Categorization Method Research Articles

Leveraging Explainable Artificial Intelligence (XAI) for Expert Interpretability in Predicting Rapid Kidney Enlargement Risks in Autosomal Dominant Polycystic Kidney Disease (ADPKD)

Autosomal dominant polycystic kidney disease (ADPKD) is the predominant hereditary factor leading to end-stage renal disease (ESRD) worldwide, affecting individuals across all races with a prevalence of 1 in 400 to 1 in 1000. The disease presents significant challenges in management, particularly with limited options for slowing cyst progression, as well as the use of tolvaptan being restricted to high-risk patients due to potential liver injury. However, determining high-risk status typically requires magnetic resonance imaging (MRI) to calculate total kidney volume (TKV), a time-consuming process demanding specialized expertise. Motivated by these challenges, this study proposes alternative methods for high-risk categorization that do not rely on TKV data. Utilizing historical patient data, we aim to predict rapid kidney enlargement in ADPKD patients to support clinical decision-making. We applied seven machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Tree, XGBoost, and Deep Neural Network (DNN)—to data from the Polycystic Kidney Disease Outcomes Consortium (PKDOC) database. The XGBoost model, combined with the Synthetic Minority Oversampling Technique (SMOTE), yielded the best performance. We also leveraged explainable artificial intelligence (XAI) techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to visualize and clarify the model’s predictions. Furthermore, we generated text summaries to enhance interpretability. To evaluate the effectiveness of our approach, we proposed new metrics to assess explainability and conducted a survey with 27 doctors to compare models with and without XAI techniques. The results indicated that incorporating XAI and textual summaries significantly improved expert explainability and increased confidence in the model’s ability to support treatment decisions for ADPKD patients.

Clinical features of vascular cognitive impairment

Vascular cognitive impairment (VCI) can occur in cerebrovascular disease of varying degree of severity, which requires further investigation.Objective: to identify specific features of attention deficit and executive function (EF) impairment in VCI and to determine the relationship between cognitive, neurological and radiological signs of the disease.Material and methods. The study involved 80 patients (51 with VCI – the main group and 29 healthy volunteers – the control group). All participants underwent clinical and neuropsychological examination and MRI of the brain. VCI was diagnosed according to 2014 VASCOG criteria. The neuropsychological examination included an assessment of the general cognitive function (MoCA test, MMSE); general EF (FAB, EXIT-25); episodic memory (12-word memory test and Benton Visual Retention Test); semantic memory (categorical association method); initiation and productivity of cognitive activity (literal associations); attention [TMT-A, Symbol-Digit Modalities Test (SDMT)]; cognitive flexibility (TMT-B); cognitive control (Stroop test).Results. As a result of the study, three subgroups of patients with VCI were identified: 1) predominantly attention deficit (AD); 2) AD and significant decrease in cognitive flexibility (CFD); 3) AD in combination with a significant cognitive control deficiency (CCD). The TMT-A and SDMT tests were significantly worse in all subgroups of VCI compared to the control group, with the worst SDMT score in subgroup 2. Subgroup 2 also had the longest time to complete the TMT-B test. Subgroup 3 was characterized by the worst Stroop test results, and uncontrolled arterial hypertension was more common in this group. Gait disturbances were significantly more frequent in subgroup 2, and increased reflexes of oral automatism and grasp reflex were observed in subgroup 3. White matter hyperintensity (WMH) was more pronounced in subgroup 2, and lacunar infarcts (LI) were more frequent in subgroup 3. Prevalence of LI and severity of WMH were lowest in subgroup 1.Conclusion. The main features of VCI are attention deficit and EF impairment, characteristics of EF disorders depend on the severity and type of vascular lesions of the brain. Progression of WMH is associated with more pronounced CFD and an increase in the number of LI is associated with CCD. There is a correlation between CFD and gait disturbances and between CCD and lack of primitive reflexes inhibition.

RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution.

