Data Constraints Research Articles

Building on Existing Classifications of Behavior Change Techniques to Classify Planned Coping Strategies: Physical Activity Diary Study.

When trying to be more physically active, preparing for possible barriers by considering potential coping strategies increases the likelihood of plan enactment. Digital interventions can support this process by providing personalized recommendations for coping strategies, but this requires that possible coping strategies are identified and classified. Existing classification systems of behavior change, such as the compendium of self-enactable techniques, may be reused to classify coping strategies in the context of physical activity (PA) coping planning. This study investigated whether coping strategies created by a student population to overcome barriers to be physically active can be mapped onto the compendium of self-enactable techniques and which adaptations or additions to the frameworks are needed. In total, 359 Flemish university students created action and coping plans for PA for 8 consecutive days in 2020, resulting in 5252 coping plans. A codebook was developed iteratively using the compendium of self-enactable techniques as a starting point to code coping strategies. Additional codes were added to the codebook iteratively. Interrater reliability was calculated, and descriptive statistics were provided for the coping strategies. Interrater reliability was moderate (Cohen κ=0.72) for the coded coping strategies. Existing self-enactable techniques covered 64.6% (3393/5252) of the coded coping strategies, and added coping strategies covered 28.52% (n=1498). The remaining coping strategies could not be coded as entries were too vague or contained no coping strategy. The added classes covered multiple ways of adapting the original action plan, managing one's time, ensuring the availability of required material, and doing the activity with someone else. When exploring the data further, we found that almost half (n=2371, 45.1%) of the coping strategies coded focused on contextual factors. The study's objective was to categorize PA coping strategies. The compendium of self-enactable techniques addressed almost two-thirds (3393/5252, 64.6%) of these strategies, serving as valuable starting points for classification. In total, 9 additional strategies were integrated into the self-enactable techniques, which are largely absent in other existing classification systems. These new techniques can be seen as further refinements of "problem-solving" or "coping planning." Due to data constraints stemming from the COVID-19 pandemic and the study's focus on a healthy Flemish student population, it is anticipated that more coping strategies would apply under normal conditions, in the general population, and among clinical groups. Future research should expand to diverse populations and establish connections between coping strategies and PA barriers, with ontologies recommended for this purpose. This study is a first step in classifying the content of coping strategies for PA. We believe this is an important and necessary step toward digital health interventions that incorporate personalized suggestions for PA coping plans.

Generating ICS Anomaly Data Reflecting Cyber-Attack Based on Systematic Sampling and Linear Regression.

Cyber threats to industrial control systems (ICSs) have increased as information and communications technology (ICT) has been incorporated. In response to these cyber threats, we are implementing a range of security equipment and specialized training programs. Anomaly data stemming from cyber-attacks are crucial for effectively testing security equipment and conducting cyber training exercises. However, securing anomaly data in an ICS environment requires a lot of effort. For this reason, we propose a method for generating anomaly data that reflects cyber-attack characteristics. This method uses systematic sampling and linear regression models in an ICS environment to generate anomaly data reflecting cyber-attack characteristics based on benign data. The method uses statistical analysis to identify features indicative of cyber-attack characteristics and alters their values from benign data through systematic sampling. The transformed data are then used to train a linear regression model. The linear regression model can predict features because it has learned the linear relationships between data features. This experiment used ICS_PCAPS data generated based on Modbus, frequently used in ICS. In this experiment, more than 50,000 new anomaly data pieces were generated. As a result of using some of the new anomaly data generated as training data for the existing model, no significant performance degradation occurred. Additionally, comparing some of the new anomaly data with the original benign and attack data using kernel density estimation confirmed that the new anomaly data pattern was changing from benign data to attack data. In this way, anomaly data that partially reflect the pattern of the attack data were created. The proposed method generates anomaly data like cyber-attack data quickly and logically, free from the constraints of cost, time, and original cyber-attack data required in existing research.

