Location Patterns Research Articles

Retrospective Analysis Comparing Lung-RADS v2022 and British Thoracic Society Guidelines for Differentiating Lung Metastases from Primary Lung Cancer.

Background and Objectives: The current study aimed to compare the effectiveness of the Lung Imaging Reporting and Data System (Lung-RADS) Version 2022 and the British Thoracic Society (BTS) guidelines in differentiating lung metastases from de novo primary lung cancer on CT scans in patients without prior cancer diagnosis. Materials and Methods: This retrospective study included 196 patients who underwent chest CT scans between 2015 and 2022 without a known history of cancer but with detected pulmonary nodules. CT images characterized nodules based on size, number, location, margins, attenuation, and growth patterns. Nodules were classified according to Lung-RADS Version 2022 and BTS guidelines. Statistical analyses compared the sensitivity and specificity of Lung-RADS and BTS guidelines in distinguishing metastases from primary lung cancer. Subgroup analyses were conducted based on nodule characteristics. Results: Of the 196 patients, 148 (75.5%) had de novo primary lung cancer, and 48 (24.5%) had lung metastases from occult primary tumors. Lung-RADS Version 2022 demonstrated higher specificity than BTS guidelines (87.2% vs. 72.3%, p < 0.001) while maintaining similar sensitivity (91.7% vs. 93.8%, p = 0.68) in differentiating lung metastases from primary lung cancer. Lung metastases were more likely to present with multiple nodules (81.3% vs. 18.2%, p < 0.001), lower lobe distribution (58.3% vs. 28.4%, p < 0.001), and smooth margins (70.8% vs. 20.3%, p < 0.001), whereas primary lung cancers were associated with solitary nodules, upper lobe location, and spiculated margins. Conclusions: Lung-RADS Version 2022 provides higher specificity than the BTS guidelines in differentiating lung metastases from primary lung cancer on CT scans in patients without prior cancer diagnosis. Recognizing characteristic imaging features can improve diagnostic accuracy and guide appropriate management.

Characteristics, location, and usage patterns of resting burrows in Chinese pangolins: Insights from radio-telemetry tracking

Characteristics, location, and usage patterns of resting burrows in Chinese pangolins: Insights from radio-telemetry tracking

Identification and characterization of soybean phytochrome-interacting factors and their potential roles in abiotic stress

Phytochrome-interacting factors (PIFs) belong to a subfamily of the bHLH transcription factor family and play a pivotal role in plant light signal transduction, hormone signal pathways, and the modulation of plant responses to various abiotic stresses. The soybean (Glycine max) is a significant food crop, providing essential oil and nutrients. Additionally, it is a vital industrial raw material and a lucrative cash crop. Nevertheless, research on PIFs in soybean is relatively scarce. In this study, we conducted a comprehensive analysis of the gene structure, chromosomal location, conserved motifs, phylogenetic relationships, and expression patterns of the Glycine max PIF (GmPIF) genes. A total of 20 GmPIF genes were identified in the soybean genome. These are unevenly distributed on 12 soybean chromosomes. The analysis of gene duplication events revealed the existence of five pairs of duplicated genes within the GmPIF gene set. Conserved motif analysis demonstrated the presence of several conserved motifs that were generally aligned with the classification of PIF protein. Cis-acting elements in the GmPIF promoters were found to be responsive to light, heat, drought, and phytohormone signaling. The expression levels of certain GmPIF genes were significantly induced under shade, high temperature and drought stress conditions. The heterologous expression of the GmPIF6c/GmPIL1 in an Arabidopsis mutant resulted in a reduction in the elongation of the hypocotyl in response to shade. It is proposed that GmPIF6c/GmPIL1 may exert an inhibitory effect on shade avoidance. This study elucidated the evolution, structural and functions of GmPIF family members.Clinical trial numberNot applicable.