Comparative genomics approaches seek to associate molecular evolution with the evolution of phenotypes across a phylogeny. Many of these methods lack the ability to analyze non-ordinal categorical traits with more than two categories. To address this limitation, we introduce an expansion to RERconverge that associates shifts in evolutionary rates with the convergent evolution of categorical traits. The categorical RERconverge expansion includes methods for performing categorical ancestral state reconstruction, statistical tests for associating relative evolutionary rates with categorical variables, and a new method for performing phylogeny-aware permutations, "permulations", on categorical traits. We demonstrate our new method on a three-category diet phenotype and we compare its performance to binary RERconverge analyses and two existing methods for comparative genomic analyses of categorical traits: phylogenetic simulations and a phylogenetic signal based method. We present an analysis of how the categorical permulations scale with the number of species and the number of categories included in the analysis. Our results show that our new categorical method outperforms phylogenetic simulations at identifying genes and enriched pathways significantly associated with the diet phenotypes and that the categorical ancestral state reconstruction drives an improvement in our ability to capture diet-related enriched pathways compared to binary RERconverge when implemented without user input on phenotype evolution. The categorical expansion to RERconverge will provide a strong foundation for applying the comparative method to categorical traits on larger data sets with more species and more complex trait evolution than have previously been analyzed.

Evaluating Students' Academic Progress in the Role of Fuzzy Logic

This paper explores the role of fuzzy logic in the assessment of student performance in local high school, Badr, Libya, positing that it offers a more flexible and accurate alternative to traditional categorization methods of academic achievement. Conventional assessment tools, such as standardized test scores and GPAs, often yield a distorted and overly simplistic view of student performance. Utilizing a mixed-methods research design, this study compares the performance, attendance, and self-reported satisfaction of students assessed through fuzzy logic with those evaluated by traditional methods. The results demonstrate that students assessed using fuzzy logic achieved higher average scores and expressed greater satisfaction with the assessment process. Additionally, educators employing fuzzy logic provided more detailed and constructive feedback, enriching the learning experience. Thus, this research indicates that fuzzy logic can effectively mitigate the shortcomings of traditional assessment methods, enhancing the recognition of diverse student abilities. The findings advocate for the integration of fuzzy logic in educational assessment practices to promote inclusivity and improved learning outcomes.

A Relative Projection-Based Multiattribute Group Decision-Making Model With Noncooperative Behavior Management and Application to NEV Supplier Selection.

This article reports a novel consensus model where a group of internal and external experts evaluate alternatives under multiple attributes and provide mutual evaluations. First, different from previous studies, the cognitive and interest conflicts of internal and external experts are considered simultaneously. But interest conflict is emphasized for internal experts, and cognitive conflict is mainly considered for external experts. Second, we explore the categorization and management methods of noncooperative behaviors (NCBs) of experts. The relative projection-based indexes are proposed for the first time to measure the degrees of cognitive and interest conflicts by using multiattribute preference matrices (MAPMs) and the weight vectors of attributes. A group of experts are divided into three categories and the corresponding management strategies are developed. Third, we investigate the consensus mechanism among experts with cognitive and interest conflicts. For reaching an acceptable consensus level, an adjustment process is proposed to revise some local entries in MAPMs and mutual evaluation matrix (MEM). A penalty mechanism is further established to dynamically update the weights of experts. An algorithm is designed to capture the consensus reaching process in multiattribute group decision making, where internal and external experts are distinguished by proposing a parameter. Finally, the high-performance battery supplier selection of new energy vehicle is studied to illustrate the proposed model. The results reveal that the efficiency of reaching consensus can be enhanced by using the developed model with effective management of NCBs.

디자인 씽킹을 활용한 주민참여 지역재생 사업 사례 조사 - 부여군 사례를 중심으로

This study aims to revitalize local commercial districts amidst the challenges of the COVID era by applying the design thinking methodology to develop a regeneration strategy for “Wonjo Food Alley” in Buyeo County. The research was conducted based on workshops involving merchants, local residents, and experts, utilizing the KJ method for idea categorization and analysis to derive a strategy for community regeneration using the cultural assets of the area. In this process, the use of design thinking played a crucial role in deeply understanding the unique requirements of the region and facilitating community participation. The results demonstrate that design thinking is an effective approach for revitalizing local economies, proving its utility and effectiveness. This underscores the importance of a participatory approach through design thinking in the development of strategies for commercial district revitalization, and it is hoped that this research will provide practical guidelines for addressing similar problems in the future.