Linear Regressor Model for Internet of Things Automated Thermal Conditioning of a Smart Classroom Environment Using Limited dataset

This research paper presents a Linear Regressor (LR) modeling approach for automated thermal conditioning of smart spaces in a classroom environment using limited data constraints. Sensitive studies were performed in order to identify the breaking point of the LR model of polynomial-order-of-1 below which it will not be possible to obtain meaningful estimates and controllability actions, considering a range between 5% and 30% limited training data points. Based on several simulation experiments, it was found that the LR breaks at the 10% training data level. This result is valuable for recommender systems that may experience difficulty in obtaining enough primary dataset. Applications that provide early warning signals will also find this study useful.

Exploring the role of student authors in the Journal of Librarianship and Information Science: Patterns, collaborations, and methodological insights

Assessing student participation in research and publication is essential, but this area has experienced limited exploration due to data constraints, despite the presence of open researcher profiles. Prior research noted modest student contributions but lacked in-depth analysis. Notably, in the Library and Information Science field, research on student-authored publications is scarce. This study analyzes data from student-authored papers in the Journal of Librarianship and Information Science from 1993 to 2022. Descriptive statistical analysis was employed, with various tables presenting yearly figures and data grouped by decades, along with charts to visualize changing trends over the years. The analysis reveals a significant increase in student engagement in research and publication, with student-authored papers constituting approximately 20% of all publications, highlighting their active participation in academic endeavors. Furthermore, this study underscores a marked shift toward collaborative research, with a substantial 88% of student authors engaging in internal or external collaborations for their publications. Additionally, nearly half of the student authors (48%) assumed the role of main authors, affirming their substantial contributions to research. Moreover, this study sheds light on the impact of research methodologies on student participation, with quantitative analysis emerging as the predominant approach for student-authored papers. Nevertheless, qualitative analysis may pose challenges for student-led research due to skill requirements. For future studies, broadening the scope of data collection to encompass a wider range of academic journals and analyzing the publication activities of graduate students from specific programs would be beneficial endeavors. In conclusion, this study enhances understanding of student authorship and its implications for academic publishing in the Library and Information Science field. It provides valuable insights into the evolving landscape of academic publishing and student involvement.

Performance of LSTM over SWAT in Rainfall-Runoff Modeling in a Small, Forested Watershed: A Case Study of Cork Brook, RI

The general practice of rainfall-runoff model development towards physically based and spatially explicit representations of hydrological processes is data-intensive and computationally expensive. Physically based models such as the Soil Water Assessment tool (SWAT) demand spatio-temporal data and expert knowledge. Also, the difficulty and complexity is compounded in the smaller watershed due to data constraint and models’ inability to generalize hydrologic processes. Data-driven models can bridge this gap with their mathematical formulation. Long Short-Term Memory (LSTM) is a data-driven model with Recurrent Neural Network (RNN) architecture, which is better suited to solve time series problems. Studies have shown that LSTM models have competitive performance in watershed hydrology studies. In this study, a comparative analysis of SWAT and LSTM models in the Cork Brook watershed shows that results from LSTM were competitive to SWAT in flow prediction with NSE of 0.6 against 0.63, respectively, given the limited availability of data. LSTM models do not overestimate the high flows like SWAT. However, both these models struggle with low values estimation. Although interpretability, explainability, and use of models across different datasets or events outside of the training data may be challenging, LSTM models are robust and efficient.

A New Highly Parameterized Linear Inversion of Water Table Change and Groundwater Depletion Rate Tested With the High Plains Aquifer, U.S.A.

AbstractUnderstanding groundwater resource dynamics is limited by the sparsity of observations of water levels, pumping rates, and hydraulic properties relative to their spatiotemporal heterogeneity. To address some of this complexity, we proposed a new highly parameterized linear inverse method to quantify water table change and groundwater depletion rate in unconfined aquifers that does not require initial or boundary conditions. The method requires linearization, and we tested the performance of six proposed water table functions with three coordinate systems with synthetic models, finding that water table functions by the dimensionless method achieve the lowest errors. Our inverted water table changes and depletion rates remained stable and accurate with head measurement error of 5%. However, inversions became less accurate when head observations contained large temporal gaps. Next, we applied the inversion for water table changes and depletion rates in the Texas High Plains Aquifer (HPA) and the HPA during 2000–2015. To address sparse water levels and uncertain hydraulic conductivity measurements, two modifications were made to improve data constraints for inversion: (a) calculating winter‐time water levels for wells with >1 head observation by linear interpolation and (b) using hydraulic conductivity geostatistical realizations. The inverted water table change and depletion rate expected mean values and uncertainties were reasonable compared to the known pumping records of the Texas HPA. With relatively minor data curation, the new inverse method can quantify spatiotemporally continuous water table change, depletion rates, and their uncertainty for heterogeneous aquifers with temporally sparse and noisy water level observations.

