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Social influence and consensus building: Introducing a q-voter model with weighted influence
We present a model of opinion formation where an individual’s opinion is influenced by interactions with a group of agents. The model introduces a novel bias mechanism that favors one opinion, a feature not previously explored. In the absence of bias, the system reduces to a mean field voter model. We identify three regimes: favoring negative opinions, favoring positive opinions, and a neutral case. In large systems, equilibrium outcomes become independent of group size, with only the bias influencing the final consensus. For smaller groups, however, the time to reach equilibrium depends on group size. Our results show that even a small initial bias leads to a consensus, with all agents eventually sharing the same opinion if the bias is not zero. The system also exhibits critical slowing down near the neutral bias, which acts as a dynamical threshold. The time to reach consensus scales logarithmically for non-neutral biases and linearly with system size for the neutral case. While short-term dynamics are influenced by group size, long-term behavior is determined solely by the bias.
Dynamic emotion intensity estimation from physiological signals facilitating interpretation via appraisal theory
Appraisal models, such as the Scherer’s Component Process Model (CPM), represent an elegant framework for the interpretation of emotion processes, advocating for computational models that capture emotion dynamics. Today’s emotion recognition research, however, typically classifies discrete qualities or categorised dimensions, neglecting the dynamic nature of emotional processes and thus limiting interpretability based on appraisal theory. In our research, we estimate emotion intensity from multiple physiological features associated to the CPM’s neurophysiological component using dynamical models with the aim of bringing insights into the relationship between physiological dynamics and perceived emotion intensity. To this end, we employ nonlinear autoregressive exogeneous (NARX) models, as their parameters can be interpreted within the CPM. In our experiment, emotions of varying intensities are induced for three distinct qualities while physiological signals are measured, and participants assess their subjective feeling in real time. Using data-extracted physiological features, we train intrasubject and intersubject intensity models using a genetic algorithm, which outperform traditional sliding-window linear regression, providing a robust basis for interpretation. The NARX model parameters obtained, interpreted by appraisal theory, indicate consistent heart rate parameters in the intersubject models, suggesting a large temporal contribution that aligns with the CPM-predicted changes.
Analysis of cervical and breast cancer screening behavior and its influencing factors among urban and rural women in Beijing
Objective To clarify the screening behavior and influencing factors of females with breast cancer and cervical cancer in suburban areas and to provide a scientific basis for the subsequent implementation of targeted health education, intervention measures and the formulation of relevant policies. Methods This study used a multi-stage stratified random sampling method to select 4, 000 women in urban and rural areas of Beijing to analyze their behavior, basic situation, and influencing factors regarding cervical and breast cancer screening. Results The sample size of the final included valid analysis was 3861 people, and the screening rate was 27.25% for cervical cancer, 20.64% for breast cancer, 30.46% for at least one screening and 17.43% for both cervical cancer and breast cancer screening. The rate of four screening conditions was greater in urban areas (PCervical cancer screening = 31.1%, PBreast cancer screening = 22.0%, PAt least one = 33.9%, PBoth cancers were screened = 19.1%) than in rural areas (PCervical cancer screening = 22.6%, PBreast cancer screening = 19.0%, PAt least one = 26.2%, PBoth cancers were screened = 15.4%) and was greater with medical insurance (PCervical cancer screening = 28.7%, PBreast cancer screening = 21.7%, PAt least one = 32.0%, PBoth cancers were screened = 18.5%) than without medical insurance (PCervical cancer screening = 12.8%, PBreast cancer screening = 10.3%, PAt least one = 15.6%, PBoth cancers were screened = 7.5%). The highest percentage of the four screening conditions was found in the 45–59-year-old group (PCervical cancer screening = 36.0%, PBreast cancer screening = 29.8%, PAt least one = 39.5%, PBoth cancers were screened = 26.4%). The rate of cervical cancer screening behavior increased with increasing education level and family per capita monthly income, and the highest percentage of respondents had a college education or above (PCervical cancer screening = 35.2%, PBreast cancer screening = 23.6%, PAt least one = 38.2%, PBoth cancers were screened = 20.6%), as did the percentage of families whose per capita monthly income was above 15, 000 yuan (PCervical cancer screening = 34.7%, PBreast cancer screening = 27.3%, PAt least one = 38.3%, PBoth cancers were screened = 23.6%). Multivariate analysis revealed that an age range of 45 to 59 years (PAll four screening conditions were obtained<0.001), an education level of junior high school, a high school (PAll four screening conditions were obtained<0.001), a college education or above (PAll four screening conditions were obtained<0.001), a marital status of a spouse (PAll four screening conditions were obtained<0.001), a divorce status (PAll four screening conditions were obtained<0.001) or a widowhood status (PAll four screening conditions were obtained<0.001), and a medical insurance status (PAll four screening conditions were obtained<0.001) were positively correlated with the percentages of the four screening behaviors. Conclusion The level of "two- cancer" screening behavior of suburban residents in Beijing still warrants improvement, and precision nutrition and health communication and intervention should be carried out continuously for rural residents, individuals under age 45, unmarried individuals, individuals with a primary school education and below, and people without medical insurance.