Who is a minor? Age assessments of refugees in Germany and the classificatory multiplicity of the state

The categories that states use to classify and govern migrants often diverge from how migrants themselves perceive and experience their conditions, biographies, and identities. Building on this insight, I argue in this article that state categories not only clash with migrants’ varied notions of categories: state categorization itself often relies on multiple definitions of categories and methods of categorization. Age assessments of young refugees in Germany demonstrate this multiplicity. These assessments are carried out by forensic medical examiners and social workers, respectively, who work with very different understandings of what constitutes age. While forensic scientists view age as countable, particular, and objective, social workers think of it as lived, assembled, and constructed. What I term the classificatory multiplicity of the state complements current scholarly endeavors aimed at critically examining state categories, particularly in the context of migration governance.

Quality of birth care and risk factors of length of stay after birth: A machine learning approach.

Length of stay (LOS) is an outcome measure and is assumed to be related to quality. The objective of this study is to examine the quality of birth care and risk factors associated with LOS after birth. A nationwide population-based Turkish Demographic and Health Survey (TDHS) was used for the year 2018. A total of 1849 women ages 15-49 were included. Explanatory factor analysis and machine learning predictors such as Random Forest, Support Vector Machine, Neural Network, k-Nearest Neighbor, and Naïve Bayes were used to identify the quality of birth care and risk factors associated with LOS after birth. As a result of the explanatory factor analysis, factor structures of quality of birth care, antenatal check-ups and supplements, and risk factors associated with birth were obtained using the Categorical Component Analysis method. The type of delivery, place of delivery, age, and type of place, which are under the quality of birth care, and risk factors associated with birth factors were found to be the variables that had the highest impact on LOS estimation. Random forest (Accuracy = 0.5789), support vector machine (radial) (Accuracy = 0.5766), and neural network (Accuracy = 0.5750) models outperformed, respectively. Type of delivery which is an indicator of quality of birth care is a strong predictor of LOS after birth according to the Random Forest model. We demonstrated that machine learning techniques offer precise LOS prediction after birth. Further studies assessing the effect of quality of birth care on predicting LOS at birth would be beneficial.

Circuit categorization approach of office building energy consumption based on data features for energy-saving diagnosis

Energy-saving in office buildings is crucial. Research on data-driven diagnostics of building energy consumption is pivotal. The emergence and development of itemized electricity platforms have expanded the scope of diagnosis from total building energy consumption to individual electrical equipment. However current studies rarely address the characteristics of sub-circuits. This paper highlights the importance of recognizing the features of electrical equipment sub-circuits based on historical energy consumption data by examining various aspects of energy use in office buildings, including electrical consumption disaggregation, equipment operational strategies, sub-item consumption proportion, and sub-circuit composition. A circuit categorization approach in office buildings by features analysis of historical data is innovatively proposed. Operational conditions, time series stationarity, and correlations with factors affecting energy consumption are considered in historical data feature analysis. The method categorizes the electric circuits into 5 categories with typical features based on the ADF-KPSS co-test and Spearman correlation analysis. The rationality and validity of the circuit categorization method were verified with actual building data in case studies. On this basis, an interpretable energy consumption anomaly diagnosis method based on the categorization results is proposed. The circuit categorization approach explores how to further investigate the typical features of sub-circuits, and provides new directions for subsequent building operation and maintenance(O&M) research, including energy consumption diagnostics. Additionally, it offers decision support for building managers for O&M evaluation from an interpretable perspective.

