Radar Chart Research Articles

Microneedling Radiofrequency for Acne Vulgaris in Adolescents as a Promising Device-Based Treatment: A Clinical Case Series.

Background: Acne vulgaris is a persistent inflammatory condition affecting the sebaceous unit, in whose pathophysiology the Propionibacterium acnes (P. acnes) play an important role, and that can have major psychological effects. Objective: The current study aims to investigate the safety and efficacy of an innovative fractional microneedle radiofrequency (RFMN) device for acne management in adolescents. Materials and Methods: A total of 21 Caucasian young patients with facial acne of all grades of severity underwent three treatment sessions spaced 15/30 days apart with an RFMN device. The Acne Radar Questionnaire was administered to all patients. The data were represented using Radar graph, and the sample population was distributed in quartiles. The first quartile, the second quartile, and the third quartile were calculated for all patient populations before and at 6 months follow-up (6 MFU). The evaluation of the severity of acne lesions was assessed by using the Global Acne Severity scale. Photographs of patients with acne were acquired with a digital camera before and at 6 MFU after the last treatment session. Results: The mean values of each item of the Acne Radar Questionnaire significantly (p < 0.001) improved for all patients treated. The analyses obtained by the quartiles showed that all variables improved after the treatment with the study device. The photographic images confirm the positive results and the improvement of acne lesions. No major side effects were observed. Conclusions: The study system appears to be an effective and safe treatment choice for patients with inflammatory active acne and acne scarring.

Abstract 4146319: Racial Disparities and Driving Factors in the AHA's Life's Essential 8 Among American Adults: Insights from NHANES 2011-2020

Backgrounds: The AHA introduced "Life's Essential 8" (LE8) in 2022 as a comprehensive metric to facilitate detailed tracking and management of cardiovascular health (CVH) at the population level. However, the racial/ethnic differences in LE8, how these differences change over time, and the driving factors of LE8 in each subgroup remain unclear. Methods: We utilized data from the continuous NHANES from 2011 to 2020 to examine racial disparities and driving factors in the AHA's LE8 among American adults. To ensure national representation, appropriate weights were applied. Survey-weighted and age-standardized trends in LE8 scores by racial and ethnic groups were analyzed using generalized linear models (GLMs). To identify the driving factors—specific metrics influencing the LE8 overall score—we calculated z-scores for all components. Then we used the spider chart to visualize the driving factors. Results: A total of 13,915 adult were included, of whom the mean age is 47.9 ± 0.37 years, 6,612 (47.55%) were women, 3,485 (14.63%) were Hispanics, 5,335 (69.74%) were non-Hispanic Whites, 3,464 (10.82%) were non-Hispanic Blacks, and 1,631 (4.81%) were Asians. From 2011-2020, the LE8 scores did not change significantly overall and across all racial groups (all p-trend &gt; 0.10, Figure 1 ). During the study period, racial/ethnic disparity persisted with Asian adults consistently having the highest LE8 scores and Black adults having the lowest scores. Notably, the lower LE8 scores in Black adults were primarily driven by lower scores of diet, blood pressure, and sleep health, whereas the higher LE8 scores in Asian adults primarily driven by higher scores of diet, nicotine exposure, and BMI. The LE8 scores in White adults were primarily driven by blood glucose and physical activity and the LE8 score in Hispanic adults were primarily driven by blood pressure ( Figure 2-A ). In 2017-2020 ( Figure 2-B ), Black adults had the lowest scores for all LE8 components, except for blood lipids. In contrast, Asian adults had the highest scores in diet, physical activity, nicotine exposure, and sleep health. Throughout the study period, the racial/ethnic disparity in LE8 score did not change significantly (all p &gt;0.10). Conclusion: We found persistent racial disparities in cardiovascular health among U.S. adults, with Black adults having the lowest LE8 scores in almost all components. The driving factors for LE8 scores varied by racial subgroups, emphasizing the need for targeted interventions.