Soil-ecological condition of the soil cover affected by the emissions of the factories of the Kazphosphate enterprise

The article provides information about the effect of phosphorus plant emissions on the soil-plant system. Emissions from phosphorus factories in Zhambyl region pose an environmental threat to the soils used for agricultural purposes, plants and residents located near them. The basis for evaluating the geochemical structure is to provide a description of the patterns of territorial and image location of heavy metals in soils, to determine the features of their migration and accumulation, and to study the stability of landscapes to pollution. About the amount of general and mobile types of heavy metals in the soils of the studied facility. Analytical data on the amount of general and mobile types of heavy metals in the soils of the studied facility showed that they were in acceptable concentrations. Some minor increases in total zinc, copper and cadmium in the soils of some sites can be explained by soil-forming rocks. In samples of amaranth plants, 10.4 times exceeding the limit permissible concentration of lead was recorded, as well as 3.2 and 3.6 times exceeding the limit permissible concentration of cadmium in the roots and leaves of large cypress.

Transdiagnostic Network Localization of Social, Language, and Motor Symptoms in Patients With Frontotemporal Lobar Degeneration.

Frontotemporal lobar degeneration (FTLD) includes different clinical syndromes with distinct patterns of symptoms and neuroanatomical locations of neurodegeneration. However, FTLD is clinically heterogeneous (with overlapping symptoms across several domains) and neuroanatomically heterogeneous (with brain atrophy in different locations in different patients). Traditional methods struggle to fully account for this heterogeneity. In this study, we use a relatively new neuroimaging approach, atrophy network mapping, to localize clinical symptoms in patients with FTLD to specific brain networks transdiagnostically. Data were obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative and 4-Repeat Tauopathy Neuroimaging Initiative. Inclusion required T1-weighted MRI and a diagnosis of behavioral-variant frontotemporal dementia (bvFTD), semantic-variant primary progressive aphasia (svPPA), nonfluent primary progressive aphasia (nfvPPA), progressive supranuclear palsy Richardson syndrome (PSP-rs), corticobasal syndrome (CBS), or normal cognition. Measures of social cognition (Interpersonal Reactivity Index, Revised Self-Monitoring Scale), language (Boston Naming Test, Animal Fluency), and motor function (Unified Parkinson Disease Rating Scale Part III, PSP Rating Scale) were correlated with neuroimaging measures, including cortical thickness, volume, and atrophy network mapping, a newer method that localizes regions connected to brain atrophy using a functional connectome from cognitively normal persons (n = 1,000). Fifty-seven patients with bvFTD (age 61.2 ± 6.8 years, 35% female), 41 PSP-rs (age 69.7 ± 7.4 years, 54% female), 39 CBS (age 66.2 ± 6.2 years, 51% female), 37 svPPA (age 63.0 ± 6.0 years, 46% female), and 36 nfvPPA (age 68.3 ± 7.3 years, 53% female) and 135 healthy age-matched controls (age 63.3 ± 7.4 years, 58% female) were included. Compared with atrophy alone, atrophy network mapping showed more consistent neuroimaging results across patients with the same clinical syndrome (Dice index mean 0.68 vs 0.11, paired t = 263.1, df = 4,452, p < 0.001), more strongly explained social cognition (F(1, 84) = 10.2, p = 0.002) and motor symptoms (F(1, 185) = 91.3, p < 0.001) across different syndromes, and showed novel neuroanatomical associations, with the temporal parietal junction relating to social cognition; Wernicke area relating to language symptoms; and association sensorimotor cortex, thalamus, and cerebellum relating to motor symptoms. Atrophy network mapping can improve understanding of brain-behavior relationships in clinically and neuroanatomically heterogeneous disorders such as FTLD.

The road to sustainable industrial land development: location patterns, morphological characteristics, and policy implications of South Korean urban high-tech industrial parks

ABSTRACT South Korean urban high-tech (UHT) industrial parks, as urban-style industrial parks designed to provide space for the industrial activities of high-tech industries, have unique location and morphological characteristics. This study empirically analyzed the location patterns and morphological characteristics of 27 South Korean UHT industrial parks using data from the 2018 Industrial Park Spatial Database. The analysis revealed that most UHT industrial parks were developed outside urban areas, primarily relying on greenfield development rather than brownfield redevelopment, thereby failing to meet sustainability goals. However, inner-urban UHT industrial parks displayed different characteristics. These parks relied more on brownfield development and featured high-density, verticalized landscapes with well-accessible open spaces and diverse land use, thereby promoting high employment density, job-housing proximity, growth of neighbouring businesses, and the revitalization of old inner-urban districts. The study highlights the dual nature of UHT industrial park development in South Korea: a prevailing trend of outer-urban greenfield development, and a more sustainable inner-urban brownfield approach. These findings emphasize the need for targeted policies to enhance the sustainability and functionality of UHT industrial parks, ensuring they meet urban development and sustainability goals.