Exploring a pico-well based scRNA-seq method (HIVE) for simplified processing of equine bronchoalveolar lavage cells
Single-cell RNA sequencing (scRNA-seq) is a valuable tool for investigating cellular heterogeneity in diseases such as equine asthma (EA). This study evaluates the HIVE™ scRNA-seq method, a pico-well-based technology, for processing bronchoalveolar lavage (BAL) cells from horses with EA. The HIVE method offers practical advantages, including compatibility with both field and clinical settings, as well as a gentle workflow suited for handling sensitive cells. Our results show that the major cell types in equine BAL were successfully identified; however, the proportions of T cells and macrophages deviated from cytological expectations, with macrophages being overrepresented and T cells underrepresented. Despite these limitations, the HIVE method confirmed previously identified T cell and macrophage subpopulations and defined other BAL cell subsets. However, compared to previous studies T helper subsets were less clearly defined. Additionally, consistent with previous scRNA-seq studies, the HIVE method detected fewer granulocytes and mast cells than anticipated in the total BAL samples. Nevertheless, applying the method to purified mast cells recovered an expected number of cells. A small set of eosinophils were also detected which have not been characterized in earlier studies. In summary these findings suggest that while the HIVE method shows promise for certain applications, further optimization is needed to improve the accuracy of cell type representation, particularly for granulocytes and mast cells, in BAL samples.
Evaluation of L-carnitine’s protective properties against AlCl3-induced brain, liver, and renal toxicity in rats
A common heavy metal in many facets of daily life is aluminum (AlCl3), which can be found in food, toothpaste, cosmetics, food additives, and numerous pharmaceutical items. The hippocampus, liver, and kidneys have the highest concentrations of this powerful neurotoxin, which also accumulates over time and contributes to the development of a number of cognitive disorders. Long-term overconsumption of AlCl3 results in hepatic and renal toxicity as well as neuronal inflammation. The purpose of the research is to assess the potential protective effects of various L-carnitine dosages as an antioxidant against hebato, renal, and neuronal toxicity in rats caused by aluminum chloride (AlCl3) (20 mg/kg, 1/20 LD 50). Six groups (n = 6), consisting of 36 adult albino rats, were randomly assigned. Saline was administered to the control group (GI) by injection. (GII) had given an injection of L-carnitine at a low-dose of 75 mg/kg body weight. An injections of L-carnitine at a high-dose (150 mg/kg) were given to (GIII), and AlCl3 (20 mg/kg) was given to (GIV). (GV) administered with L-carnitine (75 mg/kg) and AlCl3 (20 mg/kg) by injection. For 60 days, AlCl3 (20 mg/kg) and L-carnitine (150 mg/kg) were administered to GVI by injection. Furthermore, the histological structure of the cortex, hippocampus, and hepatic renal tissues appeared to change in response to AlCl3. L-carnitine therapy lessened the negative effects of AlCl3. The observable improvement in the tissues of the brain, liver, and kidneys further supported this histopathologically. It is possible to draw the conclusion that L-carnitine holds promise as a corrective measure for AlCl3, which causes renal toxicity and neural hepatotoxicity in rats. When it comes to adult albino rats, L-carnitine has a negative impact and exhibits ameliorative effects against aluminum chloride.