Grouping Of Regencies In Papua Province In 2023 Based On The Quality Of Human Resources

Since the establishment of the Golden Indonesia Vision in 2045, the government has targeted accelerating the distribution of education and improving the level of health and quality of life of the community as one of the efforts to develop the quality of human resources. This research focuses on the grouping of regencies in Papua Province in 2023 based on a composite index that describes the quality of human resources in Papua Province in 2023. The data used in this research is macro secondary data which is analyzed using descriptive analysis in the form of radar charts and inferential analysis using factor analysis. Formation of a composite index using factor analysis. Three factors were obtained which were used to create a composite index which was then used to present a better picture of various indicators supporting human resource development such as education, health, employment, economic and other variables. Then grouping by dividing into five groups based on quantiles to see spatial stratification.

Leveraging the Electronic Health Record to Measure Resident Clinical Experiences and Identify Training Gaps: Development and Usability Study.

Competence-based medical education requires robust data to link competence with clinical experiences. The SARS-CoV-2 (COVID-19) pandemic abruptly altered the standard trajectory of clinical exposure in medical training programs. Residency program directors were tasked with identifying and addressing the resultant gaps in each trainee's experiences using existing tools. This study aims to demonstrate a feasible and efficient method to capture electronic health record (EHR) data that measure the volume and variety of pediatric resident clinical experiences from a continuity clinic; generate individual-, class-, and graduate-level benchmark data; and create a visualization for learners to quickly identify gaps in clinical experiences. This pilot was conducted in a large, urban pediatric residency program from 2016 to 2022. Through consensus, 5 pediatric faculty identified diagnostic groups that pediatric residents should see to be competent in outpatient pediatrics. Information technology consultants used International Classification of Diseases, Tenth Revision (ICD-10) codes corresponding with each diagnostic group to extract EHR patient encounter data as an indicator of exposure to the specific diagnosis. The frequency (volume) and diagnosis types (variety) seen by active residents (classes of 2020-2022) were compared with class and graduated resident (classes of 2016-2019) averages. These data were converted to percentages and translated to a radar chart visualization for residents to quickly compare their current clinical experiences with peers and graduates. Residents were surveyed on the use of these data and the visualization to identify training gaps. Patient encounter data about clinical experiences for 102 residents (N=52 graduates) were extracted. Active residents (n=50) received data reports with radar graphs biannually: 3 for the classes of 2020 and 2021 and 2 for the class of 2022. Radar charts distinctly demonstrated gaps in diagnoses exposure compared with classmates and graduates. Residents found the visualization useful in setting clinical and learning goals. This pilot describes an innovative method of capturing and presenting data about resident clinical experiences, compared with peer and graduate benchmarks, to identify learning gaps that may result from disruptions or modifications in medical training. This methodology can be aggregated across specialties and institutions and potentially inform competence-based medical education.

Geosocial Tools for Community Diagnosis and Health Promotion.

Growing research highlights the role of local community contexts in health and well-being. Communities function as central arenas for health promotion as individuals and community spaces interact in daily life. To better communicate the linkages between community and health, we demonstrate the use of a set of geosocial tools for community diagnosis and health promotion, conceptually grounded in the framework of geosocial embeddedness, using data from community surveys and participatory research conducted with local inhabitants to engage their competency. Through a web-based interactive map generated using a geographic information system, we identified public territories in the municipality where greater policy attention is needed to enhance health. Through an intersectional matrix produced using multilevel analysis, we identified precise social groups by intersectional categories that are most at risk of loneliness for targeted intervention. A health radar chart on multidomain indicators illustrated information on group dynamics and longitudinal comparisons for health promotion. Overall, these tools offered not only diagnoses of the most vulnerable social groups for tailored interventions but also insight for policymakers to improve infrastructure and material resources to promote health. We conclude that geosocial tools offer a promising approach toward sharpening health communication and improving health in the community.