Predicting and Monitoring Symptoms in Patients Diagnosed With Depression Using Smartphone Data: Observational Study.

Clinical diagnostic assessments and the outcome monitoring of patients with depression rely predominantly on interviews by professionals and the use of self-report questionnaires. The ubiquity of smartphones and other personal consumer devices has prompted research into the potential of data collected via these devices to serve as digital behavioral markers for indicating the presence and monitoring of the outcome of depression. This paper explores the potential of using behavioral data collected with smartphones to detect and monitor depression symptoms in patients diagnosed with depression. Specifically, it investigates whether this data can accurately classify the presence of depression, as well as monitor the changes in depressive states over time. In a prospective cohort study, we collected smartphone behavioral data for up to 1 year. The study consists of observations from 164 participants, including healthy controls (n=31) and patients diagnosed with various depressive disorders: major depressive disorder (MDD; n=85), MDD with comorbid borderline personality disorder (n=27), and major depressive episodes with bipolar disorder (n=21). Data were labeled based on depression severity using 9-item Patient Health Questionnaire (PHQ-9) scores. We performed statistical analysis and used supervised machine learning on the data to classify the severity of depression and observe changes in the depression state over time. Our correlation analysis revealed 32 behavioral markers associated with the changes in depressive state. Our analysis classified patients who are depressed with an accuracy of 82% (95% CI 80%-84%) and change in the presence of depression with an accuracy of 75% (95% CI 72%-76%). Notably, the most important smartphone features for classifying depression states were screen-off events, battery charge levels, communication patterns, app usage, and location data. Similarly, for predicting changes in depression state, the most important features were related to location, battery level, screen, and accelerometer data patterns. The use of smartphone digital behavioral markers to supplement clinical evaluations may aid in detecting the presence and changes in severity of symptoms of depression, particularly if combined with intermittent use of self-report of symptoms.

Papillary Renal Neoplasm With Reverse Polarity: CT and MR Imaging Characteristics in 26 Patients.

To improve the diagnostic recognition of papillary renal neoplasm with reverse polarity (PRNRP) through comprehensive analysis of computed tomography (CT) and magnetic resonance imaging (MRI) findings. A retrospective multi-center study was conducted on patients with pathologically confirmed PRNRPs from 2019 to 2024, encompassing six institutions. Clinical and pathological data were meticulously documented. Preoperative CT (n=23) and MRI (n=9) features were independently evaluated in consensus by two genitourinary radiologists, focusing on tumor location, morphologic features, attenuation, signal intensity, and enhancement patterns. Postoperative outcomes were assessed through medical record review or telephone follow-up. The study cohort comprised 26 patients (mean age 62±12 years, 13 men) with 26 well-defined PRNRPs (mean diameter 2.2±1.0cm) were included. 15 cases (58%) were situated in the right kidney, 18(69%) were exophytic, and 23(88%) were quasi-spherical. A pseudocapsule was identified in eight cases (89%) on MRI. All cases demonstrated iso- or slight hyperattenuation (43.1±13.6 HU) on non-contrast CT, hypointensity on T2-weighted imaging (T2WI), and mild diffusion restriction on diffusion-weighted imaging (DWI). All cases exhibited mild or moderate enhancement in the corticomedullary phase, followed by progressive enhancement in the nephrographic and excretory phases. Concomitant renal cysts were found in 17 cases (65%). All cases showed no evidence of recurrence or metastasis. PRNRP typically presents as a small hypovascular renal mass, and characterized by a pseudocapsule, iso- or slight hyperattenuation on non-contrast CT, heterogeneous T2WI hypointensity, and mild diffusion restriction on DWI.