A sector fast encryption algorithm for color images based on one-dimensional composite sinusoidal chaos map
Images are important information carriers in our lives, and images should be secure when transmitted and stored. Image encryption algorithms based on chaos theory emerge in endlessly. Based on previous various chaotic image fast encryption algorithms, this paper proposes a color image sector fast encryption algorithm based on one-dimensional composite sinusoidal chaotic mapping. The main purpose of this algorithm is to improve the encryption and decryption speed of color images and improve the efficiency of image encryption in the big data era. First, four basic chaos maps are combined in pairs and added with sine operations. Six one-dimensional composite sinusoidal chaos maps (CSCM) were obtained. Secondly, select the two best chaotic mappings LCS and SCS. The randomness of these two chaotic mappings was verified through Lyapunov index and NIST SP 800–22 randomness tests. Thirdly, the encryption process is carried out according to the shape of a traditional Chinese fan, and the diffusion and scrambling of each pixel of the image are performed in parallel. This greatly improves encryption speed. When diffusing, changing the value of one pixel can affect the values of multiple subsequent pixels. When scrambling, each pixel changes position with the three pixels before it according to the chaotic sequence. Finally, through many experiments, it is proved that the image encryption algorithm not only greatly improves the encryption and decryption speed, but also improves various indexes. The key space reached 2192, the average information entropy was 7.9994, the average NPCR was 99.6172, and the average UACI was 33.4646. The algorithm can also resist some common attacks and accidents, such as exhaustion attack, differential attack, noise attack, information loss and so on.
Spatial-temporal evolution of the allometric relationship between urban economic and health resources in the Yangtze River Delta urban agglomeration
The evolution of the spatiotemporal relationship between urban economic growth and health resources within the Yangtze River Delta urban agglomeration provides an important context for understanding the regional development dynamics in China. Previous studies focused on equity in health-resource allocation and service efficiency, often overlooking the allometric growth relationships between health resources and economic variables. This study employs an allometric growth model to elucidate the changing interactions between the number of medical beds, doctors, and urban economic indicators in the Yangtze River Delta region from 2009 to 2022. Employing Zipf’s law and allometric growth modeling, this study analyzed growth trends and revealed significant differences in resource allocation and size changes over time. The main findings suggest that, although resource centralization is a general trend, differences persist, especially in less economically developed regions. This study innovatively introduces an allometric growth model that offers a new perspective on understanding the mechanisms of regional health-resource growth and underscores the significant influence of economic factors on health-resource allocation. This study significantly contributes to the sustainability of urban health systems and provides theoretical support for policy formulations aimed at optimizing the allocation of health resources and strengthening regional economic strategies in the Yangtze River Delta region.
Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms
Background Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition status during adolescence, in addition to infancy and childhood is very crucial. However, the available studies in Ethiopia have been usually focused in early childhood and they used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia. Methods A total of 3156 weighted samples of adolescent girls aged 15–19 years were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 80% and 20% of the observations were used for training, and testing the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. The performance of the predictive model was evaluated using evaluation metrics value through Python software. The synthetic minority oversampling technique was used for data balancing and Boruta algorithm was used to identify best features. Association rule mining using an Apriori algorithm was employed to generate the best rule for the association between the independent feature and the targeted feature using R software. Results The random forest classifier (sensitivity = 81%, accuracy = 77%, precision = 75%, f1-score = 78%, AUC = 85%) outperformed in predicting stunting compared to other ML algorithms considered in this study. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having one or more children were the top attributes to predict stunting. Association rule mining was identified the top seven best rules that most frequently associated with stunting among adolescent girls in Ethiopia. Conclusion The random forest classifier outperformed in predicting and identifying the relevant predictors of stunting. Results have shown that machine learning algorithms can accurately predict stunting, making them potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt stunting among adolescent girls.