Tensile Fractures and in situ Stress Measurement Data Constraints on Cretaceous–Present Tectonic Stress Field Evolution of the Tanlu Fault Zone in Shandong Province, North China Craton

AbstractTectonic stress fields are the key drivers of tectonic events and the evolution of regional structures. The tectonic stress field evolution of the Tanlu fault zone in Shandong Province, located in the east of the North China Craton (NCC), may have preserved records of the NCC's tectonic history. Borehole television survey and hydraulic fracturing were conducted to analyze the paleo and present tectonic stress fields. Three groups of tensile fractures were identified via borehole television, their azimuths being NNW–SSE, NW–SE and NE–SW, representing multiple stages of tectonic events. Hydraulic fracturing data indicates that the study region is experiencing NEE–SWW‐oriented compression and nearly‐N–S‐oriented extension, in accordance with strike‐slip and compression. Since the Cretaceous, the orientation of the extensional stress has evolved counterclockwise and sequentially from nearly‐NW–SE‐oriented to NE–SW‐oriented and even nearly N–S‐oriented, the stress state having transitioned from strike‐slip‐extension to strike‐slip‐compression, in association with the rotating and oblique subduction of the Pacific Plate beneath the NCC, with the participation of the Indian Plate.

DA LI UČINAK ROBOVSKOG ODNOSA PREMA DOMU DEPRESIRA MEĐUNARODNA TURISTIČKA PUTOVANJA?

This paper examines the relationship between "the house slave effect" and tourist outmigration in most European countries. The outline of this study is set up on the analysis of annual data on homeownership and tenant participants in plan to measure the relationship between housing wealth endowment and outbound traveling, with a panel framework that spans the period from 2007 to 2019. In the present empirical case, this paper examines whether or not there is a significant relationship in the sample of 32 countries by using a panel quantile regression method, a panel SGMM, and a panel OLS regression. Estimation outputs reveal that there is a strong negative trade-off between homeownership and outbound tourism for all quantile levels. This evidence along with the auxiliary SGMM and pooled panel estimation assessment strongly confirm that “the house slave effect” negatively affects outbound tourism. What is more, the results show that tenant rates help to boost the international tourism departures or expenditures per capita. The novelty of this work lies in the fact that it provides the pioneering study of the impact of home ownership on outbound travel in European area. The results suggest that a panel quantile regression method (due to data constraints arising from the skewness of international tourism outbound or per capita expenditure data) should be considered when examining the relationship between "the "house slave effect" and outbound tourism in country panel analyses.

Factors Associated with Treatment Receipt in Medicare Beneficiaries Diagnosed with Acute Myeloid Leukemia