Characterizing the Molecular Gas in Infrared Bright Galaxies with CARMA

We present the CO(1–0) maps of 28 infrared-bright galaxies from the Great Observatories All-Sky Luminous Infrared Galaxy Survey (GOALS) taken with the Combined Array for Research in Millimeter Astronomy (CARMA). We detect 100 GHz continuum in 16 of the 28 CARMA GOALS galaxies, which trace both active galactic nuclei (AGNs) and compact star-forming cores. The GOALS galaxies show a variety of molecular gas morphologies, though in the majority of cases the average velocity fields show a gradient consistent with rotation. We fit the full continuum spectral energy distributions (SEDs) of each of the sources using either magphys or SED3FIT (if there are signs of an AGN) to derive the total stellar mass, dust mass, and SFRs of each object. We adopt a value determined from luminous and ultraluminous infrared galaxies (LIRGs and ULIRGs) of α CO = 1.5−0.8+1.3 M ⊙ (K km s−1 pc2)−1, which leads to more physical values for f mol and the gas-to-dust ratio. Mergers tend to have the highest gas-to-dust ratios. We assume the cospatiality of the molecular gas and star formation and plot the CARMA GOALS sample on the Schmidt–Kennicutt relation, where we find that they preferentially lie above the line set by normal star-forming galaxies. This hyper-efficiency is likely due to the increased turbulence in these systems, which decreases the freefall time compared to star-forming galaxies, leading to “enhanced” star formation efficiency. Line wings are present in a non-negligible subsample (11/28) of the CARMA GOALS sources and are likely due to outflows driven by AGNs or star formation, gas inflows, or additional decoupled gas components.

Ayurvedic perspective of dietary risk factors of colorectal cancers - A hospital-based case control study

BackgroundThe rising prevalence of colorectal cancer (CRC) may be attributed to various nutritional and behavioural factors, making both factors as important topics for discussion to the layman and the oncology community. ObjectiveTo explore additional dietary risk factors, other than those already known, according to the Ayurvedic perspective in CRC patients. MethodsDetail dietary data was collected from 420 patients of CRC and 116 healthy volunteers registered at our institute with the help of a food frequency questionnaire. Descriptive analysis was done by plotting radar charts, whereas the logistic regression models were used to calculate the adjusted Odd Ratios (ORs) associated with consumption of green chilli, red chilli powder and curd, individually and in combinations. ResultsThe controls in the present study were younger, had more males, the majority of them belonged to middle- and lower-income groups and had a family history of cancer as compared with cohort of case studies. Green chilli consumption was found to be the maximum within the lower-income group (n = 18, 66.67%), while that of red chilli (n = 150, 48.23%), and curd (n = 107, 34.04%) within the middle class. Maximum consumption of green chillies (Males n = 48, 29.27%; Females n = 36, 21.95%), red chillies (Males n = 40, 29.85%; Females n = 16, 24.61%) and curd (Males n = 31, 28.97%) was observed in the age group 46 to 60 irrespective of sex. Tobacco was found to be the most common addiction in all groups. The maximum number of patients frequently consuming these three dietary items presented majorly with rectal cancer, and liver metastasis and were in advanced grade and stage of cancer. Curd and curd with red chilli powder had a significant association with the development of CRC with an OR of 2.7280 (95% CI 1.6346 to 4.5531) and 5.0806 (95% CI 2.4015 to 10.7485), respectively, which was highly significant (p < 0.0001). Green chilli was notably an associated risk with an OR of 2.0095 (95% CI 1.3258 to 3.0458), which was also statistically significant (p = 0.001). Red chilli powder and green chilli with curd had ORs as 1.6917 (95% CI 1.1105 to 2.5771) and 2.1778 (95% CI 1.1591 to 4.0918) with p = 0.0144 and 0.0156, respectively. ConclusionIn the present study, green chilli, red chilli, and curd are identified as additional dietary risk factors for colorectal cancers, owing to their ability to produce chronic inflammation leading to various inflammatory conditions including cancer.