Occurrence of virulence genes in multidrug-resistant Escherichia coli isolates from humans, animals, and the environment: One health perspective
Escherichia coli is one of the critical One Health pathogens due to its vast array of virulence and antimicrobial resistance genes. This study used multiplex PCR to determine the occurrence of virulence genes bfp, ompA, traT, eaeA, and stx1 among 50 multidrug-resistant (MDR) E. coli isolates from humans (n = 15), animals (n = 29), and the environment (n = 6) in Dar es Salaam, Tanzania. Their association with antimicrobial-resistant genes (ARGs) was determined using Principal Component Analysis (PCA). All 50/50 (100%) MDR E. coli isolates carried at least one virulence gene, with 19/50 (38%) carrying four genes, bfp + traT + eaeA + ompA. The findings showed a high occurrence of virulence genes bfp (82%), traT (82%), eaeA (78%), and ompA (72%); the study detected no stx1 in any of the isolates. In humans, the most detected virulence genes were bfp and traT 14/15 (93.3%); for poultry, it was eaeA 13/14 (92.9%); for pigs, was bfp and traT 13/15 (86.7%); while for river water, it was eaeA 6/6 (100%). The study observed no significant association between virulence genes and ARGs. PCA results show the genes ompA, traT, eaeA, and bfp contributed to the virulence of the isolates, and blaTEM, blaCTX-M, and qnrs contributed to ARGs. The PCA ellipses show that isolates from pigs had more virulence genes than those isolated from poultry, river water, and humans. The high frequency of numerous virulence genes in MDR E. coli isolates from humans, animals, and the environment indicates that these isolates have a very high potential to cause diseases that are difficult to treat because they are MDR.
RETRACTED: The green response of financial inclusion, infrastructure development and renewable energy to the environmental sustainability: A newly evidence from OECD economies
Recently, economic environmental degradation is being considered a leading chellenge in forefront of policy analysts. Thus, the present study introduces core environmental determinants such as infrastructure development, finacail inclusion, gross domestic product, population, and renewable energy consumption. Financial inclusion (FI) is crucial for attaining a environment. The present study selects the Organization for Economic Co-operation and Development (OECD) over period of 2004–2022. The results show that financial inclusin, infrastructure development(ID), and renewable energy (RE) play a vital influence in decreasing carbon emissions. The OECD nations should surge their investment in renewable energy and infrastructure development. Furthermore, to ensure long-term environmental sustainability, it is imperative to broaden the scope of FI. Thus, the inclusion of green infrastructure is essential in order to shift from the utilization of fossil fuels to RE sources. Similarly, policymakers should incorporate FI into climate actions at the local, national, and regional levels. However, it is crucial to promote the economic shift towards RE sources in order to mitigate the environmental impact from humn and economic activities. This study is conducive to the execution of the United Nations (UN) Sustainable Development Goals (SDG).
Determining patient activity goals and their fulfillment following total knee arthroplasty: Findings from the prospective, observational SuPeR Knee study
Background Dissatisfaction with Total Knee Arthroplasty (TKA) surgical outcomes remains between 10–20% and is associated with higher levels of societal costs. Expectations regarding post-surgical outcomes is considered as one of the major factors influencing satisfaction, however, there are no standardised methods for assessing patient’s expectations regarding activities to be achieved following surgery. Objectives The aims of this study were to identify patient expectations relating to activities of importance following TKA and to describe goal fulfillment at 3 months post-TKA. We hypothesised that activity expectation fulfillment would be associated with overall satisfaction with TKA outcomes. Methods This study comprised secondary data analysis of findings from the SuPeR Knee study. Using conventional content analysis, a classification system of activities specific to our TKA patient cohort was created. At 3 months following TKA, patients rated satisfaction with fulfilling activity goals and pain attenuation. The average level of satisfaction achieved was used as our measure of goal fulfillment. Overall satisfaction of the outcomes of surgery was rated using a 5-point Likert scale and the association between goal fulfillment and overall surgery satisfaction was compared by Spearman’s rank correlation. Results Data were collected from 861 TKA patients. Recreation and sporting pursuits were found to be important activity types (43% of all activities). At 3 months after surgery, less impactful activities were more commonly satisfied (67%), including domestic and vocational activities, low impact hobbies and leisure activities. Goal fulfillment and improvement in knee pain were both significantly positively correlated to, and significant predictors of, overall patient satisfaction (p≤0.001). Conclusions Our Australian cohort of TKA patients have a range of expectations for undertaking high-impact activities after surgery. However, at 3 months after surgery, higher rates of satisfaction were attained for lower-impact activities. Our findings support the importance of identifying activity expectations for each patient and that fulfillment of these goals contributes to overall satisfaction with the outcomes of TKA.