Background: The treatment a patient with AML receives may be influenced by where the patient is diagnosed and the physician making the initial diagnosis. Such factors could be targeted for intervention to help ensure patients receive optimal treatment; however, data constraints have previously limited a complete examination of patient, physician and hospital factors associated with treatment for AML. This study aimed to investigate the association of patient, hospital, and physician factors with treatment receipt for patients diagnosed with AML. Treatment receipt included allogeneic hematopoietic cell transplant (alloHCT) or chemotherapy without alloHCT among patients who received a National Marrow Donor Program (NMDP) donor search (search). Methods: Centers for Medicare and Medicaid (CMS) Medicare administrative claims data from 2010 to 2018 were linked to NMDP Donor Search data and to publicly available CMS and National Provider Identifier physician and hospital datasets. Patients aged 65-74 diagnosed with AML between 2010 and 2017 and enrolled in fee-for-service (Part A and B) Medicare for at least one year after diagnosis or until death, were included. The treatment groups (alloHCT and search) were identified using diagnosis and procedure codes. A multivariable hierarchical logistic regression procedure was performed to determine factors associated with treatment receipt, with hospital as a random effect. Models were adjusted for patient, hospital and physician characteristics identified at the time of diagnosis. Patients missing physician and hospital characteristics were excluded. Individuals with a racial/ethnic background of Black/African-American, Asian/Pacific Islander, Hispanic, American Indian/Alaska Native, and Other were grouped due to small numbers. All analyses were performed using Statistical Analysis Software (SAS ®) Enterprise Guide 7.1. Results: A total of 916 patients met eligibility criteria: 554 (60%) received alloHCT; 362 (40%) received search. The majority of patients in both groups were Non-Hispanic White and were age 65-69 years. A multivariable model demonstrated the variables associated with receipt of alloHCT (Figure 1). Characteristics significantly associated with decreased odds of receiving alloHCT were sex (male compared to female), race/ethnicity (a combined category of Black/African-American, Asian/Pacific Islander, Hispanic, American Indian/Alaska Native, and Other compared to Non-Hispanic White), living in an area with a lower percentage of population with a college education, age (71-75 compared to 65-69 years), and chemotherapy provision (chemotherapy not compared to provided by staff). Conclusion: This study used novel linked datasets to identify the association of patient, hospital, and physician characteristics at time of diagnosis with receipt of alloHCT, compared to patients who received a search but not an alloHCT. Notably, patient characteristics, and the hospital characteristic of provision of chemotherapy by hospital staff were associated with alloHCT receipt. Donor availability may contribute to the decreased odds of alloHCT by race/ethnicity, but studies to increase HLA-mismatched unrelated alloHCT are helping to decrease this barrier. Neighborhood education attainment was used as a proxy for socioeconomic status (SES), and SES has been associated with access to care. Ensuring treatment information is accessible and available in plain language may help patients considering different treatment options. Claims data is not collected for research, and has some inherent limitations, including the lack of disease severity, cytogenetics, patient preferences, and an algorithm was used to identify diagnosing physician and hospital. Additionally, this analysis includes patients who were able to access a transplant center to receive a search or alloHCT. Next steps will compare characteristics of those who received chemotherapy but did not receive a search to those who received a search and those who received an alloHCT. Study results will be used to help inform strategies aimed at improving access to treatment for patients diagnosed with AML.

Model based component development and analysis with ComMA

The lack of explicit and precise specifications of software interfaces between components often leads to integration issues during development and maintenance. To address this, we have developed a framework named ComMA (Component Modeling and Analysis) that supports model-based engineering of high-tech systems by precisely defining components and their interfaces. The framework is a family of Domain Specific Languages (DSLs) for modeling component interfaces, protocol state machines, time and data constraints, and constraints on relations between events of multiple interfaces. From these models a number of artifacts can be generated automatically to support analysis and various engineering tasks. ComMA has been developed in close collaboration with the Philips IGT business unit that develops minimally-invasive X-ray systems. This paper presents the experience we gained in creating the ComMA framework and its application in industrial practice. We describe and reflect on the technical, organizational and process-related aspects of deploying a non-trivial MDE solution in an industrial setting.

Unveiling the Veil: Exploring the Origin, Effects, and Coping Strategies of Menstrual Stigma

Menstruation, also referred to as the period, faces pervasive stigmatization globally despite being a natural female biological process. This stigma, rooted in historical gender inequalities and fueled by misconceptions, holds significant implications. This study investigates period stigma comprehensively, exploring its origins, impacts, and potential solutions. The stigma originates from patriarchal structures and gender inequality, reinforcing harmful biases and impeding womens career growth, exemplified by the glass ceiling effect. The resulting psychological toll on women is considerable. Efforts to combat period stigma include policy interventions, feminist movements, and NGO initiatives like United Nations International Childrens Emergency Fund. This paper enhances understanding of the stigma and its consequences, proposing practical strategies to counteract it. However, limitations include the lack of qualitative and quantitative methods due to data constraints and insufficient historical sources, impacting the portrayal of its inception. In conclusion, this study illuminates the enduring complexity of period stigma, raising awareness about its harm. Synthesizing existing literature provides valuable insights into its origins, consequences, and coping mechanisms.