Optimization of the performance of copper/graphite system for GIL Tri‐post grounding electrode based on plasma sintering technology

Grounding electrodes, as crucial components of GIL bushings, experience friction with the GIL shell during long-term operation, generating metal particles that induce partial discharge. This phenomenon can lead to internal insulation breakdown, severely compromising GIL operation. This study focuses on enhancing the wear resistance of grounding electrodes by proposing a solution involving copper-graphite composites. Copper/graphite composite materials were fabricated using Spark Plasma Sintering technology. Additionally, the impact of graphite content and particle size on wear resistance, hardness, and electrical conductivity was comprehensively analyzed. Performance evaluation using radar chart analysis identified the optimal solution. The results indicate that at a sintering temperature of 960 °C, pressure of 45 MPa, and holding time of 15 min, with a graphite mass fraction of 7 %, the grounding electrode material exhibits a smooth surface and uniform distribution of graphite. Furthermore, when the graphite particle size is 4 μm, the friction coefficient remains approximately 0.7 with minimal fluctuations. The abrasion produces scratches measuring only 190 μm, and the wear rate is recorded at 2.1128 × 10−4 mm³/N·m, while the hardness reaches 59.6 HV, an increase of 19.6 HV compared to a particle size of 40 μm. In conclusion, an appropriate graphite content effectively enhances the wear resistance of the grounding electrode, and a reduction in graphite particle size optimizes overall performance. When the composite material contains 7 % graphite with a particle size of 4 μm, the performance is optimal, allowing the grounding electrode to maintain its original properties while demonstrating excellent wear resistance. This study provides a significant approach to optimizing the performance of grounding electrodes.

Surgical and Oncological Outcomes of Level III-IV Versus Level I-II Inferior Vena Cava Thrombectomy: A Decennial Experience of a High-Volume European Referral Center.

In patients with renal cell carcinoma (RCC) the role of the extent of tumor thrombus into the inferior vena cava (IVC) has never been addressed from a surgical and oncologic standpoint. This study aims to evaluate differences between level III-IV versus level I-II patients concerning peri- and postoperative morbidity, additional treatments and long-term oncological outcomes. Overall, 40 patients with RCC underwent radical nephrectomy (RN) with IVC thrombectomy at a single European institution between 2010 and 2023. Complications were reported according to the European Union (EAU) guidelines recommendations. Spider chart served as graphical depiction of surgical and oncologic outcomes. Overall, 22 (55%) and 18 (45%) patients harbored level III-IV and I-II IVC thrombus. Level III-IV patients experienced significantly higher rates of intraoperative transfusions (68 vs 39%), but not significantly higher rates of intraoperative complications (32% vs 28%). Level III-IV patients had significantly higher rates of postoperative transfusions (82% vs 33%) and Clavien Dindo ≥3 complications (41% vs 15%). In level III-IV versus level I-II patients, median follow up was 482 and 1070 days, the rate of distant recurrence was 59% and 50%, the rate of systemic progression was 27% and 13%, and the rate of additional treatment/s was 64% and 61%, respectively (all p values > 0.05). Overall survival was 36% in level III-IV patients and 67% in level I-II (p = 0.001). Our findings suggest that patients with level III-IV RCC who are candidates for IVC thrombectomy should be counselled about the higher likelihood of postoperative severe adverse events and worse overall survival relative to level I-II counterparts.