A study on the monitoring of LNAPL migration using ERT
This study employs electrical resistivity tomography (ERT) to experimentally investigate the migration characteristics of light non-aqueous phase liquids (LNAPL) under various groundwater conditions. Through cross-hole measurements and time-lapse inversion, the migration process of LNAPL under three scenarios—unsaturated conditions, constant groundwater levels, and declining water levels—was systematically analyzed. The results indicate that LNAPL migration behavior exhibits significant differences under different conditions. Under unsaturated conditions, the vertical migration rate of LNAPL gradually decreases over time, with an average rate of 1.06 cm/h, and is influenced by preferential migration pathways formed in coarse-grained regions. At constant water levels, the migration of LNAPL is significantly constrained by the groundwater level, spreading horizontally near the water table after reaching it, with an average rate of 0.51 cm/h. When the groundwater level declines, LNAPL migrates rapidly downward along preferential flow paths, with an average rate increasing to 1.45 cm/h. Miller Soil Box experiments further reveal the relationship between LNAPL content and electrical resistivity, showing that an increase in LNAPL can significantly alter soil resistivity, especially under low moisture conditions. Overall, this study confirms the monitoring advantages of ERT technology for LNAPL migration behavior under different conditions and provides important references for remediation strategies at contaminated sites.
Physical climate risk: Stock price reactions to the historically most extreme European and United States heat waves since 1979
Climate change has heightened the need to understand physical climate risks, such as the increasing frequency and severity of heat waves, for informed financial decision-making. This study investigates the financial implications of extreme heat waves on stock returns in Europe and the United States. Accordingly, the study combines meteorological and stock market data by integrating methodologies from both climate science and finance. The authors use meteorological data to ascertain the five strongest heat waves since 1979 in Europe and the United States, respectively, and event study analyses to capture their effects on stock prices across firms with varying levels of environmental performance. The findings reveal a marked increase in the frequency of heat waves in the 21st century, reflecting global warming trends, and that European heat waves generally have a higher intensity and longer duration than those in the United States. This study provides evidence that extreme heat waves reduce stock values in both regions, with portfolio declines of up to 3.1%. However, there are marked transnational differences in investor reactions. Stocks listed in the United States appear more affected by the most recent heat waves compared to those further in the past, whereas the effect on European stock prices is more closely tied to event intensity and duration. For the United States sample only, the analysis reveals a mitigating effect of high corporate environmental performance against heat risk. This study introduces an innovative interdisciplinary methodology, merging meteorological precision with financial analytics to provide deeper insights into climate-related risks.
Travel medicine training and experience among primary care physicians in Qatar
Background Travel medicine (TM) focuses on preventing and managing travel-related issues. Evidence has become more important than expert opinions in the development of TM standards. This study aimed to evaluate the training and experience of TM among Primary Care Physicians (PCPs) in Qatar and their associated factors. Methods A cross-sectional study design was employed. A structured questionnaire was utilized to gather data from all PCPs working in publicly funded primary health centers. Results The study involved 360 PCPs (response rate: 89.5%). Of these, 42.3% reported postgraduate training (15.1%) or experience (27.5%) in TM, with common training forms including workshops (67%), postgraduate programs (24%), and short courses (15%). About 81.8% expressed interest in TM training. Regarding confidence in practicing TM, 20% felt very confident, while 50% felt moderately confident. In practice, 25.8% conducted comprehensive pre-travel risk assessments, and 22.5% responded to traveler queries without formal consultations. Multivariable logistic regression analysis showed that PCPs who graduated from medical schools in Arab countries, conducting more than ten TM consultations per month, performing comprehensive pre-travel assessments, and those expressing high confidence were more likely to be associated with TM training or experience. Conclusion Many PCPs in Qatar lack prior training and experience in TM, raising concerns about their ability to provide adequate care to traveling patients. There is a significant need for targeted TM training for PCPs, especially since the majority express a keen interest in receiving such training.