Combining machine learning (ML) and participatory rural appraisal (PRA) for disaster risk preparedness (DRP): Evidence from the poorest region of Luzon, Philippines

In the field of social science, disaster risk preparedness (DRP) is considered immeasurable due to its multidimensional nature, making it infamously difficult to quantify. The current measurements are costly, labor-intensive, and time-consuming. Consequently, policymakers struggle to target policies effectively when implementing disaster risk reduction management initiatives. By combining Participatory Rural Appraisal (PRA) and Machine Learning (ML) to train and test community-based system datasets, this work proposes novel approaches to DRP in the poorest region of Luzon, Philippines. We utilized sophisticated econometrics models along with ML categorization methods. Through the analysis of 34 locales and 4 sectors within a disaggregation system over 429 ensemble runs using cross-validation techniques, we then combined the results. The Support Vector Machine (SVM) classifier achieved the highest accuracy of 91.55 % randomly and 94.53 % within the pipeline, surpassing all other models. It also confirms the current relationship between DRP and multidimensional attributes (a total of 21 factors) in terms of correlation and causation. Our work showcases the potential of ML for disaster risk prediction, potentially reducing costs, saving labor, and optimizing time, especially in the most impoverished areas of the Philippines. Ultimately, through extensive PRA, the outcomes have provided different localities with tools for targeting policies in disaster risk management.

The efficacy of Diamond™ nucleic acid dye-stained cell counting techniques for forensic application

Touch DNA is one of the most common types of biological material collected during criminal investigations. Diamond™ Nucleic Acid Dye (DD) has been shown to aid in touch sample visualisation and target sampling. It has also been used as a method of shedder categorisation that is cheaper and quicker than DNA methods. However, the DD method routinely involves manual cell counting, which can result in intra and inter-person variability similar to other manual techniques used in forensic science, for example, fingerprint identification. Additionally, DD based shedder categorisation involves counting cells in a portion of the touch deposit to extrapolate an individual’s shedder status, and the sampling effect of such estimations is currently unknown.The present study tested different data analysis aspects of the DD method, including counting variability within and between people, shedder classification differences based on different counting methods (entire thumbprint, sub-section of a print with most cells, sub-section of a print deemed most representative of the entire thumbprint, and random sections), the use of ImageJ software to semi-automate counting and the use and extension of the DD method for investigating DNA Transfer, Persistence, Prevalence and Recovery (DNA-TPPR).The results of this study show that there are meaningful differences observed during counting processes both between and within people. These differences tended to increase as the factor of time, or the duration of counting, rather than the complexity of cell deposits being assessed. Investment in cell counting software that eliminates personal factors, such as boredom fatigue, can remedy most of these issues, however, will require optimisation, such as fibre recognition. Shedder testing was shown to be affected by the choice of sampling and categorisation methods, and suggested that using an entire finger or larger section size can provide increased precision. Finally, inverted worn gloves stained with DD may provide an acceptable alternative for hands in DNA-TPPR investigations, providing an interesting alternative for future research.

Assessment of Accelerated Aging Effect of Bio-Oil Fractions Utilizing Ultrahigh-Resolution Mass Spectrometry and k-Means Clustering of van Krevelen Compositional Space.

Bio-oils contain a substantial number of highly oxygenated hydrocarbons, which often exhibit low thermal stability during storage, handling, and refining. The primary objectives of this study are to characterize the hydroxyl group in bio-oil fractions and to investigate the relationship between the type of hydroxyl group and accelerated aging behavior. A bio-oil was fractionated into five solubility-based fractions, classified in two main groups: water-soluble and water-insoluble fractions. These fractions were then subjected to chemoselective reactions to tag molecules containing hydroxyl groups and analyzed by negative-ion electrospray ionization 21 T Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The fractions were also subjected to accelerated aging experiments and characterized by FT-ICR MS and bulk viscosity measurements. Extracting insightful information from ultrahigh-resolution data to aid in predicting upgrading methodologies and instability behaviors of bio-oils is challenging due to the complexity of the data. To address this, an unsupervised learning technique, k-means clustering analysis, was used to semiquantify molecular compositions with a close Euclidean distance within the (O/C, H/C) chemical space. The combination of k-means analysis with findings from chemoselective reactions allowed the distinctive hydroxyl functionalities across the samples to be inferred. Our results indicate that the hexane-soluble fraction contained numerous molecules containing primary and secondary alcohols, while the water-soluble fraction displayed diverse groups of oxygenated compounds, clustered near to carbohydrate-like and pyrolytic humin-like materials. Despite its high oxygen content, the water-soluble fraction showed minimal changes in viscosity during aging. In contrast, a significant increase in viscosity was observed in the water-insoluble materials, specifically, the low- and high-molecular-weight lignin fractions (LMWL and HMWL, respectively). Among these two fractions, the HMWL exhibited the highest increase in viscosity after only 4 h of accelerated aging. Our results indicate that this aging behavior is attributed to an increased number of molecular compositions containing phenolic groups. Thus, the chemical compositions within the HMWL are the major contributors to the viscosity changes in the bio-oil under accelerated aging conditions. This highlights the crucial role of oxygen functionality in bio-oil aging, suggesting that a high oxygen content alone does not necessarily correlate with an increase of viscosity. Unlike other bio-oil categorization methods based on constrained molecule locations within the van Krevelen compositional space, k-means clustering can identify patterns within ultrahigh-resolution data inherent to the unique chemical fingerprint of each sample.