Development and validation of a machine learning radiomics model based on multiparametric MRI for predicting progesterone receptor expression in meningioma: a multicenter study

ObjectivesThis study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (MRI). MethodsThe study retrospectively enrolled 739 patients with pathologically confirmed meningioma from three medical centers, dividing them into four cohorts: training (n = 294), internal test (n = 126), external test 1 (n = 217), and external test 2 (n = 102). Radiomics characteristics were derived from T2-weighted and contrast-enhanced T1-weighted MRI images, followed by feature selection. A machine learning-based combined model was developed by incorporating radiomics scores (rad-scores) from the optimal radiomics model along with clinical predictors. The Shapley additive explanation (SHAP) method was employed to visually represent the process of making predictions. The prognostic value of the model was evaluated using Kaplan-Meier analysis. ResultsAmong the 739 patients, 299 (40.5%) had negative PR expression confirmed by pathology. Twelve radiomics features derived from multiparametric MRI were selected to build the radiomics model. Tumor location and enhancement pattern were identified as key clinical predictors and were combined with rad-scores to create a combined model utilizing the extreme gradient boosting (XGBoost) algorithm. The combined model demonstrated strong accuracy and robustness, with area under the curve values of 0.907, 0.827, 0.846, and 0.807 across training, internal test, external test 1, and external test 2 cohorts, respectively. The recurrence-free survival analysis indicated that the combined model was able to effectively categorize patients based on recurrence outcomes. ConclusionsThe XGBoost combined model, utilizing multiparametric MRI, shows promise for predicting PR expression in meningioma patients.

Global banks and the picking order in internal capital markets: Do locational activity patterns matter?

This paper examines whether global banks’ liquidity reallocations via internal capital markets are driven by the locational activity patterns of their foreign branches. Using aggregated data of foreign branches of US global banks located in 52 countries, we advance evidence of a picking order that favors locations where branches are more heavily engaged in lending activities. Specifically, we find that internal liquidity support to branches in host countries with prominent local lending activities is especially significant during a contraction in local deposits.In jurisdictions where branches have higher shares of market-based activities and off-balance sheet exposures, we do not observe a significant increase in internal liquidity support following local funding contractions.

On the relation between urban road network and distribution centre location strategy of Franchise retail firms

Franchise retailers such as supermarkets and convenience stores have achieved greater efficiency in delivering products to their chain stores by locating distribution centres (DCs) within or near cities. In this study, we propose that the reduction of product transportation time due to the construction of intra-urban roads facilitates the location of distribution centres. To this end, we model the inventory management behaviour of franchise retailers and show that distribution centres have two functions: (1) consolidating the inventory holding risk faced by individual chain stores and (2) achieving larger lot sizes when purchasing goods from outside the city. We then analyse the effects of facilitating logistics by improving intra-city roads on reorganizing the location patterns of chain stores and distribution centres in the city and on improving the convenience of shopping for consumers.

Abstract 4137543: Prevalence, Genetic Correlates and Prognostic Significance of an Apical Pseudo-Infarct Late Gadolinium Enhancement Pattern in LMNA -Mediated Cardiac Laminopathy