Analysis of spatiotemporal distribution, variability, and trends of rainfall in Wollo area, Northeastern Ethiopia
Ethiopia’s agriculture is mostly dependent on rain, though the rainfall distribution and amount are varied in spatiotemporal context. The study was conducted to analyze the distribution, trends, and variability of monthly, seasonal, and annual rainfall data over the Wollo area from 1981 to 2022. To accomplish this, the study utilized the Climate Hazards Group Infrared Precipitation with Stations version two (CHIRPS-v2) data. Standard Rainfall Anomaly Index (SRA) and Coefficient of Variation (CV) were employed to examine rainfall variability and develop drought indices over southern Ethiopia. The Modified Mann Kendall (MMK) test, Sen’s slope estimator and the innovative trend analysis (ITA) were employed to detect temporal changes in rainfall trends over the study period. The study found that the area experienced considerable rainfall variability and change, resulting in extended drought and flood events within the study period. Results from SRA and CV revealed interannual and seasonal rainfall variability, with the proportions of years below and above the long-term mean being estimated at 56% and 44%, respectively. The MMK test showed that the annual rainfall during the Kiremt (summer-main rainy season) had an increasing trend. On the other hand, rainfall for the Belg (short rain season for the study area) season and the Bega (winter) season showed a significantly decreasing trend (p < 0.05). Results from the innovative trend analysis (ITA) also revealed that the annual and seasonal rainfall trends exhibited different trends in varied magnitude for different stations. On a spatial basis, the eastern and northeastern regions of the study area showed trends of increasing rainfall during the Kiremt (JJA). Decision-makers and development planners need to design strategies to mitigate the risks posed by changes in rainfall variability and distribution and enhance community adaptation and mitigation capacities in Wollo, Ethiopia.
Estimating the prevalence of adults at risk for advanced hepatic fibrosis using FIB-4 in a Swiss tertiary care hospital
Background & aims Chronic liver diseases pose a serious public health issue. Identifying patients at risk for advanced liver fibrosis is crucial for early intervention. The Fibrosis-4 score (FIB-4), a simple non-invasive test, classifies patients into three risk groups for advanced fibrosis. This study aimed to estimate the prevalence of patients at risk for advanced hepatic fibrosis at a Swiss tertiary care hospital by calculating the FIB-4 score in routine blood analysis. Methods A retrospective study was conducted using data from 36,360 patients who visited outpatient clinics at eight main clinics of the University Hospital Bern in Switzerland. The data collection period ran from January 1st to December 31st, 2022. Patients attending the hepatology outpatient clinic were excluded. We then calculated the overall and clinic-specific prevalence of patients falling into the high risk category for advanced fibrosis according to FIB-4. Results Among the 36,360 patients, 26,245 (72.2%) had a low risk of advanced fibrosis (FIB-4 <1.3), whereas 3913 (10.8%) and 2597 (7.1%) patients were flagged to have a high risk of advanced fibrosis (FIB-4 >2.67 and FIB-4 >3.25 respectively). Geriatrics and Cardiology had the highest proportions of patients at risk for advanced fibrosis over all clinics. Conclusions This study demonstrates a high prevalence of high FIB-4 score in a Swiss tertiary care hospital. The implementation of the automatically generated FIB-4 score in daily practice, not only in primary care, but also within tertiary care hospitals, could be crucial for early identification of outpatients at high risk of advanced liver fibrosis requiring further hepatological investigations.
Distribution and habitat of the painted tree rat (Callistomys pictus): Evaluating areas for future surveys and conservation efforts
Knowledge of the potential distribution and locations of poorly known threatened species is crucial for guiding conservation strategies and new field surveys. The painted tree-rat (Callistomys pictus) is a monospecific, rare, and endangered echimyid rodent endemic to the southern Bahia Atlantic Forest in Brazil. There have been no records of the species published in the last 20 years, and the region has experienced significant forest loss and degradation. According to the IUCN, only 13 specimens had been previously reported, with 12 found in the north of Ilhéus and adjacent municipalities, and one recorded approximately 200 km away from this region, suggesting that its distribution might be wider. We aimed to search for unpublished and more recent records of the C. pictus, by consulting the gray literature (including Environmental Impact Study (EIA) reports, Brazilian Red Lists, and management plans of protected areas), scientific collections, online databases, and mastozoologists working in the region. We estimated the species’ potential distribution using Ecological Niche Modeling to identify regions, municipalities, and protected areas most likely to support this species, based on factors such as climate suitability and forest cover. We reported three new sightings of the species, including the first within a protected area. We estimated suitable climate conditions across 23,151 km2, of which 9,225 km2 has a high potential for harboring the species. The area between Itacaré and Valença needs more extensive survey efforts as it has high habitat suitability and only one record has been confirmed there so far. Meanwhile, the region between Una and Ilhéus urgently requires habitat conservation initiatives. While the species may have a broader distribution than previously thought, its known occurrences are limited to a few locations, and suitable habitats are underrepresented in protected areas. Additionally, the rarity of sightings continues to indicate a concerning conservation status.