Modifying 3D composite textiles as functional panels

Honeycomb fabric-reinforced composites have been widely used recently, due to their prominent properties, when compared to other materials. This is because they are lighter in weight and stronger than other materials. Thus, they can absorb different types of energies, such as mechanical and thermal energies. A multi-layer fabric is developed by creating a unique fabric structure, to enable the presence of air voids within the panels produced, which diminishes the heat transfer rate through the material. The structure applied in samples used in this manuscript is novel, as it consists of a 3D fabric produced using a traditional loom with a special technique, by dividing the warp yarns into four layers. A skin of textile, produced from textile wastes, is applied, along with the technique of hardening the 3D fabrics to keep the open areas in the structure with the aid of pre-designed pins and a resin. The composite panel was produced with the pins used to open the fabric’s structure, and the resin was applied as a matrix holding the structure and keeping its dimensions. Panels of the 3D fabric-reinforced composite obtained from cotton and polyester yarns were produced, to achieve thermal insulation and good mechanical properties, which enable the use of the fabric in various applications. The fabric has two panels of each material, one with skin and the other without skin. This skin consists of low-quality fabric, mainly made from textile wastes. The physical and mechanical characteristics of the developed panels were measured. Functional performance, such as thermal and sound properties, were also measured. Testing thermal conductivity, it was found that panels made of cotton had higher conductivity than panels made of polyester. In addition, the presence of the skin improved the thermal insulation and mechanical strength of the panels compared to those without skin. In mechanical testing, polyester-based panels showed better mechanical properties than panels based on cotton. In sound absorption testing, all samples reached the limits for sound absorption. The number of layers affected the sound absorption coefficient (SAC) and the noise reduction coefficient (NRC). Also, the presence of the skin improved NRC. Finally, all samples had an aesthetic appearance, due to the presence of surface waves, but the sample containing the polyester material, with double layers, and the laminated skin made of waste fabric had the highest functional panel performance, in terms of radar chart areas. This means that it can be used in the field of architecture as a façade panel or as an indoor wall used for isolation, which do not require paint.

A quality by design paradigm, novel, and fast LC-MS/MS method to quantify vortioxetine in rat brain homogenate and its assessment of greenness, whiteness, and blueness tools

Vortioxetine (VOR) is an antidepressant drug with multimodal mechanism of action. In this work a sensitive and simple bioanalytical LC-MS/MS method for analysis of VOR in spiked rat brain tissue was developed and validated. Chromatographic analyses were performed in isocratic mode, and Citalopram (CT) was found as a suitable internal standard. Supelco Ascentis® Express Phenyl-Hexyl column was used as stationary phase, and the mobile phase was 0.15 % formic acid in acetonitrile:water (55:45, v/v). A Box-Behnken Design − based method optimization was applied to explore the impact of analytical conditions on chromatographic separation of VOR and CT. Detection was realized via a triple quadrupole LC-MS/MS system; the mass transition ion-pair has been followed as m/z 299.05 → 149.90 for VOR, m/z 325.20 → 109.05 for CT. Analytical method validation was performed according to International Council for Harmonisation (ICH) guideline M10 on bioanalytical method validation. The calibration curve was within the range of 30 to 450 ng/mL. LOD and LLOQ were calculated at 10 and 30 ng/mL, respectively. Recovery was 90 % for the lower limit of quantification and between 98 % to 105 % for the other concentration levels. Total run time was 5 min, retention times of citalopram and vortioxetine were 2.4 and 3.1 min, respectively, and a good chromatographic separation was obtained under optimal conditions. The solvents used to make the method greener were evaluated with the Green Solvent Selection Tool and Green Index Spider chart. The AGREE score for the greenness of the developed method was calculated as 0.61 and was also evaluated with the ComplexGAPI pictogram. The BAGI score for blueness was calculated as 72.5, and the RGBfast score for whiteness was calculated as 50. The method was determined to be most green, white, and blue.