Background: Disease-causative variants in LMNA -encoded lamin A/C cause a genetic cardiomyopathy characterized by atrioventricular (AV) block, atrial fibrillation (AF) and ventricular arrhythmias (VA). Whether domain/variant-specific late gadolinium enhancement (LGE) patterns exist and carry prognostic significance is unclear. Objective: To determine if specific LGE patterns correlate with LMNA variant type/missense variant localization and clinical outcomes in a large cohort of cardiac laminopathy patients. Methods: Retrospectively, 621 genotype-positive arrhythmogenic/dilated cardiomyopathy patients were analyzed to identify those with a disease-causative variant in LMNA -encoded lamin A/C. After exclusion of patients without cardiac magnetic resonance data, pertinent demographic/clinical data was extracted from the electronic medical record. LGE distribution (subepicardial, midmyocardial, subendocardial, transmural) and location (septal, free wall, apical) patterns were analyzed by variant type (missense vs truncating) and missense variant localization (central rod domain vs tail domain) and correlated with advanced heart failure (AHF), MVA, AV block, AF and thromboembolic (TE) events. Results: 70 out of 116 (60%) LMNA variant-positive (mean age 39±12, 43% male and 94% Caucasian) were included. Missense variants localizing to the C-terminal tail region showed a higher prevalence of transmural and apical LGE (Table). Amongst the 5 (7%) patients with this distinct apical pseudo-infarct LGE pattern, p.Arg471His-LMNA was identified in all cases. Among the 48/70 (69%) of patients with clinically manifest disease, p.Arg471His-LMNA-positive patients (n=6) were noted to have an increased prevalence of TE events [3 (50%) vs 5 (12%), 0=0.050] despite a reduced prevalence of AF [2 (33%) vs 32 (76%), p= 0.031]. No difference in AHF or MVA were observed, but AV block was less prevalent [1 (20%) vs 29 (74%), p= 0.029]. Conclusion: p.Arg471His-LMNA causes a distinct apical pseudo-infarct LGE pattern associated with an increased risk of TE events and a decreased prevalence of AV block and AF. Future studies are needed to better refine the role of domain/variant-specific risk-stratification/anticoagulation in cardiac laminopathy.

Logistic Regression Model on Nutritional Status Based on Body Mass Index (BMI) in Women of Reproductive Age (WRA) in Indonesia

The aggregate analysis of Riskesdas data in Indonesia revealed that nearly all provinces face a serious double burden of malnutrition. Both undernutrition and overnutrition negatively affect the quality of human resources, especially when they occur in adolescent girls, Women of Reproductive Age (WRA), and pregnant and breastfeeding women, as this can lead to intergenerational nutritional problems. Body Mass Index (BMI) is a nutritional status measurement technique recommended by WHO. Nutritional problems occur when BMI is abnormal or not ideal, categorized as underweight (&lt;18.5 kg/m²), pre-obesity (25-29.9 kg/m²), obesity class I (30-34.9 kg/m²), obesity class II (35-39.9 kg/m²), and obesity class III (&gt;40 kg/m²). Data from laboratory results completed up to 2017, based on selected samples from the 2013 Basic Health Research (Riskesdas), were used to assess the nutritional status of the Indonesian population. This study used 9,418 respondent data samples from 33 provinces in Indonesia. Logistic regression analysis was applied because the response variable in this study is binary, representing ideal BMI (1) and non-ideal BMI (0). The independent variables used in this study include Hemoglobin level, Ferritin level, CRP level, age group, physical activity, living location, marital status, education, and risky food consumption patterns. All independent variables are categorical. The study found that the factors influencing nutritional status based on BMI in WRA are Hemoglobin level, Ferritin level, CRP level, age, and location. The resulting logit model is logit (π)=0.27695-0.1491 (Non-anemic Hemoglobin level)-0.1721 (Normal Ferritin level)+0.741 (Non-infectious CRP level)-0.2933 (Adult age group)-0.0976 (rural location). logit (π)=0.27695-0.1491 (Non-anemic Hemoglobin level)-0.1721 (Normal Ferritin level)+0.741 (Non-infectious CRP level)-0.2933 (Adult age group)-0.0976 (rural location). The largest odds ratio The largest odds ratio was produced by the CRP level variable, indicating that CRP level is the most influential factor affecting nutritional status based on BMI in WRA, where non infectious CRP compared to infectious CRP increases the likelihood of WRA having an ideal BMI.

Clinical Relevance of Different Loads of Perivascular Spaces According to Their Localization in Patients with a Recent Small Subcortical Infarct.