Hydraulic performance of a pre-aerated stilling basin: Experimental study
Spillway chutes are critical in dam flood control, particularly in high dams where high water heads and large discharge in narrow canyons amplify the demand for safe discharging. For large unit discharges in spillways, aeration protection is essential to prevent cavitation erosion, but challenges arise from air duct choking in the traditional spillway and nonaerated regions in the stepped spillway. This paper introduces a novel spillway called the pre-aerated stilling basin spillway (PSBS). The primary distinction between PSBS and conventional spillways lies in the placement of aeration facilities. Conventional spillways feature a curved transition section that directly connects to the spillway. In contrast, the PSBS is equipped with a WES crest weir and a vertical sill of the same width, which converts supercritical to subcritical flow. This process strongly entrains air through hydraulic jumps at spillway entrances, thereby preventing cavitation damage. The study aims to experimentally investigate the flow regime, energy dissipation within the PSB, and the near-wall air concentration to ensure effective mitigation of cavitation damage downstream. The findings highlight the impact of the PSB on changing the jump type and relative local energy loss, influenced by the incoming Froude number, sill height, and sill length. Influenced by these factors, the variation in air concentration is crucial for reducing cavitation damage, necessitating potential adjustments in prototype applications to account for scale effects. Under such a pre-aeration approach, the air concentration along the downstream spillway warrants further investigation for the continuous optimization of the PSB’s design and its integration with spillways.
Maize quality detection based on MConv-SwinT high-precision model
The traditional method of corn quality detection relies heavily on the subjective judgment of inspectors and suffers from a high error rate. To address these issues, this study employs the Swin Transformer as an enhanced base model, integrating machine vision and deep learning techniques for corn quality assessment. Initially, images of high-quality, moldy, and broken corn were collected. After preprocessing, a total of 20,152 valid images were obtained for the experimental samples. The network then extracts both shallow and deep features from these maize images, which are subsequently fused. Concurrently, the extracted features undergo further processing through a specially designed convolutional block. The fused features, combined with those processed by the convolutional module, are fed into an attention layer. This attention layer assigns weights to the features, facilitating accurate final classification. Experimental results demonstrate that the MC-Swin Transformer model proposed in this paper significantly outperforms traditional convolutional neural network models in key metrics such as accuracy, precision, recall, and F1 score, achieving a recognition accuracy rate of 99.89%. Thus, the network effectively and efficiently classifies different corn qualities. This study not only offers a novel perspective and technical approach to corn quality detection but also holds significant implications for the advancement of smart agriculture.
‘‘Mitigating cancer pain: What else matters?”—A qualitative study into the needs and concerns of cancer patients in Sri Lanka
Objectives In Sri Lanka, cancer is a significant contributor to both morbidity and mortality rates. In 2022, 33,243 new cancer cases were reported, resulting in an age- standardized incidence rate of 106.9 per 100,000 individuals. The overall experience of cancer pain reflects patients’ needs and concerns. Therefore, a thorough understanding of the patient’s needs and concerns is crucial to implementing satisfactory pain outcomes. This study aims to explore the needs and concerns of patients with cancer pain in Sri Lanka. Methods This study employed a descriptive qualitative approach among purposively selected patients with cancer and registered at the pain management unit. Participants recruited were 18 years or older with cancer-related pain. Noncancerous pain and those with psychological disorders, and brain metastases were excluded. Twenty-one semi-structured interviews were conducted until data saturation using a semi-structured interview guide, each lasting 30–60 minutes. Data were analyzed by Graneheim and Lundman’s content analysis method. Results The study primarily involved participants aged 51–60 Sinhalese Buddhists. It highlighted two main themes: ’Changes in normal lifestyle’ and ’Needs and expectations’. The ’Changes in normal lifestyle’ theme included subthemes like ’Functional limitations’, ’Emotional reactions’, ’altered interpersonal relationships’, and ’Socio-financial problems’. The ’Needs and expectations’ theme covered desires for a ’Pain-free life’, a return to a ’Normal lifestyle’, and the ’Need for a caregiver’. The findings emphasize that the most significant issue for cancer patients is the disruption to their normal lifestyle due to various challenges, while their primary need is to live without pain. Conclusions ‘Life without pain’ is a cancer sufferer’s greatest need while ’changes in normal lifestyle’ owing to bio-psycho-social-spiritual problems is their primary concern.