Development of a non-radial model for resource reallocation using network data envelopment analysis

Public and private sector organizations continually seek to optimize strategies for sustained competitiveness by reallocating resources efficiently among various institutions. This research introduces a non-radial resource reallocation model employing network data envelopment analysis to address this need. The study introduces an approach to redistributing resources in a dynamic network setting over multiple periods. It takes into account indicators of both negative outcomes, aiming to improve the overall effectiveness and efficiency of companies within a holding structure. The research outlines a methodology, including data collection, modeling assumptions, decision variables, and constraints for implementing this reallocation model. By focusing on resource allocation in network systems over periods, this study provides a solid foundation for enhancing resource allocation efficiency in organizations. Additionally, it addresses the complexities associated with resource allocation compared to settings. The proposed dynamic network DEA model considers both temporal aspects in evaluations. Demonstrates its potential for resource allocation in multi-stage processes through applications within the banking industry. One key finding is the importance of recognizing shared constraints among Decision Making Units (DMUs). Considering the impact of restrictions, like government regulations, on decision-making processes and overall efficiency. The created dynamic network DEA model introduces an inventive method to redistribute resources in intricate organizational setups. It offers perspectives for decision-making procedures. Moreover, it sets the stage for further exploration into different indicators and the impact of diverse government interventions on resource allocation effectiveness.

Automated workflow for incorporation and evaluation of data uncertainty in ecological networks with autoLIMR

Ecosystems are often modelled as food webs or ecological networks, which map energy flows between species or functional groups. While there are multiple sources of uncertainty in ecological network models, a fundamental difficulty is quantifying the uncertainty of flow magnitudes, even if derived from field data, limiting the degree to which the model captures the inherent uncertainty of the empirical system. Popular techniques of incorporating data uncertainties of flow magnitudes in ecological network models are calculating multiple plausible representations of the food web within data constraints using linear inverse modelling and Markov Chain Monte Carlo (LIM-MCMC) methods. Hurdles in this process include coding input files for data-intense networks and evaluating the quality of the multiple plausible representations. To address these, we present the R package autoLIMR: an open-source automated workflow using LIM-MCMC to incorporate and evaluate data uncertainty in ecological network models. The main objectives are to improve the accessibility of LIM-MCMC, reduce errors, and provide a consistent framework to enhance model output reporting and reproducibility. We display autoLIMR on a small food web time series example and provide guidelines to facilitate the uptake of this robust methodology in the broader ecological systems sciences.

Homogeneous analysis on network effects in network autoregressive model

Network effects are pivotal for understanding the mutual influence of nodes within a network. The homogeneous group structure holds significant importance across networks in various fields. This paper introduces the Classifier-L2 regularized approach for homogeneous analysis of network effects under the network autoregressive model. This approach offers high flexibility with data constraints and provides a completely data-driven procedure. Analysis of international trade data offers meaningful perspectives for refining trade policies. Empirical results derived from Chinese mutual fund data, both before and after the outbreak of the COVID-19 pandemic, provide valuable insights for mitigating potential risks.

Multicenter Study of Mantle Cell Lymphoma Outcomes Following First-Line Bendamustine-Rituximab and Second-Line Bruton's Tyrosine Kinase Inhibitor Therapy

