to be used by departments dealing with cybercrime. Cyber-attack identification will also be made easier, and the fight against them will be more efficient.

Landslide susceptibility prediction modelling based on semi‐supervised XGBoost model

In the process of landslide susceptibility prediction (LSP) modelling, there are some problems in the model dataset relating to landslide and non‐landslide samples, such as landslide sample errors, subjective randomness and low accuracy of non‐landslide sample selection. In order to solve the above problems, a semi‐supervised machine learning model for LSP is innovatively proposed. Firstly, Yanchang County of Shanxi Province, China, is taken as the study area. Secondly, the frequency ratio values of 12 environmental factors (elevation, slope, aspect, etc.) and the randomly selected twice non‐landslides are used to form the initial model datasets. Thirdly, an extreme gradient boosting (XGBoost) model is adopted for training and testing the initial datasets, so as to produce initial landslide susceptibility maps (LSMs) which are divided into very low, low, moderate, high and very high susceptibility levels. Next, the landslide samples in initial LSMs with very low and low susceptibility levels are excluded to improve the accuracy of landslide samples, and the unlabelled twice non‐landslide samples in initial LSMs with low and very low susceptibility levels are randomly selected to ensure the accuracy of non‐landslide samples. These new obtained landslide and non‐landslide samples are reimported into XGBoost model to construct the semi‐supervised XGBoost (SSXGBoost) model. Finally, accuracy, kappa coefficient and statistical indexes of susceptibility indexes are adopted to assess the LSP performance of XGBoost and SSXGBoost models. Results show that SSXGBoost model has remarkably better LSP performance than that of XGBoost model. Conclusively, the proposed SSXGBoost model effectively overcomes the problems that the accuracy of landslide samples needs to be further improved and that non‐landslide samples are difficult to select accurately.

Investigating the Influencing Factors of the Purchase Intention of the Continuing Care Retirement Community: A Case Study of Shenzhen

China officially entered the aging society in 2000, witnessing a rapid surge in demand for senior care services. In response, the real estate industry introduced the concept of Continuing Care Retirement Communities (CCRCs). The relationship between this model and complex risks in urban security resilience encompasses various factors, including filial care, demand for senior care, and urban sustainable development. The strategic layout and planning of CCRC enhance the security resilience of urban operational systems in the face of intricate senior care risks. However, the development and operation of CCRCs have encountered the challenge of sluggish project progress, primarily due to a lack of robust purchase intention. This study investigates the factors influencing the purchase intention of CCRCs in mainland China, using the Theory of Planned Behavior (TPB) as the foundational theoretical model. Additionally, three contextual constructs (economic cost, product performance, and external stimuli) were introduced to form the initial model. Based on the initial model, six factors were identified and nine hypotheses were proposed. A questionnaire survey was conducted to collect data, and Structural Equation Modeling (SEM) analysis was employed to test the proposed hypotheses. The results indicate that consumers’ purchase intention of CCRCs is primarily influenced by product performance and subjective norms, followed by economic cost. At the same time, external stimuli have a significant indirect effect on it.

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial task integral to urban planning and economic development. This paper delves into this endeavor, highlighting the transformative impact of specifically chosen contextual open data and recent advances in eXplainable AI (XAI) to improve the accuracy and transparency of real estate price predictions within smart cities. Focusing on Lisbon’s dynamic housing market from 2018 to 2021, we integrate diverse open data sources into an eXtreme Gradient Boosting (XGBoost) machine learning model optimized with the Optuna hyperparameter framework to enhance its predictive precision. Our initial model achieved a Mean Absolute Error (MAE) of EUR 51,733.88, which was significantly reduced by 8.24% upon incorporating open data features. This substantial improvement underscores open data’s potential to boost real estate price predictions. Additionally, we employed SHapley Additive exPlanations (SHAP) to address the transparency of our model. This approach clarifies the influence of each predictor on price estimates and fosters enhanced accountability and trust in AI-driven real estate analytics. The findings of this study emphasize the role of XAI and the value of open data in enhancing the transparency and efficacy of AI-driven urban development, explicitly demonstrating how they contribute to more accurate and insightful real estate analytics, thereby informing and improving policy decisions for the sustainable development of smart cities.