Operational Performance Evaluation Model for Food Processing Machinery Industry Chain

This study aims to create a performance evaluation model for the food processing machinery industry. The goal is to help food processing plants improve both process quality and competitiveness. Additionally, component failures may disrupt the continuous operation of the food processing machine, potentially resulting in insufficient production and delays in delivery, which in turn leads to cost losses. For the sold food processing machinery, decreases in the average number of failures within a unit of time, the average repair response time when a failure occurs, and the average repair duration are three crucial factors in minimizing the total expected loss due to machine failures. Based on these three important factors, this study established the following evaluation indices: (1) the processing performance index, (2) the repair reporting performance index, and (3) the maintenance performance index. These indices serve as tools for assessing the performance of the three key operational aspects. This study employed a radar chart to construct the evaluation model, which can directly compare the critical values with the point estimates of three indices. Consequently, this approach can judge whether the operational performance has achieved the required level. This can maintain the simplicity and usability of point estimates while reducing the risk of misjudgment due to sampling errors.

An integrated strategy for deciphering quality markers of Terminaliae Belliricae Fructus based on a three-dimensional characteristic model

Terminalia bellirica (Gaertn.) Roxb. is an ethnomedicinal plant that has been utilized in Tibetan and traditional Chinese medicine (TCM). Nevertheless, its quality standard officially listed in the Chinese Pharmacopoeia does not include any content determination of the indicator components of Terminaliae Belliricae Fructus, which constrains the effective quality evaluation of medicinal material and related products. In this paper, a three-dimensional “content-pharmacokinetics-pharmacology” network strategy was developed to identify the quality markers (Q-markers) of Terminaliae Belliricae Fructus in terms of “measurability”, “traceability” and “effectiveness”. Chromatographic fingerprint analysis was performed to outline its chemical contour, and identify the differential components of 17 batches of Terminaliae Belliricae Fructus combined with multivariate statistics analysis and UPLC-QTOF-MS analysis. Serum pharmacochemistry analysis was implemented on rats, and 25 prototype components absorbed into the blood were identified. By network pharmacology analysis, a component-disease-target-pathway network was constructed, thus validating the effectiveness of the chemical components of Terminaliae Belliricae Fructus. Afterwards, the above screened candidate components were put into construction of three-dimensional "radar chart". According to the calculated regression area (RA) and coefficient of variation (CV) values, the potential Q-markers was determined, followed by “specificity” evaluation. Ultimately, ellagic acid (EA), chebulagic acid (CHA), gallic acid (GA), chebulinic acid (CA), corilagin (CO) and chebulanin (CH) were specified as the Q-markers of Terminaliae Belliricae Fructus. Owing to high content, good pharmacokinetic property, high pharmacological activities and specificity. The screened Q-markers could offer a scientific foundation for the quality control of Terminaliae Belliricae Fructus, and the proposed strategy is demonstrated to be reliable and feasible for deciphering Q-markers of TCM.

Design of ultralight multifunctional sandwich structure with n-h hybrid core for integrated sound absorption and load-bearing capacity

Multifunctionality has become an important evaluating index for structural design because engineering applications in complicated environment call for multifunctional and ultralight structures. With this in mind, this paper proposes a novel ultralight multifunctional micro-perforated sandwich structure with N-H type hybrid cores, which demonstrates superior sound absorption across a wide frequency range, while also achieving good load-bearing capacity. An analytical model is established to calculate the sound absorption coefficient, which is then validated through numerical simulation and experimental measurements. Furthermore, the underlying physical mechanisms for the satisfactory sound absorption are explored. The influences of specific parameters on the broadband sound absorption performance of the proposed structure are quantitatively demonstrated through systematic parameter studies. Additionally, in order to demonstrate the preeminent multifunctional attributes of the proposed structure, a numerical simulation method is employed to evaluate its out-of-plane compression properties. Compared to other micro-perforated sandwiches, the proposed structure exhibits an improvement in load bearing capacity. A radar chart is also created to display the advantages of the proposed micro-perforated sandwich structure over others. Overall, the proposed ultralight sandwich structure with N-H type hybrid cores shows great significance for engineering applications that demand high comprehensive performances owing to its multifunctionality.