Background: Bendamustine and rituximab (BR) is a standard-of-care first-line (1L) therapy for older or unfit patients with mantle cell lymphoma (MCL). The SHINE trial compared BR with rituximab maintenance plus ibrutinib vs placebo in patients ≥65 years old and showed that the ibrutinib arm had significantly improved progression-free survival (PFS; median 80.6 vs 52.9 months) but similar overall survival (OS; 57% vs 55% at 7 years) compared to the placebo arm. Whether sequential treatment with BR in 1L and a Bruton's tyrosine kinase inhibitor (BTKi) in second-line (2L) can result in a similar cumulative PFS compared to 1L BR plus BTKi combination therapy is unknown. To provide insight to this question, we modeled observational data to evaluate MCL outcomes after 1L BR and 2L BTKi therapy in the BTKi era. Methods: Patients with MCL who received 1L BR with or without rituximab maintenance in 2014-2020 at one of the 27 participating centers were included. Exclusion criteria included participation in the SHINE or ECHO trials, any additional 1L therapy other than BR (with or without rituximab maintenance), and stem cell transplant consolidation after 1L BR. Baseline characteristics, treatment, and follow-up data were abstracted by chart review. Event-free survival (EFS) was defined as time from index line treatment start to the first event (progression, relapse, retreatment, or death). OS was defined as time from index line treatment start to death. Using an intention-to-treat (ITT) framework, EFS2 was defined as time from 1L BR start to progression, relapse, or retreatment following 2L BTKi treatment or death. Patients who received a non-BTKi treatment at 2L were censored for EFS2 at 2L treatment start; living patients without an event following 1L BR or 2L BTKi were censored for EFS2 at last follow-up. Results: A total of 618 patients with MCL who received 1L BR in 2014-2020 were included. The median age was 71 (IQR 65-76) years, and 447 (72%) were male. 59 (11%) patients had an ECOG PS ≥2, 566 (93%) had stage III-IV disease, and simplified MIPI was low in 105 (21%), intermediate in 200 (39%), and high in 202 (40%) patients. The median follow-up following 1L BR start was 57.4 (95% CI 53.8-63.2) months. Response data were available in 580 patients, and the best ORR was 92% (79% complete response [CR] and 13% partial response [PR]). 258 (42%) patients received rituximab maintenance. As of last follow-up, 255 patients were alive and in remission after 1L BR, 92 patients died without 2L therapy, and 271 patients received a 2L therapy. The median EFS was 34.1 (95% CI 31.0-40.0) months. The median OS was 97.8 (95% CI 81.2-NA) months, the 5-year OS rate was 58.6% (95% CI 54.4-63.2), and the 7-year OS rate was 56.7% (95% CI 52.4-61.5) (Fig 1). Among the 271 patients who started a 2L treatment, 203 (75%) received a BTKi at 2L - 101 (50%) ibrutinib, 76 (37%) acalabrutinib, and 26 (13%) zanubrutinib. The median follow-up following 2L BTKi start was 38.5 (95% CI 31.3-45.1) months. Response data were available in 171 patients, and the best ORR was 64% (36% CR, 28% PR). The median EFS was 10.7 (95% CI 7.7-14.0) months, and the median OS was 24.8 (95% CI 17.3-33.1) months with 2L BTKi therapy (Fig 2). By ITT analysis, the median EFS2 following 1L BR and 2L BTKi was 72.1 (95% CI 56.7-97.8) months (Fig 1). A subset analysis of patients aged ≥65 years (n=471; 198 [42%] received rituximab maintenance) showed similar results. The median EFS with 1L BR was 32.7 (95% CI 29.1-36.3) months. The median OS was 81.5 (95% CI 65.0-NA) months, and the 7-year OS rate was 53.3% (95% CI 48.3-58.7). 208 patients received a 2L therapy, 163 (79%) with a BTKi. The median EFS was 11.5 (95% CI 7.6-15.8) months, and the median OS was 21.0 (95% CI 14.0-29.6) months with 2L BTKi therapy. By ITT analysis, the median EFS2 following 1L BR and 2L BTKi was 58.0 (95% CI 50.2-77.0) months. Conclusion: In this multicenter retrospective study, initiation of 1L BR (with or without rituximab maintenance) resulted in a 7-year OS of 57%. Median EFS2 for sequential 1L BR and 2L BTKi was 72.1 months. In context, the SHINE study reported a median PFS of 80.6 months in the BR (with rituximab maintenance) plus ibrutinib arm and a 7-year OS of 57% in the ibrutinib arm and 55% in the placebo arm, where 39% of patients received a BTKi in 2L. Within the constraints of observational data, our results provide support for sequential use of BR in 1L and BTKi in 2L for patients with MCL.