Wireless sensor network-based machine learning framework for smart cities in intelligent waste management

Environmental safety is one of the key issues that are directly related to a country's prosperity. One of the most fundamental aspects of a sustainable economy is waste management and recycling. Better recycling safety and efficiency may be achieved via the use of intelligent devices rather than manual effort. In this research, we describe a machine learning-based architecture for smart trash collection and sorting using the Internet of Things and wireless sensor networks. The goal of this study was to develop an autonomous method for producing an efficient and intelligent waste parameter monitoring system for a novel waste management system, using the Internet of Things (IoT) and Long Range (LoRa) technologies. Several possibilities are explored, all of which may be applied to the development of the three nodes. The number of trash cans, garbage stench, air quality, weight, smoke levels, and waste categories are all tracked in real-time via the Internet of Things and the Thing Speak Cloud Platform, which can be set up in numerous places. In the end, a fog layer-deployed intelligent waste classification framework consists mostly of four layers: input, feature, classification, and output. Using the Thrash Box dataset, the proposed system develops a categorization method into trash classes such as household, medical, and electronic garbage, in addition to object identification. Traditional machine learning methods, such as the multi-kernel support vector machine (SVM) and the Adaboost ensemble classifier, are employed in the classification layer, while the Resnet-101 deep convolutional neural network model is used in the feature layer. Experiments were conducted to evaluate the suggested method's ability to classify garbage and provide accurate predictions about their respective categories. Compared to other state-of-the-art models, the suggested method's performance was shown to be superior in the presented trials.

Amyand's hernia concurrent with appendicitis: A literature review

When the vermiform appendix is inside the hernia sac, an uncommon kind of inguinal hernia known as Amyand's hernia (AH) occurs. Typically, an unintentional discovery discovered during surgery serves as the basis for the diagnosis. Only a tiny percentage of patients with inguinal hernias have AH, and even fewer experience problems with acute ischemia. The referral to general surgical services was due to an inguinal tumor located in the right lower quadrant of our 65-year-old male patient. There may be consequences from ischemia because this was not touch-sensitive. The appendix was removed by open hernioplasty. Treatment for type 1 AH, which this patient has, consists of mesh repair and hernial reduction. Despite not having acute appendicitis, our patient needed an appendectomy due to anatomical variation in their AH, which raised the possibility of hernial incarceration. Because of its size and adhesions inside the hernia, this treatment was also thought to be essential for preventing the recurrence of the hernia. This paper introduces a new method for handling the accidental finding of a type 1 AH. There are few treatment suggestions available for anatomic variations of the AH. The physical characteristics of AH may be included to overcome the drawbacks of the traditional Losanoff and Basson's AH classification methods for AH management and categorization. By using this method. Amyand's hernia occurs when a vermiform appendix is discovered inside an inguinal hernia sac. The objectives of this comprehensive investigation were to gather information on its occurrence, clinical picture, diagnosis, and treatment course.

Intracranial Pressure Thresholds for Cerebral Autoregulation Impairment: Age-Stratified Analysis of Ultra-Low-Frequency Pressure Reactivity Index (UL-PRx) in Traumatic Brain Injury.