Comparative Approach to Performance Estimation of Pulsed Wave Doppler Equipment Based on Kiviat Diagram

Quality assessment of ultrasound medical systems is a demanding task due to the high number of parameters to quantify their performance: in the present study, a Kiviat diagram-based integrated approach was proposed to effectively combine the contribution of some experimental parameters and quantify the overall performance of pulsed wave Doppler (PWD) systems for clinical applications. Four test parameters were defined and assessed through custom-written measurement methods based on image analysis, implemented in the MATLAB environment, and applied to spectral images of a flow phantom, i.e., average maximum velocity sensitivity (AMVS), velocity measurements accuracy (VeMeA), lowest detectable signal (LDS), and the velocity profile discrepancy index (VPDI). The parameters above were scaled in a standard range to represent the four vertices of a Kiviat plot, whose area was considered the overall quality index of the ultrasound system in PWD mode. Five brand-new ultrasound diagnostic systems, equipped with linear array probes, were tested in two different working conditions using a commercial flow phantom as a reference. The promising results confirm the robustness of AMVS, VeMeA, and LDS parameters while suggesting further investigations on the VPDI.

Comparative analysis of advanced deep learning models for predicting evapotranspiration based on meteorological data in bangladesh.

Evapotranspiration is one of the crucial elements in water balance equations and plays a pivotal role in the water and energy cycle of an area. An accurate and precise estimation and prediction of reference evapotranspiration (ETo) is necessary for regional management of water resources and irrigation scheduling. The challenge of predicting daily evapotranspiration with limited meteorological data in Bangladesh. This study aims to predict daily evapotranspiration using limited meteorological data of Bangladesh by three deep learning (CNN, GRU, LSTM) and one hybrid (CNN-GRU) model. The novel method of hybrid CNN-GRU models, which have not been commonly used for this purpose. The performance of models was evaluated by five accuracy matrices R2, RMSE, MAE, MAPE, and CE and comparison is visualized by radar graphs. The study's novelty lies in the use of hybrid CNN-GRU models to estimate reference evapotranspiration, as this algorithm has not been commonly used for this purpose. In the case of the Rangpur station, the hybrid CNN-GRU algorithm outperformed other models, achieving the best values across various statistical metrics during both the training and testing phases. The highest correlation coefficient values of approximately 0.994 and 0.995. Moreover, during training and testing stages, the hybrid model had the lowest MAE (0.076, 0.068) and RMSE (0.138, 0.106) at the Rangpur station. Additionally, in the Sreemangal station, it can be notable that the statistical parameter RSME found superior results in the hybrid model around 0.225 and 0.174, respectively. In addition, the highest R2 and CE values were noted as 0.986, 0.987 and 0.985, 0.986 during the training and testing phases, respectively. The comparison suggests that the hybrid model will be best suited for prediction with the limited meteorological data. The outcome of the present research signifies the ability of deep learning methods in the prediction of evapotranspiration and the dominant variables affecting the changes the in context of Bangladesh.

How Do Changes in Ecosystem Services Multifunctionality Influence Human Wellbeing? Evidence From the Yangtze River Delta Urban Agglomeration in China

ABSTRACTA proclaimed goal of landscape management is to improve the multifunctionality of ecosystem services (ESs) to sustain higher levels of human wellbeing (HWB). Although the enhancement of ES multifunctionality is often considered to lead to better HWB, empirical evidence directly supporting this claim remains scarce. This study investigates the relationship between ES multifunctionality and HWB in rapidly urbanizing regions, using the Yangtze River Delta Urban Agglomeration in China as a case study. We quantified ES multifunctionality with the Gini‐Simpson Diversity Index and identified ES multifunctional types using the bundle approach. We also calculated both the HWB mean score and HWB evenness score with the improved radar chart method, examining their relationships with ES multifunctionality through Spearman correlation analysis and the Kruskal–Wallis rank‐sum test. Results showed that (1) ES multifunctionality exhibited varied relationships with HWB indicators, but overall showed significant negative associations with both HWB mean score and HWB evenness score. (2) Each HWB indicator significantly differed across the detected ES bundles, but none of the bundles exhibited the highest values of all HWB indicators. (3) HWB mean score and HWB evenness score were generally higher in the peri‐urban bundle while lower in the agriculture and forest bundles. Our findings suggest that peri‐urban landscapes can provide relatively higher and more balanced levels of HWB in urban agglomerations. This also implies that a “land‐sharing” urban development model, which balances natural and built environments, may be more beneficial for enhancing ES multifunctionality and HWB compared with a “land‐sparing” model, where natural and built areas are separated.