The impact of conflict on food security: evidence from household data in Ethiopia and Malawi

BackgroundViolent conflicts threaten food security and household welfare in sub-Saharan Africa. While a more robust understanding of the causal relationship between food security and conflict is vital in mitigating food insecurity and bolstering peace prospects, only limited research exists on this topic, especially at the household level where estimations are more empirically challenging given data constraints and identification issues. Our analysis utilizes a newly developed and novel difference-in-differences model developed by de Chaisemartin and D’Haultfoeuille (2020) to determine the causal relationship between violent conflicts and food security in two sub-Saharan African countries—Malawi and Ethiopia using household-level data from the World Bank’s Household Living Standards Measurement Survey.ResultsOur results suggest that exposure to violent conflict on average decreases the food consumption score (FCS) by 6.84 units, which corresponds to a 16.13% reduction in FCS. With respect to individual countries, Malawi shows the largest effect-size, with the FCS decreasing by 10.54 units (equivalent to a 20.22% reduction in FCS). In Ethiopia, the causal estimate was slightly smaller at − 4.32 (equivalent to a 11.67% reduction in FCS) although the baseline food security status was lower relative to Malawi. Disaggregated analyses show that the effect-size can be several orders of magnitude larger when conflict is experienced simultaneously with natural shocks. Robustness checks using different iterations of propensity score matching generate comparable causal estimates and reinforce the overall findings.ConclusionsThe findings help improve our understanding of a broader issue by providing new direct and granular evidence regarding the relationship between conflict and food security using household data. The results hold implications for aid and humanitarian efforts to help households facing food insecurity stemming from violence and other factors.

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

Geohazards pose significant risks to communities and infrastructure, emphasizing the need for accurate susceptibility assessments to guide land-use planning and hazard management. This study presents a comprehensive method that combines Variable Weight Theory (VWT) with Analytic Hierarchy Process (AHP) to assess geo-environment vulnerability based on susceptibility to various geohazards. The method was applied to the Pearl River Delta in China, resulting in the classification of areas into high vulnerability (5961.85 km2), medium vulnerability (19,227.93 km2), low vulnerability (14,892.02 km2), and stable areas (1616.19 km2). The findings demonstrate improved accuracy and reliability compared to using AHP alone. ROC curve analysis confirms the enhanced performance of the integrated method, highlighting its effectiveness in discerning susceptibility levels and making informed decisions in hazard preparedness and risk reduction. Additionally, this study assessed the risks posed by geohazards to critical infrastructures, roads, and artificial surfaces, while discussing prevention strategies. However, this study acknowledges certain limitations, including the subjective determination of its judgment matrix and data constraints. Future research could explore the integration of alternative methods to enhance the objectivity of factor weighting. In practical applications, this study contributes to the understanding of geo-environment vulnerability assessments, providing insight into the intricate interplay among geological processes, human activities, and disaster resilience.

A novel multi-agent genetic algorithm for limited-view computed tomography

Computed tomography (CT) is a non-invasive testing technique to generate the internal image of any object. It has various applications belonging to medical imaging and other engineering applications. The present article proposes a novel multiagent genetic algorithm based on hill-climbing for limited-view computed tomography (MAGAH-CT). The MAGAH combines a multi-agent system based on a genetic algorithm (MAGA) and a hill-climbing approach. The MAGAH uses multiple operators to refine the agent population during evolution. These operators are neighborhood-based competition, hybrid crossover, adaptive mutation, and self-learning. MAGAH-CT explores better solutions in neighborhood-based competition and cooperation operators. The self-learning operator learns through the agent’s population generated by adaptive mutation and hybrid crossover operator, ensuring the results’ continuity. The proposed algorithm has been tested on a Shepp-Logan head phantom with limited view projection data. The algorithm can accurately reconstruct the low-noise CT image using limited data constraints. Experimental results reveal that the presented MAGAH-CT algorithm is efficient and computationally reasonable. The presented algorithm is suitable for low-noise CT reconstruction and out-performs other limited-view CT reconstruction algorithms.