Background and Purpose: Perivascular spaces (PVS) are usually enlarged in small vessel disease (SVD). However, the significance of PVS patterns in different locations is uncertain. Hence, we analyzed the distribution of PVS in patients with a recent small subcortical infarct (RSSI) and their correlation with clinical and imaging factors. Materials and Methods: In a cohort of 71 patients with an RSSI with complete clinical data, including the Pittsburgh Sleep Quality Index (PSQI), we segmented PVS in white matter (WM-PVS), basal ganglia (BG-PVS), and brainstems (BS-PVS) on 3T-MRI T2-weighted sequences, obtaining fractional volumes (%), and calculated the WM/BG-PVS ratio. We analyzed the Pearson's correlation coefficients between PVS regional loads. We used normalized PVS measures to assess the associations with clinical and MRI-SVD features (white matter hyperintensities (WMHs), number of lacunes, and microbleeds) in univariable and multivariable linear regressions adjusted for age, sex, and hypertension. Results: In our cohort (mean age 70 years; 27% female), the Pearson's correlation coefficients between WM-PVS/BG-PVS, WM-PVS/BS-PVS, and BG-PVS/BS-PVS were 0.67, 0.61, and 0.59 (all p < 0.001). In the adjusted models, BG-PVS were associated with lacunes (p = 0.034), WMHs (p = 0.006), and microbleeds (p = 0.017); WM-PVS with lacunes (p = 0.003); while BS-PVS showed no associations. The WM/BG-PVS ratio was associated with lacunes (p = 0.018) and the PSQI (p = 0.046). Conclusions: PVS burdens in different regions are highly correlated in patients with RSSI but with different SVD patterns. Sleep quality impairment might affect waste removal mechanisms differently in the WM and BG regions.

Analysis of Secondary School Infrastructure Distributions in Osogbo Metropolis, Nigeria

This study analysed the geospatial distribution of secondary schools infrastructure in the Osogbo metropolis, Nigeria, with a focus on evaluating accessibility and spatial patterns of school locations. By examining the spatial distribution, travel distances, and clustering of schools, this research offers a novel contribution to the educational planning literature. The data were collected through both primary and secondary sources, utilizing total enumeration for spatial data and a sampling frame of 17,595 students and 452 teachers across 120 public and private secondary schools. Using a balloting approach, at 15%, 19 schools were sampled, followed by a random sampling technique to choose 5% of students and staff, resulting in a total of 906 respondents (880 students and 26 teachers). The analysis, employing Moran's I Index, revealed a value of 0.75 and a p-value of 0.14, indicating a random distribution of schools with a z-score of 1.45, falling within the expected range. The average distance traveled to schools was 1.582 km, reflecting spatial inequities in accessibility. This study provides critical insights for policymakers, emphasizing the need for both public and private stakeholders to ensure equitable distribution and accessibility of secondary schools across Osogbo metropolis.

Spatial configuration of warehouses of different sizes in the City of Cape Town municipality

Background: Various metropolitan areas have become key centres for logistics activities, leading to a significant increase in the number of warehouses. However, there is a gap in the literature regarding the locational patterns of warehouses of different sizes in Africa.Objectives: To analyse the locational patterns of warehouses of different sizes within the City of Cape Town municipality, South Africa. The analysis focuses on the main factors influencing the placement of warehouses: transport infrastructure, land availability, proximity to customers and land-use zoning.Method: The study categorised warehouses based on their building footprint sizes, using the classifications of xx-small, x-small, small, medium, x-large and mega. A total of 396 warehouses were categorised accordingly. Proportional symbol mapping was then conducted in ArcGIS 10.1, assigning each warehouse type a specific weighting.Results: The study found that the areas surrounding the Port of Cape Town predominantly accommodated x-small warehouses. A diverse concentration of warehouses was identified along the railway, near the junctions of regional and national roads and within the areas enclosed by these roads. In addition, warehouses of diverse sizes were primarily situated in the main industrial zones of the municipality.Conclusion: The City of Cape Town municipality is encouraged to promote the consolidation of particularly large warehouses in strategic locations. These include industrial areas not geographically near the Port of Cape Town but with railway access to the port.Contribution: The study will contribute towards enhancing the sustainability and efficiency of logistics processes within the City of Cape Town.

The effect of the Weibull modulus on the shape of the stress–strain curves of thin-ply pseudo-ductile hybrid composites

This paper presents a numerical approach using ABAQUS CAE scripting to simulate the mechanical response of thin-ply pseudo-ductile hybrid composites. A parametric study demonstrates that interface critical fracture energy is essential for accurately modeling damage mechanisms and mechanical behavior. Correct shear strength identification enables the model to capture experimental observations, including fragmentation and the plateau region in the stress–strain curve. The analysis shows that the mechanical behavior of these composites is largely independent of fragmentation location patterns in the low-strain layer. Results emphasize the significant impact of the Weibull modulus on the stress–strain response, with careful selection leading to strong correlation with experimental data. Notable differences in best-fit Weibull moduli were observed for different materials, with higher values for high modulus carbon fibers.