The study investigated the effectiveness of low-frequency sampling in detecting alterations in cerebrovascular reactivity (CVR) associated with changes in intracranial pressure (ICP) in patients with traumatic brain injury (TBI) across different age groups. The primary objective was to investigate an ICP threshold that indicates a decrease in CVR as evidenced by a significant increase in the ultra-low-frequency pressure reactivity index (UL-PRx). Additionally, the study aimed to develop an age-based categorization method for patients with TBI to investigate the differences between these ICP thresholds in different age groups. In this retrospective analysis, data from 263 patients with TBI were prospectively collected. ICP and mean arterial pressure were extracted from the hospital database at 5-min intervals. Demographic details, clinical presentation, computed tomography scans, neurosurgical interventions, and 12-months outcome were recorded. ICP versus UL-PRx values were categorized into ICP bins and graphically represented with boxplots for each age group, illustrating how as ICP values rise, there is a bin (age-tailored ICP [AT-ICP]) beyond which UL-PRx shows a sudden increase, indicating CVR loss. Homogeneous age groups were established to obtain a consistent AT-ICP threshold. The discriminatory ability of the AT-ICP thresholds was compared with the guideline-recommended thresholds by calculating the area under the Receiver Operating Characteristic curve of the ICP-derived indices (dose above threshold, and the hourly dosage above threshold). Age groups 0-5, 6-20, 21-60, 61-70, and 71-85years were the best age subdivisions, corresponding to AT-ICP thresholds of 20, 30, 35, 25, and 30mmHg, respectively. The AT-ICP thresholds exhibited better discriminative ability compared with the guideline-recommended thresholds. The AT-ICP thresholds offer a novel approach for estimating CVR impairment and the developed method represents an alternative solution to address the age stratification issue in patients with TBI.

N2O Emission Factors from Wastewater Treatment Plants Based on Literature Statistics and Model Fitting

The emission of nitrous oxide （N2O） during wastewater treatment cannot be ignored. The analysis of statistical data from literature based on 126 empirical studies revealed that the geographical factors of wastewater treatment plants （WWTPs） had a significant impact on N2O emission factors. However, the N2O emission factors of WWTPs in all regions of the world were generally lower than the Intergovernmental Panel on Climate Change （IPCC） recommended values. In China, the N2O emission factors （in N2O-N/Ninfluent） of WWTPs were approximately 0.000 35-0.065 20 kg·kg-1. Meanwhile, the N2O emission factors of different wastewater treatment processes were also significantly different, especially since the sequencing batch reactor （SBR） process had higher emissions. The use of uniform default emission factors for accounting was prone to overestimate N2O emissions, and it is recommended that countries conduct actual monitoring or modeling studies to develop categorical emission factors suitable for local conditions. In addition, the N2O emission factor based on total nitrogen （TN） removal was weakly negatively correlated with TN removal in 126 empirical data, which was more in line with bioprocessing stoichiometry and could provide an accurate accounting method for N2O. To this end, a digital twin model was developed to dynamically simulate a case anaerobic-anoxic-aerobic （AAO） WWTP to comprehensively quantify the dynamic emission behavior of N2O, which demonstrated that N2O emissions had significant seasonal and daily variability and were only equivalent to 11% of the calculated value of the emission factor based on the IPCC recommendation. Comparing the scatter linear fitting and categorical mean exponential fitting methods, it was found that the latter could more accurately reflect the negative correlation between the N2O emission factors and the TN removal rate, and an exponential regression equation between the average N2O emission factor based on the amount of TN removed and the TN removal rate was further developed to predict the N2O emission. The dynamic simulation and categorical index fitting methods provided in this study are important references for the accurate accounting of N2O emissions in similar WWTPs and provide help for understanding and responding to the N2O emission problems.