Mapping Urban Structure Types Based on Remote Sensing Data—A Universal and Adaptable Framework for Spatial Analyses of Cities

In the face of growing 21st-century urban challenges, this study emphasizes the role of remote sensing data in objectively defining urban structure types (USTs) based on morphology. While numerous UST delineation approaches exist, few are universally applicable due to data constraints or impractical class schemes. This article attempts to tackle this challenge by summarizing important approaches dealing with the computation of USTs and to condense their contributions to the field of research within a single comprehensive framework. Hereby, this framework not only serves as a conjunctive reference for currently existing implementations, but is also independent regarding the input data, spatial scale, or targeted purpose of the mapping. It consists of four major steps: (1) the collection of suitable data sources to describe the building morphology as a key input, (2) the definition of a spatial mapping unit, (3) the parameterization of the mapping units, and (4) the final classification of the mapping units into urban structure types. We outline how these tasks can lead to a UST classification which fits the users’ needs based on their available input data. At the same time, the framework can serve as a protocol for future studies where USTs are mapped, or new approaches are presented. This article closes with an application example for three different cities to underline the flexibility and applicability of the proposed framework while maintaining maximized objectivity and comparability. We recommend this framework as a guideline for the use-specific mapping of USTs and hope to contribute to past and future research on this topic by fostering the implementation of this concept for the spatial analysis and a better understanding of complex urban environments.

Malignant Pleural Mesothelioma and Radiotherapy: Lung Toxicity Results of an Interim Analysis in Prospective Pilot Trial

Malignant Pleural Mesothelioma (MPM) is a rare but aggressive intrathoracic malignancy with an overall poor prognosis. Neoadjuvant chemotherapy, surgery and then radiotherapy is the standard of care in early-stage disease. Our study aim is to evaluate the feasibility and toxicity of the novel adjuvant RT in accelerated hypofractionation. We report the lung toxicity preliminary results of the first patients entreated. Starting in 2017, 29 MPM patients were enrolled in the trial (IRST trial 163). All patients were treated with accelerated hypofractionated radiotherapy using a helical 3-D CRT and IMRT system and intensity-modulated arc therapy. We conducted a prospective mono-institutional clinical trial enrolling cyto-histological proven, MPM patients. The major exclusion criteria were: previous thorax radiotherapy, contralateral mediastinum involvement (N3) and/or M1, interstitial pneumopathy, active pneumonitis, and fissural disease. The prescription dose was 30 Gy in five daily fractions, while an inhomogeneous dose escalation to 40 Gy was prescribed based solely upon the presence of gross residual tumor. All patients were treated in a 3-D CRT and IMRT system. Patients underwent functional lung study before to start radiotherapy treatment, 2 months and 6 months after the end of the treatment. The organs at risk dose-volume histograms were converted to a 2-Gy equivalent dose, and we closely adhered to the dose constraints of the literature data. We in particular analyzed lung toxicity of the first 20 patients. No G3/G4 lung toxicity was found. We reported 65.0% G1 pneumonitis and 10% G2 as acute lung toxicity. The majority of the G1 patients had only mild symptoms and pneumonitis was described only for radiologic features with no need for medical therapy. Other respiratory toxicities were G1-G2 cough in 50% of the patients; G1 dyspnea occurred in 65% of the patients and G2 in the 15%. The total lung mean dose was 18 Gy (range 13 Gy-23 Gy), and the contralateral lung mean dose was 2 Gy (range 1 Gy-2 Gy). The median value of Forced Expired Volume in one second (FEV1) recorded was 75.5% (range: 46%-137%) before the treatment was started, the median value of Forced Vital Capacity (FVC) at baseline was 74 (range 46-123) with progressive decreasing values through time. DLCO has also been reported with a progressive decrease over time after radiotherapy treatment. No case of respiratory failure was reported after treatment. Treatment of the intact lung with pleural intensity-modulated arc irradiation is a novel treatment strategy that appears to be safe, feasible, and without a high grade of lung toxicity. More investigations are mandatory. The protocol is ongoing.