Assessment of the Risks of Zoonotic Infection at the Primatology Centre of the Interdisciplinary Medical Research Centre of Franceville in Gabon.

Non-human primate (NHPs) conservation sites could be sites of exchange of pathogens involved in infectious diseases. It is important to assess the potential risks associated with this type of structure. The objective of this study was to carry out a risk assessment of the Primatology Centre housed in the Interdisciplinary Centre for Medical Research of Franceville (CIRMF). A questionnaire was administered to the centre's staff to assess the risk associated with each workstation, followed by a review of the various pathogens identified in NHPs. The data were analysed using two diagrams: the Kiviat diagram and the Pareto diagram. Based on our results, a variety of pathogens such as viruses, bacteria and parasites, potentially transmissible to humans, were described in the NHPs at the Primatology Centre of CIRMF. The position most exposed to zoonotic risks was that of animal handlers. The Primatology Centre of CIRMF is a potential transfer site for the transfer of zoonotic agents. To avoid the risk of parasite exchange between staff and animals, the implementation of biosecurity measures is essential.

An integrated framework for eye tracking-assisted task capability recognition of air traffic controllers with machine learning

To effectively address the continuously increasing demands of air transport, air traffic management (ATM) systems are evolving towards a human-artificial intelligence (AI) hybrid automation paradigm. In this paradigm, air traffic controllers (ATCOs) play a crucial role in ensuring safe and efficient operations. Recognizing ATCOs’ task capability is essential for evaluating performance, optimizing task assignments, and personalizing training strategies. Physiological signals, such as eye movements, offer objective insights into human behavior and cognitive processes, making them valuable for identifying the task capability of ATCOs. In this study, an integrated framework leveraging machine learning is proposed to achieve this goal. First, a Transformer-attentional long short-term memory (LSTM) network is developed to analyze eye movement patterns, capturing both global and long-term dependencies for precise skill level detection. Next, manual parameters are extracted from the raw eye tracking data and the SHAP (SHapley Additive exPlanation) method is utilized to determine the importance of these parameters, aiding in the selection of relevant performance metrics. Furthermore, a radar chart is implemented to intuitively visualize and compare performance metrics across different skill levels based on the selected parameters. A case study and extensive experiments are conducted to validate the effectiveness of the proposed framework. This research advances task capability recognition for ATCOs in a human-in-the-loop scenario, with a focus on expertise level detection, parameter importance identification, and performance metric comparison.

The Shape(s) of Information Practice: Using Radial Mapping Qualitatively

ABSTRACTInformation practices comprise both seeking and avoidance. Although information practices scholars use qualitative and visual methods to understand seeking practices, they rarely do so to understand avoidance. This paper proposes a new visual method that supports revealing and explaining the complex interplay of seeking and avoidance in rich qualitative data. We introduce seven dimensions of information seeking and avoidance practice (intensity, granularity, engagement, control, relevance, quality, and timeliness). We conceptualize these visually as axes radiating outward from a central origin. We use Excel radar charts to depict these dimensions, allowing us to identify and characterize the shapes of information seeking and avoidance in everyday information practices. We apply our approach to two cases from published literature to show how mapping reveals the interplay between seeking and avoidance at one point in time and over time. We propose potential data collection and analysis applications of this method for information practices research.