Spatial and conventional verifications of hurricanes Dorian and Fiona using the Canadian precipitation analysis & integrated multi-satellite retrievals for GPM products

Reanalysis and satellite-based rainfall packages are useful for monitoring hydroclimatic extremes. These advanced tools can be used as an early-warning system for decision-making during extreme events. Hurricane Dorian and Fiona impacted the North Atlantic from September 6–9, 2019 and September 22–25, 2022, respectively. This study evaluated the Canadian Precipitation Analysis (CaPA) in conjunction with integrated multi-satellite retrievals for the Global Precipitation Measurement Mission (GPM) in capturing these events at Early, Late and Final run stages (IMERG-E, IMERG-L & IMERG-F, respectively). Statistical verification was conducted through continuous, categorical and spatial methods to answer multiple research questions. The results show that CaPA outperformed all the IMERG products across various intensities throughout the duration of the extremes compared to station observations; therefore, CaPA can be a proxy for gauged observations. When CaPA was used as the reference for spatial verification, IMERG products correctly captured the spatial evolution of the hurricane from day to day, indicating they are reliable for hydroclimatic extreme applications. In addition, there are no striking statistical differences between the IMERG products despite the fact that IMERG-E and IMERG-L had no adjustment or gauge observation. In general, this study shows that IMERG-E has many potential applications such as CaPA assimilation, a hazard early warning system, and hurricane tracking and prediction. Further, including IMERG-E into CaPA will leverage its high resolution and latency. This study will benefit those involved in hydroclimatic studies and decision-making in the North Atlantic.

Hubungan Antara Obesitas dan Hipertensi dengan Kanker Payudara pada Pasien di Rumah Sakit Siloam Lippo Village Gedung B

Background: Breast cancer has become one of the most common cancers in Indonesia, and being one of the largest contributors to cancer deaths. Several studies have shown association between breast cancer with obesity and hypertension, both as modifiable risk factors. Methods: This study was conducted using unpaired categorical comparativeanalytic method with a case control design. Samples were collected using medical record data using consecutive sampling method. Sample were 160 patients aged 20–55 years at Siloam Lippo Village Hospital Building B; consisting of 80 casegroup samples and 80 control group samples. Data was analysed using Chi-square test. Result: Data analysis resultshowed that there was an association between obesity and breast cancer in patients at Siloam Lippo Village Hospital Building B (p=0.000; OR=11.238; 95% CI=5.360–23.563). There was also association between hypertension and breast cancer in patients at Siloam Lippo Village Hospital Building B (p=0.007; OR=7.567;95% CI=1.648–34.740). Conclusion:There is a significant association between obesity and hypertension with the incident of breast cancer in patients at SiloamLippo Village Hospital Building B.

From the Editor-in-Chief

Welcome everybody to the third issue of 2024! Some of us are already in holiday, some are still at work; surely, many of us will meet in the forthcoming conferences, mostly our flagship conference (LICS). In the meanwhile, I invite you to read the present issue of SIGLOG News, whose contents are the following: -The SIGLOG Chair introduces this year edition of LICS and reports on the current situation of SIGLOG. -We present the outcome of the 2024 Alonzo Church Award. This prize was established in 2015 by SIGLOG, by the European Association for Theoretical Computer Science (EATCS), by the European Association for Computer Science Logic (EACSL), and by the Kurt Goedel Society (KGS). In 2022 the KGS was dissolved and, since then, the award has been sponsored by EACSL, EATCS and SIGLOG. The award is for an outstanding contribution represented by a paper or by a small group of papers published within the past 25 years. This year's recipients are Thomas Ehrhard and Laurent Regnier. -Then, you can read a new semantics column, edited by Prakash Panangaden and written by Samson Abramsky and Luca Reggio, on Game comonads ; these offer a categorical view of a number of model-comparison games central to model theory and provide a new bridge between categorical methods developed for semantics, and the combinatorial and algorithmic methods of resource-sensitive model theory. -To conclude, we wrap up this number with the latest issue of SIGLOG Monthly, prepared by Elli Anastasiadi.

An Invitation to Game Comonads

Game comonads offer a categorical view of a number of model-comparison games central to model theory, such as pebble and Ehrenfeucht-Fraïssé games. Remarkably, the categories of coalgebras for these comon-ads capture preservation of several fragments of resource-bounded logics, such as (infinitary) first-order logic with n variables or bounded quantifier rank, and corresponding combinatorial parameters such as tree-width and tree-depth. In this way, game comonads provide a new bridge between categorical methods developed for semantics, and the combinatorial and algorithmic methods of resource-sensitive model theory.