Alterable Robotic Skin Using Material Gene Expression Modulation

AbstractRobotic skins that integrate artificial tactile sensing elements can substantially complement the perception dimension of social robots, presenting an indispensable part in human‐robot interaction (HRI). However, existing design frameworks compromise between versatility and sustainability due to the restricted range of characteristics available for a single constituent. Here an alterable robotic skin constructed from homogeneous sensing units are proposed, capable of cyclically realtering their inherent characteristics across a wide spectrum. Necessary characteristics to achieve positioning and pressure sensing subunits can be encoded in the feature motifs and extracted through condition‐induced differentiation, showcasing a remarkable resemblance to the gene expression in the living system. By virtue of this, up to 100‐fold differences in feature parameters are achieved, including modulus, surface state, and conductivity, to drive the target attribute coupling. The trans‐temporal reconstruction of materials enables the superb customization of functional building blocks, advancing the flexible separation and combination of different touch modes, including location, pressure, duration, and motion pattern. As a proof of concept, the alterable robotic skin is demonstrated that integrates a position‐sensing layer and a pressure‐sensing layer. It can accurately distinguish and recognize multi‐dimensional touch motions based on less‐channel data, which showcases an efficient haptic interaction application.

Analisis Perubahan Penggunaan Lahan Kawasan Kumuh Kecamatan Kandis Kabupaten Siak Tahun 2014 dan 2023

This research aims to identify the causes of land use change in the slum areas of Kandis Sub-district, Siak Regency, and analyze land use change from 2014 to 2023 using information maps. The method used is descriptive quantitative, with data analysis techniques based on weighting the quality of slum areas through geographic information systems (GIS) using GIS applications to identify location, distance, and settlement patterns. Sampling was done through proportional stratified random sampling. Data collection used primary data in the form of interviews and secondary data in the form of documentation studies. The results showed that: (1) The causes of land use change in Kandis Sub-district slum areas include an increase in population density every year, narrow spacing between buildings (1.5 to 3 meters), lack of clean water infrastructure, inadequate drainage, inadequate toilet conditions, and lack of waste disposal facilities. Community income is also low, below Rp. 1,000,000 per month. (2) The area of the slum area has increased significantly, from 0.27 Ha in 2014 to 9.6 Ha in 2023.

Exploring Chronic Pain Patterns and Associations With All-Cause Dementia: Results From UK Biobank

The count of locations with chronic pain is widely used in research and clinical practice. However, this approach might be too simplistic to fully capture the complexity of chronic pain experiences. This study identified prevalent patterns of chronic pain locations and evaluated their associations with incident dementia among middle-aged and older adults in the UK. Data were from 445,530 participants who were free of dementia at baseline (2006–2010) in the UK Biobank. We calculated the incidence rates of all-cause dementia by the 20 most prevalent combinations of pain locations assessed at baseline. Cox models were utilized to examine the hazard ratio of incident dementia among each of these 20 combinations compared to 3 groups: 1) participants without chronic pain, 2) participants with only a single chronic pain location not included in the combination, and 3) participants with only a single chronic pain location included in the combination. The 3 most prevalent combinations were neck and back (5.7%), back and knee (5.4%), and neck and knee (4.5%). Chronic back, neck, and knee pain was commonly present either individually or simultaneously in combinations associated with higher dementia rates than persons without chronic pain. The combinations involving back, neck, and knee were associated with higher dementia rates than groups with 1 pain location not included in the combination. Chronic pain is not randomly present in body locations. Understanding how different patterns of chronic pain locations relate to dementia provides new insights into dementia prevention through pain relief. PerspectiveThis article unveils chronic pain patterns and dementia risks in the UK Biobank. Chronic pain in back, neck, and knee presents frequently, either individually or in combinations associated with increased dementia rates. Chronic pain combos correlate with diverse dementia rates, guiding targeted prevention strategies through pain management.